• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.32-37
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• 2009

Recently, electronic mail(E-mail) is the most popular communication manner in our society. In such conventional environments, spam increasingly congested in Internet. In this paper, Chinese spam could be effectively detected using text and image features. Using text features, keywords and reference templates in Chinese mails are automatically selected using genetic algorithm(GA). In addition, spam containing a promotion image is also filtered out by detecting the text characters in images. Some experimental results are given to show the effectiveness of our proposed method.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.85-91
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• 2010

In this paper, we demonstrate an autonomous design of motion control of virtual creatures (called animated robots in this paper) and develop modeling software for animated robots. An animated robot can behave autonomously by using its own sensors and controllers on three-dimensional physically modeled environment. The developed software can enable us to execute the simulation of animated robots on physical environment at any time during the modeling process. In order to simulate more realistic world, an approximate fluid environment model with low computational costs is presented. It is shown that a combinatorial use of neural network implementation for controllers and the genetic algorithm (GA) or the particle swarm optimization (PSO) is effective for emerging more realistic autonomous behaviours of animated robots.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.231-234
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• 2001

The torque of SRM depends on phase current and the derivative of inductance. But the inductance of SRM is nonlinearly changed according to rotor position angle and phase current because of saturation in magnetic circuit. Therefore this has a concern in torque ripple and speed variation, and it is difficult to control the desired torque The torque of SRM depends on phase current and the derivative of inductance. But the inductance of SRM is nonlinearly changed according to rotor position angle and phase current because of saturation in magnetic circuit, and it is difficult to control the desired torque. This paper proposes an optimization control scheme by adjusting both the turn-on and turn-off angle according to high efficiency points which are simulated by GA-Neural Network, which is used to simulate the reasonable switching angle which is nonlinearly varied with rotor speed and load.

• Korean Journal of Optics and Photonics
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• v.2 no.4
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• pp.233-241
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• 1991

Structures, fabrication, and characteristics of top-surface-emitting GaAs four quantum well microlaser are described. The microlasers have good room-temperautre CW characteristivs. The maximum CW laser output is >3mW from a 30 $\mu\textrm{m}$ diameter microlaser and the maximum differential quantum efficiency is >70% from a 10 $\mu\textrm{m}$ diameter microlaser. Active surface emitting laser logic devices are designed and fabricated as a discrete version of a top-surface-emitting laser and heterojunction phototransistor. The active surface emitting laser logic device have high optical gain (>20 overall, >200 differential) and very high on/off ratio. Two-dimensional arrays of top-surface-emitting microlasers and active surface emitting laser logic devices will be critical elements for optical computing, photonic switching and neural network applications.

• Computers and Concrete
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• v.24 no.3
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• pp.271-282
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• 2019

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to elevated temperatures. Such changes adversely alter the composition of concrete and oftentimes lead to fire-induced explosive spalling. Spalling is a multidimensional, complex and most of all sophisticated phenomenon with the potential to cause significant damage to fire-exposed concrete structures. Despite past and recent research efforts, we continue to be short of a systematic methodology that is able of accurately assessing the tendency of concrete to spall under fire conditions. In order to bridge this knowledge gap, this study explores integrating novel artificial intelligence (AI) techniques; namely, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), together with traditional statistical analysis (multilinear regression (MLR)), to arrive at state-of-the-art procedures to predict occurrence of fire-induced spalling. Through a comprehensive datadriven examination of actual fire tests, this study demonstrates that AI techniques provide attractive tools capable of predicting fire-induced spalling phenomenon with high precision.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.555-558
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• 2020

본 논문에서는 입력 영상에 담긴 다양한 물체들과 그들 간의 관계를 효과적으로 탐지하여, 하나의 장면 그래프로 표현해내는 새로운 심층 신경망 모델을 제안한다. 제안 모델에서는 물체와 관계의 효과적인 탐지를 위해, 합성 곱 신경망 기반의 시각 맥락 특징들뿐만 아니라 언어 맥락 특징들을 포함하는 다양한 멀티 모달 맥락 정보들을 활용한다. 또한, 제안 모델에서는 관계를 맺는 두 물체 간의 상호 의존성이 그래프 노드 특징값들에 충분히 반영되도록, 그래프 신경망을 이용해 맥락 정보를 임베딩한다. 본 논문에서는 Visual Genome 벤치마크 데이터 집합을 이용한 비교 실험들을 통해, 제안 모델의 효과와 성능을 입증한다.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.81-86
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• 2023

Recently, there has been an increased demand for light-emitting diode (LED) due to the growing emphasis on environmental protection. However, the use of GaN-based sapphire in LED manufacturing leads to the generation of defects, such as dislocations caused by lattice mismatch, which ultimately reduces the luminous efficiency of LEDs. Moreover, most inspections for LED semiconductors focus on evaluating the luminous efficiency after packaging. To address these challenges, this paper aims to detect defects at the wafer stage, which could potentially improve the manufacturing process and reduce costs. To achieve this, image processing and deep learning-based defect detection techniques for Sapphire Epi-Wafer used in Green LED manufacturing were developed and compared. Through performance evaluation of each algorithm, it was found that the deep learning approach outperformed the image processing approach in terms of detection accuracy and efficiency.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.145-146
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• 2024

본 논문에서는 최근 몇 년 동안 스마트시티 인프라 투자가 크게 성장하였으며 글로벌 스마트 쓰레기통 시장은 성장 가능성이 높을 것으로 예상된다. 본 논문에서는 이에 발맞추어 CNN과 MQTT를 활용한 스마트 쓰레기통을 제작하였다. 쓰레기의 종류를 구별하고 해당되는 쓰레기통의 뚜껑을 골라 여는 것은 현대인의 생활에서 비효율을 야기한다. 이러한 문제를 해결하고자 CNN을 통한 효율적인 분류와 MQTT를 통한 통신, 센서들을 활용한 더 나은 쓰레기 수거 방식을 제공한다. 스마트 쓰레기통으로 일상을 더욱 편하고 효율적이게 만드는 데 기여하고자 한다.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.