• 수산해양교육연구
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• 제28권6호
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• pp.1772-1780
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• 2016

NCS development for the treatment of waste oils from ship was carried out through steps such as analysis on characteristics, development of competency standard, utilizing package, and validation of industry sites. The results were as follows. Firstly, duty competency was classified as levels from 2 to 6. Educational training institutions were followed by 75 graduate schools, 73 universities, 54 colleges, and 37 high schools. Secondly, developed standards were consisted of duty and competency unit. The name of duty was the treatment of waste oils from ship and competency units were consisted of 8 items as classification of waste oils from ship, pickup and transport of waste oils from ship, warehousing of waste oils from ship to marine disposal company, transport of waste oils from ship to land, warehousing of waste oils from to disposal company, determination of disposal method and plant recycling treatment, and incineration treatment. 28 competency unit elements below 8 competency units were developed. Thirdly, utilizing package was developed into 3 areas of life-long career path, training criteria, and guidelines for exam according to national competency standards in order to develop development of labor's career and perform personal management such as hiring and promotion in industry sites.

• 한국안전학회지
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• 제34권6호
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.1-6
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• 2023

Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.217-218
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• 2021

The development of artificial intelligence in the field of construction and construction is revitalizing. The performance and development techniques of artificial intelligence are changing rapidly, but if you look at the cases of domestic construction sites, they are using technologies from 5 to 7 years ago. It is right to follow a stable method in consideration of commercialization, but the previous AI development method requires more manpower and time to develop than the current technology. In addition, in order to actively utilize artificial intelligence technology, customized artificial intelligence is required to be applied to ever-changing changes in construction sites. it is the reality As a result, even if good AI technology is secured at the construction site, it is reluctant to introduce it because there is no advantage in terms of time and cost compared to the existing method to apply it only to some processes. Currently, an AI technique with a faster development process and accurate recognition has been developed to cope with a fluid situation, so it will be important to understand and introduce the rapidly changing AI development method.

• 대한공간정보학회지
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• 제16권4호
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• pp.3-10
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• 2008

지적도는 토지의 소재, 지번, 지목, 경계, 면적을 지적법에 근거하여 연속하는 일필지에 의해 구성되어진 공적장부이다. 수년 전까지 지적도는 7개의 일정한 축척을 가진 2차원 평면 종이지도로 관리되어졌다. 최근 컴퓨터 시스템이 발달함으로써 지적도는 한 단계 발전할 수 있는 발판을 마련하였고, 그것의 형태가 래스터 방식에서 벡터 방식으로 전환 된 것이다. 그 결과 벡터화된 지적도는 다양하게 응용될 수 있게 되었다. 따라서 수치지적도는 한국토지정보시스템(KLIS)에 의해 다목적으로 이용될 수 있도록 시스템을 갖추었다. 본 연구에서는 수치지적도와 한국토지정보시스템을 기본으로 지적도상의 토지 이용 현황 표시를 원래의 일필지보다 구체적으로 표시하고자 한다. 필지 단위로 구성되어 있는 지적도를 "지적도의 토지현황표시를 위한 일필지 표현기법 모형 연구"에 의해 새로운 표현 기법을 적용하였다. 지적도면의 기능을 소유권의 위치관계 및 지적법에 명시된 28가지 지목별 토지이용현황의 표현뿐만 아니라 GIS 구축 사업의 기초 자료 등으로도 쓰일 수 있게 하기 위하여 도로, 철도, 구거, 하천 등의 공공용지를 중심으로 각각의 필지와 지목에 대한 일필지 표현 기법을 개발하였다. 특히, 공공용지 중 도로를 대상으로 일필지 표현 기법 개발과 이에 따른 지적도 기능 향상에 대하여 분석하였다.

• 대한공간정보학회지
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• 제19권3호
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• pp.3-11
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• 2011

본 연구의 목적은 북한 지역을 대상으로 위성영상과 공간자료를 이용하여 재조림 CDM의 정의에 적합한 대상지 선정 방안을 제시하는데 있다. 재조림 CDM이 가능한 대상지는 1990년 이전부터 현재까지 산림이 아닌 지역으로 규정되어 있다. 연구대상지역은 북한 함흥 지역이며, 1988년 9월 27일 취득된 Landsat TM 영상과 2007년 9월 24일 취득된 SPOT Pan-sharpened 영상의 두시점 영상을 이용하였다. 두시점의 영상에 대해서 각각 영상분류를 실시하여 산림황폐지(무립목지, 개간산지, 산간나지)를 구분하였다. 그리고 두 영상분류 결과를 이용한 변화탐지 분석을 수행하여 잠재적인 재조림 CDM 대상지를 도출하였다. 영상분류 결과, 19년간 산림의 1,214 ha가 개간산지, 농경지 또는 산간나지로 변화된 것으로 분석되었다. 분석 결과, 2,245 ha가 재조림 CDM의 정의에 부합하는 것으로 나타났으며, 전체 잠재적 재조림 CDM 대상지 중 79.2%가 개간산지로 나타났다. 분석된 잠재적 재조림 CDM 대상지에 대하여 지형 및 접근성 분석을 통해 적합성 지수를 산출하였고 대상지를 적합성에 따라 등급화하여 사업우선 순위의 선정 등에 활용할 수 있도록 하였다.

• 한국재난정보학회 논문집
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• 제18권2호
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• pp.269-279
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• 2022

연구목적: 본 연구의 목적은 건설현장의 재해 예방을 위해 딥러닝기반의 개인보호구 검출 모델을 개발하고, 실제 건설현장에 적용하여 분석하는 것이다. 연구방법: 본 연구의 수행 방법은 실제 환경의 데이터를 구축하고, 개발된 개인보호구 검출 모델을 적용하였다. 개인보호구 검출 모델은 크게 근로자 검출 및 개인보호구 착용 분류 모델로 구성되어 있다. 근로자 검출 모델은 딥러닝 기반의 알고리즘을 실제 현장에서 획득한 데이터셋을 구축하여 학습 및 근로자를 검출하였고, 개인보호구 착용 분류 모델은 앞단에서 추출된 근로자 검출영역에서 학습된 개인보호구 검출 알고리즘을 적용하였다. 구축된 모델의 검증을 위해 건설현장 3곳에서 획득된 데이터를 통해 실험결과를 도출하였다. 연구결과: 데이터베이스 12,000장을 구축하여 정상검출 9,460장(78.8%), 오검출 1,468(12.2%), 미검출 1,072장(8.9%)으로 나타났으며 주요 원인은 영상에서의 객체 크기, 객체간 중첩(Occulusion), 객체 잘림, 그림자에 의한 오검출로 분류되었다. 결론: 개인보호구 검출모델은 현장 상황마다 다른 검출률을 확인할 수 있었고, 본 연구의 결과가 차후 현장적용을 위한 연구에 활용될 수 있을 것으로 여겨진다.

• 대한치과보철학회지
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• 제38권3호
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• pp.321-327
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• 2000

Statement of Problem. As a standard means of diagnostics, an orthopantomogram(OPT) permits to measure the vertical and mesiodistal dimension of available bone at the desired implant site with the help of suitable radioopaque references. Based on the clinical investigation of the dentition and the edentulous sites, information upon the width of the implant site can be obtained and documented in the dental scheme. Both findings permit together systematic primary planning for endosteal implants. Purpose of Study. Contents of the present article are the representation of a semiquantitative classification of available bone with the aim to simplify the primary phase of a systematic implant planning. Results. Thus the ASCIi- system permits a clear protocol of bone findings for the implant case with all information available during the primary appointment for treatment planning as a basis of further diagnostic and therapeutic measures. Conclusion. With the ASCIi system, important parameters such as alveolar height and sub-crestal alveolar width can be documented systematically, easily and time saving in the dental scheme as a basis for exact treatment planning.