2022 8th International Conference on Systems and Informatics (ICSAI)最新文献_第6页

Chinese Biomedical Entity Relation Extraction Based On Directed Graph Convolutional Network 基于有向图卷积网络的中文生物医学实体关系提取

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005391

Baosheng Yin, Wei Zhao

引用次数: 0

Customer Portrait for Metrology Institutions Based on the Machine Learning Clustering Algorithm and the RFM Model 基于机器学习聚类算法和RFM模型的计量机构客户画像

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005470

Xiaojing Zhang, Dongshuo Zhao, Yaran Li, Yudong Liu, Gang Hu

{"title":"Customer Portrait for Metrology Institutions Based on the Machine Learning Clustering Algorithm and the RFM Model","authors":"Xiaojing Zhang, Dongshuo Zhao, Yaran Li, Yudong Liu, Gang Hu","doi":"10.1109/ICSAI57119.2022.10005470","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005470","url":null,"abstract":"With the increasing intensity of competition in the current metrology testing market, building customer portrait is an effective way for metrology institutions to improve service levels to customers. This paper is based on the basic business data of a certain metrology institution. First, recency, frequency, monetary value model (RFM model), which is widely applied in customer relationship management, is improved. Further, it is combined with the business features of the metrology institution and used to build data feature engineering, which is closely related to the business data of the metrology institution and can reflect the data situation. Then, the data are analyzed through correlation test, standardized by Z-score, and clustered with three clustering algorithms, namely K-Means, DBSCAN, and AGNES, which are in SKLEARN database based on Python. After that, the clustering results are compared. In the clustering process, the elbow method and method for traversing the silhouette coefficient are used to determine the optimal value of the clustering algorithm. Finally, with the analysis of clustering results, the customers’ features of the metrology institution are signed and the customer portrait is built, which provides data analysis methods, tools and decision basis for the metrology institution to offer better services.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114290321","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

Promoting a Hybrid Cryptosystem System’s Security based on Fresnel lens and RSA Algorithm 基于菲涅耳透镜和RSA算法的混合密码系统安全性提升

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005357

Peng Yang, Weishun Xue, Chenqi Fan, Xiaojian Xue

引用次数: 0

Test and Analysis Of Indoor And Outdoor Barometric Altimetry 室内和室外气压测高的测试与分析

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005521

Shuaichen Li, Jian-Feng Wu

{"title":"Test and Analysis Of Indoor And Outdoor Barometric Altimetry","authors":"Shuaichen Li, Jian-Feng Wu","doi":"10.1109/ICSAI57119.2022.10005521","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005521","url":null,"abstract":"Measuring the height of a site is of great significance for scientific research and daily life. This paper aims to measure the indoor and outdoor air pressure height, compare and analyze the error and fluctuation of indoor and outdoor measured data, and filter the measured value with Kalman filter to reduce the error and fluctuation; The influence of wind speed on measurement error and fluctuation is analyzed; Three methods are used to process the data of the wind measurement group and the results are compared; The predictability of air pressure sensor is tested and analyzed. The experimental results show that: 1) Compared with indoor measurement, the error and fluctuation of outdoor measurement are significantly worse. 2) In indoor and outdoor measurement, Kalman filter has a good effect, which can reduce the average error of measurement data by 31.9% at most and the measurement fluctuation by 37.5%. The effect of Kalman filtering is greatly affected by the measured data. The greater the fluctuation and error of the measured value, the more obvious the effect of Kalman filter. 3) Wind has a great influence on barometric altimetry, and the fluctuation and error of the measurement results increase with the increase of wind speed. Combine theoretical knowledge with the results of comparative experiments It is speculated that the reason for the large outdoor measurement error and fluctuation includes the interference of wind to the sensor. 4) The traditional least squares fitting is the worst, and the least squares fitting based on Kalman filter is the best. 5) In the windy state, the RMS of quadratic fitting is 60% higher than that in the windless state, and the residual error is 63.8% higher.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122106834","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

A Novel Ping-pong Task Strategy Based on Model-free Multi-dimensional Q-function Deep Reinforcement Learning 基于无模型多维q函数深度强化学习的乒乓任务策略

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005466

H. Ma, Jianyin Fan, Qiang Wang

{"title":"A Novel Ping-pong Task Strategy Based on Model-free Multi-dimensional Q-function Deep Reinforcement Learning","authors":"H. Ma, Jianyin Fan, Qiang Wang","doi":"10.1109/ICSAI57119.2022.10005466","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005466","url":null,"abstract":"Deep reinforcement learning has been widely used in table tennis decision-making tasks, but most methods have their own defects, such as relying on high-precision trajectory prediction work or requiring targeted class training, etc. None of the methods can directly get a complete hitting strategy through the initial state of the ball. In this paper, we train a ping-pong hitting policy controller with model-free reinforcement learning. By extending the multi-dimensional Q-function, the prediction part of the table tennis task and the batting strategy part are integrated, the trajectory prediction work and the batting work are completed by a single network, which simplifies the complex prediction process and does not need to build a complex dynamics model or train a neural network to predict the trajectory of ping-pong balls. In this way, the deep reinforcement learning process and supervised trajectory prediction training process are organized into a single process. The experimental results show that the best convergence effect can be basically achieved in 50,000 rounds of training. 10,000 tests were performed as a test set with a success rate of over 99%.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117084862","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

Design of a Container-based Broadband RF Pulse Signal Analysis Algorithm Integration Scheme 基于容器的宽带射频脉冲信号分析算法集成方案设计

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005340

Wenfeng Tian, Hui Deng, Pengfei Zeng

引用次数: 0

Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data 基于少量语音数据的双峰情感识别系统的学习

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005454

Junya Furutani, Xin Kang, Keita Kiuchi, Ryota Nishimura, M. Sasayama, Kazuyuki Matsumoto

{"title":"Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data","authors":"Junya Furutani, Xin Kang, Keita Kiuchi, Ryota Nishimura, M. Sasayama, Kazuyuki Matsumoto","doi":"10.1109/ICSAI57119.2022.10005454","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005454","url":null,"abstract":"This paper presents a bimodal emotion recognition system based on the voice and text information using small amount of speech data. Specifically, speech is divided into voice and text to learn emotion classifiers for each modal. The probabilities obtained from these emotion classifiers are weighted based on Mehrabian’s rule and summed up for each emotion to calculate the final score for bimodal emotion recognition. To create a highly accurate system while solving the problem that there are few Japanese speech data with emotion labels, we propose a novel data augmentation method and employ a transfer learning approach based on a pre-trained VGG16 model and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model on the tweets. In order to prove the effectiveness of the proposed method, we revealed the recognition results for 7 emotional states, i.e., anger, sadness, joy, fear, surprise, disgust, and neutral. The experiment result suggested that our novel data augmentation method improved the accuracy and that bimodal predictions based on voice and text-based outperformed the single-model predictions.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125541241","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