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Effects of Ni/Cu Bilayer Structure on The Optical and electrical Characteristics of Al-Doped ZnO Thin-Film

Effects of Ni/Cu Bilayer Structure on The Optical and Electr...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： K. Kacha H. Ferhati A. Bendjrad F. Djeffal A. Benhaya LEA University of Batna 2 Batna Algeria ISTA University of Larbi Ben M'hidi Oum El Bouaghi Algeria

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

The present study aims at investigating the impact of bimetal Ni-Cu intermediate layer on the performances of thin-film AZO-based structure, as alternative reliable broadband optical sensor. In this regard, this paper provides an in-depth experimental analysis of the Optoelectrical properties of AZO-Ni-Cu-AZO structures deposited on glass substrates for broadband (UV-Vis-NIR) photosensing applications. Employing RF Sputtering for the deposition process, we investigate the integration of Nickel (Ni) to enhance the reliability properties, particularly thermal and electromagnetic radiation effects, within the Aluminum Zinc Oxide (AZO) matrix. Moreover, the prepared structure (AZO-Ni-AZO) is re-engineered by introducing an ultrathin Copper (Cu) layer, in order to enhance the electrical properties and flexibility behavior of the sensor. The investigated AZO-Ni-Cu-AZO structure exhibits improved photodetectivity, which makes it as an alternative for developing advanced optoelectronic devices for reliable, flexible and low-cost thin-film technology.

关键词： Radio frequency Zinc oxide Photodetectors Nickel Reliability Optical sensors Optical films Copper Substrates Sputtering

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Implementation of Trajectory control Algorithm in a Dynamic Environment 4

Implementation of Trajectory Control Algorithm in a Dynamic ...

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4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Krishna Kant Sharma, Surya Renu Kumar, Saroj Shukla, Anupam Sharma, Prakash Chandra Manipal Universiy Jaipur Department of Mechatronics Engineering Rajasthan Jaipur303007 India Saroj Institute of Technology and Management Department of Electronics and Communication Engineering Utter Pradesh Lucknow226002 India Department of Electronics and Communication Engineering Mullana India OP Jindal University Department of Mechanical Engineering Chhattisgarh Raigarh496109 India Technocrats Institute of Technology and Science Department of Electrical Engineering Madhya Pradesh Bhopal India Manipal University Jaipur Department of Information Technology Rajasthan Jaipur303007 India

ISBN: (纸本)9798350306118

Trajectory planning and obstacle negotiation rate are two fundamental problems in mobile robot locomotion. The proposed work addresses the problem of locomotion in mobile robots using Radial Basis Function Neural Network (RBFNN). The RBFNN controller initializes if the sensors detected obstacles inside the environment. The proposed RBFNN navigational control algorithm provides smooth and continuous steering angle commands to the robot using sensory reading. Distance received from the sensors for obstacle negotiation and the target-seeking rate is taken as the input parameter of the proposed control algorithm. The output of the control algorithm is the steering angle and optimum trajectory length toward the target. To show the results in terms of simulation, MATLAB and CoppeliaSim GUI platform has been used. To validate the simulation results, a real-time experiment has been proposed in the same environment. The error for path length and navigational time is less than 5% and has been recorded in terms of both environments (simulation and real-time experiment). The simulation and experimental results established the stability check for the proposed control algorithm. © 2023 IEEE.

关键词： Navigation

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Reduction of Interconnected Hybrid Power System Using Direct Truncation and Routh Array Method

Reduction of Interconnected Hybrid Power System Using Direct...

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2023 IEEE international conference on Energy Technologies for Future Grids, ETFG 2023

作者： Sharma, V.P. Meena, V.P. Singh, V.P. Murari, Krishna Mathur, Akhilesh Malaviya National Institute of Technology Department of Electrical Engineering Rajasthan Jaipur302017 India The University of Toledo Electrical Engineering and Computer Science Department ToledoOH43606 United States

ISBN: (纸本)9781665471640

Model order reduction (MOR) is a computational and mathematical technique to simplify complex dynamic systems while preserving essential characteristics. This technique is used in circuit simulation, structural mechanics, and control systems engineering. The main advantages of MOR techniques are to analyze and simulate complex systems with essential accuracy, faster design iterations, improved system behavior, and reduced computational costs. In this paper, model order reduction of interconnected hybrid power systems is achieved by mixed method approaches of Routh-array and *** simplified technique of proposed work is very new and useful for model order reduction of power system. Routh-array method is used for order reduction of denominator polynomial and direct-truncation method is approached for order reduction of numerator polynomial. To define the mixed method approaches on proposed work, the thirteen-order transfer function of original inter-connected hybrid power system is reduced to sixth, fifth, fourth, third, and second order respectively. Different outputs like step response, impulse response, Bode response, and Nyquist response are also plotted for the proposed work. The main advantage of the mixed method approach is that it always gives a simplified and stable reduced lower-order system while securing essential characteristics. The final output of this proposed work is very effective and accurate. © 2023 IEEE.

关键词： aqua electrolyzer automatic load frequency control automatic voltage control battery storage power system diesel power generator fuel cell hybrid compound power system wind power conversion system

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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3D Warehousing: Enabling Intelligent Warehousing Visualization Based on *** 1

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10th international conference on Signal and Information Processing, Network and Computers, ICSINC 2022

作者： Shan, Wenqian Wan, Wenbo Chen, Anyi Ren, Leichao Sun, Jiande Fang, Meng Zhan, Yantong School of Information Science and Engineering Shandong Normal University Jinan China Shandong Port Land-Sea International Logistics Group Co. Ltd. Jinan China Shandong Luruan Digital Technology Co. Ltd. Smart Energy Branch Jinan China

ISBN: (数字)9789811999680

ISBN: (纸本)9789811999673

In the industry 4.0 era, the 3D visualization and real-time automatic monitoring of intelligent warehousing supported are becoming increasingly important. In intelligent warehousing system, 2D chart mode is key component for visualization design. However, the requirements of 3D panoramic scene for warehousing data and the gap between interactive operation and data visualization challenge the development of visual warehousing. In order to solve the problems of untimely information sharing in the warehousing system, the inability to clearly and intuitively display the stockpiling of goods, and the difficulty of overall management and control of the warehouse. In this paper, we present a ***-based warehousing system which can enable 3D warehousing visualization from the perspective of intelligent management. The proposed system includes five key components: warehousing data visualization, device data visualization, security monitoring visualization, visualization of roaming in the warehouse area, and visualization of inventory increases and decreases. Finally, the core technologies are identified to provide a visual depiction for warehousing function, particularly in intelligent warehousing visualization applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Visualization

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Test Circuit Design for Accurately Characterizing Cells' Output Currents in a Read-Decoupled 8T SRAM Array for computing-in-Memory Applications 35

Test Circuit Design for Accurately Characterizing Cells' Out...

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35th international conference on Microelectronic Test Structure, ICMTS 2023

作者： Hong, Hao-Chiao Lin, Long-Yi Chen, Bo-Chang Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan Novatek MicroElectronics Corp. Hsinchu Taiwan

ISBN: (纸本)9798350346534

computing-in-memory (CIM) is a promising technique for energy-efficiently conducting the massive amount of required multiply-And-Accumulate (MAC) calculations in neural networks (NNs). The read-decoupled 8T (RD8T) SRAM cell is popular in the CIM designs because of being read disturbance free. However, local process variations may lead significant errors to the CIM results. This work proposes an accurate on-chip test circuit design for characterizing the output current of every RD8T SRAM cell in a 8-kb RD8T SRAM array fabricated in 90nm CMOS. The experimental results show the detailed and accurate spatial distribution of the RD8T cells which helps optimize the CIM circuit design. © 2023 IEEE.

关键词： Static random access storage

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods 9

Classification of Attention Performance Post-Longitudinal tD...

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9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Laboratory Mandi India Indian Institute of Technology Mandi School of Computing And Electrical Engineering Mandi India Institute of Nuclear Medicine And Allied Sciences Cognitive Control And Machine Learning Group Delhi India

ISBN: (纸本)9798350394474

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. © 2024 IEEE.

关键词： Electroencephalography

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Current Distribution Test Analysis of Redundant Switching Power Supply in control System of Nuclear Power Plant 6

Current Distribution Test Analysis of Redundant Switching Po...

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6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： He, Weichao Niu, Yuguang Dai, Shuangfeng Du, Ming North China Electric Power University Beijing China China Techenergy Co. Ltd Beijing China Beijing Ander Medical Co. Ltd Beijing China

ISBN: (纸本)9798350374315

Switching power supply with higher efficiency and reliability is more and more applied in the design of digital instrument control system of nuclear power station. The design scheme of using a single power supply will cause a high power density for a single power module to process, and the power module will also face a large electrical stress during operation. The technical scheme of expanding single power supply brings more difficulties to the selection of power components, the improvement of switching frequency and power density of power module. At the same time, if a single power supply fails, the entire system may crash. Therefore, the research on the capacity expansion and redundancy technology using multiple small-capacity switching power supplies is becoming more and more extensive. This paper mainly focuses on the typical application of switching power supply distribution circuit in digital instrument control system engineering, and analyzes and describes the design of power supply expansion and reliability of power supply redundancy. At the same time, combined with the test analysis, the influence of the current distribution and current uneven problem of power module generated by this design on the reliability of distribution circuit is analyzed, and the subsequent improvement suggestions are put forward. The views presented in this paper are of guiding significance to the design of the switching power distribution system and the operation and maintenance of the switching power supply system of the subsequent control system of nuclear power plant, and can improve the stable operation of the control system of nuclear power plant in the long run. © 2024 IEEE.

关键词： Nuclear power plants

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Research on Power Optimization of Massive Machine Type of Communication Based on 5G Big Connection 4

Research on Power Optimization of Massive Machine Type of Co...

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4th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2023

作者： Zheng, Guoquan Liu, Yan Zhu, Enguo Zhang, Hailong Wang, Chaoliang Institute of Electrical Engineering Chinese Academy of Sciences Beijing China State Grid Zhejiang Electric Power Corporation Hangzhou China

ISBN: (纸本)9798350343113

In this paper, a resource allocation algorithm based on capacity maximization for massive Machine Type of Communication (mMTC) is studied. Taking LTE single cell as the background, considering the coexistence of LTE cellular user devices and mMTC devices, a two-layer network model is established in mMTC scenario, and mMTC gateway is introduced to analyze the transmit power control strategy of mMTC gateway (mMTCG) in mMTC scenario. On this basis, the bandwidth and power resource allocation problem in mMTC scenario is studied by using deep reinforcement learning algorithm to maximize the system capacity. The simulation results show that the proposed algorithm can improve the capacity of the system, and the power control is more consistent with the characteristics of mMTC scenario. © 2023 IEEE.

关键词： Reinforcement learning

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