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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Multi-Lens Visual SLAM with Integrated Moving Object Removal using Unsupervised Models

Multi-Lens Visual SLAM with Integrated Moving Object Removal...

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Robotics, engineering, Science, and technology (RESTCON), International Conference on

作者： Ryoichirou Hara Kousuke Matsushima Advanced Engineering Course National Institute of Technology Kurume College Fukuoka Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

In Advanced Driver-Assistance Systems (ADAS), SLAM (Simultaneous Localization and Mapping) technology is required to accurately estimate the position and orientation of onboard cameras. Compared to LiDAR SLAM, Visual SLAM has the advantage of collecting rich environmental information at a lower cost. However, the accuracy of the estimated pose and map construction is greatly reduced by moving objects in the camera scene. To solve this problem, a multi-camera SLAM has been proposed, which works robustly in dynamic environments by using multiple cameras to acquire more feature points from omnidirectional environmental information. In this paper, we propose a robust SLAM system based on a multi-camera SLAM system combined with object detection and semantic segmentation networks. We then evaluate the robustness of the proposed method by comparing it to SLAM systems that do not remove moving objects. Experimental results showed that the Absolute Trajectory Error and the Relative Position Error performed better than conventional SLAM by masking moving objects. It also showed that more static objects were detected and constructed in the map.

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Reconfigurable and Multiband Graphene THz Antenna Design Optimized Using Machine Learning

Reconfigurable and Multiband Graphene THz Antenna Design Opt...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Patel, Shobhit K. Agravat, Dhruvik Nguyen, Tien Dung Nguyen, Thi Thanh Kieu Nguyen, Truong Khang Marwadi University Department of Computer Engineering Gujrat Rajkot360003 India Marwadi University Department of Physics Gujrat Rajkot360003 India Electric Power University Faculty of Control and Automation Ha Noi Viet Nam Binh Thuan College Faculty of Engineering Binh Thuan Viet Nam School of Technology Van Lang University Faculty of Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350353983

This study investigates the concept of multiband graphene THz antennas. We investigate the advantages of graphene in THz antenna applications with standard materials. Using a graphene strip, we built a multiband GSSPlp-MIMO antenna in this study that achieves triple operating frequencies of 7.64 THz, 9.57 THz, and 10.90 THz. The reflection response (S11) of -29.2135 dB, which shows excellent signal confinement within the antenna, was greatly improved upon the inclusion of graphene. Superior impedance matching was the trade-off for this, even if the gain decreased slightly (10.41 dB) in comparison to the antenna without graphene (12.30 dB). The parameters are optimized using machine learning, yielding a maximum accuracy (R2) of 0.996622 and a mean square error of 5.3934 ×10-5. This indicates that high-performance terahertz antennas that balance gain and effective signal transmission can be designed using graphene. The antenna's capacity to handle multiple bands makes it ideal for future ultra-broadband wireless communication systems. This results in higher data transfer rates and supports emerging technologies such as 6G and beyond. © 2024 IEEE.

关键词： Data transfer rates

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Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Akashi Rintaro Kousuke Matsushima Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

In recent years, there has been a considerable amount of research conducted on the topic of road damage detection using deep learning techniques, with the aim of supporting the safe driving of mobility vehicles. To date, convolutional neural networks and supervised learning with large datasets have been employed for road damage detection, demonstrating high classification accuracy. However, all images in the dataset must be manually labeled. Moreover, images cannot be classified into classes that are not included in the training dataset. The real world presents a vast array of damage types, and the unlabeled input images encompass both known and unknown classes. This renders the system unsuitable for use in providing safe driving support for mobility. Considering this background, Generalized Category Discovery (GCD) has been put forth as a potential solution, aiming to achieve accurate classification in a comprehensive setting where both known and unknown classes are present in unlabeled data. The purpose of this study is to propose a novel model for GCD with the aim of providing safe driving assistance for mobility. The model is based on the pre-trained MobileViT. To validate its performance, this paper examines its performance on the general CIFAR-10 and CIFAR-100 datasets and on a road damage-specific dataset, and discusses the challenges encountered.

关键词： Training Deep learning Accuracy Communication systems Roads Clustering methods Supervised learning Signal processing Convolutional neural networks

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Emotion Estimation Using Single-Channel EEG and Heart Rate Variability for Industrial Applications

Emotion Estimation Using Single-Channel EEG and Heart Rate V...

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International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Chen Feng Narumon Jadram Yuri Nakagawa Midori Sugaya Department of Information Science and Engineering Shibaura Institute of Technology Tokyo Japan Functional Control System Graduate School Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331516451

ISBN: (纸本)9798331516468

The industrial world is shifting from mass production to meeting individual needs, requiring guidelines to incorporate emotions and preferences. Traditional questionnaires fall short in capturing detailed real-time emotion changes. Therefore, we propose a novel approach using Electroencephalography(EEG), which reflects central nervous system activity, and heart rate variability(HRV), which reflects autonomic nervous system activity, to estimate emotions in real-time by analyzing arousal and valence. Based on the 2D Arousal-Valence model, we plot EEG and HRV data onto this model to visualize emotion changes caused by external stimuli. Using this proposed method, we conducted joint research with the home and personal care sector to evaluate emotional responses to aroma products, and with the automotive sector to assess drivers' emotional states. These studies demonstrate the value of academic research in guiding the industrial sector, particularly through emotion estimation based on EEG and HRV. Our findings suggest that these technologies offer valuable insights into consumer emotions, which can help improve product design and user experience. Ongoing collaboration between academia and the industrial sector is crucial for overcoming challenges in interpreting physiological signals and effectively integrating these methods into practice.

关键词： Estimation Collaboration Companies Brain modeling Electroencephalography User experience Real-time systems Product design Data models Heart rate variability

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A semantic and emotion-based dual latent variable generation model for a dialogue system

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CAAI Transactions on Intelligence technology 2023年第2期8卷 319-330页

作者： Ming Yan Xingrui Lou Chien Aun Chan Yan Wang Wei Jiang State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijingChina School of Information and Communications Engineering Communication University of ChinaBeijingChina Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijingChina Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVictoriaAustralia School of Data Science and Intelligent Media Communication University of ChinaBeijingChina

With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human *** emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate ***,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the *** propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional *** from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer ***,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and *** content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features ***,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the *** results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.

关键词： conditional variational autoencoder dual latent space emotional responses latent variable generation

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Classification of Alzheimer’s Disease via Spatial-Temporal Graph Convolutional Networks

Classification of Alzheimer’s Disease via Spatial-Temporal ...

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Chinese control and Decision Conference, CCDC

作者： Yongbo Li Xiaoying Song Li Chai School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Department of Control Science and Engineering College Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In recent years, functional brain networks have been widely used for brain disease classification tasks. Graph convolutional networks (GCN), as one of the existing methods, focuses on inter-subject relationships and subjects’ brain region connectivity relationships. Though spatial features can be extracted by GCN-based methods from functional brain network, temporal relationships between brain regions are frequently ignored. In this paper, we propose a spatial-temporal graph convolutional network (STGCN) model to solve this problem. A temporal convolution module (TCM) is designed to learn information about the alteration of brain regions with time, and a spatial convolution module (SCM) is employed to extracted features of brain regions. TCM contains two parts, single-brain-region convolution and multi-brain-region convolution, to fully capture the alteration of brain regions with time from two perspectives. We finally evaluate our model on the ADNI dataset and obtain good results.

关键词： Convolution Graph convolutional networks Feature extraction Brain modeling Data mining Task analysis Alzheimer's disease

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