• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.675-685
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• 2020

As the classical response surface method (RSM), the polynomial RSM is so easy-to-apply that it is widely used in reliability analysis. However, the trade-off of accuracy and efficiency is still a challenge and the "curse of dimension" usually confines RSM to low dimension systems. In this paper, based on the univariate decomposition, the polynomial RSM is executed in a new mode, called as DPRSM. The general form of DPRSM is given and its implementation is designed referring to the classical RSM firstly. Then, in order to balance the accuracy and efficiency of DPRSM, its adaptive order revision around the most probable point (MPP) is proposed by introducing the univariate polynomial order analysis, noted as RDPRSM, which can analyze the exact nonlinearity of the limit state surface in the region around MPP. For testing the proposed techniques, several numerical examples are studied in detail, and the results indicate that DPRSM with low order can obtain similar results to the classical RSM, DPRSM with high order can obtain more precision with a large efficiency loss; RDPRSM can perform a good balance between accuracy and efficiency and preserve the good robustness property meanwhile, especially for those problems with high nonlinearity and complex problems; the proposed methods can also give a good performance in the high-dimensional cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.59-67
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• 2006

OFDM (Orthogonal frequency Division Multiplexing) systems convert serial data stream to N parallel data streams and modulate them to N orthogonal subcarriers. Thus spectrum utilization efficiency of the OFDM systems are high and high-speed data transmission is possible. However, with the OFDM systems using the same modulation method at all subcarriers, the error probability is dominated by the subcarriers which experience deep fades. Therefore, in order to enhance the performance of the system adaptive modulation is required, with which the modulation methods of the subcarriers are determined according to the estimated SNRs. The IEEE 802.11a system selects various transmission speed between 6 and 54 Mbps according to the modulation mode. There are three typical methods for SNR estimation: Direct estimation method uses the frequency domain symbols to estimate SNR directly by minimizing MSE (Mean Square Error), EVM method utilizes the distance between the demodulated constellation points and received complex values, and the method utilizing the Viterbi algorithm uses the cumulative minimum distance in decoding process to estimate the SNR indirectly. Through comparison analyses of three methods we propose a new SNR estimation method, which employs both the EVM method and the Viterbi algorithm. Finally, we perform extensive computer simulations to confirm the performance improvement of the proposed adaptive OFDM systems on the basis of IEEE 802.11a.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.299-307
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• 2016

While segmenting "complex" images (with multiple objects, many details, etc.) we experienced a need to explore new ways for time-efficient and meaningful image segmentation. In this paper we propose a new technique for image segmentation which has only one variable for controlling the expected number of segments. The algorithm focuses on the treatment of pixels in transition zones between various label distributions. Results of the proposed algorithm (e.g. on the Berkeley image segmentation dataset) are comparable to those of GMM or HMM-EM segmentation, but are achieved with significantly reduced computation time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.157-160
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain system in which conventional and mathematical models may fail to give satisfactory results. mGA(messy Genetic Algorithm) has more effective and adaptive structure than sGA with respect to using changeable-length string and VEGA(Virus Evolution Genetic) Algorithm) can search the global and local optimal solution simultaneously with reverse transcription operator and transduction operator. Therefore in this paper, the optimal fuzzy model is obtained using Virus-messy Genetic Algorithm(Virus-mGA). In this method local information is exchanged in population so that population may sustain genetic divergence. To prove the surperioty of the proposed approach, we provide the numerical example.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.311-314
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• 2001

Recently, research on Intelligent Information Process has actively been under way JD various areas and gradually extended to be adaptive to uncertain and complex dynamic environments. This paper presents a Multiple Valued Logic Automata(MVL-Automata) Model, utilizing properties of difference in a Multiple Valued Logic function. That is, MVL-Automata is able to be autonomously adapted to dynamic changing since an input stling is mapped to the value of a Multiple Valued Logic function and the property of difference in a Multiple Valued Logic function is applied to state transition. Therefore, Multiple Valued Logic Automata can be widely applied to the modeling dynamically of changing environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.49-53
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• 2011

Path following of the mobile robot is one research hot for the mobile robot navigation. For the control system of the wheeled mobile robot(WMR) being in nonhonolomic system and the complex relations among the control parameters, it is difficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive following controller based on the PID for mobile robot path following. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity and orientation tracking control of the nonholonomic WMR. The simulation results of wheel type mobile robot platform are given to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.181-184
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• 1997

This paper propose the systematic procedure of the fuzzy model based controller for the continuous nonlinear system. Fuzzy controller have been successfully applied to many uncertain and complex industrial plants. The design of the fuzzy controller mainly depends on the knowledge from the expert who are familiar with the plant by trial and error. Therefore we need more systematic approach to the design of the fuzzy controller. In this paper, we design fuzzy model based controller applied to the nonlinear system. Unlike the design procedures reported in[8] and[9], we use the nonlinear process directly in designing the controller. This controller has been successfully applied to an inverted pendulum.