2022 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第3页

Solution to Robust Two-Stage Stochastic Convex Programming Using Subgradient Method 用子梯度法求解鲁棒两阶段随机凸规划

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941320

Xinshun Ma, Qi An

引用次数: 0

A Novel Deep Learning Based Emotion Recognition Approach to well Being from Fingertip Blood Volume Pulse 一种基于深度学习的指尖血容量脉搏情绪识别方法

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941301

H. P. Fordson, Katherine Gardhouse, Nicholas G. Cicero, J. Chikazoe, A. Anderson, Eve Derosa

{"title":"A Novel Deep Learning Based Emotion Recognition Approach to well Being from Fingertip Blood Volume Pulse","authors":"H. P. Fordson, Katherine Gardhouse, Nicholas G. Cicero, J. Chikazoe, A. Anderson, Eve Derosa","doi":"10.1109/ICMLC56445.2022.9941301","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941301","url":null,"abstract":"Emotions are central to physical and mental health and general well being. There is a great need to affordably and non invasively track moment to moment changes in emotional states and their conversion into chronic conditions. Blood Volume Pulse (BVP) is a widely used sensor for measuring blood volume changes, heart rate, and is embedded in numerous biofeedback systems and applications. Nonetheless, the role of BVP features relating to emotion detection is lacking in current studies. While engineers have become more interested in the analysis of heart rate variability (HRV) and its regulation by the autonomic nervous system, there is a need to design systems that can investigate their variations due to real life stressors and how people respond to emotions differently. The study employs the database for emotion analysis using physiological signals (DEAP) in assessing emotional responses of subjects according to valence arousal scale to music videos. We demonstrate a novel approach to augmenting original features and normalized features of blood volume in peripheral vessels. The features of HRV include tachogram, multi-scale entropy (MSE), power spectral density (PSD), and statistical moments derived from BVP. We further propose embedding age and gender of participants as a weight to the augmented features. We finally used multilayer perceptron (MLP) as classifier to evaluate our approach. Obtained results show an 8.4% and 7.3% improvement in F1-score in the valence and arousal dimension respectively. Such advances may aid in building closed-loop emotion detection and intervention systems.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124146127","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

Improvement and Evaluation of Object Shape Presentation System Using Linear Actuators 基于线性执行器的物体形状呈现系统的改进与评价

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941312

Yuma Iwabuchi, T. Motoyoshi, Noboru Takagi, H. Masuta, K. Sawai

引用次数: 0

An Investigation of Formal Verification of Control Policy of Multi-Car Elevator Systems Using Statistical Model Checking 基于统计模型检验的多轿厢电梯系统控制策略形式化验证研究

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941319

Yuki Kitahara, Masaki Nakamura, K. Sakakibara

引用次数: 0

Machine Learning for Protein Solubility Prediction 蛋白质溶解度预测的机器学习

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941322

Kodai Suzuki, K. Sakakibara, Masaki Nakamura, Suguru Shinoda, Y. Asano

{"title":"Machine Learning for Protein Solubility Prediction","authors":"Kodai Suzuki, K. Sakakibara, Masaki Nakamura, Suguru Shinoda, Y. Asano","doi":"10.1109/ICMLC56445.2022.9941322","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941322","url":null,"abstract":"The proteins that have the function of catalysis, called enzymes, can be used in many different ways in the chemical industry. The catalysis function of enzymes works by solubilizing. Enzymes can be used in the chemical industry, but in the recombinant production of enzymes, some enzymes aggregate during production. In- solubilized enzymes that has lost its catalysis function cannot be used in industry. Therefore, the search for new enzymes that can be used for industrial purposes is one of the important strategies. However, the search for new enzymes takes time and costs money. In previous research, a model for predicting protein solubility from the amino add sequence of a protein was constructed using machine learning. This has made it possible to predict the solubility of a protein before it is produced. In this study, a model is constructed to predict protein solubility not only from the amino acid sequence but also from the amino acid sequence and the secondary structure information of the protein. We attempt to improve the prediction accuracy of the model by providing the model with information that is thought to influence solubility.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124849620","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

Circuit Partitioning for PCB Netlist Based on Net Attributes 基于网络属性的PCB网表电路划分

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941328

Da Meng, Yanze Zheng

{"title":"Circuit Partitioning for PCB Netlist Based on Net Attributes","authors":"Da Meng, Yanze Zheng","doi":"10.1109/ICMLC56445.2022.9941328","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941328","url":null,"abstract":"As we all know, the difficulty of automatic placement and routing is proportional to the size of the circuit. Through Printed circuit board (PCB) netlist partition algorithms, PCB circuits can be divided into different sub-modules, and the problem scale can be effectively reduced in order to obtain the optimal automatic layout and routing. It is observed that when engineers design circuits, they usually mark important nets by annotation, called net attributes. This paper proposes a PCB netlist partition approach based on net attributes. Our partition approach takes the netlist as input, and module partition set as output. Firstly, the modules are pre-partitioned using net attributes. Further, the special patterns in circuits are discovered, and the scattered resistors, capacitors and other components caused by pre-partitioning according to net attributes would be allocated to initial modules by classifying and module matching rules. Our method is evaluated on 11 PCB netlists, and experimental results show that our proposed netlist partition approach outperforms the state of the arts, which can achieve 80%–96% partition accuracy.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129630600","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

Recognition Performance of Facial Expression for the Face’s Partial Regions 人脸局部区域的表情识别性能

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941316

Tomoaki Hirose, Kazuma Yamaguchi, H. Takano

引用次数: 0

Measurement of Cardiothoracic Ratio and Detection of Cardiomegaly in X-Ray Images Using Deep Learning 利用深度学习测量 X 射线图像中的心胸比例并检测心脏肿大

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941324

Yanqin Xie, K. Nagamune

引用次数: 0

A Deep-Neural-Network-Based Approach To Detecting Forgery Images Generated From Various Generative Adversarial Networks 基于深度神经网络的各种生成对抗网络生成的伪造图像检测方法

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941295

C. Fahn, Tzu-Chin Wu

引用次数: 0

Estimation Of Cortical Currents From Eeg Signals During N-Back Working Memory Tasks N-Back工作记忆任务中脑电信号皮层电流的估计

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941314

Shinnosuke Yoshiiwa, H. Takano, Keisuke Ido, Ken-ichi Morishige

引用次数: 0