• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.21-27
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• 1998

In this paper we apply routing algorithms in circuit switched networks to B-ISDN networks and investigate the performance. B-ISDN supports a wide range of services with hetrogeneous bandwidth requirements. We assume that the network supports D classes of traffic. It is modeled as a finite D dimensional Markov chain. A call is blocked on arrival if the required bandwidth is not available on the route. The shortest path routing, alternate routing and trunk reservation are considered for performance comparison. We also consider trunk reservation with restricted access control where the network reserves certain amount of bandwidths for one class of traffic that assumes a higher transmission priority. Through the method of successive iterations, we obtain the steady state equilibrium probabilities and call blocking probabilities for dynamic routing. The results can be used to design a B-ISDN network that improves network connection availability and efficiency while simultaneously reducing the network costs.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.391-399
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• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.157-162
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• 2019

The video game Tetris is one of most popular game and it is well known that its game rule can be modelled as MDP (Markov Decision Process). This paper presents a DQN (Deep Q-Network) based game agent for Tetris game. To this end, the state is defined as the captured image of the Tetris game board and the reward is designed as a function of cleared lines by the game agent. The action is defined as left, right, rotate, drop, and their finite number of combinations. In addition to this, PER (Prioritized Experience Replay) is employed in order to enhance learning performance. To train the network more than 500000 episodes are used. The game agent employs the trained network to make a decision. The performance of the developed algorithm is validated via not only simulation but also real Tetris robot agent which is made of a camera, two Arduinos, 4 servo motors, and artificial fingers by 3D printing.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1281-1293
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• 1996

In this paper, we propose a novel recognition model of on-line cursive Korean characters using HMM(Hidden Markov Model) and level building algorithm. The model is constructed as a form of recognition network with HMM for graphemes and Korean combination rules. Though the network is so flexible as to accomodate variability of input patterns, it has a problem of recognition speed caused by 11, 172 search paths. To settle the problem, we modify the level building algorithm to be adapted directly to the Korean combination rules and apply it to the model. The modified algorithm is efficient network search procedure time complexity of which depends on the number of HMMs for each grapheme, not the number of paths in the extensive recognition network. A test with 15, 000 hand written characters shows recognition rat 90% and speed of 0.72 second per character.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.1-6
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• 2000

In this paper, the hybrid system of HMM and neural network is proposed and show better recognition rate of the post-process procedure which minimizes the process error of recognition than that of HMM(Hidden Markov Model) only used. After the HMM training by training data, testing data that are not taken part in the training are sent to HMM. The output probability from HMM output by testing data is used for the training data of the neural network, post processor. After neural network training, the hybrid system is completed. This hybrid system makes the recognition rate improvement of about $4.5\%$ in MLP and about $2\%$ in RBFN and gives the solution to training time of conventional hybrid system and to decrease of the recognition rate due to the lack of training data in real-time speech recognition system.

• ETRI Journal
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• v.42 no.1
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• pp.118-128
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• 2020

A semitransparent photovoltaic-thermal (PV/T) air collector can produce electricity and heat simultaneously. To maximize the thermal and overall efficiency of the semitransparent PV/T air collector, its availability should be maximum; this can be determined through a Markov analysis. In this paper, a Markov model is developed to select an optimized number of semitransparent PV modules in service with five states and two states by considering two parameters, namely failure rate (λ) and repair rate (μ). Three artificial neural network (ANN) models are developed to obtain the minimum cost, minimum temperature, and maximum thermal efficiency of the semitransparent PV/T air collector by setting its type appropriately and optimizing the number of photovoltaic modules and cost. An attempt is also made to achieve maximum thermal and overall efficiency for the semitransparent PV/T air collector by using ANN after obtaining its minimum temperature and available solar radiation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1108-1130
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• 2013

Wireless Body Area Networks (WBANs) are becoming increasingly important to solve the issue of health care. IEEE 802.15.6 is a wireless communication standard for WBANs, aiming to provide a real-time and continuous monitoring. In this paper, we present our development of a modified Markov Chain model and a backoff model, in which most features such as user priorities, contention windows, modulation and coding schemes (MCSs), and frozen states are taken into account. Then we calculate the normalized throughput and average access delay of IEEE 802.15.6 networks under saturation and ideal channel conditions. We make an evaluation of network performances by comparing with IEEE 802.15.4 and the results validate that IEEE 802.15.6 networks can provide high quality of service (QoS) for nodes with high priorities.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.687-698
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• 1997

In this paper, a Variation of Blast with Go-Back-N(V-BGBN) protocol is proposed, which differs from Blast with Go-Back-N(BGBN) and Blast with Full Retransmission on Error(BFRE) protocols in the retransmission strategy of packets. Performances of these three protocols under correlated packet errors are analyzed. Throughput efficiency of an arbitrary packet is obtained under the assumption that the round trip delay and the packet length are respectively constant. Recursive formula and difference equations are used as analytical tools. Correlation of packet errors is modelled by a two state Markov chain. The throughput efficiencies under these protocols are compared. V-BGBN protocol is shown to be superior to other two protocols in high speed network.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.140-150
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• 2008

Point Coordination Function (PCF) of the IEEE 802.11 protocol providing a centrally-controlled polling-based multiple access to a wireless channel is very efficient in high load conditions. However, its performance degrades with increasing the number of terminals and decreasing the load, because of wastes related to unsuccessful polling attempts. To solve the problem, we propose and study analytically the generic dynamic polling policy using backoff concept. For this aim, we develop Markov models describing the network queues changes, what allows us to estimate such performance measures as the average MAC service time and the average MAC sojourn time, to show the dynamic polling efficiency and to tune optimally the backoff rule.

• ETRI Journal
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• v.19 no.1
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• pp.13-25
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• 1997

In this paper, we propose a new queuing model, MMDP/MMDP/1/K, for an asynchronous transfer mode(ATM) multiplexer with multiple quality of service(QoS) variable bit rate (VBR) traffic in broadband-integrated services digital network (B-ISDN). We use the Markov Modulated Deterministic Process(MMDP) to approximate the actual arrival process and another MMDP for service process Using queuing analysis, we derive a formula for the cell loss probability of the ATM multiplexer in terms of the limiting probabilities of a Markov chain. The cell loss probability can be used for connection admission control in ATM multiplexer and the calculation of equivalent bandwidth for arrival traffic, The major advantages of this approach are simplicity in analysis, accuracy of analysis by comparison of simulation, and numerical stability.