• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.664-665
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• 2013

본 논문에서는 멀티미디어프로세서 구성의 한가지 방법을 제안하였다. 제안한 멀티미디어프로세서는 각각의 문자, 소리, 비디오를 한 개의 칩안에서 다룰 수 있으며, 멀티미디어의 특징인 인터렉티브의 기능을 갖고 있다. 특히 제안한 멀티미디어프로세서는 소프트웨어 없이도 메모리매상의 어드레싱이 가능하다. 제아난 멀티미디어프로세서는 가상현실에 적용이 가능하다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.277-283
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• 2013

In this study, we propose a novel method for modeling dynamic hysteresis. Hysteresis is a widespread phenomenon that is observed in many physical systems. Many different models have been developed for representing a hysteretic system. Among them, the Duhem model is a classical nonlinear dynamic hysteresis model satisfying the properties of hysteresis. The purpose of this work is to develop a novel method that expresses the local dynamics of the Duhem model by a linear system model. Our approach utilizes a certain type of fuzzy system that is based on Takagi-Sugeno (T-S) fuzzy models. The proposed T-S fuzzy Duhem model is achieved by fuzzy blending of the linear system model. A simulated example applied to shape memory alloy actuators, which have typical hysteretic properties, illustrates the applicability of our proposed scheme.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.82-87
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• 2020

In this paper, we introduce the novel idea to improve the B/W usage efficiency of on-chip networks used for TAM to test multiple DRAMs. In order to avoid the local bottleneck of test packets caused by an ATE, we make test patterns using microcode-based instructions within ATE and adopt a test bus to transmit test responses from DRAM DFT (Design for Testability) called Test Generator (TG) to ATE. The proposed test platform will contribute to increasing the test economics of memory IC industry.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.37-40
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• 2004

SONOS를 이용한 전하트랩형 플래시 메모리를 통상의 0.35um CMOS 공정을 이용하여 제작하였으며 그 구조는 소스를 공통(CSL. Common Source Line)으로 사용하는 NOR형으로 하였다. 기존의 공정을 그대로 이용하면서 멀티 비트 동작을 통한 실질적 집적도 향상을 얻을 수 있다면 그 의미가 크다고 하겠다. 따라서 본 연구에서는CSL-NOR형 플래시 구조에서 멀티 비트을 구현하기위한 최적의 프로그램/소거/읽기 전압 조건을 구하여 국소적으로 트랩된 전하의 분포를 전하펌핑 방법을 이용하여 조사하였다. 또한 이 방법을 이용하여 멀티 비트 동작 시 문제점으로 제시된 전하의 측면확산을 측정하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.98-101
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• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• 전자공학회논문지B
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• 제31B권11호
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• pp.151-162
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• 1994

다차원 시스템(multidimensional system)에 대한 정확한 모델링을 위해 “자율 분산 신경망(Self-organized Distirbuted Networks, SODN)”을 제안하였다. 제안한 신경망은 자율 신경망(Self-organized Networks)과 다수의 소규모 다층 신경망(Multilayer Neural Networks)이 조합되어 지역적 병렬 학습을 수행하는 부분 학습망으로서 학습 속도가 빠르고 학습의 정밀도를 높일 수 있으며 타 부분망 학습에서 문제가 되는 과다한 학습 메모리 소요와 학습되니 않은 영역에 대한 낮은 일반화능력 등의 문제가 보완된 새로운 신경망이다. 학습 실험 결과, 제안한 신경망은 기존의 다층 신경망과 RBF(Radial Basis Function) 신경망에 비해서 우수한 성능을 보였다.

• 제어로봇시스템학회논문지
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• 제12권7호
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• pp.693-699
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• 2006

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4412-4428
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• 2018

The purpose of this work is to solve the problem of representing an entire video using Convolutional Neural Network (CNN) features for human action recognition. Recently, due to insufficient GPU memory, it has been difficult to take the whole video as the input of the CNN for end-to-end learning. A typical method is to use sampled video frames as inputs and corresponding labels as supervision. One major issue of this popular approach is that the local samples may not contain the information indicated by the global labels and sufficient motion information. To address this issue, we propose a binary hashing method to enhance the local feature extractors. First, we extract the local features and aggregate them into global features using maximum/minimum pooling. Second, we use the binary hashing method to capture the motion features. Finally, we concatenate the hashing features with global features using different normalization methods to train the classifier. Experimental results on the JHMDB and MPII-Cooking datasets show that, for these new local features, binary hashing mapping on the sparsely sampled features led to significant performance improvements.