• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.6
• /
• pp.650-656
• /
• 1999

In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.2
• /
• pp.59-64
• /
• 2024

The small tracking radar randomly changes the transmission period to avoid deceptive signals such as RGPO (Range Gaet Pull Off) operated on the target. Since the code repeats after a specific section, it becomes difficult to avoid a deceptive signal when the repetition is exposed. In this paper, a more realistic transmission pulse code is generated by adding random code generation through FPGA and system white noise. We present the research results of code generation that generates PRF that can avoid repetition while using pseudo-random code in EPROM using FPGA. Also, the result of designing random PRF pulse was confirmed by applying it to tracking radar.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.494-496
• /
• 2020

기계학습 모델을 이용한 분류 및 회귀 문제해결에는 다양한 전처리 알고리즘 및 기계학습 모델이 활용된다. 하지만 합리적인 성능을 위해서는 주어진 데이터에 따라 적절한 알고리즘 조합에 대한 탐색 및 최적화 과정이 펄수적이다. 본 논문에서는 최적의 알고리즘 조합을 탐색하는 방법 중 랜덤 탐색과 유전 알고리즘 탐색 방법을 구현하고 8가지 데이터에 대한 성능 비교를 통해 여러 기계학습 모델을 고려하는 탐색 방법의 필요성을 보인다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.1
• /
• pp.62-66
• /
• 2011

This paper proposes a parallel architecture of random signal-based learning (PRSL), merged with simulated annealing (SA), to optimize the fuzzy logic controller (FLC). Random signal-based learning (RSL) finds the local optima very well, whereas it can not finds the global optimum in a very complex search space because of its serial nature. To overcome these difficulties, PRSL, which consists of serial RSL as a population, is considered. Moreover, SA is added to RSL to help the exploration. The validity of the proposed algorithm is conformed by applying it to the optimization of a FLC for the inverted pendulum.

• Journal of the Korea Society for Simulation
• /
• v.32 no.1
• /
• pp.23-34
• /
• 2023

The purpose of this paper is to review global stochastic optimization algorithms(GSOA) in case binary response experimentation is used and to compare the performances of them. GSOAs utilise estimator of probability of success $\^p$ instead of population probability of success p, since p is unknown and only known by its estimator which has stochastic characteristics. Hill climbing algorithm algorithm, simple random search, random search with random restart, random optimization, simulated annealing and particle swarm algorithm as a population based algorithm are considered as global stochastic optimization algorithms. For the purpose of comparing the algorithms, two types of test functions(one is simple uni-modal the other is complex multi-modal) are proposed and Monte Carlo simulation study is done to measure the performances of the algorithms. All algorithms show similar performances for simple test function. Less greedy algorithms such as Random optimization with Random Restart and Simulated Annealing, Particle Swarm Optimization(PSO) based on population show much better performances for complex multi-modal function.

• Annual Conference on Human and Language Technology
• /
• 2001.10d
• /
• pp.359-364
• /
• 2001

필기 한글문자 인식은 다양한 필기 변형, 자모 간의 접촉과 같은 문제들을 내포하고 있다. 최근 이를 해결하기 위한 방법으로 랜덤 그래프를 이용한 필기 한글 모델링이 제안되었으나, 상향식 정보처리의 한계인 시간 복잡도 문제를 겪고 있다. 영어 단어인식에 관한 인지과학적 연구에서는 하향식 정보처리의 주요한 역할 중 하나로 인식 과정에서의 계산 중복을 없애는 필터링의 역할을 들고 있다. 본 논문에서는 랜덤 그래프를 이용한 필기 한글 모델링을 기반으로 하여 필기체에 나타나는 다양한 변형을 흡수하며, 시간 복잡도를 해결하기 위한 한글 문자의 구조에 바탕을 둔 하향식 정보처리 방법을 제안한다. 제안하는 방법은 모델 발화를 이용한 자모 후보 추출 DP 정합과 동적 격자 탐색을 이용한 문자 후보 탐색, 그리고 문자의 구조적 제약을 이용한 후보 제거 기법을 포함한다. 필기 한글 데이터베이스인 SERI-DB에 대한 예비 실험 결과, 제안한 방법은 인식률의 큰 저하 없이 상향식 정보 처리에 바탕을 둔 기존 방법에 비해 높은 속도 향상을 가져 왔다.

• Geophysics and Geophysical Exploration
• /
• v.23 no.3
• /
• pp.157-167
• /
• 2020

With the recent advancement of computer hardware and the contribution of open source libraries to facilitate access to artificial intelligence technology, the use of machine learning (ML) and deep learning (DL) technologies in various fields of exploration geophysics has increased. In addition, ML researchers have developed complex algorithms to improve the inference accuracy of various tasks such as image, video, voice, and natural language processing, and now they are expanding their interests into the field of automatic machine learning (AutoML). AutoML can be divided into three areas: feature engineering, architecture search, and hyperparameter search. Among them, this paper focuses on hyperparamter search with Bayesian optimization, and applies it to the problem of facies classification using seismic data and well logs. The effectiveness of the Bayesian optimization technique has been demonstrated using Vincent field data by comparing with the results of the random search technique.

• Journal of Industrial Convergence
• /
• v.21 no.7
• /
• pp.9-17
• /
• 2023

This study was conducted to explore the factors that affect youth life satisfaction in order to find ways to increase their life satisfaction. For this purpose, we utilized data from the National Youth Policy Institute's '2021 Youth Socio-Economic Survey' to study 2,041 youth aged 18-34 as of 2021. The randomForest method was applied to explore various variables that affect youth life satisfaction. A total of 21 variables were analyzed, including demographic and socio-demographic factors and psychological and emotional factors.The results of exploring the variables affecting youth life satisfaction using randomForest are as follows. First, all 21 predictors were found to have an impact on young adults' life satisfaction. Second, the most significant impact on youth life satisfaction was found to be 'work values'. Third, it can be seen that young people's perceptions of society, such as 'political effectiveness' and 'perception of older generation', are also variables that affect youth life satisfaction. Based on these findings, the variables affecting youth life satisfaction are explained and discussion points are presented.

• Journal of the Korea Society for Simulation
• /
• v.28 no.1
• /
• pp.23-39
• /
• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.12
• /
• pp.1300-1312
• /
• 1991

In this paper we syudy the time slot sequence integrity(TSSI) of multi-slot calls in the single-buffered switching networks and analyze their traffic characteristics in terms of traffic mixture ratio, number of random searches for idle time slots, and their blocking probablities. This result can be utilized in the design of wideband switching networks in the single buffered systems, such as TDX IA/B swithcing systems, for accommodating multi slot calls.