• Proceedings of the IEEK Conference
• /
• 2000.06a
• /
• pp.240-243
• /
• 2000

In this paper, new Multi-Modulus blind Equalizer Algorithms for QAM signal set is propsed and analyzed and its performance is evaluated. The MMA algorithm combines the benifits of RCA and CMA. A new Dual-mode blind Algorithms for QAM signal set is derived. The concept of this algorithms is based on the Dual-Mode algorithm and the MMA algorithm. In order to analyze and evaluate the performance of new MMA algorithms, computer simulation are performed for the nonsquare QAM signal constellations. Form the simulation results, we can verify that new MMA algorithms converges very fast comparing to conventional MMA algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.589-598
• /
• 2006

The rectilinear Steiner tree problem is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree (MST) on some set Steiner points. A hybrid evolutionary algorithm is introduced based upon the Prim algorithm. The Prim algorithm for the fitness evaluation requires heavy calculation time. The fitness value of parents is inherited to their child and the fitness value of child is estimated by the inherited structure of tree. We introduce four alternative evolutionary algorithms, Experiment result shows that the calculation time is reduced to 25% without loosing the solution quality by using the fitness estimation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.55-60
• /
• 1992

The IEEE 802.4 priority mechanism can be used to handle multiple data access classes of traffic. Several timers are used to realize the priority mechanism. The performance and stability of a token bus network depend on the assignment of such timers. In this peper, we present a dynamic timer assignment algorithm for the token passing bus network. The presented algorithm has simple structure for real-time applications and adaptively controls the set of initial timer values according to the offered traffic load. The assignment of the set of timers becomes easy due to the presented algorithm. Based on the iterative algorithm, some solutions such as mean waiting time are derived.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.63-72
• /
• 2005

In this paper, we propose the bit-rate control algorithm for the block based image compression like H.263, H.263+ or MPEG-4. The proposed algorithm is designed to identify the quantization parameter set through the Lagrangian optimization technique based on the well-known linear source model. We set the Lagrangian cost function with the rates and distortion calculated from the linear source model. We calculate the quantization parameter set using the Vitervi algorithm to solve the Lagrangian optimization problem considering the Dquant method of H.263 and MPEG-4. The proposed algorithm improves the video quality by up to 1.5 dB compared with the TMN8 scheme, and is more effective in the video sources with dynamic activities than the consistent quality approaches.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.5
• /
• pp.1-11
• /
• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.4
• /
• pp.1661-1678
• /
• 2016

Clustering is one of the key technologies in vehicular networks. Constructing and maintaining stable clusters is a challenging task in high mobility environments. DWCM (Distributed and Weighted Clustering based on Mobility Metrics) is proposed in this paper based on the d-hop dominating set of the network. Each vehicle is assigned a priority that describes the cluster relationship. The cluster structure is determined according to the d-hop dominating set, where the vehicles in the d-hop dominating set act as the cluster head nodes. In addition, cluster maintenance handles the cluster structure changes caused by node mobility. The rationality of the proposed algorithm is proven. Simulation results in the NS-2 and VanetMobiSim integrated environment demonstrate the performance advantages.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.4 s.193
• /
• pp.84-92
• /
• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.8
• /
• pp.1360-1365
• /
• 2010

In this paper, we propose a distance relaying algorithm using a Discrete Fourier Transform (DFT)-based modified phasor estimation method to eliminate the adverse influence of exponentially decaying DC offsets. Most distance relays are based on estimating phasors of the voltage and current signals. A DFT is generally used to calculate the phasor of the fundamental frequency component in digital protective relays. However, the output of the DFT contains an error due to exponentially decaying DC offsets. For this reason, distance relays have a tendency to over-reach or under-reach in the presence of DC offset components in a fault current. Therefore, the decaying DC components should be taken into consideration when calculating the phasor of the fundamental frequency component of a relaying signal. The error due to DC offsets in a DFT is calculated and eliminated using the outputs of an even-sample-set DFT and an odd-sample-set DFT, so that the phasor of the fundamental component can be accurately estimated. The performance of the proposed algorithm is evaluated for a-phase to ground faults on a 345 kV, 50 km, simple overhead transmission line. The Electromagnetic Transient Program (EMTP) is used to generate fault signals. The evaluation results indicate that adopting the proposed algorithm in distance relays can effectively suppress the adverse influence of DC offsets.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.4
• /
• pp.436-443
• /
• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• Journal of Power Electronics
• /
• v.12 no.1
• /
• pp.104-112
• /
• 2012

In this paper a new method for the design of a simple PI controller is presented and it has been applied in the control of a Boost based PFC rectifier. The Strength Pareto evolutionary algorithm, which is based on the Pareto Optimality concept, used in Game theory literature is implemented as a multi-objective optimization approach to gain a good transient response and a high quality input current. In the proposed method, the input current harmonics and the dynamic response have been assumed as objective functions, while the PI controller's gains of the PFC rectifier (Kpi, Tpi) are design variables. The proposed algorithm generates a set of optimal gains called a Pareto Set corresponding to a Pareto Front, which is a set of optimal results for the objective functions. All of the Pareto Front points are optimum, but according to the design priority objective function, each one can be selected. Simulation and experimental results are presented to prove the superiority of the proposed design methodology over other methods.