• Journal of ILASS-Korea
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• v.19 no.1
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• pp.25-32
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• 2014

For reduction of $CO_2$ emission emitted from combustion engine, the developed nations have been focused on R&D of hybrid electric vehicle. Further more, many automobile companies are researching on various techniques related to engine used in HEV to enhance fuel economy. One of key techniques is miller cycle that control a valve timing to reduce compression stroke for saving energy and increase expansion stroke for high power. In this study, it was investigated the in-cylinder flow characteristics of miller cycle with variable intake valve timing by using the ANSYS simulation code. For simulation, the key analytic parameter defined as intake valve closing timing and cam profile. As main results, it was shown that LIVC cause a lower pressure inside cylinder and had better control turbulence intensity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.19-24
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• 2017

Nowadays, based on the advancement of IT techniques, innovative products combining IT techniques to PA system are developing. In this paper, we presented the hybrid mixer controller for digital PA system. We develop the integrated mixer controller which includes the digital mixer composing an existing digital PA system and function of digital integrated controller. Developed integrated mixer controller consists of multichannel mixer function with 16 audio input channels, 8 output channels. And, it has an equalizer for processing digital audio signal, matrix and limiter. Also, the developed controller has some features such as internet connection for controlling of overall PA system and remote monitoring of mixer process condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.5
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• pp.21-29
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• 2008

This paper proposes a carrier frequency of set estimation algorithm for IEEE802.11n system. As IEEE802.11n is a multiple input multiple output(MIMO) system, so there are several combining techniques which are used in multiple receive antenna system. In this paper, we propose hybrid carrier frequency offset estimation algorithms using combining techniques in multiple receive antenna systems, and show that the proposed selection combining carrier frequency offset (CFO) estimation algorithm can estimate carrier frequency offset within 1/10 MSE error at SNR 10 dB in channel B and within 1/2 MSE error at SNR 10 dB in channel D rather than the conventional MIMO CFO one.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.729-739
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• 2006

This paper describes an active fault-tolerant control system for an induction motor drive that propels an Electrical Vehicle(EV) or a Hybrid one(HEV). The proposed system adaptively reorganizes itself in the event of sensor loss or sensor recovery to sustain the best control performance given the complement of remaining sensors. Moreover, the developed system takes into account the controller transition smoothness in terms of speed and torque transients. In this paper which is the sequel of (Diallo et al., 2004), we propose to introduce more advanced and intelligent control techniques to improve the global performance of the fault-tolerant drive for automotive applications(e.g. EVs or HEVs). In fact, two control techniques are chosen to illustrate the consistency of the proposed approach: sliding mode for encoder-based control; and fuzzy logics for sensorless control. Moreover, the system control reorganization is now managed by a fuzzy decision system to improve the transitions smoothness. Simulations tests, in terms of speed and torque responses, have been carried out on a 4-kW induction motor drive to evaluate the consistency and the performance of the proposed fault-tolerant control approach.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.515-518
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• 1998

This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algorithms. Simulation results verify the validity of the presented method.

• Information Systems Review
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• v.5 no.2
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• pp.203-217
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• 2003

Constraint Programming and Optimization have developed in different fields to solve common problems in real world. In particular, constraint propagation and linear Programming are their own fundamental and complementary techniques with the potential for integration to benefit each other. This intersection has evoked the efforts to combine both for a solution method to combinatorial optimization problems. Attempts to combine them have mainly focused on incorporating either technique into the framework of the other with traditional models left intact. This paper argues that integrating both techniques into an old modeling fame loses advantages from another and the integration should be molded in a new framework to be able to exploit advantages from both. The paper propose a declarative modeling framework in which the structure of the constraints indicates how constraint programming and optimization solvers can interact to solve problems.

• Restorative Dentistry and Endodontics
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• v.23 no.1
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• pp.213-222
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• 1998

Advisor: Prof. The quality of apical seal obtained with 3 different gutta-percha obturation techniques was compared in 49 recently extracted single rooted teeth. The root canals were instrumented using step-back technique and obturated with laterally condensed gutta-percha, Continuous Wave gutta-percha, and hybrid technique. Teeth were suspended in black India ink for 7 days, cleared, and then examined under a stereomicroscope at ${\times}10$ magnification. The results were as follows; 1. All experimental groups produced favorable apical seal. 2. The mean leakage was $0.23{\pm}0.25mm$ for group 1, $0.17{\pm}0.21mm$ for group 2, and $0.19{\pm}0.23mm$ for froup 3, but there was no statistical difference amoung them. Within the limits of the results of this experiment, the Continuous Wave gutta-percha obturation technique demonstrated relatively favorable apical sealing effect and shorter obturation time. Thus, it is thought that this obturation technique is a acceptable method for clinical use but further studies on this metter should be conducted.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.