• Journal of KIISE:Software and Applications
• /
• v.31 no.2
• /
• pp.164-172
• /
• 2004

For a local search algorithm to find a bettor quality solution it is required to generate and evaluate a sufficiently large number of candidate solutions as neighbors at each iteration, demanding quite an amount of CPU time. This paper presents a method of selectively generating only good-looking candidate neighbors, so that the number of neighbors can be kept low to improve the efficiency of search. In our method, a newly generated candidate solution is probabilistically selected to become a neighbor based on the quality estimation determined heuristically by a very simple evaluation of the generated candidate. Experimental results on the problem of load balancing for production scheduling have shown that our candidate selection method outperforms other random or greedy selection methods in terms of solution quality given the same amount of CPU time.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.2
• /
• pp.39-47
• /
• 2020

This paper introduces a novel system-level framework that derives energy efficient electric vehicle (EV) driving speed profile to extend EV driving range without additional cost. This paper first implements an EV power train model considering forces acting on a driving vehicle and motor efficiency. Then, it derivate the minimum-energy driving speed profile for a given driving mission defined by the route. This framework first formulates an optimization problem and uses the dynamic programming algorithm with a weighting factor to derive a speed profile minimizing both of energy consumption and driving time. This paper introduces various weighting factor tracking methods to satisfy the driving time constraint. Simulation results show that runtime of the proposed scaling algorithm is 34% and 50% smaller than those of the binary search algorithm and greedy algorithm, respectively.

• Journal of KIISE:Information Networking
• /
• v.33 no.6
• /
• pp.438-450
• /
• 2006

Geographic routing protocols are very attractive choice for routing in wireless sensor networks because they have been shown to scale better than other alternatives. Under certain ideal conditions, geographic routing works correctly and efficiently. The most commonly used geographic routing protocols include greedy forwarding coupled with face routing. Existing face routing algorithms use planarization techniques that rely on the unit-graph assumption. In real world, many conditions violate the unit-graph assumption of network connectivity, such as location errors, communication voids and radio irregularity, cause failure in planarization and consequently face routing. In this paper, we propose the direction-based geographic routing, which enables energy efficient routing under realistic conditions without planarization techniques. Our proposed approach is for the case in which many sensors need to collect data and send it to a central node. Simulation results show that the protocol exhibits superior performances in terms of energy consumption, delivery success rate, and outperforms the compared protocols.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.12
• /
• pp.2343-2348
• /
• 2008

Ant algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the improved $AS_{rank}$ algorithms. The original $AS_{rank}$ algorithm accomplishes a pheromone updating about only the paths which will be composed of the optimal path is higher, but, the paths which will be composed the optimal path is lower does not considered. In this paper, The proposed method evaporate the pheromone of the paths which will be composed of the optimal path is lowest(worst tour path), it is reducing the probability of the edges selection during next search cycle. Simulation results of proposed method show lower average search time and average iteration than original ACS.

• Cross-Cultural Studies
• /
• v.29
• /
• pp.287-314
• /
• 2012

This article examines the problems with some words used in "Chinese novel Kam Pin Mui"that are presented in"the Great Chinese-Korean dictionary". The author analyses the problems into three aspects: first, error correction in meaning interpretation; second, supplementary correction in meaning interpretation; and third, additional error correction. The main points of the study are presented in the following. First, in relation to the error correction in meaning interpretation, this study finds out that the explanations of "Liezi", "daxuanmo", "kedui", "shaojian" in the "Great Chinese-Korean dictionary"are incorrect. The cases involve the explanations that have no foundation, do not get to the points, and have narrow meaning interpretations compared with original meanings. Second, as for the supplementary correction, this study points out that the explanations of "yiri", "jiaosa", "buxi", "langhu" are not sufficient. Thus, this study claims that the following meanings for each case should be added, including "long time," "abdominal pains during pregnancy," "a type of folk performing arts without stages in local areas of China, and "to devour in greedy gulps." Third, with respect to the additional error correction, this study analyses "the typos of the examples," "the setup of inaccurate meaning items," "the front-to-back arrangement of the examples," and "inconsistency between meaning interpretations and examples" displayed in the dictionary. The error correction in the dictionary can be possible only if the findings from several other disciplines should be incorporated, involving cultural history, the history of literature, philology, grammatology, linguistics, etc. It seems impossible for a person to solve all the problems with the errors in the dictionary. Thus, it will be greatly helpful to the author and the people who prepare for the new edition of "the Great Chinese-Korean dictionary" if we can get continuous supports and comments from relating scholars of other disciplines. As a result, all these efforts will contribute to the academic progress for the relevant disciplines and these academic activities may develop a new area of the study.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.33-41
• /
• 2021

This paper proposes heuristic greedy algorithm that can be find the solution within polynomial time with solution finding rule for the capacitated minimum spanning tree(CMST) problem, known as NP-hard. The CMST problem can be solved by computer-aided meta-heuristic because of the Esau-Williams heuristic polynomial time algorithm has a poor performance. Nevertheless the meta-heuristic methods has a limit performance that can't find optimal solution. This paper suggests visual by handed solution-finding rule for CMST. The proposed algorithm firstly construct MST, and initial feasible solution of CMST from MST, then optimizes the CMST with the subtree gates more adjacent to root node. As a result of total 30 cases of OR-LIB 10 data, Q=3,5,10, the proposed algorithm gets the best performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.4
• /
• pp.27-33
• /
• 2019

The min-cut problem that decides the maximum flow in a complex network flows from source(s) to sink(t) is known as a hard problem. The augmenting path algorithm divides into single path and decides the bottleneck point(edge), but the min-cut section to be decide additionally. This paper suggests O(n) time complexity heuristic greedy algorithm for the number of vertices n that applies free agent system in a pro-sports field. The free agent method assumes $N_G(S),N_G(T)$vertices among $v{\in}V{\backslash}\{s,t\}$to free agent players, and this players transfer into the team that suggest more annual income. As a result of various networks, this algorithm can be finds all of min-cut sections and min-cut value for whole cases.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.5
• /
• pp.111-118
• /
• 2019

Recent graphic processing units (GPUs) provide high throughput by using powerful hardware resources. However, massive memory accesses cause GPU performance degradation due to cache inefficiency. Therefore, the performance of GPU can be improved by reducing thread parallelism when cache suffers memory contention. In this paper, we propose a dynamic warp scheduler which controls thread parallelism according to degree of cache contention. Usually, the greedy then oldest (GTO) policy for issuing warp shows lower parallelism than loose round robin (LRR) policy. Therefore, the proposed warp scheduler employs the LRR warp scheduling policy when Miss Status Holding Register(MSHR) utilization is low. On the other hand, the GTO policy is employed in order to reduce thread parallelism when MSHRs utilization is high. Our proposed technique shows better performance compared with LRR and GTO policy since it selects efficient scheduling policy dynamically. According to our experimental results, our proposed technique provides IPC improvement by 12.8% and 3.5% over LRR and GTO on average, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.1
• /
• pp.35-57
• /
• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of the Korea Society for Simulation
• /
• v.32 no.3
• /
• pp.67-74
• /
• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.