bluuebunny/crossref_metadata_2025 huggingface dataset:
Beam alignments based on the spectrum decomposition of orbital angular momentums for acoustic-vortex communications
Authors: Guo Gepu , Li Xinjia , Wang Qingdong , Li Yuzhi , Ma Qingyu , Tu Juan , Zhang Dong
Published: April 2022

Given the enhanced channel capacity of wave chirality, acoustic communications based on the orbital angular momentum (OAM) of acoustic-vortex (AV) beams are of significant interest for underwater data transmissions. However, the stringent beam alignment is required for the coaxial arrangement of transceiver arrays to ensure the accuracy and reliability of OAM decoding. To avoid the required multiple measurements of the traditional orthogonality based algorithm, the beam alignment algorithm based on the OAM spectrum decomposition is proposed for AV communications by using simplified ring-arrays. Numerical studies of the single-OAM and OAM-multiplexed AV beams show that the error of the OAM spectrum increases with the translation distance and the deflection angle of the transceiver arrays. To achieve an ideal arrangement, two methods of the single-array translation alignment and the dual-array deflection alignment are developed based on the least standard deviation of the OAM spectrum (SD-OAM). By decreasing the SD-OAM towards zero using transceiver arrays of 16 transmitters and 16 receivers, accurate beam alignments are accomplished by multiple adjustments in three dimensions. The proposed method is also demonstrated by experimental measurements of the OAM dispersion and the SD-OAM for misaligned beams. The results demonstrate the feasibility of the rapid beam alignment based on the OAM spectrum decomposition by using simplified transceiver ring-arrays, and suggest more application potentials for acoustic communications.
Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task
Authors: Ye Huanpeng , Li Guangye , Sheng Xinjun , Zhu Xiangyang
Published: April 2022

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG. Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads. Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16–3.84 Hz for the phase and 50–85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load. Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.
Early Estimation of Daily Reference Evapotranspiration Using Machine Learning Techniques for Efficient Management of Irrigation Water
Authors: Chouaib El Hachimi , Salwa Belaqziz , Saïd Khabba , Abdelghani Chehbouni
Published: April 2022

Assessing the right amount of water needs for a specific crop is a key task for farmers and agronomists to achieve efficient and optimal irrigation scheduling, and then an optimal crop yield. To this end, the reference evapotranspiration (ET0) was developed. It represents the atmospheric evaporation demand, and therefore an important variable for irrigation management. In this regard, several methods such as the FAO’s Penman-Monteith and Hargreaves have been used to model and estimate ET0. These methods use climatic parameters data for calculation procedures such as solar net radiation (Rn), saturation vapour pressure(es), and min-max air temperatures or a combination of them. In this paper, we investigated two proposed data-driven methods to predict ET0 values in a semi-arid region in Morocco. The first approach is based on forecasting techniques and the second one uses end-to-end modeling of ET0 based on meteorological data and machine learning models. The feature selection and engineering results show that solar global radiation (Rg) and mean air temperature (Ta) have a significance of more than 87% as relevant predictors features for the ET0. We then used them as input to machine learning regression models. Regression evaluation metrics showed that The XGboost regressor model performs well in both cross-validation with R2=0.93 in the first fold, and in hold-out validation with R2=0.92 and RMSE=0.55. As a final step, we compared the univariate time series forecasting of ET0 using the Facebook Prophet model versus the machine learning modeling method that we proposed. As goodness-of-fit measures, forecasting using machine learning modeling of ET0 showed better results in terms of both R2 and RMSE.
DeepFake Video Detection through Facial Sparse Optical Flow based Light CNN
Authors: Fang Shuya , Wang Shucheng , Ye Rongjun
Published: April 2022

DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.
Research on Knowledge Transfer on Multilayer Networks Based on Link Prediction Algorithm
Authors: Li Shouwei , Wang Yihan
Published: April 2022

Multi-layered networks have great advantages in portraying the multi-attributes of links and can describe complex real-life systems better. Link prediction and knowledge transfer in complex networks have been extensively studied, but link prediction and knowledge transfer on hierarchical networks are less of a concern. Based on the definition of hierarchical network, a random walk model including link prediction and knowledge transfer is proposed. The link prediction method is proposed from the structural similarity and knowledge compatibility, and then the knowledge transfer rules are proposed. This paper also proposes the evaluation indicators for link prediction and knowledge transfer. The experimental results by using real hierarchical networks show that the link prediction has obtained better results and the complexity has been reduced; the knowledge transfer efficiency has been improved. This study has important reference value for the development of multi-layer network theory.
Attention-Based Dual-Branch Cascade Network for Multi-Label Image Recognition
Authors: Li Xingyu
Published: April 2022

Multi-label image recognition is a practical task which aims to predict all concerned objects in an image, and the correlation between labels is considered as the key to solve multilabel problem. Previous approaches focus on exploring and exploiting the underlying relations between labels, by which the performance of most labels is improved significantly. But small object and label tail problems are ignored, and negatively influences the overall results. For ameliorating above problems, we propose a unified deep neural network named Attention-Based Dual-Branch Cascade Network (ADC Net), which contains Main Branch and Auxiliary Branch. ADC Net cascades to predict labels, and obtains the final result by element-wise adding. Attention modules in each branch contribute to recognizing small objects. Top-Down-Attention Module (TDAM) utilizes the preceding prediction map to guide the Auxiliary Branch. Besides, expanding training dataset is applied for learning the feature representation of tail labels. Our proposed methods are evaluated on two benchmark datasets: MS-COCO and VOC PASCAL 2007 datasets, and achieves state-of-the-art performance. Results of small objects, tail labels and visualization also prove the effectiveness of our method.
Speech Fatigue Detection Based on Deep Learning
Authors: Chen Shuxi , Sun Yiyang , Zhang Haifei , Liu Qinqin , Lv Xingqin , Mei Xiangxiang
Published: April 2022

Medically speaking, moderate fatigue is the embodiment of workload, and excessive fatigue will cause adverse effects on the human body. Therefore, real-time monitoring of human fatigue is of great significance. In order to detect fatigue in real time and conveniently, a speech fatigue detection method based on deep learning is proposed in this paper. The main work of this paper is to screen the useful speech segments of the fatigue corpus created in the previous work; Secondly, the speech signal is preprocessed and the analog speech signal is digitized; Thirdly, deep learning can effectively reflect the phonetic features of fatigue information; Finally, the fatigue degree is classified and recognized by classifier. The best classification and recognition rate of speech fatigue detection based on BLSTM network recommended by the author can reach 92.7%, which realizes the effective recognition of sports fatigue.
Data Collector Design for Vibration Analysis by Raspberry pi 3B Embedded System Means for Industrial Applications
Authors: Cárdenas C , Sandoval C , Rincón A , Galván D , Téllez H
Published: April 2022

Currently, the industry is linked to technological advances, and the maintenance area plays an important role in this regard. Good practices are being refined, and predictive techniques have been implemented within the maintenance plan. These techniques allow decisions to be based on made equipment condition, which optimizes costs. One of these techniques is the mechanical vibrations analysis, applied commonly rotating equipment, where is common the situation of expensive measuring equipment. For this reason, this report provides a problem basic solution, through the economical measuring equipment design for industrial applications.
Enhancement of Active Swimming near Fluid Interfaces
Authors: Cartwright Avriel , Du Jian
Published: April 2022

Microorganisms often move through heterogeneous fluid medium composed of multiple materials with very different properties. Biological locomotions are significantly influenced by the physical compositions and rheology of the fluidic environment. Some micro-swimmers are able to exploit nearby deformable interfaces to enhance their speed. Through computational simulations, we investigate the movement of a finite-length undulatory swimmer near interfaces within a viscous two-fluid media. Our results show that significant speed-ups can be obtained only if the active swimmer has a large body elasticity.
Modelling of Aggregates using Aperiodic Space-Filling and Physical Simulation
Authors: Gan X , Ito T , Takeshima Y , Kikuchi T
Published: April 2022

In this paper, we will explain to you how to procedural generate realistic aggregates. The program requires two or more 3D models, and the number of elements and friction values. There are two steps: the first requires user-input elements and a provided shape and fills the defined shape with non-overlapping elements; the second, together with user-input friction data, is a physical simulation to generate a more realistic pile.
Iterative Method of Finding All Thermophysical Parameters of a Two-Layer Soil
Authors: Rysbaiuly B , Mukhametkaliyeva N E
Published: April 2022

The paper develops methods for finding the thermophysical parameters of a two-layer soil. The difference scheme for the equation of quasi-linear thermal conductivity is taken as the basis for the study. Two-layer complexes of containers have been created, the side faces of which are thermally insulated. Measurement work was carried out to obtain values at the two end borders, the environment and at the contact boundary of the two containers. This circumstance makes it possible to solve the inverse coefficient problem in each container independently of each other. We have developed methods for finding all the thermophysical parameters of the soils in both containers. A rational method of choosing the damping coefficient is also proposed, which provides an indicative rate of convergence of the approximate value of the functional to zero. Computational experiments were carried out on the basis of the developed methods and measured data. The results of which show the viability of the developed iterative methods.
The Positive Role of the Partial Discharge Monitoring System in the Partial Discharge Detection of GIS Equipment
Authors: Chen Jiangtian , Wei Dongliang , Wang Zhi , Liu Congcong , Shao Weitao , Shao Weiling
Published: April 2022

When gas insulated metal enclosed switchgear and controlgear (GIS) has internal discharge, some faults are difficult to find during inspections due to the enclosed metal casing. This paper introduces the event of the floating potential discharge inside the GIS equipment of a 500kV substation found by GIS on-line monitoring system. Through on-site ultra-high frequency detection and precise positioning of the oscilloscope to determine the position of the discharge source at a certain switch, combined with the disassembly of the equipment, it is determined that the equipment defect is the discharge of the floating electrode of the switch fork.The results show that the on-line monitoring of GIS partial discharge has an important reference role in discovering the internal discharge of equipment during the test interval, and the monitoring and maintenance of GIS partial discharge on-line monitoring system should be strengthened in daily work.
Simulation Research on Flow Boiling and Heat Transfer of Micro-channel for Electronic Cooling
Authors: Yuan Xudong , Zhao Panpan , Huang Jing , Wu Junfeng , Zhou Dao , Wang Rujin , Hu Jisun , Qu Boyi
Published: April 2022

Micro-channel heat exchanger is a popular device for the electronic cooling. Heat transfer of flow boiling in micro-channel is a research hotspot. For the convenience of the theoretical research on micro-channel, a high efficiency simulation model on flow boiling and heat transfer of micro-channel was established. In the simulation research, the influence rules of operating conditions and thermal properties of refrigerants (working fluids) on the flow boiling were studied; the distribution rules along flow direction of characteristic parameters such as vapor quality, Heat Transfer Coefficient (HTC), pressure drop and bottom wall temperature of micro-channel T
w were analyzed; the key factors on the distribution of characteristic parameters were proposed. The characteristic parameters based on the refrigerants R134a and R1234ze(E) in micro-channel were compared and researched. According to the simulation results, HTC and pressure drop of R134a are larger and smaller than those of R1234ze(E) respectively, the uniformities of HTC and T
w of R134a are all better than those of R1234ze(E) respectively.
Research on Safety of Inner Spherical Surface Machining on A--C Double-Turntable Five-axis Machine Tool
Authors: Li Tiren , Wang Qi , Zhao Gengbo , Sun Jiangong , Zhang Yanhua
Published: April 2022

This article discusses and verifies the accessibility and safety of internal spherical parts in A-C double turntable five-axis machine tools. The inner spherical surface can be machined completely by controlling the tool axis vector. The theoretical calculations determine the safe height range of the fixture to avoid collision between the tool and the workpiece and the Y-axis overrange. The correctness and reliability of the calculation are verified by machining simulation and actual machining of typical inner spherical parts.
Simulation and Statistical Analysis on the Transport Process of Salt Water Mass from the North Branch in the Yangtze River Estuary
Authors: Wu Dean , Wang Yigang , Shao Yuchen , Hu Guodong
Published: April 2022

Based on the analysis of measured data, a two-dimensional hydrodynamic Salinity Transport Model in the Yangtze River estuary is established by using Mike21 model. This model is used to study the average daily distribution and changing patterns of the saline water mass in the South Branch that are from the North Branch while only the saltwater intrusion from the North Branch is considered. It determines the intrusion process of saline water mass from the North Branch and the area that are affected by saline water intrusion. In addition, through the research of salt water mass core moving down and salinity variation in the south branch channel, the flow pattern and mechanism of salt water mass in the south branch channel are revealed. The saltwater intrusion from the North Branch has a great impact on the vertical, horizontal and vertical salinity fields of the Yangtze River estuary. The variation trend of statistical variance, deviation and standard deviation of the half month time series of salinity at each point along the route is basically the same, showing a gradual decreasing trend towards the downstream. Those show that the salinity dispersion is larger in the upstream estuary and lower outside the estuary. Combined with the salinity kurtosis and skewness, the deviation degree and direction of the salinity distribution at each point along the route can be judged, which can reflect the comprehensive process of runoff, tidal current and topography. Therefore, the kurtosis and skewness statistics can effectively describe the law of transport and dynamic characteristics of intrusion saline water masses in the Yangtze River Estuary.
Development of an Anti-radon Coating for Underground Engineering
Authors: Pan Liangliang , Qin Guoxiu , Wang Qimin , Li Fan , Li Weizhe
Published: April 2022

To satisfy the requirements of underground engineering applications, an anti-radon coating was developed by selecting suitable modified polymer emulsions, element materials, pigments, fillers, additives, etc. Experimental results on radon mitigation performance show that for a coating thickness of 2.0 mm, the radon mitigation efficiency reaches 95.1%. In addition, an anti-radon coating has a very low content of toxic substances and excellent water resistance and durability. Using the coating in an engineering application showed that the coating has the advantages of fast drying speed, no peculiar smell and excellent radon mitigation performance, where 2-3 coats produce good radon mitigation in damp and sealed underground engineering environments.
Blind Write Protocol Throughput
Authors: Herman Nanna Suryana , Anshar Khairul , Andono Pulung Nurtantio
Published: April 2022

Blind Write Protocol allows more transactions to be executed at the same time. It is achieved by removing the locking phase to any entity while the write operation is performed. To have a fair throughput comparison with the Locking Protocol then this paper introduces three different approaches using Blind Write Protocol that can preserve consistency. The throughputs are presented and compared with Locking Protocol. We found that preserving consistency using Blind Write Protocol needs more operations. As a result, it consumes more resources and makes the response times much slower compared to Locking Protocol. If Blind Write Protocol ignores consistency, then it shows better response time compared to Locking Protocol. The Blind Write Protocol aims to achieve availability rather than consistency.
Development and Implementation of a Model in MATLAB Simulink Responsible for the Analysis of Sensor Data via the CAN Bus
Authors: Elert Szymon
Published: April 2022

The paper presents a modelling of the communication system between the TI F28379D microcontroller and the sensors responsible for measuring the flight parameters of the missile. Data acquisition in aviation and their detailed interpretation are very important for the correct operation of the system. One of these devices is the AHRS module (Attitude and Heading Reference System) which was used for the test. Communication between devices is carried out via the CAN bus. The MATLAB Simulink environment was used to create the model, which made it possible to design the program and then implement the generated code on the processor. The article presents how the individual tasks have been performed by the designed controller. Then an interface was created to allow quick change of basic parameters without in-depth interference with the program. The developed program can be used as one of the autopilot subsystems and used for real-time tests.
Detect Denial of Service Attack (DoS) in MANETs Partition Scenario Using Puzzle Map Method
Authors: Alsumayt Albandari
Published: April 2022

The usage of the Internet increases significantly especially regards COVID-19. The lifestyle relies of the networks and the Internet to do all tasks such as job, meetings, and education. The mobile ad hoc network (MANET) is a type of wireless network that nodes can communicate without the occurrence of an administration point. Nodes in MANET can join and leave the network frequently, so the scenario of networks merging and partitioning can occur. In this study, the detection of Denial of Service (DoS) attack which degrades the performance of the network will be illustrated in the situation of network partitioning. The detection will be based on MrDR method and puzzle map method, the puzzle concept will help to complete the network partitioning and avoid the DoS attack. The proposed method is simulated using NS2 and the results shows the effectiveness of the puzzle map method to increase the performance of network during the partitioning scenario.
Research on Identification Method of Fragile Component of Power Grid Considering Both Structure and State
Authors: Yang Xuetao , Song Dunwen , Liu Kaixin , Li Haifeng , Jin Tao , Chen Yong
Published: April 2022

Whether in the power grid planning, design or operation stage, the identification of fragile component in the power system is always one of the core issues of power grid security analysis. In order to realize the accurate positioning of fragile lines, firstly, considering the overall distribution characteristics and local fluctuation characteristics of the power flow under the disturbance of the breaking, a composite entropy evaluation model of fragile components based on the distribution entropy and transfer entropy of the power flow is proposed. Secondly, the power flow betweenness evaluation method based on the power transmission transfer distribution factor is used to measure the transmission contribution and carrying capacity of the branch in the power grid. Through normalization, a comprehensive evaluation index for vulnerable branches is established. Aiming at the proposed identification method, the IEEE10 machine 39-node calculation example and comparison with other methods show that the method in this paper can more comprehensively and accurately identify the fragile branch of the power grid.
Distributed Control System for Agrotechnical Processes Using Customizable Procedural Regulations
Authors: Melnik V , Nikiforov A , Zelensky Al , Zelensky An , Antoshchemkov R
Published: April 2022

Nowadays, the technical and technological capabilities achieved in the field of agriculture permit to implement quickly convertible (adaptive) crop production systems. This, along with increased efficiency, also greatly complicates the management process. Automated control systems (ACS) with adaptive properties are needed. The concept of creating an ACS assumes the implementation of the principle of modularity and flexible changes in the structure of the management hierarchy, powers, tasks and functions for geographically distributed decision-making centers and executors. Key technologies in the design of such ACS are: synthesis of a temporary control loop from geographically distributed elements and synthesis of procedural regulations for a decision-making center included in the temporary control loop. The article formulates the problem of synthesis of procedural regulations in the form of a tensor transformation problem of the original electrical network (library of procedures) into a network with specified properties. A method for obtaining the tensor equation of transformation of the original network is presented based on the requirements for the composition of the input and output data of the synthesized procedural regulations. The solution obtained after tensor transformation has the form of a matrix of connections and a vector of voltages on the intermediate coils of the original electrical network (library of procedures). This forms structural links for the synthesized procedural rules. The demonstrated approach can be extended to solve a wide range of problems of adaptive changes in the structures of organizational management systems.
Theory and Practice of Creating a Two Energy Outputs Magnetron
Authors: Churyumov G , Qiu S , Wang N , Li W
Published: April 2022

This paper presents the methodology for 3-D simulation and design of the magnetron with two energy outputs. The principal functionality of the modified magnetron, both theoretically and experimentally, has been determined. The central distinguishing feature of this magnetron (for example, unlike the conventional magnetron with one output) is the availability of second energy output in the anode block. The mathematical models of both the resonant anode block and the electron-wave interaction are described. The dispersion characteristics of the anode block for cavities various geometries are given. It is supposed that forming the total RF field in the interaction space results from the interference of RF fields excited severally in the resonant anode block consisting of the small and large cavities (resonators). For PIC-simulation of electron-wave interaction in the dual-output magnetron, the non-linear system of equations is stated as a self-consistent system containing the equation of motion (for electron stream), the equation of excitation (for RF field), and Poisson’s equation for calculating the space-charge field. The fundamental feature of the self-consistent system of equations is a new algorithm for determining the Coulomb interaction forces. The implementation of the mathematical model made it possible not only to gain new knowledge about the physical processes in the magnetron but also to determine its output characteristics. On operating frequency of ∼ 13.34 GHz, at an anode voltage of 495 V, a magnetic field of ∼ 0.25 T, and with air cooling of the magnetron, there were obtained the following limiting values: the RF output power of ∼ 14.6 W and the power conversion efficiency of ∼ 40.8%. The use of the second energy output allowed extending the magnetron’s functionality and implementing the modes of the frequency tuning (adjunction) and its stabilization. The simulation results are in good agreement with the experiment.

To the 100th anniversary of magnetron
UCB-DQN based Joint Beamforming and Power Control for Software Defined UAV Swarm Network
Authors: Li Haitao , Zhang Shuai , Fang Zheng , Qiu Qiming
Published: April 2022

In this paper, we address the problem of deep Q-Network (DQN) based joint power control and beamforming for enabling interference suppression in software defined UAV swarm (SD-UAS) network. We first present the optimization model of joint beamforming and power control (JBPC). Then, to solve this joint optimization problem, we make use of UCB exploration in the learning process of DQN. Simulation results validate that the convergence obtained with the proposed UCB-DQN strategy outperform the DQN learning algorithm, which can to raise the exploration efficiency and sequentially speed up the convergence for the JBPC problem. It is benefit to supress interference and enhance the SD -UAS communication performance.
Transmission Shaft Fault Diagnosis Based on Variational Modal Decomposition (VMD) Feature Fusion
Authors: Chong Zezhong , Zhang Mengqian , Liu Sijie , Wu Yafeng
Published: April 2022

It is difficult to achieve an excellent result to accomplish the transmission shaft fault diagnosis with single feature, so thadt a feature fusion fault diagnosis method is proposed. First, the preprocessed vibration signal is decomposed by variational mode decomposition (VMD), and the Intrinsic mode function (IMF) is obtained; Then, singular value, sample entropy and approximate entropy are obtained as features and fused; Finally, the fused feature is used as the classification object of SVM for fault diagnosis. In this paper, the feasibility of this method is verified by the transmission of experimental data. The experiment included five conditions, and the diagnostic accuracy was 99%.
Neural Sliding Mode Tracking Control of Air-breathing Hypersonic Vehicles
Authors: Cui Peng , Gao Changsheng
Published: April 2022

This paper proposes a neural sliding mode control method for the tracking problem of the longitudinal dynamics of air-breathing hypersonic vehicles (ABHV). Considering the input/output feedback linearization, a high-order sliding mode law of the elevator deflection and the fuel equivalence ratio is designed. Moreover, the effect of uncertain model and control input disturbances is approximated with a Radial Basis Function Neural Network (RBFNN). The stability of the closed-loop system is analysed based on Lyapunov theorem. Simulation results shows the good tracking performance of the proposed controller and robustness with parameter uncertainties. All the signals are globally bounded and converged in short time.
Joint Optimization of Resource Allocation and UAVs’ Trajectories in Multi-UAV-Aided MEC Systems
Authors: Wu Runuyang
Published: April 2022

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has become an up-and-coming technique to provide additional computing support for smart mobile devices with limited battery capacity in the wireless communication network systems. In this paper, a multi-UAV-aided MEC system is modelled where UAVs are equipped with MEC servers and users can execute local computing and computation offloading to UAVs simultaneously. The total amount of processing bits of all users is maximized by the joint optimization of the user-UAV association, local processing frequency, transmit power, bandwidth allocation and UAVs’ trajectories, meanwhile, the energy consumption constraint and the UAVs’ speed constraint are satisfied. Because the formulated problem is not convex, it is challenging to get the global optimal solution directly. To iron out the non-convex problem, it is divided into three subproblems and solved by an alternating optimization algorithm. Simulations show that the algorithm introduced in our paper performs better than the local computing, full offloading and fixed trajectory patterns.
Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm
Authors: Liao Jiayu , Liu Xiaolan , Xie Mengying
Published: April 2022

Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.
A Rail Wear Detection Method Based on Particle Swarm Algorithm
Authors: Chen Zhiyuan , Jiang Xintao , Luo Qingli , Zhang Shubin , Chen Honghui , Chen Xiang
Published: April 2022

In China, the high-speed rail network has developed rapidly in recent years. The wear of rail is a risk to its safe operation. Compared with traditional static detection methods, the methods applying computer vision have become one of the major detection methods due to its non-contact ability, high efficiency, and low cost. However, the accuracy of dynamic detection is affected by the flexible outdoor detection conditions, random vibration during the detection process and other undesirable factors. Thus, in order to improve the accuracy of rail wear detection under complex detection conditions, this paper develops a rail wear detection method based on particle swarm algorithm. The experimental results with line-structured light data show that the proposed method improves the accuracy of rail wear detection and provides technical references for the dynamic detection of rail wear.
A Game Theory Approach in Quality Classification and Secondary Use of Electric Vehicle Used Batteries
Authors: Yao Cailiang , Ma Xinyu , Hu Tong , Gu Xiaoyu
Published: April 2022

At present, electric vehicles have become one of the main directions for the development of today’s automotive industry by virtue of their high energy utilization efficiency and low environmental pollution. With the popularity of electric vehicles, the recycling of their batteries will become a challenge in the future. At present, the recycling system of vehicle batteries is not perfect. How to evaluate different quality of batteries to decide whether to reuse them or to remanufacture them directly is a question worth exploring. The purpose of this paper is to discuss the quality grading strategy of used batteries and make decisions based on the evaluation results by constructing a game theory model that includes four parties: new energy vehicle manufacturers, consumers, battery manufacturers, and recyclers, with the goal of maximizing the utility of the parts in the closed-loop supply chain of electric vehicles. This study is beneficial for the government and managers to better understand the electric vehicle battery closed-loop supply chain, and for the sustainable development of the supply chain as well.
Predicting the mechanical properties of borophene by artificial neural networks
Authors: Moreno J D , López A , Gutierrez E D
Published: April 2022

A material of great relevance in the current research context is borophene, a monolayer nanomaterial composed only of boron atoms with extraordinary electrical and mechanical properties. In the present work, a neural network was designed and trained in order to predict the mechanical properties of this material, such as Young’s modulus, fracture strength and fracture strain. The training data set was obtained through molecular dynamics simulations, with different parameter scenarios in order to analyze the effects of temperature, strain rate and strain direction. The trained machine learning model succeeded in predicting the material’s behavior with high accuracy. Its results reflect a decrease in yield stress with increasing temperature and a slight improvement in the fracture strain with increasing strain rates, as expected. Additionally, a web application with a graphical interface was developed, which uses the trained model, in order to make this tool available to any user. This interface also makes it possible to visualize the approximate stress-strain curve, drawn based on the resulting fracture stress and strain.
Research on thermal characteristics of tapered laser chip
Authors: Wang Hongran , Liao Wenyuan , Zhang Chengwei , Lai Canxiong , lu Guoguang , Yang Shaohua , Wang Xiaohua , Wei Zhipeng
Published: April 2022

The tapered laser with high power and high beam quality is one of the research hotspots in the field of laser. The thermal effect of the tapered laser will reduce the thermal saturation current and produce thermal deformation, which will lead to the decrease of output power. Therefore, the thermal effect of the tapered laser is the main cause of failure. In order to investigate the thermal characteristics of the tapered laser chip, this paper simulates the tapered laser by using COMSOL Multiphysics. The changes of thermal stress under different ridge widths and tapered angles are simulated, and the thermal stress distributions in the ridge and tapered regions are obtained. In addition, a porous heat sink is designed. The simulation results of ordinary radiator and porous radiator are compared, which can be seen that porous heat sink has better heat dissipation capacity than ordinary heat sink. Through the simulation of tapered laser, this paper does research on the thermal characteristics of tapered laser, which can be a useful reference to improving the performance of tapered laser.
Research Status of Amplified Spontaneous Emission Sources based on Doped Materials
Authors: Lei Jiayang , Qiu Shuang , Yang Kuo , Zhao Xiaoyu
Published: April 2022

Amplified spontaneous emission (ASE) was once considered a harmful parasitic effect. This is because ASE will reduce the number of the inverted population in the gain medium, thereby reducing the efficiency of lasers. However, the output characteristics of ASE make it applicable to many studies. Therefore, the experimental settings and beam output characteristics of different doped laser ASE sources are summarized in this review. The summarization begins with the characteristics of ASE, such as threshold condition. Then, two doped fiber lasers are introduced. Based on erbium-doped fiber, an ASE source with high stability is made by measuring the best parameters and selecting a suitable structure. The ytterbium-doped fiber can benefit Q-switch pulses and high-order Stokes generation by suppressing the self-saturation of ASE and offering gain for ASE, respectively. Besides, three different ASE sources based on Nd-doped laser crystals are presented. They are spatially selective ASE sources that achieve high-intensity, narrow-bandwidth output pulses and Q-switching in a cavityless laser. Additionally, the performance of the ASE source can also be improved by doping with gold nanoparticles, Gd3+, and other particles.
Research on dielectric and microwave absorbing properties of TiO2/TiB2/Thermoplastic polyurethanes (TPU) composite materials
Authors: Liu Xiongzhang , Li Xixi , Lei Xue , Zhang Deping , Liu Zhijun , Long Xueyuan , Gong Rongzhou
Published: April 2022

TiO2/TiB2 materials are prepared by the carbothermal method and high-temperature calcination. Further, the phase composition, microstructure, dielectric and electromagnetic (EM) wave absorption characteristics are investigated. The results display that the product is composed of TiO2, TiB2 and impurity phases. The complex permittivity increases at elevated TiO2/TiB2 loading and slowly decreases with rising frequency in 2-18 GHz. When the TiO2/TiB2 content is 45 wt%, the minimum reflection loss (RLmin
) reaches -43.12 dB at 11.68 GHz with a thickness of 1.9 mm. The effective absorption band (EAB, RL<-10 dB) can reach 3.68 GHz at 2.3 mm as the TiO2/TiB2 content is 30 wt%. The enhanced EM wave absorption owes to the strong polarization loss and high conductivity loss. It goes without saying that TiO2/TiB2 materials can acquire important breakthroughs in the development of novel EM wave absorbers with outstanding absorption loss, wider EAB and smaller thicknesses.
A rapid measurement method for ship’s longitudinal and athwartship induced magnetic field
Authors: He Baowei , Zhang Junhong , Liu Shengdao , Li Fengxie
Published: April 2022

Induced magnetic field (IMF) measurement is a key step in the ship’s magnetic field treatment process. In this paper, a new method for rapid measurement of longitudinal and athwartship IMF is proposed, which can improve the traditional measurement method that is either time-consuming and labor-intensive, or dependent on the magnetic field uniformity of geomagnetic simulation coils. Using the geomagnetic simulation coil in the close-wrap magnetic treatment facility, a mapping model between the coil magnetic field and the ship’s magnetized field excited by the coil is constructed in a single-heading. Based on this single-heading-measurement method, numerical simulations and hull experiments are carried out. By combining the regularization technology, the results show that the proposed method can quickly and accurately measure the ship’s IMF, and can suppress the influence of measurement error on obtaining the IMF. Compared with the geomagnetic simulation method, proposed method not only has a great calculation accuracy and strong robustness, but also requires a lower magnetic field uniformity of the geomagnetic simulation coil, and can be used as a fast emergency measurement method for ship’s IMF in engineering.
Hog: handling HDL repository on git
Authors: Biesuz N.V. , Cieri D. , Giangiacomi N. , Gonnella F. , Peck A.
Published: April 2022

Handling HDL project development within large collaborations presents many challenges in terms of maintenance and versioning, due to the lack of standardised procedures. Hog (HDL on git) is a tcl-based open-source management tool, created to simplify HDL project development and management by exploiting git and Gitlab Continuous Integration (CI). Hog is compatible with the major HDL IDEs from Xilinx and Intel-FPGA, and guarantees synthesis and placing reproducibility and binary file traceability, by linking each binary file to a specific git commit. Hog-CI validates any changes to the code, handles automatic versioning and can automatically simulate, synthesise and build the design.
Ion beam tools for nondestructive in-situ and in-operando composition analysis and modification of materials at the Tandem Laboratory in Uppsala
Authors: Ström P. , Primetzhofer D.
Published: April 2022

Ion accelerators have demonstrated tremendous versatility in
their research applications throughout several decades. Starting
predominantly as nuclear physics tools, they subsequently provided
novel insights into fundamental atomic physics. Nowadays small and
medium size accelerators are routinely employed in several branches
of materials science. As research questions increasingly drive the
need for analyses and material modification experiments performed
in-situ and/or in-operando, facilities must adapt to service such a
need. In this work, we highlight capabilities for in-situ and
in-operando composition depth profiling and materials modification,
developed with instrumentation at the Tandem Laboratory at Uppsala
University. An overview of available ion sources and accelerators is
given. We then exemplify the specific capabilities of the available
end stations for materials synthesis, sample modification and device
operation either during simultaneous ion beam based characterization
or with such characterization performed in-situ before and after the
application of various modification steps. Describing specific
recent examples and potential future applications, we illustrate the
impact of the present national research infrastructure.
Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area
Authors: Abolafia-Rosenzweig Ronnie , He Cenlin , Chen Fei
Published: April 2022

This study predicts summer (June–September) fire burned area across the western United States (U.S.) from 1984 to 2020 using ensembles of statistical models trained with pre-fire season climate conditions. Winter and spring climate conditions alone explain up to 53% of the interannual variability and 58% of the increasing trend of observed summer burned area, which suggests that climate conditions in antecedent seasons have been an important driver to broad-scale changes in summer fire activity in the western U.S. over the recent four decades. Relationships between antecedent climate conditions with summer burned area are found to be strongest over non-forested and middle-to-high elevation areas (1100–3300 m). Statistical models that predict summer burned area using both antecedent and fireseason climate conditions have improved performance, explaining 69% of the interannual variability and 83% of the increasing trend of observed burned area. Among the antecedent climate predictors, vapor pressure deficit averaged over winter and spring plays the most critical role in predicting summer fire burned area. Spring snow drought area is found to be an important antecedent predictor for summer burned area over snow-reliant regions in the nonlinear statistical modeling framework used in this analysis. Namely, spring snow drought memory is realized through dry anomalies in land (soil and fuel) and atmospheric moisture during summer, which favours fire activity. This study highlights the important role of snow drought in subseasonal-to-seasonal forecasts of summer burned area over snow-reliant areas.
A trace formula for metric graphs with piecewise constant potentials and multi-mode graphs
Authors: Gnutzmann Sven , Smilansky Uzy
Published: April 2022

We generalize the scattering approach to quantum graphs to quantum graphs with piecewise constant potentials and multiple excitation modes. The free single-mode case is well-known and leads to the trace formulas of Roth (1983 C. R. Acad. Sci., Paris I 296 793–5), Kottos and Smilansky (1997 Phys. Rev. Lett.
79 4794). By introducing an effective reduced scattering picture we are able to propose new exact trace formulas in the more general settings. The latter are derived and discussed in details with some numerical examples for illustration. Our generalization is motivated by both experimental applications and fundamental theoretical considerations. The free single-mode quantum graphs are an extreme idealization of reality that, due to the simplicity of the model allows to understand a large number of generic or universal phenomena. We lift some of this idealization by considering the influence of evanescent modes that only open above threshold energies. How to do this theoretically in a closed model in general is a challenging question of fundamental theoretical interest and we achieve this here for quantum graphs.