• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1354-1356
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• 2012

최근 ITS(Intelligent Transportation Systems)의 개발과 함께 차량용 내비게이션의 사용이 급증하면서 경로탐색의 중요성이 더욱 가속화되고 있다. 현재 차량용 내비게이션은 멀티미디어 및 정보통신 기술의 결합과 함께 다양한 기능 및 정보를 사용자에게 제공하고 있으며 이러한 기능과 정보를 사용해서 목적지점까지의 최단경로를 탐색하는 것이 내비게이션 시스템의 핵심기능이다. 이러한 경로탐색 알고리즘은 교통시스템, 통신 네트워크, 운송 시스템은 물론 이동 로봇의 경로 설정 등 다양한 분야에 사용되고 있다. 개미 집단 최적화(Ant Colony Optimization, ACO) 알고리즘은 메타 휴리스틱 탐색 방법으로 그리디 탐색(Greedy Search)뿐만 아니라 긍정적 반응의 탐색을 사용한 모집단에 근거한 접근법으로 순환 판매원 문제(Traveling Salesman Problem, TSP)를 풀기 위해 처음으로 제안되었다. 본 논문에서는 개미 집단 최적화(ACO) 알고리즘이 기존의 경로 탐색 알고리즘으로 알려진 Dijkstra 보다 최단경로 탐색에 있어서 더 적합한 알고리즘이라는 것을 설명하고자 한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.621-622
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• 2009

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.131-137
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• 2007

Recently, Ant Colony Optimization (ACO) is emerged as a simple yet powerful optimization algorithm for routing and load-balancing of both wired and wireless networks. However, there are few researches trying to adopt ACO to enhance routing performance in WSN owing to difficulties in applying ACO to WSN because of stagnation effect. In this paper, we propose an energy-efficient path selection algorithm based on ACO for WSN. The algorithm is not by simply applying ACO to routing algorithm but by introducing a mechanism to alleviate the influence of stagnation. By the simulation result, the proposed algorithm shows better performance in data propagation delay and energy efficiency over Directed Diffusion which is one of the outstanding schemes in multi-hop flat routing protocols for WSN. Moreover, we checked that the proposed algorithm is able to mitigate stagnation effect than simple ACO adoption to WSN.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.541-551
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• 2021

Among the numerous duties of a librarian in a library, the work of arranging books is a job that the librarian has to do one by one. Thus, the cost of labor and time is large. In order to solve this problem, the interest in book-arranging robots based on artificial intelligence has recently increased. In this paper, we propose the K-ACO algorithm, which is the shortest path algorithm for multi-stops that can be applied to the library book arrangement robots. The proposed K-ACO algorithm assumes multiple robots rather than one robot. In addition, the K-ACO improves the ANT algorithm to create K clusters and provides the shortest path for each cluster. In this paper, the performance analysis of the proposed algorithm was carried out from the perspective of book arrangement time. The proposed algorithm, the K-ACO algorithm, was applied to a university library and compared with the current book arrangement algorithm. Through the simulation, we found that the proposed algorithm can allocate fairly, without biasing the work of arranging books, and ultimately significantly reduce the time to complete the entire work. Through the results of this study, we expect to improve quality services in the library by reducing the labor and time costs required for arranging books.

• Journal of Korean Neurosurgical Society
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• v.60 no.6
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• pp.635-643
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• 2017

Objective : Acute basilar artery occlusion (BAO) is associated with severe neurological dysfunction and high mortality rates. The benefits of mechanical thrombectomy in BAO have not been explored in recent clinical trials. Therefore, we analyzed outcomes of stent retriever mechanical thrombectomy for BAO, and compared with anterior circulation occlusions (ACO). Methods : In total, 161 consecutive patients (24 BAO, 137 ACO) who underwent mechanical thrombectomy with the stent retriever between January 2013 and August 2016 enrolled in our study. All patients underwent clinical assessment with the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS). Radiological results were used to evaluate thrombolysis in cerebral infarction (TICI) scores and successful recanalization was defined by TICI 2b or 3. Results : Mean NIHSS scores at 24 hours and 30 days were significantly higher in the BAO group than the ACO group (p=0.021, p=0.001). mRS at 90 days was significantly higher in the BAO group (4.2) compared with the ACO group (3.0) (p=0.003). The BAO group (2.0) performed fewer stent retriever passages than the ACO group (2.7) (p=0.049). There were no significant differences between the two groups in terms of complications, but the BAO patients experienced a higher mortality (16.6%) rate than ACO patients (5.8%) (p=0.001). In subgroup analysis of BAO, patients with short procedure times achieved successful recanalization (p=0.001) and successfully recanalized patients exhibited more favorable mRS at 90 days (p=0.027). Conclusion : In our study, mechanical thrombectomy of BAO patients showed worse clinical outcome and higher mortality rate than ACO patients. However, mechanical thrombectomy with a stent retriever in BAO is an effective treatment, because successfully recanalized patients showed good clinical outcome in BAO patients.

• Journal of Korean Neurosurgical Society
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• v.53 no.2
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• pp.112-114
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• 2013

Bilateral abducens nerve palsy related to ruptured aneurysm of the anterior communicating artery (ACoA) has only been reported in four patients. Three cases were treated by surgical clipping. No report has described the clinical course of the isolated bilateral abducens nerve palsy following ruptured ACoA aneurysm obliterated with coil. A 32-year-old man was transferred to our institution after three days of diplopia, dizziness and headache after the onset of a 5-minute generalized tonic-clonic seizure. Computed tomographic angiography revealed an aneurysm of the ACoA. Magnetic resonance imaging showed focal intraventricular hemorrhage without brain stem abnormalities including infarction or space-occupying lesion. Endovascular coil embolization was conducted to obliterate an aneurysmal sac followed by lumbar cerebrospinal fluid (CSF) drainage. Bilateral paresis of abducens nerve completely recovered 9 weeks after ictus. In conclusion, isolated bilateral abducens nerve palsy associated with ruptured ACoA aneurysm may be resolved successfully by coil embolization and lumbar CSF drainage without directly relieving cerebrospinal fluid pressure by opening Lillequist's membrane and prepontine cistern.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1409-1423
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• 2018

Compared with classical linear controllers, a nonlinear controller can result in better control performance for the nonlinear uncertainties of continuously variable transmission (CVT) systems that are driven by a synchronous reluctance motor (SynRM). Improved control performance can be seen in the nonlinear uncertainties behavior of CVT systems by using the proposed mingled revised recurrent Hermite polynomial neural network (MRRHPNN) control with mend ant colony optimization (ACO). The MRRHPNN control with mend ACO can carry out the overlooker control system, reformed recurrent Hermite polynomial neural network (RRHPNN) control with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, to help improve convergence and to obtain better learning performance, the mend ACO is utilized for adjusting the two varied learning rates of the two parameters in the RRHPNN. Finally, comparative examples are illustrated by experimental results to confirm that the proposed control system can achieve better control performance.

• Horticultural Science & Technology
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• v.32 no.2
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• pp.261-268
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• 2014

Senescence of Hosta ventricosa flowers was firstly characterized as ethylene-sensitive since the deterioration of the tepal was accompanied by increased endogenous ethylene biosynthesis. The full-length cDNAs and DNAs of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) and ACC oxidase (ACO) involved in ethylene biosynthesis were cloned from H. ventricosa flowers. The HvACS ORF with 1347 bp and two introns, encoded a polypeptide of 448 amino acids showing 79% homology with that in Musa acuminata. The HvACO ORF contained 957 bp and three introns, encoding a 318-residue polypeptide showing 83% homology with that in Narcissus tazetta. The timing of the induction of HvACS expression was in correspond to the timing of the increase in ethylene production, and that the up-regulation of HvACO transcript was closely correlated with an elevated ethylene production, but underwent a down-regulation in wounded leaves with elevated ethylene emission. The results, together with expression analysis in vegetative tissues, suggested that both HvACS and HvACO were specifically regulated by flower senescence.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.730-735
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• 2005

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there were difficulties in real experiments because the conventional method didn't reflect the precise characteristics of DNA. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve problems of Knapsack Problem. ACO was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors as compared with conventional methods, and found accurate solutions more rapidly.