• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.311-315
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• 2006

"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

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1114-1117
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• 1998

Recent applications of neural networks to power system fault diagnosis have provided positive results and have shown advantages in process speed over conventional approaches. This paper describes the application of neural network to fault detection and classification in distribution lines using the fundamental component, 2-5th harmonics index, even and odd harmonics index, and zero phase current. The Electromagnetic Transients Program (EMTP) is used to obtain fault patterns for the training and testing of neural networks. The proposed fault detection and classification method in distribution lines is obtained by analysing the difference among normal, HIF, ground fault, short circuit fault condition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.27-37
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• 1991

In this paper, the novel PLD (Preceding Layer Driven) MLP (Multi Layer Perceptron) neural network model and its learning algorithm is described. This learning algorithm is different from the conventional. This integer weights and hard limit function are used for synaptic weight values and activation function, respectively. The entire learning process is performed by layer-by-layer method. the number of layers can be varied with difficulty of training data. Since the synaptic weight values are integers, the synapse circuit can be easily implemented with CMOS. PLD MLP neural network was applied to English Characters, arbitrary waveform generation and spiral problem.

• Proceedings of the KAIS Fall Conference
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• 2004.06a
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• pp.217-220
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• 2004

'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 loaming 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.

• Journal of the Korea Safety Management & Science
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• v.22 no.2
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• pp.47-55
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• 2020

This study analyzed ignition probability about Lithium-polymer batteries of what variously were being produced wearable devices recently. The study analyzed ignition probability by PCM(Protection Circuit Module) operating state and overcharged, over-discharged, exposed to high temperatures of Lithium polymer batteries, analyzing wearable devices on the market. Then it classified experimental results to implement analysis comparison about weight, X-ray imaging, battery decomposition. With these experiments, the study analyzed combustion-possibility and fire patterns. These statistics will be used to measure and verify the cause of a fire when identify wearable devices using Lithium-polymer batteries.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.12 no.1
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• pp.53-62
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• 2018

In this study, we developed a smart 1RM system for national health management and physical fitness, which enables quantitative 1RM measurement in various types of exercise using digital pulley technology, and to test the effect on training by using it. We developed the smart 1RM system, which is composed of portable muscle strength measuring device, Bluetooth communication based mobile phone data transmission and circuit diagram, and height adjustable system body. We recruited the 30 participants with 20th aged and divided into training and non-performing groups with 15 participants randomly. The participants performed 5 sets of elbow, lumbar, knee extension / flexion 10 times using smart 1RM system and the experimental period was 3 days a week for a total of 8 weeks. The experimental results showed that the maximum strength of the elbow, lumbar, and knee joints was significantly improved before and after maximal muscle strength training in the training group. Oxygen intakes during 1RM exercise mode showed 10.91% than endurance. To verify the validity of the smart 1RM maximal strength data, the reliability was 0.895 (* p <0.00). This study can be applied to the early rehabilitation treatment of the elderly and rehabilitation patients more quantitatively using the national health care.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.413-423
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• 2024

Sixteen male college students were divided into two groups: a face-to-face group(n=8) and a real-time non-face-to-face exercise group(n=8), engaging in 30minute sessions twice a week for a duration of 8 weeks. Body composition and physical strength were measured as dependent variables before and after the home training period. For data analysis, a two-way ANOVA with repeated measures was conducted to evaluate the effects on body composition and physical strength, considering differences in exercise methods and measurement periods. Post hoc analysis using Bonferroni correction was applied. To compare the mean difference in change between groups, the pre-post difference was calculated, and an independent t-test was performed. The statistical significance level was set at p<.05. The results showed that 8 weeks of home training led to an increase in skeletal muscle mass and improvements in muscle strength, muscular endurance, and cardiorespiratory endurance in male college students, regardless of whether they participated in face-to-face or real-time non-face-to-face exercise. Moreover, there was no significant difference in exercise effectiveness between the face-to-face and real-time non-face-to-face exercise methods. Thus, these findings suggest that real-time non-face-to-face exercise can be as effective as face-to-face exercise in enhancing skeletal muscles and physical strength in male college students. Additionally, if a real-time non-face-to-face exercise program is validated for individuals with mobility issues or the elderly, it could serve as an effective alternative for those who face challenges in participating in face-to-face exercise sessions.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1251-1264
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• 2018

The systematic and effective design method of a Class-D current-source resonant inverter for use in an induction cooker with zero-ripple line current is presented. The design procedure is based on the principle of the Class-D current-source resonant inverter with a simplified load network model that is a parallel equivalent circuit. An induction load characterization is obtained from a large-signal excitation test-bench based on parallel load network, which is the key to an accurate design for the induction cooker system. Accordingly, the proposed scheme provides a systematic, precise, and feasible solution than the existing design method based on series-parallel load network under low-signal excitation. Moreover, a zero-ripple condition of utility-line input current is naturally preserved without any extra circuit or control. Meanwhile, a differential-mode input electromagnetic interference (EMI) filter can be eliminated, high power quality in utility-line can be obtained, and a standard-recovery diode of bridge-rectifier can be employed. The step-by-step design procedure explained with design example. The devices stress and power loss analysis of induction cooker with a parallel load network under large-signal excitation are described. A 2,500-W laboratory prototype was developed for $220-V_{rms}/50-Hz$ utility-line to verify the theoretical analysis. An efficiency of the prototype is 96% at full load.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.4
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• pp.47-56
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• 2000

Because of the junior college students' learning ability stands on a relatively low level, this research is accomplished to inspire students with further desires, considering students' learning ability and desire. The curriculum of junior college is organized with three parts(electronic communication tool, micro processer, integrated circuit design): the electronic communication tool and micro processer is carried out, and the training for the design skill on semiconductor devices will be focused. The main focus is reflected on the worldwide trend on the design engineering of semiconductor devices and considered for the market establishment on design engineers trained by the lab-oriented practice as well as fundamental of semiconductor technology.