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Showing papers in "Mathematical Problems in Engineering in 2022"


Journal ArticleDOI
Lindokuhle Ngcobo1
TL;DR: In this paper , a large number of core observations, casting slices, scanning electron microscopy, and X-ray diffraction methods were analyzed for the Carboniferous volcanic rocks in Xiquan block, Beisantai Oilfield, Junggar Basin as the research target.
Abstract: In recent years, petroleum exploration in the Carboniferous volcanic rock reservoirs in the Junggar Basin has been the focus of important petroleum energy development in western China. The lithologic identification of volcanic rock reservoirs seriously restricts the accuracy of reservoir prediction and affects the success rate of oil exploration. Different types of volcanic rocks have different petrological characteristics and mineral assemblages, especially affected by the depositional environment. The volcanic rocks in different regions have their own uniqueness. This paper takes the Carboniferous volcanic reservoirs in Xiquan block, Beisantai Oilfield, Junggar Basin as the research target. Through a large number of core observations, casting slices, scanning electron microscopy, and X-ray diffraction methods, the Carboniferous volcanic rocks are analyzed. The petrology, pore characteristics, physical properties, and diagenetic evolution history of the reservoir are analyzed. The study shows that the volcanic facies in the Xiquan block can be divided into explosive facies, overflow facies, and volcanic sedimentary facies, among which the explosive facies is subdivided into empty subfacies (volcanic breccia-breccia tuff combination) and thermal base wave subfacies (tuff). The lithology of the reservoir is pyroclastic rock and volcanic lava, belonging to medium-porous and ultralow permeability reservoirs, and the storage space can be divided into three types: primary pores, secondary pores, and fractures. The lithology of key exploration is breccia tuff, followed by breccia tuff and volcanic breccia.

52 citations


Journal ArticleDOI
TL;DR: In this article , a model combining incremental capacity analysis (ICA) and bidirectional long and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries.
Abstract: Lithium battery state of health (SOH) is a key parameter to characterize the actual battery life. SOH cannot be directly measured. In order to further improve the accuracy of SOH estimation of lithium batteries, a model combining incremental capacity analysis (ICA) and bidirectional long- and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries. First, the health characteristic parameters are initially selected from the lithium battery charging curve, and the health characteristics are extracted by the Pearson correlation coefficient, including the charging time of constant current, charging time of constant voltage, voltage change rate from 300 s to 1000 s, 200s of voltage per cycle at a time. Second, ICA was used to deeply mine the deep associations related to SOH and the peaks of IC curves and their corresponding voltages were extracted as additional inputs to the model. Then, Bi-LSTM is used to form a combined SOH estimation model through adaptive weighting factors. Finally, the verification is based on the 5th battery parameters of the NASA lithium battery data set. The experimental results show that the proposed combined model reduces the mean square error by 55.17%, 49.28%, and 41.47%, respectively, compared with single models such as BP neural network (BPNN), LSTM, and gated recurrent neural network (GRU).

42 citations


Journal ArticleDOI
TL;DR: In this research, a real-time VM migration algorithm for balancing fog load has been proposed and achieves 18% better cost results and optimized response time (ORT) compared to the closest data center (CDC).
Abstract: As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

40 citations


Journal ArticleDOI
TL;DR: This article intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits, and summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification.
Abstract: Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

34 citations


Journal ArticleDOI
TL;DR: A new AA distributed combined deep learning intrusion detection method for the Internet of Vehicles based on the Apache Spark framework that combines deep-learning convolutional neural network and extended short-term memory network to extract features and data for detection of car network intrusion from large-scale car network data traffic and discovery of abnormal behavior.
Abstract: As 5G and other technologies are widely used in the Internet of Vehicles, intrusion detection plays an increasingly important role as a vital detection tool for information security. However, due to the rapid changes in the structure of the Internet of Vehicles, the large data flow, and the complex and diverse forms of intrusion, traditional detection methods cannot ensure their accuracy and real-time requirements and cannot be directly applied to the Internet of Vehicles. A new AA distributed combined deep learning intrusion detection method for the Internet of Vehicles based on the Apache Spark framework is proposed in response to these problems. The cluster combines deep-learning convolutional neural network (CNN) and extended short-term memory (LSTM) network to extract features and data for detection of car network intrusion from large-scale car network data traffic and discovery of abnormal behavior. The experimental results show that compared with other existing models, the algorithm of this model can reach 20 in the fastest time, and the accuracy rate is up to 99.7%, with a good detection effect.

33 citations


Journal ArticleDOI
TL;DR: It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis.
Abstract: Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author’s keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations.

32 citations


Journal ArticleDOI
TL;DR: Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-V MD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA -VMD.
Abstract: To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals.

28 citations


Journal ArticleDOI
TL;DR: In this article , the authors examined the recent literature on estimating the state-of-charge (SOC) of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF).
Abstract: With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.

27 citations


Journal ArticleDOI
TL;DR: In this article , a comprehensive review of model-based and data-driven approaches to predict the remaining useful life of super-capacitors is presented, with the expectation of providing a reference for further research in this field.
Abstract: As a new type of energy-storage device, supercapacitors are widely used in various energy storage fields because of their advantages such as fast charging and discharging, high power density, wide operating temperature range, and long cycle life. However, the degradation and failure of supercapacitors in large-scale applications will adversely affect the operation of the whole system. To maximize the efficiency of supercapacitors without damaging the equipment and to ensure timely replacement before reaching the end of their useful life, it is critical to accurately predict the remaining useful life of supercapacitors. This paper presents a comprehensive review of model-based and data-driven approaches to predict the remaining useful life of supercapacitors, introduces the characteristics of the various methods, and foresees future trends, with the expectation of providing a reference for further research in this field.

24 citations


Journal ArticleDOI
TL;DR: A quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy and enhanced outcomes is presented.
Abstract: Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

24 citations


Journal ArticleDOI
TL;DR: A detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts, along with a detailed comparison of various hybrid approaches based on recent trends.
Abstract: Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

Journal ArticleDOI
TL;DR: In this article , a model is constructed by multi stepwise weight assessment ratio analysis (M-SWARA) and technique for order preference by similarity to ideal solution (TOPSIS) with q-rung orthopair fuzzy sets (q-ROFSs) and golden cut.
Abstract: This study aims to find an appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this context, a model is constructed by multi stepwise weight assessment ratio analysis (M-SWARA) and technique for order preference by similarity to ideal solution (TOPSIS) with q-rung orthopair fuzzy sets (q-ROFSs) and golden cut. At the first stage, five different systems are weighted for the effectiveness of the microgeneration energy investments. Secondly, four different renewable energy alternatives are ranked regarding the performance of these projects. In addition, a comparative analysis is also implemented with intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The findings are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is determined that grid-connected with battery backup is the most important system choice. On the other hand, solar energy is the most appropriate alternative for microgeneration system investments. Grid-connected system should be implemented for the performance of the microgeneration projects. Hence, providing a sustainable access to the electricity can be possible. Sufficient amount of electricity may not be obtained from wind and solar energy because of the climate changes. In this process, grid-connected system can handle this problem effectively.

Journal ArticleDOI
TL;DR: A CCTV image-based human face recognition system using different techniques for feature extraction and face recognition, which recognized faces with a minimum computing time and an accuracy of more than 90%.
Abstract: This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction algorithms, principal component analysis (PCA) and convolutional neural network (CNN). We use and compare the performance of the algorithms K-nearest neighbor (KNN), decision tree, random forest, and CNN. The recognition is done by applying these techniques to the dataset with more than 40K acquired real-time images at different settings such as light level, rotation, and scaling for simulation and performance evaluation. Finally, we recognized faces with a minimum computing time and an accuracy of more than 90%.

Journal ArticleDOI
TL;DR: A new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly.
Abstract: Leukemia is a fatal category of cancer-related disease that affects individuals of all ages, including children and adults, and is a significant cause of death worldwide. Particularly, it is associated with White Blood Cells (WBC), which is accompanied by a rise in the number of immature lymphocytes and cause damage to the bone marrow and/or blood. Therefore, a rapid and reliable cancer diagnosis is a critical requirement for successful therapy to raise survival rates. Currently, a manual analysis of blood samples obtained through microscopic images is done to diagnose this disease, which is often very slow, time-consuming, and less accurate. Furthermore, in microscopic analysis, the appearance and shape of leukemic cells seem very similar to normal cells which make detection more difficult. In the past decades, deep learning utilizing Convolutional Neural Networks (CNN) has provided state-of-the-art approaches for image classification problems; however, there is still a gap to improve their efficacy, learning procedure, and performance. Therefore, in this research study, we proposed a new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples. The proposed deep learning architecture emphasizes the channel associations on all levels of feature representation by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly. In addition, the incorporation of the squeeze-and-excitation process enhances the feature discriminability of leukemic and normal cells, and strategically assists in exposing informative features of leukemia cells while suppressing less valuable ones as well as improving feature representational power of deep learning algorithm. We show that piling these learning operations of squeeze and excite together in a deep learning model can improve the performance of the model in diagnosing leukemia from microscopic images based on blood samples of patients. Furthermore, an extensive set of experiments are performed on both cropped cells and full-size microscopic images as well as with data augmentation to address the problem of fewer data and to further boost their performance. The proposed model is tested on two publicly available datasets of blood samples of leukemia patients, namely, ALL_IDB1 and ALL_IDB2. The suggested deep learning model exhibits good results and can be utilized to make a reliable computer-aided diagnosis for leukemia cancer.

Journal ArticleDOI
TL;DR: In this article , a new methodology is planned based on an improved FCN (fully connected network) to regulate the assessment of the quality of students in Higher Education HE, which is composed of different phases: the first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method.
Abstract: EDM and LA are two fields that study how to use facts to get more academic learning and enhance the students’ entire performance. Both areas are concerned with a broad range of issues such as curriculum strategies, coaching, mental well-being of students, learning motivation, and academic achievement. The COVID-19 pandemic highly disrupted the higher education sector and shifted the old, chalk-talk teaching-learning model to an online learning format. This meant that the structure and nature of teaching, learning, assessment, and feedback methodologies also changes. With the empowerment in technology, timely and effective feedback is provided by the teachers to achieve greater learning. Through these studies, it is noted that negative feedback discourages the effort and achievement of learners, so it should be carefully crafted and delivered. In this work, a new methodology is planned based on an improved FCN (fully connected network). The key impartial of the proposed method is to regulate the assessment of the quality of students in Higher Education HE. The proposed methodology is composed of different phases: The first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method. The second phase is data orientation, in which the information is oriented in a specific file format. After that, data are cleaned, and preprocessing methods are applied. In the fourth phase, a machine learning-based model is developed to predict student’s academic performance. The fully connected neural network is enhanced with LA to better assess student quality in higher education. The proposed work is evaluated with the OULAD database, which was gathered from the students of Open University. The proposed methodology has attained an accuracy of 84%, more significant than the conventional ANN model accuracy rate. The proposed methodology’s Recall, F1-score, and precision rates are 0.88, 0.91, and 0.93, respectively.

Journal ArticleDOI
TL;DR: The primary purpose is to extend and propose ideas related to Einstein’s ordered weighted geometric aggregation operator from fuzzy structure to PFSS structure, such as the Pythagorean fuzzy soft Einstein-ordered weighted geometric (PFSEOWG) operator.
Abstract: The Pythagorean fuzzy soft set (PFSS) is the most influential and operative tool for maneuvering compared to the Pythagorean fuzzy set (PFS), which can accommodate the parameterization of alternatives. It is also a generalized form of intuitionistic fuzzy soft sets (IFSS), which delivers healthier and more exact valuations in the decision-making (DM) procedure. The primary purpose is to extend and propose ideas related to Einstein’s ordered weighted geometric aggregation operator from fuzzy structure to PFSS structure. The core objective of this work is to present a PFSS aggregation operator, such as the Pythagorean fuzzy soft Einstein-ordered weighted geometric (PFSEOWG) operator. In addition, the basic properties of the proposed operator are introduced, such as idempotency, boundedness, and homogeneity. Moreover, a DM method based on a developed operator has been presented to solve the multiattribute group decision-making (MAGDM) problem. A real-life application of the anticipated method has been offered for a capitalist to choose the most delicate business to finance his money. Finally, a brief comparative analysis with some current methods demonstrates the proposed approach’s effectiveness and reliability.

Journal ArticleDOI
TL;DR: In this article , the Glowworm Swarm Optimization (GSO) tuned proportional integral (PI) controller is used for the synchronous reluctance motor (Sync-RM) drive in a bridgeless LUO converter.
Abstract: The synchronous reluctance motor (Sync-RM) is known for its higher reliability, faster dynamic response, higher efficiency, higher speed range, higher power density, and higher torque per ampere. Consequently, the scope of research into emerging Sync-RMs drive system has been extended. For different kinds of mechanical and electrical applications, motors are important components of any machine or tool. There are several types of motors available for various kinds of industrial applications, but the benefits of Sync-RM in medium- and low-power applications have eventually been very efficient compared to other motor types, regarding the factors such as high flux density per unit volume, high efficiency, requirements of low maintenance, and low electromagnetic obstruction. This research proposed a Sync-RM drive fed by a bridgeless LUO converter (BLLC) using the Glowworm Swarm Optimization (GSO) tuned proportional integral (PI) controller. High competence, higher power density, and a small structure of the motor drive are delivered by the PI-tuned converter. The output voltages are related to the Sync-RM motor of the proposed converter. Due to its minimum effort and modest development, the Sync-RM here is chosen for the drive. The proposed Sync-RM drive framework based on the LUO converter was evaluated by simulation using the Matlab/Simulink tool.

Journal ArticleDOI
TL;DR: In this paper , a mathematical model of Burgers' fluid in the magnetic field is presented, and the thermal energy transport aspects are examined by employing the space and temperature-related heat source.
Abstract: The flow of Burgers’ fluid in the magnetic field new mathematical modeling is introduced in this article which is heated convectively and maintained. The thermal energy transport aspects are examined by employing the space- and temperature-related heat source. In the present investigation, the homogeneous-heterogeneous reactions will present the features of scrutiny of the fluid concentration. For the purpose of dimensionless similarity transformations, ordinary differential equations (ODEs) are utilized practically. Developed ODEs are solved by introducing the concepts of Runge–Kutta–Fehlberg’s fourth-fifth method. The graphs show the pertinent outcome. The relaxation time parameter is exhibited by diminishing the thermal distribution of Burgers’ fluid property, and this will depend on the relaxation time factor. Biot number and retardation time factor behaviors are analyzed by opposing the behavior of the material factor of Burgers’ fluid. The response of homogeneous strength is deteriorated by the concentration rate of the fluid, and this will augment the data using the heterogeneous response with greater magnitude. By using already published studies, it is investigated that the present investigation is validated.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors adopted the qualitative comparison analysis method to build configuration model by combining platforms, students, parents, teachers (4 level seven elements) with middle school students' continuance intention.
Abstract: The research on the users’ continuance intention behavior of online learning platforms is not a new topic. In the past, more studies were conducted on users of MOOC online learning platforms, which are based on symmetric causality. This study adopts the qualitative comparison analysis method to build configuration model by combining platforms, students, parents, teachers (4 level seven elements) with middle school students’ continuance intention. The configuration analysis identifies the user’ retention path of middle school students on online learning platform under the collaborative influence of multi-level elements, summarizes the online learning platform of continuance intention mechanism and implementation path for middle school students and reveals that the users of online learning platform for middle school students keep clear of the mechanism of action and realistic choice. In the end, the user retention mechanism of middle school students’ online learning platform is summarized, including platform quality orientation, platform interaction orientation, and “parent-teacher” dual drive.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a novel fatigue life prediction considering the temperature load, which may be neglected in the traditional assessment of suspension bridge steel deck welds under dynamic vehicle load.
Abstract: The present study proposes a novel fatigue life prediction considering the temperature load, which may be neglected in the traditional assessment of suspension bridge steel deck welds under dynamic vehicle load. Vehicle fatigue, pavement temperature, and temperature gradient models are developed based on the test data from the weight-in-motion system, U-rib welds, pavement temperature, and environment temperature. The U-rib-to-deck and U-rib-to-U-rib welds fatigue stresses are obtained considering both vehicle and temperature loads with transient analysis method in ANSYS package. Then, the temperature gradient fatigue stress spectra are calculated. After that, the fatigue life of two weld types is predicted considering the coupled vehicle-temperature loads. The results indicate that the fatigue stress varies linearly with the temperature of the asphalt concrete. The effect of the temperature on the weld’s fatigue life decreases as the distance increases between the welds and the pavement. The dynamic vehicle load results in a higher fatigue stress than the temperature gradient, indicating that the vehicle load contributes mainly to the bridge’s fatigue damage. Finally, it is calculated that the fatigue damage of two weld types is magnified 5.06 and 1.50 times when the temperature effect is considered after 100-year service of Nanxi Yangtze River Bridge.

Journal ArticleDOI
TL;DR: In this paper , a survey of experts and managers who are involved in EPC project-based organizations, and the obtained data is used to test the relationship between organizational issues, firm resources, used methods and techniques, and performance measuring tools.
Abstract: To characterize the process of lead-time reduction in EPC project-based organizations, including the engineering, procurement, and construction activities, various elements such as organizational issues, firm resources, used methods and techniques, and performance measuring tools are important. In this study, the four mentioned elements are evaluated through a survey in different EPC project-based organizations. The survey is sent to only experts and managers who are involved in EPC projects, and the obtained data is used to test the relationship between the mentioned variables and the lead time of developing new products. This evaluation is followed by a set of generalized lessons learned and courses of action to improve the development process. Additionally, the attributes of performance measurement in such projects are scrutinized. Therefore, to analyze the impacts of the mentioned variables and their variations on the performance measurement, a mathematical model based on the fuzzy approach is developed. The results show that all of the four considered variables are correlated to the lead time and affect the quality perceived by the main client and the cost of the project. Moreover, data obtained from the proposed mathematical formulations illustrated that the fuzzy modeling approach is an effective method to predict the performance measurement level when the levels of the input variables are given.

Journal ArticleDOI
TL;DR: In this paper , a voltage controller using PWM technique integrated with equal area digital modulation technique and connected to resistive loads is discussed, which reduces the harmonics in the lower order significantly and improves the power factor compared to the existing conventional line commutated voltage controllers.
Abstract: AC voltage controller using PWM technique integrated with equal area digital modulation technique and connected to resistive loads is discussed in this paper. The proposed technique reduces the harmonics in the lower order significantly and improves the power factor compared to the existing conventional line commutated voltage controllers. The voltage and current waveforms are smoothened; therefore, a sinusoidal nature is achieved. The power factor is considerably improved at the low output voltage range when compared to existing methods. The capabilities of the proposed technique are computed mathematically and simulation results are compared with the existing methods.

Journal ArticleDOI
TL;DR: In this article , the consequences of radiation and heat consumption of MHD convective flow of nanofluid on a heated stretchy plate with injection/suction and convective heating/cooling conditions are examined.
Abstract: This paper scrutinizes the consequences of radiation and heat consumption of MHD convective flow of nanofluid on a heated stretchy plate with injection/suction and convective heating/cooling conditions. The nanofluid encompasses with C u and A g nanoparticles. We enforce the suited transformation to remodel the governing mathematical models to ODE models. The HAM (homotopy analysis method) idea is applied to derive the series solutions. The divergence of fluid velocity, temperature, skin friction coefficient, local Nusselt number, entropy generation, and Bejan number on disparate governing parameters is exhibited via graphs and tables. It is seen that the fluid velocity in both directions is subsided when elevating the magnetic field and Forchheimer number. Also, the C u nanoparticles possess hefty speed compared to A g nanoparticles because the density of A g nanoparticles is high compared to that of C u nanoparticles. The fluid temperature upturns when enlarging the heat generation and radiation parameters. The skin friction coefficients and local Nusselt number are high in A g nanoparticles than in C u nanoparticles.

Journal ArticleDOI
TL;DR: An artificial intelligence technique-based power information system access control model has been designed and it has been proved that the design model can effectively support the access and modification of legitimate users and prevent illegal users from accessing and the control accuracy is high.
Abstract: Looking at the issues of low efficiency, poor control performance, and difficult access control of the traditional role-based access control model, an artificial intelligence technique-based power information system access control model has been designed. The detector is designed by artificial intelligence technology, combining artificial neural network, and artificial immune algorithm, which provide the basis for checking the access request module. It has been proved that the design model can effectively support the access and modification of legitimate users and prevent illegal users from accessing, and the control accuracy is high. The use of artificial intelligence (AI) in the power sector is now reaching emerging markets, where it may have a critical impact, as clean, cheap, and reliable energy is essential to development. Artificial intelligence can be proven very efficient for resolving the control and decision-making issues in high complex systems.

Journal ArticleDOI
TL;DR: In this paper , an Indian NGO is considered as a decision-maker in the choice of the best FDM machine based on nine common criteria because an NGO has prescribed nine different FDM machines and the NGO needs the help for the purchase of the suitable FDM to produce different fields of prototypes.
Abstract: Additive manufacturing (AM) or 3D printing has been playing a very important role in the manufacturing sector in recent decades. The AM basic process is meant to produce an object layer by layer and has many advantages that include the occurrence of only minimal production waste during production and easy manufacture of even the most geometrically complex materials. However, there are many challenging decision-making situations in the production of AM for its users, for example, the build chamber, material specification, technology types, and application requirements. This includes the choice of the best AM machine (AMM) from many AMM with slightly different features that are identical on the market, especially on a real-time basis. This research explored ways that AMM is to be selected using multi-criteria decision-making (MCDM) on a real-time basis. This includes the use of the MCDM fussy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help select the most suitable fusion deposition modeling (FDM) for an Indian nongovernment organization (NGO) from nine different machines based on contemporary. Practice in this research paper, an Indian NGO is considered as a decision-maker in the choice of the best FDM machine based on nine common criteria because an NGO has prescribed nine different FDM machines and the NGO needs the help for the purchase of the suitable FDM to produce different fields of prototypes. The outcome of this research is to recommend a suitable FDM machine from the nine similar to slightly different features of FDM machines by the suggestion of the field experts (AM machine users). The contribution of this research is not only to enable the purchase of the suitable AM machine but also to reveal the various contemporary FDM machines and the general criteria to be considered in choosing them.

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TL;DR: In this paper , the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research, is investigated, and the one-day-ahead out-of-sample forecast obtained for a whole year is evaluated using different forecasting accuracy measures.
Abstract: In today’s liberalized electricity markets, modeling and forecasting electricity demand data are highly important for the effective management of the power system. However, electricity demand forecasting is a challenging task due to the specific features it exhibits. These features include the presence of extreme values, spikes or jumps, multiple periodicities, long trend, and bank holiday effect. In addition, the forecasts are required for a complete day as electricity demand is decided a day before the physical delivery. Therefore, this study aimed to investigate the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research. To this end, the demand time series is first treated for the extreme values. The filtered series is then divided into deterministic and stochastic components. The generalized additive modeling technique is used to model the deterministic component, whereas functional autoregressive (FAR), FAR with exogenous variable (FARX), and classical univariate AR models are used to model and forecast the stochastic component. Data from the Nord Pool electricity market are used, and the one-day-ahead out-of-sample forecast obtained for a whole year is evaluated using different forecasting accuracy measures. The results indicate that the functional modeling approach produces superior forecasting results, while FARX outperforms FAR and classical AR models. More specifically, for the NP electricity demand, FARX produces a MAPE value of 2.74, whereas 6.27 and 9.73 values of MAPE are obtained for FAR and AR models, respectively.

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TL;DR: RSM effectiveness in maintaining ML algorithm performance while reducing the number of runs required to reach effective hyperparameter settings in comparison with the commonly used grid search (GS) is demonstrated.
Abstract: This study applies response surface methodology (RSM) to the hyperparameter fine-tuning of three machine learning (ML) algorithms: artificial neural network (ANN), support vector machine (SVM), and deep belief network (DBN). The purpose is to demonstrate RSM effectiveness in maintaining ML algorithm performance while reducing the number of runs required to reach effective hyperparameter settings in comparison with the commonly used grid search (GS). The ML algorithms are applied to a case study dataset from a food producer in Thailand. The objective is to predict a raw material quality measured on a numerical scale. K-fold cross-validation is performed to ensure that the ML algorithm performance is robust to the data partitioning process in the training, validation, and testing sets. The mean absolute error (MAE) of the validation set is used as the prediction accuracy measurement. The reliability of the hyperparameter values from GS and RSM is evaluated using confirmation runs. Statistical analysis shows that (1) the prediction accuracy of the three ML algorithms tuned by GS and RSM is similar, (2) hyperparameter settings from GS are 80% reliable for ANN and DBN, and settings from RSM are 90% and 100% reliable for ANN and DBN, respectively, and (3) savings in the number of runs required by RSM over GS are 97.79%, 97.81%, and 80.69% for ANN, SVM, and DBN, respectively.

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TL;DR: The paper has proposed an image inpainting network driven by multilevel attention progression mechanism that has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to performMultilevel order according to the scale size.
Abstract: Existing image inpainting schemes generally have the problems of structural disorder and blurred texture details. This is mainly because, in the reconstruction process of the damaged area of the image, it is difficult for the inpainting network to make full use of the information in the nondamaged area to accurately infer the content of the damaged area. Therefore, the paper has proposed an image inpainting network driven by multilevel attention progression mechanism. The proposed network has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to perform multilevel order according to the scale size. Attention feature progression is to achieve the goal of the full progression of high-level features including structure and details in the network. To further realize fine-grained image inpainting and reconstruction, the paper has also proposed a composite granular discriminator to achieve image inpainting process performing global semantic constraints and nonspecific local dense constraints. The related experimental results in the paper can show that the proposed method can achieve higher quality repair results than state-of-the-art ones.

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TL;DR: In this article , the authors examined the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control using MATLAB-Simulink simulation model to determine which one performed the best.
Abstract: Three-phase induction motors are becoming increasingly popular for electric cars and industrial uses because of their improved efficiency and simplicity of production, among other things. Many enterprises and industries use induction motors in several rotating applications. However, it is a difficult talent to master when it comes to controlling the speed of an induction motor for various purposes. This study examines the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control. This work utilized the fractional order Darwinian particle swarm optimization (FODPSO) method in fuzzy methodology to optimize a motor’s performance. Field-oriented control (FOC) and Direct torque control (DTC) methods are regulated by FODPSO, which is compared to standard FOC and DTC methods. MATLAB-Simulink was used to compare the outcomes of each system’s simulation model to determine which one performed the best. The support vector machine-direct torque control (SVM-DTC) technology is famous for its rapid dynamic response and decreased torque ripples. Using torque and settling time and rising time reduction, the suggested technique is proved to be superior to the present way.

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TL;DR: In this article , an extension of the cosine generalized family is presented, which adopts right and left-skewed, symmetrical, and reversed-J density shapes, while all possible monotone and nonmonotone shapes are exhibited by the hazard rate function.
Abstract: An extension of the cosine generalized family is presented in this paper by using the cosine trigonometric function and method of parameter induction concurrently. Prominent characteristics of the proposed family along with useful results are extracted. Moreover, two new subfamilies and several special models are also deduced. A four-parameter model called an Extended Cosine Weibull (ECW) with its mathematical properties is studied deeply. Graphical study reveals that the new model adopts right- and left-skewed, symmetrical, and reversed-J density shapes, while all possible monotone and nonmonotone shapes are exhibited by the hazard rate function. The maximum likelihood technique is exercised for parametric estimation, while estimation performance is accessed via Monte Carlo simulation study graphically and numerically. The superiority of the presented model over several outstanding and competing models is confirmed via three reliability and survival dataset applications.