• 대한건축학회연합논문집
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• 제20권6호
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• pp.113-119
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• 2018

Occupancy-based heating control is effective in reducing heating energy by preventing unnecessary heating during unoccupied period. Various technologies on detecting human occupancy have been developed using complicated machine learning algorithm and stochastic methodologies. This study aims at deriving low-cost and simple algorithm of occupancy inference that can be implemented to residential buildings. The core concept of the algorithm is to combine the occupancy probabilities based on indoor CO2 concentration and PIR(passive infrared) signals. The probability was estimated by applying different levels of decrement ratio depending on CO2 concentration change rate and aggregated PIR signals. The developed algorithm was validated by comparing the inference results with the occupancy schedule in a real residential building. The results showed that the inference algorithm can achieve the accuracy of 75~99%, which would be successfully implemented to the control of residential heating systems.

• Journal of Electrical Engineering and information Science
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• 제3권1호
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• pp.28-35
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• 1998

In this paper, an NxN input-buffered crosspoint packet switch which selects a Head of the Line, HOL, packet in contention randomly is analyzed with a new approach. The approach is based on both a Markov chain representation of the input buffer and the probability that a HOL packet is successfully served. The probability as a function of N is derived, and it makes it possible to express the average packet delay and the average number of packets in the buffer as a function of N. The contention resolution policy based on the occupancy of the input buffer is also presented and analyzed with this same approach and the relationship between two selection policies is analyzed in terms of the occupancy of the input buffer.

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.275-282
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• 2010

This paper analyzes a discrete-time bulk-service queue with probabilistic bulk size, where the service process is interrupted by a Markov chain. We study the joint probability generating function of system occupancy and the state of the Markov chain. We derive several performance measures of interest, including average system occupancy and delay distribution.

• 대한수학회논문집
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• 제13권3호
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• pp.603-627
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• 1998

The leaky bucket scheme is a promising method that regulates input traffics for preventive congestion control. In the ATM network, the input traffics are bursty and transmitted at high-speed. In order to get the low loss probability for bursty input traffics, it is known that the leaky bucket scheme with static leaky rate requires larger data buffer and token pool size. This causes the increase of the mean waiting time for an input traffic to pass the policing function, which would be inappropriate for real time traffics such as voice and video. We present the leaky bucket scheme with dynamic leaky rate in which the token generation period changes according to buffer occupancy. In the leaky bucket scheme with dynamic leaky rate, the cell loss probability and the mean waiting time are reduced in comparison with the leaky bucket scheme with static leaky rate. We analyze the performance of the proposed leaky bucket scheme in discrete-time case by assuming arrival process to be Markov-modulated Bernoulli process (MMBP).

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제17권2호
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• pp.103-113
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• 2013

We analyze the queueing model for leaky bucket (LB) scheme with dynamic arrivals and token rates. In other words, in our LB scheme the arrivals and token rates are changed according to the buffer occupancy. In telecommunication networks, the LB scheme has been used as a policing function to prevent congestion. By considering bursty and correlated properties of input traffic, the arrivals are assumed to follow a Markov-modulated Poisson process (MMPP). We derive the distribution of system state, and obtain the loss probability and the mean waiting time. The analysis is done by using the embedded Markov chain and supplementary variable method. We also present some numerical examples to show the effect of our proposed model.

• 전자공학회논문지
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• 제50권7호
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• pp.258-266
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• 2013

본 논문에서는 진동센서를 기반으로 하는 탐지 시스템에서 불규칙적으로 변화는 잡음의 특성을 고려하여 허위경보(false alarm)를 감소하기 위한 적응형 탐지 알고리즘을 제안한다. 제안된 알고리즘은 커널 함수(Kerenl function)을 이용한 1차 검출과 탐지 확정 단계를 적용한 2차 검출로 구성된다. 1차 검출기의 커널 함수는 측정된 신호로부터 잡음의 확률적 모수를 이용하여 잡음 변화에 따라 Neyman-Pearson 결정법으로 문턱 값을 찾아 구한다. 그리고 2차 탐지기는 1차 탐지된 표본수를 이용하여 발걸음 신호의 점유시간을 계산한 후 4단계의 탐지 확정 단계로 구성된다. 본 논문에서 제안된 알고리즘을 검증하기 위해 측정된 걷기와 뛰기 진동 신호를 이용하여 발걸음 신호에 대한 탐지 실험을 수행 하였으며 고정 문턱 값을 이용한 탐지 결과와 비교 하였다. 제안된 1차 검출기의 목표물 탐지 결과 사람의 걷기와 뛰기에 대하여 10m 구간까지 95%의 높은 탐지 성능을 획득하였다. 또한, 허위경보 확률은 고정 문턱 값과 비교할 때 40%에서 20%로 감소하였으며 탐지 확정 단계를 적용한 결과 4%미만으로 크게 감소한 결과를 얻었다.