• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.4
• /
• pp.1-9
• /
• 2007

in this paper, we propose the Adaptive Decision Algorithm for RFID Tag Anti-Collision. We study the RFID Tag anti-collision technique of ALOHA and the anti-collision algorithm of binary search. The existing technique is several problems; the transmitted data rate included of data, the recognition time and energy efficiency. For distinction of all tags, the Adaptive Decision algorithm identify smaller one ,each Tag_ID bit's sum of bit '1'. In other words, Adaptive Decision algorithm had standard of selection by actively, the algorithm can reduce unnecessary number of search even than the exisiting algorithm. The Adaptive Decision algorithm had performance test that criterions were reader's number of repetition and number of transmitted bits for understanding tag. We showed the good performance of Adaptive Decision algorithm better than exisiting algorithm.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.6
• /
• pp.21-31
• /
• 2015

The Internet users want to get real-time services for various multi-media applications. Network traffic rate has been rapidly increased, and data amounts that the Internet has to carry have been exponentially increased. A packet is the basic unit in transferring data at the Internet, and packet classification is one of the most challenging functionalities that routers should perform at wire-speed. Among various known packet classification algorithms, area-based quad-trie (AQT) algorithm is one of the efficient algorithms which can lookup five header fields simultaneously. As a representative space decomposition algorithm, the AQT requires a small amount of memory in storing classification rules, but it does not provide high-speed classification performance. In this paper, we propose a new packet classification algorithm by applying a binary search for the codewords of the AQT to overcome the issue of the AQT. Throughout simulation, it is shown that the proposed algorithm provides a better performance than the AQT in the number of rule comparisons with each input packet.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.8
• /
• pp.1382-1389
• /
• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.4B
• /
• pp.403-418
• /
• 2009

IP version 6 (IPv6) is a new If addressing scheme that has 128-bit address space. IPv6 is proposed to solve the address space problem of IP version 4 (IPv4) which has 32-bit address space. For a given IPv6 routing set, if a forwarding table is built using a trio structure, the trio has a lot more levels than that for IPv4. Hence, for IPv6 address lookup, the binary search on trio levels would be more appropriate and give better search performance than linear search on trio levels. This paper proposes a new IPv6 address lookup algorithm performing binary search on trio levels. The proposed algorithm uses a Bloom filter in pre-filtering levels which do not have matching nodes, and hence it reduces the number of off-chip memory accesses. Simulation has been performed using actual IPv6 routing sets, and the result shows that an IPv6 address lookup can be performed with 1-3 memory accesses in average for a routing data set with 1096 prefixes.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.12
• /
• pp.95-105
• /
• 1996

This paper presents structure optimization of a feedforward neural netowrk controller using the genetic algorithm. It is important to design the neural network with minimum structure for fast response and learning. To minimize the structure of the feedforward neural network, a genralization of multilayer neural netowrks, the genetic algorithm uses binary coding for the structure and floating-point coding for weights. Local search with an on-line learnign algorithm enhances the search performance and reduce the time for global search of the genetic algorithm. The relative fitness defined as the multiplication of the error and node functions prevents from premature convergence. The feedforward neural controller of smaller size outperformed conventional multilayer perceptron network controller.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1995.04a
• /
• pp.187-194
• /
• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.5
• /
• pp.2142-2148
• /
• 2015

This paper proposes novel real-time footstep planning and following methods for the navigation of humanoid robots. A footstep command is defined by a walking direction and step lengths for footstep planning. The walking direction is determined by a uni-vector field navigation method, and the allowable yawing range caused by hardware limitation is considered. The lateral step length is determined to avoid collisions between the two legs while walking. The sagittal step length is modified by a binary search algorithm when collision occurs between the robot body and obstacles in a narrow space. If the robot body still collides with obstacles despite the modification of the sagittal step length, the lateral step length is shifted at the next footstep. For footstep following, a walking pattern generator based on a 3-D linear inverted pendulum model is utilized, which can generate modifiable walking patterns using the zero-moment point variation scheme. Therefore, it enables a humanoid robot to follow the footstep command planned for each footstep. The effectiveness of the proposed method is verified through simulation and experiment.

• 한국정보통신설비학회:학술대회논문집
• /
• 2005.08a
• /
• pp.285-289
• /
• 2005

In recent years, RFID is widely used in industrial applications including factory, material flow, logistics and defense areas. In this paper, we developed a RFID baseband system with ASK modulation and convolutional channel code. A commercial ASK RF module is used its frequency range in $350{\sim}351$MHz and power is 10mW and the convolution code is constraint length k=3 and rate R=1/2 The performance is measured implemented the binary search algorithm as anti-collision method and we show the wave shapes whit collision occurrence.

• Journal of Ship and Ocean Technology
• /
• v.9 no.4
• /
• pp.35-46
• /
• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Broadcast Engineering
• /
• v.16 no.4
• /
• pp.647-656
• /
• 2011

Motion estimation using one-bit transform and two-bit transform reduces the complexity for computation of matching error; however, the peak signal-to-noise ratio (PSNR) is degraded. Modified 1BT (M1BT) and modified 2BT (M2BT) have been proposed to compensate degraded PSNR by adding conditional local search. However, these algorithms require many additional search points in fast moving sequences with a block size of $16{\times}16$. This paper provides more efficient search method by preparing candidate blocks using the number of non-matching points (NNMP) than the conditional local search. With this NNMP-based search, we can easily obtain candidate blocks with small NNMP and efficiently search final motion vector. Experimental results show that the proposed algorithm not only reduces computational complexity, but also improves PSNR on average compared with conventional search algorithm used in M1BT, M2BT and AM2BT.