• ETRI Journal
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• 제41권4호
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• 한국멀티미디어학회논문지
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• 제19권1호
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

• 대한임베디드공학회논문지
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• 제12권4호
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• 정보처리학회논문지A
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• 제13A권4호
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• pp.317-322
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• 2006

RFID 시스템은 최근 다양한 분야로 적용되면서 개발이 급증하고 있다. 특히 RFID 시스템은 공급 업체의 물류분야에서 공급 사슬 관리 시스템의 중추적인 기술로 사용된다. 물류분야에서는 신속하고 정확하게 제품을 파악해야 하는데 제품의 이동으로 인하여 제품의 재고관리를 실시간으로 처리하는데 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 RFID 네트워크의 구조적 기준이 되는 EPC-IS 네트워크를 이용하여 제품의 재고 상태를 실시간으로 파악한다. 또한 위조품이나 도난품을 파악하는 정품인증 서비스를 실시간으로 제공한다. 따라서 본 논문을 통하여 제품의 관리를 실시간으로 처리할 수 있음을 보인다.

• 대중서사연구
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• 제25권2호
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• pp.217-245
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• 2019

최근 5년 사이 한국 방송의 관찰 예능 편중 현상이 심화되었다. 그에 따라 한국 관찰 예능 특유의 소재인 '가족', 그리고 엿보기, 관음증과 같은 키워드들이 주목받기도 했다. 본 논문은 그러한 사회학적 접근이 아닌 장르와 스토리텔링이라는 문화콘텐츠, 서사학적 관점을 바탕으로 관찰 예능을 분석한다. 관찰 예능의 텍스트 내적 성질 파악을 통해 방송콘텐츠로써 관찰 예능이 지닌 가치를 탐색할 수 있다고 판단했기 때문이다. 한국 관찰 예능의 원류는 리얼 버라이어티로부터 시작한다. 리얼 버라이어티의 '사실성' 논쟁을 거치며, 그 대안적 형태로서 관찰 예능이 등장했다. 화면의 객관성을 최대한 보장하는 다큐멘터리적 양식의 이입, 다수의 출연자를 개별적으로 분리한 옴니버스식 구성, '일상'을 주 공간으로 선정해 시청자의 공감을 끌어내는 전략 등은 관찰 예능에 일관된 패턴을 만들며 하나의 장르화를 이끌었다. 특히 '관찰자'는 관찰 예능의 장르화에 있어 핵심적 요소로 기여했다. 관찰자의 성격화에 따라 프로그램의 주제가 결정되었고, 그에 따른 개별 텍스트 포맷의 변별성이 만들어진다. 관찰자는 관찰 대상을 지켜보는 '관찰자'로서 시청자의 시점을 결정하는 역할을 하고, 동시에 '서술자'로서 프로그램의 서사를 구축하는 역할을 수행한다. 관찰 예능의 스토리텔링에 있어서 가장 영향력 있는 존재이자 요소로 기능하는 것이다. 관찰 예능에서 '관찰'은 하나의 형식일 뿐이다. 이 동일 형식 안에서 서사성을 만들고 텍스트별 변별성을 만들어 내는 프로그램 요소는 관찰자이다. 또한 그동안 관찰 예능과 같은 리얼리티 쇼의 주된 명제는 관음의 문제였는데, 관찰자에 의해 걸러진 서술과 전달은 리얼리티 쇼의 선정성 문제, 관음의 문제도 상당 부분 완화시킨다. 이처럼 관찰자를 통해 분석되는 관찰 예능은 TV 속 '리얼리티'의 문화적 의미, 현대 한국 TV 예능 장르의 스토리텔링 전략을 이해하고 예측할 수 있게 한다는 점에서 의의를 찾을 수 있다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.11-18
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• 2019

One of the key objectives of control systems is to maintain a system in a specific stable state. To achieve this goal, a variety of control techniques can be used and it is often uses a feedback control method. As known this kind of control methods requires mathematical model of the system. This article presents seesaw unstable system with two propellers which are controlled without use of a mathematical model instead. The goal was to control it using training data. For system control we use a logistic regression technique which is one of machine learning method. We tested our controller on the real model created in our laboratory and the experimental results show that instability of the seesaw system can be fixed at a given angle using the decision boundary estimated from the classification method. The results show that this control method for structural equilibrium can be used with relatively more accuracy of the decision boundary.

• 한국정보통신학회논문지
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• 제13권5호
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• pp.967-973
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• 2009

최근 고속데이터통신방법을 사용하는 3세대 이동통신 서비스가 널리 보급됨에 따라 VoIP와 같은 다양한 실시간 데이터 서비스에 대한 요구 또한 증대되고 있다. VoIP 패킷은 일반적인 데이터패킷과 달리 한계지연시간이 있으며 낮은 손실율을 필요로 한다. 우리는 이러한 VoIP 패킷의 특징을 고려함과 동시에 데이터 패킷과 통합적으로 스케줄링이 가능한 스케줄링 알고리즘을 제안하고자 한다. 본 알고리즘은 기본적으로 지연시간 및 채널상태를 함께 고려하여 스케줄링 하되 VoIP 패킷이 큐에 머물러 있는 시간이 증가함에 따라 채널상태에 대한 가중치를 줄이고 지연시간에 대한 가중치를 높임으로서 스케줄링 우선권을 할당한다. 실험을 통해 우리는 이 알고리즘이 기존의 방법에 비해 높은 성능을 가지고 있음을 입증할 수 있었다.

• 대한임베디드공학회논문지
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• 제6권2호
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• pp.55-61
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• 2011

We propose power-saving real-time scheduling method for mixed task sets which consist of both time-based periodic and event-based aperiodic tasks in the automotive operating system. In this system, we have to pursue maximization of power-saving using the slack time estimation and minimization of response time of aperiodic tasks simultaneously. However, since these two goals conflict each other, one has to make a compromise between them according to the given application domain. In this paper, we find the adjustment factor which gives better response time of aperiodic tasks with slight power consumption increase. The adjustment factor denotes the gravity of response time for aperiodic tasks. We apply the ccEDF scheduling for time-based periodic tasks and then calculate new utilization to be applied to the adjustment factor. In this paper, we suggest the lccEDF algorithm to make a tradeoff between the two goals by systematically adjusting the factor. Simulation results show that our approach is excellent for variety of task sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3275-3298
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• 2022

Cloud-based architectures for precision agriculture are domain-specific controlled and require remote access to process and analyze the collected data over third-party cloud computing platforms. Due to the dynamic changes in agricultural parameters and restrictions in terms of accessing cloud platforms, developing a locally controlled and real-time configured architecture is crucial for efficient water irrigation and farmers management in agricultural fields. Thus, we present a new implementation of an independent sensor-enabled architecture using variety of wireless-based sensors to capture soil moisture level, amount of supplied water, and compute the reference evapotranspiration (ETo). Both parameters of soil moisture content and ETo values was then used to manage the amount of irrigated water in a small-scale agriculture field for 356 days. We collected around 34,200 experimental data samples to evaluate the performance of the architecture under different agriculture parameters and conditions, which have significant influence on realizing real-time monitoring of agricultural fields. In a proof of concept, we provide empirical results that show that our architecture performs favorably against the cloud-based architecture, as evaluated on collected experimental data through different statistical performance models. Experimental results demonstrate that the architecture has potential practical application in a many of farming activities, including water irrigation management and agricultural condition control.