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International Symposium on Information Theory and Its Applications, ISITA

作者： Hiroomi Isozaki Yashuhiro Sato Yoshinori Matsuura Masaaki Yamanaka dept. Maritime Safety Technology Japan Coast Guard Academy Kure Japan dept. Telecommunications and Computer Networks Osaka Electro-Communication University Neyagawa Japan

ISBN: (数字)9784885523519

ISBN: (纸本)9798331533335

Our research group aims to realize a low-cost alert system that informs the crews of small-sized ships of approaching floating objects on the sea. We previously proposed a maritime object identification system that uses a Radio Frequency Identification (RFID) tag. However, we concluded that it is difficult to measure the received signal strength, due to the effects of waves and other factors, on the sea, and the difficulty in miniaturizing the transmitter and the receiver devices. In this paper, we discuss the construction of a high-precision positioning system necessary to measure a received signal strength on the sea. We decided to use a ESP32, a low-power-consumption Wi-Fi controller, to elucidate characteristics of the received signal strength of Wi-Fi on the sea, which is necessary for a proximity warning system. Finally We make a prototype of the proximity warning system.

关键词： Accuracy Radio transmitters Sea measurements Prototypes Receivers Alarm systems Position measurement Mathematical models RFID tags Wireless fidelity

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Optimized ECG Signal Compression and Security for Efficient Cardiac Abnormality Detection 4

Optimized ECG Signal Compression and Security for Efficient ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Roy, Vandana Baghel, Abhishek Singh Jain, Sapan Kumar Mohanty, Sribidhya Srinivasarao, G. Bhatt, Anushka Ggits Dept of Elrectronics Communication M.P Jabalpur India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal India Gyan Ganga Institute of Technology & Sciences Dept of Cse MP Jabalpur India Graphic era Deemed to be University India Chalapathi Institute of Technology Dept. of Cse AP Guntur India Ggits MP India

ISBN: (纸本)9798331518981

Protecting biological data during dissemination, this ECG waves, is becoming more important, and this work is relevant to that requirement. It is of the extreme importance to guarantee the security of transferred medical information as the amount of data sent over the web continues to rise. It proposes a watermarking approach for compressing the ECG waves in addition to securing the information. By including subject information in ECG waves in a way that doesn't change their diagnostic usefulness, watermarking makes signal blocking possible. The frequency region and the domain of space are the two primary categories of watermarking methods. Improving signal quality to accept watermarking is also covered in the chapter using the Fourier Decomposition Method (FDM). In a setting with a large amount of ECG information moving around, FDM is used to accomplish realistic data compression as well, which is similar to decoding. This prevents the expansion of storage and bandwidth requirements while yet achieving a viable signal. The proposed technique beats its predecessors in relations of SNR, PRD, and compression coefficient, as shown by simulation results. As a whole, this approach improves the security of ECG signals and data minimisation for hospitals that are handled electronically. © 2024 IEEE.

关键词： Electrocardiography

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Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm

Energy Efficient Coverage Optimization in Mobile Wireless Se...

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International Conference on Computing and Networking technology (ICCNT)

作者： Ankit Gupta Mamatha K. M. Kiran M. Dept. of Information Technology National Institute of Technology Karnataka Surathkal India Dept. of Computer Science and Engineering Viseveshvaraya Technological University Belgaum India

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node’s energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adept.y authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms.

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The Dynamic Mode Construction of Mixed English Learning Based on Reinforcement Learning 2

The Dynamic Mode Construction of Mixed English Learning Base...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Liu, Yongfen Tang, Weihua Pareek, Piyush Kumar Qujing Medical College Yunnan655000 China Dept. of Computer Science Engineering AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9781665491112

With the development of new technologies and theories, the Internet has driven the changes of learning methods. With the support of various online learning platforms and social software, individual learning activities are gradually effectively organized and connected. Learning is developing in the direction of interaction, collectivity and socialization, and individual knowledge converges into group wisdom in an explicit or implicit way. The research on the construction of College English mixed learning dynamic model based on learning analysis is to study the process and results of Constructing College English dynamic model. This study aims to understand its effectiveness in improving students' performance, satisfaction and learning motivation. The main focus of this study is to investigate the effectiveness of using different types of feedback at the same time (such as formative feedback, summative feedback or two forms) to teach and evaluate students' progress. © 2022 IEEE.

关键词： Dynamic models

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Integrating NIR Hyperspectral Techniques with Neural Network for Barley Seeds Classification

Integrating NIR Hyperspectral Techniques with Neural Network...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Anuj Kumar Shveta Mahajan Dept. of Computer Science and Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Nondestructive identification of seed varieties from different origins is crucial in optimizing crop improvement, plant growth, and advancement in breeding methods. Evaluating varietal purity is essential for seed quality. Traditional barley seed classification relies on visual inspection, which is a subjective and error-prone process and is time-consuming. Moreover, chemical testing involves the breaking of seeds which highlights the necessity for exploring non-destructive methods. The integration of Near-infrared (NIR) spectroscopy with neural networks offers a promising alternative. In this study, we have used a hyperspectral imaging dataset consisting of 34 different Indian barley varieties (1008 per variety). Six spectral pre-processing techniques were used to extract and pre-treat the mean reflectance spectrum associated with each seed, namely Savitzky-Golay (SG) smoothing, SG first derivative, SG second derivative, detrending, standard normal variate (SNV), and multiplicative scatter correction (MSC). Further, we have used five different classifiers: K-nearest neighbors (KNN), partial least squares discrimination analysis (PLS-DA), artificial neural networks (ANN), support vector machine techniques (SVM), and convolutional neural networks (CNN). CNN demonstrated superior classification accuracy when implementing it on unprocessed data, whereas the ANN model outperformed it when combined based on the SG2 pre-processing method. Their respective classification accuracy rates were $98.44 \%$ and $98.88 \%$. The current study examined the use of an ANN model coupled with the NIR-HSI technology to accurately, quickly, and nondestructively classify different types of barley seeds.

关键词： Support vector machines Visualization Spectroscopy Accuracy Data models Seeds (agriculture) Convolutional neural networks

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Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM

arXiv

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arXiv 2024年

作者： Kim, Minsoo Dvorkin, Vladimir Kim, Jip Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR forecast remains challenging due to weather uncertainty. To reliably predict DLRs, we propose a new probabilistic forecasting model based on line graph convolutional LSTM. Like standard LSTM networks, our model accounts for temporal correlations between DLRs across the planning horizon. The line graph-structured network additionally allows us to leverage the spatial correlations of DLR features across the grid to improve the quality of predictions. Simulation results on the synthetic Texas 123-bus system demonstrate that the proposed model significantly outperforms the baseline probabilistic DLR forecasting models regarding reliability and sharpness while using the fewest parameters. Copyright © 2024, The Authors. All rights reserved.

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A Novel Approach Towards Gas Turbine Emission Reduction by Using Neural Networks

A Novel Approach Towards Gas Turbine Emission Reduction by U...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Roy, Atanu Pramanik, Sabyasachi Mitra, Kalyan Chakraborty, Manashi Sarisa West Bengal 743368 India Haldia Institute of Technology Dept. of Computer Science and Engineering Hatiberia West Bengal Haldia721657 India Indian Maritime University Dept. of Maritime Studies P - 19 Taratalla Road Kolkata700088 India Sarisa West Bengal 743368 India

ISBN: (纸本)9798350313987

Optimizations of controlling parameters are the key factors to achieve effectual output and emission reduction from machinery running. This study relates prediction technology for gas turbine's (GT's) running optimization and emission reduction. The tool can identify upcoming disputes and alter the engine control setting to achieve highly efficient running operations. This research objects to get a machine learning approach to predict GT performance on emissions. The forecasting capabilities of this system are based on real-time data collected from a GT plant. A neural network is employed to analyze and predict the emissions of exhaust pollutants, primarily carbon monoxide and nitrogen oxides. The model performance is studied by considering the values of MSE, MAE, and residuals. The model's accuracy and precision are revealed by its low MSE (0.0035) and MAE (0.043). This approach is a cost-effective way to gage emissions accurately and can be used to confirm compliance with environmental regulations. It is also a helpful tool for monitoring the health of the turbine and identifying any potential issues with its operation. © 2023 IEEE.

关键词： Convolutional Neural Networks Emission Gas Turbine Predictive Emission Monitoring

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Face Recognition from Partial Face Data and Fraud Detection

Face Recognition from Partial Face Data and Fraud Detection

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Thavisala Veneela Sonti Vivek Sai Yeturu Vamshidhar Reddy Vishnu Karthik Varma Kothapally dept. of Assistant professor/computer science and engineering B V RAJU INSTITUTE OF TECHNOLOGY Narsapur Medak Telangana India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Face recognition is a critical era with applications starting from safety to personalization. However, traditional face Recognition systems frequently conflict with partial face information, which can occur because of occlusions or picture excellent g troubles. I t consists of a novel technique to stand reputation using partial face statistics, specializing in fraud detection scenarios. This approach leverages deep getting to know strategies, mainly convolutional neural networks (CNNs), to extract functions from partial face snap shots. These features are then used to create a compact illustration of the face, which is robust to occlusions and noise. A similarity measure is employed to evaluate those representations, enabling the identification of individuals even from partial face records. In the context of fraud detection, it can be used to verify the identity of people based totally on partial face data captured from surveillance cameras or different sources. Experimental consequences on benchmark datasets show the effectiveness of the proposed approach, outperforming conventional face recognition procedures in eventualities with partial face information. It gives a promising solution for improving face recognition accuracy in challenging conditions, with capability packages in security, regulation enforcement, and personal identification systems. It also analyzes facial images with masks using Multi-Task Cascaded Convolutional Neural Networks (MTCNN). FaceNet algorithm is also used which adds more embeddings and verifications to face recognition. Support vector Machine (SVM) algorithm labels the data sets to produce a reliable prediction probability and along with that it also detects the frauds.

关键词： Support vector machines Accuracy Face recognition Surveillance Prediction algorithms Vectors Robustness Fraud Safety Convolutional neural networks

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Wild Animal Recognition: A Vocal Analysis

Wild Animal Recognition: A Vocal Analysis

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： P Sudharshan Duth N Manohar R Suresha M Priyanka Tian Jipeng Dept. of Computer Science Amrita Vishwa Vidyapeetham Amrita School of Computing Mysuru Campus Mysuru India Computer Science College Zhongyuan University of Technology Zhengzhou Henan Province China

The Animal Voice Recognition system has become an important application in the field of animal sound retrieval and classification; it is highly beneficial for applications involving bio-acoustics and audio retrieval. Currently existing work lacks wild animal vocal dataset and requires appropriate vocal feature engineering mechanism even in the presence of varied background noise is necessary to conduct vocal analysis for efficient recognition. In this research work, initially wild animal vocal database was created, then fusion of vocal features were extracted for developing the animal recognition system based on Multiple vocal features such as the Mel-Frequency Cepstral Coefficients (MFCC), Zero-Cross-Rate (ZCR), Spectral Roll Off, Spectral Centroid and Spectral Contrast features. Further, PCA is used to extract the transformed discriminating features. Finally, the performance of this model has been analyzed by extracting features of the collected dataset using number of conventional classifiers to identify the specific animal. Among which SVM and Random Forest classifier obtained 96.89% and 97.46% recognition accuracy respectively and it proves to be cost-effective.

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LLM4PlC: Harnessing large Language Models for Verifiable Programming of PlCs in Industrial Control Systems

LLM4PlC: Harnessing large Language Models for Verifiable Pro...

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IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP)

作者： Mohamad Fakih Rahul Dharmaji Yasamin Moghaddas Gustavo Quiros Araya Oluwatosin Ogundare Mohammad Abdullah Al Faruque Dept. of Electrical Engineering and Computer Science University of California Irvine CA USA Siemens Technology Princeton NJ USA

ISBN: (数字)9798400705014

ISBN: (纸本)9798350352177

Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings. The pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainabil-ity, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for Industrial Control Systems (ICS) op-erated by Programmable Logic Controllers (PLCs). We propose LLM4PLC, a user-guided iterative pipeline leveraging user feed-back and external verification tools - including grammar checkers, compilers and SMV verifiers - to guide the LLM's generation. We further enhance the generation potential of LLM by employing Prompt Engineering and model fine-tuning through the creation and usage of LoRAs. We validate this system using a FischerTech-nik Manufacturing TestBed (MFTB), illustrating how LLMs can evolve from generating structurally-flawed code to producing verifiably correct programs for industrial applications. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. The proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open research, we share the complete experi-mental setup, the LLM Fine-Tuning Weights, and the video demonstrations of the different programs on our dedicated webpage 1 1 https://***/***/llm4plc/home.

关键词： Codes Industrial control Pipelines Programmable logic devices Programming Reliability engineering Manufacturing

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