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Parallel Machine Scheduling with Monte Carlo Tree Search
n this article, a specific production scheduling problem (PSP), the Parallel Machine Scheduling Problem (PMSP) with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. In this article after the introduction and literature review the mathematical model of the Parallel Machines Scheduling Problem with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. After that the Monte Carlo Tree Search and Simulated Annealing are detailed. Our representation technique and its evaluation are also introduced. After that, the efficiency of the algorithms is tested with benchmark data, which result, that algorithms are suitable for solving production scheduling problems. In this article, after the literature review, a suitable mathematical model is presented. The problem is solved with a specific Monte Carlo Tree Search (MCTS) algorithm, which uses a neighbourhood search method (2-opt). In the article, we present the efficiency of our Iterative Monte Carlo Tree Search (IMCTS) algorithm on randomly generated data sets.
n this article, a specific production scheduling problem (PSP), the Parallel Machine Scheduling Problem (PMSP) with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. In this article after the introduction and literature review the mathematical model of the Parallel Machines Scheduling Problem with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. After that the Monte Carlo Tree Search and Simulated Annealing are detailed. Our representation technique and its evaluation are also introduced. After that, the efficiency of the algorithms is tested with benchmark data, which result, that algorithms are suitable for solving production scheduling problems. In this article, after the literature review, a suitable mathematical model is presented. The problem is solved with a specific Monte Carlo Tree Search (MCTS) algorithm, which uses a neighbourhood search method (2-opt). In the article, we present the efficiency of our Iterative Monte Carlo Tree Search (IMCTS) algorithm on randomly generated data sets.
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Execution Time Reduction in Function Oriented Scientific Workflows
Scientific workflows have been an increasingly important research area of distributed systems (such as cloud computing). Researchers have shown an increased interest in the automated processing scientific applications such as workflows. Recently, Function as a Service (FaaS) has emerged as a novel distributed systems platform for processing non-interactive applications. FaaS has limitations in resource use (e.g., CPU and RAM) as well as state management. In spite of these, initial studies have already demonstrated using FaaS for processing scientific workflows. DEWE v3 executes workflows in this fashion, but it often suffers from duplicate data transfers while using FaaS. This behaviour is due to the handling of intermediate data dependencies after and before each function invocation. These data dependencies could fill the temporary storage of the function environment. Our approach alters the job dispatch algorithm of DEWE v3 to reduce data dependency transfers. The proposed algorithm schedules jobs with precedence requirements to primarily run in the same function invocation. We evaluate our proposed algorithm and the original algorithm with small- and large-scale Montage workflows. Our results show that the improved system can reduce the total workflow execution time of scientific workflows over DEWE v3 by about 10% when using AWS Lambda.
Scientific workflows have been an increasingly important research area of distributed systems (such as cloud computing). Researchers have shown an increased interest in the automated processing scientific applications such as workflows. Recently, Function as a Service (FaaS) has emerged as a novel distributed systems platform for processing non-interactive applications. FaaS has limitations in resource use (e.g., CPU and RAM) as well as state management. In spite of these, initial studies have already demonstrated using FaaS for processing scientific workflows. DEWE v3 executes workflows in this fashion, but it often suffers from duplicate data transfers while using FaaS. This behaviour is due to the handling of intermediate data dependencies after and before each function invocation. These data dependencies could fill the temporary storage of the function environment. Our approach alters the job dispatch algorithm of DEWE v3 to reduce data dependency transfers. The proposed algorithm schedules jobs with precedence requirements to primarily run in the same function invocation. We evaluate our proposed algorithm and the original algorithm with small- and large-scale Montage workflows. Our results show that the improved system can reduce the total workflow execution time of scientific workflows over DEWE v3 by about 10% when using AWS Lambda.
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The fitness landscape analysis of the ant colony system algorithm in solving a vehicle routing problem
In this article, we examine the effectiveness of the Ant Colony System (ACS) algorithm for a Vehicle Routing Problem (VRP). Fitness landscape analysis determines the complexity of the optimization search space. By analyzing the search space, we can conclude the complexity of the task, whether a given algorithm (its operators) is effective for a given type of task. When most researchers develop an algorithm, they test it on benchmark data. If this achieves the best result known so far for the bench-mark data (or are close to the big ones), the results will be published. However, in addition to existing tests, various analyzes can also be performed
In this article, we examine the effectiveness of the Ant Colony System (ACS) algorithm for a Vehicle Routing Problem (VRP). Fitness landscape analysis determines the complexity of the optimization search space. By analyzing the search space, we can conclude the complexity of the task, whether a given algorithm (its operators) is effective for a given type of task. When most researchers develop an algorithm, they test it on benchmark data. If this achieves the best result known so far for the bench-mark data (or are close to the big ones), the results will be published. However, in addition to existing tests, various analyzes can also be performed
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Neutrality of Vehicle Routing Problem
Vehicle Routing is a highly investigated problem in the field of logistics, informatics, management, and engineering. Several Vehicle Routing Problem variants have appeared since the first paper was published in 1959 by Dantzig and Ramster. In this paper, the neutrality analysis of a complex Vehicle Routing Problem is presented. Neutrality analysis is a special method in the general fitness landscape analysis. The fitness landscape analysis is aimed at the examination of the complexity analysis in regard to the objective function of the optimization problem including the efficiency of the representation space and the operators. In the neutrality analysis, we select the neighbors of the solutions that are closest to them. In this paper, we present the analysis of four neighborhood operators: the 2-opt, partially matched crossover, order crossover and the cycle crossover. Based on the performed numerical analysis, the 2-opt and partially matched crossover methods dominate the other operators.
Vehicle Routing is a highly investigated problem in the field of logistics, informatics, management, and engineering. Several Vehicle Routing Problem variants have appeared since the first paper was published in 1959 by Dantzig and Ramster. In this paper, the neutrality analysis of a complex Vehicle Routing Problem is presented. Neutrality analysis is a special method in the general fitness landscape analysis. The fitness landscape analysis is aimed at the examination of the complexity analysis in regard to the objective function of the optimization problem including the efficiency of the representation space and the operators. In the neutrality analysis, we select the neighbors of the solutions that are closest to them. In this paper, we present the analysis of four neighborhood operators: the 2-opt, partially matched crossover, order crossover and the cycle crossover. Based on the performed numerical analysis, the 2-opt and partially matched crossover methods dominate the other operators.
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Using Time Series and Classification in Vehicle Routing Problem
The purpose of data mining is to process raw data and extract rules. The Vehicle Routing Problem is a logistical problem, which handles the delivery and the collection of products. In the classical problem, the position of the depot and customers are known in advance. In case of the base problem, the demand of the customer is also known in advance. But, we may need some future data, for example, the demand of the customer, so we need to forecast these data from the previous data. After the determination of the future demands of the customers, we determined whether it is worth serving customers or not with the help of the classification methods. After the time-series forecasting and classification, we also determined the route of the vehicles with the help of the genetic algorithm.
The purpose of data mining is to process raw data and extract rules. The Vehicle Routing Problem is a logistical problem, which handles the delivery and the collection of products. In the classical problem, the position of the depot and customers are known in advance. In case of the base problem, the demand of the customer is also known in advance. But, we may need some future data, for example, the demand of the customer, so we need to forecast these data from the previous data. After the determination of the future demands of the customers, we determined whether it is worth serving customers or not with the help of the classification methods. After the time-series forecasting and classification, we also determined the route of the vehicles with the help of the genetic algorithm.
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Detection of semantic obsessive text in multimedia using machine and deep learning techniques and algorithms
Word boycott has been seen frequently trending in India on various social media platforms. We studied the obsession of Indians with the word boycott; to show the protest or dissent against any government policy, Netflix series, political or religious commentary, and on various other matters, people in India prefer to trend word "Boycott" on multiple mediums. We studied how ingrained the word "Boycott" is in Indians in our research and how it affects daily life, unemployment, and the economy. The data was collected using Youtube API with the next page token to get all the search results. We preprocessed the raw data using different preprocessing methods, which are discussed in the paper. To check our data's consistency, we fed the data into various machine learning algorithms and calculated multiple parameters like accuracy, recall, f1-score. Random forest showed the best accuracy of 90 percent, followed by SVM and Knn algorithms with 88 percent each. We used word cloud to get the most dominant used words, Textblob, for sentiment analysis, which showed the mean Polarity of 0.07777707038498406 and mean subjectivity 0.2588880457405638. We calculated perplexity and coherence score using the LDA model with results -12.569424703238145 and 0.43619951201483725, respectively. This research has observed that the word boycott is a favorite to the Indians who are often using it to show opposition or support related day-to-day matters.
Word boycott has been seen frequently trending in India on various social media platforms. We studied the obsession of Indians with the word boycott; to show the protest or dissent against any government policy, Netflix series, political or religious commentary, and on various other matters, people in India prefer to trend word "Boycott" on multiple mediums. We studied how ingrained the word "Boycott" is in Indians in our research and how it affects daily life, unemployment, and the economy. The data was collected using Youtube API with the next page token to get all the search results. We preprocessed the raw data using different preprocessing methods, which are discussed in the paper. To check our data's consistency, we fed the data into various machine learning algorithms and calculated multiple parameters like accuracy, recall, f1-score. Random forest showed the best accuracy of 90 percent, followed by SVM and Knn algorithms with 88 percent each. We used word cloud to get the most dominant used words, Textblob, for sentiment analysis, which showed the mean Polarity of 0.07777707038498406 and mean subjectivity 0.2588880457405638. We calculated perplexity and coherence score using the LDA model with results -12.569424703238145 and 0.43619951201483725, respectively. This research has observed that the word boycott is a favorite to the Indians who are often using it to show opposition or support related day-to-day matters.
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Development of ontology-based model to support learning process in LMS
E-Learning is an important support mechanism for educational systems to increase the efficiency of the education process including students and teachers. The current e-learning systems typically lack the level of metacognitive awareness, adaptive tutoring, and time management skills and have not always met the expectations of the learners as required. In this study, we introduce a novel ontological model for the learning process in the e-learning domain. In the framework, we have built a domain ontology that represents knowledge of the learning, the outcome domain ontology covers the whole learning process. We focused on the learning process ontology model conceptualizing knowledge constructions, such as learning courses, and we present the created course and learning process ontology in detail. In this work, we considered three layers of learning process. The top layer defines a general framework of learning process, conceptual model layer, defines the framework of the actual process of the learning process and course ontology model contains the knowledge unit of the learning process. The prototype ontology is constructed in protégé and managed by Java web ontology language-application programming interface (OWL-API). As a result, our model can solve the problems of current e-tutor systems. Also, it can be used for different domain in e-tutor systems. It can reach the characteristics of standardization, reusability, flexibility, and open knowledge. By applying this model, we can avoid applying isolated databases. The constructed ontology can be used in the future to control adaptive intelligent e-tutor frameworks.
E-Learning is an important support mechanism for educational systems to increase the efficiency of the education process including students and teachers. The current e-learning systems typically lack the level of metacognitive awareness, adaptive tutoring, and time management skills and have not always met the expectations of the learners as required. In this study, we introduce a novel ontological model for the learning process in the e-learning domain. In the framework, we have built a domain ontology that represents knowledge of the learning, the outcome domain ontology covers the whole learning process. We focused on the learning process ontology model conceptualizing knowledge constructions, such as learning courses, and we present the created course and learning process ontology in detail. In this work, we considered three layers of learning process. The top layer defines a general framework of learning process, conceptual model layer, defines the framework of the actual process of the learning process and course ontology model contains the knowledge unit of the learning process. The prototype ontology is constructed in protégé and managed by Java web ontology language-application programming interface (OWL-API). As a result, our model can solve the problems of current e-tutor systems. Also, it can be used for different domain in e-tutor systems. It can reach the characteristics of standardization, reusability, flexibility, and open knowledge. By applying this model, we can avoid applying isolated databases. The constructed ontology can be used in the future to control adaptive intelligent e-tutor frameworks.
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Smart Contracts in the Automotive Industry
The automotive industry has undoubtedly significantly changed our societies and daily life. It is among the most advanced, complicated, and innovative industries. The automotive industry is an essential driving factor of many other advanced industries, it requires the contribution of many other technologies like advanced manufacturing systems, cyber-physical systems, and robotics. Blockchain technology can be highly beneficial for the automotive industry, to enhance its data security, integrity and reliability, tracking and location management, enhanced connectivity, mobility-as-a-service, tamper prevention, and fraud detection. One of the emerging blockchain capabilities is smart contract enforcement and autonomy. In this article, a basic overview of smart contracts is given, early history, definition and concepts, relation with the blockchain, listing its benefits, data sources, design, and describing its importance, finally we highlighted some serious, outstanding and worth to mention steps toward activation and enabling the smart contracts and blockchain roles in the automotive industry specifically and the industry in general.
The automotive industry has undoubtedly significantly changed our societies and daily life. It is among the most advanced, complicated, and innovative industries. The automotive industry is an essential driving factor of many other advanced industries, it requires the contribution of many other technologies like advanced manufacturing systems, cyber-physical systems, and robotics. Blockchain technology can be highly beneficial for the automotive industry, to enhance its data security, integrity and reliability, tracking and location management, enhanced connectivity, mobility-as-a-service, tamper prevention, and fraud detection. One of the emerging blockchain capabilities is smart contract enforcement and autonomy. In this article, a basic overview of smart contracts is given, early history, definition and concepts, relation with the blockchain, listing its benefits, data sources, design, and describing its importance, finally we highlighted some serious, outstanding and worth to mention steps toward activation and enabling the smart contracts and blockchain roles in the automotive industry specifically and the industry in general.
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Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets
Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
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Comprehensive Study on Machine Learning Techniques for Software Bug Prediction
Software bugs are defects or faults in computer programs or systems that cause incorrect or unexpected operations. These negatively affect software quality, reliability, and maintenance cost; therefore many researchers have already built and developed several models for software bug prediction. Till now, a few works have been done which used machine learning techniques for software bug prediction. The aim of this paper is to present comprehensive study on machine learning techniques that were successfully used to predict software bug. Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets. We compared the results of our proposed models with those of the other studies. The results of this study demonstrated that our proposed models performed better than other models that used the same data sets. The evaluation process and the results of the study show that machine learning algorithms can be used effectively for prediction of bugs.
Software bugs are defects or faults in computer programs or systems that cause incorrect or unexpected operations. These negatively affect software quality, reliability, and maintenance cost; therefore many researchers have already built and developed several models for software bug prediction. Till now, a few works have been done which used machine learning techniques for software bug prediction. The aim of this paper is to present comprehensive study on machine learning techniques that were successfully used to predict software bug. Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets. We compared the results of our proposed models with those of the other studies. The results of this study demonstrated that our proposed models performed better than other models that used the same data sets. The evaluation process and the results of the study show that machine learning algorithms can be used effectively for prediction of bugs.
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Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition
This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods.
This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods.
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Actuator behaviour modelling in IoT-Fog-Cloud simulation
The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.
The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.
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Comparison of workload consolidation algorithms for cloud data centers
Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation-based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT-CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population-based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real-world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population-based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.
Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation-based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT-CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population-based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real-world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population-based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.
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Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production
Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.
Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.
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Survey on New Trends of Robotic Tools in the Automotive Industry
In the last decade, industrial robotics sector has known an evolution at level of its technological platform, especially with the appearance of Industry 4.0, therefore we are entering a new era of automation where intelligent production and digital factories produce increasingly complex and high-volume quality products with less human effort. The automotive industry which is already revolutionizing the transition to electric cars has to deal with the change in construction and the tools used to produce them. The use of evermore collaborating intelligent robots with machines and humans of smart manufacturing and assembly lines represents a new level of development in automation. This article provides an overview of the latest tools, directions and intelligent methods available today in this area, from virtual reality to wearable machine intelligence devices for human-driven systems, all the while focusing on robotic applications. Describes the latest industry trends in each field, by highlighting detection systems designed to achieve security, response speed, detection completeness, and reliability, introducing security vision systems of robotic systems, ultrasonic and laser sensors and their efficiency issues. In the final stage, the paper will compare the state of the art in automotive robotics by analyzing the development of leading car manufacturers in Europe, the United States and the Far East.
In the last decade, industrial robotics sector has known an evolution at level of its technological platform, especially with the appearance of Industry 4.0, therefore we are entering a new era of automation where intelligent production and digital factories produce increasingly complex and high-volume quality products with less human effort. The automotive industry which is already revolutionizing the transition to electric cars has to deal with the change in construction and the tools used to produce them. The use of evermore collaborating intelligent robots with machines and humans of smart manufacturing and assembly lines represents a new level of development in automation. This article provides an overview of the latest tools, directions and intelligent methods available today in this area, from virtual reality to wearable machine intelligence devices for human-driven systems, all the while focusing on robotic applications. Describes the latest industry trends in each field, by highlighting detection systems designed to achieve security, response speed, detection completeness, and reliability, introducing security vision systems of robotic systems, ultrasonic and laser sensors and their efficiency issues. In the final stage, the paper will compare the state of the art in automotive robotics by analyzing the development of leading car manufacturers in Europe, the United States and the Far East.
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Simulation of industrial robots’ six axes manipulator arms – a case study
Recently, the elements of Industry 4.0 have to be implemented at manufacturing companies in order to maintain their competitiveness. So the optimal operation and control of industrial robots are important tasks. Therefore, the topic of the article is important and up-to-date. Different control techniques are used in case of serial rigid robot manipulators. In the first part of the article the most important control techniques are described. In the second part of the study a case study for the control of a six axes manipulator arm is introduced. In the case study the open-loop and closed-loop simulation of the investigated manipulator arm using PID controller is carried out. Simulation tests are achieved by the application of the MATLAB Simulink software in order to control position and velocity of different joints of the investigated robot arm. The main added value of the article is that it was confirmed in the case study – based on compared results of open-loop and closed-loop simulations – that the method applied in the study is efficient and provides the desired trajectory of the robot arm.
Recently, the elements of Industry 4.0 have to be implemented at manufacturing companies in order to maintain their competitiveness. So the optimal operation and control of industrial robots are important tasks. Therefore, the topic of the article is important and up-to-date. Different control techniques are used in case of serial rigid robot manipulators. In the first part of the article the most important control techniques are described. In the second part of the study a case study for the control of a six axes manipulator arm is introduced. In the case study the open-loop and closed-loop simulation of the investigated manipulator arm using PID controller is carried out. Simulation tests are achieved by the application of the MATLAB Simulink software in order to control position and velocity of different joints of the investigated robot arm. The main added value of the article is that it was confirmed in the case study – based on compared results of open-loop and closed-loop simulations – that the method applied in the study is efficient and provides the desired trajectory of the robot arm.
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Trial - and - Error Optimization Method of Pick and Place Task for RV-2AJ Robot Arm
In recent years, industrial robots have played an important role in the revolution of a production line in factories, and especially in the growth of Industry 4.0 concept, due to their flexibility to execute tasks and cooperate with their environment fluently, today manipulator arms takes a large part in the production chain especially in the automotive sector where the robot can be configured due to the control terminal for the different task process as welding, painting, pick and place heavy parts. Manipulator arm used in the industry is usually combined 6 degrees of freedom to have a large workspace and manipulation capability. In this article we present an optimization approach regarding a pick and place application for RV-2AJ robot arm which has five degrees of freedom in order to execute different movements, the approach aims to build a card house using one manipulator arm and a support element. Trial – and – error optimization method proposed in this article highlights a good solution regarding the positioning problem for RV-2AJ arm, which has five degrees of freedom that limits its workspace.
In recent years, industrial robots have played an important role in the revolution of a production line in factories, and especially in the growth of Industry 4.0 concept, due to their flexibility to execute tasks and cooperate with their environment fluently, today manipulator arms takes a large part in the production chain especially in the automotive sector where the robot can be configured due to the control terminal for the different task process as welding, painting, pick and place heavy parts. Manipulator arm used in the industry is usually combined 6 degrees of freedom to have a large workspace and manipulation capability. In this article we present an optimization approach regarding a pick and place application for RV-2AJ robot arm which has five degrees of freedom in order to execute different movements, the approach aims to build a card house using one manipulator arm and a support element. Trial – and – error optimization method proposed in this article highlights a good solution regarding the positioning problem for RV-2AJ arm, which has five degrees of freedom that limits its workspace.
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A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF
Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.
Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.
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Interpolative fuzzy reasoning method based on the incircle of a generalized triangular fuzzy number
Fuzzy Rule Interpolation (FRI) is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. This paper introduces a new method called “Incircle FRI” for fuzzy interpolation which is based on the incircle of a triangular fuzzy number. The suggested method is defined for triangular CNF fuzzy sets, for a single antecedent universe and two surrounding rules from the rule-base. The paper also extends the suggested “Incircle FRI” to trapezoidal, and hexagonal shaped fuzzy sets by decomposing their shapes to multiple triangulars. The generated conclusion is also a CNF fuzzy set. The performance of the suggested method is evaluated based on numerical examples and a comprehensive comparison to other current FRI methods.
Fuzzy Rule Interpolation (FRI) is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. This paper introduces a new method called “Incircle FRI” for fuzzy interpolation which is based on the incircle of a triangular fuzzy number. The suggested method is defined for triangular CNF fuzzy sets, for a single antecedent universe and two surrounding rules from the rule-base. The paper also extends the suggested “Incircle FRI” to trapezoidal, and hexagonal shaped fuzzy sets by decomposing their shapes to multiple triangulars. The generated conclusion is also a CNF fuzzy set. The performance of the suggested method is evaluated based on numerical examples and a comprehensive comparison to other current FRI methods.
Minősített cikkek
Detection of IoT-botnet attacks using fuzzy rule interpolation
Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task. There is an urgent need to handle different attack scenarios within the IoT smart environment. Attackers continuously targeted the modern aspects of technology, and trying abusing these technologies using complex attack scenarios such as Botnet attacks. Botnet attacks considered a serious challenge faces of the IoT smart environment. In this paper, we introduce a novel idea that capable of supporting the detecting of IoT-Botnet attack and in meanwhile to avoid the issues associated with the deficiencies of the knowledge-based representation and the binary decision. This paper aims to introduce a detection approach for the IoT-BotNet attack by using the Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added a benefit to enhance the robustness of fuzzy systems and effectively reduce the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts. The proposed approach was applied to an open-source BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. The proposed approach was tested, evaluated and obtained a 95.4% detection rate. Moreover, it effectively smooth the boundary between normal and IoT-BotNet traffics because of its fuzzy-nature, as well as, it had the ability to generate the required IDS alert in case of the deficiencies of the knowledge-based representation.
Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task. There is an urgent need to handle different attack scenarios within the IoT smart environment. Attackers continuously targeted the modern aspects of technology, and trying abusing these technologies using complex attack scenarios such as Botnet attacks. Botnet attacks considered a serious challenge faces of the IoT smart environment. In this paper, we introduce a novel idea that capable of supporting the detecting of IoT-Botnet attack and in meanwhile to avoid the issues associated with the deficiencies of the knowledge-based representation and the binary decision. This paper aims to introduce a detection approach for the IoT-BotNet attack by using the Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added a benefit to enhance the robustness of fuzzy systems and effectively reduce the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts. The proposed approach was applied to an open-source BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. The proposed approach was tested, evaluated and obtained a 95.4% detection rate. Moreover, it effectively smooth the boundary between normal and IoT-BotNet traffics because of its fuzzy-nature, as well as, it had the ability to generate the required IDS alert in case of the deficiencies of the knowledge-based representation.