• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• Journal of Digital Contents Society
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• v.9 no.3
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• pp.471-481
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• 2008

Test scheduling considering power dissipation is an effective technique to reduce the testing time of complex SoCs and to enhance fault coverage under limitation of allowed maximum power dissipation. In this paper, a modeling technique of test resources and a test scheduling algorithm for efficient test procedures are proposed and confirmed. For test resources modeling, two methods are described. One is to use the maximum point and next maximum point of power dissipation in test resources, the other one is to model test resources by partitioning of them. A novel heuristic test scheduling algorithm, using the extended-tree-growing-graph for generation of maximum embedded cores usable simultaneously by using relations between test resources and cores and power-dissipation-changing-graph for power optimization, is presented and compared with conventional algorithms to verify its efficiency.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1632-1638
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• 2020

In this paper, we investigate a multi-beam satellite communication system where multiple terminals transmit information signals to the gateway via a satellite. The satellite is equipped with phased array antennas to form multiple spot beams of which bandwidths are not identically allocated. We formulate an optimization problem to maximize fairness of beam bandwidth allocation. In order to solve the problem, we propose two heuristic algorithms; iterative beam bandwidth allocation (IBBA) and request ratio-based beam bandwidth allocation (RRBBA) algorithms. The IBBA algorithm iteratively equalizes the ratio of allocated bandwidth of each beam to their resource request while the RRBBA algorithm allocates beam bandwidth calculated from the ratio. Simulation results show that the IBBA algorithm has close fairness performance to the optimum while the RRBBA algorithm has less performance than the IBBA algorithm at the price of reduced computational complexity.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.45-56
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• 2019

Urban green areas are generally composed of relatively small and fragmented patches, but it is a critical factor for the quality of an urban environment. They have positive effects such as increasing green connectivity, reducing runoff, and mitigating urban heat. But, there is a lack of urban greening plans that consider the comprehensive effects of green space in real urban areas. To fill this gap in this literature, this study identifies a planning model that determines the optimal locations for maximizing green areas' multiple effects(e.g., heat mitigation and enhancement of connectivity) by using unused lots. This model also considers minimizing costs using meta-heuristic optimization algorithms. As a results, we finds 50 optimal plans that considers two effects within the limited cost in Nowon-gu. The optimal plans show the trade-off effect between connectivity, heat mitigation and cost. They also show the critical unused land lots for urban greening that are commonly selected in various plans. These optimal plans can effectively inform quantitative effectiveness of green space and their trade-off. We expect that our model will contribute to the improvement of green planning processes in reality.

• Journal of Environmental Impact Assessment
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• v.26 no.1
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• pp.44-56
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• 2017

Sustainable development is important because the ultimate objective is efficient development combining the economic, social, and environmental aspects of urban conservation. Despite Korea's rapid urbanization and economic development, the distribution of resources is inefficient, and land-use is not an exception. Land use distribution is difficult, as it requires considering a variety of purposes, whose solutions lie in a multipurpose optimization process. In this study, Yangpyeong-eup, Yangpyeong, Gyeonggi-do, is selected, as the site has ecological balance, is well-preserved, and has the potential to support population increases. Further, we have used the genetic algorithm method, as it helps to evolve solutions for complex spatial problems such as planning and distribution of land use. This study applies change to the way of mutation. With four goals and restrictions of area, spatial objectives, minimizing land use conversion, ecological conservation, maximizing economic profit, restricting area to a specific land use, and setting a fixed area, we developed an optimal planning map. No urban areas at the site needed preservation and the high urban area growth rate coincided with the optimization of purpose and maximization of economic profit. When the minimum point of the fitness score is the convergence point, we found optimization occurred approximately at 1500 generations. The results of this study can support planning at Yangpyeong-eup.ausative relationship between the perception of improving odor regulation and odor acceptance.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.33-43
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• 2011

Routing technologies considering QoS-based hypermedia services have been seen as a crucial network property in next generation optical Internet (NGOI) networks based on IP/dense-wavelength division multiplexing (DWDM). The huge potential capacity of one single fiber. which is in Tb/s range, can be exploited by applying DWDM technology which transfers multiple data streams (classified and aggregated IP traffics) on multiple wavelengths (classified with QoS-based) simultaneously. So, DWDM-based optical networks have been a favorable approach for the next generation optical backbone networks. Finding a qualified path meeting the multiple constraints is a multi-constraint optimization problem, which has been proven to be NP-complete and cannot be solved by a simple algorithm. The majority of previous works in DWDM networks has viewed heuristic QoS routing algorithms (as an extension of the current Internet routing paradigm) which are very complex and cause the operational and implementation overheads. This aspect will be more pronounced when the network is unstable or when the size of network is large. In this paper, we propose a flooding-based unicast and multicast QoS routing methodologies(YS-QUR and YS-QMR) which incur much lower message overhead yet yields a good connection establishment success rate. The simulation results demonstrate that the YS-QUR and YS-QMR algorithms are superior to the previous routing algorithms.