Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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Dynamic security assessment for the power system in the presence of wind turbines
Get PDFDynamic security is an essential requirement for operating a modern power system. Due to the global increase in load demand, modern power systems witness several dramatic changes in terms of size and implementation of new renewable sources. At the same time, the deregulation process in power operation policy is being pushed to operate closer to its security boundary limits. Based on the combined Decision Tree (DT) algorithm, namely Random Forest (RF) and advance attribute selection technique, this paper presents an approach to address these challenges related to dynamic security assessment (DSA) in the modern power system. The performance of study approach is demonstrated on a modified version of IEEE 9 and 14-bus test system models with presence of two wind turbines (WTs) type WTG 3. Results show the superiority of RF compared to other DT algorithms that are used in this study. In addition, the attribute selection technique could significantly affect the number of attributes required for DSA. This makes DT classifier more effectiveness in the online application. Thus, this approach can provide control center with vital information with high accuracy results and less attributes about security state direction that will help operator to take the right and fast steps to remedy problems and prevent a blackout from occurring
Vulnerability analysis in RF locking systems of vehicles in Bogotá, Colombia.
Get PDFA car electronic security system aims to prevent its theft, the theft of its parts or of elements in its interior. Studying these systems allows identifying and mitigating vulnerabilities. Nowadays, there are different types of attacks on these systems to exploit their vulnerabilities, such as replay, relay, brute-force or jamming attacks, among others. In the last five years in Bogotá, Colombia, the average number of stolen vehicles was 3,073 per year. This research project proposes the detection of vulnerabilities in the security system of vehicles in this city. A sample of 43 vehicles of different brands sold and registered in the city is taken. The replay attack was executed, as well as a modification of the brute-force attack. Results show that most of the implemented security systems in Bogotá are susceptible of being successfully attacked through the proposed methods. The analysis done on the brute-force attack highlights a considerable reduction in time for unlocking the vehicle compared to the conventional attack in more vulnerable RKE systems. Replay attacks turn successful in great part of the sample and, furthermore, it is concluded that the unlocking key code can be generated from the locking one
RF Energy Harvesting Wireless Networks: Challenges And Opportunities
Get PDFEnergy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced
M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems
Get PDFCurrently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS have adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine post-adoption users' satisfaction and users’ intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users’ intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model’s factors. The authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed
Design and Operation of Optical Fiber for Mass Measuring Instrument with Bending Power Loss Principle
Get PDFUtilization fiber optics macro-bending will be useful for non-destructive applications, such as a mass measurement device. This research, therefore, analyzes the fiber optic power loss due to macro bending by mass loading and explores its possible application as an instrument of mass scale. A single-mode fiber (SMF) with a core diameter of 8.2μm and laser with a wavelength of 1310 nm and 1550 nm are used in this work. Moreover, an optical power meter with a sensitivity of 0.01dBm is applied as the detector while power loss is determined using nine bending cylinders that can produce eighteen bends with a roundtrip system. It is obtaineda correlation coefficient R2of 0.9910 and 0.9959 corresponding to a sigmoid of 4 parameters for two-wavelength sources of 1310nm and 1550nm, respectively. The results showed mass suppression can produce a power loss with a relatively small error of 19.60%, 11.23%, 0.20%, 3.74%, and 9.36% for mass 300g, 600g, 100 g, 1400g, and 1800g, respectivel
Hierarchical Fuzzy Systems: Interpretability and Complexity
Get PDFHierarchical fuzzy systems (HFSs) have been regarded as a useful solution for overcoming the major issues in fuzzy logic systems (FLSs), i.e., rule explosion due to the increase in the number of input variables. In HFS, the standard FLS are reformed into a low-dimensional FLS subsystem network. Moreover, the rules in HFS usually have antecedents with fewer variables than the rules in standard FLS with equivalent functions, because the number of input variables in each subsystem is less. Consequently, HFSs manage to decrease rule explosion, which minimises complexity and improves model interpretability. Nevertheless, the issues related to the question of “Does the complexity reduction of HFSs that have multiple subsystems, layers and different topologies really improve their interpretability?” are not clear and persist. In this paper, a comparison focusing on interpretability and complexity is made between two HFS’ topologies: parallel and serial. A detailed measurement of the interpretability and complexity with different configurations for both topologies is provided. This comparative study aims to examine the correlation between interpretability and complexity in HFS
A Grid-connected Hybrid Renewable Energy System Using High-frequency Multi-winding Transformer and Phase Shift Modulation Technique
Get PDFThis paper presents a topology of multi-port phase-shift converter for integration of a PV system, a wind-turbine generator and a battery to supply a grid-connected domestic load. The main advantage of the topology is using a multi-winding high-frequency transformer to couple the input and outputs of the system. This simplifies the power flow control and provides isolation between the converter ports. Detailed operation of the proposed topology and the control system analysis are discussed. A numerical simulation of the proposed system is carried out to confirm the system operation and control technique
SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach
Get PDFSQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions
Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction
Get PDFHeterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%
Design and Evaluation of the Efficiency of Channel Coding LDPC Codes for 5G Information Technology
Get PDFThis paper proposes a result of an investigation of a topical problem and the development of models for efficient coding in information networks based on codes with a low density of parity check. The main advantage of the technique is the presented recommendations for choosing a signal-code construction is carried out taking into account the code rate and the number of iterations decoding for envisaging the defined noise immunity indices. The noise immunity of signal-code constructions based on low-density codes has been increased by combining them with multi position digital modulation. This solution eventually allowed to develop a strategy for decoder designing of such codes and to optimize the code structure for a specific information network. To test the effectiveness of the proposed method, MATLAB simulations are carried out under for various Information channels binary symmetric channel (BSC), a channel with additive white Gaussian noise (AWGN), binary asymmetric channel (BAC), asymmetric channel Z type. In addition, different code rates were used during the experiment. The study of signal-code constructions with differential modulation is presented. The efficiency of different decoding algorithms is investigated. The advantage of the obtained results over the known ones consists in determining the maximum noise immunity for the proposed codes. The energy gain was on the order of 6 dB, and an increase in the number of decoding iterations from 3 to 10 leads to a gain in coding energy of 5 dB. Envisaged that the results obtained can be very useful in the development of practical coding schemes in 5G networks