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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

computer systems Science & engineering 2023年第9期46卷 3017-3036页

作者： Sultan Almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image *** picture encryption is a crucial step in many cloud-based and healthcare *** this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed *** dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding *** suggested cryptosystem enhances the security of the delivered medical images by performing many *** validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique *** measures are used to evaluate the proposed cryptosystem,which all support its robust *** simulation results confirm the supplied cryptosystem’s ***,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and *** is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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Attracting Financing for Green Energy Projects: A City Readiness Index 9th

Attracting Financing for Green Energy Projects: A City Readi...

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9th International Conference on Decision Support System Technology, ICDSST 2023

作者： Papapostolou, Aikaterini Karakosta, Charikleia Mexis, Filippos Dimitrios Psarras, John Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Ir. Politechniou 9 Zografou Athens157 80 Greece

ISBN: (纸本)9783031325335

Standard population projections show that virtually all global growth will be in urban areas over the next thirty years. At the same time, the last decades of constant economic and population growth, the modern lifestyle of developed countries and the creation of new needs have led to an increase in energy use per capita and at an overall level resulting in environmental pollution and strengthening of the greenhouse effect. It is essential, therefore, not only at a county level but also at a city level to boost investments in green energy projects. However, important barriers hinder local authorities from investing in this kind of projects. Among others, these barriers include the lack of internal capacity to identify and implement innovative financing schemes, high cost of financing or lack of private financing. This paper presents a software tool for assessing cities’ readiness to receive the necessary financial assistance in order to implement green energy projects. The cities’ performance is evaluated along three axes (i) Investment Attractiveness, (ii) Utilisation of Financial Resources and (iii) Project Implementation, while the methodology is based on multicriteria analysis. A pilot application in ten European cities has been conducted, and fruitful outcomes have been derived from the comparative analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Investments

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IAACS: Image aesthetic assessment through color composition and space formation

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 42-56页

作者： Bailin YANG Changrui ZHU Frederick WBLI Tianxiang WEI Xiaohui LIANG Qingxu WANG School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Computer Science University of DurhamDH13LEUnited Kingdom College of Intelligence and Computing Tianjin UniversityTianjin 300072China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

关键词： Image aesthetic assessment Color composition Space formation Deep learning

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A Web-Based Service Supporting Local Governments in SECAP Implementation activities 14

A Web-Based Service Supporting Local Governments in SECAP Im...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Dimitropoulos, Nikos Milioni, Eliza Lampkowski, Marcelo Sarmas, Elissaios Kapsalis, Panagiotis Marinakis, Vangelis Doukas, Haris School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Iclei European Secretariat Freiburg im Breisgau Germany

ISBN: (纸本)9798350318067

Tackling climate change is of the utmost importance in governments' agendas. The abundance of climate related data becoming available, being published by local authorities and other sources, the digitisation of government systems under the open governance principles, and innovative data-driven services, make available resourceful and previously unseen applications that can greatly support decision-making processes. In this paper, a web-based service, to support local governments in their Sustainable Energy Climate Action Plans (SECAPs) implementation, is presented. The service incorporates open data, published from over 200 local governments, under a unified system. Data driven analytics services to classify them, compare them and recommend optimal policy actions to improve their climate change adaptation and mitigation actions are incorporated in a user-friendly application. The service aims to provide unique and tailor-made insights to local governments, monitor and follow up on their SECAPs implementation, and support their decision making processes as part of activities to tackle climate change effects they are facing. © 2023 IEEE.

关键词： Decision support systems

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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Position and Attitude Control of Quadrotor UAV Based on a New Sliding Mode Control Strategy 42

Position and Attitude Control of Quadrotor UAV Based on a Ne...

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42nd Chinese Control Conference, CCC 2023

作者： Wu, Yinghua Zhang, Ancai Pan, Guangyuan Du, Yingxue Liu, Zhi School of Automation and Electrical Engineering Linyi University Shandong Linyi276005 China Universities of Shandong Key Laboratory of Complex Systems and Intelligent Computing Linyi University Shandong Linyi276005 China

ISBN: (纸本)9789887581543

A Quadrotor unmanned aerial vehicle (UAV) is a typical underactuated nonlinear mechanical system with six degrees of freedom (DOF) and four inputs. This paper develops a new sliding mode control (SMC) strategy to solve the position and attitude control problem for this multi-DOF underactuated system. First, the dynamic motion equation model of the Quadrotor UAV is established. Second, the first and fourth inputs are designed by using the traditional SMC method and a novel Gaussian geometric function reaching law. And then, we design the second and third inputs based on a hierarchical SMC method. And the stability of sliding mode surfaces are analyzed using Lyapunov theory. The designed four sliding controllers guarantee the flight control objective of the Quadrotor UAV to be achieved. And the Gaussian reaching law used in the design of controllers can effectively reduce the chattering phenomena in the sliding mode surfaces. Numerical examples verify the effectiveness of our developed control strategy. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Sliding mode control

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Advanced extended-term simulation approach with flexible quas-isteady-state and dynamic semi-analytical simulation engines

iEnergy

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iEnergy 2022年第1期1卷 124-132页

作者： Rui Yao Dongbo Zhao A.P.Sakis Meliopoulos Chanan Singh Joydeep Mitra Feng Qiu Division of Energy Systems Argonne National LaboratoryLemontIL 60439USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege Sta-tionTX 77843USA Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI 48824USA

Power system simulations that extend over a time period of minutes,hours,or even longer are called extendedterm *** power systems evolve into complex systems with increasing interdependencies and richer dynamic behaviors across a wide range of timescales,extendedterm simulation is needed for many power system analysis tasks(e.g.,resilience analysis,renewable energy integration,cascading failures),and there is an urgent need for efficient and robust extendedterm simulation *** conventional approaches are insufficient for dealing with the extendedterm simulation of multitimescale *** paper proposes an extendedterm simulation approach based on the semianalytical simulation(SAS)*** accuracy and computational efficiency are backed by SAS's high accuracy in eventdriven simulation,larger and adaptive time steps,and flexible switching between fulldynamic and quasisteadystate(QSS)*** used this proposed extendedterm simulation approach to evaluate bulk power system restoration plans,and it demonstrates satisfactory accuracy and efficiency in this complex simulation task.

关键词： Extended-term simulation resilience multi-timescale simulation event-driven simulation dynamics quasi-steady-state(QSS) hybrid simulation holomorphic embedding(HE) semi-analytical simulation(SAS)

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AI4EF: Artificial Intelligence for Energy Efficiency in the building sector

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SoftwareX 2025年 30卷

作者： Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering Institute of Communications and Computer Systems National Technical University of Athens Greece

AI4EF (Artificial Intelligence for Energy Efficiency) is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF (Artificial Intelligence for Energy Efficiency) enables stakeholders such as public sector representatives, energy consultants, and building owners—to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the service incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. AI4EF's compatibility with open-source identity management (Keycloak) enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an overview of AI4EF's architecture, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI). © 2025 The Authors

关键词： Energy efficiency

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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