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LSTM을 이용한 식품 공장의 냉장 시설 온도 예측 모델
이지상,강민관,서현 한국정보과학회 2024 정보과학회 컴퓨팅의 실제 논문지 Vol.30 No.2
To construct a Smart Hazard Analysis and Critical Control Points (HACCP), the data from food factories, including temperature readings, counts of door openings, and defrosting events, are measured and analyzed. In this paper, we present a method utilizing Artificial Intelligence (AI) techniques to predict temperature in food factories refrigeration and freezing facilities. After collection, the dataset was further enriched by incorporating temperature and humidity data provided by the meteorological agency. Using a Long Short-Term Memory (LSTM)-based recurrent neural network model, we found that including meteorological data significantly improved its predictive performance. The results of this study showed that the proposed method is a suitable AI modeling approach for temperature predictions in food factories refrigeration and freezing facilities. The method is anticipated to help in monitoring equipment degradation, subsequently enhancing the safety and production efficiency in food factories.
Evaluation of Geometric Error Sources for Terrestrial Laser Scanner
이지상,홍승환,박일석,조형식,손홍규 대한공간정보학회 2016 대한공간정보학회지 Vol.24 No.2
As 3D geospatial information is demanded, terrestrial laser scanners which can obtain 3D model of objects have been applied in various fields such as Building Information Modeling (BIM), structural analysis, and disaster management. To acquire precise data, performance evaluation of a terrestrial laser scanner must be conducted. While existing 3D surveying equipment like a total station has a standard method for performance evaluation, a terrestrial laser scanner evaluation technique for users is not established. This paper categorizes and analyzes error sources which generally occur in terrestrial laser scanning. In addition to the prior researches about categorizing error sources of terrestrial Laser scanning, this paper evaluates the error sources by the actual field tests for the smooth in‐situ applications.The error factors in terrestrial laser scanning are categorized into interior error caused by mechanical errors in a terrestrial laser scanner and exterior errors affected by scanning geometry and target property. Each error sources were evaluated by simulation and actual experiments. The 3D coordinates of observed target can be distortedby the biases in distance and rotation measurement in scanning system. In particular, the exterior factors caused significant geometric errors in observed point cloud. The noise points can be generated by steep incidence angle, mixed‐pixel and crosstalk. In using terrestrial laser scanner, elaborate scanning plan and proper post processing are required to obtain valid and accurate 3D spatial information.
지상라이다 취득 점군자료와 영상을 이용한 실내 환경에서의 인물 감지
이지상,홍승환,박일석,손홍규 한국측량학회 2017 한국측량학회 학술대회자료집 Vol.2017 No.4
본 내용은 이미지와 라이다 데이터를 결합하여 실내 환경에서 인물을 감지 및 추출하는 방법을 제공한다. 이미지 데이터 내에서 인물을 추출하기 위한 관심영역 설정의 방법으로써 라이다 데이터를 이용해 보았으며, 이에는 라이다 데이터에 대한 K means 클러스터링 기법이 사용되었다. 인물 후보로써 클러스터링된 점군자료의 위치는 카메라와 라이다 기기 간의 기하학적 위치 관계에 의해 이미지내 영역으로 옮겨진다. 이를 통해 이미지 내 관심영역을 확정하고 이에 대한 인물여부 판단을 위하여 HOG 알고리즘과 SVM이 사용되었다.