• Journal of IKEEE
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• v.27 no.4
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• pp.636-643
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• 2023

In this paper, a high-speed calibration method for phased array antennas in the far-field is presented A max calibration, which is a simplification of the rotating-element electric-field vector (REV) method that calibrates each antenna element only through received power, and a method of grouping calibrations by sub-array unit rather than each antenna element were proposed. Using the Proximal Policy Optimization (PPO) algorithm, we found a partitioning optimized for the distribution of phased array antennas and calibrated it on a subarray basis. An adaptive max calibration method that allows faster calibration than the conventional method was proposed and verified through simulation. Not only is the gain of the phased array antenna higher while calibration is being made to the target, but the beam pattern is closer to the ideal beam pattern than the conventional method.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.43-51
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• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.69-77
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• 1998

This paper presents a new efficient and stable multilevel partitioning algorithm for VLSI circuit design. The performance of multilevel partitioning algorithms that are proposed to enhance the performance of previous iterative-improvement partitioning algorithms for large scale circuits, depend on choice of construction methods for partition hierarchy. As the most of previous multilevel partitioning algorithms forces experimental constraints on the process of hierarchy construction, the stability of their performances goes down. The lack of stability causes the large variation of partition results during multiple runs. In this paper, we minimize the use of experimental constraints and propose a new method for constructing partition hierarchy. The proposed method clusters the cells with the connection status of the circuit. After constructing the partition hierarchy, a partition improvement algorithm, HYIP$^{[11]}$ using hybrid bucket structure, unclusters the hierachy to get partition results. The experimental results on ACM/SIGDA benchmark circuits show improvement up to 10-40% in minimum outsize over the previous algorithm $^{[3] [4] [5] [8] [10]}$. Also our technique outperforms ML$^{[10]}$ represented multilevel partition method by about 5% and 20% for minimum and average custsize, respectively. In addition, the results of our algorithm with 10 runs are better than ML algorithm with 100 runs.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.525-530
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• 2006

This paper presents an efficient input variable selection method using both fixed-point independent component analysis(FP-ICA) and adaptive partition mutual information(AP-MI) estimation. FP-ICA which is based on secant method, is applied to quickly find the independence between input variables. AP-MI estimation is also applied to estimate an accurate dependence information by equally partitioning the samples of input variable for calculating the probability density function(PDF). The proposed method has been applied to 2 problems for selecting the input variables, which are the 7 artificial signals of 500 samples and the 24 environmental pollution signals of 55 samples, respectively The experimental results show that the proposed methods has a fast and accurate selection performance. The proposed method has also respectively better performance than AP-MI estimation without the FP-ICA and regular partition MI estimation.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.57-65
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• 2007

In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Fan Ye et al. proposed that statistical en-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a partition value represents a trade off between resilience and energy where the partition value is the total number of partitions which global key pool is divided. If every partition are compromised by an adversary, SEF disables the filtering capability. Also, when an adversary has compromised a very small portion of keys in every partition, the remaining uncompromised keys which take a large portion of the total cannot be used to filter false reports. We propose a fuzzy-based adaptive partitioning method in which a global key pool is adaptively divided into multiple partitions by a fuzzy rule-based system. The fuzzy logic determines a partition value by considering the number of compromised partitions, the energy and density of all nodes. The fuzzy based partition value can conserve energy, while it provides sufficient resilience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3286-3300
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• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.143-156
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• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.511-518
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• 2007

This paper presents an efficient face recognition method by using both centroid shift and mutual information estimation of images. The centroid shift is to move an image to center coordinate calculated by first moment, which is applied to improve the recognition performance by excluding the needless backgrounds in face image. The mutual information which is a measurements of correlations, is applied to efficiently measure the similarity between images. Adaptive partition mutual information(AP-MI) estimation is especially applied to find an accurate dependence information by equally partitioning the samples of input image for calculating the probability density function(PDF). The proposed method has been applied to the problem for recognizing the 48 face images(12 persons * 4 scenes) of 64*64 pixels. The experimental results show that the proposed method has a superior recognition performances(speed, rate) than a conventional method without centroid shift. The proposed method has also robust performance to changes of facial expression, position, and angle, etc. respectively.

• KSCI Review
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• v.15 no.1
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• pp.25-29
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• 2007

무선 센서 네트워크의 다양한 응용분야에서, 일어나는 심각한 보안 위협 중 하나가 공격자가의 노드 훼손을 통해 발생하는 보안정보 훼손된 및 위조된 보고서의 삽입이다. 최근에 Fan Ye 등은 이런 위협에 대한 대안으로 전역 키 풀을 전체 센서네트워크에 나누어서 할당하고, 전송 경로 중에 있는 노드들이 미리 할당받은 각자의 보안정보인 인증키를 이용해서 위조 보고서를 판단하는 통계적 여과기법을 제안하였다. 그러나 이 기법에서는 노드들의 훼손으로 인한 일부 인증키가 훼손 됐을 시 고정된 몇 개의 구획으로 나뉜 전역 키 풀 때문에 훼손된 키의 구획에 속해 있는 나머지 훼손되지 않은 인증 키들이 여과과정에서 인증키로써의 기능을 할 수 없게 된다. 본 논문에서는 전역 키 풀의 분할 여부 결정에 퍼지 로직을 적용하여 전역 키 풀을 네트워크 상황에 맞추어 나누는 적응형 분할 결정 기법을 제안한다. 전역 키 풀의 구획은 오염된 구획의 비율. 오염된 키의 비율, 노드의 에너지 비율을 고려하여 퍼지로직에 의해 분할 여부를 결정한다.