2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第4页

Improving Motor Imagery EEG Classification by CNN with Data Augmentation 基于数据增强的CNN运动图像脑电分类改进

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450227

Bin Du, Yue Liu, Geliang Tian

{"title":"Improving Motor Imagery EEG Classification by CNN with Data Augmentation","authors":"Bin Du, Yue Liu, Geliang Tian","doi":"10.1109/ICCICC50026.2020.9450227","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450227","url":null,"abstract":"Brain Computer Interface (BCI) system enables human brain to communicate with the external world without the involvement of muscle and peripheral nerves. Motor Imagery(MI) Electroencephalogram (EEG) is one of brain signals commonly used in the BCI system. Recently, deep learning models such as Convolutional Neural Network (CNN) have received widespread attention and provided better classification performance in MI EEG classification compared to other state of art approaches because they can learn the features that are most relevant to the task at hand. However, the performance of CNN largely depends on its architecture as well as the quality and quantity of training data. Current MI EEG data are scarce because the data collection is relatively expensive and therefore effective data augmentation methods are particularly important to improve the MI classification performance. In this paper, we first propose a shallow CNN architecture as well as a new and effective data augmentation method to compensate the shortcoming of data insufficiency, then we apply the method of superposing and normalizing the signals of the same labels across subjects and time to generate additional EEG data. The proposed superimposed data augmentation method can enable the signals preserve the intrinsic characteristics and reduce the signals drift over time and subjects. We evaluate the proposed architecture and method on the PhysioNet dataset, the experimental results show that the proposed CNN architecture performs better than the previous architectures and can achieve an average accuracy of 91.06% in two-class classification tasks. In addition, the proposed data augmentation method can improve the average accuracy from 73.46% to 76.78% in four-class classification tasks for all 109 subjects, which proves the effectiveness of the proposed method.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153822","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

Research on Resource Hierarchical Scheduling Based on multiple QoS for IoT 基于多QoS的物联网资源分层调度研究

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450220

Chunguang Zhang, Guangping Zeng, Zhiying Ren, Xuyan Tu

引用次数: 0

Simulation Analysis of Epidemic Trend for COVID-19 Based on SEIRS Model 基于SEIRS模型的COVID-19流行趋势仿真分析

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450226

Jike Ge, Lanzhu Zhang, Zuqin Chen, Guorong Chen, Jun Peng

{"title":"Simulation Analysis of Epidemic Trend for COVID-19 Based on SEIRS Model","authors":"Jike Ge, Lanzhu Zhang, Zuqin Chen, Guorong Chen, Jun Peng","doi":"10.1109/ICCICC50026.2020.9450226","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450226","url":null,"abstract":"The current novel coronavirus disease 2019 (COVID-19) outbreak in global has provided an opportunity to understand the spread of this pandemic linked to healthcare settings. It is very important to predict the trend of epidemic situation for timely response. In this paper, we proposed a Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model to simulate and forecast the trend of COVID-19 epidemic in China. The simulation results provide a good fit to the actual number and peak time of confirmed epidemic in Hubei province, and the simulation results also show that the epidemic of Hubei province would decline in early June. However, there are some differences between the simulation results and the real situation of other regions in China, because this model does not consider human intervention strategy. In a word, our SEIRS dynamic model is effective in simulating and predicting the COVID-19 epidemic in Hubei province, China, it has meaningful reference for the prevention and control of the pandemic situation which is raging all over the world.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115292759","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

Applying ML Algorithms to improve traffic classification in Intrusion Detection Systems 应用ML算法改进入侵检测系统中的流量分类

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450218

Laxmi Narsimha Reddy, S. Butakov, P. Zavarsky

{"title":"Applying ML Algorithms to improve traffic classification in Intrusion Detection Systems","authors":"Laxmi Narsimha Reddy, S. Butakov, P. Zavarsky","doi":"10.1109/ICCICC50026.2020.9450218","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450218","url":null,"abstract":"Traditional intrusion detection systems may have higher false-positive and false-negative rates against new malicious traffic vectors. Also, in the case of anomaly-based IDS can be bypassed by generating network traffic intelligently. The capability of machine learning algorithms in capturing complex behaviors and patterns made them increasingly popular in solving classification/detection problems. The major objective of this paper is to suggest an efficient IDS model by studying various supervised machine learning algorithms on the classification problem. For this purpose, the known NSLKDD dataset was used as a source of diverse feature columns for the model The transformed data is modeled to classify network traffic into normal or attack using machine learning algorithms SVM, KNN, neural network and ensemble learning in which KNN and SVM achieved 98 and 97% accuracy. These models can be used to differentiate anomalous traffic in intrusion systems and maybe useful as a replacement for traditional rule-based detection systems. Click here for dataset and code of IDS models.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122853695","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 Deep Learning-Based Approach for Quality Control and Defect Detection for Industrial Bagging Systems 基于深度学习的工业装袋系统质量控制与缺陷检测方法

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450251

Mathieu Juncker, I. Khriss, J. Brousseau, S. Pigeon, Alexis Darisse, Billy Lapointe

{"title":"A Deep Learning-Based Approach for Quality Control and Defect Detection for Industrial Bagging Systems","authors":"Mathieu Juncker, I. Khriss, J. Brousseau, S. Pigeon, Alexis Darisse, Billy Lapointe","doi":"10.1109/ICCICC50026.2020.9450251","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450251","url":null,"abstract":"In the competitive world of the food industry where companies have to offer quality products, quality control is essential. However, it could become expensive, especially if it is a manual process. Its automation then becomes an excellent opportunity for a company. The objective of this research is to find out whether it is possible to carry out quality control of open mouth bag sealings on industrial bagging systems using deep learning. In this paper, we propose a three-step approach: data collection, data classification, and supervised classification learning. The first step is to collect images of sealings of open mouth bags. We created a line-scan based prototype and placed it on a production line to harvest a large amount of data. Image processing is then applied to clean the data. The next step is the classification of the data to identify classes of defects and labeling of these data. Finally, supervised classification learning makes it possible to implement quality control. We propose an architecture based on convolutional neural networks for image classification of open mouth bags. Our approach gives very encouraging results for the realization of quality control of an industrial bagging system.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128980752","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

Analysis of a New 3-D Chaotic System with a Self-Excited Attractor 具有自激吸引子的新型三维混沌系统分析

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450249

Shaochun Zhang, Jun Peng, Shangzhu Jin, S. Gu

引用次数: 1

Formal Software Requirement Elicitation based on Semantic Algebra and Cognitive Computing 基于语义代数和认知计算的形式化软件需求提取

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450275

James Y. Xu, Yingxu Wang

{"title":"Formal Software Requirement Elicitation based on Semantic Algebra and Cognitive Computing","authors":"James Y. Xu, Yingxu Wang","doi":"10.1109/ICCICC50026.2020.9450275","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450275","url":null,"abstract":"Autonomous software requirement analysis and generation are a persistent challenge to theories and technologies of software engineering. A cognitive system is demanded to automatically elicit and rigorously refine informal software requirements in natural language descriptions into formal specifications. This paper presents a novel software requirements elicitation methodology based on latest advances in software science and denotational mathematics such as semantic algebra and concept algebra. It is found that user requirements for a software system in natural language may be either expressed in to-be sentences for software structures or to-do sentences for software behaviors. Thus, formal software requirements may be elicited by two sets of structural and functional models. This approach is implemented by a tool for Formal Requirement Elicitation and Analysis (FREA). Experimental results demonstrate that the FREA tool may rigorously elicit and generate formal requirements for arbitrary software systems specified in real-time process algebra (RTPA) or equivalent notations. This technology paves a way towards autonomous code generation in software engineering.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128768427","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 Neural Network Based Algorithm Selector for Radar Task Scheduling 基于神经网络的雷达任务调度选择算法

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450265

Z. Qu, Z. Ding, P. Moo

引用次数: 1

Modeling Brain-like Association Among Focal Visual Objects by a Bipartite Mesh 基于二部网格的焦点视觉对象类脑关联建模

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450256

Jinxin Yang, Xin Hu, Yufei Zhao, Qi Xu, Wen-Chi Yang

{"title":"Modeling Brain-like Association Among Focal Visual Objects by a Bipartite Mesh","authors":"Jinxin Yang, Xin Hu, Yufei Zhao, Qi Xu, Wen-Chi Yang","doi":"10.1109/ICCICC50026.2020.9450256","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450256","url":null,"abstract":"The challenge of traditional visual recognition tasks has long fallen on the segmentation of objects in two-dimensional images, whereas it is less an issue in human visual learning with the help of stereo vision and physical touches. In this kind of configuration, object classification and landmark matching are fundamentally based on the semantic similarity from inputs to conceptual prototypes in memory. Here we propose a brain-inspired cognition model that deals with visual learning tasks after the focal objects have been distinguished from their backgrounds. We designed a bipartite mesh to implement visual cognition on human faces. This mesh resolves facial landmarks into point clouds in a unique semantic space, where facial characteristics can be perceived and classified through the comparison with prototypes in the memorized ontology. These face prototypes are updatable online, and landmark matching between prototypes in the vicinity is feasible through a direct mapping between relative positions within their point clouds. Besides, the association between distant prototypes in the semantic space can be realized by a sequence of matching processes on intermediaries in memory. Our findings suggest a concise framework for simulating human visual learning mechanisms that well execute one-shot learning, online learning, and analogical reasoning, at the same time subject to certain brain-like constraints such as oblivion and lack of analogical cues between two dissimilar concepts.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060386","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

Prediction of Fatality Crashes with Multilayer Perceptron of Crash Record Information System Datasets 基于碰撞记录信息系统数据集的多层感知器预测致命碰撞

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450248

Thanh Hung Duong, F. Qiao, Jyh-haw Yeh, Yunpeng Zhang

引用次数: 1