• 경영과정보연구
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• 제3권
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• 한국정보처리학회논문지
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• 제6권7호
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• pp.1970-1979
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• 1999

본 논문에서는 수동 AVI 기술을 이용하여 차량 번호 판과 운전자 얼굴을 동시에 인식하는 시스템에 대해 제안하고자 한다. 이를 위해 우선적으로 환경에 불편인 전처리과정 알고리즘의 제시와 목표영역이 되는 차량 번호판 영역과 운전자 얼굴 영역을 추출하는 방법에 대해 다루고자 한다. 이후 목표영역에서 문자 영역분리와 인식 파라미터 추출을 행하고 차량 번호판의 경우 원형 정합으로, 운전자 얼굴 영역의 경우 퍼지 관계 행렬을 생성하여 최종적인 인식을 수행하고자 한다. 본 논문에서 제안하는 시스템은 환경에 불변인 전처리과정의 수행과 기존의 AVI 시스템에서 차량 번호 판만을 인식했던 것을 운전자 얼굴 인식까지 행함으로써 기존 AVI 시스템의 적용성 확대를 기할 수 있었다.

• 대한방사성의약품학회지
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• 제2권1호
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• pp.43-50
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• 2016

$^{89}Zr$ with the favorable nuclear decay kinetics and chemical properties is an appealing radiometal for its application in immuno-PET using radiolabeled monoclonal antibodies. Rising demand of ultrahigh purity and high-specific activity $^{89}Zr$ has propelled the radiochemist worldwide to develop an overall efficacious method for its promising separation from the target matrix $^{89}Y$. The requirement of elevated radiochemical purity (${\geq}$ 99.99%) has accelerated the efforts since last two decades to achieve higher decontamination and separation factors of carrier free $^{89}Zr$ over $^{89}Y$ using several suitable separation techniques. However, each of the technique has its own pros and cons which prior to its actual medical application needs to be optimized and thoroughly scrutinized to avoid further complications during radiolabelling of the pharmaceuticals. In this short review article we will specifically consider as well focus on the historical development and the recent advances on the radiochemical separation of $^{89}Zr$ from $^{89}Y$ which will be helpful for the separation scientist involved in this area to understand the existing available means and plan the strategy to investigate and develop the novel techniques to overcome the problems involved in the present methods.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• 한국소프트웨어감정평가학회 논문지
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• 제17권2호
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• pp.39-46
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• 2021

악성코드를 분석하여 방어하기 위해, 함수 위치 정보 등을 분석 방식으로 리버스 엔지니어링을 활용한다. 하지만, 스트립 된 바이너리는 함수 심볼 정보가 제거되어 함수 위치 등의 정보를 찾기가 쉽지 않다. 이를 해결하기 위해, BAP, BitBlaze IDA Pro 등 다양한 바이너리 분석 도구가 존재하지만, 휴리스틱을 기반으로 하므로 일반적인 성능이 우수하진 못하다. 본 논문에서는 재귀 하강 방식으로 역 어셈블리어에 대응되는 바이너리를 데이터로 N-byte 기법의 알고리즘을 제시해 LSTM 기반 모델을 적용하여 함수정보를 추출하는 기법을 제안한다. 실험을 통해 제안 기법이 수행 시간과 정확도 면에서 기존 기법들보다 우수함을 보였다.

• 품질경영학회지
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• 제51권2호
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• pp.223-245
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• 2023

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.912-919
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• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

• 한국컴퓨터정보학회논문지
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• 제28권11호
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• pp.89-101
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• 2023

사이버 공격으로 인한 국가, 기업 등의 피해를 막기 위해 공격자의 접근을 사전에 감지하는 이상 탐지 기술이 꾸준히 연구되어왔다. 외부 혹은 내부에서 침입하는 공격들을 즉각적으로 막기 위해 실행시간의 감축과 오탐지 감소는 필수불가결하다. 본 연구에서는 공격 이벤트의 유형과 빈도가 이상 탐지 정탐률 향상 및 오탐률 감소에 영향을 미칠 것으로 가설을 세우고, 검증을 위해 Los Alamos National Laboratory의 2015년 로그인 로그 데이터셋을 사용하였다. 전처리 된 데이터를 대표적인 이상행위 탐지 알고리즘에 적용한 결과, 공격 이벤트 유형과 빈도를 동시에 적용한 특성을 사용하는 것이 이상행위 탐지의 오탐률과 수행시간을 절감하는데 매우 효과적임을 확인하였다.

• Mass Spectrometry Letters
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• 제15권2호
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• pp.69 -78
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• 2024

The advancement of high-throughput omics technologies and systems biology is essential for understanding complex biological mechanisms and diseases. The integration of proteomics and metabolomics provides comprehensive insights into cellular functions and disease pathology, driven by developments in mass spectrometry (MS) technologies, including electrospray ionization (ESI). These advancements are crucial for interpreting biological systems effectively. However, integrating these technologies poses challenges. Compared to genomic, proteomics and metabolomics have limitations in throughput, and data integration. This review examines developments in MS equipped electrospray ionization (ESI), and their importance in the effective interpretation of biological mechanisms. The review also discusses developments in sample preparation, such as Simultaneous Metabolite, Protein, Lipid Extraction (SIMPLEX), analytical techniques, and data analysis, highlighting the application of these technologies in the study of cancer or Huntington's disease, underscoring the potential for personalized medicine and diagnostic accuracy. Efforts by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and integrative data analysis methods such as O2PLS and OnPLS extract statistical similarities between metabolomic and proteomic data. System modeling techniques that mathematically explain and predict system responses are also covered. This practical application also shows significant improvements in cancer research, diagnostic accuracy and therapeutic targeting for diseases like pancreatic ductal adenocarcinoma, non-small cell lung cancer, and Huntington's disease. These approaches enable researchers to develop standardized protocols, and interoperable software and databases, expanding multi-omics research application in clinical practice.