• 스마트미디어저널
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• 제13권3호
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• pp.53-61
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• 2024

본 논문은 기존 방법보다 우수한 성능을 달성하는 피부 병변 분할을 위한 새로운 M자 모양 인코더-디코더 아키텍처를 제안한다. 제안된 아키텍처는 왼쪽과 오른쪽 다리를 활용하여 다중 스케일 특징 추출을 가능하게 하고, 스킵 연결 내에서 어텐션 메커니즘을 통합하여 피부 병변 분할 성능을 더욱 향상시킨다. 입력 영상은 네 가지 다른 패치로 분할되어 입력되며 인코더-디코더 프레임워크 내에서 피부 병변 분할 성능의 향상된 처리를 가능하게 한다. 제안하는 방법에서 어텐션 메커니즘을 통해 입력 영상의 특징에 더 많은 초점을 맞추어 더욱 정교한 영상 분할 결과를 도출하는 것이다. 실험 결과는 제안된 방법의 효과를 강조하며, 기존 방법과 비교하여 우수한 정확도, 정밀도 및 Jaccard 지수를 보여준다.

• 한국측량학회지
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• 제34권6호
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• 한국산학기술학회논문지
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• 제4권2호
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• pp.114-119
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• 2003

본 연구는 폐수로부터 몇 가지의 유기오염물(Phenol, 2-Chlorophenol, Nitrobenzene)을 비분산 용매추출법으로 동시 제거하는 실험이다. 몇 가지의 용매(MIBK, IPAc, Hexane)에 대하여 분배계수를 구하였고, 용매와 폐수사이의 향류와 병류 흐름에 의한 추출 실험을 하였다. 수용액상의 유량이 증가함에 따라 용매와의 접촉시간이 짧아져 제거율이 떨어지고, 용매의 유량이 증가함에 따라 제거율이 증가함을 알 수 있었다. 그리고 병류보다는 향류에서 유기오염물의 제거율이 증가하는 것을 알 수 있었다. 이러한 결과를 토대로 유기오염물만이 아닌 다른 중금속 오염물 둥도 처리할 수 있는 산업용 폐수처리장치의 개발을 위한 기초자료로 쓰일 수 있다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
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• pp.238-240
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• 2002

If there is an Information Retrieval system which comprehends the semantic content of documents and knows the preference of users. the system can search the information better on the Internet, or improve the IR performance. Therefore we propose the IR model which combines semantic based indexing and fuzzy relevance model. In addition to the statistical approach, we chose the semantic approach in indexing, lexical chains, because we assume it would improve the performance of the index term extraction. Furthermore, we combined the semantic based indexing with the fuzzy model, which finds out the exact relevance of the user preference and index terms. The proposed system works as follows: First, the presented system indexes documents by the efficient index term extraction method using lexical chains. And then, if a user tends to retrieve the information from the indexed document collection, the extended IR model calculates and ranks the relevance of user query. user preference and index terms by some metrics. When we experimented each module, semantic based indexing and extended fuzzy model. it gave noticeable results. The combination of these modules is expected to improve the information retrieval performance.

• 지능정보연구
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• 제9권2호
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.300-306
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• 2000

This paper introduces a new concept of automation for bio-production with tele-operative system. The proposed system showed practical and feasible way of automation for the volatile bio-production process. Based on the proposition, recognition of the job environment with object identification was performed using computer vision system. A man-machine interactive hybrid decision-making, which utilized a concept of tele-operation was proposed to overcome limitations of the capability of computer in image processing and feature extraction from the complex environment image. Identifying watermelons from the outdoor scene of the cultivation field was selected to realize the proposed concept. Identifying watermelon from the camera image of the outdoor cultivation field is very difficult because of the ambiguity among stems, leaves, shades, and especially fruits covered partly by leaves or stems. The analog signal of the outdoor image was captured and transmitted wireless to the host computer by R.F module. The localized window was formed from the outdoor image by pointing to the touch screen. And then a sequence of algorithms to identify the location and size of the watermelon was performed with the local window image. The effect of the light reflectance of fruits, stems, ground, and leaves were also investigated.

• Current Photovoltaic Research
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• 제7권1호
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• pp.21-27
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• 2019

Lifetime of Si photovoltaics modules are about 25 years and a large amount of waste modules are expected to be discharged in the near future. Therefore, the extraction and collection of valuable metals out of discharged Si modules will be one of the important technologies. In this study, we demonstrated that supercritical $CO_2$ extraction method can be effectively used to remove Cu, one of the abundant elements in the module, as well as its oxide form, $Cu_2O$. Especially, we proved that the addition of hexane as co-solvent is effective for the removal of both materials. The optimal ratio of $CO_2$ and hexane was 4:1 at a fixed temperature and pressure of $250^{\circ}C$ and 250 bar, respectively. In addition, it was proven that the removal of $Cu_2O$ was preceded via reduction of $Cu_2O$ to Cu.

• Smart Structures and Systems
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• 제29권1호
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• pp.153-166
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• 2022

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.

• 물과 미래
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• 제28권2호
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• pp.115-124
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• 1995

수문순환과정의 해석을 위한 기본 자료로써 하천유역의 형태학적 요인들은 그 형태에 따라 다양할 뿐 아니라 광범위하여 인간의 접근이 곤란하다. 이러한 하천유역의 기하형태학적 특성을 정량화하기 위해서는 많은 시간, 경비 및 인력을 필요로 한다. 최근 컴퓨터 주변기기 및 소프트웨어의 개발로 수자원 분야에서 GIS를 이용, 유역과 유추분석을 위한 응용연구를 시작으로 수질 및 물분배 등과 같은 문제에 적용하기 시작하였으며 여기서 추출된 자료를 이용하여 수자원의 많은 공가적 분석이 시도되었다. 따라서 본 연구에서는 지형정보시스템을 이용하여 평창강 유역을 대상으로 1:250,000의 지형도를 scanning하여 vectorizing함으로서 DEM(digital elevation model)자료를 생성하였고 이를 사용하여 지형도를 이용한 유역경계를 추출하였다. 또한 추출된 자료로부터 지표유출량을 산정하기 위한 수문학적 유역특성인자들을 정량화하여 기존 자료들과 비교 검토하였다. 여기서 얻은 결과를 토대로, 수자원의 운용 및 관리를 위한 정확하고 신속한 하천유역의 기하형태학적 자료를 수문정보로 사용함으로써 지형정보시스템을 이용한 하천유역의 공간정보 즉, 수문 및 지형학적 인자의 추출과정을 자동화할 수 있는 방법을 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.