Search results for: industrial machine

Authors: Afzeri, A.G.E Sujtipto, R. Muhida, M. Konneh, Darmawan

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Increasing the demand for effectively use of the production facility requires the tools for sharing the manufacturing facility through remote operation of the machining process. This research introduces the methodology of machining technology for direct remote operation of networked milling machine. The integrated tools with virtual simulation, remote desktop protocol and Setup Free Attachment for remote operation of milling process are proposed. Accessing and monitoring of machining operation is performed by remote desktop interface and 3D virtual simulations. Capability of remote operation is supported by an auto setup attachment with a reconfigurable pin type setup free technology installed on the table of CNC milling machine to perform unattended machining process. The system is designed using a computer server and connected to a PC based controlled CNC machine for real time monitoring. A client will access the server through internet communication and virtually simulate the machine activity. The result has been presented that combination between real time virtual simulation and remote desktop tool is enabling to operate all machine tool functions and as well as workpiece setup..

Keywords: Remote Desktop, PC Based CNC, Remote Machining.

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Authors: Jian Wang, Lu Yang, Jiong Peng

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Internal circulation two-platen clamping system for injection molding machine (IMM) has many potential advantages on energy-saving. In order to estimate its properties, experiments were carried out in this paper. Displacement and pressure of the components were measured. In comparison, the model of hydraulic clamping system was established by using AMESim. The related parameters as well as the energy consumption could be calculated. According to the analysis, the hydraulic system was optimized in order to reduce the energy consumption.

Keywords: AMESim, energy-saving, injection molding machine, internal circulation.

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Authors: Abubakar Rashid, Muhammad Saad, Faheem Ahmed

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This paper provides a comprehensive study pertaining to diagnosis of intermittent high vibrations on an industrial gas turbine using detailed vibrations analysis, followed by its rectification. Engro Polymer & Chemicals Limited, a Chlor-Vinyl complex located in Pakistan has a captive combined cycle power plant having two 28 MW gas turbines (make Hitachi) & one 15 MW steam turbine. In 2018, the organization faced an issue of high vibrations on one of the gas turbines. These high vibration peaks appeared intermittently on both compressor’s drive end (DE) & turbine’s non-drive end (NDE) bearing. The amplitude of high vibration peaks was between 150-170% on the DE bearing & 200-300% on the NDE bearing from baseline values. In one of these episodes, the gas turbine got tripped on “High Vibrations Trip” logic actuated at 155µm. Limited instrumentation is available on the machine, which is monitored with GE Bently Nevada 3300 system having two proximity probes installed at Turbine NDE, Compressor DE &at Generator DE & NDE bearings. Machine’s transient ramp-up & steady state data was collected using ADRE SXP & DSPI 408. Since only 01 key phasor is installed at Turbine high speed shaft, a derived drive key phasor was configured in ADRE to obtain low speed shaft rpm required for data analysis. By analyzing the Bode plots, Shaft center line plot, Polar plot & orbit plots; rubbing was evident on Turbine’s NDE along with increased bearing clearance of Turbine’s NDE radial bearing. The subject bearing was then inspected & heavy deposition of carbonized coke was found on the labyrinth seals of bearing housing with clear rubbing marks on shaft & housing covering at 20-25 degrees on the inner radius of labyrinth seals. The collected coke sample was tested in laboratory & found to be the residue of lube oil in the bearing housing. After detailed inspection & cleaning of shaft journal area & bearing housing, new radial bearing was installed. Before assembling the bearing housing, cleaning of bearing cooling & sealing air lines was also carried out as inadequate flow of cooling & sealing air can accelerate coke formation in bearing housing. The machine was then taken back online & data was collected again using ADRE SXP & DSPI 408 for health analysis. The vibrations were found in acceptable zone as per ISO standard 7919-3 while all other parameters were also within vendor defined range. As a learning from subject case, revised operating & maintenance regime has also been proposed to enhance machine’s reliability.

Keywords: ADRE, bearing, gas turbine, GE Bently Nevada, Hitachi, vibration.

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Authors: V. Malathi, N.S.Marimuthu

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This paper presents a wavelet transform and Support Vector Machine (SVM) based algorithm for estimating fault location on transmission lines. The Discrete wavelet transform (DWT) is used for data pre-processing and this data are used for training and testing SVM. Five types of mother wavelet are used for signal processing to identify a suitable wavelet family that is more appropriate for use in estimating fault location. The results demonstrated the ability of SVM to generalize the situation from the provided patterns and to accurately estimate the location of faults with varying fault resistance.

Keywords: Fault location, support vector machine, supportvector regression, transmission lines, wavelet transform.

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Authors: Kashif Bilal, Uzair Muhammad, Atif Khan, M. Nasir Khan

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Machine Translation, (hereafter in this document referred to as the "MT") faces a lot of complex problems from its origination. Extracting multiword expressions is also one of the complex problems in MT. Finding multiword expressions during translating a sentence from English into Urdu, through existing solutions, takes a lot of time and occupies system resources. We have designed a simple relational data approach, in which we simply set a bit in dictionary (database) for multiword, to find and handle multiword expression. This approach handles multiword efficiently.

Keywords: Machine Translation, Multiword Expressions, Urdulanguage processing, POS (stands for Parts of Speech) Tagging forUrdu, Expert Systems.

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Authors: Ferenc Kovács, Gábor Hosszú

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The paper presents an advanced control system for tennis ball throwing machines to improve their accuracy according to the ball impact points. A further advantage of the system is the much easier calibration process involving the intelligent solution of the automatic adjustment of the stroking parameters according to the ball elasticity, the self-calibration, the use of the safety margin at very flat strokes and the possibility to placing the machine to any position of the half court. The system applies mathematical methods to determine the exact ball trajectories and special approximating processes to access all points on the aimed half court.

Keywords: Control system, robot programming, robot control, sports equipment, throwing machine.

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing domain presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: Classification, climbing, data imbalance, data scarcity, machine learning, time sequence.

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: Crime prediction, machine learning, public safety, smart city.

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Authors: Nebi Gedik

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Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: Matthias Kirmse, Uwe Petersohn, Elief Paffrath

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As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.

Keywords: Ensemble methods, fault detection, machine learning, semiconductor test.

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Authors: Sara Tessitore, Tiberio Daddi, Fabio Iraldo

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The article aims to investigate the presence of a correlation between eco-innovation and economic performance within industrial districts. The case analyzed in this article is based on a study concerning a sample of 54 Italian industrial clusters entitled "Eco-Districts" that has compiled a list of the most eco-efficient districts at the national level. After selecting two districts, this study assesses the economic performance of the last three years through the analysis of trends in four indicators. The results show that only in some cases there is a connection between eco innovation and economic performance.

Keywords: clusters, industrial districts, eco-innovation, economic performance.

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Authors: Rami H. Fouad

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The purpose of this study is to identify and evaluate the scale of implementation of Just-In-Time (JIT) in the different industrial sectors in the Middle East. This study analyzes the empirical data collected by a questionnaire survey distributed to companies in three main industrial sectors in the Middle East, which are: food, chemicals and fabrics. The following main hypotheses is formulated and tested: (The requirements of JIT application differ according to the type of industrial sector).Descriptive statistics and Box plot analysis were used to examine the hypotheses. This study indicates a reasonable evidence for accepting the main hypotheses. It reveals that there is no standard way to adopt JIT as a production system. But each industrial sector should concentrate in the investment on critical requirements that differ according to the nature and strategy of production followed in that sector.

Keywords: Just-In-Time, JIT, Questionnaire, Types of Industrial Sectors.

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

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Authors: M. M. Islam, Rajib K. Saha, Abdullah A. Amin, M. Z. Haq

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The autonomous mobile robot was designed and implemented which was capable of navigating in the industrial environments and did a job of picking objects from variable height and delivering it to another location following a predefined trajectory. In developing country like Bangladesh industrial robotics is not very prevalent yet, due to the high installation cost. The objective of this project was to develop an autonomous mobile robot for industrial application using the available resources in the local market at lower manufacturing cost. The mechanical system of the robot was comprised of locomotion, gripping and elevation system. Grippers were designed to grip objects of a predefined shape. Cartesian elevation system was designed for vertical movement of the gripper. PIC18F452 microcontroller was the brain of the control system. The prototype autonomous robot was fabricated for relatively lower load than the industry and the performance was tested in a virtual industrial environment created within the laboratory to realize the effectiveness.

Keywords: Industrial application, autonomous mobile robot, appropriate technology.

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Authors: Sangram Keshori Mohapatra, Sidhartha Panda, Prasant Kumar Satpathy

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This paper describes the power-system stability improvement by a static synchronous compensator (STATCOM) based damping controller with Differential evolution (DE) algorithm is used to find out the optimal controller parameters. The present study considered both local and remote signals with associated time delays. The performances of the proposed controllers have been compared with different disturbances for both single-machine infinite bus power system and multi-machine power system. The performance of the proposed controllers with variations in the signal transmission delays has also been investigated. To show the effectiveness and robustness of the proposed controller the Simulation results are presented under different disturbances and loading conditions.

Keywords: Controller Design, Differential Evolution Algorithm Static Synchronous Compensator, Time Delay, Power System Stability, Single Machine Infinite-bus Power System, Multi-Machine Power System.

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Authors: M. Rahou, A. Cheikh, F. Sebaa

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Manufacturing tolerancing is intended to determine the intermediate geometrical and dimensional states of the part during its manufacturing process. These manufacturing dimensions also serve to satisfy not only the functional requirements given in the definition drawing, but also the manufacturing constraints, for example geometrical defects of the machine, vibration and the wear of the cutting tool. In this paper, an experimental study on the influence of the wear of the cutting tool (systematic dispersions) is explored. This study was carried out on three stages .The first stage allows machining without elimination of dispersions (random, systematic) so the tolerances of manufacture according to total dispersions. In the second stage, the results of the first stage are filtered in such way to obtain the tolerances according to random dispersions. Finally, from the two previous stages, the systematic dispersions are generated. The objective of this study is to model by the least squares method the error of manufacture based on systematic dispersion. Finally, an approach of optimization of the manufacturing tolerances was developed for machining on a CNC machine tool

Keywords: Dispersions, Compensation, modeling, manufacturing Tolerance, machine tool.

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Authors: Cristina Vatamanu, Doina Cosovan, Dragoş Gavriluţ, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through (semi)-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: Detection Rate, False Positives, Perceptron, One Side Class, Ensembles, Decision Tree, Hybrid methods, Feature Selection.

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Authors: M. M. Agheli, M. J. Nategh

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Hexapod Machine Tool (HMT) is a parallel robot mostly based on Stewart platform. Identification of kinematic parameters of HMT is an important step of calibration procedure. In this paper an algorithm is presented for identifying the kinematic parameters of HMT using inverse kinematics error model. Based on this algorithm, the calibration procedure is simulated. Measurement configurations with maximum observability are decided as the first step of this algorithm for a robust calibration. The errors occurring in various configurations are illustrated graphically. It has been shown that the boundaries of the workspace should be searched for the maximum observability of errors. The importance of using configurations with sufficient observability in calibrating hexapod machine tools is verified by trial calibration with two different groups of randomly selected configurations. One group is selected to have sufficient observability and the other is in disregard of the observability criterion. Simulation results confirm the validity of the proposed identification algorithm.

Keywords: Calibration, Hexapod Machine Tool (HMT), InverseKinematics Error Model, Observability, Parallel Robot, ParameterIdentification.

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Authors: Siwar Fadhel, Imen Bahri, Man Zhang

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The switched reluctance machine (SRM) has undeniable qualities in terms of low cost and mechanical robustness. However, its highly nonlinear character and its uncertain parameters justify the development of complicated controls. In this paper, authors present the design of a robust H-infinity current controller for an 8/6 SRM with taking into account the nonlinearity of the SRM and with rejection of disturbances. The electromagnetic torque is indirectly regulated through the current controller. To show the performances of this control, a robustness analysis is performed by comparing the H-infinity and PI controller simulation results. This comparison demonstrates better performances for the presented controller. The effectiveness and robustness of the presented controller are also demonstrated by experimental tests.

Keywords: Current regulation, experimentation, robust H-infinity control, switched reluctance machine.

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: Artificial Intelligence, machine learning, deep learning, convolutional neural networks.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans – including asset maintenance operations – in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: Degradation models, ageing, anomaly detection, soft sensor, incremental learning.

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Authors: Muhammad F. Yaqub, Iqbal Gondal, Joarder Kamruzzaman

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Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.

Keywords: Envelop detection, machine defect frequency, multiple faults, machine health monitoring.

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Authors: Esther T. Akinlabi, Stephen A. Akinlabi

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The design of temperature measuring approach for a re-configured milling machine to produce friction stir welds is reported in this paper. The product design specifications for the redesigning of a milling machine were first outlined and the ranking criteria were determined. Three different concepts were generated for the temperature measurement on the reconfigured system and the preferred or the best concept was selected based on the set design ranking criteria. Further simulation and performance analysis was then conducted on the concept. The Infrared Thermography (IRT) concept was selected for the temperature measurement among other concepts generated because it is an ideal and most effective system of measurement in this regard.

Keywords: Clamping system, Friction Stir Welding, Reconfiguration, Support systems.

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Authors: Yue Sun, Yuan Wang

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Most of the earliest national industries in Wuhan are located along the Hanjiang River, and Qiaokou is considered to be a gathering place for Dahankou old industrial base. Zongguan Waterworks, Pacific Soap Factory, Fuxin Flour Factory, Nanyang Tobacco Factory and other hundred-year-old factories are located along Hanjiang River in Qiaokou District, especially the Gutian Industrial Zone, which was listed as one of 156 national restoration projects at the beginning of the founding of the People’s Republic of China. After decades of development, Qiaokou has become the gathering place of the chemical industry and secondary industry, causing damage to the city and serious pollution, becoming a marginalized area forgotten by the central city. In recent years, with the accelerated pace of urban renewal, Qiaokou has been constantly reforming and innovating, and has begun drastic changes in the transformation of old cities and the development of new districts. These factories have been listed as key reconstruction projects, and a large number of industrial heritage with historical value and full urban memory have been relocated, demolished and reformed, with only a few factory buildings preserved. Through the methods of industrial archaeology, image analysis, typology and field investigation, this paper analyzes and summarizes the spatial characteristics of industrial heritage in Qiaokou District, explores urban renewal from the perspective of industrial heritage protection, and provides design strategies for the regeneration of urban industrial sites and industrial heritage.

Keywords: Industrial heritage, urban renewal, protection, urban memory.

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Authors: Dengpan Wu, Dan Liu

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In view of the present cloud security problem has increasingly become one of the major obstacles hindering the development of the cloud computing, put forward a kind of memory based on Xen cloud environment security isolation technology implementation. And based on Xen virtual machine monitor system, analysis of the model of memory virtualization is implemented, using Xen memory virtualization system mechanism of super calls and grant table, based on the virtual machine manager internal implementation of access control module (ACM) to design the security isolation system memory. Experiments show that, the system can effectively isolate different customer domain OS between illegal access to memory data.

Keywords: Cloud security, memory isolation, Xen, virtual machine.

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Authors: Essam Al-Daoud

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Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.

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Authors: Patrick Innocenti, Elias Montini, Silvia Menato, Marzio Sorlini

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Documented experiences of industrial symbiosis are always triggered and driven only by economic goals: environmental and (even rarely) social results are sometimes assessed and declared as effects of virtuous behaviours, but are merely casual and un-pursued side externalities. Even worse: all the symbiotic project candidates entailing economic loss for just one of the (also dozen) partners are simply stopped without considering the overall benefit for the whole partnership. The here-presented approach aims at providing methodologies and tools to effectively manage these situations and fostering the implementation of virtuous symbiotic investments in manufacturing aggregations for a more sustainable production.

Keywords: Business model, industrial symbiosis, industrial agglomerations, sustainability.

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.

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Authors: C. Schneider, M. M. Seizmeir, T. Suchanek, M. Hutter-Mironovová, M. Bdiwi, M. Putz

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In this paper, a suitable measurement setup is presented to conduct force and pressure measurements for transient contact cases at the example of lathe machine tending. Empirical measurements were executed on a selected collaborative robot’s behavior regarding allowable operating speeds under consideration of sensor- and workpiece-specific factors. Comparisons between the theoretic calculations proposed in ISO/TS 15066 and the practical measurement results reveal a basis for future research. With the created database, preliminary risk assessment and economic assessment procedures of collaborative machine tending cells can be facilitated.

Keywords: biomechanical thresholds, collaborative robots, force and pressure measurements, machine tending, transient contact

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