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2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献

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Construction method of network feasible path for power data platform 电力数据平台网络可行路径的构建方法
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00020
Mingming Zhang, Kai Liu, Guo Qian
{"title":"Construction method of network feasible path for power data platform","authors":"Mingming Zhang, Kai Liu, Guo Qian","doi":"10.1109/MLISE57402.2022.00020","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00020","url":null,"abstract":"There are complex functional components and various network paths on a power data platform. To enable security personnel to monitor the distributed network of the data platform clearly and intuitively, the construction method of network feasible path of power data platform is studied in this paper, which is mainly composed of the following three parts: firstly, the feasible model of power data platform is formed by constructing the physical connection relation and extracting the configuration rules; secondly, the connected graph of power data platform is constructed according to the generated feasible model; finally, the network feasible path of power data platform is constructed. The simulation results show that the proposed algorithm is of good feasibility.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127434538","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
Recognition of Brain Tumor by Means of Deep Learning Neural Networks 基于深度学习神经网络的脑肿瘤识别
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00078
Yangchen Chi, Yutao Li, Jiayi Zhang
{"title":"Recognition of Brain Tumor by Means of Deep Learning Neural Networks","authors":"Yangchen Chi, Yutao Li, Jiayi Zhang","doi":"10.1109/mlise57402.2022.00078","DOIUrl":"https://doi.org/10.1109/mlise57402.2022.00078","url":null,"abstract":"Due to the irregular and manifold shapes of brain tumors, it is somewhat complicate to match the tumor images to the right result and provide the guidance to assess and improve the quality as well as the improvement of individual’s actions. Traditional methods using simple method to deal with the problem. The result shows that it is time-consuming and hard to put into practice in clinical application. In this paper, this study made the attempt to improve the algorithm and it made sense. In this work, an efficient algorithm combining 3 methods was proposed and a comparison was made to their performance. The new algorithm briefly makes a lower complexity of the layer function. Experimental results apparently shows that our algorithm is rather competitive. It can provide accurate feedback for learners in brain tumor recognition. The precision of proposed CNN can reach 0.69 while MobileNet method and VGG16 can reach 0.75 and 0.71. Moreover, the low rate of loss makes our model much more stable. The satisfied result makes our method a remarkably promising tool in medical treatment.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324572","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
Lightweight Real-time Object Detection System Based on Embedded AI Development Kit 基于嵌入式AI开发工具包的轻量级实时目标检测系统
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00010
Junjie Li, Xinsen Zhou, Qianqiu Wang, Xianlu Luo
{"title":"Lightweight Real-time Object Detection System Based on Embedded AI Development Kit","authors":"Junjie Li, Xinsen Zhou, Qianqiu Wang, Xianlu Luo","doi":"10.1109/MLISE57402.2022.00010","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00010","url":null,"abstract":"Lightweight object detection focuses on the lightweight and real-time performance of the model, aiming to reduce the size of the model as much as possible without significantly reducing the detection accuracy, so that it can be deployed in practical application scenarios. This system improves the MobileNet-SSD object detection algorithm and uses standard datasets for training and testing. Through channel pruning, the parameters of the model are greatly reduced while the accuracy remains unchanged, and the model compression ratio is 1.17:1, which reduces the model’s occupation of the device memory capacity, and obtains about 44% improvement in detection speed. Finally, the trained model is deployed on the embedded artificial intelligence development kit EAIDK-610 to process and detect the collected video content in real time. This system can be extended to practical detection tasks with limited computing resources in various specific occasions.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133426814","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
Development of Launch Vehicle Shape Design Software Based on Parameterization Method 基于参数化方法的运载火箭外形设计软件开发
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00028
Tian Xie, Zhiqin Liu, Jizheng Li, Jun Huang, Qingfeng Wang
{"title":"Development of Launch Vehicle Shape Design Software Based on Parameterization Method","authors":"Tian Xie, Zhiqin Liu, Jizheng Li, Jun Huang, Qingfeng Wang","doi":"10.1109/mlise57402.2022.00028","DOIUrl":"https://doi.org/10.1109/mlise57402.2022.00028","url":null,"abstract":"Shape design is one of the most important parts in concept design stage of launch vehicle, which directly affects the overall aerodynamic performance of launch vehicle. In order to help users to quickly select the conceptual design scheme of launch vehicle, this study designed and implemented the launch vehicle design software based on parametric method, involving the overall design field of launch vehicle. The software obtains the parameterized shape of the launch vehicle and generates its surface mesh by using the method of model line design, taking into account various types of generatrix; The mesh quality is tested from three aspects: cell side-length ratio, cell skew angle and cell warp angle; STL files in standard format are generated by using the shape data of the launch vehicle, which can be used for aerodynamic engineering estimation, overall optimization of the launch vehicle, CFD calculation, CNC machining, etc.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132319160","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
Early Warning Model for Financial Risks of Listed Companies Based on Machine Learning 基于机器学习的上市公司财务风险预警模型
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00100
Xu Wei, Yonghui Chen
{"title":"Early Warning Model for Financial Risks of Listed Companies Based on Machine Learning","authors":"Xu Wei, Yonghui Chen","doi":"10.1109/MLISE57402.2022.00100","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00100","url":null,"abstract":"The descriptive text information in the annual reports of listed companies is an essential part of the information disclosure of listed companies. The prediction ability of their financial risks can be improved by mining and analysing listed companies’ disclosure text. By extracting the textual characteristics of the “Discussion and Analysis of Business Conditions” in the annual reports of listed companies in the A-share market, we construct textual characteristics indicators that can reflect financially distressed companies and normal companies. Subsequently, the text feature indicators are combined with financial indicator data and classified using convolutional neural networks to construct the financial risk warning fusion model E-CNN. AUC evaluates the performance of the early warning model. The experimental results show that the financial text features extracted by the word2vec-ES model can improve the AUC values predicted by the financial early warning model. The word2vec-ES improves the AUC values predicted by the financial early warning model more significantly compared with other methods, indicating that the word2vec-ES model effectively extracts the financial text features and improves the prediction ability of the financial risk early warning model of listed companies.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125202489","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
Adversarial Audio Detection Method Based on Transformer 基于变压器的对抗性音频检测方法
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00023
Yunchen Li, Da Luo
{"title":"Adversarial Audio Detection Method Based on Transformer","authors":"Yunchen Li, Da Luo","doi":"10.1109/MLISE57402.2022.00023","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00023","url":null,"abstract":"Speech recognition technology has been applied to all aspects of our daily life, but it faces many security issues. One of the major threats is the adversarial audio examples, which may tamper the recognition results of the acoustic speech recognition system (ASR). In this paper, we propose an adversarial detection framework to detect adversarial audio examples. The method is based on the transformer self-attention mechanism. Spectrogram features are extracted from the audio and divided into patches. Position information are embedded and then fed into transformer encoder. Experimental results show that the method achieves good performance with the detection accuracy of above 96.5% under the white-box attacks and blackbox attacks, and noisy circumstances. Even when detecting adversarial examples generated by the unknown attacks, it also achieves satisfactory results.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114635266","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
Initial clustering center optimization and feature auto-weighting for k-Means clustering algorithm k-Means聚类算法的初始聚类中心优化及特征自动加权
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00036
Fu-zhou Zhao
{"title":"Initial clustering center optimization and feature auto-weighting for k-Means clustering algorithm","authors":"Fu-zhou Zhao","doi":"10.1109/mlise57402.2022.00036","DOIUrl":"https://doi.org/10.1109/mlise57402.2022.00036","url":null,"abstract":"We focus on two main issues. First, the effectiveness of clustering is strongly related to selecting the initial clustering center. Traditional algorithms and their tendency to select multiple initial clustering centers in the same cluster, we use the maximum distance principle, which ensures the initial clustering centers attribute to different categories to avoid this problem. Second, the k-means algorithm cannot assign greater weights to essential features in high dimensions because it treats all features equitably in the clustering process. We acquire a proposed algorithm that is more efficient and accurate than the traditional k-means by improving the algorithm with the multidimensional feature weights technique to give more weight to the more essential features. Experimentally, our enhancements have significantly improved efficiency by 33% and accuracy by 36%.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114723228","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
Position prediction algorithm design for FIRA Simulation of 5VS5 Robot 5VS5机器人FIRA仿真位置预测算法设计
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00061
Yu-Hua Zhang, Weili Ge
{"title":"Position prediction algorithm design for FIRA Simulation of 5VS5 Robot","authors":"Yu-Hua Zhang, Weili Ge","doi":"10.1109/MLISE57402.2022.00061","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00061","url":null,"abstract":"Robot football simulation match is a kind of simulated football game with software. For the fira5vs5 robot simulation football project, many scholars have proposed the optimization of the team’s strategy on this project. On the basis of the dynamic role assignment of players in the game, this paper analyzes the principle of the least square method and the application process of the least square method, gives the algorithm design and obtains the final result. Platform verification and game results show that this strategy can greatly improve the goal rate of shooting players and achieve better shooting effect.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131806964","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
Research on cloud computing resource scheduling based on machine learning 基于机器学习的云计算资源调度研究
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00090
Yansong Li
{"title":"Research on cloud computing resource scheduling based on machine learning","authors":"Yansong Li","doi":"10.1109/MLISE57402.2022.00090","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00090","url":null,"abstract":"In the cloud computing environment, concurrent training of multiple machine learning models will cause serious competition for shared cluster resources and affect the execution efficiency. Aiming at this problem, this paper proposes a cloud computing resource scheduling method for distributed machine learning. Based on historical monitoring data, a model between the number of iterations and model quality improvement is established, the impact of resource allocation on model quality improvement is predicted online, resource optimization scheduling strategies are formulated, and a resource scheduling framework is designed. Experimental results show that the proposed method can quickly adapt to the dynamic changes of tasks and loads and maximize the overall performance of multiple model training jobs.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970183","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
Anomaly Detection in Dynamic Graph based on Deep Graph Auto-encoder 基于深度图自编码器的动态图异常检测
2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00069
Peng Gao, Gu Feng, Fei Liang
{"title":"Anomaly Detection in Dynamic Graph based on Deep Graph Auto-encoder","authors":"Peng Gao, Gu Feng, Fei Liang","doi":"10.1109/MLISE57402.2022.00069","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00069","url":null,"abstract":"Dynamic networks are ubiquitous in daily life, such as the power data center and social network. Anomalies in dynamic networks seriously endanger the security of the network. Therefore, it is a critical task to detect anomalies in dynamic networks. This paper proposes an anomaly detection system based on network embedding learning, which encodes the dynamic network, learns the embedding vector of each node in the network, and performs anomaly detection by clustering the embedding vector. We propose a depth graph autoencoder model to learn the dynamic node embedding vectors. The we calculate the anomaly score based on the distance of the node to its nearest cluster center. Extensive experiments on real-life datasets are conducted to illustrate that proposed method outperforms state-of-the-art baselines. Compared with the existing methods, the method in this paper improves the AUC by up to 11%.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128675272","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: 3
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