bluuebunny/crossref_metadata_2025_split huggingface dataset:
Offloading Decision Making for Workflow Applications Using an Enhanced Genetic Algorithm in the Edge Environment
Authors: Balasubramanian S. , Meyyappan T. 
Published: April 2022

Abstract
Edge computing is an environment suitable for processing the workflow applications produced by the IoT devices to reduce time and energy consumption. The execution of the workflow task in the Cloud computing environment increases the consumption of both time and energy. In order to solve this issue, this paper proposes a new approach, namely, Decision making Regarding the offloading of A subset of the Workflow application (DRAW). In the DRAW approach, selecting the destination environment for executing the subset of the workflow application occurs in the Edge environment. The DRAW uses the genetic algorithm for offloading the subset to minimize the objective factors, including total execution time and energy consumption. It equally prioritizes both the objective factors for improving the execution of the subset in the Cloud environment using the improved genetic algorithm. The DRAW approach improves the genetic algorithm by removing its traditional limitations and produces an effective possible solution in terms of better offspring. Finally, the algorithm stops by attaining the best solution from the possible solutions. Thus, the implementation results show that the DRAW approach significantly outperforms the existing approach by minimizing execution time and energy consumption.
Stock Market Prediction Based on Time-frequency Analysis and Convolutional Neural Network
Authors: Jia Dandi , Gao Qiang , Deng Hui 
Published: April 2022

Abstract
Recently, researchers have shown an increased interest in stock market prediction with neural networks. Stock market is affected by a multiplicity of factors with different active periods, thus financial time series possess multiscale frequency characteristics, which can be exploited to facilitate prediction of stock market. In this paper, we propose a stock market prediction model combining time-frequency analysis and convolutional neural network (CNN), in which the influence extent of different frequency components has been considered. We transform original financial time series into the spectrogram reflecting time-localized frequency information by short-time Fourier transform (STFT). The 2-dimensional time-frequency feature is obtained from the spectrogram by frequency bands extraction, which is then pre-weighted and input into CNN to forecast the future price change. The frequency bands extraction and pre-weight are set according to the frequency influence. The results of experiments on Shanghai Composite Index show that the proposed model with frequency bands extraction considering frequency influence achieves a 4% relative decrease in mean absolute error (MAE) compared with that does not consider the frequency influence. Moreover, the pre-weight gives an additional 3% relative decrease of MAE.
Pedestrian’s Abnormal Behaviour Detection Based on Machine Vision
Authors: Zhao Rongyong , Jia Ping , Yanwang  , Zhu Wenjie , Li Cuiling , Ma Yunlong 
Published: April 2022

Abstract
It is significant to automatically analyse video sequences for timely and accurate detection of abnormal pedestrian behaviour by using computer vision and analysis technology in public places. To realize the real-time monitoring of abnormal behaviour, this study uses a hybrid behaviour detection model based on the colour characteristics of the pixels and the gradient characteristics of the pixels mathematically. According to the hybrid behaviour detection model, the criterion of abnormal behaviour of the same pedestrian based on two frames of video images is proposed. This is conducive to realizing early awareness of emergencies caused by abnormal behaviours and taking early warning measures, reducing casualties, and maintaining public safety and social stability.
Research and Implementation of Flow Pattern Recognition for Gas-liquid Two-phase Flows Based on GoogLeNet
Authors: Yuan Jiaxin , Kan Zhe 
Published: April 2022

Abstract
In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level.
Model of Hydrodynamic Friction in the Boundary Layer on an Inclined Flat Surface of an Aircraft
Authors: Klyuev Nikolay , Polyakov Konstantin , Klyuev Nikolay 
Published: April 2022

Abstract
An integral method for solving the problem of longitudinal flow of an inclined flat plate located in a stream of air flow is presented. As a result of the integration of the equations of motion over the thickness of the boundary layer, the two-dimensional problem becomes one-dimensional. The problem was solved numerically in the Mathcad software package. The graphs of the dependence of the thickness of the boundary layer and friction on the longitudinal coordinate at different angles of inclination of the plate are presented. The flow mathematical model can be used to determine the characteristics of two-dimensional boundary layers with arbitrary boundary conditions.
Simulation of Bee Aggregation in The Hive During Changes in Honey Supply
Authors: Oskin S V , Shishigin I N , Loza A A , Tsokur D S 
Published: April 2022

Abstract
Much research has been devoted to bee overwintering because it is the most dangerous period of the bee family’s life. The critical period for bees is the end of winter and the beginning of spring. During this time, there are large fluctuations in air temperature, and bees are forced to change their state from passive to active. By the end of winter, the club rises to the top of the hive, and the honey supply decreases significantly. Thus, this article considers a model that takes these changes into account. According to the results of the simulation, it was found that the movement of the bee club and the decrease of honey reserves in winter increase the fluctuations of the air temperature inside the hive. This affects the internal temperature regime of the bee club. At the same time, the stock of heat energy in the honey decreases and smoothes temperature fluctuations within the bee aggregation less. It is proposed to add a hive weight strain gauge to the previously developed electric bee heating control circuit, which will allow to correct the PWM output voltage coming to the heaters.
Effects of Manufacturing-induced Operating-conditions Amplified Stresses on Fatigue-life Prediction of Micro Gas Turbine Blades – A Simulation-based Study
Authors: Fameso Festus , Ramakokovhu Unarine , Desai Dawood , Salifu Smith 
Published: April 2022

Abstract
This study investigates the influence of residual stresses induced by the manufacturing process, on the fatigue life prediction of micro gas turbine blades manufactured by laser metal deposition additive manufacturing technique and heat-treated at different heat-treatment parameters. Finite element modelling, using commercial software Abaqus CAE ® and FE-Safe ®, was used to simulate the turbine blade and disk assembly’s operations and analyze induced stresses, as well as estimate the life cycle of the assembly. Results show internal stress build-ups, up to 400 MPa in magnitude can be induced in engineered components right from the point of additive manufacture, the effects of which can become amplified to as much as double that scale or more by the time the component is in operation. As much as between 75% to 300% fatigue life prediction error reduction can hence be attained by simply accounting for induced stress contribution of the process of its manufacture, rendering cost-saving condition-based monitoring and maintenance/overhaul decision making options.
Stimulation and Acquisition’ System to Obtain the Visual Evoked Potential Related to the Optic Nerve Pathologies
Authors: Montalvo Aguilar J A , Bazan Trujillo I , Ramírez-García A 
Published: April 2022

Abstract
The development and integration of an electroencephalographic signal acquisition and stimulation system is proposed to support the detection of pathologies related to the optic nerve. It is based on the extraction and analysis of a characteristic waveform called Visual Evoked Potential immersed in the electroencephalographic register. Some parameters of this potential, as latency and amplitude, are affected by the optic nerve pathologies, and measurement of changes from its normal states can be used as indicators of abnormal visual functionality of a patient. The proposed stimulation system can generate Visual Evoked Potential and multifocal Visual Evoked Potential which can help to locate pathologies in different areas of the visual field.
A Novel Progressive Response Surface Method for High-order polynomial Metamodel
Authors: Zheng Guanchao , Ariffin Mohd Khairol Anuar Bin Mohd , Ahmad Siti Azfanizam Binti , Aziz Nuraini Binti Abdul , Xu Wen 
Published: April 2022

Abstract
The response surface method (RSM) model as a general metamodel has already widely been applied to many practical problems. However, the majority of studies only focus on the 2nd-order terms RSM model instead of how to determine the high-order terms. This paper introduced a novel progressive response surface method (NPRSM) combining optimal Latin hypercube design (OLHD), moving least square method (MLSM) and statistical test. This NPRSM model can decide the high-order terms by progressive procedure. Then three numerical function can be proposed to validate the accuracy and the approximated performance of the NPRSM model.
Numerical Investigation of Charge Packet Dynamics in Polyethylene Assuming Barrier Height-Electric Field Hysteresis Hypothesis
Authors: Cai Shikai , Li Hongpeng , Sun Zhipeng 
Published: April 2022

Abstract
Charge packet phenomenon is a sign of the impending breakdown of the dielectric materials. In order to discuss the formation mechanism of charge packets, space charge characteriscs in polyethylene under the electric field of 150kV/mm are simulated based on the bipolar transport model assuming hysteresis in the barrier height for holes. It is found that if hysteresis in the barrier height is not introduced, net charge densities in the vicinity of the electrodes increase with the increase of polarization time. However, charge packets could not be reproduced without hysteresis hypothesis. When hysteresis loop in the barrier height are incorporated, charge packets are generated and propagated with the velocity about 3μm/s. The dying away of charge packets is not observed, which disagrees with the experimental data.
Study on Two Residual Flux Suppression Strategies of Current Transformer During Automatic Reclosing
Authors: Deng Chenglin , Deng Chuanhai , Wang Han , Zhong Jianying , Cai Xinjing 
Published: April 2022

Abstract
After power system short-circuit fault occurs, there is a large amount of residual magnetism in current transformer core, which will lead to wrong operation of connected relay protection and affect its reliability. Based on the direct current method, this paper puts forward two kinds of current transformer residual flux suppression strategies, which are constant voltage variable frequency (CVVF) and variable voltage constant frequency (VVCF), the magnetic flux of the core is gradually reduced by controlling the polarity of the output voltage of the switch module. The principle of residual flux suppression is introduced, and the method of parameter selection is given. The suppression effect of two residual flux suppression strategy is simulated and analyzed by experiment simulation. The results show that the two residual flux suppression strategies can demagnetize the current transformer core during automatic reclosing, but the constant voltage CVVF residual flux suppression strategy is simpler and more efficient.
Design and Implementation of a Mixed Reality Night Hoisting Assistance System Driven by Digital Twins
Authors: Li Hengwei , Wang Wei , Qu Jue , Qiu Ang , Niu Chenghong , Li Chengyu 
Published: April 2022

Abstract
Hoisting at night has low visibility and high operational complexity, which has the problems of large cognitive load and high risk. With the help of digital twin (DT) and mixed reality (MR), the article designs and implements a MR night hoisting assistance system driven by DT. The system is developed on the Unity3D platform and based on the UNet networking communication mechanism. It realizes the functions of fusion highlighting, motion synchronization, multi-terminal collaboration and error display. It also has multiple interaction methods such as voice interaction, gesture recognition, and sight tracking. The research results have positive significance for improving the effect of night hoisting.
Direction Relation Matrix Model Considering the Location Characteristics
Authors: Cheng Mianmian , Sun Qun , Kang Shilun , Chen Huanxin , Yang Na , Li Chunyan 
Published: April 2022

Abstract
To resolve the problem that the conventional direction relation matrix (DRM) models are not sensitive to the location characteristics of the target object, a DRM model that takes into account the location characteristics is proposed. Based on spatial cognition, the concept of taking the direction field of the target into consideration is provided, and a probability density function of the direction field is established. The cumulative probability of the region where the target object is located is used to replace the area in the conventional DRM model, and two methods are given for cumulative probability calculation. The cumulative probability is used to assign values to the direction relationship matrix. Experimental results show that, compared with the conventional DRM model, when the target is present in multiple regions, the method proposed in this paper leads to results that are more consistent with human intuitive experience and describes the direction accurately. When the target is only present in a certain fixed region, the proposed method has the same performance as the conventional DRM model. Therefore, the method proposed in this paper alleviates to a certain extent the problem that the conventional DRM models are not sensitive to location characteristics.
A Hybrid-ARQ Method with Adaptive Receiving Buffer Duration in Real Time Video Streaming
Authors: Cheng Baoping , Tao Xiaoming , Lei Peng , Zhou Junhua , Wang Duo , Chen Min 
Published: April 2022

Abstract
To decrease end-to-end delay for streaming media over internet, the transmission method based on best-effort model is always employed in Real Time Communication (RTC) system. However, the packet-loss, jitter and congestion occur frequently because the quality of transmission cannot be guaranteed, which causes lag and splash of video. Therefore, an optimized hybrid Automatic Repeat Query (ARQ) solution with adaptive receiving buffer duration is proposed to guarantee the quality of video transmission. In the method, the loss rate was used to adjust the buffer duration and calculate the redundancy for Forward Error Correction (FEC). The results show that the method in this paper gains lower end-to-end delay and better Mean Opinion Score (MOS) values while ensuring higher fluency in weak network scenarios.
Synthesis of NaA Zeolite from Blast Furnace Slag (BFS) and Its Utilization for Adsorption of Basic Dye (Methylene Blue)
Authors: Li C X , Zhang Q W , Li L 
Published: April 2022

Abstract
The adsorption process of methylene blue (MB) onto blast furnace slag (BFS) based zeolite (BFSZ) was discussed. Via the analyses of XRD, FE-SEM, XRF, CEC and SSA, the main composition in in the product was Na-A zeolite. And the Na2O content, CEC value and SSA value of the BFSZ samples was significantly increased to 19.61 wt.%, 3.06 meq/g and 27.55 m2.g1 respectively. Moreover, the adsorption kinetic for MB onto BFSZ was examined and best represented by Pseudo-second-order kinetic model. Moreover, the adsorption capacity of MB from aqueous solutions reaches up to 21.89 mg.g1 at 30 °C.
Over-the-Air Programming System for IoT Devices with a Non-native Wireless Update Service
Authors: Lozano Domínguez J.M. , Mateo Sanguino T.J. , Redondo González M. J. , Fernández de Viana González I.J. 
Published: April 2022

Abstract
In the last years, many solutions have been developed based on the Internet of Things (IoT) applied to several fields such as agriculture, road safety or electric lighting, among others. These devices are usually located in places that are not easy to access, which makes their software difficult to update. These updates should be carried out to improve the software of the devices to include new functionalities and/or solve security problems. This paper presents an Over-the-Air (OTA) programming system for devices that do not natively integrate a wireless update service. A description of the hardware used is included, as well as the update management application developed to carry out this task. The approach proposed has been validated by updating a microcontroller-based system applied to the area of road safety. The validation consisted in measuring the additional consumption required by the auxiliary update system compared to the base consumption, as well as determining the time required to update an IoT node in both wireless and wired mode. The results obtained show a reduction in consumption of 577% and a reduction in the updating time of 66%.
Extraction and Application of Inertial Sensors-Based Kinematic Parameters in a Clinical Walk Test among Stroke Survivors
Authors: Han S L , Cai M L , Sugiharto T , Pan M C 
Published: April 2022

Abstract
A new framework for adopting inertial sensors in a clinical test was proposed and tested in this study. A self-assembled-and-coded, inertial sensor-based wearable system was validated by comparing it with a commercialized optical tracking apparatus. Twenty-five post-stroke patients were enrolled in a clinical walk test while wearing this validated system to simultaneously assess the affected lower extremities’ functional walk competency through extracted kinematic parameters. Their average walking speeds were correlated with various gait parameters, such as the ranges of motion of the individual joints along the sagittal plane and the low back motion. The validation results proved this developed system is precise and accurate. The average walking speeds showed a modest correlation with the range of motion of the hip (r = 0.33) and a moderate and negative correlation with the motion along the coronal plane of the low back (r = -0.55). Thus, this framework supports a new method to adopt wearable devices for clinical application. It also broadens the application of the clinical walk test as an integral assessment tool for assessing functional walking competency and gait parameters, which is feasible for rehabilitation canters to monitor post-stroke patients.
Research on Server Green Computing in 5G Data Center
Authors: Li Rui , Qu Yan , Zuo Jun 
Published: April 2022

Abstract
The global 5G commercial era is approaching. The throughput and number of connections of 5G will be 100 times that of 4G. The processing power of massive data will determine the experience of users surfing the Internet. In order to cope with the geometric growth of network data, the computing power and storage capacity of 5G data centers need to be correspondingly improved. With the surge of servers, the energy consumption of the 5G data center has also been greatly improved. How to effectively improve the performance of the server and reduce the energy consumption has become an extremely urgent problem. This paper focuses on the management method of the data center server in the context of 5G network transformation, and proposes effective solutions in combination with the application scenarios of large-scale 5G data centers.
Design of Network Security Protection System for Internet of Vehicles under Electric Internet of Things Background
Authors: Wang Xueer , Jin Min 
Published: April 2022

Abstract
At present, China’s Internet of Vehicles (IoV) industry is booming and constantly promoting the society to the intelligent era. However, the development of network technology not only brings the opportunity of technology promotion for the IoV, but also brings new network security risks. Therefore, it is urgent to further strengthen the construction of the protection system for the IoV. Based on the original architecture of the IoV, this paper first analyses the security risks of the terminal layer, the network layer, the platform layer and the application layer under the background of the electric internet of things. According to the new security threats faced by the four layers of the IoV, the network security protection system is constructed, which strictly follows the overall information security protection strategy of the State Grid Corporation of China and the new requirements of “network security level protection 2.0”. Deepening the construction of the intelligent defence system of the IoV would be significant for the development of coordinated network security and the IoV, and would continuously improve the protection ability of the IoV.
An Energy-Balance Unequal Clustering Routing Protocol for Wireless Sensor Networks
Authors: Zhang Wen-mei 
Published: April 2022

Abstract
On the basis of traditional unequal clustering protocol for wireless sensor networks, an energy-balance unequal clustering routing protocol is proposed. In the clustering stage, the entire network is divided into clusters of different sizes based on the remaining energy and the distance from the sink node to the node. In the cluster establishment phase, each node calculates the cluster head selection time based on the relative remaining energy, the position, the average distance between the nodes in the cluster. The cluster head is selected by time series, and non-cluster head nodes are selected to join the cluster based on the distance between cluster heads and the remaining energy of the relay cluster head. In the route establishment phase, an optimal transmission path based on minimum spanning tree is built, based on the distance between cluster heads, the residual energy and the distance from node to the sink node. During data transmission, a single hop within a cluster is adopted, and a minimum spanning tree multi-hop method is used between clusters, and the data is finally transmitted to the sink node. The simulation shows that the routing protocol can effectively balance energy consumption and save energy, and the life cycle of the network is prolonged.