2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)最新文献_第3页

Dynamical Representation Learning for Ethereum Transaction Network via Non-negative Adaptive Latent Factorization of Tensors 基于张量非负自适应潜分解的以太坊交易网络动态表示学习

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736256

Zeshi Lin, Hao Wu

{"title":"Dynamical Representation Learning for Ethereum Transaction Network via Non-negative Adaptive Latent Factorization of Tensors","authors":"Zeshi Lin, Hao Wu","doi":"10.1109/ICCSI53130.2021.9736256","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736256","url":null,"abstract":"As a common cryptocurrency platform, Ethereum involves massive accounts and numerous real-time transactions. Moreover, as the involved accounts increase drastically, it is impossible to have transactions among all accounts at one time slot, which results in a high-dimensional and incomplete (HDI) dynamic transaction network. In spite of its HDI nature, such HDI dynamic transaction network contains much useful knowledge regarding involved accounts' behavior patterns like potential transaction links. To extract such knowledge from an HDI dynamic transaction network, this paper proposes a Non-negative Adaptive Latent Factorization of Tensors (NAL) model with two interesting ideas: a) adopting an HDI tensor to describe an HDI dynamic transaction network and building a non-negative learning objective based on the principle of data density-oriented, and b) implementing model hyper-parameter self-adaptive via using a particle swarm optimization (PSO) algorithm in the training process. Empirical studies on three real Ethereum transaction networks show that compared with state-of-the-art methods, the proposed NAL model achieves superior performance in terms of accuracy and computational efficiency in predicting potential transaction links.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125896267","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}

引用次数: 4

Research on the Influence of Camera Velocity on Image Blur and a Method to Improve Object Detection Precision 相机速度对图像模糊的影响及提高目标检测精度的方法研究

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736224

Xuan Yang, Fan Sang, Tianle Wang, Xuan Pei, H. Wang, Taogang Hou

{"title":"Research on the Influence of Camera Velocity on Image Blur and a Method to Improve Object Detection Precision","authors":"Xuan Yang, Fan Sang, Tianle Wang, Xuan Pei, H. Wang, Taogang Hou","doi":"10.1109/ICCSI53130.2021.9736224","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736224","url":null,"abstract":"The relative motion between camera and target objects will inevitably result in image blurring, which will cause poor performance of visual perception algorithms. The existing methods focus on coarse-grained classification of random camera jitter to study the degradation of object detection performance. However, there are few studies on blur caused by camera with high-speed movement. In this paper, a novel idea that focuses on the relationship between the performance of the camera's visual perception algorithm (taking object detection as an example) and the forward speed of the camera is proposed. A four-wheeled experimental platform that can move in a straight line at a uniform speed of up to 10km/h was designed. Some sensors such as cameras and speed meters were mounted to take motion blurred pictures at different driving speeds. Then, object detection method YOLO-v5 was performed on the above image data to get the confidence of detection frames at different motion speeds, thus a precise expression of relationship between the confidence and speed could be obtain using polynomial regression method. Further, a preliminarily practice that to improve the object detection performance of this type of motion blur image was conducted. DeblurGAN-v2, a deblurring algorithm based on the generative adversarial networks, is used to deblur the original blurred pictures. By comparing with the result of object detected before and after deblurring, the conclusion that the object detection performance is improved as a whole after deblurring was drawn. These results may help expand the range of image perception (blurred images due to high speed) and generate some potential applications.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250798","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}

引用次数: 1

A Denoising algorithm of MEMS Gyro Based on CEEMDAN and Improved LWT 基于CEEMDAN和改进LWT的MEMS陀螺去噪算法

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736255

Zhenxing Wu, Liang Shan, Daqing Rui, Jia Chen

引用次数: 2

Discussion on the Innovative Model of Remote Experimental Teaching For Engineering Education 工程教育远程实验教学创新模式探讨

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736246

Jun Deng, Wenxin Yang, Mingzhang Luo, Qisen Zuo, Xiaolong Yuan, Guodong Zhang

{"title":"Discussion on the Innovative Model of Remote Experimental Teaching For Engineering Education","authors":"Jun Deng, Wenxin Yang, Mingzhang Luo, Qisen Zuo, Xiaolong Yuan, Guodong Zhang","doi":"10.1109/ICCSI53130.2021.9736246","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736246","url":null,"abstract":"The remote experimental teaching for engineering education is supported by the remote open experimental environment, and the experimental teaching process is implemented through remote resource sharing and remote control. The remote experiment platform of this paper is based on remote experiment as the basic carrier, connecting electronic information, artificial intelligence, structural engineering and other engineering education-related experiments, and attaching importance to technical support. Starting from the core of “importing knowledge more effectively” and the five elements of “learners, teachers, teaching tasks, learning resources and environment, and teaching practice process”, the teaching concept of remote experiments, the construction of experimental platforms and links, the content design of experimental cases, the construction and support of teaching teams, and the application of artificial intelligence have carried out model innovations. At the same time, facing the social needs and the direction of industrial development, the new technology is integrated into the experimental case teaching, and the students are guided to learn the engineering thinking method that integrates theory with practice.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128011334","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}

引用次数: 0

Prediction of Ballistic trajectories based on Gaussian Mixture Model 基于高斯混合模型的弹道轨迹预测

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736174

Jihuan Ren, Yi Liu, Xiang Wu, Y. Bo

引用次数: 0

Operator-based nonlinear forced vibration control for a vertical wing-plate 基于算子的垂直翼板非线性强迫振动控制

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736249

Guang Jin, M. Deng

引用次数: 0

Parallel intelligent command decision-making technology based on combat prior knowledge and reinforcement learning algorithm 基于战斗先验知识和强化学习算法的并行智能指挥决策技术

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736221

Bojian Tang, Yuxiang Sun, Jiahui Yu, Tao Jin, Xianzhong Zhou

{"title":"Parallel intelligent command decision-making technology based on combat prior knowledge and reinforcement learning algorithm","authors":"Bojian Tang, Yuxiang Sun, Jiahui Yu, Tao Jin, Xianzhong Zhou","doi":"10.1109/ICCSI53130.2021.9736221","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736221","url":null,"abstract":"Intelligent command decision-making technology based on enhanced learning algorithm has gradually become a new trend in the field of intelligent chess. However, in the complex chess environment, relying solely on intensive learning algorithms, it is difficult to gain effective experience quickly in the initial stage of the opening, convergence speed is very slow. We present the WKP-BCQ (War-Knowledge-Prior Batch-constrained deep Q-learning) algorithm based on prior knowledge to solve the problem of long-term ineffective exploration of agents. Our model generate a knowledge-based cache in the same reward environment according to the rules of experts and the actual per-person competition data. Based on this cache library, combined with discrete BCQ algorithms to train directly from cached data, an effective strategy is generated to solve the cold start problem of relying solely on intensive learning. The proposed WKP-BCQ algorithm is verified by tactical-level chess projection and confrontation system, and the experiment proves that the algorithm can efficiently generate intelligent decision suggestions and have stronger intelligence.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123680196","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}

引用次数: 0

Application of Small Sample Information Processing in High Dimensional Space 小样本信息处理在高维空间中的应用

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736206

Shanshan Yuan

引用次数: 1

Graph Neural Network-based Node Classification with Hard Sample Strategy 基于硬样本策略的图神经网络节点分类

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736175

Yinhao Tang, Zhenhua Huang, Jiujun Cheng, Guangtao Zhou, Shuai Feng, Hongjiang Zheng

引用次数: 0

The Research of a Data-Driven Intelligent SCUC Decision-making Approach based on Gated Recurrent Unit and Seq2Seq Technique 基于门控循环单元和Seq2Seq技术的数据驱动scc智能决策方法研究

2021 International Conference on Cyber-Physical Social Intelligence (ICCSI) Pub Date : 2021-12-18 DOI: 10.1109/ICCSI53130.2021.9736250

Juncong Hao, Nan Yang, Di Ye, Ye He, Cong Yang, Xietian Zhang

{"title":"The Research of a Data-Driven Intelligent SCUC Decision-making Approach based on Gated Recurrent Unit and Seq2Seq Technique","authors":"Juncong Hao, Nan Yang, Di Ye, Ye He, Cong Yang, Xietian Zhang","doi":"10.1109/ICCSI53130.2021.9736250","DOIUrl":"https://doi.org/10.1109/ICCSI53130.2021.9736250","url":null,"abstract":"With the challenges of independent system operators and the integration of artificial intelligence technology and power system, it is of great significance to study the approach of security-constrained unit commitment decision with high adaptability and high precision. In this paper, based on the gated recurrent unit neural network, an improved intelligent data-driven SCUC decision-making approach is proposed by constructing a compound gated recurrent unit neural network frame with Seq2Seq technology for SCUC problems. Firstly, a sample encoding technique for high-dimensional sample matrices is used to compress the dimension of matrices. After that, a compound neural network frame that is facing unit commitment decision-making is studied. Finally, the compound neural network is constructed by gated recurrent unit neural network and Seq2sSeq technique, forming a unit commitment deep learning model. The mapping model between system daily load and unit on/off schemes is constructed by the historical data training process. A series of simulation results based on standard examples verify the correctness and effectiveness of the proposed approach.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"10863 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134087802","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}

引用次数: 0