• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.469-474
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• 2009

Q-algorithm is proposed at EPC global Class-1 Generation-2 RFID systems to determine the frame size of next query round. In Q-algorithm, the reader calculates the frame size without estimating the number of tags. But, it uses only the slot conditions: empty, success, or collision. Therefore, it wastes less computational cost and is simpler than other algorithms. However, the constant parameter C value, which is used for calculating the next frame size, is not optimized. In this paper, we propose the optimized C values of Q-algorithm according to the number of tags within the identification range of reader through a lot of computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.164.1-164
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• 2001

Q-learning is a recent reinforcement learning algorithm that does not need a modeling of environment and it is a suitable approach to learn behaviors for autonomous agents. But when it is applied to multi-agent learning with many I/O states, it is usually too complex and slow. To overcome this problem in the multi-agent learning system, we propose the successive Q-learning algorithm. Successive Q-learning algorithm divides state-action pairs, which agents can have, into several Q-functions, so it can reduce complexity and calculation amounts. This algorithm is suitable for multi-agent learning in a dynamically changing environment. The proposed successive Q-learning algorithm is applied to the prey-predator problem with the one-prey and two-predators, and its effectiveness is verified from the efficient avoidance ability of the prey agent.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.635-637
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• 2012

In Gen-2 Q-algorithm, the value of initial $Q_{fp}$ and weight C is not defined in the standard. If we let the initial $Q_{fp}$ be large or small, the number of empty slot will be large during the initial query round or almost all the slots will be collided, respectively. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, it is anticipated that 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 analyze how the performances of Gen-2 Q-algorithm will be affected by the weight and initial slot-count size.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.93-101
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• 2013

This paper suggests optimal location algorithm of new firm $A(F_A)^{\prime}s$ p(p$B(F_B)$ already operating q outlets of sports in the market. This algorithm selects top q nodes among $V=V{\backslash}F_B$ nodes that covers maximum nodes based on the shortest distance. Then, q nodes choose next node that has a maximum cover with inclusion-exclusion principle. At the time of same number of cardinality in q sets to pre-defined q, we select the maximum cover node set. This algorithm called by competitive algorithm. The competitive algorithm simply decides the optimal location of the outlets p=1,2,3,4 for q=5. Also, we show that the market share of competitive algorithm can be maximize.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.519-523
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• 2010

In Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter of Q-algorithm, is not defined in the standard. In this case, if the number of tags within the reader's identification range is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. 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 allocate the optimal initial $Q_{fp}$ through the tag number estimation before the query round begins. Through computer simulations, it is demonstrated that the proposed scheme achieves more stable performance than Gen-2 Q-algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1444-1451
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• 2009

This paper first proposes diverse C determining models in the Q algorithm which is proposed in the EPCglobal C1 Gen 2 standard and then compares and analyzes its performance. EPCglobal C1 Gen 2 standard proposes the slot-count (Q) selection algorithm for multiple tag identification environment, but there is no such definition for the C value which modifies the Q value depending on collision or no reply. During the tag anti-collision process, the Q algorithm adds C to the Q when there is a collision and reduces the Q by C when there is no reply. The modified Q value updates new slot-counts for tags which determines the tag identification speed, so the C value is an important factor. However, many researches only intend to increase the tag identification speed by proposing a new method or modifying the Q algorithm without any research about the C value. This paper suggests diverse C models which satisfies the EPCglobal C1 Gen 2 and analyzes their performance in the multi tag identification environment. The result of this paper can be used as an index for future researches on EPCglobal C1 Gen 2 C models and multiple tag identification performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.653-655
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• 2011

EPCglobal proposed a Q-algorithm, which is used for selecting a slot-count in the next query round. However, it is impossible to allocate an optimized slot-count because the original Q-algorithm did not define an optimized weight C value. In this paper, we propose an adaptive Q-algorithm, in which we differentiate the weight values with respect to collision and empty slots. The weight values are defined with the identification time as well as the collision probability.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.1
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• pp.35-42
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• 2001

The primitive polynomial on GF(q) is used in the area of the scrambler, the error correcting code and decode, the random generator and the cipher, etc. The algorithm that generates efficiently the primitive polynomial on GF(q) was proposed by A.D. Porto. The algorithm is a method that generates the sequence of the primitive polynomial by repeating to find another primitive polynomial with a known primitive polynomial. In this paper, we propose the algorithm that is improved in the A.D. Porto algorithm. The running rime of the A.D. Porto a1gorithm is O($\textrm{km}^2$), the running time of the improved algorithm is 0(m(m+k)). Here, k is gcd(k, $q^m$-1). When we find the primitive polynomial with m odor, it is efficient that we use the improved algorithm in the condition k, m>>1.