• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2063-2081
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• 2018

A resource allocation algorithm is proposed in this paper to simultaneously minimize the total system power consumption and maximize the system throughput for the downlink of multi-user multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. With the Lagrange dual decomposition method, we transform the original problem to its convex dual problem and prove that the duality gap between the two problems is zero, which means the optimal solution of the original problem can be obtained by solving its dual problem. Then, we use convex optimization method to solve the dual problem and utilize bisection method to obtain the optimal dual variable. The numerical results show that the proposed algorithm is superior to traditional single-objective optimization method in both the system throughput and the system energy consumption.

• 한국통신학회논문지
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• 제33권2B호
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• pp.82-89
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• 2008

단말의 이동을 인터넷 프로토콜 계층에서 지원하기 위하여 사용되는 모바일 아이피는 하나의 단말이 아닌 다수의 단말이 함께 이동할 경우 발생하는 문제를 해결하기 위하여 네트워크 이동성이라는 개념으로 확장되었다. 네트워크 이동성 시나리오에서 이동 라우터들 사이에 계층 구조가 존재할 경우를 특히 중첩 이동 네트워크라고 부르며 이러한 중첩 이동 네트워크에서는 계층의 깊이에 비례하여 비효율적인 라우팅 경로를 따르게 되는 핀볼 라우팅 문제가 발생한다. 본 논문에서는 기존의 이동 네트워크 지원 프로토콜과 호환성을 가지며 이질적인 이동 네트워크 환경을 고려하는 경로 최적화 기법을 제안하고 정량적 정성적 비교 분석을 수행한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.33-39
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• 1995

One dimensional optimization problem is considered, we propose a method to find the global minimum of one-dimensional function with on gradient information but only the finite number of input-output samples. We construct a learning network which has a good learning capability and of which global maximum(or minimum) can be calculated with simple calculation. By teaching this network to approximate the given function with minimal samples, we can get the global minimum of the function. We verify this method using some typical esamples.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.157-164
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• 2022

The proposed privacy preserving scheme has identified the drawbacks of existing schemes in Vehicular Networks. This prototype enhances the number of nodes by decreasing the cluster size. This algorithm is integrated with the whale optimization algorithm and Block Chain Technology. A set of results are done through the NS-2 simulator in the direction to check the effectiveness of proposed algorithm. The proposed method shows better results than with the existing techniques in terms of Delay, Drop, Delivery ratio, Overhead, throughout under the denial of attack.

• 한국통신학회논문지
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• 제30권4B호
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• pp.178-185
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• 2005

네트워크 단위의 이동성을 지원하는 network mobility (MEMO) 기술에서는 중첩된 네트워크 환경 (nested NEMO)에서 전달 지연 시간을 줄이는 경로를 최적화에 관현 연구가 활발히 이루어지고 있다. 현재 대표적인 경로 최적화 방안으로는 확장 헤드를 이용하여 경로 정보를 모두 기록하는 RRH (Reverse Routing Header)와 최상위 MR에서 하부에 위치한 MR의 상태 정보를 관리하는 BHT (Bi-directional tunnel between Home agent and Top level mobile route)이 제안되어 있다. 하지만 기존의 방안들은 중첩 깊이가 증가할수록 패킷 전달을 위한 오버헤드가 증가하는 문제가 발생한다. 본 논문에서는 중첩된 이동 네트워크 환경에서 지역적 정보를 이용한 경로 최적화 방안 (RIRO; Regional Information-based Route Optimization)을 제안하고자 한다. RIRO 방안에서는 모든 MR들은 자신의 하부에 위치한 MR들의 위치 정보를 관리하고 라우팅 헤더를 이용하여 패킷 전달 경로를 최적화하는 방안으로 중첩된 환경에서도 패킷이 전달을 위한 오버헤더가 증가하지 않는 장점이 있다.

• 한국수자원학회논문집
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• 제32권2호
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• pp.143-152
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• 1999

우리나라 경북 동·남부지역은 지형조건과 원래 부족한 수자원으로 용수개발에 어려움을 겪는 지역이다. 이와 같은 물 문제를 완화시키기 위해 새로운 댐의 개발과 광역용수공급, 기존 용수공급 시스템의 조정, 오래된 댐의 개·증축 그리고 저류용댐의 건설 방안이 검토되었다. 새롭게 제시된 수자원 개발 대안의 평가는 수자원 시스템의 의사결정 도구로 많이 이용되고 있는 수학적 모형의 하나인 네트워크 최적화 모형을 이용하였다. 연구결과 용수공급 시스템이 2011년까지 건설된다면 포항 및 경주권의 용수공급 신뢰도는 95% 이상을 확보할 수 있을 것으로 분석되었으며 네트워크 최적화 모형이 수리권 혹은 용수공급 우선 순위를 고려한 수자원 시설물의 운영을 분석하는데 사용 될 수 있을 것으로 판단된다.

• 한국해양공학회지
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• 제33권5호
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Journal of Ship and Ocean Technology
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• 제8권2호
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.