• 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 Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.83-85
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• 2013

무인 자가 운반 차량은 컨테이너 터미널 내 선박과 장치장 사이를 오가며 컨테이너를 운반하는 무인 장비로 컨테이너를 집고 내리는 하역 능력을 갖고 있다. 터미널에서 컨테이너의 처리량을 극대화하기 위해서는 컨테이너 운송 시간을 최소화하여야 하는데, 이를 위해서는 차량의 효율적인 주행 경로 설정이 필요하다. 최적의 주행경로를 설정하기 위한 방법으로 A*, ant colony optimization과 같은 탐색알고리즘을 이용해서 주행경로를 찾는 방안이 연구된바 있다. 하지만 교통 상황에 따라 최적의 주행 경로는 바뀌게 되는데 기존의 연구에서는 결정된 주행 경로에 대한 수정이 없기 때문에 이러한 변화를 반영하지 못하는 문제가 있었다. 이에 본 논문에서는 주행 중인 차량이 다른 차량의 간섭에 의하여 대기하는 경우 대기 시간을 이용하여 새로운 주행 경로를 탐색 하여 현재 교통 상황에 맞는 최적의 경로를 찾는 방안을 제안하였으며 실험을 통해 기존 방안보다 더 효율적임을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3197-3218
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• 2012

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user's predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles' physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users' satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.285-291
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• 2022

WSN is the major component for information transfer in IoT environments. Source Location Privacy (SLP) has attracted attention in WSN environments. Effective SLP can avoid adversaries to backtrack and capture source nodes. This work presents a Two-Level Randomized Sector-based Routing (TLRSR) model to ensure SLP in wireless environments. Sector creation is the initial process, where the nodes in the network are grouped into defined sectors. The first level routing process identifies sector-based route to the destination node, which is performed by Ant Colony Optimization (ACO). The second level performs route extraction, which identifies the actual nodes for transmission. The route extraction is randomized and is performed using Simulated Annealing. This process is distributed between the nodes, hence ensures even charge depletion across the network. Randomized node selection process ensures SLP and also avoids depletion of certain specific nodes, resulting in increased network lifetime. Experiments and comparisons indicate faster route detection and optimal paths by the TLRSR model.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.36-40
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• 2023

Recently, in the problem of multi-order processing in logistics warehouses, multi-pickup systems are changing from the form in which workers walk around the warehouse to the form in which goods come to workers. These changes are shortening the time to process multiple orders and increasing production. This study considered the sequence problem of which warehouse the items to be loaded on each truck come first and which items to be loaded first when loading multiple pallet-unit goods on multiple trucks in an industrial smart logistics automation warehouse. To solve this problem efficiently, we use the mutant algorithm, which combines the GA algorithm and ACO algorithm, and compare with original system.

• IE interfaces
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• v.23 no.2
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• pp.147-155
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• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.1-10
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• 2011

Proportional.Integral.Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, and Particle Swarm Optimization and Ant colony optimization. The proposed algorithm is used to tune the PID parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Lambda method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on Machine Direction of basics weight control in pulp and paper industry. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.