Conference on Computer Graphics, Artificial Intelligence, and Data Processing最新文献_第4页

Research on system-level origin graph design for APT attack detection 面向APT攻击检测的系统级原点图设计研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674706

Yuxiang Zhang, Jiujiang Han, Ming Xian, Huimei Wang

{"title":"Research on system-level origin graph design for APT attack detection","authors":"Yuxiang Zhang, Jiujiang Han, Ming Xian, Huimei Wang","doi":"10.1117/12.2674706","DOIUrl":"https://doi.org/10.1117/12.2674706","url":null,"abstract":"With the rapid development of science and technology, the world has accelerated into the network information era, and the high sustained and high intensity attack and defense confrontation in cyberspace has become the new normal of the game between countries, the organization of attackers, the standardization of attack equipment, and the automation of attack methods have evolved. The research on APT attack detection has become a hot and difficult issue for academia and industry. To address these challenges, this paper proposes a system-level origin graph model for APT attack detection, analyzes and discusses the advantages and disadvantages of different granularity of origin graphs, selects a reasonable granularity of origin graph models, and focuses on multi-operating system origin graph models to determine different origin graph models for the respective characteristics of different operating system platforms, specifically, to build different entity objects, and elaborates on the technical details. The technical details are elaborated. Finally, the validity and feasibility of the system-level origin graph model are clarified to provide model support for the subsequent research on effective APT attack detection.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122956163","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

Impact of coal price and hydropower output uncertainty on electricity spot market for Hubei 煤炭价格和水电产量不确定性对湖北电力现货市场的影响

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674529

Yang Tang, Yifeng Liu, Meiting Liu, Junxuan Zou, Yuxin Zhang, Yuhong Fan

{"title":"Impact of coal price and hydropower output uncertainty on electricity spot market for Hubei","authors":"Yang Tang, Yifeng Liu, Meiting Liu, Junxuan Zou, Yuxin Zhang, Yuhong Fan","doi":"10.1117/12.2674529","DOIUrl":"https://doi.org/10.1117/12.2674529","url":null,"abstract":"Hubei is the second batch of power spot market pilot areas, and the construction of the spot market is imminent. At present, there are many difficulties and challenges in the construction of the spot market in Hubei. First, the price of thermal coal has fluctuated significantly in recent years, and many thermal generators have incurred losses due to the rising fuel prices, disrupting the stable operation of the market. Secondly, Hubei has a high installed capacity of hydropower, and there are many large-capacity hydropower generators with storage capacity. How to participate in the spot market of hydropower generators is a difficult problem that needs to be solved urgently at present. Based on the actual data of Hubei Power Grid, the electricity spot market is simulated. The market price and the profitability of various generators under different coal prices and different periods of hydropower dispatch are compared. The results show that in the spot market, the market price can correctly reflect the real price of electricity and ensure the profits of various generators. For hydropower generators, compared with the daily dispatch, the monthly dispatch of hydropower can better stimulate the storage capacity adjustment ability of hydropower and reduce the total cost of electricity consumption in the whole society.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576453","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

Quantitative refueling action recognition algorithm 定量加油动作识别算法

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674614

Lei Wang, Dasheng Guan, Cong Liu, Zhijun Zhang

引用次数: 0

A sample survey study of poly-semantic neurons in deep CNNs 深度cnn中多语义神经元的抽样调查研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674650

Chang-Bin Zhang, Yue Wang

{"title":"A sample survey study of poly-semantic neurons in deep CNNs","authors":"Chang-Bin Zhang, Yue Wang","doi":"10.1117/12.2674650","DOIUrl":"https://doi.org/10.1117/12.2674650","url":null,"abstract":"Although deep CNN networks have excellent image classification performance, they do not provide interpretability, and furthermore existing work reveals that these models have complex internals, for example, mysterious polysemantic neurons activate to multiple features. In this work, we analyze the intermediate data of the network dissection paper made by Bau et al. to understand to what extent polysemantic neurons exist. We divide the polysemantic neurons into five types and calculate the percentage of each type by sampling. We find that above 50% neurons identify one concept but there are a quite proportion of neurons that recognize two or more features. This can explain the high classification accuracy and some capacity saving of a deep CNN. By case studies, we draw some conclusions and hypotheses: First, unlike the human visual system, a CNN cannot distinguish detailed features (metaphor: a CNN is like a nearsighted eye). Second, the reason that the CNN is prone to adversarial attacks may be partially due to the polysemantic neurons. Third, polysemantic neurons may partially explain why people wrongly visualize one thing as another in neuroscience.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121402700","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

Occlusion face recognition based on improved attention mechanism 基于改进注意机制的遮挡人脸识别

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674629

Mai Fu, Zhihui Wang, Daoerji Fan, Huijuan Wu

引用次数: 0

Research on denoising of skinned point cloud based on multi-feature point parameter weight optimization 基于多特征点参数权重优化的蒙皮点云去噪研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674547

Binpeng Li, Jian Mao, Jie Yang, Hang Cai

引用次数: 0

Vehicle turning simulation with analysis and resolution of understeer and oversteer 车辆转向仿真及欠转向和过转向的分析与解决

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674982

Chuhan Zhang

引用次数: 0

Research on semantic segmentation algorithm of road pollution based on deep learning 基于深度学习的道路污染语义分割算法研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674939

Xin Zhang, Yan Zhu, Li-Juan Deng, Long Qi, Zhang Tao, Zhihao Tan

引用次数: 0

Market power monitoring and mitigation mechanism of spot market under new power system 新电力系统下现货市场市场电力监测与缓解机制研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674578

Yifeng Liu, Yang Tang, Meiting Liu, Fangmei Bie, Yuliang He, Yuxin Zhang

{"title":"Market power monitoring and mitigation mechanism of spot market under new power system","authors":"Yifeng Liu, Yang Tang, Meiting Liu, Fangmei Bie, Yuliang He, Yuxin Zhang","doi":"10.1117/12.2674578","DOIUrl":"https://doi.org/10.1117/12.2674578","url":null,"abstract":"In recent years, with the continuous development of China's electric power spot market construction, the market power problem brought by the structure of China's power generation-side has gradually become severe, and has evolved as the focus issue in the process of power market reform. The injection of renewable sources and the participation of distributed generation resources have brought profound influence on the power system and the power market, and it is urgent to establish and complete the market power monitoring and mitigation mechanism of China's electricity spot market. This paper primarily discusses the market power monitoring methods and mitigation mechanisms, then introduces the mature experience of typical international power markets in market power monitoring and mitigation mechanisms. Secondly, this paper summarizes the existing power monitoring and mitigation mechanisms in China, and analyzes the development and challenges of market power monitoring and mitigation mechanism construction under the new power system. We aim to provide some reference for the evaluation, monitoring and mitigation of market power in a series of practical aspects.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131116156","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

Rice yield prediction based on LSTM and GRU 基于LSTM和GRU的水稻产量预测

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674760

Yu Qiu

{"title":"Rice yield prediction based on LSTM and GRU","authors":"Yu Qiu","doi":"10.1117/12.2674760","DOIUrl":"https://doi.org/10.1117/12.2674760","url":null,"abstract":"Rice yield prediction is a vital problem in the national agriculture and economy. The development of deep learning overcomes the obstacles of traditional machine learning and shows superior performance in solving complicated problems. Especially for natural language processing (NLP) models such as LSTM and GRU, these models outperform the time series data, thus having great potential for complex agricultural spatiotemporal data with high dimensionality and nonlinearity. However, there is little discussion about performance of these two models in rice yield prediction. In this article, we adopted two popular NLP models to build and test 12 different model frameworks based on optimal hyperparameter configurations. And we compared model depth as well as bidirectional setting on the rice yield prediction by observing the performance of MSE losses throughout the training process. The results illustrated that both simple and complex models had outstanding fitting for small-sample training, and the depth and direction of the models did not significantly impact the performance of the experiment. But the complex model notably increases the training cost and decreases the convergence rate, implying that it’s not necessarily suitable for time-series problems with small-sample data. Further, the results could provide insights into a deep learning framework construction and hyperparameter selection for subsequent studies with comparable characteristics.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"72 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975218","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