2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)最新文献

{"title":"Path Planning of a Non-Standard Heavy Cargo in Dual-Crane Operation","authors":"Fu Yu-ping, Wang Hong-gui, YU Yang, Fang Yi-wang","doi":"10.1109/ISCTT51595.2020.00099","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00099","url":null,"abstract":"For the safe transfer of non-standard heavy cargo to the inboard by dual-crane operation, an algorithm which can automatically generate the rotational path that movement with rotation in horizonal plane in the process of transverse-moving is designed to plan the rotation path of the cargo. However, this algorithm does not consider the stability of the ship in the process of transverse-moving, and only aims to meet the requirements of safe operation of cranes and collision-free between cargo and derricks. Then a two degrees of freedom (DOF) of collision-free rotation path is generated by determining the boom elevation angle and horizontal slewing angle during rotation, making the calculation process is faster. At the same time, in order to consider the control of subsequent stability and floating state of ship, this paper refines the rotation process. Finally, the feasibility of the algorithm is verified by the real ship data, which provides a reference for the path planning of heavy lifting ship using dual crane operation mode to load non-standard heavy cargos.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multimodal Biometric Recognition System based on Finger Snapping and Information Fusion","authors":"Liangyuan Chen","doi":"10.1109/ISCTT51595.2020.00024","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00024","url":null,"abstract":"Biometrics, physiological and/or behavioral traits used to distinguish different individuals, have now been widely used in authentication sites because they have shown advantage over passwords and token-based verification methods. However, biometric systems that use static and unimodal biometric information are susceptible to spoofing, which poses a security threat. Finger snapping, which is dynamic and can be collected multimodally, has the potential to overcome the current challenges, but a good information fusion method, model, as well as dataset is needed. In this study, three main questions are proposed and studied: (1) Is snapping a viable biometric? (2) Is feature-level information fusion of snapping practical? (3) How do traditional ML models and DL models differ in their performances on a snapping dataset? To answer the three questions, a preparative dataset consisting of kinetic information of snaps from 33 participants, as well as a main dataset consisting of kinetic and acoustic information of snaps from 50 participants have been built using a self-constructed data collection system. The preparative dataset is used to decide which subset of sensors provides the most useful information, and the main dataset is used to answer the above three questions. After testing, it is discovered that (1) snapping is a highly feasible biometric, (2) the kinetic and acoustic data can be fused at the feature level, and (3) The traditional ML model tested has greater potential over DL models due to the higher AUROC metric (0.967 ± 0.034 versus 0.943 ± 0.032), but has a lower weighted accuracy score with the default decision threshold than the DL model (87.5% ± 4.04% versus 89.9% ± 2.74%).","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129968221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Social and Economic Benefits of Beijing-Shanghai High-Speed Railway","authors":"Sun Jihong","doi":"10.1109/ISCTT51595.2020.00126","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00126","url":null,"abstract":"Since its inception, the Beijing-Shanghai High-Speed Rail revolutionized the development of the regional economy, the urbanization process, the labor market, the tourism and the aviation industry. At the same time, risks are gradually being clarified. This paper briefly analyzes the social and economic benefits of the Beijing-Shanghai High-speed Rail. Conclusion: In the actual operation of the Beijing-Shanghai High-speed Rail, siphon effect, filtering effect, urbanization effect and urban space reconstruction effect occur in the city along the line, with regional spatial structure and economic industry along the line Affect urbanization process and labor market along the line. Although the tourism industry and the Beijing-Shanghai route have had a different degree of impact, they are inconsistent with the “last mile” of urban traffic, which is the danger of the Beijing-Shanghai High-speed Rail. Finally, hoping to provide a reference for the construction of the second line of the Beijing-Shanghai High-speed Rail, this paper proposed a corresponding proposal for the construction of the second line of Beijing and Shanghai.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on Warehouse Site Selection based on AHP","authors":"Feng Bingqing, Cao Liting","doi":"10.1109/ISCTT51595.2020.00055","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00055","url":null,"abstract":"As a storage unit and logistics center, the location of the warehouse will change according to the needs of enterprises and urban planning. The location of the warehouse determines the efficiency of warehousing operations and the response speed of logistics links. How to select warehouse location scientifically is particularly important. A logistics enterprise in Tangshan is located in G112, the first ring of Tangshan city. According to the requirements of municipal planning and its own development needs, a new warehouse site selection is needed now. In order to solve the site selection problem of this enterprise, analytic hierarchy process (AHP) is used for analysis and decision-making. Finally, the recommended site location is obtained, and site selection suggestions are given to help enterprises reduce operating costs and improve economic benefits.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131189097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Location of Base Station Based on Wireless Communication Density for 5G Network","authors":"Ruoyan Wei, Junfeng Wang, Yasu Jin, Siyuan Huo","doi":"10.1109/ISCTT51595.2020.00038","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00038","url":null,"abstract":"In order to improve the efficiency of 5G Network communication, a method of dynamic spotting setting for base station based on communication demand density is proposed. First, we grid the area and count the demands of each grid. Second, these areas form a scatter plot of regional demand density, and then remove the scatter points in the sparse demand area. Third, we homogenize filtered distribution points. Fourth, we use SOM to perceive regional needs. Finally, a series of experiments were conducted, and the results are great.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127456956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Care and Pension Plan Planning Based on Improved Leslie Model","authors":"Ruifeng Bian, Wenyi Tan, Weixiong Yang, Yichen Hou","doi":"10.1109/ISCTT51595.2020.00050","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00050","url":null,"abstract":"LesLie model is popular for the analysis of the population structure. However, on basis of the characteristics of the Leslie model, it can only predict the years in which the age range is taken as a unit, making decision makers fail to flexibly formulate effective plans for population changes. Therefore, in this paper, the fertility rate combination model and the Lee Carter model are combined with the Leslie model to cooperate with the GM (1, 1) gray-scale prediction model and multiple linear regression model to predict the population structure, and conduct algorithmic verification and medical resource allocation of the Shenzhen population status, thus verifying the feasibility and value of the algorithm.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127359478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path Planning for CAVs Considering Dynamic Obstacle Avoidance Based on Improved Driving Risk Field and A* Algorithm","authors":"Ye Tian, Huaxin Pei, Yi Zhang","doi":"10.1109/ISCTT51595.2020.00056","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00056","url":null,"abstract":"In vehicle path planning, safety is the most important consideration. As a main and popular index of traffic risk assessment, driving risk field can describe the comprehensive driving risk of complex traffic scenes, and has strong real-time performance. In view of the defects of the existing driving risk field model, this paper proposes an improved driving risk field with ellipse modification formula. Compared with the existing research, the improved model can take into account the safety differences in different directions of the road. Then, a path planning algorithm based on improved driving risk field and A * algorithm is proposed for CA V s, which considers dynamic obstacle avoidance. The simulation result shows that the algorithm is accurate and feasible, which can ensure the driving safety of vehicle in the process of path planning and improve the efficiency of the algorithm.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130198654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive machine learning pipeline for predicting the recurrence of gastric cancer","authors":"Yifan Gao, Haoran Wang, Minhan Guo, Yajin Li","doi":"10.1109/ISCTT51595.2020.00076","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00076","url":null,"abstract":"The advancement of medical science and technology has provided more methods for the diagnosis and treatment of malignant tumors, and the survival period of cancer patients has been significantly extended. However, many patients with malignant tumors still have recurrence and metastasis after effective treatment. Exploring the mechanism of tumor recurrence and metastasis to predict the recurrence and metastasis of cancer is a major clinical issue. At the same time, the rapid development of the Human Genome Project and gene microarray technology has enabled the activity of many genes in the patient's body to be intuitively measured through the chip. The rapid development of machine learning has contributed to the data mining and medical science of this DNA microarray technology. Therefore, this project aims at the above-mentioned problems and predicts the location and time of recurrence by analyzing a large amount of clinical data and 2enetic data. Firstly, we perform simple data cleaning and normalization processing on clinical data and genetic data; second, perform differential gene screening; next, select principal component analysis, sparse principal component analysis, nuclear principal component analysis, and multi-dimensional scaling algorithms to reduce the dimensional of the data. Finally, the genetic data uses random forest, support vector machine, linear support vector machine, guided aggregation algorithm, gradient boosting algorithm, and ensemble learning for machine learning, and then finds the best parameters and methods through grid search, and selects the appropriate model Evaluation method. The clinical data is manually selected and classified using machine learning. Finally, the results of clinical data and genetic data are combined to predict the site of recurrence. Using the above method to predict the recurrence and the location of the recurrence, a good effect was achieved. Taking whether to recurrence as an example, the accuracy rate of the verification set reached 0.825, the recall rate reached 0.801, the Fl score reached 0.800. Through the retrospective study and prediction of gastric cancer recurrence, the model proposed in this paper has potential clinical value.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133994172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalography Signal Analysis and Classification Based on Deep Learning","authors":"Zheng Li","doi":"10.1109/ISCTT51595.2020.00029","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00029","url":null,"abstract":"Brain computer interface (BCI) bridges the interaction between the brain activities and an external machines/device. Electroencephalography (EEG) has its advantages over other brainwave monitoring tools in cost, portability and monitoring frequency and accuracy. The interpretation of the relatively large amount of EEG signals is of key importance to understand brain functionality. Traditional machine learning and signal preprocessing methods alone fail to provide robust and in-time EEG signal interpretation and partially relied on professionally trained expert. For further developments and applications of robust and in-time EEG interpretation, we investigated the application of the deep learning models on a classification task of a motor imagery EEG signal dataset with both spatial and temporal information. We adopt sliding window with specified window sizes and strides to generate training samples for deep learning models and standardize the training samples to improve the model performance. Convolutional neural network, variants of recurrent neural network are investigated and compared on the classification and interpretation performance over the interested dataset. The convolutional neural network demonstrates superior performance in terms of both training efficiency and accuracy to other models.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133500531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Logistics Distribution Routes Optimization Based on ACO","authors":"Tianshi Liu, Yike Yin, Xiaobo Yang","doi":"10.1109/ISCTT51595.2020.00122","DOIUrl":"https://doi.org/10.1109/ISCTT51595.2020.00122","url":null,"abstract":"Aiming at the problem of route optimization, a multi-objective optimized route distribution model is constructed with the goal of the smallest total transportation cost and the shortest total delivery route. In order to overcome the shortcomings of ant colony optimization, such as too long search time and easy to fall into local optimum, the ant colony pheromone volatilization coefficient is improved by the method of adaptive change value, which improves the adaptability of ant colony optimization. Therefore, the improved ant colony optimization is used to solve the logistics distribution route optimization problem. The experimental results show that the improved ant colony optimization can obtain a better logistics distribution route plan, which provides valuable reference information for improving the economic benefits of logistics enterprises.","PeriodicalId":178054,"journal":{"name":"2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)","volume":"3 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114024828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}