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检索条件"机构=Computer Engineering and Control Systems Dept."

共 6680 条记录，以下是1-10 订阅

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Distance Based Single-Channel Target Speech Extraction

Distance Based Single-Channel Target Speech Extraction

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shi, Runwu Yen, Benjamin Nakadai, Kazuhiro Dept. of Systems & Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350368741

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures by solely utilizing distance information. This is the first work that utilizes only distance cues without using speaker physiological information for single-channel TSE. Inspired by recent single-channel Distance-based separation and extraction methods, we introduce a novel model that efficiently fuses distance information with time-frequency (TF) bins for TSE. Experimental results in both single-room and multi-room scenarios demonstrate the feasibility and effectiveness of our approach. This method can also be employed to estimate the distances of different speakers in mixed speech. Online demos are available at https://***/distance-demo-page/. © 2025 IEEE.

关键词： distance based sound separation single-channel Target speech extraction

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

引用

4th International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture 7

Augmented Karuta: Interactive Playing Cards on the Floor for...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Umezu, Nobuyuki Bando, Hideki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798331521578

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 'grabbing' cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding 'reading' card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected 'grabbing' card that matches the 'reading' card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness. © 2025 IEEE.

关键词： Tropics

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Datta, Gourav Liu, Zeyu Li, Anni Beerel, Peter A. Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9798350368741

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10. © 2025 IEEE.

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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