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Aamir Raza, Muhammad Noor-Ul-Amin
Journal of Reliability and Statistical Studies pp 527–540-527–540; https://doi.org/10.13052/jrss0974-8024.1427

Abstract:
The estimation of population mean is not meaningful using ordinary least square method when data contains some outliers. In the current study, we proposed efficient estimators of population mean using robust regression in two phase sampling. An extensive simulation study is conduct to examine the efficiency of proposed estimators in terms of mean square error (MSE). Real life example and extensive simulation study are cited to demonstrate the performance of the proposed estimators. Theoretical example and simulation studies showed that the suggested estimators are more efficient than the considered estimators in the presence of outliers.
Brijesh P. Singh
Journal of Reliability and Statistical Studies pp 541–550-541–550; https://doi.org/10.13052/jrss0974-8024.1428

Abstract:
Population scientists are generally developing mathematical models/techniques in demography and to provide brief explanation of extensive data sets. The prime objective of the present paper is to propose a probability model to illustrate the distribution of female’s age at first menstrual onset. Menarcheal age distribution is used to evaluate risk associated to reproductive issues and may be used as a demographic indicator of female fecundity. The suitability of proposed model is tested with the real data sets. Parameters of the proposed distribution have been estimated through least square estimation technique. It is observed that older female’s age at menarche is somewhat higher than the younger female’s age at menarche. Also we have constructed a life table for menarcheal age using a probability model. This life table is enable to provide expected duration of getting menarche for a girl of a particular age.
P. Jini Varghese, G. Michael Rosario
Journal of Reliability and Statistical Studies pp 491–526-491–526; https://doi.org/10.13052/jrss0974-8024.1426

Abstract:
The weaving machine’s reliability is assessed using newly introduced fuzzy numbers. The fuzzy numbers introduced in this study give a better method to improve the reliability than other techniques. Pendant Fuzzy Number, Hexant Fuzzy Number, and Octant Fuzzy Number are all introduced in this present study. Pendant Fuzzy Number, Hexant Fuzzy Number, and Octant Fuzzy Number,α-cuts are defined, as well as their mathematical operations. The numerical examples are utilised to conduct a comparative research of reliability using various Fuzzy Numbers, and their defuzzification is accomplished using various ways such as Signed Distance method, Graded Mean Integration Method and Centroid Method. The purpose of this study is to discover the most reliable value for a weaving machine.
Yuan Zheng, Xiangbin Wen
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2067

Abstract:
With the continuous innovation and development of modern computer science and mobile Internet and other information technologies, artificial intelligence (AI) is not a new thing. It has been widely studied and applied in many fields, and it is very important for people in modern society. The research fields of artificial intelligence mainly include: deep learning, natural language processing, computer vision, intelligent robot, automatic programming, data mining and so on. All kinds of industrial production and daily life will bring a very important practical significance and far-reaching influence. The rapid development and improvement of AI have effectively changed the daily life of modern people and improved work efficiency, and promoted the vigorous and healthy development of human economic and social civilization and the progress of information technology. When widely used, traditional network information and big data processing technologies are difficult to adapt to its development needs. Only by closely combining cloud computing technology with other technologies can it play a better role and give full play to AI technology and its development. The enthusiasm and promotion of related application technologies have promoted the smooth progress of AI technology and related undertakings. With the development and improvement of cloud computing technology, more and more users tend to use the cloud to work. However, a large number of cloud service failures occurred, causing huge losses for enterprises and individuals. In order to prevent damage to the interests of enterprises and individuals, cloud service providers will provide high-quality services as much as possible. This paper aims to study the application of AI technology in cloud computing environment resources, research on the indicator of reliability, and propose a cloud service reliability verification method for the infrastructure-as-a-service layer. Experimental research shows that through the reliability detection method in this paper, users can easily and quickly obtain the reliability of the purchased cloud service, and can intuitively feel whether the performance of each server meets the promised situation in the cloud service provider’s SLA.
Imjae Hwang, Juwon Yun, Woonam Chung, Jaeshin Lee, Cheong-Ghil Kim, YoungSik Kim, Woo-Chan Park
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2065

Abstract:
In a computing environment, higher resolutions generally require more memory bandwidth, which inevitably leads to the consumption more power. This may become critical for the overall performance of mobile devices and graphic processor units with increased amounts of memory access and memory bandwidth. This paper proposes a lossless compression algorithm with a multiple differential pulse-code modulation variable sign code Golomb-Rice to reduce the memory bandwidth requirement. The efficiency of the proposed multiple differential pulse-code modulation is enhanced by selecting the optimal differential pulse code modulation mode. The experimental results show compression ratio of 1.99 for high-efficiency video coding image sequences and that the proposed lossless compression hardware can reduce the bus bandwidth requirement.
, Ugur Cekmez, Ali Buldu
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2062

Abstract:
With the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders’ level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.
, Ying Wang
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2061

Abstract:
Ontology technology has been investigated in a wide range of areas and is currently being utilized in many fields. In the e-learning context, many studies have used ontology to address problems such as the interoperability in learning objects, modeling and enriching learning resources, and personalizing educational content recommendations. We systematically reviewed research on ontology for e-learning from 2008 to 2020. The review was guided by 3 research questions: “How is ontology used for knowledge modeling in the context of e-learning?”, “What are the design principles, building methods, scale, level of semantic richness, and evaluation of current educational ontologies?”, and “What are the various ontology-based applications for e-learning?” We classified current educational ontologies into 6 types and analyzed them by 5 measures: design methodology, building routine, scale of ontology, level of semantic richness, and ontology evaluation. Furthermore, we reviewed 4 types of ontology-based e-learning applications and systems. The observations obtained from this survey can benefit researchers in this area and help to guide future research.
Mingyi Huang, Chengyu Song
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2066

Abstract:
With the rapid advancement of hardware and internet technologies, we are surrounded by more and more Internet of Things (IoT) devices. Despite the convenience and boosted productivity that these devices have brought to our lives and industries, new security implications have arisen. IoT devices bring many new attack vectors, causing an increment of cyber-attacks that target these systems in the recent years. However, security vulnerabilities on numerous devices are often not fixed. This may due to providers not being informed in time, they have stopped maintaining these models, or they simply no longer exist. Even if an official fix for a security issue is finally released, it usually takes a long time. This gives hackers time to exploit vulnerabilities extensively, which in many cases requires customers to disconnect vulnerable devices, leading to outages. As the software is usually closed source, it is also unlikely that the community will review and modify the source code themselves and provide updates. In this study, we present ARMPatch, a flexible static binary patching framework for ARM-based IoT devices, with a focus on security fixes. After identified the unique challenges of performing binary patching on ARM platforms, we have provided novel features by replacing, modifying, and adding code to already compiled programs. Then, the viability and usefulness of our solution has been verified through demos and final programs on real devices. Finally, we have discussed the current limitations of our approach and future challenges.
Rekha P. M., Nagamani H. Shahapure, Punitha M., Sudha P. R.
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2064

Abstract:
The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.
Sobhan Mohammadnia, ,
Published: 13 October 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2063

Abstract:
REST Web Services is a lightweight, maintainable, and scalable service accelerating client application development. The antipatterns of these services are inadequate and counter-productive design solutions. They have caused many qualitative problems in the maintenance and evolution of REST web services. This paper proposes an automated approach toward antipattern detection of the REST web services using Genetic Programming (GP). Three sets of generic, REST-specific and code-level metrics are considered. Twelve types of antipatterns are examined. The results are compared with the manual rule-based approach. The statistical analysis indicates that the proposed method has an average precision and recall scores of 98% (95% CI, 92.8% to 100%) and 82% (95% CI, 79.3% to 84.7%) and effectively detects REST antipatterns.
Mahmoud Magdy, Mahmoud Kamal, Ashraf Mostafa Hamed, Ahmed Eldein Hussin, W. Aboelsoud
European Journal of Computational Mechanics pp 169–196-169–196; https://doi.org/10.13052/ejcm2642-2085.30232

Abstract:
This study uses Ansys 16 commercial package to investigate an accurate numerical model that can trace the flame shape from inverse diffusion combustion of LPG with a focus on the effect of air pulsation on the combustion characteristics. The simulation is based on solving the energy, mass and momentum equations. The large eddy simulation turbulence model and the non-premixed combustion model are used to simulate the pulsating combustion reaction flows in a cylindrical chamber with an air frequency of 10,20,50,100 and 200 rad/sec. The numerical results are in great agreement with the experimental results in the flame shape and the temperature distribution along the combustion chamber in both pulsating and non-pulsating combustion. Diffusion combustion responds positively to pulsating combustion and increases mixing in the reaction zone. Increasing the air frequency increases the temperature fluctuations, the peak turbulent kinetic energy and maximum velocity magnitude, respectively, by 27.3%, 300%, and 200%. Increasing the Strouhal number to 0.23 shortens the flame by 40% and reduces nitric oxide and carbon monoxide by 12% and 40%, respectively, including an environmentally friendly combustion product. The maximum average temperature dropped from 1800 K to 1582 K with a very homogeneous temperature distribution along the combustion chamber which is very important for furnaces.
, Bindi Thakkar, R. R. Kumar, Vaishali, Sudip Dey
European Journal of Computational Mechanics pp 223–254-223–254; https://doi.org/10.13052/ejcm2642-2085.30234

Abstract:
Purpose: To investigate the probabilistic low-velocity impact of functionally graded (FG) plate using the MARS model, considering uncertain system parameters. Design/methodology/application: The distribution of various material properties throughout FG plate thickness is calculated using power law. For finite element (FE) formulation, isoparametric elements with eight nodes are considered, each component has five degrees of freedom. The combined effect of variability in material properties such as elastic modulus, modulus of rigidity, Poisson’s ratio, and mass density are considered. The surrogate model is validated with the FE model represented by the scatter plot and the probability density function (PDF) plot based on Monte Carlo simulation (MCS). Findings: The outcome of the degree of stochasticity, impact angle, impactor’s velocity, impactor’s mass density, and point of impact on the maximum value of contact force (CFmax ), plate deformation (PDmax), and impactor deformation (IDmax ) are determined. A convergence study is also performed to determine the optimal number of the constructed MARS model’s sample size. Originality/value: The results illustrate the significant effects of uncertain input parameters on FGM plates’ low-velocity impact responses by employing a surrogate-based MARS model.
Tesfaye Aga Bullo, Guy Aymard Degla, Gemechis File Duressa
European Journal of Computational Mechanics pp 197–222-197–222; https://doi.org/10.13052/ejcm2642-2085.30233

Abstract:
A parameter-uniform finite difference scheme is constructed and analyzed for solving singularly perturbed parabolic problems with two parameters. The solution involves boundary layers at both the left and right ends of the solution domain. A numerical algorithm is formulated based on uniform mesh finite difference approximation for time variable and appropriate piecewise uniform mesh for the spatial variable. Parameter-uniform error bounds are established for both theoretical and experimental results and observed that the scheme is second-order convergent. Furthermore, the present method produces a more accurate solution than some methods existing in the literature.
Tom Saju, M. Velu
European Journal of Computational Mechanics pp 255–274-255–274; https://doi.org/10.13052/ejcm2642-2085.30235

Abstract:
In this paper, two different nickel-based superalloys, namely Inconel 718 and Nimonic 80A were joined using electron beam welding techniques with three different welding parameters. A finite element analysis (FEA) using Abaqus software was carried out to calculate the residual stresses due to welding. Both transverse and longitudinal residual stresses were determined. Also, an X-ray residual stress measurement system, μ-X360 Ver. 2.5.6.2 was used for measuring transverse residual stress along and across the weld centerline. The transverse residual stress found by FEA and that measured experimentally was nearly the same thus validating the FEA. Also, the peak values of longitudinal residual stress found using the FEA were close to the yield strengths of the base metals as found elsewhere.
Dushyant Tyagi, Vipin Yadav
Journal of Reliability and Statistical Studies pp 471–490-471–490; https://doi.org/10.13052/jrss0974-8024.1425

Abstract:
Statistical Process Control (SPC) is an efficient methodology for monitoring, managing, analysing and recuperating process performance. Implementation of SPC in industries results in biggest benefits, as enhanced quality products and reduced process variation. While dealing with the theory of control chart we generally move with the assumption of independent process observation. But in practice usually, for most of the processes the observations are autocorrelated which degrades the ability of control chart application. The loss caused by autocorrelation can be obliterated by making modifications in the traditional control charts. The article presented here refers to a combination of EWMA and CUSUM charting techniques supplementing modifications in the control limits. The performance of the referred scheme is measured by comparing average run length (ARL) with existing control charts. Also, the referred scheme is found reasonably well for detecting particularly smaller displacements in the process.
Laor Boongasame, Supansa Chaising, Punnarumol Temdee
Published: 4 October 2021
Journal of Mobile Multimedia pp 203–230-203–230; https://doi.org/10.13052/jmm1550-4646.1823

Abstract:
Without trust, buyers may not join a coalition. Despite the tremendous need for trustworthy relationships in buyer coalitions, no current buyer coalition scheme explicitly tackles confidence issues with blockchain technology. This study proposes an algorithmic design, the blockchain-based trusty buyer coalition scheme, to satisfy the trust requirement among different actors while forming the coalition. All activities forming a coalition through a decentralized public ledger can be explicitly examined. Consequently, the proposed algorithm can ensure anonymity within a community, resulting in trusting relationships. Furthermore, the proposed algorithm can ensure correctness and accountability by recognizing misbehavior and enforcing alternative forms of punishment. Additionally, the discovered algorithm can be applied to mobile commerce applications.
Haijuan Wang, Haibo Jiang, Fengchun Yang
European Journal of Computational Mechanics pp 145–168-145–168; https://doi.org/10.13052/ejcm1779-7179.30231

Abstract:
In order to explore the temporal and spatial evolution of the mechanical characteristics of hydraulic tunnels with high ground temperature during construction, a numerical simulation study was performed on the construction process of the tunnel using finite element calculation software, and the results were compared with the field monitoring results. The results showed that the displacements of the top arch and waist arch of the tunnel increased with time, and the top arch of the tunnel entrance was greatly affected by the excavation. The plastic zone was larger in the top arch and the bottom arch of surrounding rock, and smaller in the waist arch. The plastic strain of the top arch and bottom arch of surrounding rock was smaller, while the plastic strain of the waist arch was larger. The parallel excavation and lining construction have less impact on the surrounding rock.
Jin-Yan Sh, Ke-Chang Zhang
International Journal of Fluid Power pp 393–408-393–408; https://doi.org/10.13052/ijfp1439-9776.2235

Abstract:
Hydraulic excavator is important mechanical equipment in engineering construction, which is widely used in mining enterprises, construction industry, etc. Variable axial piston pump is the main power component of hydraulic excavator. The negative flow control variable axial piston pump is deduced and the mathematical model is established. The dynamic simulation model of negative flow control variable axial piston pump is built by using SIMULINK in MATLAB software, and the simulation analysis is carried out. The influence of the main parameters of negative flow control mechanism on the dynamic characteristics of negative flow control variable axial piston pump is obtained, which provides a reference for the parameter design of negative flow control mechanism.
Ke-Chang Zhang, Jin-Yan Shi
International Journal of Fluid Power pp 409–424-409–424; https://doi.org/10.13052/ijfp1439-9776.2236

Abstract:
In order to reduce the pulsation and the energy consumption of the hydraulic system, the series pump and valve cooperative control hydraulic system is designed, and the pulsation simulation and energy consumption analysis of it is carried out. Firstly, the working principle of series pump valve co control system is studied. Secondly, the mathematical model of series pump valve cooperation control system is established. And then the Controller of series pump valve cooperation control system is designed. Finally, the simulation analysis of the proposed hydraulic system is carried out, and results show that the proposed system has high stability and low energy consumption.
Kuditi Kamalapathi, Ponugothu Srinivasa Rao Nayak, Vipul Kumar Tyagi
Distributed Generation & Alternative Energy Journal pp 73–102-73–102; https://doi.org/10.13052/dgaej2156-3306.3714

Abstract:
Investigation of on-board renewable solar PV and wireless EV charging station integration is studied in this paper. Integration of on-board solar PV power with EV charger power will reduce the stress on the grid without the need for extra ground for solar plant installation. A dual-input buck-boost converter (DIBBC) is used to integrate the two power sources and charge the EV battery. A small-signal model of the converter is used to design the controller for three switches of the DIBBC. The simulation model of the integrated solar PV system and wireless power transfer (WPT) system is designed for charging a battery of 120V/165Ah at 130V. The hardware prototype of the proposed EV battery charging system is designed for 1.5kW to verify the simulation results. WPT system is developed for circular spiral-shaped coils, which are series-series compensated for 85kHz resonance frequency. Solar PV is replaced by a solar simulator programmed to operate with the same specifications used in the simulation. Results and analysis of the DIBBC based charger with charging voltage 130V showed higher efficiency up to 92% when both the sources are supplying power to DIBBC. The proposed charging system gives better efficiency with higher source voltages and when the difference in power supplied by the two sources is less. Thus, higher voltage sources are beneficial for improving the efficiency of the integrated charging system. Further, loss analysis in major components of the converter is discussed.
Lucas Diniz da Costa, Paulo Victor de Magalhães Rozatto, André Luiz Cunha de Oliveira,
Published: 31 August 2021
Journal of Mobile Multimedia pp 27–42-27–42; https://doi.org/10.13052/jmm1550-4646.1812

Abstract:
This paper aims to introduce a free educational web platform that offers a repository for viewing interactive content, using Computer Graphics (CG), Augmented Reality (AR) and Virtual Reality (VR) resources. The platform provides curatorship for third party and copyright projects. Within the scope of this research, two projects of the platform were subject to a qualitative evaluation. The evaluation carried out focused on the analysis of the motivating potential of using the platform during the distance teaching-learning process. It was possible to collect positive evidence about its viability for use in supporting both presential and remote learning.
V. Vinodhini, Akula Vishalakshi, , S. Sankar,
Published: 31 August 2021
Journal of Mobile Multimedia pp 135–162-135–162; https://doi.org/10.13052/jmm1550-4646.1817

Abstract:
Vasovagal syncope (VVS) refers to fainting of people with a drop in blood flow to the brain more serious disease in paraplegia patients. Precognitive diagnoses are characterized by lightheadedness, nausea, severe fatigue, and an elevated heart rate. As a result, it’s important to seek care as soon as possible after experiencing syncope. Since receiving a correct diagnosis and appropriate care, the majority of patients may avoid complications with syncope. Syncope appears to be a sign of COVID 19 in people with coronary artery disease. Furthermore, a sudden heart attack might result in acute syncope. In a few circumstances, machine learning classification techniques may not be precise. For paraplegia patients, prediction vasovagal syncope needs more precise results in order to save their lives. The aim of this paper is to use the ensemble technique to improve the accuracy of conventional machine learning algorithms. EEG (ElectroEncephaloGram) brainwave dataset from kaggle is used to implement it. The accuracy of the proposed AWET algorithm is 82%. It improves the accuracy by 17% compare to Support Vector Machine, Random Forest, Naive Bayes, and MultiLayer Perceptron classifiers.
Published: 31 August 2021
Journal of Mobile Multimedia pp 43–60-43–60; https://doi.org/10.13052/jmm1550-4646.1813

Abstract:
Clustering algorithms are most probably and widely used analysis method for grouping agricultural data with high similarity. For example, one of the most widely used approaches in previous study is K-means, which is simpler, more versatile, and easier to understand and formulate. The only disadvantage of the K-means algorithm has always been that the predetermined set of cluster centres must be prepared ahead of time and provided as feedback. This paper addresses the issue of estimating cluster random centres for data segmentation and proposes a new method for locating appropriate random centres based on the frequency of attribute values. As a consequence of calculating cluster random centres, the number of iterations required to achieve optimum clusters in K-means will be reduced, as will the time required to shape the final clusters. The experimental findings show that our approach is efficient at estimating the right random cluster centres that indicate a fair separation of objects in the given database. The technique observation and comparative test results showed that the new strategy does not use present manual cluster centres, is more efficient in determining the original cluster centres, and therefore more successful in terms of time to converge the actual clusters especially in agricultural data bases.
G Kavitha, N. M. Elango
Published: 31 August 2021
Journal of Mobile Multimedia pp 119–134-119–134; https://doi.org/10.13052/jmm1550-4646.1816

Abstract:
The evolution of computing is increasing in a vast manner that will integrate many physical objects and the internet to generate a new interconnection, such as the Internet of Things (IoT). It is estimated that the number of devices that will be interconnected to the internet will be more than trillions until 2025. Due to the lack of interoperability when these devices are interconnected in a vast heterogeneous network, it is tough to define and apply security mechanisms. The IoT networks have been exposed to many vulnerable attacks that disturb the network. Therefore, designing an intrusion detection system that provides additional security tools specific to IoT is needed to apply security mechanisms to detect the attacks in the network. In this paper, we propose a novel hybrid GA-CMIM machine learning algorithm that improves the efficiency in detecting the botnet intrusions with the set of optimal features that are selected from the dataset using a feature selection method.
Published: 31 August 2021
Journal of Mobile Multimedia pp 1–26-1–26; https://doi.org/10.13052/jmm1550-4646.1811

Abstract:
Industrial applications, including autonomous systems and vehicles, rely on processing data on multiple physical devices. The composition of functionality across heterogeneous computing infrastructure is challenging, and will likely get even more challenging in the future as software in vehicles is updated to introduce new features and ensure the safety. New soft real-time use cases emerge and in such cases the model of offloading processing from a limited or malfunctioning device is a viable solution. This study examines orchestration of services across edge and cloud for an industrial vehicle application use case involving image based object detection using machine learning (ML) based models. First, service orchestration requirements are defined taking into account the dependable nature of industrial vehicle applications. Second, an implementation based on Arrowhead framework is presented and evaluated. The open Arrowhead framework offers means for dynamic service discovery, authorization and late binding of computational units. The feasibility of object detection as a service and the suitability of Arrowhead framework to support such orchestrations across edge and cloud is assessed.
Kiruba Thangam Raja, Bimal Kumar Ray
Published: 31 August 2021
Journal of Mobile Multimedia pp 89–118-89–118; https://doi.org/10.13052/jmm1550-4646.1815

Abstract:
Polygonal approximation (PA) techniques have been widely applied in the field of pattern recognition, classification, shape analysis, identification, 3D reconstruction, medical imaging, digital cartography, and geographical information system. In this paper, we focus on some of the key techniques used in implementing the PA algorithms. The PA can be broadly divided into three main category, dominant point detection, threshold error method with minimum number of break points and break points approximation by error minimization. Of the above three methods, there has been always a tradeoff between the three classes and optimality, specifically the optimal algorithm works in a computation intensive way with a complexity ranges from O (N2) to O (N3).The heuristic methods approximate the curve in a speedy way, however they lack in the optimality but have linear time complexity. Here a comprehensive review on major PA techniques for digital planar curve approximation is presented.
Meenigi Ramesh Babu, K. N. Veena
Published: 31 August 2021
Journal of Mobile Multimedia pp 61–88-61–88; https://doi.org/10.13052/jmm1550-4646.1814

Abstract:
With the advanced technologies, IoT has widely emerged with data collection, processing, and communication as well in smart applications. The wireless medium in the IoT devices would broadcast the data, which makes them easily targeted by the attacks. In the local network, the normal communication attack is restricted to small local domain or local nodes. However, the attack present in IoT devices gets expanded to a large area that would cause destructive effects. The heterogeneity and distribution of IoT services/applications make the security of IoT a more challenging and complex one. This paper aims to propose a bi-level flow based anomalous activity identification system in IoT. Initially, the flow based features get extracted along with the statistical features like mean, median, variance, correlation, and correntropy. Subsequently, Bi-level classification is carried out in this work. In level 1, the presence of attack is detected and the level 2 classification classifies the type of attack. A decision tree is used for detecting the attacks by checking whether the network traffic is anomalous traffic or normal traffic. In level 2, an Optimized Neural network (NN) is used for categorizing the attacks in IoT with the knowledge of flow features and statistical features. To make the detection and classification more accurate, the weight of NN will be optimally tuned by a new Combined Whale SeaLion Algorithm (CWSA) that hybridizes the concepts of both SLnO and WOA. At last, the performance of the adopted method is computed over other traditional models in terms of accuracy, sensitivity, specificity, precision, FPR, FDR, FNR, NPV, F1-score, and MCC.
Venkatesh Boddapati, S Arul Daniel
Distributed Generation & Alternative Energy Journal pp 41–72-41–72; https://doi.org/10.13052/dgaej2156-3306.3713

Abstract:
Mobility has been changing precipitously in recent years. With the increasing number of electric vehicles (EV), travel-sharing continues to grow, and ultimately, autonomous vehicles (AV) move into municipal fleets. These changes require a new, distributed, digitalised energy system, maintenance, and growing electrification in transportation. This paper proposes the designing of an Electric Vehicle Charging Station (EVCS) by using hybrid energy sources such as solar PV, wind, and diesel generator. The proposed system is mathematically modelled and designed using the Hybrid Optimization Model for Multiple Energy Resources (HOMER). The system is analysed and assessed in both autonomous mode and grid-connected mode of operation. The optimum sizing, energy yields of the system in each case is elaborated, and the best configuration is found for design. The variations in Levelized Cost Of the Energy (LCOE), Net Present Cost (NPC), initial cost, and operating cost of the various configuration are presented. From the results, it is observed that the grid-connected EVCS is more economical than the autonomous EVCS. Further, a sensitivity analysis of the EVCS is also performed.
S.K. Yadav, Dinesh K Sharma, Ayodele Julius Alade, A.K. Shukla
Journal of Reliability and Statistical Studies pp 451–470-451–470; https://doi.org/10.13052/jrss0974-8024.1424

Abstract:
In this study, three novel regression models are introduced for estimating and forecasting peppermint yield production. Several indices of the goodness of fit are used to assess the quality of the suggested models. The proposed models for yield production are compared to current regression models that are well-known. Primary data from the Banki block of the Barabanki District of Uttar Pradesh State in India was used to validate the efficiency conditions for the suggested models to outperform the competition models. The empirical results suggest that the proposed models for estimating and predicting peppermint yield production are more efficient than competing estimators.
Yogesh M Gajmal, R. Udayakumar
Published: 30 August 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.2054

Abstract:
Access control is a major factor in enhancing data security in the cloud storage system. However, the existing data sharing and the access control method have privacy data leakage and key abuse, which is a major challenge in the research community. Therefore, an effective method named Blockchain-based access control and data sharing approach is developed in the cloud storage system to increase data security. The proposed Blockchain-based access control and data sharing approach effectively solve single-point failure in the cloud system. It provides more benefits by increasing the throughput and reducing the cost. The Data user (DU) makes the registration request using the ID and password and forwards it to the Data Owner (DO), which processes the request and authenticates the Data user. The information of the data owner is embedded in the transactional blockchain using the encrypted master key. The Data owner achieves the data encryption process, and encrypted files are uploaded to the Interplanetary File System (IPFS). Based on the encrypted file location and encrypted key, the Data owner generates the ciphertext metadata and is embedded in the transactional blockchain. The proposed Blockchain-based access control and data sharing approach achieved better performance using the metrics, like a better genuine user detection rate of 95% and lower responsiveness of 25sec with the blockchain of 100 sizes.
Xinhe Liu, Wenwu Hang, Haitao Wu
Distributed Generation & Alternative Energy Journal pp 103–116-103–116; https://doi.org/10.13052/dgaej2156-3306.3715

Abstract:
At present, the detection of transformer winding deformation in the offline phase, in order to discover the small transformer winding deformation, realize the online monitoring, in this paper, using nanosecond pulse frequency response analysis method, the detection of transformer winding deformation is studied, the results show that the nanosecond pulse frequency response analysis method in the detection of transformer winding deformation with high reliability and sensitivity.In order to verify the repeatability of the method, the method was used again at a 30-day interval under the condition that the detection environment was basically unchanged. The ρ value was 0.9719 in the range of 1 kHz∼1 MHz.
Wang Zongbao
Distributed Generation & Alternative Energy Journal pp 1–22-1–22; https://doi.org/10.13052/dgaej2156-3306.3711

Abstract:
The distributed power generation in Gansu Province is dominated by wind power and photovoltaic power. Most of these distributed power plants are located in underdeveloped areas. Due to the weak local consumption capacity, the distributed electricity is mainly sent and consumed outside. A key indicator that affects ultra-long-distance power transmission is line loss. This is an important indicator of the economic operation of the power system, and it also comprehensively reflects the planning, design, production and operation level of power companies. However, most of the current research on line loss is focused on ultra-high voltage (≧110 KV), and there is less involved in distributed power generation lines below 110 KV. In this study, 35 kV and 110 kV lines are taken as examples, combined with existing weather, equipment, operation, power outages and other data, we summarize and integrate an analysis table of line loss impact factors. Secondly, from the perspective of feature relevance and feature importance, we analyze the factors that affect line loss, and obtain data with higher feature relevance and feature importance ranking. In the experiment, these two factors are determined as the final line loss influence factor. Then, based on the conclusion of the line loss influencing factor, the optimized random forest regression algorithm is used to construct the line loss prediction model. The prediction verification results show that the training set error is 0.021 and the test set error is 0.026. The prediction error of the training set and test set is only 0.005. The experimental results show that the optimized random forest algorithm can indeed analyze the line loss of 35 kV and 110 kV lines well, and can also explain the performance of 110-EaR1120 reasonably.
Sajid Ali, Sanku Dey, M H Tahir, Muhammad Mansoor
Journal of Reliability and Statistical Studies pp 415–450-415–450; https://doi.org/10.13052/jrss0974-8024.1423

Abstract:
Estimation of parameters of Poisson Nadarajah-Haghighi (PNH) distribution from the frequentist and Bayesian point of view is discussed in this article. To this end, we briefly described ten different frequentist approaches, namely, the maximum likelihood estimators, percentile based estimators, least squares estimators, weighted least squares estimators, maximum product of spacings estimators, minimum spacing absolute distance estimators, minimum spacing absolute-log distance estimators, Cramér-von Mises estimators, Anderson-Darling estimators and right-tail Anderson-Darling estimators. To assess the performance of different estimators, Monte Carlo simulations are done for small and large samples. The performance of the estimators is compared in terms of their bias, root mean squares error, average absolute difference between the true and estimated distribution functions, and the maximum absolute difference between the true and estimated distribution functions of the estimates using simulated data. For the Bayesian inference of the unknown parameters, we use Metropolis–Hastings (MH) algorithm to calculate the Bayes estimates and the corresponding credible intervals. Results from the simulation study suggests that among the considered classical methods of estimation, weighted least squares and the maximum product spacing estimators uniformly produces the least biases of the estimates with least root mean square errors. However, Bayes estimates perform better than all other estimates. Finally, we discuss a practical data set to show the application of the distribution.
Amirfarhad Nilizadeh, Shirin Nilizadeh, , Cliff Zou,
Journal of Cyber Security and Mobility pp 1–28-1–28; https://doi.org/10.13052/jcsm2245-1439.1111

Abstract:
Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses. This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.
Qiangshan Zhang
Distributed Generation & Alternative Energy Journal pp 23–40-23–40; https://doi.org/10.13052/dgaej2156-3306.3712

Abstract:
In order to achieve grid connected optimal dispatch of micro-grid, a improved bee colony method is put forward to carry out optimization of grid connected dispatch. Firstly, the optimal scheduling model of micro-grid grid connection, and the overall cost of generating electricity and environmental cost of micro-grid grid connection is used as objective function, and system power balance constraint, power constraint of micro power supply, contact line constraint that interacted with main grid and charge and discharge cycle of battery are used as constraint conditions. Secondly, the improved bee colony algorithm is established through introducing particle swarm algorithm. Finally, a residential area is used as an example, and the optimal dispatch of micro-grid grid connection is carried out based on proposed model, and simulation results showed that the proposed model has higher correctness and efficiency.
Jiangping Nan
Distributed Generation & Alternative Energy Journal pp 117–128-117–128; https://doi.org/10.13052/dgaej2156-3306.3716

Abstract:
In order to improve the algorithm of time-varying parameters and unknown parameters adaptability, avoid assuming the approximate part deviation caused by the algorithm, this paper proposes a adaptive control algorithm, the algorithm based on lyapunov direct method to predict the output voltage in the process of estimating each parameter in a reasonable manner to parameter estimation error with the actual output current and current automatic adjustment. The adaptive control of current tracking is realized and the error caused by assuming voltage or current and neglecting line resistance is avoided in the predictive current control algorithm. The simulation results show that the tracking current can track the target current with high precision from t = 0 in the presence of random noise, and the power factor is close to 1, showing a good steady-state performance. Frequency domain waveform, the calculated harmonic distortion rate is 2.2418%, waveform quality is good and each harmonic amplitude is small. Conclusion: adaptive control algorithm can quickly and accurately realize current tracking and greatly suppress the noise.
Yu Guoji, Zhong Jianxu, Yu Shaofeng, Liao Chongyang, Ma Yining
Published: 26 August 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.20515

Abstract:
With the development of online information sharing, high-tech equipment for collaborative production management of power enterprises emerges endlessly. Therefore, it is necessary to design the collaborative production management system of power enterprises based on online information sharing to meet the information sharing needs. In terms of the hardware, the B/S structure was built, and the computer was debugged with Cascading Style Sheet (CSS). In terms of the software, Hadoop horizontal architecture technology framework was designed, the physical deployment was carried out, the production management center module was designed, and the production operation chain was monitored and managed to realize the collaborative production management of power enterprises. The experimental results showed that the designed collaborative production management system of power enterprise had high reliability and friendliness, the highest reliability is 97.2%, the highest friendliness is 99.8%, which meets the current demand.
Hong Zhao, Lupeng Yue, Weijie Wang, Zeng Xiangyan
Published: 26 August 2021
Journal of Web Engineering; https://doi.org/10.13052/jwe1540-9589.20511

Abstract:
Speech signal is a time-varying signal, which is greatly affected by individual and environment. In order to improve the end-to-end voice print recognition rate, it is necessary to preprocess the original speech signal to some extent. An end-to-end voiceprint recognition algorithm based on convolutional neural network is proposed. In this algorithm, the convolution and down-sampling of convolutional neural network are used to preprocess the speech signals in end-to-end voiceprint recognition. The one-dimensional and two-dimensional convolution operations were established to extract the characteristic parameters of Meier frequency cepstrum coefficient from the preprocessed signals, and the classical universal background model was used to model the recognition model of voice print. In this study, the principle of end-to-end voiceprint recognition was firstly analyzed, and the process of end-to-end voice print recognition, end-to-end voice print recognition features and Res-FD-CNN network structure were studied. Then the convolutional neural network recognition model was constructed, and the data were preprocessed to form the convolutional layer in frequency domain and the algorithm was tested.
Huaqin Wu
Distributed Generation & Alternative Energy Journal pp 441–458-441–458; https://doi.org/10.13052/dgaej2156-3306.3646

Abstract:
Traditional fault detection methods for power generation systems use centralized fault processing analysis, which leads to long accuracy and response time of fault detection. To address these problems, a data mining-based distributed power generation system fault artificial intelligence detection method is studied. The depth-first search tree algorithm is used to divide the grid of distributed generation system. The network structure is modified to locate fault zones by processing anomaly mining of the system data after grid division. The combination of fuzzy logic and wavelet singular entropy is used to complete the detection and identification of system faults. Through simulation experiments, it is verified that the response time of the detection method is only 0.016 s, and its detection error rate and false negative rate are 1.23% and 1.25%, which are far lower than other methods.
Anand Pavithran, Meeta Sharma, Anoop Kumar Shukla
Distributed Generation & Alternative Energy Journal pp 335–362-335–362; https://doi.org/10.13052/dgaej2156-3306.3641

Abstract:
The energy generation from the fossil fuels results to emit a tremendous amount of carbon dioxide into the atmosphere. The rise in the atmospheric carbon dioxide level is the primary reason for global warming and other climate change problems for which energy generation from renewable sources is an alternative solution to overcome this problem. However, the renewables sources are not as reliable for the higher amount of energy production and cannot fulfil the world’s energy demand; fossil fuels will continue to be consumed heavily for the energy generation requirements in the immediate future. The only possible solution to overcome the greenhouse gas emission from the power plant is by capturing and storing the carbon dioxide within the power plants instead of emitting it into the atmosphere. The oxy-fuel combustion power cycle with a carbon capture and storage system is an effective way to minimize emissions from the energy sectors. The oxy-fuel power cycle can reduce 90–99% of carbon dioxide emissions from the atmosphere. Moreover, the oxy-fuel power cycles have several advantages over the conventional power plants, these include high efficiency, lesser plant footprint, much easier carbon-capturing processes, etc. Because of these advantages, the oxy-fuel combustion power cycles capture more attention. In the last decades, the number of studies has risen exponentially, leading to many experimental and demonstrational projects under development today. This paper reviews the works related to oxy-fuel combustion power generation technologies with carbon capture and storage system. The cycle concepts and the advancements in this technology have been briefly discussed in this paper.
Zhengang Zhao, Zhengyu Yang, Yuyuan Wang, Ke Liang, Nengsi Jin, Kaiqiang Shi, Chuan Li, Yingna Li
Distributed Generation & Alternative Energy Journal pp 403–424-403–424; https://doi.org/10.13052/dgaej2156-3306.3644

Abstract:
According to the national standard GB/T 1094.7-2008, the method of hot spot measurement of oil-immersed transformer is used to place several temperature sensors inside the gasket within the predicted hot spot position to measure the temperature of winding transformer. The highest temperature measured is regarded as the hot spot temperature of transformer. Since the winding and gasket are bad conductors of heat, there exists certain temperature difference between the gasket and the hot spot temperature of the winding. In order to ensure safe operation of transformer, the thermal environment of temperature measuring point is analyzed and the discrete equation of boundary node is established. The parameters are set according to the heat transfer mode of the oil-immersed transformer and the temperature characteristics of each heat transfer node is analyzed. Gauss-Seidel Iteration method is used to calculate the theoretical value of the measuring point of the oil-immersed transformer and the heat transfer model of the measuring point is established for further analysis. The experimental platform of the oil-immersed transformer simulator is established according to the method described in the national standard and used to measure the hot spot temperature and winding surface temperature. The results show that when the winding temperature is 77 ℃, the heat transfer model of the temperature measuring point is 74.7 ℃ and the experimental temperature of the temperature measuring point is 74.9 ℃. The error between theoretical calculation temperature and experimental temperature is 0.2. As the temperature of the experiment increases, the temperature difference between the temperature point and the winding temperature gradually increases, and the maximum absolute error is 2.1 ℃.
Jiaqi Song, Jing Li, Di Wu, Guangye Li, Jiaxin Zhang, Jiantie Xu, Tian Lan
Distributed Generation & Alternative Energy Journal pp 385–402-385–402; https://doi.org/10.13052/dgaej2156-3306.3643

Abstract:
Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.
Bo Zhang, Qiang Lu, Zheng Shen, Yaokun Yang, Yunlin Liang
Distributed Generation & Alternative Energy Journal pp 363–384-363–384; https://doi.org/10.13052/dgaej2156-3306.3642

Abstract:
Based on the localized data of environmental load, this study has established the life cycle assessment (LCA) model of battery electric passenger vehicle (BEPV) that be produced and used in China, and has evaluated the energy consumption and greenhouse gases (GHGs) emission during vehicle production and operation. The results show that the total energy consumption and GHG emissions are 438GJ and 37,100kg (in terms of CO2 equivalent) respectively. The share of GHG emissions in total emissions at the production stage is 24.6%, and 75.4% GHG emissions are contributed by the operational stage. The main source of energy consumption and GHG emissions at vehicle production stage is the extraction and processing of raw materials. The GHG emissions of raw materials production accounts for 75.0% in the GHG emissions of vehicle production and 18.0% in the GHG emissions of full life cycle. The scenario analysis shows that the application of recyclable materials, power grid GHG emission rates and vehicle energy consumption rates have significant influence on the carbon emissions in the life cycle of vehicle. Replacing primary metals with recycled metals can reduce GHG emissions of vehicle production by about 7.3%, and total GHG emissions can be reduced by about 1.8%. For every 1% decrease in GHG emissions per unit of electricity, the GHG emissions of operation stage will decrease by about 0.9%; for every 1.0% decrease in vehicle energy consumption rate, the total GHG emissions decrease by about 0.8%. Therefore, developing clean energy, reducing the proportion of coal power, optimizing the production of raw materials and increasing the application of recyclable materials are effective ways to improve the environmental performance of BEPV.
Yong-Chao Xie, Jin-Yan Shi
Distributed Generation & Alternative Energy Journal pp 425–440-425–440; https://doi.org/10.13052/dgaej2156-3306.3645

Abstract:
Based on the small H-shaped vertical axis wind wheel model (NACA0016), a CFD wind wheel model was constructed. Based on the principle of moving grid, the grid division of the CFD wind wheel model is completed by using GAMBIT software, and the boundary conditions such as the inlet boundary and the outlet boundary are set reasonably. Then, the turbulence model and the couple algorithm are used to carry out transient simulation calculations, and finally the aerodynamic parameter curves of the two-dimensional CFD wind wheel model are obtained. Based on this, the matching characteristics of the wind turbine and generator of the small H-shaped vertical axis wind turbine are studied. The research results show as follows: when the incoming wind speeds change in range of (2 m/s, 12 m/s), and the power characteristic curve and torque characteristic curve of the generator wind wheel are respectively overlap the best power curve and best torque of the generator, the matching characteristics of the small H-shaped vertical axis wind turbine rotor and generator are optimal, which provides reference for carrying out related research.
Medani Bhandari
Strategic Planning for Energy and the Environment pp 381–402-381–402; https://doi.org/10.13052/spee1048-4236.391415

Abstract:
The purpose of this paper is to illustrate the importance of strategic planning in general and its application in energy and the environment in particular. In the contemporary world, planning is so common that we cannot even manage everyday life without plan. Strategic planning is a formalized, structured, planned way to manage planning from formulation to implementation, evaluation, and control. When we talk about energy, we mostly talk about the various forms (physical) of energy such as nuclear, thermal, chemical, electrical, or other forms which create and transform energy. The sources of energy can be solar, wind, water, nuclear, electromagnetism or related to fossil – coal, gas, petroleum etc. The world rarely has sufficient energy therefore, strategic planning for energy is essential to sustain and maintain the energy supply and demand. The environment is our entire surroundings including the land, air, water, or the combination of all biotic and abiotic factors of the planet. We have ample evidence that anthropogenic disturbances have already destroyed the balance of nature, as a result the global climatic pattern is changed, and there has been unprecedented damage to our ecosystem. Such severe impacts due to global environmental change mean that it is extremely urgent that we formulate a strategic plan (or plans) to protect the environment. There are as yet no alternatives for Planet Earth, therefore we need planned strategies to minimize the environmental problems. This review outlines why strategic planning is so important for the future of energy and the environment since they go hand in hand.
Sven Osterland, Lutz Müller,
International Journal of Fluid Power pp 373–392-373–392; https://doi.org/10.13052/ijfp1439-9776.2234

Abstract:
This article gives experimentally evidence that cavitation erosion in hydraulic components like valves and pumps is caused by vapour cavitation not gas or pseudo cavitation. In fact, the free air content which is released by vapour and gas cavitation reduces the erosion significantly. In order to clearly separate the different cavitation types, a test rig with a specially designed reservoir with integrated degassing capability is presented. As flow geometry a valve model with realistic dimensions and under realistic operating conditions was used, which ensures very high transferability of the results to the reality of hydraulic components in practical applications and typical operating conditions. A total of 4 five-hour long tests are performed and analysed. The quantification of the cavitation erosion is determined by the mass loss of the copper samples. The experimental results show a 4.4–5.1 times higher mass loss in tests with air-free oil compared to tests with air-saturated or oversaturated hydraulic oil. The experimental fact that air-free hydraulic oil causes significantly more cavitation erosion than normal (saturated) hydraulic oil, and its implications are discussed. The conclusion can be drawn, that further developments of hydraulic components and systems towards the use of air-free oil or increasing power densities will be disproportionately challenged by cavitation erosion.
European Journal of Computational Mechanics pp 121–144-121–144; https://doi.org/10.13052/ejcm1779-7179.3015

Abstract:
A linearised finite element numerical scheme for the vibration of inextensible beams is developed. The proposed scheme is based on the methodology introduced by S. Bartels [15] and satisfies a linearised form of the inextensibility constraint. The time m arching procedure is based on repeated use of the theta-parameter integration quadrature. Three parameters are introduced in total and appropriately selected such that the energy conservation features are improved compared to the Bartels algorithm while the inextensibility constraint is satisfied as accurately as possible. Cubic Hermite polynomials are employed for the spatial discretisation. The Bartels algorithm is retrieved as a special case. Several numerical experiments are presented demonstrating the theoretically predicted enhanced inextensibility mimicking and optimum values of the method parameters are identified.
Haijin Pan
European Journal of Computational Mechanics pp 81–98-81–98; https://doi.org/10.13052/ejcm1779-7179.3013

Abstract:
Due to the lack of more precise and complete data support, the reliability of posture stability evaluation method based on common technology is poor. In the face of such problems, the application of multi-body system coupling dynamic model in the evaluation of sports posture stability is proposed. The coupling dynamic model of human motion posture is established, and the relevant data of human motion posture is collected. The complete data of human motion posture is obtained by solving the dynamic model. Choose the appropriate stability evaluation index, calculate the stability evaluation index, divide the stability level, and realize the evaluation of posture stability. The experimental results show that: the application of multi-body system coupling dynamic model in the stability evaluation method makes the time delay and data error of the evaluation method small, and its overall reliability is improved.
Taochun Yang, Yanjun Li, Xiaohui Zhai
European Journal of Computational Mechanics pp 99–120-99–120; https://doi.org/10.13052/ejcm1779-7179.3014

Abstract:
In order to study the degradation law and seismic performance of reinforced concrete frame structure with the extension of service time under normal service environment, the multi-scale modeling of corroded reinforced concrete frame is carried out by using the general finite element analysis software ABAQUS. The correctness of the multi-scale modeling method is verified by the experimental data of corroded reinforced concrete members and single frame. The pushover analysis and elastic-plastic time history analysis of a four story reinforced concrete frame structure are carried out by using a multi-scale model. Then the seismic response and damage of RC frame structures with different service time are compared. The experimental results show that the established seismic performance model of reinforced concrete frame structure is more practical in practical application and can meet the research requirements.
European Journal of Computational Mechanics pp 51–80-51–80; https://doi.org/10.13052/ejcm1779-7179.3012

Abstract:
Continuum Damage Mechanics is successfully employed to describe the behaviour of metallic materials up to the onset of fracture. Nevertheless, on its own, it is not able to accurately trace discrete crack paths. In this contribution, Continuous Damage Mechanics is combined with the XFEM and a Cohesive Law to allow the full simulation of a ductile fracture process. In particular, the Cohesive Law assures an energetically consistent transition from damage to crack for critical damage values lower than one. Moreover, a novel interpretation is given to the parameters of the cohesive law. A fitting method derived directly from the damage model is proposed for these parameters, avoiding additional experimental characterization.
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