• Electrical & Electronic Materials
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• v.9 no.8
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• pp.825-830
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• 1996

In this study, Characteristics of Ultrasonic Transducer fabricated with porous piezoelectric resonator, 3-D underwater object restoration using the self made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network are investigated. The self-made transducer was satisfied the required condition of ultrasonic transducer in water, and the modified SCL neural network using the acquired object data 16*16 low resolution image was used for object restoration of $32{\times}32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with porous piezoelectric resonator could be applied for SONAR system.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.939-941
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• 1999

Recognition rates of 3-D underwater object using the SCL neural network and the SOFM neural network were 91.8% and 95.9%, respectively.

• Electrical & Electronic Materials
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• v.10 no.9
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• pp.916-921
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• 1997

In this study Characteristics of 3-D underwater object recognition independent of translation using the self-made ultrasonic sensor fabricated with porous piezoelectric resonator and presented. The sensor was satisfied with requirement of ultrasonic sensor. The recognition rates for the training data and the testing data are 97.45 and 91.25[%] respectively using the self-made ultrasonic sensor and SCL(Simple Competitive Learning) neural network. According to the experimental results It is believed that the self-made ultrasonic sensor can be applied as sensor of SONAR system.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.68-73
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• 2020

This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 ㎛ CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.326-328
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• 1994

A modified Kohonen's simple Competitive Learning(SCL) algorithm which has binary reinforcement function and a constant adaptation gain is proposed. In contrast to the time-varing adaptation gain of the original Kohonen's SCL algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SCL due to the constant adaptation gain. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than one of the original SCL.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.6
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• pp.456-461
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• 1999

In this study, the characteristics of Ultrasonic Transducer fabricated with PZT-Polymer 1-3 type piezoelectric ceramic/polymer composite are investigated. 3-D underwater object restoration using the self-made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network was presented. The ultrasonic transducer was satisfied with the required condition of commerical ultrasonic transducer in underwater. The modified SCL neural network using the acquired object data $16\times16$ low resolution image was used for object restoration of $32\times32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with PZT-Polymer 1-3 type piezoelectric ceramic/polymer composite could be applied for SONAR system.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.349-352
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• 1994

An object recognition and restoration using ultrasound sensors and neural networks are presented. The planar arrangement of the sensor is used to reduce the interference effects between sensors. The SOFM(Self-Organizing Feature Map) Neural Network and SCL(Simple Competitive Learning) method are learned with the acquired data. Lab experiments were performed that the object can be recognized ed the resolutions of the object can be enhanced by using the small number of the ultrasound array and neural networks.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.