• 한국엔터테인먼트산업학회논문지
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• 제15권7호
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• pp.249-257
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• 2021

최근 세계적 감염질환 확산 방지 및 사회적 대처 방안으로 다중이용시설의 필수적 환경개선인 스마트기기를 활용한 비대면 본인인증, 출입관리서비스 제공이 가능한 얼굴정보처리기술에 대한 관심과 요구가 증가하고 있다. 본 연구는 지속적인 출입관리체계를 수립하기 위한 다중이용시설분류체계 및 적용서비스분야를 정의하고 이원화된 출입관리 시스템, 개인·측정정보 유형 분석을 통해 확장성을 고려한 서비스플랫폼의 사용성 개선방안과 이에 따른 서비스 로드맵을 제안하고자 한다. 또한 활용도에 따른 일회성, 다회성으로 인증해야하는 다중이용시설 적용서비스분야인 물리적 출입관리 시스템 서비스플랫폼 개선을 목표로 한다. 향후 본 연구의 방법론이 논리적 출입관리 시스템 유형의 서비스플랫폼으로 적용될 수 있을 것으로 기대한다.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• 한국환경복원기술학회지
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• 제26권1호
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• pp.1-16
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• 2023

The current study aims to analyze the vegetation characteristics of the rocky slopes in expressway applied by different types of greening work. A field survey on the current status of vegetation were conducted in 50 rock slopes along 13 expressways in two years, 2020 to 2021. Specifically, the type of implemented greening and slope stabilization work, the soil properties, the vegetation coverage, and the emerged species were investigated on a every single slope. As the result of the implemented work types, the soil-media hydroseeding and the gabion work appeared to be the most implemented greening and slope stabilization work, respectively. As a result of the vegetation survey, 126 classification groups (42 families, 93 genera and 126 species) were identified in total and it was observed 26 IAP species and 5 invasive species were growing. The longer the time after greening work, the more frequent appearance of IAP species were observed. Woody species such as Robinia pseudoacacia and Lespedeza bicolo, and perennial herbs such as Artemisia princeps, Erigeron annuus, and Festuca arundinacea were appeared with high frequencies at the rocky slopes in expressway. It was also observed Pinus densiflora, Quercus dentata, Rubus crataegifolius and Miscanthus sinensis which had invaded from the adjacent forests naturally, and the largest number of species were invaded between 5~10 years usually after greening work in this study.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1997년도 춘계학술대회논문집
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.314-324
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• 1996

A high speed NMR quality evaluation sensor was designed , constructed and tested . The device consists of an NMR spectrometer coupled to a conveyor system. The conveyor was run at speeds ranging from 0 to 250 mm/s. Spectral of avocado fruits and one-dimensional magnetic resonance images of pickled olives were acquired while the samples were moving on a conveyor belt mounted through a 20Tesla NMR magnet with a 20 mm diameter surface coil and a 150 mm diameter imaging coil respectively. Fro a magnetic resonance spectrum analysis, motion through variations in the magnetic field tends to narrow spectral line width just like using sample rotation in high resolution NMR to narrow spectral line width. Spectrum analysis was used to detect the dry weight of avocado fruits using the ratio oil and water resonance peaks. Good correlations maximum r=0.970@ 50 mm/s and minimum r=0.894@250mm/s ) between oil and water resonance peak ratio and dry weight of avocados were observed at speeds ra ging from0 to 250mm/s. For the application of magnetic resonance imaging (MRI) method, the projections were used to distinguish between pitted and non-pitted olives . Effect of fruit position in the coil was tested and coil degree effects were noticed when projects were generated under dynamic conditions. Various belt speeds (up to 250mm/s) were tested and detection results were compared to static measurements. Higher classification errors were occurred at dynamic conditions compared to errors while olives were at rest.

• The Plant Pathology Journal
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• 제39권2호
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• pp.171-180
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• 2023

Spring black stem and leaf spot, caused by Phoma medicaginis, is an issue in annual Medicago species. Therefore, in this study, we analyzed the response to P. medicaginis infection in a collection of 46 lines of three annual Medicago species (M. truncatula, M. ciliaris, and M. polymorpha) showing different geographic distribution in Tunisia. The reaction in the host to the disease is explained by the effects based on plant species, lines nested within species, treatment, the interaction of species × treatment, and the interaction of lines nested within species × treatment. Medicago ciliaris was the least affected for aerial growth under infection. Furthermore, the largest variation within species was found for M. truncatula under both conditions. Principal component analysis and hierarchical classification showed that M. ciliaris lines formed a separate group under control treatment and P. medicaginis infection and they are the most vigorous in growth. These results indicate that M. ciliaris is the least susceptible in response to P. medicaginis infection among the three Medicago species investigated here, which can be used as a good candidate in crop rotation to reduce disease pressure in the field and as a source of P. medicaginis resistance for the improvement of forage legumes.

• 산업경영시스템학회지
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• 제46권3호
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• 산업경영시스템학회지
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• 제46권4호
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• 환경영향평가
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• 제32권5호
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• pp.363-376
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• 2023

기후변화와 무분별한 개발 등으로 인해 생물다양성의 감소와 멸종 위험이 심각해짐에 따라 자연생태계에 대한 보전의 중요성이 높아지고 있다. 이러한 상황에서 본 연구는 자연자원과 생태계 변화 등에 대한 정보제공을 목적으로 한 평가기반의 자연생태 전망평가체계에 대한 국외 사례를 검토하였다. 결과로서 국외에서는 국내와 유사하게 다양한 조사기관에서 분류군별 조사를 수행하고 있으며 조사된 자료가 통합적으로 수집·관리되고 단기간내 지속적으로 제공되고 있다. 또한 기초조사 자료와 함께 평가기반의 예측·전망 정보를 제공함으로써 국가정책 및 환경영향평가 등에서의 활용도가 높은 것으로 조사되었다. 이를 바탕으로 국내 자연생태 조사 현황과 환경영향평가 등에서의 정보이용의 한계성을 분석하고 자연생태 정책수립 지원 등 정보로서의 자연생태 전망평가체계 작성 필요성에 대해 고찰하였다. 또한 제안된 자연생태 전망평가 등의 다양한 정보를 통해 국내 자연생태계 정책에 효과적으로 활용할 수 있는 정책방향을 제안하였다.

• 환경영향평가
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• 제25권2호
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• pp.89-102
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• 2016

담양하천습지는 영산강 본류에 위치하는 하천습지로서 2004년 습지보호지역으로 지정되었다. 이번 연구에서는 총 30개의 식생자료가 획득되었으며, 총 101종(미동정종 1종)을 포함하는 22개의 식생유형이 구분되었다. 현존식생도는 6개의 범례(하천림, 대상식생, 터주식생, 연안대식생, 습생초원, 개방수역)로 구분되었으며, 습지식생의 면적은 약 35%($386,841.86m^2$)를 차지하였다. 본 연구 결과 담양하천습지에 분포하는 식물사회는 보호지역 상단에 설치된 물막이보와 상부 유역에 운영 중인 담양댐 등으로 인해 급격한 수환경 변화로 형성된 것으로 유추되었다. 고수부지는 최근까지 경작이 진행되었으나 보호지역 지정 이후 방치됨으로써 현재와 같은 하천변 휴경작지의 식생경관이 형성되었다. 조사지역 내 환경부 지정생태계 교란 야생식물인 털물참피가 우점하는 군락이 넓게 발달하고 있으며, 국내 미기재된 새로운 귀화식물군락인 앵무새깃군락이 관찰되었다. 이들 식물군락들은 나도겨풀군락이 발달하는 환경과 중복되어 그 밖의 유사한 생태적 지위를 가지는 고유식물군락의 서식처를 점유하게 될 것이다. 담양하천습지의 다양한 식물사회들은 인공 시설물 등에 의한 하천환경 변화 및 교란, 훼손에 기인한 것으로 이해된다.