Top Read Articles

    Published in last 1 year |  In last 2 years |  In last 3 years |  All
    Please wait a minute...
    For Selected: Toggle Thumbnails
    Cross-domain text sentiment classification based on domain-adversarialnetwork and BERT
    Guoyong CAI,Qiang LIN,Kaiqi REN
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 1-7,20.   DOI: 10.6040/j.issn.1672-3961.0.2019.293
    Abstract997)   HTML38)    PDF(pc) (1549KB)(635)       Save

    In order to capture more sentence semantic information from the extracted shared sentiment features for cross-domain sentiment analysis, a deep network model based on domain adversarial mechanism and BERT (bidirectional encoder representations from transformers) was proposed. The model firstly used BERT to obtain the semantic representation vectors of sentences, and then extracted the local features of sentences with a convolutional neural network. A domain adversarial neural network was designed to make the representations of features extracted from different domains to be as indistinguishable as possible, that was, the features extracted from source domain and target domain had much more similarities; and a sentiment classifier was trained on the source domain dataset with sentiment labels, and it was expected that the trained sentiment classifier would have good classification performance in the source domain, and in the target domain. The experimental results on Amazon product reviews dataset showed that the proposed method achieved the expectation and was competent for achieving cross-domain text sentiment classification.

    Table and Figures | Reference | Related Articles | Metrics
    Image denoising based on 3D shearlet transform and BM4D
    Shengnan ZHANG,Lei WANG,Chunhong CHANG,Benli HAO
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 83-90.   DOI: 10.6040/j.issn.1672-3961.0.2019.262
    Abstract962)   HTML10)    PDF(pc) (12734KB)(207)       Save

    Aimed at the disadvantage that the traditional block matching denoising method could only deal with two-dimensional images, an image denoising method based on 3D shearlet transform and BM4D(block-matching and 4D filtering) was proposed. This method used 3D shearlet transform to obtain transform domain coefficients, and realized joint filtering in transform domain through hard threshold and Wiener filtering stage. The 3D shearlet transformation was localized through two filtering stages: multi-scale decomposition and directional decomposition. The hard threshold and Wiener filtering were performed, which include grouping, collaborative filtering and aggregation. The 4D transformation of the cubes was based on the local correlationandon-local correlation cubes. The estimated values of each grouped cube were obtained by inverse transformation of 3D shearlet transform, and self-adaptive aggregation was performed at their original positions. PSNR(peak signal to noise ratio) and SSIM(structural similarity) were used as evaluation criteria. The results showed that this method could effectively remove image noise in high noise environment, and effectively improved the visual effect of the image with high accuracy.

    Table and Figures | Reference | Related Articles | Metrics
    Review on smart highways critical technology
    Jianqing WU,Xiuguang SONG
    Journal of Shandong University(Engineering Science)    2020, 50 (4): 52-69.   DOI: 10.6040/j.issn.1672-3961.0.2020.149
    Abstract887)   HTML478)    PDF(pc) (2579KB)(990)       Save

    Giving highway engineering "wisdom" and establishing new generation five-in-one system of "Internet+" design, construction, management, monitoring and operation, namely, the smart highway, was the hot issue of the interdisciplinary study of civil engineering, control engineering, mechanical engineering, transportation engineering, and computer science. To comprehensively understand the smart highway, this review focused on the critical technology in the integrated system in full life-cycle of the smart highway as well as systematically investigated the relevant previous efforts, critical common technologies, and future scopes on multi-function pavement material, smart construction, smart detection, autonomous vehicles, connected vehicles, and internet of things technology.

    Table and Figures | Reference | Related Articles | Metrics
    Review of energy consumption and demand forecasting methods
    Ming YANG,Pingjing DU,Fengquan LIU,Xupeng HAO,Yifan BO
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 56-62,71.   DOI: 10.6040/j.issn.1672-3961.0.2019.180
    Abstract878)   HTML937)    PDF(pc) (2290KB)(793)       Save

    In view of the increasing dependence of energy planning on energy demand forecasting and the difficulty of energy demand forecasting, this paper analyzed various energy forecasting methods and discussed the direction of energy development. The article analyzed the current demand situation of energy development methods from the direction of global energy demand development in recent years. The existing main energy forecasting methods were summarized and compared. The advantages and disadvantages of the existing research methods and applicable occasions were summarized. Combined with the new direction of energy development, the future development prospects of energy forecasting were given. Furthermore, this paper applied the LEAP model to predict the energy demand of the African region, and analyzed the regional energy complementation effect and the role of "electricity substitution" in the development of energy demand.

    Table and Figures | Reference | Related Articles | Metrics
    Entity alignment method based on adaptive attribute selection
    Jialin SU,Yuanzhuo WANG,Xiaolong JIN,Xueqi CHENG
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 14-20.   DOI: 10.6040/j.issn.1672-3961.0.2019.415
    Abstract864)   HTML20)    PDF(pc) (1167KB)(477)       Save

    Most existing entity alignment methods typically relied on external information and required expensive manual feature construction to complete alignment. Knowledge graph-based methods used only semantic information and failed to use structural information. Therefore, this paper proposed a new entity alignment method based on adaptive attribute selection, training an entity alignment model based on the joint embedding of the two knowledge graphs, which combined the semantic and structural information. Also, this paper proposed the use of strong attribute constraint based on adaptive attribute selection, which could adaptively generate the most effective attribute category and weight, to improve the performance of entity alignment. Experiments on two realistic datasets showed that, compared with traditional methods, the precision of the proposed method was improved by 11%.

    Table and Figures | Reference | Related Articles | Metrics
    Air quality prediction approach based on integrating forecasting dataset
    Minghe GAO,Ying ZHANG,Rongrong ZHANG,Zihao HUANG,Linyan HUANG,Fanyu LI,Xin ZHANG,Yanhao WANG
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 91-99.   DOI: 10.6040/j.issn.1672-3961.0.2019.404
    Abstract843)   HTML15)    PDF(pc) (4733KB)(566)       Save

    Towarding the air quality prediction research problem, LightGBM was employed to propose and design a predictive feature-based air quality prediction approach, which could effectively predict the PM2.5 concentration, i.e., the key indicator reflecting air quality, in the upcoming 24-hour within Beijing. During constructing the prediction solution, the features of the training data set was analyzed to execute data cleansing, and the methods of random forest and linear interpolation were used to solve the problem of high data loss and noise interference. The predictive data features were integrated into the dataset, and meanwhile the corresponding statistical features were designed to imiprove the prediction accurancy. The sliding window mechanism was used to mine high-dimensional time features and increase the quantity of data features. The performance and result of the proposed approach were analyzed in details through comparing with the basedline models. The experimental results showed that compared with other model methods, the proposed LightGBM-based prediction approach with integrating forecasting data had higher prediction accuracy.

    Table and Figures | Reference | Related Articles | Metrics
    A multi-microcontroller communication method based on UART asynchronous serial communication protocol
    Jinping MA
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 24-30.   DOI: 10.6040/j.issn.1672-3961.0.2019.705
    Abstract817)   HTML12)    PDF(pc) (3677KB)(160)       Save

    To reduce the additional communication modules, complexity and costs of the different Internet of Things(IoT) devices communication, a multi-microcontroller communication method based on UART asynchronous serial ports was proposed. Based on the universal asynchronous receiver/transmitter(UART) serial communication method of the microcontroller, the control line was utilized to control the usage of the communication lines by the communication device, and a method of occupying the signal line by multiple machines in a time-sharing manner was realized. The master-slave control strategy was used to set the communication protocol. The master implements signal forwarding and identification, and the slave got signals from the master to achieve reliable and stable communication among multiple machines. By transplanting the μC/OS-Ⅱ operating system to the STM32 microcontroller, and using the real-time multitasking characteristics of μC/OS-Ⅱ, the signal reception, transmission and identification were designed into tasks of different priorities, and the master and the slave were realized. The functions of information receiving, sending and identification and the characteristics of multi-slave expansion were achieved through the communication protocol, solving the problem of multi-microcontroller communication that the traditional UART method could not achieve. The feasibility of the proposed method was verified through experiments, which provided a new solution for multi-microcontroller communication of edge devices in the Internet of Things.

    Table and Figures | Reference | Related Articles | Metrics
    Key frame extraction based on ViBe algorithm for motion feature extraction
    Qiuling LI,Baomin SHAO,Lei ZHAO,Zhen WANG,Xue JIANG
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 8-13.   DOI: 10.6040/j.issn.1672-3961.0.2019.276
    Abstract776)   HTML14)    PDF(pc) (4029KB)(418)       Save

    Aiming at the fact that the background was dominant in the key frame extraction algorithm, in which the foreground target was too small and it was not easy to extract the features of moving targets in sports video, a key frame extraction algorithm for foreground moving target feature extraction based on background modeling algorithm was proposed, which was called visual background extractor (ViBe) algoritm. The foreground target detection of video sequence was firstly carried out using ViBe algorithm, afterwards the scale-invariant feature transformation (SIFT) features of the foreground moving target were extracted. Based on the similarity calculated from video frame series, the key frames of video were output according to the key frame discrimination method. The experimental results showed that the proposed algorithm could solve the problem of missed selection and misselection in traditional key frame extraction. Compared with the algorithm based on SIFT distribution histogram, the F1 score was well improved. The algorithm based on ViBe could effectively identify key frames in sports video.

    Table and Figures | Reference | Related Articles | Metrics
    End-to-end security encryption scheme of NB-IoT for smart grid based on physical unclonable function
    Donglan LIU,Xin LIU,Jianfei CHEN,Wenting WANG,Hao ZHANG,Lei MA,Dong LI
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 63-71.   DOI: 10.6040/j.issn.1672-3961.0.2019.034
    Abstract751)   HTML18)    PDF(pc) (3224KB)(189)       Save

    In order to improve the high security of power network data transmission, an end-to-end security encryption scheme of NB-IoT (narrow band internet of things, NB-IoT) for smart grid based on physical unclonable function and domestic cipher algorithm SM3 was proposed in this paper. A self-controllable NB-IoT application layer security architecture was designed by introducing the SM3, extending the existing key derivation structure of LTE, and combining the physical unclonable function to ensure the generation of encryption keys between NB-IoT terminals and power grid business platforms. Analysis and experiment showed that the proposed scheme realized secure data transmission and bidirectional identity authentication between IoT devices and terminals. Its features included high compatibility, low communication costs, lightweight and flexible key update. In addition, the scheme also supported terminal authentication during key agreement, which furtherly enhanced the security of business systems in smart grid.

    Table and Figures | Reference | Related Articles | Metrics
    Review of developments in titanium-based coagulants
    Baoyu GAO,Xin HUANG,Guangping YAO,Qinyan YUE
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 109-114.   DOI: 10.6040/j.issn.1672-3961.0.2019.359
    Abstract741)   HTML45)    PDF(pc) (1129KB)(579)       Save

    The development process and its applications of titanium-based coagulant were reviewed, including the mono-titanium coagulants and poly-titanium coagulants, and the single titanium coagulants and composite titanium coagulants, and the recent research process and the future development of titanium coagulants, which could provide guidances and references for the research and development of titanium-based coagulants.

    Reference | Related Articles | Metrics
    Vehicle classification and tracking for complex scenes based on improved YOLOv3
    Shiqi SONG,Yan PIAO,Zexin JIANG
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 27-33.   DOI: 10.6040/j.issn.1672-3961.0.2019.412
    Abstract698)   HTML16)    PDF(pc) (5481KB)(292)       Save

    Aiming at the influence of weather conditions and mutual occlusion of vehicles on vehicle classification and tracking accuracy and stability, a hybrid model based on improved YOLOv3 and matching tracking was proposed. The improved YOLOv3 network refered to DenseNet′s design idea, replaced the residual layer in the network with a dense convolution block and changed the design structure of the network. The fused features of dense convolution blocks and convolution layers were classified by using Softmax classifier. According to the detection result of single frame image, the target matching function was designed to solve the vehicle tracking problem in video sequence. In the KITTI dataset test, the improved algorithm achieved an average precision of 93.01%, the number of frames per second reached 48.98, and the average recognition rate in the self-built dataset was 95.79%. The experimental results showed that the proposed method could effectively distinguish the types of vehicles in complex scenes with higher accuracy. At the same time, the method had higher accuracy and robustness in vehicle tracking.

    Table and Figures | Reference | Related Articles | Metrics
    LDA-based topic feature representation method for symbolic sequences
    Chao FENG,Kunpeng XU,Lifei CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 60-65.   DOI: 10.6040/j.issn.1672-3961.0.2019.760
    Abstract650)   HTML6)    PDF(pc) (1403KB)(247)       Save

    To address the problems of high feature dimensionality and high algorithm time complexity in the existing methods, a topic feature representation method was proposed to transform the symbolic sequences into a set of topic probability vectors, based on the topic model latent Dirichlet allocation (LDA) commonly used in text mining. In the new method, each short sequence gram was considered as the shallow feature (word) of the sequence, and the topics with their probability distributions were extracted as the deep features of the sequences using the LDA model learning algorithm.Experiments were carried out on six real-world sequence sets, and compared with the existing grams-based and Markov model-based methods. The results showed that the new method improved the learning efficiency of the representation model while reducing the feature dimensionality, and achieved better accuracy in the application of symbolic sequence classification.

    Table and Figures | Reference | Related Articles | Metrics
    Abnormal sound detection of washing machines based on deep learning
    Chunyang LI,Nan LI,Tao FENG,Zhuhe WANG,Jingkai MA
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 108-117.   DOI: 10.6040/j.issn.1672-3961.0.2019.419
    Abstract645)   HTML15)    PDF(pc) (5582KB)(594)       Save

    Based on the convolutional neural network (CNN) framework, a model for abnormal sounds recognition of washing machine was proposed. According to the remarkable feature extraction ability and translation invariance of convolutional neural network, the abnormal sound features of washing machines were learned, so as to achieve the purpose of the automatic intelligent recognition of abnormal sounds for washing machines in production line. This method provided a complete process to solve the problems of training datasets establishment and data imbalance. A network model for data augmentation called advanced deep convolution generated adversarial network (ADCGAN)was proposed to solve the problem of training data scarcity. The traditional deep convolution generated adversarial network (DCGAN) model was improved to better adapt to the generation of industrial sounds. This model could be used to extend the original data and generate the abnormal sound augmented datasets of washing machine. The augmented datasets was used to train the convolutional neural network, and the test accuracy reached 0.999. The generalization ability of abnormal sounds recognition model for washing machine network was tested by using the data set with background noise signal added. The correct recognition rate reached 0.902, which indicated that this network had good robustness in recognizing abnormal noises of washing machines.

    Table and Figures | Reference | Related Articles | Metrics
    Bridge monitoring and warning system based on digital measurement technology
    Chengxin YU,Guojian ZHANG,Yongqian ZHAO,Xiaodong LIU,Xinhua DING,Tonglong ZHAO
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 115-122.   DOI: 10.6040/j.issn.1672-3961.0.2019.063
    Abstract623)   HTML11)    PDF(pc) (3123KB)(340)       Save

    In order to make up the deficiency of the traditional measuring method in monitoring the whole deformation and real-time monitoring of the bridge and overcome the defect of digital photography in monitoring the three-dimensional deformation of a bridge, this paper presented the image matching-time baseline parallax method: a digital camera was set up in the proper place, and a reference plane, consisting of reference points, was not far from the digital camera. Reference plane was perpendicular to the photographic direction, and the monitoring points were on the bridge. The two-dimensional deformation on the object plane of the deformation point was obtained based on image matching-time baseline parallax method, then the horizontal deformation on object plane was disassembled into the bridge direction and the direction perpendicular to bridge direction based on the principle of three-dimensional deformation monitoring, The three-dimensional deformation of the bridge was got. Results showed that measurement accuracy of the bridge was 0.72 mm and 1.16 mm in X and Z direction on object plane, respectively. It could meet accuracy requirements of deformation monitoring. As Phoenix mountain road-bridge showed good flexibility in X, Y, Z and comprehensive direction, and the absolute of the maximum positive and negative deflection was 7.57 mm, which was approximately equal to 1/8 of the allowable deflection of the bridge (L/1 000), and the bridge was in good health. Bridge health monitoring and warning system could achieve the integration of data acquisition, storage, processing and display. The deformation curves could show the deformation trend of the bridge and effectively warn the potential danger.

    Table and Figures | Reference | Related Articles | Metrics
    GRU-based collaborative filtering recommendation algorithm with active learning
    Delei CHEN,Cheng WANG,Jianwei CHEN,Yiyin WU
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 21-27,48.   DOI: 10.6040/j.issn.1672-3961.0.2019.411
    Abstract614)   HTML13)    PDF(pc) (1435KB)(406)       Save

    The traditional collaborative filtering recommendation algorithm failed to reflect short-term user interest. In order to reflect the short-term interests of users better, a collaborative filtering recommendation algorithm based on Gated Recurrent Unit (GRU) neural network with active learning was proposed. Based on the GRU neural network, the algorithm processed the data into time-series data to reflect the change of the user's interest and used active learning to sample high-quality data dynamically for accelerating the training of GRU neural network. The result on MovieLens1M dataset showed that the GRU model with active learning could obtain higher short-term prediction success rate, recall rate, item coverage, and user coverage compared with the user-based collaborative filtering method (UCF), the markovian chain model-based collaborative filtering method (MC) and the matrix factory-based collaborative filtering method (LFM), so it could effectively predict the short-term interest of users, improve the accuracy, discover the long-tail items. Meanwhile, it could achieve the same effect with fewer iterations compared with the original GRU model.

    Table and Figures | Reference | Related Articles | Metrics
    Fire detection based on lightweight convolutional neural network
    Yunyang YAN,Chenxi DU,Yian LIU,Shangbing GAO
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 100-107.   DOI: 10.6040/j.issn.1672-3961.0.2019.424
    Abstract608)   HTML5)    PDF(pc) (4400KB)(229)       Save

    A novel lightweight flame detection method was proposed based on MobileNet. The video flame detection rate was promoted by the feature receptive field of DCB(dilated convolution block)module expand based on depthwise separable convolution and dilated convolution to strengthen the feature semantic information. The SSD(single shot multibox detector) detection framework was also optimized. The lightweight detection model DMSSD(Dilated MobileNet-SSD) was provided. Experiments showed that the mean average precision was increased by 1.7% and 3.8% respectively on the PASCAL VOC dataset and the VisiFire dataset of Bilkent University. Furthermore, the detection speed was up to 80 frames per second. The robustness and real-time performance of DMSSD were strong.

    Table and Figures | Reference | Related Articles | Metrics
    Multi-protocol heterogeneous fieldbus control system regulated by GPRS
    Pengfei HOU,Zhumei SUN,Qi WANG,Jianyun BAI
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 49-55.   DOI: 10.6040/j.issn.1672-3961.0.2019.228
    Abstract607)   HTML6)    PDF(pc) (3417KB)(139)       Save

    The current fieldbus control system usually only supports one or two kinds of fieldbus protocols. In view of the current situation that there were many standards of fieldbus protocols and many types of bus devices, a multi-protocol heterogeneous compatible fieldbus control system with three layers of monitoring level, control level and field equipment level was constructed for the purpose of supporting multiple protocols. By configuring gateway to solve the communication problems of heterogeneous fieldbus between PROFIBUS-DP and PROFIBUS-PA, HART, Modbus, DeviceNet, GPRS, etc., a fieldbus monitoring system with Siemens PCS7 as the upper monitor system and heterogeneous controller as the field control system was built, and the wireless and real-time embedded remote monitoring function of FCS and fieldbus intelligent instrument was realized by GPRS. The test results showed that the monitoring level could monitor and control heterogeneous fieldbus devices with different protocols remotely and real-time, and realize remote management and start-stop control of fieldbus devices. This system could be used as the feasibility verification of multi-protocol heterogeneous compatible FCS.

    Table and Figures | Reference | Related Articles | Metrics
    Improved bird swarm algorithms based on mixed decision making
    Wei YAN,Damin ZHANG,Huijuan ZHANG,Ziyun XI,Zhongyun CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 34-43.   DOI: 10.6040/j.issn.1672-3961.0.2019.294
    Abstract599)   HTML7)    PDF(pc) (1356KB)(483)       Save

    Aiming at the problems of low precision and easy to fall into local optimum in solving complex function problems of traditional bird swarm algorithm (BSA), an improved bird swarm algorithm based on mixed decision-making was proposed while retaining the simplicity of BSA. The centroid opposition-based learning was used to initialize the bird population and maintain the better spatial solution distribution of the bird flock. In order to balance the global search ability and local detection ability of the algorithm in the optimization process, the period time of the birds flying to another area was dynamically adjusted. The weighting strategy of adaptive cosine function and weighted averaging idea were introduced to improve the producer's foraging formula, so as to increase the ability of the algorithm to get rid of difficulties after falling into local optimum. The performance of improved bird swarm algorithm based on mixed decision-making, bird swarm algorithm and particle swarm optimization were compared on the basis of nine test functions. The results showed that the accuracy and speed of the improved algorithm were greatly improved in the test of single-peak and multi-peak functions.

    Table and Figures | Reference | Related Articles | Metrics
    Modified SuBSENSE algorithm via adaptive distance threshold based on background complexity
    Keyang CHENG,Shuang SUN,Yongzhao ZHAN
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 38-44.   DOI: 10.6040/j.issn.1672-3961.0.2019.413
    Abstract599)   HTML7)    PDF(pc) (2419KB)(214)       Save

    In order to solve the problem of poor adaptability of SuBSENSE algorithm in updating distance threshold in real complex scenes, which resulted in poor detection effect, SuBSENSE algorithm is proposed based on adaptive distance threshold correction of background complexity. A measure of background complexity is defined based on temporal consistency and spatial consistency, and the distance threshold correction strategy to get the accurate distance threshold as a criterion to achieve better detection results. This algorithm was compared with PBAS and traditional SuBSENSE algorithm. Experiments showed that the prospects of the proposed algorithm were more accurate in dynamic scenarios. The precision of the proposed algorithm was 6.70% and 0.80% higher than that of the PBAS algorithm and the traditional SuBSENSE algorithm, and the recall was 9.37% and 1.24% higher than that of the PBAS algorithm and the traditional SuBSENSE algorithm, respectively. After a comprehensive study of the three indicators, it was found that the proposed algorithm was superior to the contrast algorithms, and had higher robustness and detection accuracy in dynamic scenarios.

    Table and Figures | Reference | Related Articles | Metrics
    Imprecise conditional probability prediction of wind power ramp events
    Bo WANG,Buwei WANG,Ming YANG,Yuanchun ZHAO,Wenli ZHU
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 82-94.   DOI: 10.6040/j.issn.1672-3961.0.2019.178
    Abstract591)   HTML6)    PDF(pc) (2669KB)(518)       Save

    Wind power ramp events (WPRE) could easily destroy the active power balance of the power system, as well as did harm to the frequency stability and power quality, which threatened the safe and stable operation of power grid. A novel imprecise conditional probability prediction approach was proposed based on the credal network (CN), which could provide the interval range of the occurrence probability of each WPRE state. The approach excavated the dependence relationships between WPRE and meteorological variables using the greedy search algorithm, and constructed a CN structure to express the relationships in an abstract way. The proposed approach extended the imprecise Dirichlet model (IDM) on hyperparameter settings to quantify the uncertain conditional dependences among the variables, thus to realize the parameter estimation of the CN. Based on the constructed CN model, a CN probability inference algorithm was employed to estimate the imprecise probability distribution of the multi-state WPRE. The case study with wind-farm operating measurements in Ningxia Province demonstrated that the proposed approach had excellent performance even under the prediction scenarios with insufficient samples.

    Table and Figures | Reference | Related Articles | Metrics
    Voltage control method of urban distribution network considering street light charging pile access
    Shizhan SONG,Haoyu CHEN,Jian ZHANG,Kun WANG,Qingshui HAO
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 104-110.   DOI: 10.6040/j.issn.1672-3961.0.2019.138
    Abstract584)   HTML8)    PDF(pc) (1450KB)(78)       Save

    Aiming at the problem of voltage over-limit in urban distribution network, this paper proposed a voltage control method for electric vehicles participating in charging and discharging of street charging piles. The method utilized the transformer capacity released by the traditional high-pressure sodium light replaced by using the LED street light, and built a street light charging pile to participate in the voltage control of the urban distribution network as a controllable resource for the electric vehicle charged and discharged by the street light charging pile. Based on the analysis of the load characteristics of electric vehicle charging and discharging of street light charging pile, according to the characteristics of various voltage regulating resources in the distribution network, the multi-level voltage control strategy of urban distribution network was studied, and the optimal control cost of each voltage regulating measure was taken as the objective function. The voltage regulation model was established and the model was solved by particle swarm optimization. According to the characteristics of urban street lighting load, the daytime and nighttime scenarios were simulated. The simulation results verified the effectiveness of electric vehicle′s use of streetlight pile charging and discharging in urban distribution network voltage control. The control effect was verified by comparative analysis. It was better than traditional voltage control methods.

    Table and Figures | Reference | Related Articles | Metrics
    Semantic relation recognition for natural language question answering
    Jiangli DUAN,Xin HU
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 1-7.   DOI: 10.6040/j.issn.1672-3961.0.2019.417
    Abstract581)   HTML35)    PDF(pc) (1642KB)(213)       Save

    To avoid the deficiency of excessive dependence on named entity recognition during the understanding perio, logic relationships among vital information in Chinese natural language question were understood by semantic relation. An algorithm for recognizing semantic relations based on dependency structures was proposed, which first recognized three kinds of valuable dependency structures that were vital for obtaining semantic relations, and then combined or transformed these dependency structures to obtain semantic relations. The effectiveness and scalability of the proposed method were verified by extensive experiments over Chinese benchmark question answering datasets, and the experiments results showed that this method could also understand Chinese natural language questions when recognition of named entity failed.

    Table and Figures | Reference | Related Articles | Metrics
    Liquid-liquid phase separation and solidification behavior of Al65Bi28Cu7 monotectic alloy
    Na ZHANG,Yanjun YU,Yuqing WANG,Degang ZHAO
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 123-128.   DOI: 10.6040/j.issn.1672-3961.0.2019.002
    Abstract578)   HTML8)    PDF(pc) (7510KB)(174)       Save

    The liquid-liquid phase separation of Al65Bi28Cu7 monotectic alloy melt cast in copper mould was investigated by resistivity method. The formation mechanism of core-shell structure of Al65Bi28Cu7 monotectic alloy was discussed. The results showed that the anomalous changes in ρ-T curve confirmed the occurrence of liquid phase separation, monotectic reaction and eutectic reaction in the solidification of Al65Bi28Cu7 monotectic alloy melt. The anomalous change above monotectic temperature in ρ-T curve should be attributed to the concentration fluctuation of melt. The core-shell structure of Al-rich core covered by Bi-rich could form in the Al65Bi28Cu7 monotectic alloy.

    Table and Figures | Reference | Related Articles | Metrics
    Fake comment detection based on heterogeneous ensemble learning
    Dapeng ZHANG,Yajun LIU,Wei ZHANG,Fen SHEN,Jiansheng YANG
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 1-9.   DOI: 10.6040/j.issn.1672-3961.0.2019.402
    Abstract573)   HTML23)    PDF(pc) (2118KB)(252)       Save

    In view of the problem of small data set and inaccurate labeling in the field of fake comment detection, in order to prevent the vicious competition of sellers, ensure the fair trading of e-commerce platform, and protect the rights of consumers, the latest fake comment data set released by Amazon was used. The research was carried out and the related algorithms were improved. The Word2vec model could not recognize the word pairs in English. The Bigram-Word2vec model was proposed. The "two-class weighted hard voting" was proposed to solve the heterogeneous integration learning's case where the number of votes of the classifier was equal. The "weighted soft voting" was studied for how to set the weight of the classifier in heterogeneous integration learning. The experimental results showed that the improvement of related algorithms in this paper had achieved more ideal results.

    Table and Figures | Reference | Related Articles | Metrics
    Construction expansion online for a class of nonaffine nonlinear large-scale systems
    Xiaojie CAO,Xiaohua LI,Hui LIU
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 35-48.   DOI: 10.6040/j.issn.1672-3961.0.2019.039
    Abstract571)   HTML6)    PDF(pc) (1687KB)(243)       Save

    The problem of construction expansion online was studied for a class of nonaffine nonlinear interconnected large-scale systems. An adaptive neural decentralized connective stabilization method was proposed for expansion online of large-scale systems based on backstepping technique. By using neural network adaptive technique, the decentralized controller of the newly added subsystem was designed under the condition that the decentralized control laws and adaptive laws of the original system were kept to be unchanged. The interconnection parts caused the a new subsystem were dealt with in the controller of the new subsystem. An adaptive decentralized connective stabilization controller for the newly added subsystem was obtained. The controller could ensure that all the signals both in the newly added closed-loop nonaffine subsystem and the resultant expanded closed-loop large-scale system were uniformly ultimately connective bounded. The simulation results were given to verify the effectiveness of the proposed control method.

    Table and Figures | Reference | Related Articles | Metrics
    Visual sentiment analysis based on spatial attention mechanism and convolutional neural network
    Guoyong CAI,Xinhao HE,Yangyang CHU
    Journal of Shandong University(Engineering Science)    2020, 50 (4): 8-13.   DOI: 10.6040/j.issn.1672-3961.0.2019.422
    Abstract565)   HTML108)    PDF(pc) (1354KB)(199)       Save

    Existing visual sentiment analysis based on deep learning mainly ignored the intensity differences of emotional presentation in different parts of the image. In order to solve this problem, the convolutional neural network based on spatial attention (SA-CNN) was proposed to improve the effect of visual sentiment analysis. The affective region detection neural network was designed to discover the local areas of sentiment induced in images. The spatial attention mechanism was used to assign attention weights to each location in the sentiment map, and the sentiment features of each region were extracted appropriately, which was helpful for sentiment classification by using local information. The discriminant visual features were formed by integrating local region features and global image features, and were used to train the neural network classifier of visual sentiment. Classification accuracy of the method achieved 82.56%, 80.23% and 79.17% on three real datasets Twitter Ⅰ, Twitter Ⅱ and Flickr, which proved that the method could improve the visual emotion classification effect by making good use of the difference of emotion expression in the local area of the image.

    Table and Figures | Reference | Related Articles | Metrics
    Research advance in the source, water pollution status and treatment processes of perchlorate
    Baoyu GAO,Wen SONG,Guangping YAO,Qinyan YUE
    Journal of Shandong University(Engineering Science)    2020, 50 (5): 107-117.   DOI: 10.6040/j.issn.1672-3961.0.2019.572
    Abstract558)   HTML317)    PDF(pc) (2943KB)(209)       Save

    Perchlorate was widely used in military manufacturing, aerospace and industrial production, which had high stability, high water solubility, strong oxidizability and low volatility. With the flow of air and water migration, increasingly serious perchlorate water pollution caused widespread concern worldwide. Therefore, many countries carried out a comprehensive research and investigation on the source, water pollution status and treatment technology of perchlorate. However, China still had a few reports on perchlorate study, seriously neglecting the situation and treatment research of perchlorate pollution and lacking relevant environmental quality standards and safety concentration limits. This paper reviewed the source, hazard and water pollution status of perchlorate, analized the existing worldwide perchlorate concentration limit standards, and summarized the research progress of perchlorate treatment process in order to provide some references for further development of perchlorate study and formulation of relevant regulations in China.

    Table and Figures | Reference | Related Articles | Metrics
    Label distribution learning based on kernel extreme learning machine auto-encoder
    Yibin WANG,Tianli LI,Yusheng CHENG,Kun QIAN
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 58-65.   DOI: 10.6040/j.issn.1672-3961.0.2019.295
    Abstract551)   HTML6)    PDF(pc) (2735KB)(148)       Save

    In the label distribution learning framework, the example could be associated with the degree of description of the label. However, most of the algorithms were designed with complete data, and didn′t consider the noise in the data. Therefore, combined the noise reduction characteristics of the auto-encoder and the stability of the kernel extreme learning machine, the Label Distribution Learning Algorithm based on Kernel Extreme Learning Machine with auto-encoder was proposed in this paper. Firstly, we used the auto-encoder in kernel extreme learning machine to map the original feature space to obtain more robust feature representation. Secondly, we constructed the extreme learning machine model that adapted to the label distribution learning as a classifier to improve the classification efficiency and performance. Finally, the experimental results showed the proposed algorithm had certain advantages over other label distribution learning algorithms, and the hypothesis test method further illustrated the effectiveness of the algorithm.

    Table and Figures | Reference | Related Articles | Metrics
    An integrated learning approach for O3 mass concentration prediction model
    Yan PENG,Tingting FENG,Jie WANG
    Journal of Shandong University(Engineering Science)    2020, 50 (4): 1-7.   DOI: 10.6040/j.issn.1672-3961.0.2019.423
    Abstract547)   HTML244)    PDF(pc) (2691KB)(166)       Save

    In order to accurately predict O3 mass concentration and development trend and to analyze inducing factors, an O3 mass concentration prediction model based on integrated learning was proposed. A multilayer FS-IL model for the O3 pollutant mass concentration was established in accordance with the data of O3 pollutant mass concentration and meteorological factors from 2015 to 2016 in Beijing, on the basis of missing value filling and outlier analysis, Pearson correlation analysis and Lasso regression analysis were used to select features of the cleaned meteorological data to eliminate data redundancy and improve prediction accuracy; an integrated learning algorithm based on self-organizing featuremap (SOFM)-Elman neural network (ENN) was proposed. After clustering sample data with SOFM to realize reasonable distribution of samples, ENN was used for simulation training to predict O3 mass concentration. The experimental results showed that the accuracy of ENN-based O3 pollutant mass concentration prediction was improved from 74.6% to 82.1% after the preliminary processing of data with Pearson-Lasso feature selection and SOFM sample clustering.

    Table and Figures | Reference | Related Articles | Metrics
    Pollution characteristics and atmospheric transmission of PM2.5 and PM1.0 in Jinan city
    Qi HUANG,Lingxiao YANG,Yanyan LI,Pan JIANG,Ying GAO,Wenxing WANG
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 95-100, 108.   DOI: 10.6040/j.issn.1672-3961.0.2019.001
    Abstract547)   HTML5)    PDF(pc) (3561KB)(277)       Save

    To study the characteristics of PM2.5 and PM1.0 in the North China Plain, atmospheric particulate samples were collected from October, 2014 to June, 2016 in Jinan urban area by using mid-volume samplers. Then we determined iron composition of Water-soluble inorganic ions with ion chromatography(IC)and carbonaceous component with thermal-optical transmittance (TOT) carbon aerosol analyzer. It was shown that the fine particle pollution of the atmosphere was serious in winter. The secondary ions SO42-, NO3- and NH4+ were the major water-soluble ions of PM2.5 and PM1.0, especially easily enriched in PM1.0. Compared with autumn and winter, concentrations of organic carbon(OC) and elemental carbon (EC) were much lower in spring and summer. The mass concentration of SOC, most of which was distributed in particles with particle size >1 μm, increased obviously in winter. Indicated by the 72 h backward trajectories, long-distance transmission from Hebei and Inner Mongolia, as well as local transmission from Shan- dong had an important influence on the ion mass concentration of PM2.5 and PM1.0 in the atmosphere of Jinan. The extinction coefficient of Jinan was up to 789.13 Mm-1 in winter. The extinction coefficient had a high correlation with secondary particles NH4+, SO42- and NO3- in PM2.5, which was the chief reason of the reduction in the visibility of the atmosphere.

    Table and Figures | Reference | Related Articles | Metrics
    MR image classification and recognition model of breast cancer based onGabor feature
    Gaoteng YUAN,Yihui LIU,Wei HUANG,Bing HU
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 15-23.   DOI: 10.6040/j.issn.1672-3961.0.2019.305
    Abstract546)   HTML11)    PDF(pc) (7621KB)(243)       Save

    To investigate the clinical value of breast tumor magnetic resonance (MR) images in differentiating fibroadenoma of breast (FB), invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), the region of interest of MR image was selected and the MR image was decomposed by wavelet transform, and the region of tumor was segmented by K-means algorithm. Gabor wavelet was used to filter the region of interest from 8 directions and 5 scales, and the mean value of the tumor location was taken as the feature. The extracted features were analyzed and the key features were obtained. Different classification algorithms were compared in machine learning, such as support vector machine, Bayesian, and neural network, to classify and predict the key features, and calculate the accuracy, sensitivity and specificity of classification, so as to get the most suitable parameter settings for classification model. Texture analysis of breast MR images could distinguish three kinds of common breast tumors, and the prediction accuracy was 77.36%, which showed that MR image had important clinical value in differentiating FB, IDC and ILC.

    Table and Figures | Reference | Related Articles | Metrics
    Design of triple-cables limiting-location anti-swing device for shipboard crane
    Zhaopeng REN,Rui XI,Shenghai WANG,Zhijiang ZHANG,Haiquan CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 125-132142.   DOI: 10.6040/j.issn.1672-3961.0.2019.004
    Abstract538)   HTML4)    PDF(pc) (4845KB)(474)       Save

    To reduce the payload pendulation of shipboard cranes, a mechanical anti-swing device based on triple-cables limiting-location was proposed. Three cables were used to pull the hook, which limited the spatial position of the payload to prevent the payload pendulation. According to the established kinematic model of the shipboard crane, the effect of length on the anti-swing was analyzed. The dynamic model of the payload system was established, and the effect of tension value on the anti-swing was analyzed. The models were verified by physical experiment based on a self-built test platform. The experimental results proved that the proposed mechanical anti-swing device based on triple-cables limiting-location had good anti-swing effect in practical applications. The overall anti-swing effect could reach more than 61%.

    Table and Figures | Reference | Related Articles | Metrics
    Impact analysis of road traffic on urban air quality in congested environment
    FENG Haixia, WANG Qi, YANG Licai, KOU Junying, XIE Qingmin, ZHAO Junxue, MENG Xianglu, WANG Yanfeng
    Journal of Shandong University(Engineering Science)    2021, 51 (1): 128-134.   DOI: 10.6040/j.issn.1672-3961.0.2020.211
    Abstract533)      PDF(pc) (3465KB)(166)       Save
    Focusing on research hot issues of traffic congestion, haze(air quality), and the main urban area of Jinan taken as an example, the paper quantitatively analyzed the impact of peak congestion delay index and traffic operation index on urban air quality. Combining with satellite retrieval of aerosol optical depth(AOD ), the impact of road traffic on air quality in congestion environment was quantitatively analyzed based on geographical weighted regression model. The results showed that there was a strong correlation between the peak congestion delay index and the air quality index. The traffic operation had great influence on air quality. Geographically weighted regression(GWR)refined local spatial features. Under traffic congestion conditions, the road area occupancy rate had the greatest impact on air quality in the region. The paper had certain guiding significance for traffic planning and provided support for traffic planning and control.
    Reference | Related Articles | Metrics
    A syntactic element recognition method based on deep neural network
    Yanping CHEN,Li FENG,Yongbin QIN,Ruizhang HUANG
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 44-49.   DOI: 10.6040/j.issn.1672-3961.0.2019.313
    Abstract532)   HTML7)    PDF(pc) (1711KB)(258)       Save

    It was difficult to obtain structural information in Chinese sentences by the traditional feature method. To solve the problem, according to characteristics of Chinese sentence, a Bi-LSTM-Attention-CRF model was proposed based on deep neural network. A Bi-LSTM network was used to automatically extract structural information and semantic information from raw input sentences. Attention mechanism was adopted to weight abstract semantic features for classification. An optimized label sequence was output through the CRF layer. Comparing with other methods, our model could effectively identify syntactic elements in sentences. The performance reached to 84.85% in F1 score in the evaluation data sets.

    Table and Figures | Reference | Related Articles | Metrics
    Semantic analysis and vectorization for intelligent detection of big data cross-site scripting attacks
    Haijun ZHANG,Yinghui CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 118-128.   DOI: 10.6040/j.issn.1672-3961.0.2019.043
    Abstract531)   HTML7)    PDF(pc) (2001KB)(203)       Save

    The access traffic corpus big data were processed with word vectorization based on the methods of semantic scenario analysis and vectorization, and the intelligent detection oriented to big data cross-site scripting attack was realized. It used the natural language processing methods for data acquisition, data cleaning, data sampling, feature extraction and other data preprocessing. The algorithm of word vectorization based on neural network was used to realize word vectorization and get big data of word vectorization. Through theoretical analysis and deductions, the intelligent detection algorithms of varieties of long short term memory networks with different layers were realized. With different hyperparameters and repeated tests, lots of results were got, such as the highest recognition rate for 0.999 5, the minimum recognition rate for 0.264 3, average recognition rate for 99.88%, variance for 0, standard deviations for 0.000 4, the curve diagram of recognition rates change, the curve diagram of error of loss change, the curve diagram of cosine proximity change of word vector samples and the curve diagram of mean absolute error change etc. The results of the study showed that the algorithm had the advantages of high recognition rates, strong stability and excellent overall performance, etc.

    Table and Figures | Reference | Related Articles | Metrics
    Numerical simulation of mechanical properties of layered jointed rock mass
    Ziyao XU,Song YU,Qiang FU
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 66-72.   DOI: 10.6040/j.issn.1672-3961.0.2019.393
    Abstract528)   HTML8)    PDF(pc) (8008KB)(100)       Save

    GDEM software was used to analyze the mechanical properties of parallel layered jointed rock masses from different angles, and explore the influence of the change of the angle between the joint angle and the loading direction on the failure mode of the specimen, the parallel layered joint model was established by using different interlayer rock materials commonly in engineering. Through three loading methods, such as static load uniaxial compression, biaxial compression and pure shear, failure forms of such rock mass model at different inclined angles, the stress-strain relationship in the loading process and the variation trend of peak load were analyzed. This study found that the mechanical properties and peak strength of the parallel-level jointed rock mass were directly related to the joint inclination angle. It was found by simulation that the parallel-level jointed rock mass obvious elastic-brittle mechanics under three loading conditions.

    Table and Figures | Reference | Related Articles | Metrics
    A Chirplet neural network for automatic target recognition
    Yifei LI,Zunhua GUO
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 8-14.   DOI: 10.6040/j.issn.1672-3961.0.2019.062
    Abstract528)   HTML7)    PDF(pc) (1794KB)(191)       Save

    Aiming at automatic target recognition of aircrafts, a Chirplet neural network for joint feature extraction and target classification was proposed to realize recognition of one-dimensional high resolution range profiles. Based on the multilayer feedforward neural network structure, the Chirplet-atom transform was used to replace the conventional excitation function in the input layer for feature extraction, and the hidden layer and output layer constituted the classifier of the network. The network weights and the parameters of Chirplet-atom node were simultaneously adjusted and optimized to achieve joint feature extraction and target classification. The simulation results of the four types of aircrafts showed that the Chirplet neural network method with the four-feature-parameters had higher recognition rate and anti-noise performance than the time-frequency transformation and Gabor atoms network.

    Table and Figures | Reference | Related Articles | Metrics
    Study on modeling methods of wastewater treatment processes with canonical correlation analysis
    Hongbin LIU,Liu SONG
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 101-108.   DOI: 10.6040/j.issn.1672-3961.0.2018.552
    Abstract516)   HTML7)    PDF(pc) (7972KB)(203)       Save

    With the improvement of public awareness of environmental protection, the discharge of industrial wastewater became a crucial issue in industrial production. The typical water quality models were based on static models which ignored the dynamic information in process variables, resulting in the reduction in the accuracy of model prediction and the generalization ability of the models. Considering the time-varying and dynamic characteristics of process variables, a time difference model embedded into canonical correlation analysis was proposed in this paper. The effect of the order of the time difference model on the prediction accuracy was also analyzed. Compared with the traditional canonical correlation analysis, the root mean square error values of effluent chemical oxygen demand and effluent total nitrogen were reduced from 1.502 8 to 0.564 5 and from 2.344 0 to 1.192 6, respectively. The correlation coefficient values were increased from 0.422 7 to 0.847 0 and from 0.405 9 to 0.793 6, respectively. The results indicated that the prediction accuracy and generalization ability of the model were both improved.

    Table and Figures | Reference | Related Articles | Metrics
    Treatment of coastal soft foundation with cement-soil mixing pile
    Guoren LÜ,Jiandong GE,Haitao XIAO
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 73-81.   DOI: 10.6040/j.issn.1672-3961.0.2019.256
    Abstract509)   HTML5)    PDF(pc) (10192KB)(104)       Save

    Direct construction of roads and railways on coastal soft soil foundation leads to subgrade instability and other problems, and foundation treatment must be carried out. Based on the actual project, the pile arrangement simulation analysis was carried out and the pile arrangement parameters of cement-soil mixing piles were optimized. Through indoor mix proportion test and on-site pile forming test, the influencing factors of cement-soil strength and pile forming quality of cement-soil mixing pile were analyzed. The results showed that the proposed pile arrangement scheme was safe and feasible and cost saving. The optimal range of cement content in cement soil was 16%-18%, and the unconfined compressive strength at short-term age could reach 60%-70% of the standard age, which shortened the construction period; During the construction, the pile-forming technology of four stirring and four spraying was of the best quality. Preservatives were very important to the quality and durability of pile body. Through pile quality inspection, it was comprehensively judged that the reinforcement effect of cement-soil mixing pile in this project meeted the requirements. The research results had certain reference value for similar projects and provide on-site basis for the formulation of technical standards and construction methods.

    Table and Figures | Reference | Related Articles | Metrics
    Identification of the same product feature based on multi-dimension similarity and sentiment word expansion
    Longmao HU,Xuegang HU
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 50-59.   DOI: 10.6040/j.issn.1672-3961.0.2019.403
    Abstract501)   HTML8)    PDF(pc) (1624KB)(233)       Save

    Because the existing methods for identifying the same product features were limited by the lack of dictionary coverage or corpus size, an identification method was proposed based on multidimensional similarity and sentiment word expansion. Extracting emotional words of product features through bi-directional long short-term memory and conditional random field (Bi-LSTM-CRF), combining the morpheme similarity, Cilin similarity and term frequency-inverse document frequency (TF-IDF) cosine similarity of product feature words, the same product features were identified by K-medoids clustering algorithm. The experimental results showed that, on mobile and notebook datasets, the maximum adjusted rand index (ARI) reached 0.579 and 0.595 9 respectively, while the minimum entropy reached 0.782 6 and 0.745 7. The proposed method was superior to the adjusted Jaccard similarity combined morpheme, Word2Vec similarity and Word2Vec similarity based on bisecting K-means.

    Table and Figures | Reference | Related Articles | Metrics
    Ant colony optimization for solving maximization problem based ondouble heuristic information
    Jun QIN,Weidong LI,Jinli YI,Jing LIU,Maode MA
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 45-50.   DOI: 10.6040/j.issn.1672-3961.0.2019.306
    Abstract491)   HTML5)    PDF(pc) (1564KB)(150)       Save

    With How to use influence of social individuals to expand the scope of information dissemination was an Influence Maximization problem, which had become an important research field. A new ant colony algorithm was propsed to solve the problem, in the initial node selection process, we introduced two heuristic information to measure node-influence: priority selected nodes that were less likely to be activated by the precursor node; considered the impact of successors, especially multi-level successors node on the influence of spread. Based on this, a new ant colony optimization algorithm was proposed. The experiments showed that our method improved the problem of initial node selection, which was easy to fall into the local optimum, the results were better than the greedy method and the traditional ant colony optimization algorithm in the efficiency(raise 25%) and range of initial node dissemination(add 150 nodes).

    Table and Figures | Reference | Related Articles | Metrics
    A scheduling algorithm based on multi-objective container cloud task
    Xiaolan XIE,Qi WANG
    Journal of Shandong University(Engineering Science)    2020, 50 (4): 14-21.   DOI: 10.6040/j.issn.1672-3961.0.2018.210
    Abstract487)   HTML96)    PDF(pc) (1365KB)(132)       Save

    In order to solve the unrealistic, unfair, inefficient and unbalanced problems caused by container cloud scheduling model facing isomorphic tasks, isomorphic resources and single objectives, a tree scheduling objective model with constraint repair was proposed. Based on heterogeneous tasks and resources, constraint repair was adopted to avoid the impracticability of mapping scheme, and then priority to synthesize multiple sub-goals and attributed them to sub-spaces under different tree branches, and eventually achieved a fair, efficient, economical and balanced scheduling model among multiple upper application frameworks. The experimental results showed that the tree scheduling objective model with constrained repair was not inferior to other single-objective models in fairness, which could meet more tasks, and had higher resource utilization and load balancing under the preceding conditions. It was superior to the single-objective model in practicability, fairness, efficiency and balancing and ensured fair allocation of resources, which increased the benefits of container services, decreased the cost of physical resources, increased the stability and availability.

    Table and Figures | Reference | Related Articles | Metrics
    Risk assessment method based on spatial hidden danger distribution and motion intention analysis
    Yuenan ZHAO,Guiyou CHEN,Chen SUN,Ning LU,Liwei LIAO
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 28-34.   DOI: 10.6040/j.issn.1672-3961.0.2019.179
    Abstract486)   HTML9)    PDF(pc) (7769KB)(254)       Save

    To solve problems of slow detection and lack of behavioral safety analysis in intelligent nursing, a risk assessment method based on spatial hidden danger distribution and motion intention analysis was proposed. The k-means++ algorithm and background elimination method were used to enhance the object detection capability of YOLO(you only look once) v3, which led to the classification and localization of human targets as well as dangerous objects. The Kalman filter was used to predict the moving trajectory, where two parameters, namely the deviation angle of motion and the distance between the human target and danger, were used to construct the human movement patterns. The risk assessment model was established, and the degree of danger was evaluated according to different movement behavior patterns. Experimental results showed that for identifying different objects in the test set, both the detection precision and the recall rate of the enhanced YOLOv3 algorithm were over 95%. An increasement of IOU(intersection over union) at 7% was witnessed, and frames rate reached 31.3 frames/s. These results proved the real-time performance of the system. Since the proposed risk assessment model incorporated motion intentions of the human target, this method was expected to boost the performance in fitting the risk progression of different movement patterns, making the risk assessment more reasonable.

    Table and Figures | Reference | Related Articles | Metrics
    Visual tracking algorithm based on verifying networks
    Ningning CHEN,Jianwei ZHAO,Zhenghua ZHOU
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 17-26.   DOI: 10.6040/j.issn.1672-3961.0.2019.418
    Abstract484)   HTML16)    PDF(pc) (3959KB)(169)       Save

    In order to solve the problem that the existing deep learning based visual tracking algorithms paid attention to the deep features but neglected the shallow features, and the tracking network did not evaluate the tracking results, a visual tracking algorithm based on verifying network was proposed. The proposed algorithm consisted of tracking network and verifying network. In the tracking network, considering the fusion of deep features and shallow edge features, a multi-input residual network was designed to learn the relationship between the target and its corresponding Gaussian response map to obtain the position information of the target. In the verifying network, a shallow chain discriminate network was designed, and this paper compared the tracking results of tracking network and verifying network, and updated the tracking network according to the compared results. Therefore, the proposed algorithm not only took the deep features into account, but also avoided the loss of detail information. Furthermore, the tracking results were evaluated to prevent the continuation of error messages in the update. The experimental results illustrated that the proposed tracking algorithm achieved better tracking results than some other existing tracking methods.

    Table and Figures | Reference | Related Articles | Metrics
    Multi-infeed HVDC simultaneous commutation failure risk evaluation method considering synchronous condenser reactive power
    Changhui MA,Liang WANG,Shaoqing TAN,Yi LU,Huan MA,Kang ZHAO
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 98-103.   DOI: 10.6040/j.issn.1672-3961.0.2019.575
    Abstract482)   HTML4)    PDF(pc) (1571KB)(147)       Save

    In order to improve the "Strong alternating current and weak direct current" characteristics of multi-infeed high voltage direct current systems, the synchronous condenser gradually gained attention.For the problem of multi-infeed high voltage direct current with synchronous condenser simultaneous commutation failure, a risk evaluation method considering the synchronous condenser reactive voltage characteristics was proposed. Firstly, the reactive voltage characteristics of the synchronous condenser were analyzed, and the multi-infeed interaction factor was calculated by reactive voltage sensitivity of the synchronous condenser, high voltage direct current system and static reactive power compensation device. Then, based on the nature of the commutation failure, the commutation failure evaluation factor was defined, and the simultaneous commutation failure risk of the multi-infeed high voltage direct current system with synchronous condenser was evaluated. Finally, the Shandong power grid was taken as an example to verify the results that the proposed method could effectively evaluate the multi-infeed high voltage direct current simultaneous commutation failure, and it was important in the early planning of the high voltage direct current transmission system and ensuring the stability and security of the power system.

    Table and Figures | Reference | Related Articles | Metrics
    Experimental study on mechanical parameters and wave velocity variation of sandstone under high ground stress
    Jiachen GONG,Shihai CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 82-87, 97.   DOI: 10.6040/j.issn.1672-3961.0.2019.622
    Abstract473)   HTML7)    PDF(pc) (1822KB)(107)       Save

    A large number of studies showed that high ground stress had a certain influence on the wave velocity of deep buried rock, based on the wave equation, a mathematical model of the relationship between longitudinal wave velocity of sandstone and hydrostatic confining pressure was proposed. Based on the conventional triaxial test of rock, the static elastic modulus, static Poisson's ratio and longitudinal wave velocity of sandstone under different hydrostatic confining pressures were obtained, and the fitting curves and fitting formulas of static elastic modulus-hydrostatic confining pressure and static Poisson's ratio-hydrostatic confining pressure were obtained respectively. The test results showed that the static elastic modulus and static Poisson's ratio of sandstone increased with the increase of hydrostatic pressure, and the rate of increase of static elastic modulus decreased slowly. Based on the wave equation, the mathematical model of the longitudinal wave velocity-hydrostatic confining pressure was obtained, the longitudinal wave velocity calculated by the mathematical model showed that the longitudinal wave velocity of the sandstone increased with the increase of the hydrostatic pressure, and the increasing rate gradually became slower. The calculated longitudinal wave velocity was compared with the measured, the error range was 7.0%-8.3%. Therefore, the mathematical model of sandstone longitudinal wave velocity-hydrostatic confining pressure based on wave equation was reliable and accurate, it was of guiding significance to analyze and judge the physical and mechanical parameters of rock under high ground stress and the variation law of wave velocity.

    Table and Figures | Reference | Related Articles | Metrics
    Discussion on emergency control of central air conditioner at large receiving-end grid to cope with HVDC blocking fault
    Meng LIU,Dingyi CHENG,Wen ZHANG,Hengxu ZHANG,Kuan LI,Guohui ZHANG,Jianjun SU
    Journal of Shandong University(Engineering Science)    2020, 50 (1): 72-81.   DOI: 10.6040/j.issn.1672-3961.0.2019.201
    Abstract473)   HTML6)    PDF(pc) (5590KB)(322)       Save

    The central air conditioner had the potential to cope with the DC blocking fault through emergency control, ensure the safe and stable operation of the receiving-end grid after suffering from large power shortage. The basic principle of the central air conditioner was introduced. On this basis, the physical model of the central air conditioner which included central air conditioning unit, temperature change of frozen inflow and outflow water, heat exchange between the cooling water of the fan coil and the room, indoor average temperature change as well as the proportion of the room in the open state were established. An emergency control strategy for direct power cut and flexible recovery of central air conditioning was proposed. The feasibility of the emergency control of the central air conditioning system in response to stability control and under frequency/voltage load shedding were discussed respectively. The characteristics of the central air conditioning emergency control were simulated. The emergency control of central air conditioner was simulated after HVDC blocking fault occurs in Shandong power grid, verifying that the power grid frequency could be increased by 0.04 Hz when central air conditioners accounted for 1% of the total load in Shandong power grid.

    Table and Figures | Reference | Related Articles | Metrics
    Method for super-resolution using parallel interlaced sampling
    An ZHU,Chu XU
    Journal of Shandong University(Engineering Science)    2020, 50 (2): 10-16,26.   DOI: 10.6040/j.issn.1672-3961.0.2019.318
    Abstract459)   HTML11)    PDF(pc) (5209KB)(101)       Save

    Various Internet-based images and artificial intelligence applications were more sensitive to the quality of image data. The image quality had been seriously affected due to the limitations of previous acquisition equipment and transmission methods. In order to compensate for the loss of image data quality and enhance the image effect, a parallel interlaced up and down sampling network (PSUDN) was proposed as a better solution to this problem, which using parallel high resolution feature (HR Feature) and low resolution feature (LR Feature) interleaving sample to generated advanced feature maps, and improved the quality of the output high-resolution pictures by building parallel high resolution feature modules and low resolution feature modules. The model constructed by parallel upsampling and downsampling could reconstruct 8 times high resolution pictures and achieved better results.

    Table and Figures | Reference | Related Articles | Metrics
    Application of variable selection in soft sensor modeling of wastewater treatment processes
    Hongbin LIU,Qiyue WU,Liu SONG
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 133-142.   DOI: 10.6040/j.issn.1672-3961.0.2019.009
    Abstract456)   HTML4)    PDF(pc) (9397KB)(130)       Save

    Chemical oxygen demand and suspended solid were important monitoring indices of effluent discharge in paper-making industry. An effective model of effluent quality of wastewater treatment processes was of key importance to monitoring and controlling pollution emission. Concerning the strong correlations among the input variables and the complicated characteristics of wastewater treatment processes in paper-making industry, partial least squares (PLS) method was applied to extract information of variables importance in projection (VIP) for variable selection (VS). Then the optimal variables were chosen as new input variables for soft sensor models to predict the effluent qualities of a papermaking wastewater treatment process. Compared to the LSSVM model, the root mean square error (RMSE) of VS-based LSSVM model was reduced by 15.2%, and the correlation coefficient (r) was increased by 14.4%. For the effluent SS, the value of RMSE was decreased by 20.5%, and the value of r was increased by 16.1%. The results showed that the proposed method not only reduced the model complexity, but also enhanced the model generalization capacity.

    Table and Figures | Reference | Related Articles | Metrics
    Eye tracking in human-computer interaction control
    Hui HE,Junhao HUANG
    Journal of Shandong University(Engineering Science)    2021, 51 (2): 1-8.   DOI: 10.6040/j.issn.1672-3961.0.2020.346
    Abstract442)   HTML46)    PDF(pc) (4362KB)(195)       Save

    To actualize the simple and low-cost eye-tracking based human-computer interaction, an exact interaction method based on the visual directions estimation and eye tracking with webcam videos was proposed. A simple and fast convolution neural network model was used to roughly estimate the user′s viewpoints on the screen. And then an accurate human-computer interaction method was proposed on the basis of the eye movements recognition and sight line tracking results. To verify the effectiveness of the method, the key operations of eye mouse and eye typing were developed. The test results show that the proposed method enabled users to achieve eye tracking and to actualize most precise human-computer interactions with only one common monocular camera, which was expected to completely replace the mouse and keyboard hardwares.

    Table and Figures | Reference | Related Articles | Metrics