112 research outputs found

    An Application of Web-Supported Mental Tools in Technology Education

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    This article presents the pedagogical model “Network Oriented Study with Simulations” (NOSS) using simulation software and the ICT-based tool “Web Orientation Agent” (WOA) (Page et al., 2006) and supporting findings. The NOSS model was developed and implemented in CSCL (computer supported collaborative learning) type learning situations around computers in technology education utilising novel technologies such as case web-supported simulations and to evaluate the effectiveness of those in teaching, studying and learning settings. The preliminary findings appear to support the effectiveness of this pedagogical model with some limitations which are subsequently discussed

    An application of web-supported mental tools in technology education

    Get PDF
    This article presents the pedagogical model “Network Oriented Study with Simulations” (NOSS) using simulation software and the ICTbased tool “Web Orientation Agent” (WOA) (Page et al., 2006) and supporting findings. The NOSS model was developed and implemented in CSCL (computer supported collaborative learning) type learning situations around computers in technology education utilising novel technologies such as case web-supported simulations and to evaluate the effectiveness of those in teaching, studying and learning settings. The preliminary findings appear to support the effectiveness of this pedagogical model with some limitations which are subsequently discussed

    Applied Metaheuristic Computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

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    With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

    Optimal power flow solutions for power system operations using moth-flame optimization algorithm

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    Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective function such as: minimization of total fuel cost, emission, real power transmission loss, voltage deviation, etc. while fulfilling certain operation constraints like bus voltage, line capacity, generator capability and power flow balance. Optimal reactive power dispatch (ORPD) is a sub-problem of optimal power flow. ORPD has a considerable impact on the economic and the security of the electric power system operation and control. ORPD is considered a mixed nonlinear problem because it contains continuous and discrete control variables. Another sub-problem of OPF is Economic dispatch (ED) which one of the complex problems in the power system which its purposes is to determine the optimal allocation output of generator unit to satisfy the load demand at the minimum economic cost of generation while meeting the equality and inequality constraints. In this thesis, a recent metaheuristic nature-inspired optimization algorithm namely: Moth-Flame Optimizer (MFO) is applied to solve the two subproblems of Optimal power flow (OPF) namely: Economic dispatch (ED) and Optimal reactive power dispatch (ORPD) simultaneously. Three objective functions will be considered: generation cost minimization, transmission power loss minimization, and voltage deviation minimization using a weighted factor. The IEEE 30-bus test system and IEEE 57-bus test system will be employed, to investigate the effectiveness of the proposed MFO in solving the above-mentioned problems. Then the obtained MFO methods results is compared with other reported well-known methods. The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961/hcomparedto971.411400/h compared to 971.411400 /h, 983.738069/h,975.346233/h, 975.346233/h, 985.198050/h,1035.537820/h, 1035.537820/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. In IEEE 57-bus test system, MFO offers a minimization of 19.16% compared to 19.03% and 18.98% for Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) respectively. Moreover, the MFO have speedy convergence rate and smooth curves more than the other algorithms

    Evolutionary Computation 2020

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    Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

    Applied Methuerstic computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    The Extraction and Usage of Patterns from Video Data to Support Multi-Agent Based Simulation

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    The research work presented in this thesis is directed at addressing the knowledge acquisition bottleneck frequently encountered in computer simulation. The central idea is to extract the required knowledge from video data and use this to drive a computer simulation instead of the more conventional approach of interviewing domain experts and somehow encapsulating this knowledge in a manner whereby it can be used in the context of computer simulation. More specifically the idea presented in this thesis is to extract object location information from video data and then to mine this information to identify Movement Patterns (MPs) and then to utalise these MPs in the context of computer simulation. To act as a focus for the work rodent behaviour simulation was considered. Partly because video data concerning rodent behaviour was relatively easy to obtain and partly because there is a genuine need to achieve a better understanding of rodent behaviour. This is especially the case in the context of crop damage. There are a variety of computer simulation frameworks. One that naturally lends itself to rodent simulation is Multi Agent Based Simulation (MABS) whereby the objects to be simulated (rodents) are encapsulated in terms of software agents. In more detail the work presented is directed at a number of research issues in the context of the above: (i) mechanisms to identify a moving object in video data and extracting associated location information, (ii) the mining of MPs from the extracted location information, (iii) the representation of MPs in such a way that they are compatible with computer simulation frameworks especially MABS frameworks and (iv) mechanisms where by MPs can be utilized and interacted with so as to drive a MABS. Overall two types of mechanisms are considered, Absolute and Relative. The operation of rodent MABSs, driven using the proposed MP concept, is fully illustrated in the context of different categories of scenarios. The evaluation of the proposed MP driven MABSs was conducted by comparing real world scenarios to parallel simulated scenarios. The results presented in the thesis demonstrated that the proposed mechanisms for extracting locations, and consequently mining MPs, from video data to drive a MABS provides a useful approach to effective computer simulation that will have wide ranging benefits

    Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies

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    In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions
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