• 전자통신동향분석
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• 제38권5호
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• pp.12-22
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• 2023

The development of neural networks is evolving towards the adoption of transformer structures with attention modules. Hence, active research focused on extending the concept of lightweight neural network algorithms and hardware acceleration is being conducted for the transition from conventional convolutional neural networks to transformer-based networks. We present a survey of state-of-the-art research on lightweight neural network algorithms and hardware architectures to reduce memory usage and accelerate both inference and training. To describe the corresponding trends, we review recent studies on token pruning, quantization, and architecture tuning for the vision transformer. In addition, we present a hardware architecture that incorporates lightweight algorithms into artificial intelligence processors to accelerate processing.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.

• 한국정밀공학회지
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• 제20권7호
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized teaming architecture. It is proposed a learning controller consisting of too neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by three independent wheels.

• 전자공학회논문지A
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• 제30A권9호
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• pp.71-79
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• 1993

This paper proposes a new test pattern generation methodology for combinational logic circuits using neural networks based on a modular structure. The CUT (Circuit Under Test) is described in our gate level hardware description language. By conferring neural database, the CUT is compiled to an ATPG (Automatic Test Pattern Generation) neural network. Each logic gate in CUT is represented as a discrete Hopfield network. Such a neual network is called a gate module in this paper. All the gate modules for a CUT form an ATPG neural network by connecting each module through message passing paths by which the states of modules are transferred to their adjacent modules. A fault is injected by setting the activation values of some neurons at given values and by invalidating connections between some gate modules. A test pattern for an injected fault is obtained when all gate modules in the ATPG neural network are stabilized through evolution and mutual interactions. The proposed methodology is efficient for test generation, known to be NP-complete, through its massive paralelism. Some results on combinational logic circuits confirm the feasibility of the proposed methodology.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.235-240
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.210-215
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• 1999

In this paper, a new approach to the dynamic control technique for track vehicle system using fuzzy-neural network control technique is proposed. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.