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IEEE Photonics Conference (IPC)

作者： Muhammad Mahmudul Hasan Nezih Pala Michael Shur Electrical and Computer Engineering Florida International University Miami USA Dept. of Electrical Computer and Systems Engineering and the Dept. of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy NY USA Electronics of the Future Inc. Vienna VA USA

This work provides a nondestructive terahertz testing technique for the detection of counterfeit, damaged, or faulty ICs by measuring the response to incident terahertz or subterahertz radiation at the circuit pins an...

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Sub-Terahertz Plasma Wave Generation by Dyakonov-Shur Instability in p-Diamond TeraFET

Sub-Terahertz Plasma Wave Generation by Dyakonov-Shur Instab...

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IEEE Photonics Conference (IPC)

This study investigates sub-terahertz (THz) emission from p-diamond TeraFET using the Dyakonov-Shur instability phenomenon. Results indicate that a minimum carrier drift velocity is required to form a continuous plasm...

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Sallen-Key Dual Channel Low-Pass Filter with Tunable Cutoff Frequency

Sallen-Key Dual Channel Low-Pass Filter with Tunable Cutoff ...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics engineering, SIBIRCON

作者： Darya Yu. Denisenko Nikolay N. Prokopenko Nikolay V. Butyrlagin Dept. of Information Systems and Radio Engineering DTSU Dept. of Automatic Control Systems SFU Rostov-on-Don Russia Dept. of Information Systems and Radio Engineering DTSU Institute for Design Problems in Microelectronics of RAS Rostov-on-Don Zelenograd Russia Dept. of Information Systems and Radio Engineering DTSU Rostov-on-Don Russia

ISBN: (纸本)9781665464819

A two-channel Sallen-Key low-pass filter on three non-inverting amplifiers is studied, which has the property of unrelated digital set of the upper cut-off frequency at constant index of the transmission coefficient in the bandwidth range. The restructuring of the upper cutoff frequency and the attenuation of the pole is carried out by modify the resistors’ resistance of one in the upper channel of the low-pass filter. It is shown that a non-inverting amplifier in the lower channel of the low-pass filter can have a small value of the systematic zero bias voltage component, what is important for the operation of the low-pass filter at the input of analog-to-digital converters. It has been established that due to the construction of a low-pass filter according to a two-channel architecture, the requirement for the magnitude of the output static voltage of the upper channel is removed. The base equations of the new low-pass filter scheme are given and the summary modeling in the Micro-Cap was pictured.

关键词： Resistance Operational amplifiers Time-frequency analysis Systematics Cutoff frequency Capacitors Low-pass filters

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Design and Simulation of Short Circuit Fault Detection in DC Power Supply using Daubechies Wavelet and Convolutional Neural Networks

Design and Simulation of Short Circuit Fault Detection in DC...

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International Conference on Mechatronics (ICOM)

作者： Ralph Laurence G. Visaya Ralph Gerard B. Sangalang Department of Electronics Engineering Batangas State University- The Nat'l Eng'g University Batangas City Philippines Dept. of Electronics Engineering Dept. of Computer Engineering Tanauan City College Electronics Systems Research Center Batangas State University- The Nat'l Eng'g University Batangas City Philippines Dept. of Electronics Engineering Batangas State University- The National Engineering University Philippines

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Power supply systems are an essential component of current infrastructure and must operate reliably. Short-circuit defects pose a danger to power supply stability and may potentially result in equipment damage and system outages. This research presents a novel approach for predicting and detecting short circuit faults in power supply systems by combining the Daubechies wavelet transform with convolutional neural networks (CNNs). The researcher will make use of MATLAB Simulink and focuses on the design and simulation with short circuit faults introduced at known points of linear power supplies, boost converters, and buck converters. The voltage and current from the simulations were exported and then fed to Python with the use of PyWavelet for mathematical analysis. The researcher will make use of the db4 transform for the Daubechies wavelet. This decomposes the signals into detail coefficients at various scales, which are then converted into 1D vectors to be flattened and used for training with CNN. Based on the data and results, this shows 94.22% accuracy and 94.67% mAP (mean average precision) at 35 epochs. The researchers concluded that this study was feasible and reliable for implementation in real-world scenarios.

关键词： Wavelet transforms Buck converters Accuracy Power supplies Software packages Voltage Vectors

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Developmental Scaffolding with Large Language Models

arXiv

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arXiv 2023年

作者： Celik, Batuhan Ahmetoglu, Alper Ugur, Emre Oztop, Erhan Computer Engineering Dept. Bogazici University Istanbul Turkey Computer Science Dept. Ozyegin University Istanbul Turkey Symbiotic Intelligent Systems Research Center Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan

Exploration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding to accelerate skill and knowledge acquisition. In developmental robotics, this approach has been adopted often by having a human acting as the source of scaffolding. In this study, we investigate whether Large Language Models (LLMs) can act as a scaffolding agent for a robotic system that aims to learn to predict the effects of its actions. To this end, an object manipulation setup is considered where one object can be picked and placed on top of or in the vicinity of another object. The adopted LLM is asked to guide the action selection process through algorithmically generated state descriptions and action selection alternatives in natural language. The simulation experiments that include cubes in this setup show that LLM-guided (GPT3.5-guided) learning yields significantly faster discovery of novel structures compared to random exploration. However, we observed that GPT3.5 fails to effectively guide the robot in generating structures with different affordances such as cubes and spheres. Overall, we conclude that even without fine-tuning, LLMs may serve as a moderate scaffolding agent for improving robot learning, however, they still lack affordance understanding which limits the applicability of the current LLMs in robotic scaffolding tasks. © 2023, CC BY.

关键词： Scaffolds

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On the Trade-Offs Between Accuracy, Privacy, and Resilience in Average Consensus Algorithms

On the Trade-Offs Between Accuracy, Privacy, and Resilience ...

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IEEE Conference on Decision and control

作者： Guilherme Ramos André M. H. Teixeira Sérgio Pequito Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Sweden

There can be none. In this paper, we address the problem of a set of discrete-time networked agents reaching average consensus privately and resiliently in the presence of a subset of attacked agents. Existing approaches to the problem rely on trade-offs between accuracy, privacy, and resilience, sacrificing one for the others. We show that a separation-like principle for privacy-preserving and resilient discrete-time average consensus is possible. Specifically, we propose a scheme that combines strategies from resilient average consensus and private average consensus, which yields both desired properties. The proposed scheme has polynomial time-complexity on the number of agents and the maximum number of attacked agents. In other words, each agent that is not under attack is able to detect and discard the values of the attacked agents, reaching the average consensus of non-attacked agents while keeping each agent's initial state private. Finally, we demonstrate the effectiveness of the proposed method with numerical results.

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Online Complaint Management System using Image Recognition

Online Complaint Management System using Image Recognition

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International Conference on Communication and Electronics systems (ICCES)

作者： S. Balakrishnan J. Janet Rohith T Sakthivel R Sanjaiy Kumar T N Dept. of Computer Science and Business Systems Sri Krishna College of Engg. and Technology Coimbatore Tamilnadu India Dept. of Computer Science and Engineering Sri Krishna College of Engg. and Technology Coimbatore Tamilnadu India

Clean and safe environments are the responsibility of the government. Currently, people have to lodge their complaints with the Municipal Corporation, and then workers take time to respond to them. In this paper we have made a platform to overcome the communication gap between the user and the municipal corporation. This research study proposes an online complaint management system in a web application, where the citizen can lodge a complaint by taking a picture. After the picture being uploaded, image recognition is used to automatically detect the problem raised by the user and Google map API to detect the location automatically. As soon as the problem has been resolved, the municipal corporation must upload a completed status picture to verify that the work has been completed. The aim of this application is to overcome the communication gap between user and Municipal Corporation, to simplify the process of raising complaints and to maintain a clean and safe environment.

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A Model for Intelligible Interaction Between Agents That Predict and Explain

arXiv

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arXiv 2023年

作者： Baskar, A. Srinivasan, Ashwin Bain, Michael Coiera, Enrico Dept. of Computer Science Information Systems BITS Pilani Goa Campus India Dept. of Computer Science Information Systems and APPCAIR BITS Pilani Goa Campus India School of Computer Science Engineering University of New South Wales Australia Centre for Health Informatics Macquarie University Sydney Australia

Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents. However, relatively little attention has been paid to the interaction between people and ML systems. In this paper we view interaction between humans and ML systems within the broader context of communication between agents capable of prediction and explanation. We formalise the interaction model by taking agents to be automata with some special characteristics and define a protocol for communication between such agents. We define One- and Two-Way Intelligibility as properties that emerge at run-time by execution of the protocol. The formalisation allows us to identify conditions under which run-time sequences are bounded, and identify conditions under which the protocol can correctly implement an axiomatic specification of intelligible interaction between a human and an ML system. We also demonstrate using the formal model to: (a) identify instances of One- and Two-Way Intelligibility in literature reports on humans interacting with ML systems providing logic-based explanations, as is done in Inductive Logic Programming (ILP);and (b) map interactions between humans and machines in an elaborate natural-language based dialogue-model to One- or Two-Way Intelligible interactions in the formal model. © 2023, CC BY.

关键词： Inductive logic programming (ILP)

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Comparative Analysis of MLP and CNN-LSTM Models for Solar Power Generation Forecasting 9

Comparative Analysis of MLP and CNN-LSTM Models for Solar Po...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Jannah, Nurul Hanifah, Mohd Shahrin Abu Gunawan, Teddy Surya Zabidi, Suriza Ahmad Yusoff, Siti Hajar Sapihie, Siti Nadiah Mohd International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia National Petroleum Limited Petronas Research Sdn Bhd Bandar Baru Bangi43000 Malaysia

ISBN: (纸本)9798350343380

Solar energy, a cornerstone of renewable energy, for optimal grid integration and management, requires precise forecasting. Photovoltaic (PV) forecasting must be accurate to ensure energy stability and maximize resource utilization. This study compares Multi-Layer Perceptron (MLP) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models for forecasting solar power generation. Both models were trained with 13 features using an open-source dataset from 10 PV sites in Hebei Province, China, spanning 300 days (2018-07-01 to 2019-06-13). The CNN-LSTM was configured with 50 epochs and particular hyperparameters. CNN-LSTM demonstrated superior performance, with Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values of 0.088, 0.051, and 0.227 versus MLP's 0.260, 0.156, and 0.395. The findings demonstrate CNN-LSTM's potential for enhancing solar power forecasting and facilitating the management of renewable energy sources. © 2023 IEEE.

关键词： Long short-term memory

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