• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• 전기전자학회논문지
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• 제28권2호
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• pp.199-203
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• 2024

본 논문에서는 단일 프레임 지도 시간적 행동 지역화에서 1D 합성곱 층의 커널 사이즈 변화를 제안한다. 본 논문에서는 두 개의 1D 합성곱 층의 커널 사이즈를 각각 3과 1을 사용하는 기존 방법을 기반으로, 각각의 1D 합성곱 층의 커널 사이즈를 변화시키는 방법을 제안하였다. 제안하는 방법의 효율성을 검증하기 위하여 THUMOS'14 데이터셋을 활용하여 비교실험을 수행하였다. 또한 성능 평가를 위해 전체 비디오에 대한 분류 정확도(Accuracy), mAP(mean Average Precision) 그리고 Average mAP를 성능 지표로 사용하였다. 본 논문의 실험 결과에 따르면 제안하는 방법이 기존 방법보다 더 정확한 mAP와 Average mAP를 제공할 수 있음을 관찰하였다. 또한 커널 사이즈를 7과 1로 변화시킨 방법이 전체 비디오에 대한 분류 정확도에서 8.0% 개선된 것을 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제24권10호
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• pp.11-23
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• 2019

본 연구에서는 토목분야의 다양한 상용 BIM 모델링 설계 소프트웨어의 사용자들에게 상호 호환성과 상호 운용성을 보장하기 위해 BIM 국제 표준 파일 포맷인 IFC 파일로의 파일 변환 프로세스를 새롭게 설계하여 제시한다. 제안된 프로세스는 상용 BIM 모델링 소프트웨어를 위한 add-in 방식의 컨버터(Converter)를 사용하여, 변환되는 IFC 파일의 3차원 객체 형상 정보에 수량 산출식 코드 속성과 토목 분야 CBS/OBS/WBS 표준분류체계 속성으로 구성되는 추가적인 속성들을 삽입한다. 또한, 개방형(Open) 웹 기반 수량, 공정(4D) 및 공사비(5D) 관리를 위한 IFC 파일의 통합 활용 프로세스를 추가로 설계하고 구축한다. 이러한 작업을 통해 토목 분야의 BIM 모델링 설계 단계에서 최종적인 시공 단계에 이르는 개방형 웹 기반 수량, 공정(4D) 및 공사비(5D)의 연계적 활용에 대한 새로운 프로세스를 제시하는 것이 본 연구의 궁극적인 목적이다.

• 한국건축시공학회지
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• 제20권3호
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• pp.245-252
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• 2020

합성수지 거푸집은 내부식성이 우수한 경량의 고밀도 폴리에틸렌(HDPE)를 재료로 사용한다. 합성수지 거푸집의 전과정 평가를 위하여 ISO FDIS 13352에서 요구하는 시스템 경계를 만족하도록 공정 흐름도를 고려하였다. 이에 따라 고려된 시스템 경계는 Cradle-to- Product shipmen이다. 고려된 시스템 경계에서 투입 에너지원, 사용재료, 운송수단, 제작공정 등으로부터 산정한 전과정 목록(LCI) 데이터베이스를 분석하였다. 합성수지 거푸집의 LCI 데이터 분석으로 부터 환경부의 환경영향평가지수 방법론에 기반하여 분류화, 정규화, 특성화 및 가중치 과정을 거쳐 환경영향평가를 수행하고, 그 결과는 유로폼의 환경영향 평가값과 비교하였다. 실험결과, 전용횟수를 고려한 CO₂ 배출량은 유로폼 대비 2배 이상의 전용성을 갖는 합성수지 거푸집이 약 32 % 가량 낮았다. 이는 합성수지 거푸집 사용은 유로폼 대비 자재 생산을 1/2로 줄일 수 있으며, CO₂ 배출량 저감으로 이어질 수 있다.

• 한국환경복원기술학회지
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• 제8권3호
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• pp.53-66
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• 2005

The objective of this study is the classification of priority areas for the implementation of green roof by evaluating environmental deterioration in Seoul. Non-permeable pavement, air pollution, habitual floods, energy use, heat island and green space are considered in this assessment indicators. The expert questionnaire survey was conducted in order to determine the most important indicators. These indicators were then, thoroughly evaluated. As a result, high priority areas for the implementation of green roof were deduced in the following order of the districts : Jung, Sungdong, Jungrang, Youngdungpo, Jongro and Kangnam. The highest priority areas were determined to be crowded business-commercial areas. Low priority areas are analyzed in the following order of the districts : Kwanak, Nowon, Seocho and Dobong. The result of this study can be utilized for environmental planning and decision of related policies. Additionally, it can be promoted that awareness of implementing green roof of citizens, policy makers and building owners and effect of green networking between inside and outside Seoul can be increased.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.868-871
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• 2006

Mortality of domestic people from cardiovascular disease ranked second, which followed that of from cancer last year. Therefore, it is very important and urgent to enhance the reliability of medical examination and treatment for cardiovascular disease. Heart Rate Variability (HRV) is the most commonly used noninvasive methods to evaluate autonomic regulation of heart rate and conditions of a human heart. In this paper, our aim is to extract a quantitative measure for HRV to enhance the reliability of medical examination for cardiovascular disease, and then develop a prediction method for extracting multi-parametric features by analyzing HRV from ECG. In this study, we propose a hybrid Bayesian classifier called FP-based Bayesian. The proposed classifier use frequent patterns for building Bayesian model. Since the volume of patterns produced can be large, we offer a rule cohesion measure that allows a strong push of pruning patterns in the pattern-generating process. We conduct an experiment for the FP-based Bayesian classifier, which utilizes multiple rules and pruning, and biased confidence (or cohesion measure) and dataset consisting of 670 participants distributed into two groups, namely normal and patients with coronary artery disease.

• 대한기계학회논문집A
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• 제24권11호
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• pp.2875-2882
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• 2000

Typically, the ultrasonic sensors can be used in navigation systems for modeling of the enviornment, obstacle avoidance, and map building. In this paper, we tried to approach an object classification method using the range data of the ultrasonic sensors. A characterization of the sonar scan is described that allows the differentiation of planes, corners, edges, cylindrical and rectangular pillars by processing the scanned data from three sonars. To use the data from the ultrasonic sensors as input to the neural networks, we have introduced a clustering, threshold, and bit operation algorithm for the obtained raw data, After repeated training of the neural network, the performance of the proposed method was obtained through experiments. Also, the recognition ranges of the proposed method were investigated. As a result of experiments, we found that the proposed method successfully recognized the objects within the accuracy of 78%.

• 한국조경학회지
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• 제29권6호
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• pp.27-36
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• 2002

The objective of this study is to improve the quality of the atmospheric environment by incorporating the factors of meteorology and urban climate into the field of urban and environmental planning. To this end, we have conducted a study on CLIMATOP and the mapping of urban climate, which are basic data used to analyze changes in climatic factors and the stagnation and accumulation of air pollutants. In particular, we focused on understanding the formation and movement of cold fresh air and its influx into urban areas by measuring and analyzing climatic factors. As a study result, classification criteria far CLIMATOP and a urban climatic map were made. In addition, we analyzed a digital elevation model, climatic data, and isothermal curves. As a result, we identified the corridor through which cold fresh air moves. We also observed that the temperature of the fluxed cold fresh air increased as land use changed. When the results of this study are applied to urban re-development and re-building projects, which require preliminary environmental assessment and environmental impact assessment, the practice proposed by this study is expected to contribute to the natural purification of air pollution activating the movement of cold fresh air and its influx into urban areas.

• 지식경영연구
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• 제19권4호
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• Smart Structures and Systems
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• 제30권4호
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.