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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
    Abstract1170)   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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    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
    Abstract1700)   HTML105)    PDF(pc) (4331KB)(998)       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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    Unmanned vehicle path planning based on deep Q learning in real environment
    Hao XIAO,Zhuhua LIAO,Yizhi LIU,Silin LIU,Jianxun LIU
    Journal of Shandong University(Engineering Science)    2021, 51 (1): 100-107.   DOI: 10.6040/j.issn.1672-3961.0.2020.247
    Abstract481)   HTML22)    PDF(pc) (6074KB)(904)       Save

    It was an important problem for the intelligent navigation of unmanned vehicles that planning the optimal path in the actual traffic environment. At present, many researches about global path planning of unmanned vehicle mainly focused on the improvement of algorithm solution speed in the simulation environment. Most of them just only considered the optimal path distance or the current road conditions, also ignored other factors and future changes in the actual environment. In order to complete the complex task that competing global path planning of unmanned vehicle in dynamic road network, this research put forward a framework of unmanned vehicle driving system for practical environment based on the thought of planning after prediction, and put forward DP-DQN which was a fast global path planning method combined with deep Q learning and deep prediction network technology. This method used the road characteristic data such as time and space, weather et al to predict the future traffic situation, and then competed the global optimal path. Finally, experimental results based on open datasets showed that the proposed method reduced driving time 17.97% at most than Dijkstra, A*, algorithm et al.

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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
    Abstract944)   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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    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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    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
    Abstract815)   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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    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)(826)       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
    Abstract2241)   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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    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
    Abstract1192)   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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    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
    Abstract1078)   HTML19)    PDF(pc) (4733KB)(688)       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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    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
    Abstract1280)   HTML150)    PDF(pc) (4120KB)(680)       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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    Experimental research on mechanical features model of prestressed mutual-anchored retaining wall
    Jianhong JIANG,Zhenyu YANG,Qi CHEN,Qingyu MENG,Hongbo ZHANG
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 61-69.   DOI: 10.6040/j.issn.1672-3961.0.2018.165
    Abstract690)   HTML3)    PDF(pc) (3546KB)(678)       Save

    To study the mechanical behaviors of the prestressed mutual anchoring retaining walls under different structural parameters, an indoor model test device was designed considering the mechanical mechanism and different prestress levels and different anchor placement positions. The results showed that due to the lateral restraint effect of the anchor (cable) on the wall and under the lateral prestress, the wall moved towards the direction of the filler, and gradually increased with the prestressing of the anchor. It evolved from T-model into the T+RB model. The earth pressure behind the wall gradually increased and it had a parabolic distribution with the peak at the anchor. According to the test results, the optimal anchoring position was recommended, and the range of anchored prestressing and optimal prestressing. The research results could be used to guide the design calculation of the prestressed mutual anchoring retaining walls.

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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
    Abstract835)   HTML19)    PDF(pc) (5582KB)(672)       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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    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
    Abstract876)   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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    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
    Abstract856)   HTML46)    PDF(pc) (1129KB)(595)       Save

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

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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)(571)       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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    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
    Abstract1048)   HTML11)    PDF(pc) (4187KB)(568)       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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    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
    Abstract727)   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 of triple-cables limiting-location anti-swing device for shipboard crane
    Zhaopeng REN,Rui XI,Shenghai WANG,Zhijiang ZHANG,Haiquan CHEN
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 125-132142.   DOI: 10.6040/j.issn.1672-3961.0.2019.004
    Abstract696)   HTML5)    PDF(pc) (4845KB)(548)       Save

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

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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
    Abstract807)   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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    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
    Abstract979)   HTML17)    PDF(pc) (1178KB)(500)       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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    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
    Abstract656)   HTML3)    PDF(pc) (5046KB)(479)       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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    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
    Abstract982)   HTML24)    PDF(pc) (2077KB)(468)       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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    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)(463)       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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    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
    Abstract584)   HTML6)    PDF(pc) (5590KB)(457)       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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    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
    Abstract966)   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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    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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    Bi-level optimal configuration of energy storage system in an active distribution network
    Liyan WANG,Fei WANG,Yongji CAO,Tao ZHANG,Yaxin ZHANG,Yi LU,Zihan LIU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 37-43, 51.   DOI: 10.6040/j.issn.1672-3961.0.2019.082
    Abstract648)   HTML2)    PDF(pc) (3422KB)(424)       Save

    In order to optimize the configuration of energy storage system in an active distribution network, a bi-level optimization method was proposed, considering the impact of operation strategy on planning scheme. In the short-scale inner optimization, the high-frequency components of integration power was extracted by the low-pass filtering algorithm. And a multi-objective optimization model was constructed to minimize the variation coefficient of extracted high-frequency component and the rate of the loss of renewable energy, which was simplified into a scalar optimization problem and solved by the particle swarm optimization algorithm. In the long-scale outer optimization, a multi-objective optimization model was established to minimize the investment cost and the rate of the loss of renewable energy, of which the Pareto optimal solutions were searched by the NSGA-Ⅱ. The location and capacity of energy storage system and the rate of the loss of renewable energy were taken as coupling variables, based on which the inner and outer models with different time scales were solved in a united optimization frame. The case study validated the effectiveness of the proposed model and corresponding solving methods, of which the results indicate that the optimal configuration of energy storage system in an active distribution network could enhance the accommodation ability of renewable energy.

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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
    Abstract669)   HTML4)    PDF(pc) (4106KB)(409)       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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    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
    Abstract843)   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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    The influence of shape parameters of wave energy device floating body on energy capture characteristics
    Yanjun LIU,Wei WANG,Zhi CHEN,Donghai WANG,Dengshuai WANG,Gang XUE
    Journal of Shandong University(Engineering Science)    2020, 50 (6): 1-8,16.   DOI: 10.6040/j.issn.1672-3961.0.2020.160
    Abstract527)   HTML147)    PDF(pc) (6369KB)(401)       Save

    To clarify the effect of the floating body′s shape parameters on the energy capture performance and working stability, an oscillating float type wave energy converter (WEC) model with linear power take-off system was established. Frequency domain calculation theory was used to deduce the formulas of energy capture power and energy capture width ratio. After introducing the numerical simulation steps of floating body in frequency domain, ANSYS-AQWA software was used to investigate the floater's energy capture power and energy capture width ratio with different bottom shapes and half vertex angles. Influence of shape parameters on the energy capture performance was drawn to provide a theoretical basis for the shape optimization of the floating body applied to the wave power supply device and floating platform. The results showed that the practical fabrication feasibility of circular truncated cone bottom was higher than that of cone and sphere. The energy capture characteristics and stability of circular truncated cone bottom were better than that of general cylindrical floating body in the intermediate wave frequency band. The energy capture performance and working stability of circular truncated cone floating body with big top and small bottom were better. The energy capture performance under intermediate frequency waves could be improved with the increase of the half vertex angle. The optimal power capture performance and working stability could be achieved with a proper apex angle.

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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
    Abstract873)   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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    Weak signal detector based on all-digital phase-shifting lock-in method
    Lu WANG,Hong WANG,Guoping CHEN
    Journal of Shandong University(Engineering Science)    2019, 49 (4): 24-28.   DOI: 10.6040/j.issn.1672-3961.0.2018.526
    Abstract668)   HTML3)    PDF(pc) (3505KB)(385)       Save

    In order to solve the problem of the uncontrollable phase difference in existing lock-in detection and the vulnerable to interference or high power consumption in analog phase-shifting circuit. The lock-in detection equipment based on all-digital phase-shifting method was designed, which the analog correlation signal was replaced by an all-digital reference signal with frequency traversal and co-frequency shift, then combined with the lock-in correlation detection to extract weak signals in low SNR. The reference signal and the weak signal to be tested were transmitted by direct memory access(DMA), the analog to digital converter(ADC) and digital to analog converter(DAC) were used to realize the transmission of synchronous frequency data with low power and faster transfer speed. The results showed that the lowest SNR detectable by this method was -46 dB with good stability and efficiency in a strong noise environment of 90 dB. It had great practical value for high-precision quality testing in high noise environment passive speaker equipment of the industrial plant.

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    Heat transfer performance analysis of phase change energy storage heat exchanger based on entransy theory
    Xinchen ZHOU,Xuelai ZHANG,Yue CHEN,Lu LIU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 72-84.   DOI: 10.6040/j.issn.1672-3961.0.2019.151
    Abstract623)   HTML9)    PDF(pc) (4434KB)(385)       Save

    Entransy transfer efficiency, entransy dissipation number and thermal resistance of heat exchanger based on entransy dissipation were applied to heat transfer performance analysis of phase change energy storage heat exchanger on the basis of successful application of entransy theory on conventional heat exchanger. The generalized entransy dissipation rate was defined to derive (instantaneous) entransy transfer efficiency of phase change energy storage heat exchanger in heat storage, heat release and total process, and heat transfer rate was determined to calculate entransy dissipation number and thermal resistance of heat exchanger based on entransy dissipation. A kind of phase change energy storage heat exchanger was selected as the object, and the temperature variation of main parts were described by theoretical analyses. The temperature variation of outlet of silicon oil and water were further simplified to derive their expression, as the basis of calculation and analyses. The results showed that the application range of entransy transfer efficiency was the widest, which was used to calculate the (instantaneous) irreversible heat loss of phase change energy storage heat exchanger in heat storage, heat release and total process. The evaluation results of entransy transfer efficiency were consistent with heat transfer performance and its instantaneous values were increased first, then unchanged, finally increased, with increasing heat storage time, and were decreased first, then unchanged, finally decreased, with increasing heat release time. The evaluation results of entransy dissipation number in heat storage and total process were consistent with that of entransy transfer efficiency. With increasing heat storage time, its instantaneous values were decreased first, then unchanged, finally decreased, while its application was limited in heat release process. The application of thermal resistance of heat exchanger based on entransy dissipation was the most limited since parts of its evaluation results were inconsistent with actual state. In heat storage and total process, the entransy transfer efficiency, entransy dissipation number and thermal resistance of heat exchanger based on entransy dissipation were nearly unchangeable when the heat storage quantity, heat release quantity and stage time in the process of heat storage and release were synchronous. The entransy transfer efficiency was increased, while the entransy dissipation number and the thermal resistance of heat exchanger based on entransy dissipation were decreased when the heat transfer efficiency was improved. In the heat release process, entransy transfer efficiency was unchangeable since the heat transfer performance of system was not influenced by the change of parameters setted.

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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
    Abstract780)   HTML11)    PDF(pc) (3123KB)(377)       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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    Transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm
    Runjia SUN,Hainan ZHU,Yutian LIU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 17-23.   DOI: 10.6040/j.issn.1672-3961.0.2019.123
    Abstract635)   HTML4)    PDF(pc) (4095KB)(373)       Save

    This paper proposed a transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm, which incorporated the preferences for different objectives to obtain network reconfiguration schemes. Considering the factors about generators, transmission lines and loads, three objectives were proposed to establish a preference multiobjective optimization model. Considering the preference and discreteness of the model, a preference-based nondominated sorting genetic algorithm Ⅱ was designed. To improve the solve efficiency, the preference-based dominance relation, population sacle control technique and repetitive individual filtration technique were proposed to obtain a preference Pareto solution set with a controllable number of solutions. The simulation results demonstrated that the strategy could reasonably leverage the tradeoff among different factors about network reconfiguration, and the proposed algorithm was highly efficient in solving network reconfiguration optimization problems.

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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
    Abstract1048)   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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    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
    Abstract705)   HTML6)    PDF(pc) (1908KB)(349)       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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    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
    Abstract668)   HTML3)    PDF(pc) (5769KB)(347)       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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    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
    Abstract974)   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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    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
    Abstract874)   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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    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
    Abstract833)   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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    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
    Abstract643)   HTML5)    PDF(pc) (3561KB)(313)       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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    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
    Abstract678)   HTML12)    PDF(pc) (7621KB)(310)       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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    Research on BP neural network rainfall runoff forecasting model based on elastic gradient descent algorithm
    Baoming JIN,Guangyi LU,Wei WANG,Lunyue DU
    Journal of Shandong University(Engineering Science)    2020, 50 (3): 117-124.   DOI: 10.6040/j.issn.1672-3961.0.2019.504
    Abstract583)   HTML7)    PDF(pc) (2025KB)(304)       Save

    The improved elastic gradient descent algorithm of back propagation was used, and 14 rainfall runoff processes from 1997 to 2014 in the upper reaches of Chongyang River were selected. The back propagation (BP) neural network rainfall-runoff forecasting model of the elastic gradient descent algorithm was established, which took the measured rainfall of six rainfall stations in Yangzhuang, Wubian, Da′an, Kengkou, Lingyang, and Langu in the basin and the preliminary flow data of Wuyishan Hydrological Station as inputs, and selected the corresponding flow of Wuyishan Hydrological Station as output. The 7-rainfall runoff process was used to test the model, the test results showed that the proposed method required fewer parameters and had higher operation speed than the traditional back propagation algorithm. The prediction accuracy of the model could meet the requirements, and provide the basis for flood control and disaster reduction.

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    Lightweight self-adaptive CSI-based positioning algorithm in underground mine
    Junmei YUE,Dongmei ZHANG
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 112-118.   DOI: 10.6040/j.issn.1672-3961.0.2018.356
    Abstract677)   HTML3)    PDF(pc) (2758KB)(298)       Save

    To solve the problem of high cost and working hazard factor of traditional downhole positioning methods, a lightweight self-adaptive CSI-based positioning algorithm in underground mine was proposed. The fine-grained CSI was used to obtain higher positioning accuracy rather than coarse-grained RSSI, inverse fast Fourier transform was adopted to transform CSI data to channel impulse response so as to get the line-of-sight signal, an attenuation model of line-of-sight signal of CSI was built to implement accurate ranging, position features of existing point access points (APs) in wireless fidelity and characteristics of rock roadways was utilized to calculate orientation of target relative to AP, which finally completed location according to orientation and distance. LSA was adaptive to arbitrary deployment modes, and the corner recognition optimization algorithm was used to improve positioning accuracy. The experimental results showed that LSA method median error could reach 0.53 m and eliminate the need to deploy any positioning system in the well alone, the performance was superrior to CDPF and FILA.

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    TRCC series system based on LNG cold energy and fuel cellwaste heat utilization
    Yinglun GUO,Fuqiang XI,Ruizhi SU,Guoxiang LI,Zeting YU
    Journal of Shandong University(Engineering Science)    2019, 49 (5): 52-57.   DOI: 10.6040/j.issn.1672-3961.0.2019.154
    Abstract715)   HTML5)    PDF(pc) (1261KB)(292)       Save

    A cogeneration system based on solid oxide fuel cell (SOFC for short) and transcritical carbon dioxide cycle (TRCC for short) was proposed. The transcritial carbon dioxide cycle was used to recover the exhaust heat of the SOFC while utilizing the LNG refrigeration capacity. The mathematical model of the system was established, and the influence of parameter changes on system performance was analyzed. The results showed that under the design conditions, the thermal efficiencies of SOFC, TRCC, and the whole system were 64.2%, 22.4%, and 74.1%, respectively. The system thermal efficiency increased with the inlet temperature of the fuel cell and decreased with the increase of the steam-carbon ratio. The thermal efficiency increased as the turbine inlet pressure to the TRCC increased.

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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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    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
    Abstract682)   HTML7)    PDF(pc) (1711KB)(289)       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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    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
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    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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