• ETRI Journal
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• 제44권6호
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• pp.915-924
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• 2022

In this paper, we present a two-stage scalable channel estimator (TSCE), a deep learning (DL)-based scalable, and robust channel estimator for wireless cellular networks, which is made up of two DL networks to efficiently support different resource allocation sizes and reference signal configurations. Both networks use the transformer, one of cutting-edge neural network architecture, as a backbone for accurate estimation. For computation-efficient global feature extractions, we propose using window and window averaging-based self-attentions. Our results show that TSCE learns wireless propagation channels correctly and outperforms both traditional estimators and baseline DL-based estimators. Additionally, scalability and robustness evaluations are performed, revealing that TSCE is more robust in various environments than the baseline DL-based estimators.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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• pp.160-166
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• 2002

Since conventional mobile robot control with one module has limitation to solve complex problems, there have been a variety of works on combining multiple modules for solving them. Recently, many researchers attempt to develop mobile robot controllers using artificial life techniques. In this paper, we develop a mobile robot controller using cellular automata based neural networks, where complex tasks are divided to simple sub-tasks and optimal neural structure of each sub-task is explored by genetic algorithm. Neural network modules are combined dynamically using the action selection mechanism, where basic behavior modules compete each other by inhibition and cooperation. Khepera mobile robot simulator is used to verify the proposed model. Experimental results show that complex behaviors emerge from the combination of low-level behavior modules.

• 전자공학회논문지B
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• 제31B권12호
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• pp.124-131
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• 1994

In this paper, we propose a new approach of solving the stereopsis problem with a discrete-time cellular neural network(DTCNN) where each node has connections only with its local neithbors. Because the matching process of stereo correspondence depends on its geometrically local characteristics, the DTCNN is suitable for the stereo correspondence. Moreover, it can be easily implemented in VLSI. Therefore, we employed a two-layer DTCNN with dual templates, which are determined with the back propagation learning rule. Based on evaluation of the proposed approach for several random-dot stereograms, its performance is better than that of the Marr-Poggio algorithm.

• 한국통신학회논문지
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• 제21권4호
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• pp.1008-1018
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• 1996

For assigning channel in a cellular sireless network, we purpose satisfying constraints with a related probability between cells and channels on the channel interference matrix formed by m terminals and n channels. And we purpose to get efficient channel assigning to limited channels in a wireless cell using parallelism of neural networks. In this paper, we solve the problem according to the number of requeirements when channel change with 11-533 procession elements. We demonstrate efficiency of proposed algorithm through same simulations in a specific time period.

• ETRI Journal
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• 제27권3호
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• pp.294-303
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• 2005

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.

• 전자공학회논문지B
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• 제33B권1호
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• pp.153-160
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• 1996

본 논문에서는 이산시간 cellular 신경회로망(DTCNN)의 효율적인 디지털 하드웨어 구조를 제안한다. DTCNN은 셀간의 연결 형태를 결정하는 템플릿(template)내에서 국소적이며 공간 불변적인 특징을 가진다. 이와 같은 DTCNN의 특징과 분산연산 방식을 결합하여 간단한 하드웨어와 적은 연결선으로 DTCNN 하드웨어를 구현하였다. 또한 분산연산의 특징인 비트별 연산 방식을 사용하여 셀 간의 연결을 위한 넓은 버스 폭을 단일 비트로 줄였다. 본 논문에서는 제안한 구조를 프로그래밍이 가능한 FPGA를 사용하여 가변적인 구조를 갖는 DTCNN 보드로 구현하였다.

• 산업경영시스템학회지
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• 제21권48호
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• pp.123-132
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• 1998

The group formation problem of the machine and part is a very important issue in the planning stage of cellular manufacturing systems. This paper investigates Self-Organizing Map(SOM) neural networks approach to machine-part grouping problem. We present a two-phase algorithm based on SOM for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. Output layer in SOM network is one-dimensional structure and the number of output node has been increased sufficiently to spread out the input vectors in the order of similarity. The proposed algorithm performs remarkably well in comparison with many other algorithms for the well-known problems shown in previous papers.

• Biomolecules & Therapeutics
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• 제26권2호
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• pp.93-100
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• 2018

Carbon monoxide (CO) is a gaseous molecule produced from heme by heme oxygenase (HO). Endogenous CO production occurring at low concentrations is thought to have several useful biological roles. In mammals, especially humans, a proper neurovascular unit comprising endothelial cells, pericytes, astrocytes, microglia, and neurons is essential for the homeostasis and survival of the central nervous system (CNS). In addition, the regeneration of neurovascular systems from neural stem cells and endothelial precursor cells after CNS diseases is responsible for functional repair. This review focused on the possible role of CO/HO in the neurovascular unit in terms of neurogenesis, angiogenesis, and synaptic plasticity, ultimately leading to behavioral changes in CNS diseases. CO/HO may also enhance cellular networks among endothelial cells, pericytes, astrocytes, and neural stem cells. This review highlights the therapeutic effects of CO/HO on CNS diseases involved in neurogenesis, synaptic plasticity, and angiogenesis. Moreover, the cellular mechanisms and interactions by which CO/HO are exploited for disease prevention and their therapeutic applications in traumatic brain injury, Alzheimer's disease, and stroke are also discussed.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.