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14th International Conference on Frontier Computing, FC 2024

作者： Dofitas, Cyreneo Gil, Joon-Min Byun, Yung-Cheol Department of Electronic Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

The state of a road encompasses various factors, including the presence of vehicles and pedestrians. On-the-road surveillance cameras are crucial in tracking information regarding pedestrians, moving vehicles, and objects in the road vicinity. With the increasing adoption of deep learning models in vehicle monitoring systems, there is a focus on identifying and classifying conditions relevant to driving comfort and road safety. This study addresses the issue of an imbalanced dataset. However, the scarcity of publicly available datasets and efficient detection techniques poses challenges in accurately classifying road situations. In this paper, we propose a long-term recurrent convolutional network (LRCN) comprised of two groups integrated with convolutional neural networks (CNN) and long short-term memory (LSTM) networks with multiple layers of LSTM blocks for road situation detection in videos. Our model for road situation classification achieves an accuracy of 71% when using the original dataset captured from the camera. However, employing data augmentation to balance the dataset resulted in an increased accuracy of 90%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Optimizing HVAC cooling system chilled water temperature and water flow rate using energy efficiency input-output model-based PSO 33

Optimizing HVAC cooling system chilled water temperature and...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Adenuga, Olukorede Tijani Mquqwana, Manduleli Krishnamurthy, Senthil Ratshitanga, Mukovhe Adewuyi, Oludamilare Bode Department of Electrical Electronic and Computer Engineering Cape Town Bellville7535 South Africa

ISBN: (纸本)9798331535162

High industries motor-driven systems energy consumption has been growing significantly at an alarming rate, owing to higher cooling system demand. This contributes to a large portion of building total energy usage and an appropriate system operation control strategy promises large energy saving with embedded energy efficiency enhancement. The paper proposes a Particle Swarm Method (PSO) for the cooling system with chilled water temperature and water flow rate using an energy efficiency input-output model to minimize energy consumption. This work investigates the energy consumption of the 12-zone building with a variable frequency drive (VFD) to control an HVAC fixed-speed cooling water pump and the data is recorded every 15 minutes resulting in 32160 data sets. The methodology is based on the system delivering cold air at an HVAC system when the cooling water flow rate has been varied between 3.57 m3/h and 50 m3/h, while the evaporation outlet temperature has been kept constant at 2.1°C. In this scenario, chiller power consumption is constant while pump power varies in line with the cooling water flow rate. Optimizing cooling systems' chilled water flow rate reduced chilled water pump energy savings by 41.9%. It is proposed to use sustainable energy in the industrial sector, with a special emphasis on South African goals and regulations on energy efficiency prospects in the industry sector value chain. © 2025 IEEE.

关键词： HVAC

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ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models 24

ADO-LLM: Analog Design Bayesian Optimization with In-Context...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Yin, Yuxuan Wang, Yu Xu, Boxun Li, Peng Department of Electronic and Computer Engineering University of California Santa BarbaraCA United States

ISBN: (纸本)9798400710773

Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine-learning-based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black-box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high-dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM’s ability to infuse domain knowledge to rapidly generate viable design points to remedy BO’s inefficiency in finding high-value design areas specifically under the limited design space coverage of the BO’s probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high-quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO’s exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness. © 2024 Copyright is held by the owner/author(s).

关键词： Integrated circuit layout

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A Gate Driving Scheme for GaN GIT with Enhanced Short Circuit Capability for Motor Drive Application 14

A Gate Driving Scheme for GaN GIT with Enhanced Short Circui...

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14th Annual IEEE Applied Power electronics Conference and Exposition, APEC 2025

作者： Zhou, Zongjie Cheng, Yan Chen, Kevin J. The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331516116

Short circuit capability in power switching devices is highly desired for motor drive application, and it is a challenging issue for GaN power high-electron-mobility transistor (HEMT) devices because of concentrated heat generation and inefficient thermal dissipation that partially stems from much smaller die size. This paper proposes a current mode gate driving scheme with a shunt resistor in the gate loop for a GaN gate injection transistor (GIT) to prolong short circuit duration time and thus improve the system robustness and reliability. The shunt resistor not only can limit the short circuit peak current as a result of the source negative feedback but also can be used as a current sensing element that is indispensable for cost-sensitive motor control. The experiments demonstrate a greatly increased short circuit capability and no obvious compromise on other performances in motor drive application with the proposed gate driving scheme. © 2025 IEEE.

关键词： Traction motors

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Md. Ali Islam, Md. Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2025 IEEE.

关键词： Global positioning system

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A Scalable Multi-Hop IoT Network Using ESP-NOW for Remote Zone Monitoring Applications

A Scalable Multi-Hop IoT Network Using ESP-NOW for Remote Zo...

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2025 International Conference on electronics, Information, and Communication, ICEIC 2025

作者： Uddin, Raihan Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of

ISBN: (纸本)9798331510756

This paper presents the development of a scalable multi-hop Internet of Things (IoT) network utilizing ESP-NOW for remote zone monitoring applications. The proposed network employs ESP32 microcontrollers, where the source node is equipped with a DHT22 sensor to collect temperature and humidity data, which is then transmitted every 2 seconds through multiple relay nodes to a gateway node. The gateway node forwards data to a Node-RED server via Message Queuing Telemetry Transport (MQTT), enabling remote access to monitor the visualized data. The network design emphasizes scalability and reliability, addressing the challenges of long-distance data transmission without an internet connection by using ESP-NOW's peer-to-peer communication. The core features of this work include the implementation of relay nodes to extend the communication range, integration with Node-RED for real-time monitoring, and practical demonstration with environmental data. Experimental results validate the network's efficacy, high-lighting low latency and zero packet loss, even in complex environments. This approach provides a robust solution for remote monitoring in areas lacking traditional network infrastructure. © 2025 IEEE.

关键词： Gateways (computer networks)

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A Parallel Color Image Encryption Algorithm Based on a 2D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Ho-Ching Iu, Herbert Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong The School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China School of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computer Engineering Faculty of Engineering Karamanoglu Mehmetbey University Karaman70200 Turkey Department of Artificial Intelligence and Data Engineering Engineering Faculty Ankara University Ankara06830 Turkey Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This paper aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2D Logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multi-timescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a two-dimensional framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Mitigating Power System Oscillations in PMSGWind Integrated Grids Using Power electronics and Inertia Control: A Comprehensive Review 16

Mitigating Power System Oscillations in PMSGWind Integrated ...

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16th Annual Power electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

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An encoder-decoder network for crowd counting based on multi-scale attention mechanism

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Multimedia Tools and Applications 2025年第3期84卷 1187-1210页

作者： Chuang, Hao-Hsiang Chen, Yi-Cheng Lin, Chang Hong Department of Electronic and Computer Engineering National Taiwan University of Science and Technology No. 43 Keelung Rd. Da’an Dist. Taipei Taiwan

Crowd counting is a challenging computer vision task, which is widely used in video surveillance and public safety applications. With the increase of camera resolution and the complexity of crowd image, it becomes an important problem to predict crowd density and crowd count accurately. Recent CNN-based density estimation methods have shown effectiveness in densely populated scenes. In this paper, we present a novel approach to crowd counting through the development of an Encoder-Decoder Multi-Scale Attention Network. Our approach leverages the robust U-net architecture as the backbone network, strengthened by the strategic integration of an attention mechanism. We adopt a multi-scale attention method to each different layers in the U-net backbone to make the network extract features which focus on the crowds, instead of the background in the images. The attention mechanism and the skip-connections can adjust the weights of feature maps while maintaining features at different scales. Extensive experiments on ShanghaiTech Part_A & B and UCF-QNRF dataset demonstrate that our network can achieve better performances with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values outperforming existing methodologies: ShanghaiTech Part_A (MAE/RMSE: 60.0/104.9), Part_B (MAE/RMSE: 7.8/13.8), and UCF-QNRF (MAE/RMSE: 98.6/179.7). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decoding

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Elevated-temperature dry sliding wear behaviors of dual-cathode HiPIMS TiN and TiZrN films

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Emerging Materials Research 2025年第1期14卷 98-106页

作者： Lin, Yu-Tse Liu, Wen-Chung Kuo, Chin-Chiuan Department of Power Mechanical Engineering National Formosa University Huwei Yunlin County Taiwan Department of Mechanical and Computer Aided Engineering National Formosa University Huwei Yunlin County Taiwan Graduate Institute of Aeronautical and Electronic Engineering National Formosa University Huwei Yunlin County Taiwan

Titanium–zirconium nitride films with three Zr/Ti ratios (16/35, 22/29, and 25/26) were deposited using Ti and Zr dual-cathode reactive high-power impulse closed-field magnetron sputtering by adjusting the average power ratio of two cathode materials. The sliding wear resistances of the obtained TiZrN films at different temperatures were compared. The results revealed that the ternary TiZrN film exhibited a higher hardness (approximately 35 GPa) and lower elastic modulus (approximately 360–380 GPa) than those of the binary TiN film. This contributed to the lower friction coefficient and wear resistance at room temperature. However, the addition of Zr became disadvantageous with an increase in wear temperature. Above 4008C, the surface hardness of the TiZrN film decreased dramatically with an increase in the Zr fraction. This is attributed to the increased vulnerability of the film to oxidation. Structural discontinuity induced by the inhomogeneous oxidation (a multilayer structure with voids, embedded oxides, and oxynitrides formed in the surface layer) roughened the film surface and reduced the surface strength, which accelerated the wear of TiZrN films at elevated temperature in an ambient atmosphere. Such ternary nitride coatings should not be applied in oxidative high temperature environments without obstructing the oxidation through designed composition and structure. © 2025 Emerald Publishing Limited: All rights reserved.

关键词： Vacuum deposited coatings

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