• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.281-285
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• 2011

One of the key issues in implementing RFID systems is to design anti-collision protocols for identifying all the tags in the interrogation zone of a RFID reader with the minimum identification delay. In this paper, Furthermore, in designing such protocols, the limited resources in tags and readers in terms of memory and computing capability should be fully taken into consideration. we first investigate two typical RFID anti-collision algorithms, namely RFID Gen2 Q algorithm (accepted as the worldwide standard in industrial domain) and FAFQ algorithm including their drawbacks and propose a new RFID anti-collision algorithm, which can improve the performance of RFID systems in terms of tag identification time considerably. Further, we compared performance of the proposed algorithm with Q algorithm and FAFQ algorithm through computer simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.45-52
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• 2021

The space vector control based voltage control method for a three-phase PWM rectifier requires a lot of effort to design an optimal switching pattern since a switching pattern for the switching section must be designed. In this study, a D-Q control based SPWM output voltage control algorithm was studied for the three-phase six-pulse CVS type rectifier. In the output voltage control algorithm, three-phase reference signals are obtained from the D-Q transformation based on the space vector representation method, instead of the switching pattern, SPWM method is used to generate rectifier switching control signals. Next, a three-phase six-pulse CVS PWM rectifier based on D-Q transformation and SPWM was modeled using EMTP-RV. Finally, the validity of the D-Q control-based SPWM voltage control algorithm was confirmed by comparing the output voltage waveform obtained through EMTP-RV simulation works with a reference value and confirming that the output voltage accurately follows the reference voltage.

• ETRI Journal
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• v.30 no.5
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• pp.653-662
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• 2008

Arbitration of tag collision is a significant issue for fast tag identification in RFID systems. A good tag anti-collision algorithm can reduce collisions and increase the efficiency of tag identification. EPCglobal Generation-2 (Gen2) for passive RFID systems uses probabilistic slotted ALOHA with a Q algorithm, which is a kind of dynamic framed slotted ALOHA (DFSA), as the tag anti-collision algorithm. In this paper, we analyze the performance of the Q algorithm used in Gen2, and analyze the methods for estimating the number of slots and tags for DFSA. To increase the efficiency of tag identification, we propose new tag anti-collision algorithms, namely, Chebyshev's inequality, fixed adjustable framed Q, adaptive adjustable framed Q, and hybrid Q. The simulation results show that all the proposed algorithms outperform the conventional Q algorithm used in Gen2. Of all the proposed algorithms, AAFQ provides the best performance in terms of identification time and collision ratio and maximizes throughput and system efficiency. However, there is a tradeoff of complexity and performance between the CHI and AAFQ algorithms.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.219-224
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• 2022

Q-Learning is a technique widely used as a basic algorithm for reinforcement learning. Q-Learning trains the agent in the direction of maximizing the reward through the greedy action that selects the largest value among the rewards of the actions that can be taken in the current state. In this paper, we studied a policy that can speed up agent training using Q-Learning in Frozen Lake 8×8 grid environment. In addition, the training results of the existing algorithm of Q-learning and the algorithm that gave the attribute 'direction' to agent movement were compared. As a result, it was analyzed that the Q-Learning policy proposed in this paper can significantly increase both the accuracy and training speed compared to the general algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.358-366
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• 2012

LED-ID (Light Emitting Diode - Identification) is one of the key technologies for identification, data transmission, and illumination simultaneously. This is the new paradigm in the identification technology environment. There are many issues are still now challenging to achieve high performance in LED-ID system. Collision issue is one of them. Actually this is the most significant issue in all identification system. LED-ID system also suffers from collision problem. In our system, collision occurs when two or more readers transmit data to tag at the same time or vice versa. There are many anti-collision protocols to resolve this problem; such as: Slotted ALOHA, Basic Frame Slotted ALOHA, Query Tree, Tree Splitting, and Q-Algorithm etc. In this paper, we propose modified Q-Algorithm to resolve collision at tag. The proposed protocol is based on Q-Algorithm and used the information of arrived readers to a tag from neighbor. The information includes transmitting slot number of readers and the number of readers that can be arrived in next slot. Our proposed protocol can reduce the numbers of collision slot and the successful time to identify all readers. In this paper our simulation and theoretical results are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.1035-1048
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• 2023

The IEEE 802.11 WLAN adopts a random backoff algorithm for its collision avoidance mechanism, and it is well known that the contention-based algorithm may suffer from performance degradation especially in congested networks. In this paper, we design an efficient backoff algorithm that utilizes a reinforcement learning method to determine optimal values of backoffs. The mobile nodes share a common contention window (CW) in our scheme, and using a Q-learning algorithm, they can avoid collisions by finding and implicitly reserving their optimal time slot(s). In addition, we introduce Frame Size Control (FSC) algorithm to minimize the possible degradation of aggregate throughput when the number of nodes exceeds the CW size. Our simulation shows that the proposed backoff algorithm with FSC method outperforms the 802.11 protocol regardless of the traffic conditions, and an analytical modeling proves that our mechanism has a unique operating point that is fair and stable.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.172-176
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• 2011

In the Gen-2 Q-algorithm, the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not optimized in the standard. However, the standard suggests that the reader uses small values of C when the slot-count is large and larger values of C when the slot-count is small. In this case, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to select the weight based on the slot-count size of current query round. Through various computer simulations, it is demonstrated that the proposed scheme achieves more stable performances than Gen-2 Q-algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1431-1438
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• 2004

Reinforcement learning learns policies for accomplishing a task's goal by experience through interaction between agent and environment. Q-learning, basis algorithm of reinforcement learning, has the problem of curse of dimensionality and slow learning speed in the incipient stage of learning. In order to solve the problems of Q-learning, new function approximation methods suitable for reinforcement learning should be studied. In this paper, to improve these problems, we suggest Fuzzy Q-Map algorithm that is based on online fuzzy clustering. Fuzzy Q-Map is a function approximation method suitable to reinforcement learning that can do on-line teaming and express uncertainty of environment. We made an experiment on the mountain car problem with fuzzy Q-Map, and its results show that learning speed is accelerated in the incipient stage of learning.