Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9181900
Gan Wenfeng, Su Bo, Zhang Hong
This article analyzes and designs a SOA-based equipment asset management and maintenance system. The system is based on the asset and equipment ledger, takes work order submission, approval, and execution as the main line to optimize the resource configuration of the electrical enterprise, reduce the maintenance cost, and improve the maintenance efficiency according to fault disposal, planned repair and preventive maintenance and other business management modes, so as to increase the competitive capability of the electrical enterprise.
{"title":"Research on Application of SOA-based Asset Management System for Power Equipment","authors":"Gan Wenfeng, Su Bo, Zhang Hong","doi":"10.1109/ICAICA50127.2020.9181900","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9181900","url":null,"abstract":"This article analyzes and designs a SOA-based equipment asset management and maintenance system. The system is based on the asset and equipment ledger, takes work order submission, approval, and execution as the main line to optimize the resource configuration of the electrical enterprise, reduce the maintenance cost, and improve the maintenance efficiency according to fault disposal, planned repair and preventive maintenance and other business management modes, so as to increase the competitive capability of the electrical enterprise.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121624773","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182650
Lu Di
The Internet in the information age gives birth to the intelligent way of stadium operation. In the field of Shanghai public stadiums and gymnasiums, the public attribute of the stadiums and gymnasiums endows the fitness groups with the sociality and commonweal, while the economic characteristics of the stadiums and gymnasiums cause the problem of how to operate intelligently under the wave of Shanghai public fitness. This paper uses the methods of literature, induction and logical analysis to sort out the resources sharing materials of all kinds of public sports venues in Shanghai. Based on the application of PHP technology system, it designs the logical framework of the intelligent service operation platform of Shanghai public sports venues, in order to provide reference for the innovation of resources sharing mode of Shanghai public sports venues.
{"title":"Analysis of Intelligent Operation Path of Shanghai Public Sports Venues under Internet + Background—Take PHP Technology System as an Example","authors":"Lu Di","doi":"10.1109/ICAICA50127.2020.9182650","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182650","url":null,"abstract":"The Internet in the information age gives birth to the intelligent way of stadium operation. In the field of Shanghai public stadiums and gymnasiums, the public attribute of the stadiums and gymnasiums endows the fitness groups with the sociality and commonweal, while the economic characteristics of the stadiums and gymnasiums cause the problem of how to operate intelligently under the wave of Shanghai public fitness. This paper uses the methods of literature, induction and logical analysis to sort out the resources sharing materials of all kinds of public sports venues in Shanghai. Based on the application of PHP technology system, it designs the logical framework of the intelligent service operation platform of Shanghai public sports venues, in order to provide reference for the innovation of resources sharing mode of Shanghai public sports venues.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125030590","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182637
Jing Wang, Tianyou Yu, Zebin Huang
Brain-computer interface is a technology that is helpful for these people with dyspraxia or strokes to obtain the ability to communicate with others or control devices again. However, due to the brain signal collected by the system has terrible quilty, the error decision is often made by the BCI system, which hinders the development of the technology. Therefore, detecting the error from the BCI system holds a great significance by error-related potential (ErrP) generated when erroneous feedback from the system is found by the subject. In this paper, we propose an integrated transfer learning based on Group Sparse Bayesian Linear Discriminant Analysis (ITL_GSBLDA) to detect ErrPs. In this way, the Group Sparse Bayesian Linear Discriminant (GSBLDA) has better performance with the help of transfer learning. The experiment has been finished with the dataset of Kaggle competition. In the experiment, sensitivity, specificity, and Area Under Curve (AUC) are used to evaluate the performance of the decoder. Finality, the results are 71.49% sensitivity, 66.49% specificity, and 0.7624 AUC when using the signal features and meta-feature. And in this condition, our decoder surpasses the 5th place in the competition.
{"title":"Integrated Transfer Learning Based on Group Sparse Bayesian Linear Discriminant Analysis for Error-Related Potentials Detection","authors":"Jing Wang, Tianyou Yu, Zebin Huang","doi":"10.1109/ICAICA50127.2020.9182637","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182637","url":null,"abstract":"Brain-computer interface is a technology that is helpful for these people with dyspraxia or strokes to obtain the ability to communicate with others or control devices again. However, due to the brain signal collected by the system has terrible quilty, the error decision is often made by the BCI system, which hinders the development of the technology. Therefore, detecting the error from the BCI system holds a great significance by error-related potential (ErrP) generated when erroneous feedback from the system is found by the subject. In this paper, we propose an integrated transfer learning based on Group Sparse Bayesian Linear Discriminant Analysis (ITL_GSBLDA) to detect ErrPs. In this way, the Group Sparse Bayesian Linear Discriminant (GSBLDA) has better performance with the help of transfer learning. The experiment has been finished with the dataset of Kaggle competition. In the experiment, sensitivity, specificity, and Area Under Curve (AUC) are used to evaluate the performance of the decoder. Finality, the results are 71.49% sensitivity, 66.49% specificity, and 0.7624 AUC when using the signal features and meta-feature. And in this condition, our decoder surpasses the 5th place in the competition.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123329170","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182386
Jianwei Ren
Vision sensors can simulate human eyes to process visual scenes. Vision-based object localization technology has been widely studied and applied in some fields such as autonomous driving. SLAM technology can estimate the surrounding environment and locate itself in real time without prior knowledge. The existing binocular vision positioning SLAM has problems such as insufficient positioning accuracy, high data requirements and high computational cost. This paper proposes an improved binocular LSD_SLAM method with census transform, which purpose is to reduce the requirement for the initial value and optimize the localization accuracy. Experiments in several office scenarios show that the proposed method is improved on Average Precision and Average Recall, and its computing cost still needs to be improved.
{"title":"An improved binocular LSD_SLAM method for object localization","authors":"Jianwei Ren","doi":"10.1109/ICAICA50127.2020.9182386","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182386","url":null,"abstract":"Vision sensors can simulate human eyes to process visual scenes. Vision-based object localization technology has been widely studied and applied in some fields such as autonomous driving. SLAM technology can estimate the surrounding environment and locate itself in real time without prior knowledge. The existing binocular vision positioning SLAM has problems such as insufficient positioning accuracy, high data requirements and high computational cost. This paper proposes an improved binocular LSD_SLAM method with census transform, which purpose is to reduce the requirement for the initial value and optimize the localization accuracy. Experiments in several office scenarios show that the proposed method is improved on Average Precision and Average Recall, and its computing cost still needs to be improved.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123595351","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182389
Qiubo Huang, Xuezhi Song, Guozheng Fang
We proposed a plagiarism detection approach based on code similarity and student behavior characteristics in educational scenarios. The traditional plagiarism check is based on the code only, which enables that students can escape inspection by modifying a small amount of code. We proposed that if the behavioral characteristics of students when submitting code can be considered, the suspected plagiarism can be more accurately identified. We proposed the concept of code similarity concentration (SCD) with reference to the Gini coefficient idea. SCD can reflect the similarity distribution between all the codes submitted by a student and others' codes. A large value of SCD means that a student's codes are always the most similar to the codes of some particular classmates. In addition, we also extracted other features to help detection. Finally, we classify the plagiarism detection problem as a binary classification problem and use LightGBM to make decisions. The experimental results show that the accuracy is close to 99% and f1-score is close to 98%.
{"title":"Code Plagiarism Detection Method Based on Code Similarity and Student Behavior Characteristics","authors":"Qiubo Huang, Xuezhi Song, Guozheng Fang","doi":"10.1109/ICAICA50127.2020.9182389","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182389","url":null,"abstract":"We proposed a plagiarism detection approach based on code similarity and student behavior characteristics in educational scenarios. The traditional plagiarism check is based on the code only, which enables that students can escape inspection by modifying a small amount of code. We proposed that if the behavioral characteristics of students when submitting code can be considered, the suspected plagiarism can be more accurately identified. We proposed the concept of code similarity concentration (SCD) with reference to the Gini coefficient idea. SCD can reflect the similarity distribution between all the codes submitted by a student and others' codes. A large value of SCD means that a student's codes are always the most similar to the codes of some particular classmates. In addition, we also extracted other features to help detection. Finally, we classify the plagiarism detection problem as a binary classification problem and use LightGBM to make decisions. The experimental results show that the accuracy is close to 99% and f1-score is close to 98%.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125654398","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182639
Zihui Jia, Yuesheng Zhu
Single image raindrop removal is an extremely challenging task since the raindrop regions of various shapes and sizes are not given and the background information of the occluded regions is completely lost for most part. In this paper, a novel two-stage residual adaptive mask generative adversarial network (RAM-GAN) is developed for single image raindrop removal, in which the raindrop regions can be automatically detected and a restored image without raindrops is generated. Moreover, the residual adaptive mask block (RAMB) structures and residual dense adaptive mask modules (RDAMM) are proposed to be the main components constructing the network. The proposed RAMB structure can serve as a feature selector which adaptively enhances the effective information and suppress the invalid information. Each block is processed into two branches: soft mask branch and trunk branch. A mask is generated by the soft mask branch to softly weigh the features processed by the trunk branch. In addition, RDAMM, the residual densely connected module based on RAMB structure, is proposed to maximize the information flow among different blocks and guarantee better convergence. Our experimental results have demonstrated that our method can effectively remove raindrops while well preserving the image details, which outperforms the state-of-the-art methods quantitatively and qualitatively.
{"title":"Residual Adaptive Mask Generative Adversarial Network for Image Raindrop Removal","authors":"Zihui Jia, Yuesheng Zhu","doi":"10.1109/ICAICA50127.2020.9182639","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182639","url":null,"abstract":"Single image raindrop removal is an extremely challenging task since the raindrop regions of various shapes and sizes are not given and the background information of the occluded regions is completely lost for most part. In this paper, a novel two-stage residual adaptive mask generative adversarial network (RAM-GAN) is developed for single image raindrop removal, in which the raindrop regions can be automatically detected and a restored image without raindrops is generated. Moreover, the residual adaptive mask block (RAMB) structures and residual dense adaptive mask modules (RDAMM) are proposed to be the main components constructing the network. The proposed RAMB structure can serve as a feature selector which adaptively enhances the effective information and suppress the invalid information. Each block is processed into two branches: soft mask branch and trunk branch. A mask is generated by the soft mask branch to softly weigh the features processed by the trunk branch. In addition, RDAMM, the residual densely connected module based on RAMB structure, is proposed to maximize the information flow among different blocks and guarantee better convergence. Our experimental results have demonstrated that our method can effectively remove raindrops while well preserving the image details, which outperforms the state-of-the-art methods quantitatively and qualitatively.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584548","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182577
Fu-Wen Liu, Zhiyun Xiao
Early treatment of potato diseases can increase the yield in the later stage, so correct diseased areas identification of potato leaves is of great significance. Deep learning can effectively obtain invariant features and avoid the limitations of artificial feature extraction, it is gradually applied to hyperspectral image classification. Aiming at the local disease spots of potato leaves with different diseases, this paper used 1D-CNN to adaptively extract invariant features, so as to realize the identification of spots of different diseases. In order to verify the accuracy of the algorithm, the labels of the calibration data are needed, and the traditional calibration methods are cost in high. In this paper, a method of calibrating data for rough calibration followed by fine calibration is proposed. In the experiment, a total of 126 hyperspectral potato disease leaves were collected in Hohhot, there are three types of diseases, including 28 anthracnose, 49 leaf blight, 7 early blight and 42 mixed diseases of varying degrees. Among them, 9 of datasets were used to train and 117 tests. In the diseased area, the average accuracy of traditional SVM was 95.66%, and the number of misclassification pixels was 88,939. The average time of single data recognition was about 395s; the average accuracy of one-dimensional convolutional neural network was 97.72%, 39,684 pixels were misclassification, and the average time required to identify a single data was about 15s. The results showed that the one-dimensional convolutional neural network is faster and better in disease spots identifying of potato leaves in hyperspectral.
{"title":"Disease Spots Identification of Potato Leaves in Hyperspectral Based on Locally Adaptive 1D-CNN","authors":"Fu-Wen Liu, Zhiyun Xiao","doi":"10.1109/ICAICA50127.2020.9182577","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182577","url":null,"abstract":"Early treatment of potato diseases can increase the yield in the later stage, so correct diseased areas identification of potato leaves is of great significance. Deep learning can effectively obtain invariant features and avoid the limitations of artificial feature extraction, it is gradually applied to hyperspectral image classification. Aiming at the local disease spots of potato leaves with different diseases, this paper used 1D-CNN to adaptively extract invariant features, so as to realize the identification of spots of different diseases. In order to verify the accuracy of the algorithm, the labels of the calibration data are needed, and the traditional calibration methods are cost in high. In this paper, a method of calibrating data for rough calibration followed by fine calibration is proposed. In the experiment, a total of 126 hyperspectral potato disease leaves were collected in Hohhot, there are three types of diseases, including 28 anthracnose, 49 leaf blight, 7 early blight and 42 mixed diseases of varying degrees. Among them, 9 of datasets were used to train and 117 tests. In the diseased area, the average accuracy of traditional SVM was 95.66%, and the number of misclassification pixels was 88,939. The average time of single data recognition was about 395s; the average accuracy of one-dimensional convolutional neural network was 97.72%, 39,684 pixels were misclassification, and the average time required to identify a single data was about 15s. The results showed that the one-dimensional convolutional neural network is faster and better in disease spots identifying of potato leaves in hyperspectral.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121127281","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182535
F. Pan, Yuewei Bai, Kefeng Xu, L. Nie
Faced with fierce competition, the concept of virtual factory comes out. As an important part of virtual factories, Coordinate Measuring Machines' accuracy is important. The paper introduces the efficient and economic method, error compensation, to enhance the Coordinate Measuring Machines precision. As the basic steps of error compensation, the significant error sources-geometric errors are found out; the errors relationship is established and the squareness errors' measurement is analyzed. The experiment is done, which validates the feasibility of the method.
{"title":"The squareness errors compensation for Coordinate Measuring Machines based on virtual factory","authors":"F. Pan, Yuewei Bai, Kefeng Xu, L. Nie","doi":"10.1109/ICAICA50127.2020.9182535","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182535","url":null,"abstract":"Faced with fierce competition, the concept of virtual factory comes out. As an important part of virtual factories, Coordinate Measuring Machines' accuracy is important. The paper introduces the efficient and economic method, error compensation, to enhance the Coordinate Measuring Machines precision. As the basic steps of error compensation, the significant error sources-geometric errors are found out; the errors relationship is established and the squareness errors' measurement is analyzed. The experiment is done, which validates the feasibility of the method.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129706542","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9181947
Qing Shao, Tao Xu, Yoshino Tatsuo
An adaptive RL-BFGS (ARL-BFGS) algorithm was proposed for fatigue life design to speed up the convergence and obtain the global optimal solution under the circumstances of fewer optimization times. Fatigue life is one of the most essential criteria for the optimal design of double row angular contact ball bearings. The contact angle was selected as a design parameter besides the basic geometric parameters. The design constraints considering the manufacturing and mounting situations were processed by a penalty function. Three different constraint non-linear optimization models were established for the optimal dynamic and static load capacity, and their weighted form. The bearing model 3210 was optimized successfully to prove the correctness and effectiveness of the proposed algorithm. The overall performance of the ARL-BFGS algorithm was checked by the comparative experiments of different optimization methods and different bearing models. The result showed that the dynamic load capacity and static load capacity of the optimized bearing series 32 are approximately 60% and 30% higher than the standard value in Rolling Bearing Handbook by using the ARL-BFGS algorithm, respectively. The weighted form of the dynamic and static load capacity was also optimized to provide more selection for designers.
{"title":"Optimal fatigue life design of double row angular contact ball bearings by an adaptive RL-BFGS algorithm","authors":"Qing Shao, Tao Xu, Yoshino Tatsuo","doi":"10.1109/ICAICA50127.2020.9181947","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9181947","url":null,"abstract":"An adaptive RL-BFGS (ARL-BFGS) algorithm was proposed for fatigue life design to speed up the convergence and obtain the global optimal solution under the circumstances of fewer optimization times. Fatigue life is one of the most essential criteria for the optimal design of double row angular contact ball bearings. The contact angle was selected as a design parameter besides the basic geometric parameters. The design constraints considering the manufacturing and mounting situations were processed by a penalty function. Three different constraint non-linear optimization models were established for the optimal dynamic and static load capacity, and their weighted form. The bearing model 3210 was optimized successfully to prove the correctness and effectiveness of the proposed algorithm. The overall performance of the ARL-BFGS algorithm was checked by the comparative experiments of different optimization methods and different bearing models. The result showed that the dynamic load capacity and static load capacity of the optimized bearing series 32 are approximately 60% and 30% higher than the standard value in Rolling Bearing Handbook by using the ARL-BFGS algorithm, respectively. The weighted form of the dynamic and static load capacity was also optimized to provide more selection for designers.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"60 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116434872","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}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9181941
Dafeng Xu, Tongtong Xu, Chunmei Liu, Jingbo Yang
In recent years, China's direct investments in Africa have grown substantially, making the risk analysis of direct investments by logistics companies increasingly important. Many specialised agencies in the world measure the national investment risk. However, their assessments only analyse the political and economic environment of each country without emphasising the importance of risk assessment in terms of logistics projects. This study fills the gap by presenting the risk assessment of logistics projects in 10 major African countries where China has the highest foreign direct investment. On the basis of existing research, an artificial neural network is mainly used to establish China's risk index system for African logistics projects and a macro early warning model for the investment risk of such logistics projects. Several suggestions on how to prevent the risks of logistics projects in African countries are provided.
{"title":"Risk Assessment of Logistics Projects based on BP Neural Network: A Case Study on African Countries","authors":"Dafeng Xu, Tongtong Xu, Chunmei Liu, Jingbo Yang","doi":"10.1109/ICAICA50127.2020.9181941","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9181941","url":null,"abstract":"In recent years, China's direct investments in Africa have grown substantially, making the risk analysis of direct investments by logistics companies increasingly important. Many specialised agencies in the world measure the national investment risk. However, their assessments only analyse the political and economic environment of each country without emphasising the importance of risk assessment in terms of logistics projects. This study fills the gap by presenting the risk assessment of logistics projects in 10 major African countries where China has the highest foreign direct investment. On the basis of existing research, an artificial neural network is mainly used to establish China's risk index system for African logistics projects and a macro early warning model for the investment risk of such logistics projects. Several suggestions on how to prevent the risks of logistics projects in African countries are provided.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128218151","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}
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