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ANALYSIS OF THE EFFECT OF PROJECTILE IMPACT ANGLE ON THE PUNCTURE OF A STEEL PLATE USING THE FINITE ELEMENT METHOD IN ABAQUS SOFTWARE 利用abaqus有限元软件分析了弹丸冲击角对钢板击穿的影响
Q3 Economics, Econometrics and Finance Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-5
Kuba Rosłaniec
This paper deals with the punctureability of a steel plate by a projectile at different angles of attack. The effect of the projectile angle on the force required to penetrate a plate made of A36 steel is presented using Finite Element Method calculation software. Using Abaqus software, a dynamic model of a projectile striking a plate was modelled and the force required to penetrate a 5 mm thick steel plate was presented. The introduction gives an overview of the genesis of the topic and a brief historical background. The chapter on numerical analysis presents the numerical model used and how the simulation was modelled. In the conclusions, a summary of the results was formulated and conclusions were drawn regarding the observations and insights of the analysis. The force required to penetrate the plate was observed to increase with increasing projectile angle of attack and it was found that, as the angle of the plate increased, the force required to penetrate increased. 
本文研究了不同攻角弹丸对钢板的穿透性。利用有限元计算软件,研究了弹丸角度对A36钢板穿板力的影响。利用Abaqus软件建立了弹丸撞击钢板的动力学模型,给出了弹丸穿透5mm厚钢板所需的力。引言部分概述了选题的起源和简要的历史背景。数值分析一章介绍了所使用的数值模型以及如何进行模拟。在结论中,对结果进行了总结,并得出了关于分析的观察和见解的结论。观察到穿透板所需的力随着弹丸攻角的增大而增大,并且发现,随着板角的增大,穿透板所需的力也随之增大。
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引用次数: 0
A METHOD OF VERIFYING THE ROBOT'S TRAJECTORY FOR GOALS WITH A SHARED WORKSPACE 一种利用共享工作空间验证机器人目标轨迹的方法
Q3 Economics, Econometrics and Finance Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-3
Jakub Anczarski, Adrian Bochen, MArcin Głąb, Mikolaj Jachowicz, J. Caban, R. Cechowicz
The latest market research (Fanuc Polska 2019) shows that the robotization of the Polish industry is accelerating. More and more companies are investing in robotic production lines, which enable greater efficiency of implemented processes and reduce labour costs. The article presents the possibilities of using virtual reality (VR) for behavioural analysis in open robotic systems with a shared workspace. The aim of the article is to develop a method of verification of programmed movements of an industrial robot in terms of safety and efficiency in systems with a shared workspace. The method of the robot program verification on the digital model of the working cell made in VR will be checked. The obtained research results indicate a great potential of this method in industrial applications as well as for educational purposes.
最新的市场研究(Fanuc Polska 2019)表明,波兰工业的机器人化正在加速。越来越多的公司正在投资机器人生产线,这可以提高实施过程的效率并降低劳动力成本。本文介绍了在具有共享工作空间的开放机器人系统中使用虚拟现实进行行为分析的可能性。本文的目的是开发一种在具有共享工作空间的系统中从安全性和效率的角度验证工业机器人程序化运动的方法。将检查在VR中制作的工作单元数字模型上进行机器人程序验证的方法。所获得的研究结果表明,这种方法在工业应用和教育目的方面具有巨大的潜力。
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引用次数: 4
BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI 乳腺癌cad系统应用迁移学习,提高ROI
Q3 Economics, Econometrics and Finance Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-8
Muayed S. Al-Huseiny, Ahmed S. Sajit
Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%. 
计算机系统正被用于医疗诊断等专业领域,以减轻一些成本并提高可靠性和可扩展性。本文实现了一个计算机辅助癌症诊断系统。它利用了公开可用的迷你MIAS乳房X光摄影图像数据集。对图像进行预处理以清洁分离乳房组织区域。提取的区域用于调整和验证预训练的卷积深度神经网络GoogLeNet。与其他已发表的工作相比,所实现的模型显示出良好的性能结果,准确率为86.6%,灵敏度为75%,特异性为88.9%。
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引用次数: 0
DETECTION OF FILLERS IN THE SPEECH BY PEOPLE WHO STUTTER 口吃者对言语中的填充物的检测
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-28
Waldemar Suszynski, M. Charytanowicz, Wojciech Rosa, L. Koczan, R. Stegierski
Stuttering is a speech impediment that is a very complex disorder. It is difficult to diagnose and treat, and is of unknown initiation, despite the large number of studies in this field. Stuttering can take many forms and varies from person to person, and it can change under the influence of external factors. Diagnosing and treating speech disorders such as stuttering requires from a speech therapist, not only good profes-sional preparation, but also experience gained through research and practice in the field. The use of acoustic methods in combination with elements of artificial intelligence makes it possible to objectively assess the disorder, as well as to control the effects of treatment. The main aim of the study was to present an algorithm for automatic recognition of fillers disfluency in the statements of people who stutter. This is done on the basis of their parameterized features in the amplitude-frequency space. The work provides as well, exemplary results demonstrating their possibility and effectiveness. In order to verify and optimize the procedures, the statements of seven stutterers with duration of 2 to 4 minutes were selected. Over 70% efficiency and predictability of automatic detection of these disfluencies was achieved. The use of an automatic method in conjunction with therapy for a stuttering person can give us the opportunity to objectively assess the disorder, as well as to evaluate the progress of therapy.
口吃是一种非常复杂的言语障碍。尽管在该领域进行了大量研究,但它很难诊断和治疗,而且起源不明。口吃有多种形式,因人而异,而且在外部因素的影响下会发生变化。诊断和治疗口吃等言语障碍不仅需要言语治疗师做好专业准备,还需要通过该领域的研究和实践获得经验。声学方法与人工智能元素相结合的使用使客观评估疾病以及控制治疗效果成为可能。这项研究的主要目的是提出一种自动识别口吃者陈述中填充词不流畅的算法。这是基于它们在幅频空间中的参数化特征来完成的。这项工作也提供了示范性的结果,证明了它们的可能性和有效性。为了验证和优化程序,选择了7名持续时间为2-4分钟的口吃者的陈述。实现了70%以上的效率和可预测性的自动检测这些障碍。将自动方法与口吃患者的治疗相结合,可以让我们有机会客观评估这种障碍,并评估治疗的进展。
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引用次数: 0
PRODUCTIVITY OF A LOW-BUDGET COMPUTER CLUSTER APPLIED TO OVERCOME THE N-BODY PROBLEM 用于克服n体问题的低预算计算机集群的生产率
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-32
T. Nowicki, Adam Gregosiewicz, Z. Łagodowski
The classical n-body problem in physics addresses the prediction of individual motions of a group of celestial bodies under gravitational forces and has been studied since Isaac Newton formulated his laws. Nowadays the n-body problem has been recognized in many more fields of science and engineering. Each problem of mutual interaction between objects forming a dynamic group is called as the n-body problem. The cost of the direct algorithm for the problem is O(n2) and is not acceptable from the practical point of view. For this reason cheaper algorithms have been developed successfully reducing the cost to O(nln(n)) or even O(n). Because further improvement of the algorithms is unlikely to happen it is the hardware solutions which can still accelerate the calculations. The obvious answer here is a computer cluster that can preform the calculations in parallel. This paper focuses on the performance of a low-budget computer cluster created on ad hoc basis applied to n-body problem calculation. In order to maintain engineering valuable results a real technical issue was selected to study. It was Discrete Vortex Method that is used for simulating air flows. The presented research included writing original computer code, building a computer cluster, preforming simulations and comparing the results.
物理学中的经典n体问题涉及在引力作用下预测一组天体的单个运动,自艾萨克·牛顿制定定律以来一直在研究这一问题。如今,n体问题已被越来越多的科学和工程领域所认识。形成动态群的物体之间相互作用的每个问题都被称为n体问题。该问题的直接算法的代价为O(n2),从实际角度来看是不可接受的。由于这个原因,已经成功地开发了更便宜的算法,将成本降低到O(nln(n))或甚至O(n)。因为算法的进一步改进不太可能发生,所以硬件解决方案仍然可以加速计算。这里显而易见的答案是一个可以并行预处理计算的计算机集群。本文重点研究了在特定基础上创建的低预算计算机集群在n体问题计算中的性能。为了保持工程上有价值的结果,选择了一个真正的技术问题进行研究。离散涡流法用于模拟气流。所进行的研究包括编写原始计算机代码、构建计算机集群、预成型模拟和比较结果。
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引用次数: 0
KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS 基于机器学习方法的击键动力学分析
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-30
Nataliya Shabliy, S. Lupenko, N. Lutsyk, O. Yasniy, Olha Malyshevska
The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.
本文的主要目的是使用机器学习的方法,根据用户的击键动力学来确定用户。这类问题可以表述为分类任务。为了解决这一问题,采用了四种监督机器学习方法,即逻辑回归、支持向量机、随机森林和神经网络。三个用户中的每一个都输入了同一个有7个符号的单词600次。数据集的行由7个值组成,这些值是按下特定键的时间段。基本事实值是用户id。在应用机器学习分类方法之前,将特征转换为z分数。获得了每种应用方法的分类度量。确定了以下参数:精确度、召回率、f1评分、支持度、预测和受试者工作特征曲线下面积(AUC)。获得的AUC得分相当高。在线性回归分类器的情况下,AUC得分最低,为0.928。AUC得分最高的是在神经网络分类器的情况下。与神经网络方法相比,支持向量机和随机森林方法的结果略低。精确度、召回率和F1成绩也是如此。然而,所获得的分类度量在每种情况下都相当高。因此,机器学习的方法可以有效地用于基于击键模式对用户进行分类。解决这类问题最推荐的方法是神经网络。
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引用次数: 2
CYBER-PHYSICAL SYSTEMS TECHNOLOGIES AS A KEY FACTOR IN THE PROCESS OF INDUSTRY 4.0 AND SMART MANUFACTURING DEVELOPMENT 网络物理系统技术是工业4.0和智能制造发展过程中的关键因素
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-31
J. Zubrzycki, A. Świć, Łukasz Sobaszek, J. Kovác, R. Králiková, R. Jencík, N. Šmídová, P. Arapi, Peter Dulenčin, Jozef Homza
The continuous development of production processes is currently observed in the fourth industrial revolution, where the key place is the digital transformation of production is known as Industry 4.0. The main technologies in the context of Industry 4.0 consist Cyber-Physical Systems (CPS) and Internet of Things (IoT), which create the capabilities needed for smart factories. Implementation of CPS solutions result in new possibilities creation – mainly in areas such as remote diagnosis, remote services, remote control, condition monitoring, etc. In this paper, authors indicated the importance of Cyber-Physical Systems in the process of the Industry 4.0 and the Smart Manufacturing development. Firstly, the basic information about Cyber-Physical Production Systems were outlined. Then, the alternative definitions and different authors view of the problem were discussed. Secondly, the conceptual model of Cybernetic Physical Production System was presented. Moreover, the case study of proposed solution implementation in the real manufacturing process was presented. The key stage of the verification concerned the obtained data analysis and results discussion.
生产过程的持续发展目前体现在第四次工业革命中,其中的关键是生产的数字化转型,即工业4.0。工业4.0背景下的主要技术包括网络物理系统(CPS)和物联网(IoT),它们创造了智能工厂所需的能力。CPS解决方案的实施带来了新的可能性——主要是在远程诊断、远程服务、远程控制、状态监测等领域。在本文中,作者指出了网络物理系统在工业4.0和智能制造发展过程中的重要性。首先,概述了网络物理生产系统的基本信息。然后,讨论了该问题的替代定义和不同作者的观点。其次,提出了控制论物理生产系统的概念模型。此外,还对所提出的解决方案在实际制造过程中的实施进行了案例研究。验证的关键阶段涉及所获得的数据分析和结果讨论。
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引用次数: 1
ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE 基于人工神经网络的需求预测与联邦基金利率集成
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-27
Anupa Arachchige, Ranil Sugathadasa, Oshadhi Herath, Amila Thibbotuwawa
Adverse effects of inaccurate demand forecasts; stockouts, overstocks, customer loss have led academia and the business world towards accurate demand forecasting methods. Artificial Neural Network (ANN) is capable of highly accurate forecasts integrated with many variables. The use of Price and Promotion variables have increased the accuracy while the addition of other relevant variables would decrease the occurrences of errors. The use of the Federal Funds Rate as an additional macroeconomic variable to ANN forecasting models has been discussed in this research by the means of the accuracy measuring method: Average Relative Mean Absolute Error.
需求预测不准确的不利影响;缺货、库存过剩、客户流失,已经促使学术界和商界采用准确的需求预测方法。人工神经网络(Artificial Neural Network, ANN)具有多变量综合预测精度高的特点。Price和Promotion变量的使用提高了准确性,而添加其他相关变量将减少错误的发生。本研究通过平均相对平均绝对误差的精度测量方法,讨论了在人工神经网络预测模型中使用联邦基金利率作为一个额外的宏观经济变量。
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引用次数: 5
IMPLEMENTATION OF A HARDWARE TROJAN CHIP DETECTOR MODEL USING ARDUINO MICROCONTROLLER 用ARDUINO微控制器实现硬件木马芯片检测模型
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-30 DOI: 10.35784/acs-2021-26
Kadeejah Abdulsalam, J. Adebisi, Victor Durojaiye
These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba¬bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training.
如今,硬件设备及其相关活动受到各种技术威胁的极大影响。硬件特洛伊木马是对集成电路电路进行的恶意修改。本工作考虑利用这种修改并访问设备的损坏程度。当这些木马出现在敏感的硬件系统部署中时,往往会对系统造成潜在的损坏和感染。本研究利用机器学习技术构建了一个硬件木马检测器。这项工作结合了逻辑测试和功率侧通道分析(SCA)以及功率跟踪的机器学习。使用获取的数据对该模型进行了5个时期的训练、验证和测试。对目标硬件设备以及电源SCA进行了初步逻辑测试。使用Arduino微控制器实现了所设计的机器学习模型,结果表明,硬件木马检测器能够准确识别木马芯片。硬件的功耗读数从1035-1040mW开始,功率时间序列数据使用混合了不同标准偏差的加性高斯白噪声(AWGN)的直流功率测量进行模拟。该模型实现了准确度、精确度和准确的召回值。然而,将特洛伊木马类的阈值概率设置为小于0.5会增加召回率,这是在几个时期的训练后获得超过95%的总体准确率的最重要指标。
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引用次数: 0
BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING 利用机器学习方法的电驱动单元黑箱效率建模
Q3 Economics, Econometrics and Finance Pub Date : 2021-12-08 DOI: 10.35784/acs-2021-25
L. Bauer, Leon Stütz, M. Kley
The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.
动力系统电气化程度的提高导致对测试技术的需求增加,以确保所需的功能。特别是对于传统的测试设备,有必要了解可应用于实际测试的测试技术的能力。建模使人们能够尽早了解测试平台的动态能力和计划测试场景的可行性。本文以传输效率为例,通过人工神经网络的实验建模,描述了复杂子系统的建模。对于数据生成,描述了实验设计和执行。生成的数据用合适的方法进行预处理,并针对神经网络进行优化。使用不同的输入变体以及不同的算法来执行建模。这些变体相互比较和竞争。使用统计方法和其他适当的技术来验证最合适的变体。该结果很好地代表了现实,并使测试系统能够以现实的方式进行性能调查。
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引用次数: 3
期刊
Applied Computer Science
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