SmartMicro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

Development of a Novel Noise Reduction Algorithm for Smart Checkout RFID System in Retail Stores

This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’checkout counters.This is achieved by integrating a novel AI algorithm,called Improved Laser Simulator Logic(ILSL)into the RFID system.The enhanced RFID system was able to improve the accuracy of item identification,reduce noise interference,and streamline the overall checkout process.The potential of the systemfor noise detection and elimination was initially investigated through a simulation study usingMATLAB and ILSL algorithm.Subsequently,it was deployed in a small-scale environment to validate its real-world performance.Results show that RFID with the proposed new algorithm ILSL and AI basket is capable of accurately detecting the related itemswhile eliminating noise originating fromunrelated objects,achieving an accuracy rate of 88%.

Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study

BACKGROUND Psychological problems affect economic development.However,there is a huge gap between mental health service resources and mental health service needs.Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people.Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible.AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors.METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects.The general condition Questionnaire,Eysenck Personality Questionnaire,Symptom Checklist-90,General Health Questionnaire,and Smart Medical Service Use Intention Questionnaire were used to collect data.Pearson’s correlation was used to analyze the correlation between the participants’willingness to use smart medical services and their personality characteristics,psychological symptoms,and mental health.RESULTS The results revealed that the mental health of young and middle-aged people was poor,and some had psycho-logical problems such as anxiety,depression,and physical discomfort.Familiarity,acceptance,and usage of smart healthcare in this population are at a medium level,and these levels correlate with psychological characteristics.Acceptance was positively correlated with E,and negatively correlated with P,anxiety,fear,anxiety/insomnia,and social dysfunction.The degree of use was negatively correlated with P,obsessive-compulsive symptoms,depression,anxiety,hostility,paranoia,and somatic symptoms.CONCLUSION The familiarity,acceptance,and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.

Do smart city policies improve energy efficiency?Evidence from China

To address air pollution and offer a convenient and comfortable living environment,the Chinese government launched a smart city pilot(SCP)project in 2012,accompanied by a comprehensive set of environmental and energy-related laws and regulations.Although academic interest in smart cities has surged,there remains a notable gap in empirical research exploring the economic,environmental,and energy effects of such initiatives.Taking 232 prefecture-level cities from 2003 to 2017 as research subjects,this study measures energy effi‐ciency by using energy consumption per unit of GDP and adopts a difference-in-differences(DID)analysis to investigate the impact of SCPs on energy efficiency.The empirical results indicate that SCPs improved energy efficiency by promoting urban technological innovation capabilities and green total factor productivity,and this effect was more pronounced in cities that were more dependent on traditional fossil fuel energy sources and had more developed fiscal and financial levels.Studying the impact of smart city construction on energy utilization efficiency in developing countries,such as China,is not only significantly enlightening for China’s green and low-carbon transition but also provides reference opinions for constructing smart cities and the path to enhancing energy efficiency in other developing countries.The findings provide valuable insights into the global development of smart cities,urban sustainability,and high-quality economic growth.

Optimization of Stealthwatch Network Security System for the Detection and Mitigation of Distributed Denial of Service (DDoS) Attack: Application to Smart Grid System

The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.

Integration of Ideological and Political Education in College Physics Courses Under the Reform of Smart Teaching

The development of the times has prompted China to enhance the quality of education and the value of talent.As guides for students,teachers should conscientiously implement ideological and political education,create college physics courses that are more in line with modern talent cultivation,eliminate the fixed and singular nature of traditional teaching,and find the integration points of ideological and political education.Teachers need to use the textbook itself,the expansion of resources in smart classrooms,and current technological progress to implement ideological and political education in order to cultivate more high-quality and high-level comprehensive talents for society.

中国加入CPTPP的农产品贸易效应研究--基于WITS-SMART模型

粮食安全是国家安全的基石,面对国际市场不稳定性,加入CPTPP有望为中国夯实农产品贸易韧性提供新契机。在考察中国与CPTPP国家农产品贸易竞合关系的基础上,运用WITS-SMART模型建立局部均衡分析框架,研究中国加入CPTPP的农产品贸易效应,结果发现:第一,中国农产品竞争力整体不强,但与CPTPP国家依存关系较高,双边贸易潜力在关税完全削减时充分释放。第二,零关税情境下,贸易创造效应普遍大于转移效应,中国对CPTPP多数国家农产品贸易规模大幅扩张,进口增长主要源自种植业、畜牧业部门以及加拿大、澳大利亚、英国、日本等国家,出口增长主要集中于种植业部门以及日本、英国、马来西亚、墨西哥等国家。第三,加入CPTPP显著改善了双边经济福利,不过关税损失较为严重。因此,中国应破除部门利益障碍,加强与墨西哥、日本、英国等国经贸联系以充分挖掘CPTPP国家市场潜力,释放贸易自由化福利,持续提高农产品出口质量和国际竞争力以应对加入CPTPP带来的挑战。

二维同步插补算法及其在S7-200 Smart PLC上的应用

针对插补运动系统中存在机械振动较大的问题,提出一类基于恒定加加速度的二维直线插补算法。在确定加加速度的前提下,将直线插补的运动过程分为7个不同的阶段。利用运动学定律分析每个阶段的加速度、速度和位移的表达式,获取各运动阶段的初始条件。在基于二维系统的位移要求确定二维同步关系的基础上,实现了各阶段算法的离散化,最终完成了基于PLC(可编程逻辑控制器)的算法设计。实测效果表明,该算法同步精度小于0.5%,运行时间误差小于1 s,运行效果良好,满足应用场景的需求。

Achieving 500X Acceleration for Adversarial Robustness Verification of Tree-Based Smart Grid Dynamic Security Assessment

Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart grid DSA has received significant research interests in recent years.

Introduction to the Special Issue on ComputerModeling for Smart Cities Applications

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

MWCNT Doped Reverse-Mode Polymer Network Liquid Crystals with Frequency Response Property

Polymer-liquid crystals(PLCs)are common materials for smart windows.However,PLC smart windows usually require high driving voltage to maintain transparency.We synthesized a novel PLC smart film by doping multi-wall carbon nanotubes(MWCNTs)into a reverse-mode polymer network liquid crystal(R-PNLC).

Call for Papers:Special Section on Smart Electromagnetic Environment(SEME)

Guest Editors Prof.Andrea Massa Prof.Shi-Wen Yang University of Trento University of Electronic Science and Technology of China andrea.massa@unitn.it swnyang@uestc.edu.cn Prof.Yu-Mao Wu Fudan University yumaowu@fudan.edu.cn,Next-generation communication systems will play a pivotal role in supporting an intensely immersive and interconnected global landscape.In this dynamic realm,the exchange of enormous volumes of data between physical entities,individuals,and their digital devices has become the norm.The Smart Electromagnetic Environment(SEME)is a rapidly evolving paradigm aiming at revolutionizing the design of next-generation mobile communication systems.It is founded on the main idea that the environment is no more an obstacle to wireless signals,but instead enables controlling and tailoring the propagation of electromagnetic waves.

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

SMART管理模式联合PDCA循环管理在提高ICU心电监护报警设置正确率中的应用

探讨SMART管理模式联合PDCA循环管理在提高ICU心电监护报警设置正确率中的应用研究。 方法 随机选取2022年6月~2023年6月收治80名患者作为研究对象。依据实施干预措施的时间进行划分,将 2022年6月至12月在我院ICU行心电监护的40例患者作为对照组,实施常规心电监护护理方法,2023年1月至6月收治的40例患者作为观察组,在对照组的基础上应用SMART管理模式联合PDCA循环管理,比较两组心电监护报警设置正确率。 结果 实施SMART管理模式联合PDCA循环管理后,心电监护报警设置正确率高于实施前,差异具有统计学意义,P<0.05。 结论 SMART管理模式联合PDCA循环管理有利于提高ICU心电监护报警设置正确率,可以帮助护理人员更好的识别患者的异常生命体征,保障患者安全。

Construction of smart propellant with multi-morphologies

Smart materials,which exhibit shape memory behavior in response to external stimuli,have shown great potential for use in biomedical applications.In this study,an energetic composite was fabricated using a UV-assisted DIW 3D printing technique and a shape memory material(SMP)as the binder.This composite has the ability to reduce the impact of external factors and adjust gun propellant combustion behavior.The composition and 3D printing process were delineated,while the internal structure and shape memory performance of the composite material were studied.The energetic SMP composite exhibits an angle of reversal of 18 s at 70°,with a maximum elongation typically reaching up to 280% of the original length and a recovery length of approximately 105%during ten cycles.Additionally,thermal decomposition and combustion behavior were also demonstrated for the energetic SMP composite.