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2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献

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An Optimal QoS Multicast Routing Protocol in IoT Enabling Cognitive Radio MANETs: A Deep Q-Learning Approach 物联网中支持认知无线电manet的最优QoS多播路由协议:深度q -学习方法
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415188
T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An
{"title":"An Optimal QoS Multicast Routing Protocol in IoT Enabling Cognitive Radio MANETs: A Deep Q-Learning Approach","authors":"T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An","doi":"10.1109/ICAIIC51459.2021.9415188","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415188","url":null,"abstract":"In this paper, we propose an optimal quality-of-service (QoS) multicast routing protocol (QMR) in Internet-of-Things (IoT) enabling cognitive radio mobile ad hoc networks (ICR-MANETs) based on deep Q-learning approach. To this end, we formulate a joint end-to-end queuing delay and link’s stability optimization problem. The formulated optimization is typically NP-complete. We then leverage a novel deep Q-learning method to solve this challenging problem, which arrives at optimal convergence $Q^{ast}$-values. Based on the obtained $Q^{ast}$-values, we propose a QoS multicast routing protocol to select the best set of neighbors associated with minimum $Q^{ast}$-values to establish the multicast tree to the multicast members. Simulation results show that the proposed QMR protocol outperforms the current state-of-the-art routing protocols, which emerges as an efficient multicast routing protocol in ICR-MANETs.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105794","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: 4
Predictive Maintenance of Relative Humidity Using Random Forest Method 基于随机森林方法的相对湿度预测维护
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415213
Aji Teguh Prihatno, Himawan Nurcahyanto, Y. Jang
{"title":"Predictive Maintenance of Relative Humidity Using Random Forest Method","authors":"Aji Teguh Prihatno, Himawan Nurcahyanto, Y. Jang","doi":"10.1109/ICAIIC51459.2021.9415213","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415213","url":null,"abstract":"The massive development of Industry 4.0 inseparable with improvement of Machine Learning. In order to protect manufacturing sector from unwanted events such as electrical failures due to high level of humidity, the predictive maintenance based on Machine Learning should be developed accurately. This paper describes the implementation work of predicting Relative Humidity (RH) in the smart factory’s environment by using Random Forest method as a part of Machine Learning. In order to support data reliability and interoperability in smart factory environment, IIoT devices based oneM2M standard platform was used to collect the data. The result of this Random Forest method for predict relative humidity shows 82.49% which considered as an excellent accuracy. This research goal may contribute to the manufacturing fields to be able to lower the cost and increase efficiency in maintenance.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126837713","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
Comparison of CNN-based Speech Dereverberation using Neural Vocoder 基于cnn的神经声码器语音去噪比较
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415259
Chanjun Chun, Kwang Myung Jeon, Chaejun Leem, Bumshik Lee, Wooyeol Choi
{"title":"Comparison of CNN-based Speech Dereverberation using Neural Vocoder","authors":"Chanjun Chun, Kwang Myung Jeon, Chaejun Leem, Bumshik Lee, Wooyeol Choi","doi":"10.1109/ICAIIC51459.2021.9415259","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415259","url":null,"abstract":"Reverberation degrades the speech quality and intelligibility, particularly for hearing impaired people. In an automatic speech recognition (ASR) system, a dereverberation technique, which removes reverberation, is widely employed as a pre-processing to increase the performance of the ASR system. In this paper, we compare the performance of the CNN-based dereverberation method by applying various vocoders. The U-Net architecture is employed as the dereverberation technique. WaveGlow, MelGAN, and Griffin Lim are used as vocoders. Such vocoders play a role in converting speech features into speech samples in time domain, and are capable of generating high-quality speech from mel-spectrograms. In order to compare the results, PESQ was measured. As a result, it was confirmed that PESQ was higher than that of the reverberant speech when speech was synthesized with the reverberation removal and vocoder.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484637","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: 4
Big Data Platform for Intelligence Industrial IoT Sensor Monitoring System Based on Edge Computing and AI 基于边缘计算和人工智能的智能工业物联网传感器监控系统大数据平台
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415189
Sothearin Ren, Jaesung Kim, W. Cho, Saravit Soeng, Sovanreach Kong, Kyung-Hee Lee
{"title":"Big Data Platform for Intelligence Industrial IoT Sensor Monitoring System Based on Edge Computing and AI","authors":"Sothearin Ren, Jaesung Kim, W. Cho, Saravit Soeng, Sovanreach Kong, Kyung-Hee Lee","doi":"10.1109/ICAIIC51459.2021.9415189","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415189","url":null,"abstract":"The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data — vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121864687","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: 7
RNN Based Optimal Sensing Schedule Control for Wireless Sensor Networks 基于RNN的无线传感器网络最优感知调度控制
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415235
Seung-Hee Choi, S. Yoo
{"title":"RNN Based Optimal Sensing Schedule Control for Wireless Sensor Networks","authors":"Seung-Hee Choi, S. Yoo","doi":"10.1109/ICAIIC51459.2021.9415235","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415235","url":null,"abstract":"With the growth of the IoT technologies, the development of WSNs becomes increasingly more important. Since batteries are commonly used as energy sources for sensors in WSNs, high energy efficiency can extend the life of sensors and free them from interference such as energy harvesting. Mobile object tracking is one of the areas where WSNs are used. To save the energy, sensors usually manage multi-mode operation, in which they periodically switch active and inactive modes. There exists a tradeoff between object detection accuracy and energy efficiency. Depending on the object speed, direction and sensor deployment topology, different sensing schedules should be applied. In this paper, we propose a novel RNN-based sensor dynamic duty cycle control method that can determine the optimal sensing schedule of each sensor node. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129548112","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
Low-Cost Real-time Driver Drowsiness Detection based on Convergence of IR Images and EEG Signals 基于红外图像和脑电信号收敛的低成本驾驶员困倦实时检测
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415193
Kwang-Ju Kim, Kil-Taek Lim, J. Baek, Miyoung Shin
{"title":"Low-Cost Real-time Driver Drowsiness Detection based on Convergence of IR Images and EEG Signals","authors":"Kwang-Ju Kim, Kil-Taek Lim, J. Baek, Miyoung Shin","doi":"10.1109/ICAIIC51459.2021.9415193","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415193","url":null,"abstract":"This paper focused on low-cost real-time driver’s drowsiness detection by fusing facial image information obtained through the IR camera (Infrared Camera) and EEG (Electroencephalogram) signal acquired through the EEG sensor. The proposed method was tested on the target board (i.MX6Quad). The i.MX6Quad is the SoCs (System-on-Chip) that integrate many processing units into one die, like the main CPU, a video processing unit and a graphics processing unit for instance. Instead of the RGB camera, the IR camera is applied to driver condition monitoring and drowsiness detection technology by extracting the driver’s facial feature information robustly against daytime, night-time, and frequent change of brightness around the face. The headphone type EEG sensor is also used to minimize the user’s discomfort. On the target board, the processing time per image frame is about 60ms, so that it can process about 17 frames per second. This processing time can be suitable for the driver’s drowsiness detection systems.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129815160","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
An Generational SDE based Indicator for Multi and Many-objective optimization 基于分代SDE的多目标优化指标
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415230
Jamshid Yusupov, Vikas Palakonda, Samira Ghorbanpour, R. Mallipeddi, K. Veluvolu
{"title":"An Generational SDE based Indicator for Multi and Many-objective optimization","authors":"Jamshid Yusupov, Vikas Palakonda, Samira Ghorbanpour, R. Mallipeddi, K. Veluvolu","doi":"10.1109/ICAIIC51459.2021.9415230","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415230","url":null,"abstract":"Recently, the study of designing multi-objective evolutionary algorithms (MOEAs) to solve multi and many-objective optimization has received lot of recognition. In this paper, we have proposed an indicator based MOEA (IgSDE-MOEA) in which the information from the shift based density estimation is utilized to a greater extent. In the past, the shift based density estimation (SDE) is employed in conjunction with the other indicators and metrics. However, in this work, we employ the indicator based on SDE solely to approximate the Pareto front. The indicator proposed in this paper is adaptively controlled over the generations. The performance of the proposed IgSDE-MOEA is evaluated by performing experiments on 14 benchmark problems and 7 real-world problems. The experimental results demonstrate that the proposed IgSDE-MOEA exhibits better performance in comparison with the state-of-art algorithms.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129101976","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
Prioritized-MAC Model for Intelligent UAV-to-BS Communication in Industrial-WSN Systems 工业无线传感器网络系统中uav - bs智能通信的优先mac模型
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415264
Williams-Paul Nwadiugwu, Seung-Hwan Kim, Jae-Min Lee, Dong-Seong Kim
{"title":"Prioritized-MAC Model for Intelligent UAV-to-BS Communication in Industrial-WSN Systems","authors":"Williams-Paul Nwadiugwu, Seung-Hwan Kim, Jae-Min Lee, Dong-Seong Kim","doi":"10.1109/ICAIIC51459.2021.9415264","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415264","url":null,"abstract":"In this paper, a prioritized-MAC protocol model for unmanned aerial vehicle (UAV) data collection and processing in an industrial wireless sensor network system (IWSN) is proposed. The model encapsulates the existing legacy IEEE 802.11 carrier-sense multiple access collision avoidance (CSMA/CA) MAC protocol, where nodes are kept active when in UAV’s coverage area. A level-sided access channel approach is then deployed to distinguish the priority allotment to each ground node using the distributed coordinated function inter-frame spacing (DCFIS) approach. Consequently, nodes located at the highest priority level-sided frame have shorter time-length with more DCFIS values than those of lower priority level-sided frames. On simulation, our proposed protocol not only improves fairness, but enhances network fairness with reduced delay time.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"530 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120869560","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
Reverse Engineering the Hamming Code with Automatic Graph Learning 基于自动图学习的汉明码逆向工程
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415240
N. Jacobsen
{"title":"Reverse Engineering the Hamming Code with Automatic Graph Learning","authors":"N. Jacobsen","doi":"10.1109/ICAIIC51459.2021.9415240","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415240","url":null,"abstract":"Probabilistic graphical models are used extensively across the information theory, artificial intelligence and machine learning disciplines. In this paper, we work towards realizing a generalized graph-based framework for automated learning in intelligent systems. The proposed automatic graph learning framework employs factor graphs, i.e. Tanner graphs from coding theory, to represent an arbitrary stochastic system of variables and factorized realizations of their joint probability density function. We develop algorithms that are capable of learning statistical relationships between system variables, which involves constructing an appropriate factor graph representation and generating estimates of its component probability density functions, from training data. In this paper, automatic graph learning is used to reverse engineer the Hamming code, based on training data comprised of input-output codeword pairs. We show that automatic graph learning is capable of replicating known decoder performance with an order of magnitude less training data than a multi-layer dense neural network.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"134 13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125803932","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
Positional estimation of invisible drone using acoustic array with A-shaped neural network 基于a型神经网络声阵的隐形无人机位置估计
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415272
Jong-Deuk Ahn, M. Y. Kim
{"title":"Positional estimation of invisible drone using acoustic array with A-shaped neural network","authors":"Jong-Deuk Ahn, M. Y. Kim","doi":"10.1109/ICAIIC51459.2021.9415272","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415272","url":null,"abstract":"Image-based object detection is a commonly used algorithm for anti-drone surveillance system. However, there is a disadvantage that it cannot be detected if the target is not visible within the image. In this paper, we propose drone position estimation algorithm using acoustic array to detect objects complementing the difficulty of estimating sudden directional shifts in hiding, occurrence situations and quickly out of the vision of the camera. Sound data is converted into an image via mel-spectrogram to facilitate image sensor and sound sensor fusion and the drone position is estimated via the Convolution Neural Network. The proposed neural network is the A-shape neural network, which consists of up-sampling and down-sampling. Through these methods, we achieve RMSE of 13.045 pixels and show that the location of the drone can be estimated efficiently.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005899","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
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