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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Bridging the Gap: Secure Northbound Interface in SDN with gRPC 5

Bridging the Gap: Secure Northbound Interface in SDN with gR...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Rathnayake, R.M.K.G. Kalana, K.A.V. Dharmakeerthi, Uditha Senarathne, Amila Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Software Defined Networking (SDN) promises to introduce flexibility and programmability into networks by offering developers a northbound interface (NBI) to create SDN applications. However, current designs and implementations have drawbacks, including the lack of Proper Standardization, Controller-Lock-In Situation, and Loss of Compatibility with SDN applications. Using Google Remote Procedure Call (gRPC) Application Programming Interface (API), the SDN controller can offer network resources, such as statistics, flow information, or topology data, to registered SDN applications. Furthermore, the gRPC enables controller-agnostic and language-agnostic development to enable proactive and reactive applications and services to interact with controller services. Transport Layer Security (TLS) enables confidentiality with encryption, authenticity with mutual authentication, and integrity with Message Authentication Code (MAC). Finally, Attribute-based Access Control (ABAC) and Logging capability for controller services allow fine-grained access control and accountability enforcement to ensure the security goals of the NBI. This research paper provides a complete architecture to converge different Northbound API streams into one secure, language-agnostic, controller-agnostic API stream with the help of the gRPC framework. © 2023 IEEE.

关键词： Controllers

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Dynamic Android Malware Detection Using Temporal Convolutional Networks

Dynamic Android Malware Detection Using Temporal Convolution...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Joomye, Abdurraheem Ling, Mee Hong Yau, Kok-Lim Alvin Sunway University Department of Computing and Information Systems Malaysia Lee Kong Chian Faculty of Engineering & Science Malaysia

ISBN: (纸本)9798350302684

As new malware emerges and continues to grow in number with increasing complexity, user applications are foreseen to face greater security challenges. Hence, there is a need to leverage machine learning and deep learning (DL) approaches in Android malware detection. Temporal convolutional network (TCN), is a DL approach which uses causal and dilated 1-dimensional convolution layers to process sequential data, has not been implemented with features extracted through dynamic analysis (e.g. system calls) of Android malware applications. TCN has been shown to achieve higher accuracy in applications dealing with sequential data, with lower delay due to its parallelism and less computationally exhaustiveness. This paper implements TCN for dynamic Android malware detection. Compared to long short-term memory (LSTM), TCN is shown to achieve a similar validation accuracy and a higher steady convergence, with TCN achieving an accuracy of 89.25% and LSTM achieving an accuracy of 88.42% with 1000 system calls. Therefore, it is an effective model for Android malware detection. © 2023 IEEE.

关键词： Android malware

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Exploring information hiding in images – problems and measures: a survey

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Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Graph Neural Network Meets de Bruijn Genome Assembly 13

Graph Neural Network Meets de Bruijn Genome Assembly

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13th International Symposium on Image and Signal Processing and Analysis, ISPA 2023

作者： Simunovic, Marijo Vrcek, Lovro Sikic, Mile University of Zagreb Faculty of Electrical Engineering and Computing Croatia Croatia Genome Institute of Singapore A-STAR Singapore Singapore

ISBN: (纸本)9798350315363

The genome assembly problem, which involves reconstructing the sequence of nucleotides in a DNA molecule from its short, error-prone substrings called reads, is one of the most extensively studied computational problems in biology. The majority of existing solutions for this problem rely on representing input reads in the form of an assembly graph, where each vertex and edge represents a sequence of nucleotides, and the genome corresponds to a path in this graph. However, assembly graphs often contain millions of erroneous edges due to read errors, which significantly complicates the graph analysis. Traditionally, these erroneous edges are detected and removed using a variety of heuristics. However, these heuristics often require manual parameter tuning. In this paper, we propose a novel approach to detect erroneous edges in a specific type of assembly graph called the de Bruijn graph. We employ a graph neural network-based architecture to detect these erroneous edges, replacing the corresponding heuristic step in traditional genome assembly algorithms. To overcome the lack of datasets with known ground truth, we simulate realistic read sets using error profiles learned from real reads. We propose a new method for erroneous edge classification in de Bruijn graph that relies on Graph Neural Networks. In our experiments, we demonstrate that the new method can reliably replace a more complicated heuristic approach and reduce the overall execution time. © 2023 IEEE.

关键词： Errors

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Collective Citizen Energy Investment Models for Solar Power: Case Study Croatia

Collective Citizen Energy Investment Models for Solar Power:...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Herenčić, Lin Capuder, Tomislav Kirac, Mislav Čačić, Goran University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia Zagreb Croatia

ISBN: (纸本)9798350332308

Cost-competitiveness of decentralized renewable energy sources (RESs) has opened opportunities for citizens' ownership of RES and for the development of new business models. Citizen energy investment models based on cooperative principles have gained increased attention lately due to promises to support local economic welfare, boost local economy, increase social cohesion, help in grid optimization, and increase citizens' support for new RES projects. In this paper, we provide an overview of collective citizen energy investment concepts and describe the six types of models being developed in Croatia. We provide an overview of the initiatives together with the challenges, threats, and opportunities found when concepts are faced with the real-life practice in Croatia. We show that even though citizens' ownership of RES (mainly individual solar PV systems) is strongly increasing, the current developments of collective (group) citizen energy investment models are predominantly related to sporadic research projects and enthusiasts' initiatives, meaning still no wider-scope investments in RES have been conducted this way. Following the findings, we provide recommendations for removing the barriers and speeding up the development of such projects in the future. © 2023 IEEE.

关键词： Solar energy

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The effect of the Radon field for efficient medical image compression

The effect of the Radon field for efficient medical image co...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Cherifi, Mehdi Akrour, Leila Lahdir, Mourad Mouloud Mammeri University of Tizi- Ouzou Faculty of Electrical Engineering and Computing Department of Electronics Tizi-Ouzou Algeria

ISBN: (纸本)9781665492942

The medical image accumulated over time for the purpose of research confronts us with the problems of transmission and storage. So, an efficient compression scheme is necessary to eliminate this serious problem. The integration process is one of the powerful characteristics of the Radon transform leading to the attenuation of the quantization noise. We introduce in this paper a medical image compression method based on the Radon transform and the Discrete Cosine Transform (DCT) with high quantification. The primary goal of this method is to demonstrate the possibility of exploiting a high scale of quantization in the Radon field for effective compression of the medical image databases. A comparative study with the basic DCT compression scheme is presented. The results of this comparison showed that despite the high quantization scale, the presented method ensures an excellent rate-distortion compromise with a high compression with an excellent quality. A comparative study is performed to show the power of the Radon transform against the quantization noise. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Linear Machine Learning Algorithm for Early Annual Corn Yield Prediction 17th

Linear Machine Learning Algorithm for Early Annual Corn Yie...

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17th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2023

作者： Kralj, Ivan Kusek, Mario Jezic, Gordan Internet of Things Laboratory Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9789819930678

Machine learning is an important decision support tool for predicting crop yields, including supporting decisions about which crops to grow and what to do during the crop growing season. Various machine learning algorithms have been used to support crop yield prediction research, including K-Nearest Neighbor, Random Forest Classifier, logistic regression and many more. The objective of this paper is to develop a linear machine learning algorithm based on a Generalized Linear Model (GLM) that is more accurate and also provides early prediction of corn yield with a relative error of less than 20%, which is crucial for decisions on allocating harvesting and storage resources in Croatia. Input parameters for our model include various climate and greenhouse gas parameters. We examined how accurate the corn yield prediction would be if farmers wanted to know what the corn yield would be at a given harvest date during the R1 (Silking) phase of the corn and thereafter. For this test case, the relative error for our model was 11.63%, while for GLM it was 12.55%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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"Hand Model" - A Static Sinhala Sign Language Translation Using Media-Pipe and SVM Compared with Hybrid Model of KNN, SVM and Random Forest Algorithms 5

"Hand Model" - A Static Sinhala Sign Language Translation Us...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Gedaragoda, G.H.M.S.C. Wijesinghe, W.R.S. Abeywickrama, T.R. Lokuliyana, Shashika Mahaadikara, Hansika Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study of sign language recognition has been a thriving area of research for nearly twenty years. SSL translation using computer vision relies on extensive training using large number of images or video sequences. Yet, it poses a challenge for languages with limited resources, like Sinhala Sign Language. Due to the absence of accessible datasets for such investigations and lack of SSL recognition systems for real-time scenarios, the authors of this research have developed a completed system that consist of three components which are, static sign recognition, dynamic sign recognition and natural language processing to output a meaningful sentence. This paper describes the models and the results which are developed for static sign recognition. The final model for the Sinhala Static recognition has developed using media-pipe and SVM algorithm. And to compare the results and performance authors have used CNN, SVM, KNN and Random-forest algorithms to compare the results and performance. In this research, authors have made a valuable contribution by introducing a new dataset and developing a system for translating static SSL that gives 99.67% of accuracy and best performance when compared to the other two models and algorithms described in this paper. © 2023 IEEE.

关键词： Support vector machines

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