Automated Bandwidth Allocation across Heterogeneous Networks (AutoBAHN) is a tool under active development that supports a Bandwidth on Demand (BoD) service, intended to operate in a multi-domain environment using het...
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The application of Remote Laser Welding (RLW) has become an attractive assembly technology in various branches of industry, as it offers higher efficiency at lower costs compared to traditional Resistance Spot Welding...
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The application of Remote Laser Welding (RLW) has become an attractive assembly technology in various branches of industry, as it offers higher efficiency at lower costs compared to traditional Resistance Spot Welding (RSW) when high volumes of sheet metal assemblies are to be produced. However, the introduction of RLW technology raises multiple new issues in designing the configuration, the layout, and the behavior of the assembly system. Since configuring an RLW workstation and planning the welding process are closely interrelated problems, a hierarchical decision process must be applied where configuration and planning go hand in hand. The paper presents a hierarchical workflow for workstation configuration and process planning for RLW operations, and proposes methods for solving the decision problems related to each step of this workflow. A software toolbox is introduced that has been developed to facilitate a semi-automatic, mixed-initiative workstation design and to guide the expert user throughout the configuration, planning, programming, evaluation, and simulation of the RLW workstation. A case study from the automotive industry is presented, where the software tools developed are applied to configuring and planning the behavior of an RLW workstation that replaces RSW technology in assembling a car door.
We investigate here several categories of strategic games and antagonistic situations that are known to admit potential functions, and are thus guaranteed to either possess pure Nash equilibria or to stabilize in some...
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Companies with diverse product portfolio often face capacity planning problems due to the diversity of the products and the fluctuation of the order stream. High volume products can be produced cost-efficiently in ded...
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Companies with diverse product portfolio often face capacity planning problems due to the diversity of the products and the fluctuation of the order stream. High volume products can be produced cost-efficiently in dedicated assembly lines, but the assembly of low-volume products in such lines involves high idle times and operation costs. Reconfigurable assembly lines offer reasonable solution for the problem; however, it is still complicated to identify the set of products which are worth to assemble in such a line instead of dedicated ones. In the paper a novel method is introduced that supports the long-term decision to relocate the assembly of a product with decreasing demand from a dedicated to a reconfigurable line, based on the calculated investment and operational costs. In order to handle the complex aspects of the planning problem a new approach is proposed that combines discrete-event simulation and machine learning techniques. The feasibility of the approach is demonstrated through the results of an industrial case study.
Service-oriented computing is a widely adopted paradigm in real applications. Considering the continuous evolution of services, adaptive service composition has always been a major concern. It is a big challenge to ad...
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ISBN:
(纸本)9781479932801
Service-oriented computing is a widely adopted paradigm in real applications. Considering the continuous evolution of services, adaptive service composition has always been a major concern. It is a big challenge to adjust the composition to be optimal in real-time. In this paper, a learning automata-based approach is proposed to attack this problem. It consists of two important components: random environment and a learning automaton. The former can be mapped to the service's execution environment. The latter is responsible for the adaptation achievement using reward and penalty functions, while we take the service composition structures into account to compute the usefulness value of all services. At last, simulation study has shown that our approach is efficient to find the optimal (sub-optimal) composition.
Time-frequency analysis methods such as short-time Fourier transform and wavelet transform are applied to investigate characteristic from non-stationary signal. In this study, we proposed redundant morphological wavel...
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In this paper we propose an optimal algorithm for the problem of least expected transmissions multicasting in wireless networks. The algorithm starts by transforming the network graph into an expanded graph that captu...
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ISBN:
(纸本)9781479923601
In this paper we propose an optimal algorithm for the problem of least expected transmissions multicasting in wireless networks. The algorithm starts by transforming the network graph into an expanded graph that captures the wireless broadcast advantage (WBA) while simplifying point-to-multi-point transmissions in the original graph into point-to-point transmissions in the auxiliary expanded graph. Using an appropriate function to calculate the weights of the expanded graph links we also capture the wireless unreliable transmission (WUT) characteristics of the wireless medium. By solving the minimum Steiner tree problem on the expanded graph we obtain the optimal solution of the initial problem. Since the optimal algorithm is of non-polynomial complexity, we proceed to propose a heuristic algorithm. Simulation results show that the proposed heuristics have performance close to that of the optimal algorithm, at least for the instances for which we were able to track optimal solutions, while outperforming other heuristic multicast algorithms.
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this pu...
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ISBN:
(纸本)9781479969357
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic environments. In addition, we compare different preprocessing robot audition variants (single channel and separated signal from multiple channels) and test different acoustic models, learned a priori, to tackle multiple noise conditions of increasing complexity in the presence of noises of different natures (e.g., robot motion, speech). The results with six different musical genres suggest improved results, in the order of 43.6pp for the most complex conditions, when recurring to Sound Source Separation and acoustic models trained in similar conditions to the testing scenarios. A robot dance demonstration session confirms the applicability of the proposed integration for genre-adaptive dancing robots in real-world noisy environments.
In order to cultivate human resources with an advanced skill of software development, it is necessary to make the learner acquire the programming technology as the basis at an early point and with certainty. Programmi...
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In order to cultivate human resources with an advanced skill of software development, it is necessary to make the learner acquire the programming technology as the basis at an early point and with certainty. Programming education is conducted with a focus on exercise, and it is often the case that the result is made submitted as a report, in order to evaluate the degree of fixation for the knowledge by the exercise. By using the report, one can check the final result of the exercise, but it is difficult for the educator to know the process by the learner to complete making the software, or on its way, to face what kind of problems are faced and to cope with them. In this study, in conjunction with Eclipse, a learning environment to support software development exercise is constructed by collecting fine-grained record of learner's programming in real time and by storing it to the server connected to the network.
In recent years, practical software development exercises have been carried out in many higher education institutions. To carry out the exercises effectively, it is important that teachers understand the difficulty of...
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In recent years, practical software development exercises have been carried out in many higher education institutions. To carry out the exercises effectively, it is important that teachers understand the difficulty of learners in exercises and advise appropriately for it. Currently, a common way to check the results of the exercises is that teachers review artifacts which learners submitted. However, there is a problem in this way that it can't obtain information regarding the learners' artifacts creation process. Therefore, teachers can't fully understand difficulties of the learners. We focus on the learners' artifacts creation process and propose a method for detecting learners' difficult points during the exercises. We develop a tool that collects the class diagram creation process by learners during exercises and analyze it.
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