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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

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A Diagnosis Model Based on Federated Learning for Lung Cancer Classification 2nd

A Diagnosis Model Based on Federated Learning for Lung Cance...

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2nd International Conference on science, engineering Management and Information technology, SEMIT 2023

作者： Babu, Ann Mary Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031722837

Lung cancer, causing a significant number of fatalities annually, is a prominent contributor to global mortality rates among individuals of both genders, with an estimated five million cases resulting in death each year. The utilization of Computed Tomography (CT) scans contribute valuable data for the detection of lung cancer. The main objectives of this study are to diagnose malignant lung nodules in the provided input lung images and to classify lung nodule based on its level of severity. This study proposes a solution wherein locally computed updates are combined to stimulate the learning of a shared model, while the training data remains dispersed throughout mobile devices. Federated Learning (FL) is an advanced technology that operates in a collaborative and decentralized manner, with the primary objective of preserving privacy. Its purpose is to address the obstacles posed by data silos and the sensitivity of data. Therefore, the FL approach is employed for the classification of medical images in collaborative and real-world circumstances. A new approach is proposed in this paper, where the model is trained on data distributed across multiple institutions, and the training process occurs locally at each institution. The performance of the proposed method in terms of accuracy, specificity and sensitivity is evaluated and compared with that of the existing models such as Googlenet, Alexnet, and SVM. The proposed method performs comparably better in all three performance measures indicating that it works better than other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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Intelligent Systems for Data Driven Agriculture: Enhancing Farmer Productivity Through Automation and Artificial Intelligence

Intelligent Systems for Data Driven Agriculture: Enhancing F...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Lokesh, S. Madhavan, Arvind Ramanathan, R.M. Prakash Anand, Krteen PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350378177

Numerous obstacles endanger the livelihoods of smallholder farmers and global food security. Crop output losses due to plant diseases alone are estimated to be between 15% to 25% every year. However, farmers' capacity to recognize and treat illnesses in a timely manner is hampered by things like a lack of agronomic expertise, limited access to actionable insights, and information gaps. The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection. Thus, enabling farmers to identify the infection using their smart phones and obtain crop - fertilizer recommendations for the crops to be treated in the farmer's area based on historical data analysis of yield-influencing elements such as geography, weather, and soil. The proposed system will employ Generative Artificial Intelligence, such as ChatGPT's natural language processing and generation capabilities, to engage in conversational interactions with farmers. The work achieves close to 95% accuracy after utilizing 30 epochs in the area of disease detection and 97% accurate models are achieved for fertilizer outputs. These features along with it's high accuracy can assist farmers in making informed decisions, utilising the data obtained to reduce crop output losses. © 2024 IEEE.

关键词： Smart agriculture

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A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Srichandan, Suresh Kumar Jena, Sudarson Majhi, Santosh Kumar Mishra, Kaushik Veer Suredra Sai University of Technology Department of Computer Science and Engineering Odisha Sambalpur Burla India Sambalpur University Institute of Information Technology Sambalpur Univesity Department of Computer Science and Engineering Odisha India Guru Ghasidash Vishwavidyalaya Department of Computer Science and Information Technology C. G. Birlaspur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350379525

The Internet of Things (loT) is rapidly advancing, but its limited resources are often supplemented by integrating fog computing to address these constraints. Despite this, fog devices encounter challenges such as heterogeneity, widespread distribution, dynamic nature, and resource limitations. To tackle these issues, a robust task scheduling strategy is crucial for efficiently utilizing fog resources and enhancing Quality of Service (QoS). Moreover, tasks generated are dynamic in nature with disparate requirements such as deadline, cost, priority, etc. Fog resources could be utilized effectively with proper mapping of tasks based on ingress traffic on each node to meet the requirements. This research formulates the task mapping problem (TMP) with the goal of minimizing cost while improving QoS by reducing response times and deadline violations for loT tasks. It uses SVM (Support Vector Machine) to predict fog node traffic and categorizes nodes into peak-traffic and low-traffic groups. The Quantum-inspired PSO-based TM Algorithm (QPSO) has been implemented to map the tasks on these nodes. Simulation results reveal that the proposed method offers significant improvements, achieving up to 19.66% better response times, 30% cost reduction, and a 26% deadline violation rate i.e., an increase in meeting task deadlines compared to other approaches. © 2024 IEEE.

关键词： Cost reduction

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Enhanced and Efficient Guiding Template Design for Lamellar DSA with Graph Monomorphism

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 4329-4333页

作者： Ciou, Yi-Sian Shih, An-Jie Fang, Shao-Yun Liu, Yi-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Lamellar directed self-assembly (DSA) technology in combination with the self-aligned via (SAV) process has emerged as a novel and promising choice for via/contact layer fabrication, where a via between Metal- x and Metal- (x+1) is generated at the intersection of a wire segment on Metal- (x+1) and a rectangular and perpendicular guiding template. Compared to the highly investigated cylindrical DSA, lamellar DSA does not suffer from the overlay error of generated holes and thus can have better yield, and it also benefits from the better flexibility of various via pitches a single template can produce. A state-of-the-art work has addressed the guiding template design problem for the new process, and an integer linear programming (ILP) formulation that can optimally solve the problem as well as a simple heuristic approach are proposed. However, solving the ILP formulation is time consuming, and the heuristic method suffers from unignorable degradation in solution quality due to several algorithm deficiencies. In this article, we propose an enhanced and efficient template design algorithm flow for the lamellar DSA and SAV process by using graph monomorphism for pattern conflict check in multiple patterning lithography. Experimental results show that the proposed flow can greatly reduce the numbers of conflicts and short templates and is almost as efficient as the existing heuristic approach. © 1982-2012 IEEE.

关键词： Integer programming

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Prevent the Counterfeit of Pharmaceutical Products by Using Data Matrix Code and Distributed Blockchain 1

Prevent the Counterfeit of Pharmaceutical Products by Using ...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Hasan, M. M. Rakibul Fatema, Sadia Jahan Hossain, Gazi Md. Shakil Alam, Mohammad Mahabub International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350392111

False medications are a serious risk, threatening lives and undermining confidence. This research paper offers an innovative approach to fight counterfeiting and transform the transparency of the pharmaceutical supply chain combining blockchain technology with data matrix code. Consumers with cell phones can easily verify the validity of products by using unique data matrix codes linked to an immutable blockchain database. Authorized participants trace the movement of products within the network, from pharmacies to producers, producing an informative audit trail. Attempts at counterfeiting are exposed and are replaced with efficiency and trust. Envision an open supply chain promoting early identification and mitigation of counterfeiting. The goal of this research is to be the driving force behind this change by utilizing blockchain technology and data matrix to dramatically increase patient and consumer safety and create a more reliable pharmaceutical industry. © 2024 IEEE.

关键词： Blockchain Data Matrix Code IPFS Pharmaceutical Product Prevent Counterfeit Proof of Stack Public Key Cryptography SHA 256 Supply Chain Transparency

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DRCS: Document Recommendation Framework for Cultural Studies as a Prospective Domain 15th

DRCS: Document Recommendation Framework for Cultural Studies...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

The need for extensions of documents for highly special sensitive domains like cultural studies which involves countries, and which involves cultural relationships and political scenarios between cultures and across countries. As the worldwide web continues to evolve, it is transitioning into a densely populated semantic web, which is considered the standard for the next generation of the web, commonly known as web 3.0. This paper suggests a framework for Document Recommendation framework for Cultural Studies (DRCS) that utilizes methods like term frequency - inverse document frequency (TF-IDF) and LDA (Latent Dirichlet Allocation) to analyze the term data set. Metadata generation is used to collect and arrange the information, employing transformers for classification. The XGBoost classifier, which is feature-controlled, is used for data set classification. To measure semantic relevance, strong similarity measures like APMI (Adaptive Pointwise Mutual Information) and Pianka Index, Ren-Konen Index, Sim Rank are used. The model incorporates LDA for topic modelling and utilizes resources like the Google Knowledge Graph API to access community-verified knowledge. To optimize the solutions, the UnderCricket Algorithm optimization algorithm is employed. The proposed model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 94.75%, 96.74%, 95.745%, and 95.7346598% respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.06% is achieved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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Comparative Analysis of Neonatal Facial Expression from Images using Deep Learning 15

Comparative Analysis of Neonatal Facial Expression from Imag...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Chirantan Nanda, Dibyashree Chatterjee, Sudip Dutta, Dipankar University Institute of Technology Burdwan University Department of Computer Science and Engineering Burdwan India

ISBN: (纸本)9798350370249

Understanding and interpreting infant emotions is crucial for early childhood development and effective caregiving. In this work, we implemented various deep learning (DL) models to detect emotions in infants from images. By leveraging advancements in computer vision and DL techniques, our goal is to develop a comprehensive system capable of accurately recognizing and classifying a wide range of emotional states in infants. We analyze facial expressions in images of infants, utilizing DL models to extract relevant features and identify emotional patterns. Our approach is evaluated using a comprehensive dataset of infants exhibiting various emotional states. Through rigorous experimentation, we assess different feature extraction methods, machine learning (ML) algorithms, and deep convolutional neural networks (CNNs), measuring performance using accuracy metrics to determine the system's effectiveness in identifying and classifying infant emotions. © 2024 IEEE.

关键词： CNN computer vision Deep Learning (DL) Infant emotions Multi-stream Fusion Neonatal CNN (NCNN) Squeeze and Excitation Block

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