• Structural Engineering and Mechanics
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• v.39 no.1
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• pp.147-168
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• 2011

To ensure safety and long term performance, structural control has rapidly matured over the past decade into a viable means of limiting structural responses to strong winds and earthquakes. Nonlinear response history analysis requires rigorous procedure to compute seismic demands. Therefore the simplified nonlinear analysis procedures are useful to determine performance of the structure. In this investigation, application of improved capacity demand diagram method in the control of structural system is presented for the first time. Developed pole assignment method (DPAM) in structural systems control is introduced. Genetic algorithm (GA) is employed as an optimization tool for minimizing a target function that defines values of coefficient matrices providing the placement of actuators and optimal control forces. The ground acceleration is modified under induced control forces. Due to this, performance of structure based on improved nonlinear demand diagram is selected to threshold of nonlinear behavior of structure. With small energy consumption characteristics, semi-active devices are especially attractive solutions for limiting earthquake effects. To illustrate the efficiency of DPAM, a 30-story steel moment frame structure employing the semi-active control devices is applied. In comparison to the widely used linear quadratic regulation (LQR), the DPAM controller was shown to be just as effective and better in the reduction of structural responses during large earthquakes.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.21-31
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• 2017

In this paper, the Zynq system-on-chip (SoC) platform is used to design an adaptive noise reduction and edge-detection system using partial reconfiguration. Filters are implemented in a partially reconfigurable (PR) region to provide high computational complexity in real-time, 1080p video processing. In addition, partial reconfiguration enables better utilization of hardware resources in the embedded system from autonomous replacement of filters in the same PR region. The proposed edge-detection system performs adaptive noise reduction if the noise density level in the incoming video sequences exceeds a given threshold value. Results of implementation show that the proposed system improves the accuracy of edge-detection results (14~20 times in Pratt's Figure of Merit) through self-reconfiguration of filter bitstreams triggered by noise density level in the video sequences. In addition, the ZyCAP controller implemented in this paper enables about 2.1 times faster reconfiguration when compared to a PCAP controller.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.194-201
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• 2004

Optical fibers are indispensable fer optical communication systems that transmit large volumes of data at high speed. But the aligning technology under the sub-micron accuracy is required for the precise axis adjustment and connection. For the purpose of precise alignment of the optical arrays, in this research, we have developed the 12-axis(with 8 automated axis and 4 manual axis) automatic optical fiber alignment system including the image processing-based searching system, the automatic loading system using the robot and the suction toot and the automatic UV bonding system. In order to obtain the sub-micron alignment accuracy, two 4-axis PC-based motion controllers and the two 50nm resolution 6-aixs micro-stage actuated by micro stepping motors are adopted. The fiber aligning procedure consists of two steps. Firstly, the optical wave guide and an input optical array are aligned by the 6-axis input micro-stage with the IR camera. The image processing technique is introduced to reduce primary manual aligning time and result in achieving the 50% decrease of aligning time. Secondly, the IR camera is replaced by the output micro-stage and a wave guide and two optical arrays are aligned simultaneously before the laser power intensity delivered to the optical powermeter reached the threshold value. When the aligning procedure is finished, the wave guide and arrays are W bonded. The automatic loading/unloading system is also introduced and the entire wave guide handing time is reduced significantly compared to the former commercial aligning system.

• Journal of Sensor Science and Technology
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• v.23 no.5
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• pp.342-348
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• 2014

This paper introduced a novel multi-gas sensor detector with simple signal processing algorithm. This device was evaluated by investigating the characteristics of combustible materials using fire-generated smell and smoke. Plural sensors including TGS821, TGS2442, and TGS260X were equipped to detect carbon monoxide, hydrogen gas, and gaseous air contaminants which exist in cigarette smoke, respectively. Signal processing algorithm based on the difference of response times in fire-generated gases was implemented with early and accurately fire detection from multiple gas sensing signals. All fire experiments were performed in a virtual fire chamber. The cigarette, cotton fiber, hair, polyester fiber, nylon fiber, paper, and bread were used as a combustible material. This analyzing software and sensor controlling algorithm were embedded into 8-bit micro-controller. Also the detected multiple gas sensor signals were simultaneously transferred to the personnel computer. The results showed that the air pollution detecting sensor could be used as an efficient sensor for a fire detector which showed high sensitivity in volatile organic compounds. The proposed detecting algorithm may give more information to us compared to the conventional method for determining a threshold value. A fire detecting device with a multi-sensor is likely to be a practical and commercial technology, which can be used for domestic and office environment as well as has a comparatively low cost and high efficiency compared to the conventional device.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.871-877
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• 2004

In this paper, for manufacturing a custom-made shoes, shape of foot acquired three-dimensional measurement device which makes shoe-last data for needing a custom-made shoes is founded on artificial intelligence technique and it shows method restoring to the original shape in optimized state. the developed system for this study is based on PC which uses existing three dimensional measurement method. And it gains shoe-last and data of foot shape going through 8 CCD(Charge Coupled Device) Which equipped top and bottom, right and left sides and 4 lasers which also equipped both sides and upper and lower sides. The acquired data are processed image processing algorithm using artificial intelligence technique. And result of data management is better quality of removing noise than other system not using artificial intelligence technique and it can simplify post-processing. So, this paper is constituted hardware and software system and it used neural network for determining threshold value, when input image on pre-processing step is being stage of image binarization and present that results.

• Journal of Biomedical Engineering Research
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• v.22 no.2
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• pp.157-163
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• 2001

본 논문에서는 후취자극 제어장치를 이용하여 후각자극에 대한 인간의 뇌의 활성화 영역을 뇌기능자기공명영상(functional magnetic resonance imaging : fMRI)장치로 측정 또는 가시화하고 이의 임상적용에 대한 기초자료를 마련하고자 하였다. 우선 후각에 이상이 없고 코 수술 경험이 없는 오른손잡이 피험자 4명을 대상으로 5번에 걸쳐 Echo Plannar Imaging(EPI)에 의한 혈액산소농도의존(blood oxygen level dependent : BOLD)법을 이용하여 후각자극에 의한 뇌기능자기공명영상 실험을 수행하였다. 후각자극은 MRI 장치에서 사용할 수 있도록 제작된 후취제어장치를 사용하였으며, 제시된 향은 천연 향의 일종인 lavender-like fragrance를 사용하였다. 향의 제시는 후각의 피로도를 감안하여 3회의 휴식기관과 2회의 자극기간을 각 30초씩 번갈아 시행하였으며, 동시에 5초 간격으로 각 절편 당 30 영상을 연속적으로 획득하였다. Correlation법으로 0.4∼0.7의 문턱치(threshold)범위에서 통계 처리된 뇌의 활성화 영상은 EPI영상과 같은 부위의 T1 강조영상에 overlapping 시켰다. 호흡에 의한 artifact를 제거하기 위해 실험실에 만든 장치로 호흡을 측정하여 post-processing 할 때 반영하였다. 이렇게 얻어진 fMRI 영상의 신호변화를 관찰하여 활성 영역의 위치를 분석하였다. 그 결과 후각자극에 의해 뇌의 전두엽 피질(frontal cortex), 소뇌(cerebellum), 그리고 뇌교(pons)에서 활성화된 신호를 발견할 수 있었다. 또한, 측두엽(temporal lobe)과 뇌섬(insula)에서도 의미 있는 신호가 관찰되었다. 그러나, 일차 후각영역인 piriform cortex와 entorhinal cortex, amygdaloid complex, 그리고 이차후각영역인 orbitofrontal cotex에서는 그다지 많은 빈도로 신호가 발견되지 않았다. 결론적으로 BOLD법을 이용한 fMRI에 의하여 후각자극에 대한 뇌의 활성화영역을 관찰할 수 있었으며, 후각자극에 대한 뇌의 기능을 연구하는데 있어서 중요한 정량적 자료를 제공할 수 있다는 점을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.10
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• pp.77-83
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• 2004

In low-power VLSI systems, it is effective to suppress leakage current by shutting down megablocks in idle states. Recently, multi-threshold voltage CMOS (MTCMOS) is widely accepted to shutdown power supply. However, it requires short wake-up time as operating frequency increases. This causes large current surge during wake-up process, and it often leads to system malfunction due to severe Power line noise. In this paper, a novel wake-up scheme is proposed to solve this problem. It exploits pipelined wake-up strategy in several stages that reduces maximum current on the power line and its corresponding power line noise. To evaluate its efficiency, the proposed scheme was applied to a multiplier block in the Compact Flash memory controller chip. Power line noise in shutdown and wake-up process was simulated and analyzed. From the simulation results, the proposed scheme was proven to greatly reduce the power line noise compared with conventional schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.837-852
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• 2021

Recurrent neural network (RNN) architectures have been used for language modeling (LM) tasks that require learning long-range word or character sequences. However, the RNN architecture is still suffered from unstable gradients on long-range sequences. To address the issue of long-range sequences, an attention mechanism has been used, showing state-of-the-art (SOTA) performance in all LM tasks. A differentiable neural computer (DNC) is a deep learning architecture using an attention mechanism. The DNC architecture is a neural network augmented with a content-addressable external memory. However, in the write operation, some information unrelated to the input word remains in memory. Moreover, DNCs have been found to perform poorly with low numbers of weight parameters. Therefore, we propose a robust memory deallocation method using a limited retention vector. The limited retention vector determines whether the network increases or decreases its usage of information in external memory according to a threshold. We experimentally evaluate the robustness of a DNC implementing the proposed approach according to the size of the controller and external memory on the enwik8 LM task. When we decreased the number of weight parameters by 32.47%, the proposed DNC showed a low bits-per-character (BPC) degradation of 4.30%, demonstrating the effectiveness of our approach in language modeling tasks.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.817-824
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• 2021

The SDN(Software Defined Networking) technology, which appeared to overcome the limitations of the existing network system, resolves the rigidity of the existing system through the separation of HW and SW in network equipment. These characteristics of SDN provide wide scalability beyond hardware-oriented network equipment, and provide flexible load balancing policies in data centers of various sizes. In the meantime, many studies have been conducted to apply the advantages of SDN to data centers and have shown their effectiveness. The method mainly used in previous studies was to periodically check the server load and perform load balancing based on this. In this method, the more the number of servers and the shorter the server load check cycle, the more traffic increases. In this paper, we propose a new load balancing technique that can eliminate unnecessary traffic and manage server resources more efficiently by reporting to the controller when a specific level of load occurs in the server to solve this limitation.