Multi-thread Method Research Articles

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

ПРИМЕНЕНИЕ МЕТОДОВ ПАРАЛЛЕЛЬНОГО ПРОГРАММИРОВАНИЯ В SIMINTECH

The article discusses an alternative approach to creating projects that require a large amount of calculations in the SimInTech environment. At the same time, it is proposed to use parallel programming methods and a master/slave model of interaction between projects, as they are more suitable for the tasks of studying and optimizing the parameters of complex systems. In general, parallel programming is becoming increasingly relevant in design systems due to the increase in computer power, the development of parallel computing technologies, the increasing complexity of design systems and the development of cloud computing. This approach can significantly speed up the modeling process and increase its accuracy, which makes it increasingly relevant in design systems. The article analyzes the main advantages and disadvantages of the recommended method of parallel computing using a signal database and batch processing of projects in SinInTech. Based on this analysis, an alternative method for organizing parallel computing using an embedded programming language is proposed. A program code is given that implements a multi-threaded method of launching slave projects with control from the master project. An analysis of the performance improvement when using this approach was carried out to study the effect of PI speed controller settings on the control error in a two-loop slave control system with a DC motor. Recommendations are given for the implementation of combined methods that include both batch processing and a master/slave model of project interaction. These methods will allow you to implement multi-threaded calculations of projects with distributed computing on computers united in a single network. In conclusion, it is noted that parallel programming is currently becoming an integral part in the creation and selection of modern design tools and this approach is fully implemented in the SimInTech environment.

Monitoring and alarm method for wildfires near transmission lines with multi‐Doppler weather radars

AbstractMonitoring and alarm play an important role in preventing trip‐out caused by wildfires for high‐voltage transmission lines. This paper proposes a multi‐Doppler weather radar‐based method for monitoring and alerting of wildfires near transmission lines. Firstly, characteristic parameters are mathematically proposed to distinguish wildfire's radar echoes. Then, the four‐site neighbourhood algorithm and the multithreading method are used to extract all possible wildfire echo units detected by various Doppler weather radars, and a cumulative probability is calculated to identify the wildfire. Finally, a regional block search method is used to quickly locate wildfire‐affected transmission towers. The membership function of spread time in the fuzzy set is proposed to determine the alarm level, which takes into account the influences of environment, topography, and vegetation on wildfire spread rate. The application to a provincial power grid demonstrates that the proposed method has an accuracy of 82.4%, a missing alarm rate of 27.4%, and a delay of fewer than 15 min. In addition, the joint observation of wildfires by multi‐Doppler weather radars and satellites indicates a promising application prospect for transmission line wildfire fighting.

Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption.

With the development of society and the continuous progress of science and technology, it has become the mainstream measure to promote the development of the social economy through science and technology. Therefore, to improve the current situation of tourism consumption, improve the consumer sentiment of tourists, and promote the development of the tourism economy, the convolutional neural network (CNN) technology model is used to analyze the tourist's consumer psychology and behavior. Based on this, the user's consumption situation is analyzed, thus providing support for the intelligent improvement of tourism consumption. First, the basic characteristics of tourism consumption mood and behavior are introduced, and the methods to improve the tourism consumption mood and behavior are briefly introduced. Then, the CNN algorithm is employed to identify consumers' travel consumption behaviors and emotions. To improve the recognition effect, the algorithm is combined with skeleton node behavior recognition and video image behavior recognition. Finally, the performance of the designed algorithm is tested. The accuracy of the human behavior recognition (HBR) algorithm is more than 0.88. Compared with the detection effect of the HBR algorithm, the combined algorithm adopted in this work can reduce the image processing time and improve the detection efficiency. The multithread method can effectively reduce the complexity of the model and improve the recognition accuracy. The test results on different data sets show that the proposed algorithm can better adapt to the changes in identification samples and obtain more accurate recognition results compared with similar algorithms. In summary, this study not only provides technical support for the rational analysis of consumer sentiment and consumer behavior but also contributes to the comprehensive development of the tourism market.

PERANCANGAN APLIKASI BASE STATION DALAM SISTEM KOORDINASI ROBOT SEPAK BOLA BERODA DENGAN MULTI THREAD

The Indonesian Wheeled Football Robot Contest (KRSBI Beroda) is one of the divisions of the Indonesian Robot Contest (KRI). At KRSBI Beroda robots are expected to be able to move automatically by using the Base Station as a communication center between robots in the same team. Base Station has a function to forward orders from the referee or RefBox (Referee Box) to the robot. This study aims to add features to the Base Station by displaying the position of two robots in the field in animation using the multi-thread method to facilitate strategy formulation. The result is that the Base Station that is made can display the position of two robots in the field without any errors.

Random-sampling-based multi-UAV cooperative search planning for high-rise firefighting

This paper proposes a multi-UAV cooperative search planning method based on random sampling for high-rise fire rescue. First, an optimal distribution method of search points inspired by the random tree sampling search algorithm is proposed, which uses its repeated search feature in a limited complex space to solve the problems of waypoint distribution and task assignment for multiple UAVs simultaneously. Second, a waypoint optimization method based on transaction rules is designed to further improve search coverage. Third, the optimal traversal order of waypoints for each UAV is obtained by using the improved harmony search algorithm to acquire the planned path of each UAV. Finally, the multithreading method is used to simulate the distributed operation in the actual task to verify the feasibility of the proposed method.

Positioning Tank-Wall Magnetic Shunts of a Three-Phase Power Transformer Considering Thermal Effects

This article presents an investigation of the optimal positioning of tank-wall magnetic shunts in power transformers. Magnetic shunts are made of grain-oriented magnetic silicon steel laminations and are used to avoid hot spots in metallic regions inside the power transformer. The magnetodynamic equations are solved in terms of the electric vector potential and the magnetic scalar potential. Thermal effects are calculated using heat transfer coefficients. This article proposes and solves an optimization problem where the objective is to position several shunts near the tank wall. Several design variables, temperature, and geometrical constraints are considered. As the finite-element (FE) model to be solved is in 3-D, the computing resources must be efficiently used. A multi-threading method was developed and numerically tested where the 3-D FE model, temperature calculation, and the particle swarm optimizer (PSO) are executed in a multicore computer in a parallel scheme.

Multithreaded wedge detection method on triangular 3D CAD objects using mesh traversal method

In this study, a multithreaded method for triangular mesh three-dimensional computer aided design objects is proposed to detect and extract wedges. Wedge detection is time consuming process for such objects that have large number of facets. To take the advantage of parallel computing opportunities, the algorithm is refactored in this study. Scope of variables, memory management and stack use are optimized for efficient use of computational resources. The proposed method is focused to calculation efficiency and performance on multicore / multithreaded processors and it is evaluated with benchmark, complex and realistic objects.

Information Steganography Technology of Optical Communication Sensor Network Based on Virtual Reality Technology

In order to solve the problems of narrow object orientation and insufficient visual rendering ability of the optical communication network experimental system, the author proposes a multithreaded scheduling using virtual reality, and the experimental system design method of optical communication network with multichannel serial port design is proposed. The system builds the overall structure model of the optical communication network experimental system in the virtual reality environment, and uses the PCI bus technology to build the transmission channel model of the optical communication network, the basic entity object of the optical communication network experimental system is constructed, and the multithread scheduling method is used to process the local information of the experimental system. The result obtained is as follows: The experimental system of optical communication network designed by the author’s method, the optical communication transmission bit error rate is reduced by about 30%, the time overhead is saved by about 60%, and the memory overhead is saved by about 50%, which is superior. Using the author’s method, the maximum bit error rate is about 22%, while the maximum bit error rate of traditional method 1 is about 43%, and the maximum bit error rate of traditional method 2 is about 70%. It is proven that the visual simulation effect of the optical communication network experimental system is good, the reliability and stability of the optical communication network are improved, and the communication transmission error is reduced.

The Academic Portrait of the Creator of the Pure Theory of Law. Several Facts from Thomas Olechowski’s Book Entitled Hans Kelsen. Biographie eines Rechtswissenschaftlers. Tübingen: Mohr Siebeck, 2020 (1027 pp.)

The Academic Portrait of the Creator of the Pure Theory of Law was written by Thomas Olechowski, a professor of the University of Vienna, and a historian of law with an established academic position, having outstanding expertise in the field of the history of the system of law in Austria in the 19th and 20th centuries. Olechowski collected impressive source material - mainly archival, including Kelsen’s extensive correspondence, university and administrative files connected with all the stages of his life and academic activity, and interviews with still-living persons (oral history) who had met Kelsen directly or indirectly. Owing to the obtained material, often secured through detailed source query in Austrian, Czech, German, and American archives, the author managed to correct and complete many details from his subject’s life and works. Hence, the reviewed biography of Kelsen provides a great deal of new information, which presents a view of his life and academic achievements through a multithreaded method. Various examples of little-known or completely unknown facts from H. Kelsen’s biography will be presented in the review.

A nonparametric algorithm for automatic classification of large multivariate statistical data sets and its application

A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the «compression» of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one- and two-dimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data.

Development of multithread acquisition system for high quality EEG signal measurement

This work was concerned on development of the EEG acquisition and EEG signal processing by adding active electrodes and implementing multithread techniques. By using active electrodes, mounting them on the scalp surface would be easier to capture low signals of less than 1µV. The active electrodes were used to reduce noise when transfer signals from the electrode to the acquisition systems which equipped 20 gain. The verification was performed by comparing the active and passive electrodes using NETECH MiniSIM EEG Simulator 330. The advantage of this research was to reduce time delay for EEG signal computation on 32 channels. The acquisition system was based on Raspberry Pi and ADS1299 with multithread signal treatment. Signal filtering was performed into different threads and put all the EEG features into the database. A PC was used to process signal calculation such as processing FFT, signal feature extractions, and signal analysis. These calculations were divided into several functionally independent computations. The signals of each channel were calculated into different threads. The results of this work showed the effectiveness of the multithreaded method for processing large amounts of data (32 channels of 24 bits EEG signal) with low noise levels on the active electrodes.

A Multi-threaded Method to Assemble a Sparse Stiffness Matrix for Quasi-static Solutions of Linearized Bond-Based Peridynamics

Peridynamics is generally applied to obtain dynamic solutions of crack propagation problems where crack initiation, crack branching and usually fast fracture are involved. A fairly straightforward explicit time-stepping scheme is most commonly used. Quasi-static solutions can be obtained using an adaptive dynamic relaxation method or an energy minimization method applied to the dynamic equations of motion or by solving a linear system of equations in the case of linearized peridynamics for an implicit solution. A direct solution approach by forming a linear system of equations is advantageous as the implementation is fairly straightforward and does not require multiple iterations. However, memory requirements for storing full stiffness matrices scale quadratically with the number of degrees of freedom. For example, in 2D, a grid of nominal size, say 104 particles, would require memory of nearly 3 gigabytes when using double precision. This work presents detailed algorithms that use coordinate and neighbor information to directly generate the sparse matrices required to solve a peridynamic system of equations for quasi-static simulations. The algorithms are first prototyped in MATLAB®; and the stiffness matrix and solutions are verified by comparing with their counterparts obtained by assembling the full stiffness matrix. The algorithms are also implemented in a C++ code that is run on multiple threads and the performance is evaluated as a function of parameters such as problem size, horizon, and the number of threads used. The performance and solutions of two problems with a static crack and a propagating crack are compared with solutions obtained using more traditional methods. It is found that a massive computational gain can be achieved in terms of both memory and performance by taking advantage of multi-threading and sparse implementation simultaneously.

Optimizing the location of multiple laser guide stars in ground layer adaptive optical systems

At present, the ground layer adaptive optical systems are using multiple laser guide stars arranged in regular polygons as reference targets to measure the effects of atmospheric turbulence. Obtaining the optimal position of laser guide stars becomes an interesting problem to analyze. This paper proposes a method to obtain the optimal position of laser guide stars by using a genetic algorithm as the optimization algorithm and a simplified geometry model of the ground layer adaptive optic system as the evaluation function. Furthermore, multi-process, Numba library, and multi-thread techniques methods are used to accelerate calculation speed. Based on these methods, real atmospheric turbulence profiles are used to analyze the relationship between the optimal position of laser guide stars with different numbers and the different atmospheric turbulence profiles from the same site, through an example of a ground layer adaptive optics system with 14 arcmin field of view. The results show that the optimal position of laser guide stars in the same site is almost the same and their statistically optimal positions are all regular polygon. Besides, we also find that the spatial resolution of turbulence profiles has strong effects to positions of laser guide stars, showing that the more equivalent layers in the measurement results, the closer the position distribution of laser guide stars is to the regular polygon.

Metody badania prawa rolnego według koncepcji Profesora Andrzeja Stelmachowskiego

The deliberations are devoted to the methods of legal research that Professor Andrzej Stelmachowski conducted in the field of agricultural law. This outstanding civil lawyer and co-creator of agricultural law as a separate branch within the system of law used methods that shaped the philosophical schools of law research methodology, that is, the positivist, natural law and historical law school. Using the methodology of these schools, Professor Stelmachowski created an interesting conglomerate of methods of studying law through the analysis of the purpose of a legal norm, the empirical study of law in action and the historical and legal analysis of the evolution of legal institutions. The multithreaded method of legal research adopted by Professor Stelmachowski created a specific methodology of agricultural law research, which constitutes a significant legacy of the doctrine of agricultural law in Poland.

Development of a Pyrometer That Measures the True Temperature Field of the Two-Dimensional Array

This paper develops a two-dimensional array pyrometer, which can measure the true temperature field of the two-dimensional array. The pyrometer consists of an optical part, a circuit part and a software part. In the optical part, the radiation energy of the two-dimensional array target is obtained by scanning with the rotating mirror. Then, the radiation signal is converted and amplified by the circuit part. The software component realizes the functions of the pyrometer calibration, signal acquisition and data processing. The data processing adopts the secondary measurement method to calculate the true temperature and uses the multi-threaded method to improve the operational efficiency. Experiments show that the uncertainty of the two-dimensional pyrometer array can reach 1.43%. Compared with the single-threaded method, the true temperature operation time of the two-dimensional pyrometer array is improved by 77%, which verifies that the software operational efficiency is greatly improved.

Multi-threading parallel reinforcement learning

With respect to the problem of the slow convergence of the traditional reinforcement learning algorithm in practical applications, we propose a novel multi-threading parallel reinforcement learning algorithm - MPRL. MPRL is mainly composed of two parts. One is the FCM-based reinforcement learning multi-threading partitioning method, which transforms the multi-threading partitioning problem into a clustering partition problem to obtain the optimal multi-threading partitioning solution. The other is the parallel reinforcement learning framework, which makes the parallel execution between the policy evaluation and the interaction with the environment. In the learning process, the experience replay is adopted to update the value function, which can also solve the problem of the non-convergence in the off-policy evaluation. Experimentally, the MPRL algorithm is applied to the windy grid world problem and the cart pole problem, and compared with Q-Learning, Sarsa and KCACL. The experimental results show that MPRL has a faster convergence rate and better convergence performance.

Automatic and multithreaded method for determining the number of vector fitting poles based on empirical data

AbstractThe vector fitting method has been largely used to generate accurate and passive time‐domain macromodels for circuits or structures characterized by frequency‐domain data such as scattering parameters. Nevertheless, very few approaches to determine the vector fitting order were discussed in the literature. In this paper, an iterative method to automatically identify the number of vector fitting poles is proposed. It can achieve acceptable performance and accuracy by capitalizing on the fact that the number of vector fitting poles lies in the range that spans the number of resonance peaks in the model and double that number as deduced from the empirical data that was acquired by experimentation on different types of models. Therefore, the algorithm's awareness of the frequency response of the model and confining the search for an acceptable order within the aforementioned range proved to be key factors to enable it to mostly outperform its MATLAB counterpart as shown in the experimental results. Furthermore, a multithreaded implementation of the proposed method is introduced to distribute the vector fitting passes on multicore processors in order to obtain a better speedup factor. The experiments that demonstrate the positive effect of the multithreaded version on performance are also presented.

An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring

Developing an efficient and robust lightweight graphic user interface (GUI) for industry process monitoring is always a challenging task. Current implementation methods for embedded GUI are with the matters of real-time processing and ergonomics performance. To address the issue, an embedded lightweight GUI component library design method based on quasar technology embedded (Qt/E) is proposed. First, an entity-relationship (E-R) model for the GUI library is developed to define the functional framework and data coupling relations. Second, a cross-compilation environment is constructed, and the Qt/E shared library files are tailored to satisfy the requirements of embedded target systems. Third, by using the signal-slot communication interfaces, a message mapping mechanism that does not require a call-back pointer is developed, and the context switching performance is improved. According to the multi-thread method, the parallel task processing capabilities for data collection, calculation, and display are enhanced, and the real-time performance and robustness are guaranteed. Finally, the human-computer interaction process is optimized by a scrolling page method, and the ergonomics performance is verified by the industrial psychology methods. Two numerical cases and five industrial experiments show that the proposed method can increase real-time read-write correction ratios by more than 26% and 29%, compared with Windows-CE-GUI and Android-GUI, respectively. The component library can be tailored to 900 KB and supports 12 hardware platforms. The average session switch time can be controlled within 0.6 s and six key indexes for ergonomics are verified by different industrial applications.

Optimizing Raytracing Algorithm Using CUDA

Now, there are many codes to generate images using raytracing algorithm, which can run on CPU or GPU in single or multi-thread methods. In this paper, an optimized algorithm has been designed to generate image using raytracing algorithm to run on CPU or GPU in multi-thread algorithm. This algorithm employs light with depth of 8 to generate images. It is optimized by changing pixel travel priority and ray of light to thread, dedicating depth function to empty threads, and using optimized functions from MSDN library. Its code has been written in C++ and CUDA. In addition, we do the following to show its performance: comparing implementation in different compiler mode, changing thread number, examining different resolution, and investigating data bandwidth. The results show that one can generate at least 11 frames per second in HD (720p) resolution by GPU processor and GT 840M graphic card, using trace method. If better graphic card employ, this algorithm and program can be used to generate real-time animation.