• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.430-435
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.185-188
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• 2006

생체 신경세포를 모방하는 진동성 신경 셀을 아날로그 집적회로로 설계한다. 진동성 신경셀은 입력신호 취합을 위한 취합회로와 신경 펄스 발생회로, 신경펄스 발생을 위한 범프회로와 트랜스콘덕터로 이루어지는 부성저항 블록으로 구성된다. $0.35{\mu}m$ 2중 폴리 공정 파라미터를 이용하여 SPICE 모의실험을 실시하여 입력 신호 유무 및 크기변화에 따른 출력 펄스의 발생을 얻어 진동성 신경회로의 가능성을 확인한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.14-24
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• 2002

In this paper, a design of the pulsewidth-modulation(PWM) neural network with both retrieving and learning function is proposed. In the designed PWM neural system, the input and output signals of the neural network are represented by PWM signals. In neural network, the multiplication is one of the most commonly used operations. The multiplication and summation functions are realized by using the PWM technique and simple mixed-mode circuits. Thus, the designed neural network only occupies the small chip area. By applying some circuit design techniques to reduce the nonideal effects, the designed circuits have good linearity and large dynamic range. Moreover, the delta learning rule can easily be realized. To demonstrate the learning capability of the realized PWM neural network, the delta learning nile is realized. The circuit with one neuron, three synapses, and the associated learning circuits has been designed. The HSPICE simulation results on the two learning examples on AND function and OR function have successfully verified the function correctness and performance of the designed neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.804-810
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• 2004

There are two important things for the general purpose neural network processor. The first is a capability to build various structures of neural network, and the second is to be able to support suitable learning method for that neural network. Some way to process various learning algorithms is required for on-chip learning, because the more neural network types are to be handled, the more learning methods need to be built into. In this paper, an improved hardware structure is proposed to compute various kinds of learning algorithms flexibly. The hardware structure is based on the existing modular neural network structure. It doesn't need to add a new circuit or a new program for the learning process. It is shown that rearrangements of the existing processing elements can produce several neural network learning modules. The performance and utilization of this module are analyzed by comparing with other neural network chips.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.

• Korean Journal of Biological Psychiatry
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• v.18 no.1
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• pp.15-24
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• 2011

Mood disorder is unlikely to be a disease of a single brain region or a neurotransmitter system. Rather, it is now generally viewed as a multidimensional disorder that affects many neural pathways. Growing neuroimaging evidence suggests the anterior cingulate-pallidostriatal-thalamic-amygdala circuit as a putative cortico-limbic mood regulating circuit that may be dysfunctional in mood disorders. Brain-imaging techniques have shown increased activation of mood-generating limbic areas and decreased activation of cortical areas in major depressive disorder(MDD). Furthermore, the combination of functional abnormalities in limbic subcortical neural regions implicated in emotion processing together with functional abnormalities of prefrontal cortical neural regions probably result in the emotional lability and impaired ability to regulate emotion in bipolar disorder. Here we review the biological correlates of MDD and bipolar disorder as evidenced by neuroimaging paradigms, and interpret these data from the perspective of endophenotype. Despite possible limitations, we believe that the integration of neuroimaging research findings will significantly advance our understanding of affective neuroscience and provide novel insights into mood disorders.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.246-254
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• 2001

In this paper, we described an approach to the automation of visual inspection of BGA solder joint defects of surface mounted components on printed circuit board by using neural network. Inherently, the BGA solder joints are located underneath its own package body, and this induces a difficulty of taking good image of the solder joints by using conventional imaging systems. To acquire the cross-sectional image of BGA sol-der joint, X-ray cross-sectional imaging method such as laminography and digital tomosynthesis has been cur-rently utilized. However, the cross-sectional image obtained by using laminography or DT methods, has inher-ent blurring effect and artifact. This problem has been a major obstacle to extract suitable features for classifi-cation. To solve this problem, a neural network based classification method is proposed int his paper. The per-formance of the proposed approach is tested on numerous samples of printed circuit boards and compared with that of human inspector. Experimental results reveal that the method provides satisfactory perform-ance and practical usefulness in BGA solder joint inspection.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.111-118
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• 2001

In this paper, in order to implement the multi-layered perceptron neural network using pure digital logic circuit model, we propose the new logic neuron structure, the digital canonical multi-layered logic neural network structure, and the multi-stage multi-layered logic neural network structure for pattern recognition applications. And we show that the proposed approach provides an incremental additive learning algorithm, which is very simple and effective.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.241-246
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• 2020

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.147-149
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• 2007

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.