• 한국ITS학회 논문지
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• 제19권4호
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• pp.67-80
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• 2020

고속축중기는 통행 흐름의 통제나 속도의 감속 없이 주행 중인 화물차량의 중량을 실시간 무인 검측하는 시스템으로, 국내에서는 축 조작 행위방지 및 주행 중 과적행위 적발을 위해 주로 고속국도 및 일반국도 본선에 설치하여 이동단속반 검차를 위한 사전선별용으로 활용된다. 본 연구에서는 고속축중기의 중량 측정정확도를 법정 기준까지 향상시키고자 기존 적분형 및 첨두형 중량환산 알고리즘에 대한 분석을 통해 개선사항을 제시하고, 매트타입형 고속축중기에 적용할 수 있는 새로운 융합형 알고리즘을 고안하였다. 이 알고리즘을 적용하여 현장 실차 주행시험을 통한 정확도 향상 효과를 분석한 결과, 최상급의 정확도 등급을 확인할 수 있었다.

• Journal of Information Processing Systems
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• 제10권1호
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.