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arXiv 2024年

作者： Yu, Sangwon Song, Jongyoon Hwang, Bongkyu Kang, Hoyoung Cho, Sooah Choi, Junhwa Joe, Seongho Lee, Taehee Gwon, Youngjune L. Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung SDS Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of their decisions on specific issues. In this work, we observe that language models exhibit a negative bias in the binary decisions of complex reasoning tasks. Based on our observations and the rationale about attention-based model dynamics, we propose a negative attention score (NAS) to systematically and quantitatively formulate negative bias. Based on NAS, we identify attention heads that attend to negative tokens provided in the instructions as answer candidate of binary decisions, regardless of the question in the prompt, and validate their association with the negative bias. Additionally, we propose the negative attention score alignment (NASA) method, which is a parameter-efficient fine-tuning technique to address the extracted negatively biased attention heads. Experimental results from various domains of reasoning tasks and large model search space demonstrate that NASA significantly reduces the gap between precision and recall caused by negative bias while preserving their generalization abilities. Our codes are available at https://***/ysw1021/NASA. Copyright © 2024, The Authors. All rights reserved.

关键词： NASA

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Super-Resolution CNN Accelerator on Low-Cost Zynq-7000 Series SoC-Based FPGA

Super-Resolution CNN Accelerator on Low-Cost Zynq-7000 Serie...

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USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)

作者： Obaidullah Ahmed Hussain Ali Yasir Javed Naveed Iqbal Department of Electrical Engineering National University of Sciences and Technology Military College of Signals Islamabad Pakistan Renzym Islamabad Pakistan Department of Electrical Engineering Interdisciplinary Research Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

This work presents an implementation of a super-resolution (SR) convolutional neural network (CNN) accelerator on low-cost Zynq-7000 series SoC-based FPGA. On Xilinx FPGA boards, $\mathbf{C}$ programming is employed for deploying a trained SR CNN model using Vivado high-level synthesis. Key optimizations are strategically implemented to efficiently utilize the limited on-board resources, leading to the creation of an optimized IP core. With deterministic latency, the system consistently delivers high-resolution images with a processing timing of 4.35 ns. The outcomes not only showcase the viability but also the effectiveness of implementing intricate machine learning models on cost-effective FPGA platforms. This underscores the potential for broader applications in fields that demand real-time machine learning execution.

关键词： Conferences Superresolution Machine learning programming Real-time systems Timing Convolutional neural networks

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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

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IEEE Transactions on Wireless Communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

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Starfish Optimization Algorithm for Enhanced Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Starfish Optimization Algorithm for Enhanced Parameter Ident...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Ahmed S. Menesy Hamdy M. Sultan Salah Kamel Mahmoud Kassas Mohamed A. Tolba Mohamed Kourany Saad Electrical Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dhahran Saudi Arabia Reactors Department Nuclear Research Center Egyptian Atomic Energy Authority Cairo Egypt Electrical and Renewable Energy Department Faculty of Engineering Nahda University in Beni Suef Beni Suef Egypt

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This study evaluates the performance of the starfish optimization algorithm (SFOA) for parameter identification in Proton Exchange Membrane Fuel Cells (PEMFCs). SFOA, inspired by the decentralized movement and regeneration behavior of starfish, combines global exploration and local exploitation strategies to efficiently estimate unknown PEMFC parameters. Four different PEMFC stacks, namely, 250W, BCS 500W, SR-12 500W, and NetStack_PS6, are analyzed using SFOA, with results benchmarked against state-of-the-art optimization techniques. The optimization is executed 50 times on each PEMFC. Statistical measurements have been conducted, and the statistical results confirm that SFOA yields the minimum total squared deviation (TSD) for all the four PEMFCs studied. The results of convergence characteristics identify that optimum results are achieved within 200 iterations and 30 agents. In addition, the proposed algorithm is capable of capturing the PEMFC performance under varying temperatures and gas pressure and is applicable in real-world situations. The results confirm the better precision, rate of convergence, and efficiency of SFOA and conclude that it is a valuable tool in PEMFC simulation and optimization.

关键词： Protons Parameter estimation Temperature Fuel cells Stability analysis Robustness Optimization Thermal stability Standards Convergence

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Performance Evaluation of Multi-ONU Customers Access Network using Spectrum Slicing and Duplication Techniques 5

Performance Evaluation of Multi-ONU Customers Access Network...

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5th International Conference on Emerging Electrical Energy, Electronics and Computing Technologies, ICE4CT 2024

作者： Rahman, Juwairiyyah Abdul Mugunthan, Sivaguru A.L. Hassan, Hasliza Abu Muslim, Norliana Ab-Rahman, Mohammad Syuhaimi Department of Electrical Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Electrical and Computer Engineering Program Faculty of Engineering Sohar University PC:311 Sohar Oman

This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solution allows for the determination of power requirements at each ONU based on bus-star topologies, facilitating an increase in user capacity. The power received by each ONU is a critical parameter that system designers must consider when planning the implementation of Fiber to the Home (FTTH), as it impacts the output data in terms of Bit Error Rate (BER) and Signal Quality Factor (QF) for each user. Various parameters were evaluated to ensure that the enhanced network improves scalability while adhering to standard specifications. These parameters included the Q-factor, BER, eye diagram, user count, and received power. The study employed slicing and duplication techniques, utilizing Arrayed Waveguide Grating (AWG) for signal duplication and Wavelength Division Multiplexing for signal slicing. The simulations were conducted using a transmitter operating at a frequency of 1491 nm with a power output of 0 dBm, utilizing the latest version of OptiSystem 20 software. The results highlighted the system's capability to support a total of 73,728 users, meeting stringent performance criteria. Specifically, the system achieved a Q-factor greater than 6, indicating high signal quality, and a bit error rate (BER) lower than 1 × 10-9, ensuring reliable data transmission. For the sampled 30 users, the average Q factor was 8.1527, and the average BER was 5.3648 × 10^-11, with an output power level of -54.278 dBm. Despite an increase in users, the quality of the data supplied to clients remained unchanged. These outcomes underscore the robustness and efficiency of the design in delivering high-capacity, error-resilient optical communication. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Q factor measurement

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Outage, Capacity, and Error Performance of Downlink RSMA-based systems: Analysis and Resource Optimization

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IEEE Transactions on Communications 2025年

作者： Vu, Thai-Hoc Da Costa, Daniel Benevides Kim, Sunghwan Pham, Quoc-Viet University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Kyonggi University School of Electronic Engineering Kyonggi Korea Republic of University of Dublin School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02PN40 Ireland

This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP) and ergodic capacity (EC) frameworks, deriving exact expressions for both, along with asymptotic analysis in high signal-to-noise ratio (SNR) and low-rate regions, which serve as the foundation for deducing the approximate and maximal energy-reliability and energy-spectral formulas. To enhance system performance, we tackle the non-convex problems of jointly optimizing common and private power allocation (PA) coefficients to minimize the maximal OP performance. Moreover, we also delve into optimizing PA coefficients and rate-splitting factors concurrently to maximize the ergodic sum capacity (ESC). Addressing the influence of modulation schemes on user error rates, we introduce mathematical frameworks for symbol error rate (SER) considering four combined modulation schemes based on binary phase-shift keying (BPSK) and quadrature-phase shift keying (QPSK), the whole cases are quantified in terms of exact and asymptotic manners. Furthermore, we present a straightforward approach to optimize the PA coefficients to minimize the maximal SER performance. Finally, Monte-Carlo simulations are presented to validate our developed frameworks and optimization solutions. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Exploring the Effectiveness of Work from Home: A Text Mining Analysis of Employee Perceptions and Experiences

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Procedia Computer Science 2024年 234卷 448-454页

作者： Reny Nadlifatin Satria Fadil Persada Amanda Beatrice Nadya Permata Putri Michael Nayat Young Yogi Tri Prasetyo Anak Agung Ngurah Perwira Redi Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department Universitas Pembangunan Nasional "Veteran" Jawa Timur Surabaya 60294 Indonesia School of Industrial Engineering and Engineering Management Mapua University 658 Muralla St. Intramuros Manila 1102 Philippines Industrial Engineering Department Faculty of Engineering and Technology Sampoerna University Jakarta 12780 Indonesia

Work from home (WFH) has become a global phenomenon that continues to grow, especially since the COVID-19 pandemic hit the world. Many organizations and companies are forced to adopt a remote working model to keep their employees safe. The usefulness of WFH is still up for dispute, though. Text mining analysis can be utilized to determine how beneficial working from home is. Sentiment analysis uses text analysis to gather ten thousand data from social media Tweets. Joy emotion is predicted to dominate with 83.98 percent according to the mining performed by the vocabulary valence aware dictionary and sentiment reasoner (VADER).

关键词： Sentiment Text mining Twitter Work from home

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Conceptual Design of a Robotic Vehicle Refuelling System

Conceptual Design of a Robotic Vehicle Refuelling System

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2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Acharya, Pratik Sinha, Arpita Guha, Anirban Indian Institute of Technology Bombay Centre of Excellence in Oil Gas Energy Mumbai India Indian Institute of Technology Bombay Interdisciplinary program in Systems Control Engineering Mumbai India Indian Institute of Technology Bombay Department of Mechanical Engineering Mumbai India

ISBN: (纸本)9781665433235

Robotics has been hailed as a solution for repetitive, tedious, and dangerous physical activities. One such activity which has a need for robotics is Vehicle Refuelling. Automatic Vehicle Refuelling systems have been developed by a few organisations and have also been described in patent and research literature. Most of these systems require radical changes in the design of the fuel tank of the vehicle or changes in the already existing refuelling station infrastructure. These challenges, along with technological barriers, made the adoption of such systems rare and difficult. In this paper we present two conceptual designs which do not have these limitations. Being a critical operation, we have designed for an independent safety check to preclude fuel spillage. An inbuilt option to shift to manual operation makes it an attractive candidate for implementation in existing fuel pumps. © 2021 IEEE.

关键词： Conceptual design

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Estimating Blood Glucose Levels Using PPG Signals: A Deep Learning Approach with Diverse Patient Profiles

Estimating Blood Glucose Levels Using PPG Signals: A Deep Le...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mahdi Zeynali Khalil Alipour Bahram Tarvirdizadeh Mohammad Ghamari Department of Mecha-tronics Eng. School of Intelligent Systems Engineering Advanced Service Robots (ASR) Laboratory College of Interdisciplinary Science and Technolog Tehran Iran Department of Electrical Engineering California Polytechnic State University San Luis Obispo California USA

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

Accurate and continuous monitoring of blood glucose levels is crucial for effective diabetes management. Photoplethysmography (PPG) has emerged as a promising modality for non-invasive blood glucose monitoring. In this study, we investigate how blood glucose levels can be measured using PPG signals. To do this, we analyze a largescale dataset from VitalDB, which includes information from 6,388 subjects. By including diverse patient profiles in our dataset, we ensure that our models can be applied to a wide range of individuals. To capture the complex patterns and relationships in PPG signals, we employ a complicated deep learning approach based on convolutional neural networks (CNNs). Our models have achieved a mean absolute error (MAE) of 23.99, demonstrating their accuracy and reliability. Furthermore, we evaluate the clinical accuracy of the predictions using the Clarke Error Grid analysis, which reveals a high agreement within clinically acceptable ranges. By utilizing the comprehensive VitalDB dataset, our study contributes to a deeper understanding of PPG-based blood glucose prediction and validates the effectiveness of our models on a large-scale cohort. Additionally, we perform a comparison with recent papers, demonstrating why our model excels in various aspects such as model generality with a large dataset, high clinical accuracy, and the requirement of only 10 seconds of PPG data for effective prediction. The results highlight the potential of PPG signals as a valuable tool for non-invasive blood glucose monitoring.

关键词： Deep learning Accuracy Predictive models Data models Glucose Diabetes Convolutional neural networks Reliability Blood Monitoring

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