Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

Strategic Alliance Pattern Evaluation Model for Taiwan’s Machine Tool Industry: A Hierarchical DEMATEL Method

The worldwide machine tool market is anticipated to reach a value of USD 68.9 billion by 2021, from USD 65.6 billion in 2020. This projection is based on the progressive production drop within the car industry, which is the largest customer of machine devices, and supply chain disruption. The machine tool industry in Taiwan faces a severe challenge and has been unobtrusively experiencing an inner reshuffling and innovative transformation. The developing strategic alliances reflect a basic endeavor by numerous firms to improve their specialized capabilities. This study applied the DEMATEL, a suitable method for gathering group knowledge to form a structural model and visualize the casual relationship between subsystems through a casual diagram, revealing that the causal relationships between measurement criteria and the proposed model can provide a viable assessment of the alliance with satisfactory criteria that fit the decision-makers requirements, especially when the assessment criteria are various and interrelated. Financial resources were the strongest factor within the strategic behavior dimension (D1), whereas the minimize manufacturing cost was the foremost basic determinant in the cost perspective (D2). The specialists also demonstrated that obtaining dominant technology was a determinative component within organizational learning (D3). This paper offers proposals for government authorities to plan a machine tools industry strategy for Taiwan and for companies to formulate business directions for long-run advancement.

Existence of Fixed Points in Fuzzy Strong b-Metric Spaces

In the present research, modern fuzzy technique is used to generalize some conventional and latest results. The objective of this paper is to construct and prove some fixed-point results in complete fuzzy strong b-metric space. Fuzzy strong b-metric (sb-metric) spaces have very useful properties such as openness of open balls whereas it is not held in general for b-metric and fuzzy b-metric spaces. Due to its properties, we have worked in these spaces. In this way, we have generalized some well-known fixed-point theorems in fuzzy version. In addition, some interesting examples are constructed to illustrate our results.

Backward Trajectory and Multifractal Analysis of Air Pollution in Zhengzhou Region of China

With the continuous promotion of industrialization and urbanization, China's environmental pollution is becoming increasingly serious, which has caused considerable damage to the natural balance. Air pollution seriously harms people's physical and mental health, the ecological environment, and the social sustainable development of society. In this study, the backward trajectory model and multifractal methods were adopted to analyze air pollution in Zhengzhou. The backward trajectory analysis showed that most clusters of air pollution were from southern Hebei, eastern Shandong, and mid-western Henan, which were then transported to Zhengzhou. For the PSCF and CWT analyses, we selected four representative cities to explore how close the air pollution of Zhengzhou is to other areas on the basis of air polluted concentration. The results of several multifractal methods indicated that multifractality existed in the AQI time series of Zhengzhou and cross-correlations between Zhengzhou and each of the four cities. The widths of multifractal spectra showed that the air pollution in Zhengzhou was closest to that in Jinan, followed by Shijiazhuang, Zibo, and Luoyang. The CDFA analysis showed that carbon monoxide (CO), nitrogen dioxide (NO2), and inhalable particulate matter (PM10) had important influences on air pollution in Zhengzhou. These findings offer a useful reference for air pollution sources and their potential contributions in Zhengzhou, which can support policy makers in environmental governance and in achieving sustainable urban development.

A Low-Cost and Ultralight Unmanned Aerial Vehicle-Borne Multicamera Imaging System Based on Smartphones

Newly developed oblique photogrammetry (OP) techniques based on unmanned aerial vehicles (UAVs) equipped with multicamera imaging systems are widely used in many fields. Smartphones cost less than the cameras commonly used in the existing UAV OP system, providing high-resolution images from a built-in imaging sensor. In this paper, we design and implement a novel low-cost and ultralight UAV OP system based on smartphones. Firstly, five digital cameras and their accessories detached from the smartphones are then fitted into a very small device to synchronously shoot images at five different perspective angles. An independent automatic capture control system is also developed to realize this function. The proposed smartphone-based multicamera imaging system is then mounted on a modified version of an existing lightweight UAV platform to form a UAV OP system. Three typical application examples are then considered to evaluate the performance of this system through practical experiments. Our results indicate that both horizontal and vertical location accuracy of the generated 3D models in all three test applications achieve centimeter-level accuracy with respect to different ground sampling distances (GSDs) of 1.2 cm, 2.3 cm, and 3.1 cm. The accuracy of the two types of vector maps derived from the corresponding 3D models also meet the requirements set by the surveying and mapping standards. The textural quality reflected by the 3D models and digital ortho maps (DOMs) are also distinguishable and clearly represent the actual color of different ground objects. Our experimental results confirm the quality and accuracy of our system. Although flight efficiency and the accuracy of our designed UAV OP system are lower than that of the commercial versions, it provides several unique features including very low-cost, ultralightweight, and significantly easier operation and maintenance.

Survey on Reverse-Engineering Tools for Android Mobile Devices

With the presence of the Internet and the frequent use of mobile devices to send several transactions that involve personal and sensitive information, it becomes of great importance to consider the security aspects of mobile devices. And with the increasing use of mobile applications that are utilized for several purposes such as healthcare or banking, those applications have become an easy and attractive target for attackers who want to get access to mobile devices and obtain users’ sensitive information. Developing a secure application is very important; otherwise, attackers can easily exploit vulnerabilities in mobile applications which lead to serious security issues such as information leakage or injecting applications with malicious programs to access user data. In this paper, we survey the literature on application security on mobile devices, specifically mobile devices running on the Android platform, and exhibit security threats in the Android system. In addition, we study many reverse-engineering tools that are utilized to exploit vulnerabilities in applications. We demonstrate several reverse-engineering tools in terms of methodology, security holes that can be exploited, and how to use these tools to help in developing more secure applications.

Assessing the Effect of Instructor’s Emotional Intelligence (EI) on the Students’ Satisfaction Index (SSI): Meta-Analysis of University Students

The present study aims to examine the relationship of instructors’ emotional intelligence (EI) with the satisfaction index of their corresponding students. For this purpose, data were collected from 650 full-time students and 6 male instructors from a major Middle Eastern University. Emotional intelligence of the instructors was measured with the help of average of students’ responses with the weightage of each assessing parameter, i.e., self-awareness, self-management, social awareness, and relationship management which also reflected the students’ satisfaction index (SSI). Moreover, authenticity of the data was confirmed with the help of Cronbach’s alpha, and the analysis of data was carried out using descriptive statistics, correlation, and box plots. The students’ satisfaction index is calculated by correlating various parameters such as comfort, skill, learning, and motivation in order to identify the most critical parameter. For identifying the most critical parameter, box plots are used. Final results reveal a strong correlation of instructor’s EI with student satisfaction index (r = 0.951, p < 0.005 , F >> Fcritical). Findings of the study can be beneficial to highlight the importance of students’ satisfaction index (SSI) which is correlated with instructor’s EI.

Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Electrically excited synchronous motor (EESM) is widely used in many large equipment drives because of its strong overload capacity, high efficiency, and adjustable power factor. The research and development of a high-performance EESM control system can realize the high combination of energy-saving speed regulation and green environmental protection and has a high social effect and economic value. In this paper, the signal injection method is used to obtain the initial rotor position information of EESM. Sliding Fourier transform is used to improve the initial position angle detection method based on the rotor signal injection method, and the improved method is compared with the traditional voltage integration method. Rotor high-frequency signal injection method was used to detect the rotor position information of the motor during operation, and the influence of the damping winding on the rotor signal injection method was analyzed. On the premise that the damping winding had no influence on the method, a method of obtaining the rotor position information of EESM without a speed sensor was designed. Finally, the speed sensorless regulation system using the initial rotor position detection method is simulated, which verifies the accuracy of the proposed speed sensorless control scheme.

Optimal Replenishment Strategy for Inventory Mechanism with a Known Price Increase and Backordering in Finite Horizon

For the finite horizon inventory mechanism with a known price increase and backordering, based on minimizing the inventory cost, we establish two mixed integer optimization models. By buyer’s cost analysis, we present the closed-form solutions to the models, and by comparing the minimum cost of the two strategies, we provide an optimal ordering policy to the buyer. Numerical examples are presented to illustrate the validity of the model, and sensitivity analysis on major parameters is also made to show some insights to the inventory model.

Multidimensional Connection Cloud Model Coupled with Improved CRITIC Method for Evaluation of Eutrophic Water

The degree of eutrophication in the water environment is deepening. For the appropriate treatment of eutrophication, it is essential to evaluate it accurately. However, the evaluation of eutrophication has not been well solved because it is full of uncertainty. Herein, a multidimensional connection cloud model, combined with the improved CRITIC (Criteria Importance Through Inter-criteria Correlation) method, was put forward here to assess water eutrophication and depict the randomness, ambiguity, and interaction of evaluation factors. First, an improved CRITIC was adopted to determine indicator weight so that the correlation among different indicators and more information were depicted. Secondly, a multidimensional connection cloud was simulated to characterize fuzzy indicators and ambiguous classification boundary values according to classification criteria. Next, the connection degree was calculated relative to the evaluation standard. The eutrophication grade was specified under the “maximum connection degree” principle. At last, the effectiveness and practicality of the model proposed here were affirmed by two cases and comparisons with supplementary methods. The results suggest that the proposed model can avoid shortcomings of the original CRITIC method and cloud model, and make the assessment result more realistic.