The role of electrical insulation is critical for the proper operation of electrical equipment. Power equipment cannot operate without energy losses, which lead to rises in temperature. It is therefore essential to dissipate the heat generated by the energy losses, especially under high load conditions. Failing to do so results in premature aging, and ultimately to failure of the equipment. Heat dissipation can be achieved by circulating certain liquids, which also ensure electrical insulation of energized conductors. The insulating-fluids market is therefore likely to be dominated by liquids, leaving to gases (such as compressed air and SF6) limited applications in power equipment such as circuit breakers and switchgear [1]-[3]. Several billion liters of insulating liquids are used worldwide in power equipment such as transformers (power, rectifier, distribution, traction, furnace, potential, current) [4], resistors [5], reactors [6], capacitors [7], cables [8], bushings [9], circuit breakers [10], tap changers [11], thyristor cooling in power electronics, etc. [12]. In addition to their main functions of protecting solid insulation, quenching arc discharges, and dissipating heat, insulating liquids can also act as acoustic dampening media in power equipment such as transformers. More importantly, they provide a convenient means of routine evaluation of the condition of electrical equipment over its service life. Indeed, liquids play a vital role in maintaining the equipment in good condition (like blood in the human body). In particular they are responsible for the functional serviceability of the dielectric (insulation) system, the condition of which can be a decisive factor in determining the life span of the equipment [13]. Testing the physicochemical and electrical properties of the liquids can provide information on incipient electrical and mechanical failures. In some equipment, liquid samples can be obtained without service interruption.

50 years in the development of insulating liquids

A review on feature selection in mobile malware detection

Internet connected smartphone devices play a crucial role in the application domain of Internet of Things. These devices are being widely used for day-to-day activities such as remotely controlling lighting and heating at homes, paying for parking, and recently for paying for goods using saved credit card information using Near Field Communication (NFC). Android is the most popular smartphone platform today. It is also the choice of malware authors to obtain secure and private data. In this paper we exclusively apply the machine learning ensemble learning algorithm Random Forest supervised classifier on an Android feature dataset of 48919 points of 42 features each. Our goal was to measure the accuracy of Random Forest in classifying Android application behavior to classify applications as malicious or benign. Moreover, we wanted to focus on detection accuracy as the free parameters of the Random Forest algorithm such as the number of trees, depth of each tree and number of random features selected are varied. Our experimental results based on 5-fold cross validation of our dataset shows that Random Forest performs very well with an accuracy of over 99 percent in general, an optimal Out-Of-Bag (OOB) error rate [3] of 0.0002 for forests with 40 trees or more, and a root mean squared error of 0.0171 for 160 trees.

Random Forest Classification for Detecting Android Malware

In parallel with the meteoric rise of mobile software, we are witnessing an alarming escalation in the number and sophistication of the security threats targeted at mobile platforms, particularly Android, as the dominant platform. While existing research has made significant progress towards detection and mitigation of Android security, gaps and challenges remain. This paper contributes a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area. We have carefully followed the systematic literature review process, and analyzed the results of more than 300 research papers, resulting in the most comprehensive and elaborate investigation of the literature in this area of research. The systematic analysis of the research literature has revealed patterns, trends, and gaps in the existing literature, and underlined key challenges and opportunities that will shape the focus of future research efforts.

A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software

Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on Android-based devices. Moreover, due to extensive exploitation of the Android platform in the IoT devices creates a task challenging of securing such kind of malware activities. This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for Android IoT devices. The proposed technique is implemented using a sequential approach, which includes clustering, classification, and blockchain. Machine learning automatically extracts the malware information using clustering and classification technique and store the information into the blockchain. Thereby, all malware information stored in the blockchain history can be communicated through the network, and therefore any latest malware can be detected effectively. The implementation of the clustering technique includes calculation of weights for each feature set, the development of parametric study for optimization and simultaneously iterative reduction of unnecessary features having small weights. The classification algorithm is implemented to extract the various features of Android malware using naive Bayes classifier. Moreover, the naive Bayes classifier is based on decision trees for extracting more important features to provide classification and regression for achieving high accuracy and robustness. Finally, our proposed framework uses the permissioned blockchain to store authentic information of extracted features in a distributed malware database blocks to increase the run-time detection of malware with more speed and accuracy, and further to announce malware information for all users.

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A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features

because android phones are widespread and steadily gaining popularity. Unfortunately, such phenomenon draws attention to malicious application developers so that malicious applications are increasing rapidly. To detect these malicious applications, several researches have been done using machine learning. In machine learning methods, there are two ways which are dynamic detection and static detection to detect these malicious applications. The dynamic detection has a high detection rate but uses a lot of resources and takes long time to detect malicious behaviors. In addition, it is possible to infect malicious code after implementing detection method. On the other hand, the static detection has a small amount of resources and quick detection time but has low detection rate. To these weaknesses, we propose a malicious application detection framework on android market and an automatic extraction tool of malicious features for static detection. This framework is able to perform both detection methods using machine learning and is expected to perform a thorough analysis than previous framework. Also, the proposed automatic extraction tool is expected to help a code analysis for static detection.

http://psrcentre.org/images/extraimages/1.%20113082.pdf

A Malicious Application Detection Framework using Automatic Feature Extraction Tool on Android Market

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, the fuzzy set-based fuzzy neural network (FNN) was investigated and designed. Using PD data measured from laboratory defect models, this algorithm was designed and tested. Considering the on-site situation where it is not easy to obtain voltage phases in PRPDA (phase resolved partial discharge analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithm. The result of the proposed FNN algorithm was compared with that of conventional BP-NN (back propagation neural networks) algorithm using same data. The FNN algorithm proposed in this study was appeared to have better performance than BP-NN algorithm.

Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm

In the design of DC superconducting cables, the electrical insulation design is a critical factor in the cable's performance and reliability. To evaluate the insulation design of DC superconducting cables, DC electric field analysis and experimental verifications should be performed. Wrapped polypropylene laminated paper (PPLP) tape has generally been used to insulate superconducting cable. During the wrapping process, butt gaps are inevitably introduced, and these are filled with liquid nitrogen (LN2) during normal operating conditions. Therefore, the insulation characteristics of the resulting combination of PPLP and liquid insulation should be carefully verified. The objective of this work was to determine the DC electric field transitions when a butt gap is present in a LN2/PPLP composite insulation system. DC electric field distribution and transition were simulated by using COMSOL Multiphysics® software. Also, to verify the characteristics of DC electric field transitions, two kinds of breakdown tests were performed: a ramp voltage breakdown test and a step voltage breakdown test. In both the experimental and analytical works, it was observed that the electric field distributions were totally different while the DC field transition. And, due to the different distributions of electric field, the breakdown characteristics of LN2/PPLP composite insulation systems could be altered.

Correlation between DC electric field intensity and electrical breakdown of butt gap in LN2/PPLP composite insulation system

Even though a lot of research has been devoted to find partial discharge (PD) sources based on the time difference of pulses, it has been reported that studies have been unable to obtain precise locations in joints due to the errors caused by the attenuation or distortion of PD pulses. In this study, in order to reduce the issues, ‘Precise PD finding’ is developed to obtain the accurate locations of PD sources by considering not only the time difference of pulses, but also pulse polarity. Accordingly, a PD detection algorithm is implemented. An attachable inductive sensor is designed and fabricated and then applied to a real scaled 154 kV XLPE cable with a joint. From the experimental results, the algorithm was verified to successfully find the locations of PD sources. The algorithm was then applied to an on-site, 345 kV XLPE cable. Consequently, PD sources were detected in a normal straight joint box and confirmed through a disassembly investigation.

Development of a partial discharge detection algorithm and verification test for extra-high-voltage cable system

Recently, the superconductivity projects to develop commercial superconducting devices for extra high voltage transmission lines have been undergoing in many countries. One of the critical components to be developed for high voltage superconducting devices, including superconducting transformers, cables, and fault current limiters, is a high voltage bushing, to supply high current to devices without insulating difficulties, that is designed for cryogenic environments. Unfortunately, suitable bushings for HTS equipment were not fully developed for some cryogenic insulation issues. Such high voltage bushings would need to provide electrical insulation capabilities from room temperature to cryogenic temperatures. In this paper, design factors of cryogenic bushings were discussed and test results of specimen were introduced in detail. First, the dielectric strength of three kinds of metals has been measured with uniform and non-uniform electrodes by withstand voltage of impulse and AC breakdown test in LN2. Second, puncture breakdown voltage of glass fiber reinforced plastics (GFRPs) plates has been analyzed with non-uniform electrodes. Finally, creepage discharge voltages were measured according to the configuration of non-uniform and uniform electrode on the FRP plate. From the test results, we obtained the basic design factors of extra high voltage condenser bushing, which could be used in cryogenic environment.

Jeong-Tae Kim

Papers

A Malicious Application Detection Framework using Automatic Feature Extraction Tool on Android Market

Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm

Correlation between DC electric field intensity and electrical breakdown of butt gap in LN2/PPLP composite insulation system

Development of a partial discharge detection algorithm and verification test for extra-high-voltage cable system

Insulation design of cryogenic bushing for superconducting electric power applications