• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1637-1638
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• 2006

HVAC 시스템은 쾌적하고 깨끗한 운전환경을 만들어 줌으로써 운전자에게 향상된 안락성과 안전성을 제공한다. 이때 센서는 시시각각으로 변화하는 차실 내외의 환경변화에 대한 정보를 검출하여 HVAC 제어 유니트에 제공한다. 현재 HVAC 시스템에 사용되고 있는 후막 가스센서는 소자 크기와 소비전력이 크고, 제작공정이 까다로워 생산성이 낮은 단점이 있다. 이와 같은 문제점을 해결하기 위해서 최근에는 초소형화, 저소비전력, 대량생산에 의한 저가격화가 가능한 MEMS 가스센서의 연구개발이 활발히 진행되고 있다. 본 연구에서는 MEMS 구조체를 이용한 마이크로 가스센서를 설계 및 제작하였고, 감도특성을 고찰하였다. 가스 감지막은 금속산화물 페이스트를 스크린 프린팅 하는 종래의 방법 대신 MEMS 구조체에 적용 가능한 sol-gel 프로세스에 의해 형성하였다. 또 가스 감지전극과 micro-heater를 동일 평면상에 제작, 공정을 간소화하여 저가화를 시도하였다. MEMS 구조체 위에 제작된 Pt 박막 micro-heater의 인가전압에 따른 발열특성을 조사한 결과, 발열온도가 인가전압에 비례하는 이상적인 선형성을 나타내었으며, $300^{\circ}C$의 동작 온도에 도달하기 위해 65mW 이하의 저전력 동작이 가능하였다. 가스 센서의 감도특성 확인 실험은 CO 가스 10ppm, NO 가스 0.3ppm을 기준으로 수행되었으며, CO 및 NO에 대해 Rs(sensitivity, 가스반응저항/초기저항) 값은 각각 0.753 과 2.416로 우수한 성능을 나타내었다.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.81-97
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• 2003

In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.5
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• pp.50-57
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• 2004

For the application of structural health monitoring such as AE detection, a stabilized FBG sensor system with wide dynamic range was proposed. A tunable Fabry-Perot filter with narrow free spectral range(FSR) was used to simplify the multiplexing demodulator for FBG vibration sensors. Stabilization controlling system was also developed for the maintenance of maximum sensitivity of the sensors. In order to verify the performance of the proposed FBG vibration sensor system, we measured sensitivity, and the system showed the average sensitivity of 256 $n{\in}_{mas}/{\sqrt{Hz}}$. Finally, multi-points vibration tests using in-line FBG sensors were conducted to validate the multiplexing performance of the FBG system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.70-79
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• 2020

The main objective of this research to develop an IoT based wireless contactless ultrasonic system (ICUS) and its application to concrete structure. The developed system consists of 16 mems, 2Mhz digitizer, amplifying circuit, FPGA, and wifi module, enabling to measure leaky surface waves from concrete specimens without physical coupling process and wires. Multi-channel analysis is performed to improve the accuracy of data analysis, and the velocity of leaky surface waves and acoustics are derived. Field inspection of railroad concrete sleepers is conducted to evaluate the performance of the system and to compare the results with conventional ultrasonic pulse velocity (UPV). As a result of the field inspection, UPV was limited to evaluate damages. This is because crack pattern of railroad sleepers is parallel to ultrasonic ray path and accessibility of the railroad at the field is disadvantageous to contact-based UPV. On the other hand, ICUS possibly detect the damages as reduction of dynamic modulus by up to 59% compared to non-damaged specimen.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1073-1081
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• 2009

In this paper, we proposed new method to enhance choroidal vessels by suppressing retina vessels brightness. It is well-known that CNV(choroidal neovascularization) is related with sight loss. The main feature of CNV is the occurrence of new vessels in choroid. Unfortunately, because retina vessels brightness is stronger than choroidal vessels brightness in ICGA(indocynanine green angiography) image, so that the choroidal vessels were hardly recognized. Therefore, for correct diagnosis, the choroidal vessels must be enhanced in ICGA image. The proposed enhancement method consists of 3 strategies. First, the retina vessels were detected by multi scale enhancement technique, hysteresis thresholding, KNN(Kth-nearest neighbor) classification method. And then, a retina vessel mask was generated from detection result. Next, the brightness of retina vessels was suppressed by the proposed conditional region erosion method and mask region until the mask region was vanished. Finally, the brightness of choroidal vessel was enhanced on processed image. Through an experiment, we had confirmed that the proposed method was robust and efficient.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.804-809
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• 2013

The current state monitoring system at a semiconductor manufacturing process is based on the manually collected sensor data, which involves limitations when it comes to complex malfunction detection and real time monitoring. This study aims to design a situation recognition algorithm to form a network over time by creating a domain ontology and to suggest a system to provide users with services by generating events upon finding risk factors in the semiconductor process. To this end, a multiple sensor node for situational inference was designed and tested. As a result of the experiment, events to which the rule of time inference was applied occurred for the contents formed over time with regard to a quantity of collected data while the events that occurred with regard to malfunction and external time factors provided log data only.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.11
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• pp.1133-1144
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• 2019

Recently, the amount of image on the Internet has rapidly increased, due to the advancement of smart devices and various approaches to effective image retrieval have been researched under these situation. Existing image retrieval methods simply detect the objects in a image and carry out image retrieval based on the label of each object. Therefore, the semantic gap occurs between the image desired by a user and the image obtained from the retrieval result. To reduce the semantic gap in image retrievals, we connect the module for multiple objects classification based on deep learning with the module for human behavior classification. And we combine the connected modules with a behavior ontology. That is to say, we propose an image retrieval system considering the relationship between objects by using the combination of deep learning and behavior ontology. We analyzed the experiment results using walking and running data to take into account dynamic behaviors in images. The proposed method can be extended to the study of automatic annotation generation of images that can improve the accuracy of image retrieval results.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.39-46
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• 2019

This study conducted a comparative analysis of differences between cartesian trajectory in a linear rectangular coordinate system and MultiVane trajectory in a nonlinear rectangular coordinate system axial T1 and axial T2 images using an American College of Radiology(ACR) phantom. The phantom was placed at the center of the head coil and the top-to-bottom and left-to-right levels were adjusted by using a level. The experiment was performed according to the Phantom Test Guidance provided by the ACR, and sagittal localizer images were obtained. As shown in Figure 2, slices # 1 and # 11 were scanned after placing them at the center of a $45^{\circ}$ wedge shape, and a total of 11 slices were obtained. According to the evaluation results, the image intensity uniformity(IIU) was 93.34% for the cartesian trajectory, and 93.19% for the MultiVane trajectory, both of which fall under the normal range in the axial T1 image. The IIU for the cartesian trajectory was 0.15% higher than that for the MultiVane trajectory. In axial T2, the IIU was 96.44% for the cartesian trajectory, and 95.97% for the MultiVane trajectory, which fall under the normal range. The IIU for the cartesian trajectory was by 0.47% higher than that for the MultiVane trajectory. As a result, the cartesian technique was superior to the MultiVane technique in terms of the high-contrast spatial resolution, image intensity uniformity, and low-contrast object detectability.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.171-178
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• 2019

The use of anti-scatter grids in radiographic imaging has the advantage of preventing the image distortion caused by scattered radiation. However, it carries the side effect of leaving artifacts in the X-ray image. In this paper, we propose a grid line suppression technique using discrete cosine transform(DCT). In X-ray images, the grid lines have different characteristics depending on the shape of the object and the area of the image. To solve this problem, we adopt the DCT transform based on a dynamic segmentation, and propose a filter transfer function for each individual segment. An algorithm for detecting the band of grid lines in frequency domain and a band stop filter(BSF) with a filter transfer function of a combination of Kaiser window and Butterworth filter have been proposed. To solve the blocking effects, we present a method to determine the pixel values using multiple structured images. The validity of the proposed theory has been evaluated from the experimental results using 140 X-ray images.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.