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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
    Abstract2298)   HTML63)    PDF(pc) (7881KB)(827)       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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    Object detection of 3D point clouds based on F-PointNet
    Peng WAN
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 98-104.   DOI: 10.6040/j.issn.1672-3961.0.2018.348
    Abstract2243)   HTML40)    PDF(pc) (3128KB)(808)       Save

    Aiming at the problem of poor detection accuracy of the current 3D point cloud object detection model, the F-PointNet model, which directly processed point cloud data, was used to detect cars, pedestrians and cyclists, and the model was fine-tuned to further improve the object detection accuracy. The model was tested by different parameter initialization methods, $\ell $2 regularization and modifying convolution kernels. The experimental results showed that the Xavier parameter initialization method converged faster 0.09s than the truncated normal distribution method, and the vehicle detection accuracy and the cyclists detection accuracy was about 3% and 2% higher respectively. By adding $\ell $2 regularization, the detection accuracy of pedestrians and cyclists was increased by about 2% and 1% respectively. By reducing the number of convolution kernels in the first layer of T-Net (Transformer Networks) to 128, the detection accuracy of cars and cyclists was increased by about 1% and 2% respectively, which confirmed that the model could effectively improve object detection accuracy.

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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
    Abstract1703)   HTML105)    PDF(pc) (4331KB)(999)       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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    An automatic reading method for pointer meter
    Yanghao ZHOU,Yifan LIU,Li LI
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 1-7.   DOI: 10.6040/j.issn.1672-3961.0.2018.275
    Abstract1288)   HTML151)    PDF(pc) (4120KB)(683)       Save

    An automatic reading method for automatically monitoring pointer meter in substation was proposed based on the machine learning and image processing algorithms, which was consisted of two stages: meter detection and pointer recognition. The position of the meter in the input image was detected by using the fully convolutional networks, and then the patch of the meter was extracted. The interference of illumination and shadow on the pointer recognition was reduced by using histogram equalization, median filtering and bilateral filtering, and the tilt of shooting was rectified by using the affine transformation. The position of the pointer was detected via the improved Hough transform. The reading was obtained by computing the angle of the pointer. The results showed that the method could detect the pointer meter and recognize the reading accurately for the pointer instrument in the substation. The method showed good robustness to the disturbances such as illumination and shadow, which could significantly reduce the substation inspection personnel workload and improve the work efficiency.

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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
    Abstract1215)   HTML14)    PDF(pc) (12734KB)(251)       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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    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
    Abstract1196)   HTML47)    PDF(pc) (1549KB)(711)       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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    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
    Abstract1178)   HTML20)    PDF(pc) (3677KB)(204)       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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    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
    Abstract1171)   HTML484)    PDF(pc) (2579KB)(1076)       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.

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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
    Abstract1119)   HTML21)    PDF(pc) (1167KB)(572)       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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    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
    Abstract1083)   HTML19)    PDF(pc) (4733KB)(690)       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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    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
    Abstract1064)   HTML942)    PDF(pc) (2290KB)(836)       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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    An ensemble learning algorithm for unbalanced data classification
    Zongtang ZHANG,Sen WANG,Shilin SUN
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 8-13.   DOI: 10.6040/j.issn.1672-3961.0.2019.050
    Abstract1050)   HTML11)    PDF(pc) (1159KB)(359)       Save

    For unbalanced data classification problem in underwater acoustic target recognition, a random subspace AdaBoost algorithm called RSBoost was proposed. Subtraining sample set was extracted by random subspace method in different underwater acoustic feature space and base classifier was trained in every subtraining sample set. The base classifier with the maximum margin mean of minority class was chosen as the base classifier of this round, the final ensemble classifier was formed iteratively. The experiment was carried out on the measured data, the performance of RSBoost and AdaBoost in different feature space was evaluated by F-measure and G-mean. The results showed that, compared with AdaBoost, the F-measure of RSBoost improved from 0.07 to 0.22 and the G-mean improved from 0.18 to 0.25, which showed that RSBoost was superior to AdaBoost in underwater acoustic unbalanced classification problem.

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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
    Abstract1049)   HTML11)    PDF(pc) (4187KB)(569)       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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    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
    Abstract983)   HTML24)    PDF(pc) (2077KB)(469)       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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    Prediction method of wind power and PV ramp event based on deep learning
    Zhixiang LIANG,Xiaoming LIU,Ying MU,Yutian LIU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 24-28.   DOI: 10.6040/j.issn.1672-3961.0.2019.132
    Abstract983)   HTML17)    PDF(pc) (1178KB)(502)       Save

    With the gradual increase of the renewable energy penetration rate, the ramp event that caused the unbalanced active power occured sometimes, and even a large load loss. Due to the insufficient accuracy of wind power and photovoltaic prediction, there were many operational scenarios to be considered. The time domain simulation could not meet the online assessment requirements. A method based on deep learning was proposed in this paper. Considering the generation unit and tie line adjustment ability, the stacked denoising autoencoder was used to extract each layer feature to train support vector machine. The wind power, photovoltaic and load forecast data, and the power of the tie line at the previous moment were taken as inputs, and whether the ramp event occured as an output. The vector machine was used to quickly predict whether a ramp event occured. The simulation results of practical power grid showed that the proposed method was fast and accurate. It could effectively identify ramp events.

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    Visualization of interactive ThemeRiver based on time-series data
    Xindi CHEN,Tianrui LI,Huanhuan YANG
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 29-35, 43.   DOI: 10.6040/j.issn.1672-3961.0.2017.400
    Abstract975)   HTML16)    PDF(pc) (3445KB)(341)       Save

    ThemeRiver was a widely used technique when visualizing time-series data, which showed the whole trend efficiently. But the user often encountered ambiguity when observing a single river due to the influence of the lower river. However, the current visualization technique couldn't solve the issue. The following improvements of ThemeRiver were proposed upon the above issue: The river fluctuation was evaluated by the coefficient of variance instead of standard deviation, and the rivers with smaller fluctuation were arranged in lower position in order to reduce the impact on the upper rivers; The concept of ambiguity point was proposed, and the user could set fluctuation and max contribution as filtering parameters in the user interface so that the ambiguity points would be dynamically displayed by the system; The ambiguity point was visualized as bellow(The peak and valley of a river were represented by triangle and inverted triangle, respectively; The triangles would be filled with the color of the river with max contribution; A reorderable interactive ThemeRiver was proposed, in which the user was able to reorder the rivers by dragging them according to the information given by the ambiguity points). The proposed method was applied on the data containing 20 themes of 2015 from Zhihu and the results showed that the system could efficiently eliminate the ambiguity of ThemeRiver when presenting the trend of a single river and provided a flexible and personalized visualization.

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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
    Abstract967)   HTML14)    PDF(pc) (4029KB)(446)       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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    Review on application of artificial intelligence in power system restoration
    Yutian LIU, Runjia SUN, Hongtao WANG, Xueping GU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 1-8.   DOI: 10.6040/j.issn.1672-3961.0.2019.122
    Abstract948)   HTML55)    PDF(pc) (1128KB)(890)       Save

    The research results of expert system, fuzzy mathematics, evolutionary algorithm and machine learning in power system restoration were summarized. It was pointed out that the existing researches were mainly offline restoration method and the researches about online decision-making were in the initial stage. In addition, the application potential of artificial intelligence technology in system restoration was prospected.

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    An improved algorithm of maximal information coefficient based on dynamic equipartition
    Yanxia MENG,Yuchen GUO,Li WANG
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 105-111.   DOI: 10.6040/j.issn.1672-3961.0.2018.209
    Abstract937)   HTML6)    PDF(pc) (2337KB)(251)       Save

    In order to solve the problem of high computational time complexity of the maximal information coefficient algorithm, an improved algorithm of the maximal information coefficient(MIC) based on dynamic equipartition was presented. The scattered points shown in the grid were iterated and optimized by using dynamic mean division pairs of variables. The obtained mutual information entropy was regularized to obtain the optimal MIC value, and the multi-thread computation of the data set was carried out by using the POSIX parallel strategy, which made the computation more efficient in the computation of large data sets. Compared with the RapidMIC method on multiple data sets, the DE-MIC algorithm was faster and more efficient under the premise of preserving the generality and equitability of the maximal information coefficient algorithm.

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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
    Abstract922)   HTML19)    PDF(pc) (3224KB)(230)       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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    Laminar flow field characteristics in the stirred vessel equipped with an eccentric-shaft impeller
    Meiting LI,Wei LI,Xiaoguang LI,Fengling YANG
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 93-98, 107.   DOI: 10.6040/j.issn.1672-3961.0.2018.530
    Abstract880)   HTML7)    PDF(pc) (2868KB)(627)       Save

    For the purpose of improving mixing efficiency of the stirred tank with high viscosity fluid laminar flow condition, an eccentric-shaft agitation method was proposed. With a purity of 99% glycerol as medium, and the traditional 2-flat-blade impeller as the research object, the laminar flow fields were numerically studied. The inner diameter of the stirred vessel was 0.3m and the agitated fluid was glycerol. The modeling reliability and simulation methods of the concentric agitation were validated by experimental results. In comparison with the concentric agitation, flow fields generated by eccentric and eccentric-shaft agitation were asymmetrical, and theoverall volume weitghted average velocity of the groove fluid generated by eccentric-shaft agitation could be raised by 68%. And furthermore, when operated under the same speed, the power consumption of eccentric-shaft agitation increased 15.3% than that of concentric agitation. However, the eccentric shaft agitation increased the speed in the tank and expanded the range of disturbance. Accordingly, the superiority of eccentric-shaft agitation of improving the mixing efficiency in the tank was apparent.

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    A force-based method for robot hole-searching and assembly
    Yujun WU,Wei WU,Yu GUO,Jian GUO
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 119-126.   DOI: 10.6040/j.issn.1672-3961.0.2018.396
    Abstract876)   HTML12)    PDF(pc) (7332KB)(337)       Save

    In the process of replacing arresters for live working robots, a method based on force control for robot to search hole on insulated cross-arms and assembling arresters was proposed for accurate assembly. In the control process, a force sensor was installed at the end of the robot for the special shape of the arrester's lower end and the scene of the pole cross arm, and the converted force was used as the controlled amount. A peg-in-hole assembly strategy was designed, which included four processes of touching, hunting, evolving and inserting. The robot could insert the arrester's lower end into the mounting hole on the cross arm accurately. The experimental results verified the effectiveness of the proposed method.

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    TBM comprehensive advanced geological prediction in a tunnel andits application in Yangling Tunnel
    Jun LIU,Lijun HAN,Qingbin MENG
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 51-60.   DOI: 10.6040/j.issn.1672-3961.0.2018.093
    Abstract875)   HTML5)    PDF(pc) (5462KB)(385)       Save

    Advanced geological prediction was used to play an irreplaceable role in the aspect of informative tunnel construction, effective disaster prevention, economical construction support in tunnel engineering. The effectiveness of the advanced geological prediction method in the drilling and blasting method under the tunnel boring machine (TBM) specific construction environment was compared and analyzed. The comprehensive advanced geological prediction system under TBM tunnel construction with "long and short distance, geophysical and drilling exploration, sound wave reflection and electromagnetic method, key and normal" was established. Based on the horizontal sonic profiling (HSP) sound wave reflection, drilling process monitoring (DPM) advanced drilling, transient electromagnetic instrument, the multiple solutions and error superposition of single advanced geological prediction method were suppressed effectively. And then the 3D forward theory calculation was considered as an appropriate way for enhancing the accuracy and reliability. Based on the law of electromagnetic response and the study of removing electromagnetic interference, the results summarized from the paper were used to guide the application of transient electromagnetic method in Yixing Yangling Tunnel and to provide a reference for future projects.

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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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    Flow characteristics of rough rock fractures under wide range of Reynolds numbers
    Jie LIU,Zhechao WANG,Yupeng ZHANG,Huayang SUN
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 70-77, 85.   DOI: 10.6040/j.issn.1672-3961.0.2018.533
    Abstract846)   HTML11)    PDF(pc) (6754KB)(404)       Save

    Through experiments and theoretical analysis, the flow characteristics of rock rough fractures with different geometric parameters were studied, including non-Darcy coefficient β, critical Reynolds number Rec and non-Darcy effect factor E. Testing equipment for fluid flow in fractures was developed and 9 rough single fracture models with different apertures and Joint Roughness Coefficients (JRC) were prepared. Laboratory tests on flow in rough fractures with Reynolds numbers, i.e., from 2 to 5000 were performed. The characteristics of flow in single fractures with different roughness (JRC=2-20) under wide range of Reynolds numbers were obtained. It was shown that fracture roughness had a significant impact on the nonlinear flow characteristics of the fracture. Based on the Forchheimer equation, the effects of fracture roughness on equation parameters were quantitatively studied. The larger fracture roughness was, the more likely it was to cause the nonlinearity of the fracture flow, the smaller critical Reynolds number was, and the stronger non-linear effect would be.

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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
    Abstract837)   HTML19)    PDF(pc) (5582KB)(673)       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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    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
    Abstract835)   HTML16)    PDF(pc) (5481KB)(335)       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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    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
    Abstract826)   HTML330)    PDF(pc) (2943KB)(240)       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.

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    Phase change characteristics of paraffin in rectangular storage unit
    Huilin ZHOU,Yan QIU
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 99-107.   DOI: 10.6040/j.issn.1672-3961.0.2019.018
    Abstract817)   HTML8)    PDF(pc) (3235KB)(834)       Save

    To study the heat transfer characteristics of phase change material (PCM) in a storage unit and improve the heat transfer efficiency of phase change heat exchanger, the enthalpy-porous media model and FLUENT program were used to carry out a numerical investigation on the heat transfer process of paraffin in a rectangular heat storage unit. The element liquid fraction β and dimensionless Fo, Ste and Ra were introduced to analyze the influences of different positions outside the tube and inlet temperatures of heat transfer fluid on the melting and solidification process of paraffin. The results showed that the paraffin outside the tube was melted in order from the upper to the left/right part, then the lower part. The total melting time of paraffin in upper part was shorten by at least 20% compared with other parts. Conversely, in the heat release process, the paraffin was solidified in order from the lower, the left/right part and the upper part. The heat transfer mechanism in the unit changed gradually from heat conduction to natural convection in the thermal storage process. The efficiency of heat storage and release could be improved significantly by increasing the temperature difference between heat transfer fluid and paraffin. The criterion of β was obtained by polynomial fitting.

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    Crack initiation theory and experimental verification of pre-existing plane cracks with seepage pressure
    Yajuan XIE,Song YU,Bangxiang LI,Xiang XU,Weishen ZHU
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 36-43.   DOI: 10.6040/j.issn.1672-3961.0.2018.140
    Abstract808)   HTML4)    PDF(pc) (3494KB)(174)       Save

    In order to study the crack initiation characteristics of pre-existing plane cracks with seepage pressure, cracks were divided into two types: open type and closed type. Theoretical analysis and experimental verification were carried out. The theoretical derivation of crack stress intensity factor, crack initiation angle and crack initiation stress was expounded in detail, and the crack initiation strength formula for closed crack under compression was given. Precast concrete mortar specimens with open-type crack were produced and subjected to biaxial compression experiments. The variation of stress intensity factor and crack initiation angle with crack inclination angle, hydraulic pressure and crack thickness were investigated through experimental verification and analysis. The relationship of crack initiation strength with hydraulic pressure and lateral pressure were also investigated. The results showed that type Ⅰ stress intensity factor of open-type crack with seepage pressure increased with the increase of crack angle. The type Ⅰ crack was most easily initiate when the crack angle was 75°. K decreased with the increase of water pressure and crack thickness. Type Ⅱ stress intensity factor of shear crack was distributed symmetrically with the crack inclination angle, and the maximum value was obtained at the crack inclination angle of 45°, irrespective of hydraulic pressure and crack thickness. Crack initiation angle of open-type crack decreased with the increase of crack dip, hydraulic pressure and crack thickness; crack initiation strength was inversely proportional to hydraulic pressure and proportional to lateral pressure. The research results could provide reference for crack initiation and crack propagation with seepage pressure in geotechnical engineering.

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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
    Abstract808)   HTML16)    PDF(pc) (1435KB)(438)       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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    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
    Abstract808)   HTML9)    PDF(pc) (1356KB)(541)       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.

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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
    Abstract805)   HTML6)    PDF(pc) (1403KB)(269)       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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    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
    Abstract800)   HTML10)    PDF(pc) (2985KB)(235)       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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    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
    Abstract798)      PDF(pc) (3465KB)(208)       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.
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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
    Abstract785)   HTML19)    PDF(pc) (2325KB)(280)       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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    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
    Abstract784)   HTML6)    PDF(pc) (4400KB)(251)       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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    Impact analysis on construction of large section and small spacing road tunnel
    Mingcai LIU
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 78-85.   DOI: 10.6040/j.issn.1672-3961.0.2018.055
    Abstract781)   HTML4)    PDF(pc) (3052KB)(289)       Save

    The construction project of Fo Yangling Tunnel was taken as the research background, the construction was carried out by using benching tunneling construction method. The fast Lagrangian analysis of continua 3D (FLAC3D) finite element software was used as the calculation platform to establish the finite element model of tunnel with large section and small clear distance. The tunnel model was numerically simulated and subsidence, stress of surrounding rock and lining structural were analyzed. And the corresponding monitoring and reinforcement measures were put forward. The results of numerical analysis showed that the settlement of the upper part of the advance tunnel was larger than the subsequent tunnel. The construction of the subsequent tunnel increased the vertical displacement of the advance tunnel, which caused the increase of maximum principal stress and the minimum principal stress. The stress of tunnel lining structure was concentrated in the area from arch waist to arch foot. The support structure of advance tunnel played the role of stabilizing rock mass in the excavation of subsequent tunnel, and the excavation of the subsequent tunnel would make the stress concentration of the middle rock. In the construction, attention should be payed to the surrounding rock and reinforcement measures should be taken. The analysis results could provide a scientific basis for the design and construction of large section and small spacing road tunnel.

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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
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    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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    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
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    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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    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
    Abstract753)   HTML110)    PDF(pc) (1354KB)(289)       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.

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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
    Abstract752)   HTML8)    PDF(pc) (2419KB)(248)       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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    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
    Abstract747)   HTML25)    PDF(pc) (2118KB)(282)       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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    Optimization method for parallel coordination restoration strategy of asynchronous power grid
    Xueping GU, Chao YANG, Haiping LIANG, Yuanbo WANG, Shaoyan LI
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 9-16.   DOI: 10.6040/j.issn.1672-3961.0.2019.096
    Abstract743)   HTML16)    PDF(pc) (1429KB)(216)       Save

    Aiming at asynchronous power grid restoration after a blackout, an optimization method of parallel coordinated restoration strategy was proposed. This method adopted the thinking of serial and parallel restoration, used the high voltage direct current (HVDC) to coordinate sending end and receiving end power grids, and finally restored whole power system. Based on determining each AC subnet as a sending role or receiving role, the source characteristic of converter station in sending end gird and the load characteristic of converter station in receiving end gird were analysed. An optimization model for parallel coordinated restoration of asynchronous power grid was established, and the coordinated restoration process of HVDC, sending end and receiving end power grids were emphatically studied. The multi-population genetic algorithm (MPGA) was used to solve this model, then the optimal restoration scheme and the optimal starting time of HVDC were obtained. An asynchronous power grid example which was constructed based on the IEEE 39 bus system was given to verify the proposed method. The results showed that this optimization method was feasible, and it was suitable for the formulation of asynchronous power grid restoration scheme.

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    Review on structural resistance to downburst wind loads
    TIAN Li,Wenzhe BI,Sarim Saleem SIDDIQUI,Kaiyue LIU
    Journal of Shandong University(Engineering Science)    2021, 51 (5): 32-41.   DOI: 10.6040/j.issn.1672-3961.0.2020.102
    Abstract743)   HTML27)    PDF(pc) (2558KB)(57)       Save

    Researches of downburst in the field of structural wind resistance were introduced. Concerning all kinds of building structures related to people's production and life, this paper reviewed the research and the existing problems to date on the structural resistance to downburst from five aspects: the field measurements, the analytical models, the numerical simulation of the downburst, the wind-tunnel test for downburst, and the wind-induced response analysis of structures. Some problems in view of the structural resistance to downburst for further study were proposed.

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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
    Abstract738)   HTML6)    PDF(pc) (3417KB)(167)       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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    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
    Abstract731)   HTML6)    PDF(pc) (2669KB)(559)       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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    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
    Abstract726)   HTML9)    PDF(pc) (2354KB)(464)       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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    None-consumption users filtering algorithm based on decision tree and data-driven methods
    Bo ZHANG,Feng LU,Hanyu DONG,Qingtai CHEN,Zhenzhi LIN,Hongtao WANG
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 29-36.   DOI: 10.6040/j.issn.1672-3961.0.2019.116
    Abstract723)   HTML4)    PDF(pc) (2084KB)(133)       Save

    With the increasing of power consumers and diversification of power consumption in power systems, the number of none-consumption users (NCUs) was also increasing rapidly. Thus, lots of manpower and material resources of power supply companies were arranged to perform troubleshooting on NCUs. Given this background, a data-driven method based on the electricity information of NCUs collected by electricity information acquisition system (EIAS) was proposed to determine the filtering results of normal NCUs and abnormal NCUs. The decision tree was utilized to analyze the electricity data of NCUs, and determine the types of NCUs. The key factors suitable for NCUs filtering were determined based on the original data to filter the NCUs that could not be screened by the decision tree, and the evaluation system for NCUs filtering was constructed. On this basis, CRITIC and radar chart methods were adopted to determine the weights of the key factors and to determine the filtering results of NCUs, respectively. The NCUs power-supplied by an actual power supply station in Zhejiang Province were served for demonstrating the proposed algorithm of NCUs filtering, and the simulation and on-site inspection results showed that the proposed data-driven method was effective for screening out the abnormal NCUs.

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    Object tracking algorithm based on deep residual features and entropy energy optimization
    Jinchao HUANG
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 14-23.   DOI: 10.6040/j.issn.1672-3961.0.2018.461
    Abstract719)   HTML8)    PDF(pc) (10095KB)(223)       Save

    To solve the low rate of accuracy, real-time and robustness of object tracking algorithm based on model updating, a new algorithm based on deep residual features and entropy energy optimization was proposed. Deep residual features were first extracted from original video sequence by deep residual network. The entropy energy from deep residual features were calculated, and the deep frequency from entropy energy by two-dimension kernel transformation could be calculated, after that we got the deep balance by deep frequency with differential equation, and then the object state by MLE was estimated, including object position and speed. To validate the feasibility and efficiency of the proposed algorithm, the comparing experiments on the object tracking basis (OTB) dataset for the state-of-the-art algorithms were done, and the comparison results showed that the proposed algorithm had significant improvement on tracking accuracy and robustness. By using entropy energy optimization for deep residual features, the proposed algorithm had more flexibility and robustness for object tracking.

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