2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献_第4页

EEG-based Emotion Recognition Under Convolutional Neural Network with Differential Entropy Feature Maps 基于脑电信号的差分熵特征映射卷积神经网络情感识别

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071612

Yifan Li, C. Wong, Yudian Zheng, F. Wan, P. Mak, S. Pun, M. Vai

{"title":"EEG-based Emotion Recognition Under Convolutional Neural Network with Differential Entropy Feature Maps","authors":"Yifan Li, C. Wong, Yudian Zheng, F. Wan, P. Mak, S. Pun, M. Vai","doi":"10.1109/CIVEMSA45640.2019.9071612","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071612","url":null,"abstract":"In recent electroencephalograph (EEG)-based emotion recognition, the differential entropy (DE) features extracted from multiple electrodes are organized as a 2D feature map for convolutional neural network (CNN) in order to utilize the information hidden in the electrodes. In this study, we attempt to investigate the influence of different feature maps on the recognition performance. Six different 2D feature maps (M1-M4: baseline feature maps without sparsity and location relationship, M5-M6: pre-defined feature maps with sparsity and location relationship) are used to organize the DE features for the traditional CNN model. Evaluation study on the DEAP dataset finds that the 2D feature map configuration exhibits statistically significant effect on the classification performance of the traditional CNN model in classifying the high/low arousal and high/low valence, respectively. However, the differences are rather limited, e.g., only 1% improvement can be resulted from selecting the optimal 2D feature map among 6 feature maps. This implies that the feature map may not be a critical issue when applying the DE features to classifying the emotion states in a CNN.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117188292","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: 5

Portable Image Based Moon Date Detection and Declaration: System and Algorithm Code Sign 基于便携式图像的月球日期检测与声明:系统与算法代码符号

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071604

A. Moshayedi, Zuyan Chen, Liefa Liao, Shuai Li

{"title":"Portable Image Based Moon Date Detection and Declaration: System and Algorithm Code Sign","authors":"A. Moshayedi, Zuyan Chen, Liefa Liao, Shuai Li","doi":"10.1109/CIVEMSA45640.2019.9071604","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071604","url":null,"abstract":"The Moon is one of the interesting objects in the sky. Over the human life history, this shining object attracts mankind all over the world. Even in some civilization, people worship or coordinate their ethical plan and calendar based on its shape. Nowadays, some researches show over than the moon shape on earth environment which has an effect on the human body as well. The main problem to track the moon is that the moon shape always in the sky is not visible which may leads to lose tracking the shape. Sometimes similar shape for some dates may causes the wrong evaluation for a date. So, knowing and determining the shape as well as date of the moon is always challenging. All these problems which may mix with cloudy nights encourage researchers to make a device to sort the date deceleration. In this research paper, design of the portable Raspberry Pi bases system as the recent development in the embedded system has considered. The designed system has the capability to capture the image. Then, after preprocessing on the image, with the help of the written portable GUI in python, declares the moon phase for the user. Finally, over than system, to perform an effective system, the novel SAZ algorithm proposed which is the extension of HSV and shape detection based on blob algorithm. Then, the algorithm tested on some real image samples. The result shows Raspberry pi and proposed algorithm (SAZ) has the ability to do the moon date declaration.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"7 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117338982","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: 8

Attribute Selection Techniques to Clustering the Entrepreneurial Potential of Student based on Academic Behavior 基于学业行为的大学生创业潜力聚类属性选择技术

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071597

Nova Rijati, S. Sumpeno, M. Purnomo

引用次数: 3

A Systematic Analysis of Noninvasive Sensory Feedback Reconstruction Methods for Upper Limb Amputees 上肢截肢者无创感觉反馈重建方法的系统分析

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071603

Wenjie Wang, Yuan Liu, Zhicai Li, Feng He, Dong Ming

引用次数: 1

EEG Controlled Automated Writing Robotic Arm Based on Steady State Visually Evoked Potential 基于稳态视觉诱发电位的EEG控制自动书写机械臂

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071613

Xuequan Zhu, Meng Mu, Abdelkader Nasreddine Belkacem, Duk Shin, Rui Xu, Kun Wang, Zhongpeng Wang, Changming Wang, Chao Chen

{"title":"EEG Controlled Automated Writing Robotic Arm Based on Steady State Visually Evoked Potential","authors":"Xuequan Zhu, Meng Mu, Abdelkader Nasreddine Belkacem, Duk Shin, Rui Xu, Kun Wang, Zhongpeng Wang, Changming Wang, Chao Chen","doi":"10.1109/CIVEMSA45640.2019.9071613","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071613","url":null,"abstract":"Brain-Computer Interface(BCI) refers to devices that allow people to communicate or control the outside devices only through brain waves without relying on their own output pathways, such as the human nervous system and muscle tissue. The problem of insufficient information transfer and interaction between organism and electromechanical device in electromechanical integration system is pointed out. This paper uses BP(Brain Products) equipment to collect Steady State Visual Evoked Potential (SSVEP) signals. The collected SSVEP signals were preprocessed, feature extracted and feature classified. Then it is connected with the robot arm to build a portable brain-computer interface control system. Six subjects participated in the online experiment of the system. Experimental results show that the system can write some simple Chinese characters with high accuracy, and the system is feasible and effective. Then the signal is taken by Open Brain-computer Interface (OpenBCI) to complete the connection with the robotic arm. We will realize the control of the robotic arm in the later experiment. Our research aim is to find an relatively effective control method by comparing BP and OpenBCI based control on the robotic arm.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130531068","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

Touchless Palmprint and Finger Texture Recognition: A Deep Learning Fusion Approach 非触摸掌纹和手指纹理识别:一种深度学习融合方法

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071620

A. Genovese, V. Piuri, F. Scotti, Sarvesh Vishwakarma

{"title":"Touchless Palmprint and Finger Texture Recognition: A Deep Learning Fusion Approach","authors":"A. Genovese, V. Piuri, F. Scotti, Sarvesh Vishwakarma","doi":"10.1109/CIVEMSA45640.2019.9071620","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071620","url":null,"abstract":"Biometric systems based on touchless and less-constrained palmprint are being increasingly studied since they allow a favorable trade-off between high-accuracy and high usability recognition. Another advantage is that with a palmar hand acquisition, it is possible to extract the palmprint as well as the Inner Finger Texture (IFT) and increase the recognition accuracy without requiring further biometric acquisitions. Recently, most methods in the literature consider Deep Learning (DL) and Convolutional Neural Networks (CNN), due to their high recognition accuracy and the capability to adapt to biometric samples captured in heterogeneous and less-constrained conditions. However, current methods based on DL do not consider the fusion of palmprint with IFT. In this work, we propose the first novel method in the literature based on a CNN to perform the fusion of palmprint and IFT using a single hand acquisition. Our approach uses an innovative procedure based on training the same CNN topology separately on the palmprint and the IFT, adapting the neural model to the different biometric traits, and then performing a feature-level fusion. We validated the proposed methodology on a public database captured in touchless and less-constrained conditions, with results showing that the fusion enabled to increase the recognition accuracy, without requiring multlple biometric acquisltions.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131428357","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: 10

Redundant Reader Elimination in large-scale IoT City Networks 大规模物联网城市网络中的冗余读取器消除

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071635

Haoning Shi, Huiquan Wang, R. Raad, Saeid Iranmanesh, Lingshun Meng

{"title":"Redundant Reader Elimination in large-scale IoT City Networks","authors":"Haoning Shi, Huiquan Wang, R. Raad, Saeid Iranmanesh, Lingshun Meng","doi":"10.1109/CIVEMSA45640.2019.9071635","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071635","url":null,"abstract":"Radio Frequency Identification (RFID) is an important communication technology for the Internet of Things (IoT). With the development of IoT, RFID technology is widely deployed in large-scaled city networks. Under the circumstances, how to effectively optimize RFID network to decrease the operational cost of IoT is an ongoing research direction. In this area, one of the problems is the redundant reader issue, which means multiple RFID readers cover and interact with one RFID tag or many same tags, thereby resulting in massive energy cost. In order to solve this problem, quite a lot redundant reader elimination algorithms were presented to reduce unnecessary RFID readers. In this paper, the author proposes and compares three existing redundant reader elimination algorithms, including Redundant Reader Elimination Algorithm (RRE), Three-Count Based Algorithm (TCBA) and Threshold Selection Algorithm (TSA), to observe their performances. In the simulations, the author designs three experiments to test RFID reader's detection radius, detection accuracy and algorithmic efficiency by observing the number of readers eliminated in different environmental sets. The simulation results show that TCBA can detect more redundant readers than RRE and TSA, but it costs more time. Compared with TCBA and RRE, the performance of TSA is more practical and efficient to satisfy real RFID environment and market's needs.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123319681","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

Texture Estimation Using Thermography and Machine Learning 基于热成像和机器学习的纹理估计

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071610

Tamás Aujeszky, Georgios Korres, M. Eid, F. Khorrami

引用次数: 0

Predicting Electricity Usage Based on Deep Neural Network* 基于深度神经网络的用电量预测*

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071602

Ran Wei, Jinhai Wang, Qirui Gan, Xin Dang, Huiquan Wang

{"title":"Predicting Electricity Usage Based on Deep Neural Network*","authors":"Ran Wei, Jinhai Wang, Qirui Gan, Xin Dang, Huiquan Wang","doi":"10.1109/CIVEMSA45640.2019.9071602","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071602","url":null,"abstract":"This paper describes a deep neural network (DNN) based method for forecasting short-term hospital electricity usage. In Experiment One, a 4-layer DNN stack auto-encoder (SAE) based model is constructed to verify the accuracy of the method. Kilowatt-hours (kwh), capacitance (pf), power factor (phi), voltage (v), electricity reactive power (var), and electricity active power (w) are the main input variables. After training the model, the prediction accuracy can reach 77.60%. In the improvement phase, the model is altered to use more common variables; specifically, kilowatt-hours (kwh), electric charge (charg), average active power (avg-w), and maximum active power (max-w) are used as input variables. In order to optimize the training of the model, Experiment Two improves on the basis of the original DNN model. As a result, the prediction accuracy can be increased to 85.17%. Finally, the four power data with the best measurement are used, namely current(I), voltage(V), reactive power(Var) and active power(W), and the predicted result is 98.14%. This method indicates that the planning and scheduling of the hospital’ s electricity usage will also be improved.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125225971","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

Functional Corticomuscular Coupling Based on Bivariate Empirical Mode Decomposition - Multiscale Transfer Entropy 基于二元经验模态分解-多尺度传递熵的功能皮质-肌肉耦合

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071605

Shengcui Cheng, Xiaoling Chen, P. Xie, Xiaohui Pang, Xiaolin Bai

{"title":"Functional Corticomuscular Coupling Based on Bivariate Empirical Mode Decomposition - Multiscale Transfer Entropy","authors":"Shengcui Cheng, Xiaoling Chen, P. Xie, Xiaohui Pang, Xiaolin Bai","doi":"10.1109/CIVEMSA45640.2019.9071605","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071605","url":null,"abstract":"The functional corticomuscular coupling (FCMC) is a physiological phenomenon to reflect the multilayered characteristics of the information interaction between electroencephalogram (EEG) and electromyographic (EMG) signals. The multilayered characteristics such as local frequency band, complex and multiscale between the brain and muscles are of great significance to understand the cooperative function of the motor-sensory neural network. Though the multiscale transfer entropy (MSTE) method can effectively describe the multiscale and complex characteristics of the coupling signals to some extent, it fails to describe the FCMC on the local frequency band. Therefore, in this study, we combined the bivariate empirical mode decomposition (BEMD) with the MSTE to construct a new model, named bivariate empirical mode decomposition-multiscale transfer entropy (BMTE), to quantify the synchronous coupling between EEG and EMG signals on the local frequency band at different scales. The results show that the FCMC is significant in both EEG→EMG and EMG → EEG directions at betal and beta2 bands during steady-state grip task. Meanwhile, the maximum coupling strength value at beta2 band on different scales alomost occur on the high scales (9–16 scales), and the significant value at betal band was on the lower time scale. Additionally, the coupling strength at gamma band in EEG→ EMG direction is also significant in the higher scale. The results show that the BMTE method can quantitatively describe the local frequency band and multiscale characteristics between the motor cortex and the contralateral muscle in motor control system. This study extends the relative researches on the FCMC.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"50 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129335927","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