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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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TACNET: TEMPORAL AUDIO SOURCE COUNTING NETWORK

arXiv

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arXiv 2023年

作者： Ahmadnejad, Amirreza Darviishani, Ahmad Mahmmodian Asadi, Mohmmad Mehrdad Saffariyeh, Sajjad Yousefi, Pedram Fatemizadeh, Emad Department of Electrical Engineering Sharif University of Technology Tehran11155-4365 Iran Department of Computer Science Institute for Advanced Studies in Basic Sciences Zanjan45137-66731 Iran

In this paper, we introduce the Temporal Audio Source Counting Network (TaCNet), an innovative architecture that addresses limitations in audio source counting tasks. TaCNet operates directly on raw audio inputs, eliminating complex preprocessing steps and simplifying the workflow. Notably, it excels in real-time speaker counting, even with truncated input windows. Our extensive evaluation, conducted using the LibriCount dataset, underscores TaCNet's exceptional performance, positioning it as a state-of-the-art solution for audio source counting tasks. With an average accuracy of 74.18 % over 11 classes, TaCNet demonstrates its effectiveness across diverse scenarios, including applications involving Chinese and Persian languages. This cross-lingual adaptability highlights its versatility and potential impact. © 2023, CC0.

关键词： Deep neural networks

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide. © 2025 IEEE.

关键词： Precision agriculture

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Intelligent Energy Management Strategies for Hybrid Electric Transportation

Intelligent Energy Management Strategies for Hybrid Electric...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Pankaj Yadav Vikas Vikash Kumar Saini Ameena S. Al-Sumaiti Rajesh Kumar Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Center for Energy and Environment Malaviya National Institute of Technology Jaipur India Electrical Engineering and Computer Science Advanced Power and Energy Center Khalifa University UAE

Shortage of fossil fuels around the world, oil prices, and environmental concerns like climate change and air pollution are big challenges for the transport sector. Hybrid electric vehicles (HEVs) are an alternative solution to overcome the above challenge. HEVs require an efficient energy management strategy (EMS) to promote consumption efficiency in the different test cycles. The learning-based algorithms are frequently used to control how efficiently HEVs use energy. The tremendous processing intensity, the extensive data training, and the stringent necessity of accurately predicting the future state of operation all work against the maximum use of these systems. In this study, the model-free reinforcement control mechanism is used for the energy management of HEVs. A deep $\mathbf{Q}$ network (DQN) and deep deterministic policy gradient (DDPG) are utilized to the enhance the learning process and reliability of the EMS framework. Since it is a memoryless random process, the Markovian decision process has been used to formulate the issue. Both learning algorithms are compared with different test driving cycles. The simulation results present that the deep DDPG performance is more reliable and fast converges than the deep $\mathbf{Q}$ learning algorithm.

关键词： Training Renewable energy sources Simulation Transportation Reinforcement learning Robustness Real-time systems

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Accuracy Enhancing Time Limited Model Reduction Framework: Computational Efficiency in Power Systems

Accuracy Enhancing Time Limited Model Reduction Framework: C...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Latif Hira Ambreen Muhammad Imran Samra Zafar Sajid Bashir Department of Electrical Engineering Military College of Signals (MCS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Sir Syed Center for Advanced Studies in Engineering Institute of Technology (SS-CASE-IT) Islamabad Pakistan Henan University School of Computer and Information Engineering Kaifeng China College of Electrical & Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

The analytical and modelling precision required to build modern wind turbines makes them stable, controlled, and observable. This article presents a state-of-the-art method for lowering the model order of a wind turbine driven by a double-fed induction generator, where the rotational speed varies over time. The suggested approach uses state-space representations to set up a comprehensive and systematic framework for analysing wind turbine performance and checking for compliance with grid standards. A time-constrained Gramians-based novel balanced realization approach is employed to reduce the dimensionality of the large-scale wind turbine model. Stability and reduced approximation error are two benefits of the lower-order system. The proposed method also gives researchers an a priori formula for the error-bound, making computations faster and more efficient. Using substantial simulation data, we show that our method improves upon previous methods in optimising models that include wind turbines with changing rotational speeds, taking into account both stability and error computation. To enhance the efficacy and accuracy of their modeling procedures, academics and researchers analyzing wind turbines should incorporate this new methodology.

关键词： Analytical models Accuracy Uncertainty Computational modeling Stability criteria Velocity control Power system stability Approximation error Wind turbines Computational efficiency

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Precision agriculture Technological innovation Adaptation models Accuracy Visual analytics Crops Artificial neural networks Genetics Sustainable development Optimization

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Deep Learning-Based Image Analysis to Track Vehicles

Deep Learning-Based Image Analysis to Track Vehicles

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Disruptive Technologies (ICDT), International Conference on

作者： P Prabakaran R. Jayasudha Mukta Sandhu Sandeep Gupta Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Palwal Haryana India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun India

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches.

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Self-Immolative Electrochemical Redox Substrates: Emerging Artificial Receptors in Sensing and Biosensing

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ACS Measurement Science Au 2024年第2期4卷 163-183页

作者： Manibalan, Kesavan Arul, Ponnusamy Wu, Hsin-Jay Huang, Sheng-Tung Mani, Veerappan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Advanced Membranes and Porous Materials Center (AMPMC) Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia High-Value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology. © 2024 The Authors. Published by American Chemical Society.

关键词： alkaline phosphatase artificial receptors electrochemical mediators electrochemical sensors and biosensors galactosidase hydrogen peroxide neuraminidase organic electrochemistry ratiometric sensors self-immolative sensors

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Optimization of Violet Emitting Quantum Wells with Insertion of AlGaN Layers and Temperature Ramp-up

Optimization of Violet Emitting Quantum Wells with Insertion...

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2022 Compound Semiconductor Week, CSW 2022

作者： Liu, Cheng Seitz, Matthew Dwyer, Matt Kirch, Jeremy Pasayat, Shubhra S Tansu, Nelson Zhang, Jing Mawst, Luke University of Wisconsin-Madison Department of Electrical and Computer Engineering MadisonWI53706 United States Rochester Institute of Technology Electrical and Microelectronic Engineering RochesterNY14623 United States DRS Daylight Solutions MadisonWI53704 United States University of Adelaide School of Electrical and Electronic Engineering AdelaideSA5005 Australia University of Adelaide Institute for Photonics and Advanced Sensing AdelaideSA5005 Australia

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

High-power InGaN-based violet laser diodes (LDs) have received great attention for their applications such as long-haul illuminations and material processing. 1 In order to achieve robust high-power devices, maintaining a high-quality multiple quantum well (MQW) active region is needed but challenging, since the laser structure requires a higher temperature growth for p-type (Al)GaN cladding layer. Some degree of degradation has been observed for InGaN/GaN quantum wells (QWs) after such high temperature growth. A concept of using an AlGaN cap layer to improve the thermal stability of InGaN/GaN MQWs has been developed and utilized in red-blue wavelength regime. 2 – 4 Besides the thermal stability, the insertion of the AlGaN cap also increases the quantum barrier height, reduces electron leakage, prevents indium out-diffusion from the QW. In this work, we extend such studies to the violet-emission regime for potential high-power laser applications.

关键词： Gallium Nitride Quantum Well Cap AlGaN Interlayer Barrier Violet Emitters MOCVD III-nitrides

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Relative Permittivity of Natural Ester Oil-Based Nanofluids with Iron Oxide Nanoparticles

Relative Permittivity of Natural Ester Oil-Based Nanofluids ...

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International Universities Power engineering Conference (UPEC)

作者： Thomas E. Tsovilis Evangelos T. Staikos Alexandros Y. Hadjicostas Zacharias G. Datsios George Litsardakis Eleftheria C. Pyrgioti Aristeidis Bakandritsos George D. Peppas High Voltage Laboratory School of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece Laboratory of Materials for Electrotechnics School of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece Electrical & Computer Engineering Department High Voltage Laboratory University of Patras Greece Regional Centre of Advanced Technologies and Materials Czech Advanced Technology and Research Institute (CATRIN) Palacký University Olomouc Czech Republic Research & Development Department Raycap SA Industrial Area of Drama Drama Greece

The real part of the relative permittivity and the electrical conductivity of a natural ester oil, that serves as a liquid matrix for nanofluids, are measured for a wide frequency and electric field range. The focus of this work is the exploration of the modification of these basic electrical parameters of the liquid matrix after the integration of iron oxide nanoparticles. A series of experiments with the aid of an impedance analyzer, an LCR meter, and a variable-frequency power supply employing two different liquid test fixtures reveal an increase of both real and imaginary parts of relative permittivity with increasing nanoparticles concentration up to 0.5% w/w.

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