• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.477-479
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• 2011

본 논문에서는 1차원 은닉 마코프 모델을 2차원으로 확장하기 위하여 노드들의 마코프 특성이 인과적인 관계를 갖는 마코프 메쉬 모델을 이용하여 완전한 2차원 HMM의 구조를 갖는 모델을 제안한다. 마코프메쉬 모델은 이웃시스템을 통하여 이전의 시점을 정의하고, 인과적인 관계를 통하여 전이확률의 계산을 가능하게 한다. 또한 영상의 최적의 분할을 위하여 계층적 디리슐레 과정을 사전분포로 두어 고정된 상태의 수가 아닌 무한의 상태 수를 갖는 2차원 HMM을 제안한다. HDP로 정의된 사전분포와 관측된 표본 자료의 정보를 갖는 우도함수를 결합한 사후분포의 베이스 추정은 깁스샘플링 알고리즘을 이용하여 계산된다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.442-449
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• 2012

Cognitive Radio(CR) is the technology that allocates the frequency by using dynamic spectrum access. We proposed a model to calculate the optimal level of the cognitive radiotelegraph, where secondary users opportunistically share the spectrum with primary users through the spectrum sensing. When secondary user with cognitive radio detects the arrival of a primary user in its current channel, the secondary user moves to the idle channel or be placed in the virtual queue. We assume that the primary users have finite buffers and the population of secondary users is finite. Using a two-dimensional Makov model with preemptive priority queueing, we could derive the blocking and waiting probability as well as the optimal level of cognitive radiotelegraph under a various range of parameter circumstances.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.316-328
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• 2003

In this paper, we propose and analyze two adaptive channel allocation schemes for supporting multimedia traffics in hierarchical cellular systems. It is guaranteed to satisfy the required quality of service of multimedia traffics according to their characteristics such as a mobile velocity for voice calls and a delay tolerance for multimedia calls. In the scheme 1, only slow-speed voice calls are allowed to overflow from macrocell to microcell and only adaptive multimedia calls can overflow from microcell to macrocell after reducing its bandwidth to the minimum channel bandwidth. In the scheme II, in addition to the first scheme, non-adaptive multimedia calls can occupy the required channel bandwidth through reducing the channel bandwidth of adaptive multimedia calls. The proposed scheme I is analyzed using 2-dimensional Markov model. Through computer simulations, the analysis model and the proposed schemes are compared with the fixed system and two previous studies. In the simulation result, it is shown that the proposed schemes yield a significant improvement in terms of the forced termination probability of handoff calls and the efficiency of channel usage.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.9
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• pp.25-37
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• 2007

The existing route re-establishment methods which intend to extend the lifetime of the network attempt to find new routes in order not to overly consume energy of certain nodes. These methods outperform other routing algorithms in the network lifetime extension aspect because they try to consume energy evenly for the entire network. However, these algorithms involve heavy signaling overheads because they find new routes based on the flooding method and route re-acquisition occurs often. Because of the overhead they often can not achieve the level of performance they intend to. In this paper, we propose a new route re-acquisition algorithm ARROW which takes into account the cost involved in the packet transmission and the route re-acquisition. Since the proposed algorithm considers future route re-acquisition costs when it first finds the route, it spends less energy to transmit given amount of data while evenly consuming the energy as much as possible. Using 2-dimensional Markov Chain model, we compare the performance of the proposed algorithm and that of other algorithms. Analysis results show that the proposed algorithm outperforms the existing route re-acquisition methods in the signaling overhead and network lifetime aspects.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.81-86
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• 2014

In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.