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2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献

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Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting 同质集合前馈神经网络在CIMB股价预测中的应用
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638452
K. S. Gan, C. K. On, P. Anthony, C. S. Vui
{"title":"Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting","authors":"K. S. Gan, C. K. On, P. Anthony, C. S. Vui","doi":"10.1109/IICAIET.2018.8638452","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638452","url":null,"abstract":"In stock forecasting, the main concern is the accuracy of the stock market prediction. Various methods, techniques and algorithms have been proposed to achieve higher prediction accuracy. One of the popular techniques is Artificial Neural Network (ANN). ANN is a bio-inspired algorithm that tries to simulate the brain’s mechanism which is achieved through simulating a connected artificial neuron network. Although ANN has shown promising results in forecasting, researches have shown that combining multiple neural networks can further improve the forecasting result. The aim of this paper is to compare the performance of a single and multiple homogeneous ANN in forecasting CIMB stock market closing price. The empirical result shows that homogeneous ensemble ANN performs better than a single ANN in predicting the stock market price.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487990","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: 9
Changes in fundamental frequency and gesture of response corresponding to the understanding level of partner’s talk 基本频率和反应姿态的变化与伴侣谈话的理解程度相对应
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638457
Natsumi Kana, Yuya Saitoh, Yumi Wakita, Y. Nakatoh
{"title":"Changes in fundamental frequency and gesture of response corresponding to the understanding level of partner’s talk","authors":"Natsumi Kana, Yuya Saitoh, Yumi Wakita, Y. Nakatoh","doi":"10.1109/IICAIET.2018.8638457","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638457","url":null,"abstract":"This study explores a system that estimates the degree of decline in driving ability in elderly people using nonverbal information from daily conversations. To create this system, it is necessary to find factors that are effective for estimating the degree of decline that has reached a problematic level in daily life. In this paper, we consider the case where elderly people cannot understand the partner’s talk as their hearing and concentration abilities drop, and we analyze the relation between the degree of understanding of the partner’s talk and the nonverbal characteristic of the response utterance. According to the analysis results, both fundamental frequency (F0) values of response utterance and brightness values of the frame difference image for a response scene when a person can understand their partner’s talk tend to be higher than that when he/she does not understand the talk. These results indicate that the F0 value of utterances and brightness values of the frame difference images are important factors to estimate the decline in hearing and concentration abilities.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131612790","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
An Optimized Controller for Region of Attraction Enlargment of Underactuated Systems Using Impulse Manifold Method 基于脉冲流形法的欠驱动系统吸引力区域扩大优化控制器
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638448
Reza Pooshaneh, M. Baradarannia, A. Farzamnia, F. Hashemzadeh
{"title":"An Optimized Controller for Region of Attraction Enlargment of Underactuated Systems Using Impulse Manifold Method","authors":"Reza Pooshaneh, M. Baradarannia, A. Farzamnia, F. Hashemzadeh","doi":"10.1109/IICAIET.2018.8638448","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638448","url":null,"abstract":"Impulsive control is an alternative method for enlarging the region of attraction of nonlinear underactuated systems. The goal of this paper is to propose a method based on impulsive control to enlarge the region of attraction and meanwhile, minimize the consumed control energy. A performance index based on the consumed control energy is defined. Then, minimizing this performance index along with the use of impulsive manifold method enlarge the region of attraction with minimum consumption of control energy. The efficiency of the presented method is shown through a simulation.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127778181","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
Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier 基于RUSBoost分类器的单通道脑电信号睡眠阶段评分
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638465
S. Sheykhivand, T. Y. Rezaii, A. Farzamnia, M. Vazifehkhahi
{"title":"Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier","authors":"S. Sheykhivand, T. Y. Rezaii, A. Farzamnia, M. Vazifehkhahi","doi":"10.1109/IICAIET.2018.8638465","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638465","url":null,"abstract":"Automatic sleep stages detection in medical applications using an intelligent method is one of the most important issues in recent years which leads to reducing the workload of specialist in analyzing sleep data with visual inspection. In this paper, a single-channel EEG-based algorithm is presented for automatic recognition of sleep stages using complete ensemble empirical mode decomposition (CEEMD), hybrid genetic algorithm (GA) and neural network. First, the EEG signal is decomposed into IMFs using CEEMD and statistical and temporal features are extracted. For dimensionality reduction of feature space, a hybrid GA and multi-layer back propagation neural network is applied. Then, McNemar’s test is used to validate the features. The RUSBoost algorithm performs the final classification on these optimized features and on average, the accuracies of classification for 2 up to 6 classes are 98.10%, 95.12%, 93.09%, 90.16% and 91.0% and Kappa Cohen coefficients are 0.98, 0.95, 0.93, 0.89 and 0.91, respectively. It is shown that the proposed method has better performance in classification of sleep stages compared to the previous studies.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115451049","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: 13
IICAIET 2018 Conference Program Committee IICAIET 2018会议计划委员会
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/iicaiet.2018.8638453
{"title":"IICAIET 2018 Conference Program Committee","authors":"","doi":"10.1109/iicaiet.2018.8638453","DOIUrl":"https://doi.org/10.1109/iicaiet.2018.8638453","url":null,"abstract":"","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122939329","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
Normal Estimation of Surface in PointCloud Data for 3D Parts Segmentation 三维零件分割中点云数据表面的法向估计
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638451
Takuya Tsujibayashi, Katsufumi Inoue, M. Yoshioka
{"title":"Normal Estimation of Surface in PointCloud Data for 3D Parts Segmentation","authors":"Takuya Tsujibayashi, Katsufumi Inoue, M. Yoshioka","doi":"10.1109/IICAIET.2018.8638451","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638451","url":null,"abstract":"3D object parts segmentation is a fundamental task and essential for many higher level tasks, such as object detection, human pose estimation and environment recognition. For 3D object parts segmentation, there are methods using local 3D concave/convex relationships. Those relationships are distinguished by using normals in local adjacent regions of 3D object surface. To obtain these relationships, precise estimation of normals are very important. However, the normal estimation method for a whole surface of an object which are represented by PointCloud is not discussed sufficiently. Therefore, in this research, we propose an automatic normal estimation method. In our method, the number of times that a normal go through surfaces of the object is used to determine the direction of the normal, inside or outside of the object. As results of experiments on the benchmark which consists of 380 3D objects in 19 categories, we obtained the average normal estimation accuracy of 99%","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116955956","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: 2
Non-linear Approximation Intellectual Property Targeting Edge Inferencing in Field Programmable Gate Array 现场可编程门阵列非线性逼近知识产权定位边缘推理
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638460
Teo Sje Yin, Soon Ee Ong, Siaw Chen Lee, P. Ehkan, R. Raof
{"title":"Non-linear Approximation Intellectual Property Targeting Edge Inferencing in Field Programmable Gate Array","authors":"Teo Sje Yin, Soon Ee Ong, Siaw Chen Lee, P. Ehkan, R. Raof","doi":"10.1109/IICAIET.2018.8638460","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638460","url":null,"abstract":"Non-linear function is the core computation for every neural network’s activation function. Many researchers attempted different methods to implement non-linear functions onto hardware to achieve better system performance. Despite its popularity, non-linear functions’ implementation methods available today are still costly for edge inferencing. This paper proposes an approximation Intellectual Property (IP) to implement non-linear functions in FPGA with high accuracy, fast computation, controllable error rate, adjustable granularity, and yet light in resource utilization. Test result shows that the proposed approximation IP has significant reduction in computation time (improvement rate up to 71%), DSP utilization (improvement rate up to 97%) as well as number of logic utilization (improvement rate up to 74%) and total register count (improvement rate up to 69%).","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121334526","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
IICAIET 2018 Conference Overview IICAIET 2018会议综述
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/iicaiet.2018.8638468
{"title":"IICAIET 2018 Conference Overview","authors":"","doi":"10.1109/iicaiet.2018.8638468","DOIUrl":"https://doi.org/10.1109/iicaiet.2018.8638468","url":null,"abstract":"","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125936911","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
Clustering of Rippling Traffic Waves for Driving Behavioural Pattern Learning 用于驾驶行为模式学习的波纹交通波聚类
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638459
L. Angeline, M. Y. Choong, B. Chua, R. Chin, K. Teo
{"title":"Clustering of Rippling Traffic Waves for Driving Behavioural Pattern Learning","authors":"L. Angeline, M. Y. Choong, B. Chua, R. Chin, K. Teo","doi":"10.1109/IICAIET.2018.8638459","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638459","url":null,"abstract":"Extending traffic infrastructures to reduce congestion is not always viable and is a very costly solution. The new road may reduce the congestion, but those effects are generally momentary as more drivers use those newly expanded roads. Thus, there is a serious need for a fundamental understanding of the emergence of traffic jam. This paper aims to study the existence of cluster of jams in an ideal traffic flow. The traffic model is developed on a cellular automata platform, with bad driving behaviour such as tailgating is modelled to initiate traffic waves and create a ripple effect that forces the following vehicles to sudden brake when they run into such waves. Congestion state can be determined by clustering the rippling traffic waves. Simulation results show that, the average speed of a particular lane is determined by the spacing between vehicles.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041597","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: 2
Optical Network Performance Analysis through Prediction of Q Factor on Simulated and Validated Dataset 基于仿真和验证数据集Q因子预测的光网络性能分析
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2018-11-01 DOI: 10.1109/IICAIET.2018.8638446
Ken Fahmidur Rahman, Farhana Afrin, F. S. Taip
{"title":"Optical Network Performance Analysis through Prediction of Q Factor on Simulated and Validated Dataset","authors":"Ken Fahmidur Rahman, Farhana Afrin, F. S. Taip","doi":"10.1109/IICAIET.2018.8638446","DOIUrl":"https://doi.org/10.1109/IICAIET.2018.8638446","url":null,"abstract":"With the advent of big data, systems nowadays have their performance analyzed with machine learning algorithms. However these machine learning algorithms require huge amount of dataset that not be available. Optoelectronics dataset is scarce. In this paper, an optoelectronics communication network was simulated and the simulation data was validated with an empirical data. Then the performance of the system was analyzed by predicting Q-factor from the dataset, at first with regression then with classification.","PeriodicalId":308127,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122573995","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: 2
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