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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Liu, Chuxin Guo, Yuyao Xu, Ruiyang Lu, Liangjun Chen, Jianping Zhou, Linjie Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We implement an analog EO-PLL for a III/V-Si3N4 hybrid integrated ECL to generate a highly-linear FMCW signal over multiple wavelengths. The ranging resolution is improved from 5 m to 35 cm for a 100-m target. © ... 详细信息

ISBN: (纸本)9781957171180

关键词： Phase locked loops

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A Human-Flow Analysis Based on PCA:A Case Study on Population data Near Railway 2

A Human-Flow Analysis Based on PCA:A Case Study on Populatio...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Kim, SeongIn Shibuya, Takeshi Toride, Shingo Endo, Yasunori University of Tsukuba Programs in Intelligent and Mechanical Interaction Systems Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan National Research Institute for Earth Science and Disaster Prevention Disaster Information Research Division Tsukuba Japan

ISBN: (纸本)9781665459440

The damage caused by natural disasters and accidents is increasing every year. To reduce such damage from spreading, it is important to detect an accident promptly. However, current sensing systems are difficult to use because they have narrow coverage and are specialized in few detectable accidents. In this paper, we propose a method to detect disasters and accidents by calculating the degree of an anomaly in human flow by treating a common human flow as a single large sensor. Human flow can be assumed to have typical patterns in people's daily life, such as going to work and leaving work. Developing an anomaly detection method of human flow can lead to the discovery of any hidden causes such as accidents and disasters. In this paper, we study a method that aims to detect anomalies in human flow, considering the operational status of railways as an example. We confirm that our method can detect the actual suspension of operations. © 2022 IEEE.

关键词： Principal component analysis

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Optimizing System Costs in Local Electricity Market through Peer-to-Peer Energy Trading: Impact of Photovoltaic and Battery Storage Adoption

Optimizing System Costs in Local Electricity Market through ...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Mochi, Pratik Pandya, Kartik S. Soares, Joao Faia, Ricardo Charotar University of Science and Technology Charusat Cspit Fte Dept.of Electrical Engineering Anand Gujarat India Parul University Parul Institute of Engineering & Technology Faculty of Engg. & Technology Electrical Engineering Dept. Gujarat Vadodara India - Polytechnic of Porto Gecad Lasi - Intelligent Systems Associate Laboratory Porto4200-072 Portugal

ISBN: (纸本)9798350393378

As the global energy landscape undergoes a paradigm shift towards decentralization and sustainability, local electricity markets have emerged as a promising avenue for efficient energy exchange among prosumers. This conference paper presents a comprehensive study focused on optimizing system costs within local electricity markets through peer-to-peer energy transactions. Our primary objective is to investigate the impact of photovoltaic and battery storage adoption on system costs, shedding light on the potential economic benefits of these technologies in diverse scenarios. The study encompasses a rigorous analysis of various cases and scenarios to provide a holistic understanding of the dynamics involved in local electricity markets. By simulating a range of scenarios, we explore the implications of different levels of PV and battery storage adoption, evaluating their capacity to mitigate system costs effectively. Our findings reveal that, in the best-case scenario, substantial cost savings of 24.42% can be achieved through the integration of PV and battery storage systems into the local electricity market. This research contributes to the growing body of knowledge on local electricity markets, emphasizing the pivotal role of peer-to-peer energy transactions in shaping the future of energy distribution. Furthermore, it provides valuable insights for policymakers, utilities, and consumers seeking to optimize their participation in decentralized energy systems while promoting sustainable energy practices. © 2023 IEEE.

关键词： Power markets

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MDAG-Net: Multi-Domain Association-Guided Network for Image-based Long-term Visual Localization

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Ge, Fawei Zhang, Yunzhou Wang, Li Tan, Yanhai Coleman, Sonya Kerr, Dermot Northeastern University College of Information Science and Engineering Liaoning Shenyang110819 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China University of Ulster Intelligent Systems Research Centre DerryBT52 1SA United Kingdom

In the case of long-term changing environment, long-term visual localization is a challenging problem in autonomous driving and mobile robots. Due to the influence of season, illumination and other changing weather conditions, the traditional image retrieval methods are difficult to achieve ideal results in long-term visual localization. Therefore, inspired by the human brain associative recognition function, an image retrieval based on a multi-domain association-guided network is proposed to solve the long-term visual localization problem. The key idea is to extract the discriminative domain-invariant features in different scenes through multi-domain image transformation of the perceptual network and the conceptual network. In addition, in order to better associate image features of different scenes in the conceptual network and guide the perceptual network to obtain more robust domain invariant features, an association-guided module is designed without the need of external datasets. On this basis, the domain feature loss function and the guidance mechanism of the loss function are introduced to assist these two network models training to obtain better performance. Finally, experiments are carried out on the CMU-Seasons dataset and the RobotCar-Seasons dataset. Compared with some state-of-the-art methods, the proposed method improved the high-precision localization result of urban, suburban and park scenes in the CMU-Seasons dataset by 1.5%, 0.5% and 0.7%, respectively, which also can verify the effectiveness of the proposed method under various seasonal and illumination conditions. © 2016 IEEE.

关键词： Mobile robots

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Regularized Multivariate Functional Principal Component Analysis

arXiv

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arXiv 2023年

作者： Haghbin, Hossein Zhao, Yue Maadooliat, Mehdi Artificial Intelligence and Data Mining Research Group ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Department of Mathematical and Statistical Sciences Marquette University MilwaukeeWI53233 United States

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, controlling the roughness of the extracted Principal Components (PCs) can be challenging. This paper introduces a novel approach called regularized MFPCA (ReMFPCA) to address this issue and enhance the smoothness and interpretability of the multivariate functional PCs. ReMFPCA incorporates a roughness penalty within a penalized framework, using a parameter vector to regulate the smoothness of each functional variable. The proposed method generates smoothed multivariate functional PCs, providing a concise and interpretable representation of the data. Extensive simulations and real data examples demonstrate the effectiveness of ReMFPCA and its superiority over alternative methods. The proposed approach opens new avenues for analyzing and uncovering relationships in complex multivariate functional *** Codes 62Hxx, 46-xx (Primary) 62H25 (Secondary) © 2023, CC BY.

关键词： Principal component analysis

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Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

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Computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

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A Review on Solar Panel Adoption in Residential Malaysia: Trends, Benefits and Technologies 2nd

A Review on Solar Panel Adoption in Residential Malaysia: Tr...

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2nd International Conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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Pose Estimation and Its Impact on Quadcopter Control: An Experimental Study 9

Pose Estimation and Its Impact on Quadcopter Control: An Exp...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Nguyen, Minh-Quan Tran, Van-Ho Vo, Minh-Tai Le, My-Ha Faculty of International Education Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology and Education Intelligent Systems Laboratory Ho Chi Minh City Viet Nam Rmit University Vietnam School of Science Engineering & Technology Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331505042

Autonomous quadcopter control based on computer vision has become one of the popular research methods. However, challenges persist due to constraints in processing speed and memory associated with embedded edge computing, thereby limiting accuracy and real-time performance. In this study, we introduce a novel gesture controlled autonomous quadcopter system capable of achieving high accuracy in real-time scenarios. Firstly, the Lightweight OpenPose method is employed to extract gesture sequences. Secondly, we propose a gesture classifier based on the SimVP architecture. In order to validate our approach, we conduct an evaluation using a generated dataset, followed by testing in a real-world environment. Results of our evaluation and experiments demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Embedded systems

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