A single acrylic resin has poor conductivity and toughness, but strong hydrophilicity, which limits its application in high-voltage transmission corona prevention. In response to this issue, this article investigates the preparation of four types of acrylic composite conductive coatings with different additions of carbon fiber powder, multi-walled carbon nanotubes, and nano titanium dioxide. Through experimental testing, compare and analyze the effects of various fillers on the mechanical properties, conductivity, hydrophobicity, and heat resistance of acrylic composite conductive coatings. The experimental results show that the acrylic conductive coating prepared with thermoplastic acrylic resin as the matrix, carbon fiber powder and multi-walled carbon nanotubes as conductive fillers, and nano titanium dioxide as a self-cleaning agent has excellent performance characteristics in all aspects, and is suitable for corona prevention in ultra-high voltage transmission. This acrylic composite coating can be used to repair burrs and scratches on the surface of transmission lines, fill gaps in stranded wires, and achieve the goal of reducing the local electric field of high-voltage transmission lines to prevent wire corona.

The use of grid systems for distributing and managing resources such as computing power and data storage has become increasingly widespread in recent years. However, as the demand for these resources continues to grow, the capacity of traditional grid systems to meet this demand has become a concern. When dealing with the constantly expanding system scale and its many uncertainties, traditional model-based techniques are becoming unsuitable. A better alternative to these techniques involves considering data-driven control (DDC) methodologies. In this paper, we begin by reviewing the current state of the art in DDC usage in grid systems in monitoring, improving, error detection, etc. with a particular focus on improving host capacity. We then describe our proposed approach, which involves improving the host capacity of grid systems using historical data. Finally, we present experimental results demonstrating the effectiveness of our approach and discuss its potential impact and future directions.

Orthogonal Frequency Division Multiplexing (OFDM) is employed in the current 4G cellular technologies. Because of its resistance to multipath propagation channels, OFDM is a popular multicarrier modulation technique. Although OFDM is one of the effective approaches for a 4G network, out-of-band radiation, inter-symbol interference (ISI), and inter-carrier interference make this method unsuitable for 5G. (ICI). A new approach or next-generation cellular network, i.e., very high data rate, low latency, greater throughput, more device connectivity, and improved device-to-device communication, should be used to deliver effective and dependable communication. The system introduces generalized frequency division multiplexing (GFDM). In GFDM, a single cyclic prefix is utilized throughout the frame and an adjustable pulse-shaping filter can be used to monitor the transmitted signal’s out-of-band emission. Using this ISI, the multipath channel can be controlled. In the proposed system, with large signal constellations, higher-order modulation schemes, and advanced receiver structure, a very high data rate can be achieved. By using various iterative receiver structures the performance of the GFDM system in 5G cellular networks can be analyzed.

Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm.

In current research a hybrid autonomous supplying system for electric vehicles applications is presented. The hybrid system is consisted of fuel cell, micro gas turbine and supercapacitor. There are realized with averaged models in MATLAB/Simulink environment. The supplying elements are connected to a DC bus for charging a different type of EVs. In this case as a load is use two EVs: BMW-i3 and Nissan Leaf. This system can operate autonomously in hard-to-reach places where there is no supplying from the distributed grid and other sources. These places could be remote holiday villages, research centers positioned at hard-to-reach places and also for production of agricultural crops with the aids of electric vehicles. This requires the necessity for searching of different structural and conceptual solutions for production and storage of electric energy. An optimization problem is resolved in order to reduce the value of the capacitance of the supercapacitor with which it will decrease his price. Thus, it also decreases the price for construction of the entire charging station. Recently, the usage of natural gas and his transportation is well organized which can contribute for assuring of the reserved energy for the autonomous charging station.

We present in this paper a new approach for predicting creditworthiness of borrowers that we call “Method of splitting the learning set into two region”. The aim of this approach consists on the construction of two regions from a learning set, the first called "Solvency Region" that contains the feature vectors of the elements that have paid their financial obligations on time and the second one called "Non-Solvency Region", which contains the feature vectors of the elements that have defaulted in paying their debts. Therefore, to predict creditworthiness borrowers, it is sufficient to identify which of the two regions includes his feature vectors; if it doesn’t correspond to any region, the credit decision-making requires further analysis. To develop and test our predictive proposed approach, a large set of real and recent credit data obtained from the UCI repository is used, we trained also on a real credit database from a Moroccan bank and the creditworthiness of borrowers are analyzed using two performance measurement indicators such as Classification accuracy and the AUC of the ROC curve as a robustness measurement criteria. The proposed model was compared to three traditional machine-learning algorithms: LR, RBF-NN and the MLP-NN. The experimental results show the improved performance of our proposed predictive method for predicting creditworthiness of borrowers.

Due to the diversity of defocus blurred images, causing the unsatisfied effect of sharpness enhancement of the defocused and blurred image. On this basis, the paper has proposed an enhancement algorithm of defocused and blurred image base on non-local constraints. The estimated results similar to the original image was acquired, the actual defocus blur optical transfer function was calculated and the low inverter over-amplifying the noise was avoided through the improvement of inverse filtering by the Wiener filter. Based on the symmetry of a defocused and blurred circular, the diffusion curve of straight marginal function was filtered and restored to eliminate the noise in the image. On this basis, the non-local self-similarity and total variational regularity of the defocused and blurred image were complemented, and the sharpness of the defocused and blurred image was finally enhanced by using the non-local model to restore the marginal and detailed texture information of the defocused and blurred image. The results of the simulation have shown that the proposed method could not only increase the computational efficiency, but also obtain satisfactory sharpness enhancement of the defocused and blurred image, and a better retain effect of the marginal information.

Yu and Cao proposed “Cryptography based on delayed chaotic neural networks” in 2006. However, in 2009, Yang et al. pointed out the Yu-Cao scheme can not against chosen plaintext attack. Liu et al. studies exclusiveor logical operation very well, and provided Boolean algebra proofs in 2012. Ye et al. used Liu et al.’s method to reinterpret and analyze Yu-Cao scheme in 2018. In this paper the authors would like to give a formal verification by Galois field expression on the exclusive-or operation problem again. As this result, it makes more effective insecure to Yu-Cao algorithm.

The primary goal of Vehicular Ad-hoc Network (VANET) technology’s development is to increase road safety and enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication in order to share crucial information and prevent road accidents. VANET establishes a mobile network between moving cars by considering each vehicle as a separate entity. Safety applications in VANET are currently receiving a lot of attention from researchers as well as automobile manufacturers. This paper concentrate on simulation-based safety-critical techniques in vehicle networks employing radar and video sensors in various road styles. This paper discusses about how to create a simulation-based driving scenario environment and calculates the fluctuations in sensor detection rates depending on different driving conditions. And also created a basic Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication network. An integrated approach is also proposed to Forward Collision Warning System (FCWS) in different cases.