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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Emil Agusalim Habi Talib Paulus Insap Santosa Sunu Wibirama Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Signal Processing Laboratory Universitas Gadjah Mada Indonesia

The use of a learning management system (LMS) in the world of education is encouraged to support and to optimize the teaching and learning process. Using the LMS, the learning process that has previously been performed in schools can be performed anywhere and anytime. A good LMS provides convenience and comfort to its users. However, to the best of our knowledge, there is no review study that focusses on the usability and user experience of LMS. Therefore, in this literature study, we surveyed several methods used to measure usability and user experience in the LMS. We managed to collect 40 papers discussing the evaluation of usability and user experience in LMS. Furthermore, we successfully answered four formulated research questions. Based on our study, we found that there are 9 methods used to measure usability and user experience, 5 stages carried out in evaluating usability and user experience, 25 characteristics measured, and there are 13 data analysis methods that can be used to analyze usability and user experience in LMS. These findings are important for academics and practitioners working on information technology to support the educational process.

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Machine Learning Isolation Forest-Based Target Detection Algorithm for Airborne Radar

Machine Learning Isolation Forest-Based Target Detection Alg...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Jing Liu Cao Zeng Filbert H. Juwono Pengcheng Huang Haihong Tao National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

While the advantages of airborne radar are widely recognized, it is prone to target overwhelm by clutter during detection tasks, and most traditional detectors rely on clutter mathematical models to obtain detection thresholds. This paper proposes a new machine learning isolation forest-based airborne radar target detection algorithm, fully exploiting the advantages of ensemble decision trees. The proposed algorithm first designs new feature data with feature extraction and space-time weight vector, and the new data enable separate utilization of multidimensional features, serving as inputs for the target and clutter classifier to be constructed. Then, the proposed algorithm designs a unique isolation forest-based target and clutter detector, constructing isolation forest anomaly scores for each designed feature data, and determining the presence of targets for airborne radar based on score discrimination criteria. Compared with various classical detectors, the proposed algorithm significantly enhances target detection performance, providing higher detection probabilities, and offering a new idea and direction for radar target detection. The proposed algorithm is confirmed by simulations to be effective and advantageous.

关键词： Machine learning algorithms Airborne radar signal processing algorithms Forestry Object detection Detectors Machine learning Feature extraction Classification algorithms Clutter

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Joint Precoding and Fronthaul Compression for Cell-Free MIMO with Hybrid Topology

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IEEE Internet of Things Journal 2025年第11期12卷 18125-18136页

作者： Chen, Yijie Xia, Wenchao Zhang, Jun Hou, Xiaoyun Wong, Kai-Kit Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China The Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China The Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Cell-free multiple-input multiple-output (MIMO) generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity. © 2014 IEEE.

关键词： Iterative methods

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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ADMM-Based Solution for mmWave UL Channel Estimation with One-Bit ADCs via Sparsity Enforcing and Toeplitz Matrix Reconstruction

ADMM-Based Solution for mmWave UL Channel Estimation with On...

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IEEE International Conference on Communications (ICC)

作者： Majdoddin Esfandiari Sergiy A. Vorobyov Robert W. Heath Department of Signal Processing and Acoustics Aalto University Espoo Finland Department of Electrical and Computer Engineering North Carolina State University Raleigh USA

Low-power millimeter wave (mmWave) multi-input multi-output communication systems can be enabled with the use of one-bit analog-to-digital converters. Owing to the extreme quantization, conventional signal processing tasks such as channel estimation are challenging, making uplink (UL) multiuser receivers difficult to implement. To address this issue, we first reformulate the UL channel estimation problem, and then combine the idea of $\ell_{1}$ regularized logistic regression classification and Toeplitz matrix reconstruction in a properly designed optimization problem. Our new method is referred to as $\ell_{1}$ regularized logistic regression with Toeplitz matrix reconstruction (L1-RLR-TMR). In addition, we develop a computationally efficient alternating direction method of multi-pliers (ADMM)-based implementation for the L1-RLR-TMR method. Numerical results demonstrate the performance of the L1-RLR-TMR method in comparison with other existing methods.

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Introducing Multilingual Phonetic Information to Speaker Embedding for Speaker Verification

Introducing Multilingual Phonetic Information to Speaker Emb...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhida Song Liang He Penghao Wang Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China Department of Electronic Engineering Tsinghua University China

Incorporating frame-level phonetic information during the extraction of speaker embeddings has been shown to enhance the performance of speaker verification systems. However, previous studies have primarily relied on phonetic information obtained from pre-trained models of monolingual automatic speech recognition (ASR). Considering that speaker verification datasets typically consist of multiple languages, there are instances where speakers are proficient in multiple languages, resulting in discrepancies between the languages used in the enrolled and test utterances. To address these challenges, we employ a pre-trained multilingual ASR Conformer encoder to initialize the MFA-Conformer network for speaker verification. Experimental results on the VoxCeleb dataset demonstrate a significant improvement in the performance of the system that incorporates multilingual phonetic information across different evaluation sets, including VoxCeleb1-O, E, and H, as well as the VoxSRC21 validation set, which focuses on multilingual verification. The source code is released at https://***/zds-potato/multilingual-phonetic-sv.

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Deep Learning for Fault Detection and Diagnosis: Application to Photovoltaic System 19

Deep Learning for Fault Detection and Diagnosis: Application...

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19th IEEE International Multi-Conference on Systems, signals and Devices, SSD 2022

作者： Marweni, Manel Fezai, Radhia Hajji, Mansour Mansouri, Majdi National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Im-age 5019 Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

With expanding requests for effectiveness and product quality and advancing integration of au-tomatic control systems in high-cost and safety-critical processes, Fault Detection and Diagnosis (FDD) in photo-voltaic (PV) have gained a very important role. However, PV systems can be subject to failures. These faults may include the distinctive system components such as PV modules, converters/inverters, connection lines, which can lead to a reduce in the operation performance. Thus, FDD is one of the key technologies to improve the efficiency of grid-connected PV performances. The most well-known data-driven based FDD methods are Deep Learning (DL)-basd approaches. The main advantages of DL algorithms, are that they can learn automatically high- level features from PV data in a high-order. This paper therefore presents the appli-cation of DL-based tools for fault diagnosis purposes and provides a comparative study using PV systems. These techniques include Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and BIdirectional Long-Short Term Memory (BILSTM). The implemented FDD based frameworks are validated using an emulated Grid-Connected PV (GCPV) system. The results show that the DL-based diagnosis methods are able to detect and diagnose faults under different operating modes. © 2022 IEEE.

关键词： Long short-term memory

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Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons

Deeper Bidirectional Neural Networks with Generalized Non-Va...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

The new NoVa hidden neurons have outperformed ReLU hidden neurons in deep classifiers on some large image test sets. The NoVa or nonvanishing logistic neuron additively perturbs the sigmoidal activation function so that its derivative is not zero. This helps avoid or delay the problem of vanishing gradients. We here extend the NoVa to the generalized perturbed logistic neuron and compare it to ReLU and several other hidden neurons on large image test sets that include CIFAR-100 and Caltech-256. Generalized NoVa classifiers allow deeper networks with better classification on the large datasets. This deep benefit holds for ordinary unidirectional backpropagation. It also holds for the more efficient bidirectional backpropagation that trains in both the forward and backward directions.

关键词： Backpropagation Training Neurons Machine learning Delays Biological neural networks Logistics

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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