- E-ISSN2508-7894
- KCI
E-ISSN : 2508-7894
Vol.11 No.2
Abstract
This paper presents a comprehensive scheme for assessing the importance of multiple microgrids (MGs) network that includes distributed energy resources (DERs), renewable energy systems (RESs), and energy storage system (ESS) facilities. Due to the uncertainty of severe weather, large-scale cascading failures are inevitable in energy networks. making the assessment of the structural vulnerability of the energy network an attractive research theme. This attention has led to the identification of the importance of measuring energy nodes. In multiple MG networks, the energy nodes are regarded as one MG. This paper presents a modified PageRank algorithm to assess the importance of MGs that include multiple DERs and ESS. With the importance rank order list of the multiple MG networks, the core MG (or node) of power production and consumption can be identified. Identifying such an MG is useful in preventing cascading failures by distributing the concentration on the core node, while increasing the effective link connection of the energy flow and energy trade. This scheme can be applied to identify the most profitable MG in the energy trade market so that the deployment operation of the MG connection can be decided to increase the effectiveness of energy usages. By identifying the important MG nodes in the network, it can help improve the resilience and robustness of the power grid system against large-scale cascading failures and other unexpected events. The proposed algorithm can point out which MG node is important in the MGs power grid network and thus, it could prevent the cascading failure by distributing the important MG node's role to other MG nodes.
Abstract
The purpose of this study is to investigate how consumers perceive electric vehicles and factors that affect attitude, satisfaction, and intention to use electric vehicles and to explore policy issues regarding climate change and global environment. By classifying actual and potential users, this study developed the following research questions: i) factors including economic feasibility, sociality, environmental sustainability, inefficiency, inconvenience, convenience, and uncertainty affect attitude to electric vehicles; ii) attitude to electric vehicles affects actual consumers' satisfaction; and iii) attitude to electric vehicles affects potential users' intention to use. This study conducted an online survey and applied factor and regression analyses and ANOVA to test hypotheses. The results of this study found that economic feasibility and convenience factors significantly affect attitude in both cases of actual and potential users. How actual users perceive efficiency of electric vehicles negatively and uncertain issues such as battery technology affect attitude to electric vehicles. This study provides policy implications that foster promotional policies for the adoption of electric vehicles for environment and regulate negative aspects. This study also provides managerial implications for manufacturers to develop better technology competences to enhance reliability on electric vehicles.
Abstract
In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.
Abstract
The purpose of this study is to explore how citizens perceive application of the metaverse platforms for city marketing and investigate factors that affect overall attitude for citizen relationship management in the public sector. In particular, this study investigates the following: i) how factors including perceived city brand value, public service, emotional value, experience, personalization, economic value, social value, and cultural value on overall attitude and ii) how overall attitude affects intention to use of metaverse for the public sector and citizen satisfaction. This study conducted an online survey with the assistance of a well-known research firm. This study applied factor, ANOVA, and regression analysis to test hypotheses. The results found that effects of perceived city brand value, emotional value, information, economic value, social value, and cultural value on overall attitude toward metaverse application for the public sector showed significance. The results provide managerial and policy implications for the public sector on how to apply metaverse to provide public services and enhance engagement with citizens. The results also provide implications which aspects should be considered to enhance citizen relationship management and to build the better city brand value by applying metaverse.
Abstract
The creativity of thesis is that the significance of cyber security challenges in blockchain. The variety of enterprises, including those in the medical market, are the targets of cyberattacks. Hospitals and clinics are only two examples of medical facilities that are easy targets for cybercriminals, along with IoT-based medical devices like pacemakers. Cyberattacks in the medical field not only put patients' lives in danger but also have the potential to expose private and sensitive information. Reviewing and looking at the present and historical flaws and vulnerabilities in the blockchain-based IoT and medical institutions' equipment is crucial as they are sensitive, relevant, and of a medical character. This study aims to investigate recent and current weaknesses in medical equipment, of blockchain-based IoT, and institutions. Medical security systems are becoming increasingly crucial in blockchain-based IoT medical devices and digital adoption more broadly. It is gaining importance as a standalone medical device. Currently the use of software in medical market is growing exponentially and many countries have already set guidelines for quality control. The achievements of the thesis are medical equipment of blockchain-based IoT no longer exist in a vacuum, thanks to technical improvements and the emergence of electronic health records (EHRs). Increased EHR use among providers, as well as the demand for integration and connection technologies to improve clinical workflow, patient care solutions, and overall hospital operations, will fuel significant growth in the blockchain-based IoT market for linked medical devices. The need for blockchain technology and IoT-based medical device to enhance their health IT infrastructure and design and development techniques will only get louder in the future. Blockchain technology will be essential in the future of cybersecurity, because blockchain technology can be significantly improved with the cybersecurity adoption of IoT devices, i.e., via remote monitoring, reducing waiting time for emergency rooms, track assets, etc. This paper sheds the light on the benefits of the blockchain-based IoT market.
