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    A survey of image captioning methods based on deep learning
    Zhifu CHANG,Fengyu ZHOU,Yugang WANG,Dongdong SHEN,Yang ZHAO
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 25-35.   DOI: 10.6040/j.issn.1672-3961.0.2019.244
    Abstract998)   HTML32)    PDF(pc) (7881KB)(352)       Save

    Image captioning is the cross-research direction of computer vision and natural language processing. This paper aimsed to summarize the deep learning methods in the field of image captioning. Imgage captioning methods based on deep learning was summarized into five categories: multimodal space based method, multi-region based method, enconder-deconder based method, reinforcement learning based method, and generative adversarial networks based method.The datasets and evaluation metrics were demonstrated, and experimental result of different methods were compared. The three key problems and future research direction for image captioning were presented and summarized.

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    Review of capsule network
    Jucheng YANG,Shujie HAN,Lei MAO,Xiangzi DAI,Yarui CHEN
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 1-10.   DOI: 10.6040/j.issn.1672-3961.0.2019.312
    Abstract601)   HTML49)    PDF(pc) (4331KB)(495)       Save

    Recently capsule network with dynamic routing was the new neural network model which was considered a significant model in next generation. In recent years, much research evidenced capsule network exceptional ability to fit features. But the high computational overhead made it unable to fit complex and large datasets. Consequently, reducing computational became a research hotspot. There were two methods, including optimized capsule and optimized routing, to solve the issue. Optimized capsule was usually driven by application purpose which was designed as a model of specific classification tasks. And optimized routing was the way to improve the performance of the model from an algorithmic perspective.

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    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
    Abstract438)   HTML14)    PDF(pc) (1549KB)(400)       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.

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    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
    Abstract343)   HTML899)    PDF(pc) (2290KB)(536)       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.

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    Research status and development trend of autonomous cognition and learning of robot manipulation skills
    Wei WANG,Feng WU,Fengyu ZHOU
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 11-24.   DOI: 10.6040/j.issn.1672-3961.0.2019.229
    Abstract319)   HTML10)    PDF(pc) (2077KB)(230)       Save

    Autonomous cognition and learning of manipulation skills, being one of the most important skills for robots, has been one of the hot issues in the field of robotics research. Combining with the authors' work in the field of robotics, this paper's focus is placed on giving a comprehensive overview of the mainstream modes, methods, algorithms, as well as advantages and disadvantages of different methods in terms of robots' manipulation skill learning. It concludes the challenges faced by autonomous learning and the key issues that need to be addressed for the individual cloud robots learning manipulation skills in the knowledge sharing mode. At the end, a potential solution for the above issues is given, and that is to integrate individual learning mode and shared learning model for the purpose of enhancing autonomous cognition and learning ability for robots.

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    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
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    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.

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    Design of robot cloud service platform based on microservice
    Lei YIN,Fengyu ZHOU,Ming LI,Yugang WANG,Yinbo GUO,Ke CHEN
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 55-62, 80.   DOI: 10.6040/j.issn.1672-3961.0.2019.251
    Abstract296)   HTML1)    PDF(pc) (4187KB)(181)       Save

    To improve the intelligent level of service robots, a cloud service design method for service robots based on micro-service was proposed in this paper. The framework of cloud service based on micro-service was designed based on virtual model of service robot. Kinematics model, sensor model and environment model were proposed to map the robot parameters to cloud center. The interactive interface for cloud services was proposed. The adaptive matching of heterogeneous protocols was realized by using the protocol response of robots. Cloud service development methods were proposed in detail. The experiment result were done to demonstrate the cloud service results, real time and fine-grained quality of service of the proposed cloud service design for service robots.

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    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
    Abstract249)   HTML7)    PDF(pc) (5481KB)(108)       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.

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    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
    Abstract230)   HTML0)    PDF(pc) (4733KB)(220)       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.

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    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
    Abstract222)   HTML10)    PDF(pc) (3224KB)(94)       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.

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    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
    Abstract219)   HTML4)    PDF(pc) (1167KB)(263)       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%.

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    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
    Abstract209)   HTML8)    PDF(pc) (2118KB)(161)       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.

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    Flow resistance characteristics of wire mesh porous media channel based on pore-scale
    Wei HU
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 119-126.   DOI: 10.6040/j.issn.1672-3961.0.2019.503
    Abstract206)   HTML12)    PDF(pc) (2325KB)(97)       Save

    Through the pore-scale of the mesh porous media channel numerical analysis, the flow resistance characteristics of wire mesh channel with different geometric parameters were studied, including pressure drop ΔP, viscous resistance Au and inertial resistance Bu2. A three-dimensional steady-state modified k-ωturbulence model was developed by CFD software, and five four-cell pore models with different wire diameters and pore diameters were selected. Numerical analysis on flow resistance characteristics in wire mesh velocity numbers, i.e., from 0.2 m/s to 1.0 m/s were performed. The characteristics of flow in pore-level channels with different configurations under the range of low velocity numbers were obtained. It was shown that the configuration had a significant influence on the nonlinear flow characteristics of the wire mesh channel. The results showed that the smaller the mesh configuration angle (θ=45°~90°), the greater flow resistance in the channel, however, the partial pressure ratio was the same. It also indicated that faster the flow velocity (v=0.2~1.0 m/s), the greater the nonlinear effect and more inertial resistance would be.

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    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
    Abstract194)   HTML2)    PDF(pc) (12734KB)(58)       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.

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    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
    Abstract193)   HTML1)    PDF(pc) (1403KB)(127)       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.

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    A robot service recognition mechanism based on ontology in smart home
    Linglong KONG,Guohui TIAN
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 45-54.   DOI: 10.6040/j.issn.1672-3961.0.2018.495
    Abstract192)   HTML0)    PDF(pc) (5046KB)(208)       Save

    In order to enhance the ability of robots to provide different types of services, a classification service reasoning method based on semantic web rule language was proposed for service robots in smart home environments. The ontology model of smart home was established by ontology technology, which integrated data from different data sources and eliminated the heterogeneity between devices. The classification of service types were based on the service characteristics of robot service system in smart home. With historical context information, the service rule bases were set up. The reasoning engine could match the real-time context information and service rules to realize the service reasoning of the robot. The experimental results showed that the robot service reasoning method could achieve different types of service reasoning in smart home environments and further improve the intelligence of the robot service.

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    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
    Abstract189)   HTML4)    PDF(pc) (4029KB)(84)       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.

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    Study on a low-speed direct drive transverse-flux switched reluctance motor
    Zhenwei ZHAO,Zhigang DONG,Yongbin LI
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 81-85.   DOI: 10.6040/j.issn.1672-3961.0.2019.339
    Abstract188)   HTML1)    PDF(pc) (2985KB)(125)       Save

    In order to improve the torque density of switched reluctance motor (SRM), achieve low speed and large torque output, and meet the requirements of direct electrical drive systems such as servo and electric vehicle, a new type of transverse magnetic flux switch reluctance motor (TFSRM) was proposed. The principle of transverse flux motor was applied to switch reluctance motor, which simplified the complexity of the motor structure and improved the torque density of the motor. The working principle and structural characteristics were introduced in detail. The magnetic field distribution of the stator and rotor in different relative positions were analyzed by the three-dimensional equivalent magnetic network method, and the torque angle characteristics of the motor were calculated under constant current control. A closed-loop current control system based on digital signal processor was designed, and the prototype was tested. The experimental results were consistent with the theoretical analysis, which verified the feasibility and validity of the theoretical analysis and design method.

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    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
    Abstract183)   HTML3)    PDF(pc) (1435KB)(277)       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.

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    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
    Abstract175)   HTML0)    PDF(pc) (2669KB)(326)       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.

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    Weighted value of solar tariff based on time-of-use electricity price
    Fangyun HAN,Liang QIAO,Bincheng ZHAO,Li ZHANG
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 93-97,106.   DOI: 10.6040/j.issn.1672-3961.0.2019.617
    Abstract171)   HTML1)    PDF(pc) (1384KB)(84)       Save

    Value of solar tariff is a new way to solve the problem of unfair allocation of power supply costs and revenue decrease of power grid company caused by distributed photovoltaic power generation. Because the existing model did not consider the change of power supply cost under different operating conditions of power grid, a weighted solar energy value calculation method based on time-of-use electricity price was proposed in the paper. The operation conditions of the grid were typificated as peak, flat and valley periods, the setting of relevant parameters was discussed and an improved model was established. The new model accounted more for the time consistency of photovoltaic power generation and grid operation, and could reflect the value of photovoltaic power generation more accurately. Simulation analysis were done, the results demonstrated the effectiveness of the proposed model.

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    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
    Abstract169)   HTML4)    PDF(pc) (3123KB)(161)       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.

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    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
    Abstract165)   HTML0)    PDF(pc) (4400KB)(99)       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.

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    Complete coverage path planning for mobile robots based on hyperchaotic synchronization control
    Caihong LI,Chun FANG,Zhiqiang WANG,Bin XIA,Fengying WANG
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 63-72.   DOI: 10.6040/j.issn.1672-3961.0.2019.173
    Abstract165)   HTML1)    PDF(pc) (6359KB)(170)       Save

    Based on the requirements of randomness and completeness of the mobile robots under special tasks such as the surveillance, patrol, etc., a complete coverage path planning method for robots by the hyperchaotic synchronization control strategy was proposed. The four-dimensional hyperchaotic Lorenz system was used as the main driving equation, and the hyperchaotic synchronization response equation was constructed by the single-coupled hyperchaotic synchronization control. A path planner of the chaotic robot was constructed by combining synchronized hyperchaotic synchronous response system with kinematics equation of mobile robot to produce the complete coverage path and satisfy the requirements of the special tasks. The mirror mapping method was used to limit the running range of the coverage trajectory and to avoid the static obstacles at the workspace boundary. Qualitative analysis and quantitative calculations of the planned trajectories showed that the coverage trajectories produced by hyperchaotic synchronization method had better coverage rate and randomness, which could meet the requirements of autonomous mobile robots for special tasks.

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    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
    Abstract162)   HTML3)    PDF(pc) (7510KB)(79)       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.

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    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
    Abstract162)   HTML2)    PDF(pc) (7972KB)(137)       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.

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    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
    Abstract161)   HTML2)    PDF(pc) (1687KB)(155)       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.

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    Configuration design and interference analysis of multi-arm robot for mandible reconstruction
    Honghua ZHAO,Jian ZHAO,Xingguang DUAN,Zhitong HU,Qianqian TIAN,Yaohua ZHAO
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 73-80.   DOI: 10.6040/j.issn.1672-3961.0.2019.185
    Abstract160)   HTML0)    PDF(pc) (5769KB)(207)       Save

    Based on the requirements of mandible reconstruction surgery and the design criteria of surgical robots, a seven degree of freedom three-arm configuration with active and passive combination was proposed. In order to meet the requirement of high efficiency of interference analysis for manipulator of surgical robot, the linear swept sphere element model was used to simplify the manipulator with complex structure. The interference mathematical models between two cylinders, between cylinders and hemispheres, and between two hemispheres were constructed. The concept of effective interference points and the method of judging the effectiveness of interference points were proposed. The interference judgment of the manipulator at any position was obtained. The experiment of non-interference control verified the correctness of the method by robot platform. The method simplified the criterion and process of interference analysis of basic geometric model, reduced the number of interference judgments between models, and improved the efficiency of interference analysis.

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    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
    Abstract159)   HTML3)    PDF(pc) (2419KB)(59)       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.

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    Analysis of wind power convergence trend quantitation based on sub-scene reconstruction
    Yuanxi YAO
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 86-92.   DOI: 10.6040/j.issn.1672-3961.0.2019.117
    Abstract158)   HTML1)    PDF(pc) (3095KB)(66)       Save

    The output power of wind power was fluctuating. Due to the output of unit stabilized each other, with the increase of wind power cluster size, the fluctuation of wind power gradually decreased and the wind power showed "convergence effect". Grasping the trend of the convergence effect had an important guiding significance for transmission capacity configuration of large-scale wind power network. In this paper, the scene of wind power output was defined, and the convergence characteristics of wind power in each scene were analyzed. By fitting the continuous output curve in each scene, a scene reconstruction method based on convergence characteristics analysis was proposed. The validity of the method was verified by the measured. The example showed that the sub-scene reconstruction method analyzed the trend of convergence for wind power more accurately. The number of scenarios affected the fitting accuracy of continuous output curve, comparing to the scene divided by 5, 10 and 20, scene reconstruction in 10 scenes was more accurate for the description of wind convergence trend.

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    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
    Abstract157)   HTML3)    PDF(pc) (1794KB)(66)       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.

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    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
    Abstract152)   HTML4)    PDF(pc) (7769KB)(148)       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.

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    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
    Abstract151)   HTML4)    PDF(pc) (5582KB)(282)       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.

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    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
    Abstract151)   HTML22)    PDF(pc) (1642KB)(107)       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.

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    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
    Abstract150)   HTML7)    PDF(pc) (5209KB)(48)       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.

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    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
    Abstract148)   HTML5)    PDF(pc) (7621KB)(66)       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.

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    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
    Abstract148)   HTML2)    PDF(pc) (5590KB)(152)       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.

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    Analysis of factors affecting heat transfer of double U-shaped buried tubes based on TRNSYS
    Tao LIU,Ye TIAN,Yongzhi MA
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 113-118.   DOI: 10.6040/j.issn.1672-3961.0.2019.317
    Abstract146)   HTML2)    PDF(pc) (1908KB)(96)       Save

    Because the horizontal buried tubes covered a large area, the heat transfer effect was poor, and the initial investment of the vertical buried tubes was high, the construction was difficult. A transient real-time simulation model of double U-shaped buried tubes heat exchanger was established with TRNSYS software. The factors, which could affect the heat transfer capacity of double U-shaped buried tubes heat exchanger, including the number and the spaceing of holes, and the depth of the buried tubes were analyzed under the condition of unique variable. The simulation experiment results showed that the heat transfer effect of buried tubes could be improved by increasing the number of holes, deepening drilling depth and increasing the spacing of the holes. It provided an analytical basis for the balance between the heat transfer effect of the buried tubes and the construction difficulty.

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    The composite control of backstepping control based on uncertain model compensation of wheeled mobile robot
    Meizhen LIU,Fengyu ZHOU,Ming LI,Yugang WANG,Ke CHEN
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 36-44.   DOI: 10.6040/j.issn.1672-3961.0.2019.236
    Abstract143)   HTML0)    PDF(pc) (4106KB)(289)       Save

    Given these factors of model uncertainty, non-linearity and unmodeled dynamic characteristics existing in wheeled mobile robots, which seriously affected the stability and accuracy of trajectory tracking, a backstepping composite control strategy based on model uncertainty compensation was proposed. Based on the kinematics model of a nonholonomic wheeled mobile robot, backstepping control and Lyapunov stability criterion were adopted to design virtual velocity control quantity as continuous incentive input for trajectory tracking. Considering the model uncertainty and external bounded moment disturbance of wheeled mobile robots, the uncertainties of the system were derived from the dynamic model of wheeled mobile robots, and the moment control quantity of model was acquired by using the neural network with highly nonlinear fitting characteristics, and then adaptive law of uncertainties was obtained from Lyapunov stability analysis to realize self-adjustment and real-time trajectory tracking. The simulation results showed that the proposed composite control strategy could track the reference trajectory adaptively, and had better robustness and tracking accuracy than the single backstepping control strategy, model uncertainty compensation control strategy and PID controller.

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    Size-dependenct intrinsic properties of the bilayer piezoelectric microbeam
    Yu LIU,Shenjie ZHOU,Kanghui WU
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 107-112.   DOI: 10.6040/j.issn.1672-3961.0.2019.061
    Abstract143)   HTML4)    PDF(pc) (1188KB)(81)       Save

    A size-dependent dynamic model of a bilayer piezoelectric microbeam was established based on the couple stress piezoelectric theory incorporating flexoelectric effects to explore the variation of the intrinsic properties of the bilayer piezoelectric microbeam with the characteristic size. The natural frequency of the bilayer piezoelectric microbeam was obtained. The influence of piezoelectric effects and flexoelectric effects on the natural frequency of the microbeam was discussed by numerical analysis method. The results indicated that the dimensionless natural frequency of the bilayer piezoelectric microbeam increased significantly as the beam thickness decreased. It was also found that the dimensionless natural frequency of the bilayer piezoelectric microbeam showed stronger size-dependency than that of the model without considering electromechanical coupling effects, which mainly resulted from flexoelectric effects, and piezoelectric effects exerted minor influence on the beam natural frequency.

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    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
    Abstract143)   HTML10)    PDF(pc) (3959KB)(103)       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.

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    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
    Abstract142)   HTML0)    PDF(pc) (3561KB)(94)       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.

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    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
    Abstract140)   HTML3)    PDF(pc) (3677KB)(49)       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.

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    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
    Abstract137)   HTML2)    PDF(pc) (3417KB)(49)       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.

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    Design and verification of power system for ECVT hybrid electric city bus
    Youming TANG,Kun DONG,Yuanwei ZHANG
    Journal of Shandong University(Engineering Science)    2019, 49 (6): 98-106.   DOI: 10.6040/j.issn.1672-3961.0.2019.072
    Abstract137)   HTML3)    PDF(pc) (2354KB)(247)       Save

    Aiming at the problem that the integrated starter/generator(ISG) hybrid power system was not ideal in fuel saving effect, this study selected the electronic continuously variable transmission(ECVT) hybrid power system scheme, which was applied and verified in plug-in hybrid electric city bus. Based on the equivalent lever analysis of the kinematics characteristics of a single planetary line, the parameters of key components of a plug-in ECVT power system were matched and calculated. According to the calibration test data, Matlab/Simulink software was used to establish the engine simulation model, drive motor simulation model and generator simulation model, to build the vehicle simulation model. Under typical urban bus cycle conditions in China, the fuel economy, dynamic performance and pure electric maximum continuous voyage characteristics of the target vehicle were studied, and the road tests of economy and dynamic performance were completed. The results showed that the ECVT hybrid system vehicle designed in this study could achieve fuel saving rate of 57.47% compared with traditional vehicles, and 24.12% higher than ISG hybrid system vehicle. Therefore, the adoption of ECVT hybrid power system for plug-in hybrid city bus was feasible and effective, and had obvious fuel saving effect.

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    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
    Abstract137)   HTML0)    PDF(pc) (1711KB)(175)       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.

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    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
    Abstract129)   HTML2)    PDF(pc) (2735KB)(33)       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.

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    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
    Abstract129)   HTML0)    PDF(pc) (1450KB)(27)       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.

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    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
    Abstract128)   HTML5)    PDF(pc) (2001KB)(96)       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.

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    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
    Abstract126)   HTML1)    PDF(pc) (9397KB)(45)       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.

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