• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.29-37
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• 2015

The wavefront printer records a volume-reflection hologram as a two-dimensional array of elemental holograms from computer-generated holograms (CGHs) displayed on a spatial light modulator (SLM). The wavefront coming from the object is extracted by filtering in the spatial-frequency domain. This paper presents a method to improve color reproduction in a wavefront printer with spatial division of exposures at primary colors, by adaptive partitioning of the SLM in accordance with the color content encoded in the input CGHs, and by the controllable change of exposure times for the recording of primary colors. The method is verified with a color wavefront printer with demagnification of the object beam. The quality of reconstruction achieved by the proposed method proves its efficiency in eliminating the stripe artifacts that are superimposed on reconstructed images in conventional mosaic recording.

• ETRI Journal
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• v.34 no.6
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• pp.900-910
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• 2012

In this paper, a novel trellis-coded spatial modulation (TCSM) design method is presented and analyzed. Inspired by the key idea of trellis-coded modulation (TCM), the detailed analysis is firstly provided on the unequal error protection performance of spatial modulation constellation. Subsequently, the Ungerboeck set partitioning rule is proposed and applied to develop a general method to design the novel TCSM schemes. Different from the conventional TCSM approaches, the novel one based on the Ungerboeck set partitioning rule has similar properties as the classic TCM, which has simple but effective code design criteria. Moreover, the novel designed schemes are robust and adaptive to the generalized Rician fading channels, which outperform the traditional TCSM ones. For examples, the novel 4-, 8-, and 16-state TCSM schemes are constructed by employing different transmit antennas and different modulation schemes in different channel conditions. Simulation results clearly demonstrate the advantages of the novel TCSM schemes over the conventional ones.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.478-487
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• 2004

We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.24-27
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• 1996

The storage function method(SFM) is one of hydrologic flood routings which has been used most widely in Korea and Japan. This paper presents a storage function method using multiple model adaptive estimation(MMAE), in which a model set is generated by partitioning storage parameters over feasible range, and each storage function model is estimated, and then the weighted average of them is calculated. Finally, the future runoff is predicted in real time by means of observed data of water level at dam and rainfall. Simulation results applied to actual data show that the proposed method has much better performance than that of conventional SFM.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.166-169
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• 1997

For parallel processing, the compiler partitions a loaded program into a set of tasks and makes a schedule for the tasks that will minimize parallel processing time for the loaded program. Building an optimal schedule for a given set of partitioned tasks of a program has known to be NP-complete. In this paper we introduce a GA(Genetic Algorithm)-based scheduling method in which a chromosome consists of two parts of a string which decide the number and order of tasks on each processor. An additional computation is used for feasibility constraint in the chromosome. By granularity theory, a partitioned program is categorized into coarse-grain or fine-grain types. There exist good heuristic algorithms for coarse-grain type partitioning. We suggested another GA adaptive to the coarse-grain type partitioning. The infeasibility of chromosome is overcome by the encoding and operators. The number of processors are decided while the GA find the minimum parallel processing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.677-686
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• 2012

In the presence of a high power cellular network, picocells are added to a Macro-cell layout aiming to enhance total system throughput from cell-splitting. While because of the different transmission power between macrocell and picocell, and co-channel interference challenges between the existing macrocell and the new low power node-picocell, these problems result in no substantive improvement to total system effective throughput. Some works have investigated on these problems. Pico Cell Range Expansion (CRE) technique tries to employ some methods (such as adding a bias for Pico cell RSRP) to drive to offload some UEs to camp on picocells. In this work, we propose two solution schemes (including cell selection method, channel allocation and serving process) and combine new adaptive frequency partitioning reuse scheme to improve the total system throughput. In the simulation, we evaluate the performances of heterogeneous networks for downlink transmission in terms of channel utilization per cell (pico and macro), call blocking probability, outage probability and effective throughput. The simulation results show that the call blocking probability and outage probability are reduced remarkably and the throughput is increased effectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.64-76
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• 2009

Adaptive Random Testing through Iterative Partitioning(IP-ART) is one of Adaptive Random Testing(ART) techniques. IP-ART uses an iterative partitioning method for input domain to improve the performances of early-versions of ART that have significant drawbacks in computation time. Another version of IP-ART, named with EIP-ART(IP-ART with Enlarged Input Domain), uses virtually enlarged input domain to remove the unevenly distributed parts near the boundary of the domain. EIP-ART could mitigate non-uniform test case distribution of IP-ART and achieve relatively high performances in a variety of input domain environments. The EIP-ART algorithm, however, have the drawback of higher computation time to generate test cases mainly due to the additional workload from enlarged input domain. For this reason, a revised version of IP-ART without input domain enlargement needs to improve the distribution of test cases to remove the additional time cost. We explore three smoothing algorithms which influence the distribution of test cases, and analyze to check if any performance improvements take place by them. The simulation results show that the algorithm of a restriction area management achieves better performance than other ones.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.451-461
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• 2009

The Adaptive Random Testing(ART) aims to improve the performance of traditional Random Testing(RT) by reducing the number of test cases to find the failure region which is located in the input domain. Such enhancement can be obtained by efficient selection algorithms of test cases. The ART through Iterative Partitioning(IP-ART) is one of ART techniques and it uses an iterative input domain partitioning method to improve the performance of early-versions of ART which have significant drawbacks in computation time. And the IP-ART with Enlarged Input Domain(EIP-ART), an improved version of IP-ART, is known to make additional performance improvement with scalability by expanding to virtual test space beyond real input domain of IP-ART. The EIP-ART algorithm, however, have the drawback of heavy cost of computation time to generate test cases mainly due to the virtual input domain enlargement. For this reason, two algorithms are proposed in this paper to mitigate the computation overhead of the EIP-ART. In the experiments by simulations, the tiling technique of input domain, one of two proposed algorithms, showed significant improvements in terms of computation time and testing performance.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.395-401
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• 2010

Image enhancement algorithm aims to improve the visual quality of low contrast image through eliminating the noise and blurring, increasing contrast, and raising detail. This paper proposes adaptive dynamic range linear stretching(ADRLS) algorithm based on advantages of existing methods. ADRLS method is focused on generating sub-histograms of the majority through partitioning the histogram of input image and applying adaptive scale factor. Generated sub-histograms are finally applied by linear stretching(LS) algorithm. In order to validate proposed method, it is compared with LS and histogram equalization(HE) algorithm generally used. As the result, the proposed method show to improve contrast of input image and to preserve distinct characteristics of histogram by controlling excessive change of brightness.