2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )最新文献_第7页

FPGA Implementation of ARM MCU with Five-stage Pipeline ARM单片机五级流水线的FPGA实现

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983568

Chi-Lin Chiang, Mao-Hsu Yen, Che-Wei Chang, Yih-Hsia Lin, Yuan-Fu Ku

引用次数: 0

Cloud and AI-supported Mobile Information Multi-agent System for Smart Energy-saving 基于云与ai的移动信息多智能体智能节能系统

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983592

Sheng-Yuan Yang

引用次数: 0

Ensemble Algorithm of Convolution Neural Networks for Enhancing Facial Expression Recognition 增强面部表情识别的卷积神经网络集成算法

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983573

Gwo-Chuan Lee, Zi-Yang Li, Tsai-Wei Li

{"title":"Ensemble Algorithm of Convolution Neural Networks for Enhancing Facial Expression Recognition","authors":"Gwo-Chuan Lee, Zi-Yang Li, Tsai-Wei Li","doi":"10.1109/ICKII55100.2022.9983573","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983573","url":null,"abstract":"Artificial intelligence (AI) cooperates with multiple industries to improve the overall industry framework. Especially, human emotion recognition plays an indispensable role in supporting medical care, psychological counseling, crime prevention and detection, and crime investigation. The research on emotion recognition includes emotion-specific intonation patterns, literal expressions of emotions, and facial expressions. Recently, the deep learning model of facial emotion recognition aims to capture tiny changes in facial muscles to provide greater recognition accuracy. Hybrid models in facial expression recognition have been constantly proposed to improve the performance of deep learning models in these years. In this study, we proposed an ensemble learning algorithm for the accuracy of the facial emotion recognition model with three deep learning models: VGG16, InceptionResNetV2, and EfficientNetB0. To enhance the performance of these benchmark models, we applied transfer learning, fine-tuning, and data augmentation to implement the training and validation of the Facial Expression Recognition 2013 (FER-2013) Dataset. The developed algorithm finds the best-predicted value by prioritizing the InceptionResNetV2. The experimental results show that the proposed ensemble learning algorithm of priorities edges up 2.81% accuracy of the model identification. The future extension of this study ventures into the Internet of Things (IoT), medical care, and crime detection and prevention.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115678197","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

Computing Resource Optimization for a Log Monitoring System 日志监控系统的计算资源优化

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983580

Thanin Srithai, V. C. Barroso, P. Phunchongharn

{"title":"Computing Resource Optimization for a Log Monitoring System","authors":"Thanin Srithai, V. C. Barroso, P. Phunchongharn","doi":"10.1109/ICKII55100.2022.9983580","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983580","url":null,"abstract":"A Large Ion Collider Experiment (ALICE) at the Large Hadron Collider (LHC) in the European Organization for Nuclear Research (CERN) laboratory was built to study heavy-ion collisions and the properties of the quark-gluon plasma. The Online and Offline (O2) software systems of the experiment generate a huge amount of log data that is used for monitoring to detect a potential system failure. Elasticsearch was selected as a log storage and search engine for the monitoring system. One of the main problems is how to allocate the computing resources for Elasticsearch while minimizing cost and satisfying performance thresholds, i.e., throughput). Moreover, lacking knowledge of the search engine's behavior makes it difficult to find the best configuration. The exhaustive search method is a potential approach for solving. However, it is not practical since it consumes a lot of time and computing resources. Due to the limited resources, Bayesian optimization is applied as a solution. The Bayesian method requires only a few samples to create a surrogate function that roughly represents the objective function, i.e., minimizing cost while satisfying the performance needs. Then, the method explores only the area where the optimal solution exists with a high probability. The results show that Bayesian optimization provides the optimal or near-optimal computing resource configuration for given benchmark experiments while requiring only about half of the evaluations compared to other methods, e.g., exhaustive search, regression, and machine learning. The impact of several acquisition functions and initial sample generators were studied in order to find the best solution. These insights can help system operators search for an optimal computing resource configuration quickly and efficiently.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115890951","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

Simulation, Fabrication, Measurement, and Characteristic Analysis of Ka-band Frequency Selective surface ka波段频率选择表面的仿真、制作、测量与特性分析

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983582

Ke-Yang Liao, W. Lau, Jyh-Shin Chen

引用次数: 0

Private Data Preprocessing for Privacy-preserving Federated Learning 隐私保护联邦学习的私有数据预处理

2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII ) Pub Date : 2022-07-22 DOI: 10.1109/ICKII55100.2022.9983518

Ruei-Hau Hsu, Ting-Yun Huang

{"title":"Private Data Preprocessing for Privacy-preserving Federated Learning","authors":"Ruei-Hau Hsu, Ting-Yun Huang","doi":"10.1109/ICKII55100.2022.9983518","DOIUrl":"https://doi.org/10.1109/ICKII55100.2022.9983518","url":null,"abstract":"Privacy-preserving federated learning can accomplish model aggregation without leaking to a local model and avoid the problem of sensitive data exposure caused by model leakage. Even though it protects privacy in the training process, any data analysis task proposed by the initiator and the type of data required for the task contains the research or trade secrets of the organization. Being intercepted in the transmission process or known by other data providers, the disclosure of essential research secrets occurs, leading to the theft of research or business ideas. Therefore, it is a critical issue to achieve data matching between the initiator and the participant under the premise of privacy protection. In this study, we propose a federated learning framework that considers the above security issues. A privacy-preserving federated learning architecture based on homomorphic encryption is designed to protect each participant's data and local model. In addition, encrypted query technology is used in this architecture to provide data privacy matching. The data provider searches the data in ciphertext, finds the encrypted data that meets the conditions, and completes the training process without disclosing any requirements of the task initiator.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"49 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132974322","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