• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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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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• v.3 no.1
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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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• v.28 no.1_2
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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.

• Communications of the Korean Mathematical Society
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• v.13 no.3
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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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• v.17 no.2
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.