• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.59-61
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• 2018

Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.805-810
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• 1993

In this paper, we propose a compliance control scheme using a neurofuzzzy compliance model(NFCM). as a new control paradigm for telerobot systems. A NFCM, used as a compliance controller, is composed of a fuzzy compliance model(FCM), a neural network and a low pass filter. The NFCM is trained through a reinforcement learning algorithm, and then, can generate suitable compliant motion for a given task. A series of simulations have been performed to show applicability of the proposed algorithm send it is found that the NFCM can implement suitable compliant motion for a given task through the learning procedure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1154-1160
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• 1999

A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.62-69
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• 2003

In most shipyards, the measurement of 3-dimensional relative position of pipes should be connected in the block depends on the manual operation. It results a very tedious and inefficient procedure, thus the proper measurement system is needed to improve productivity and accuracy. This paper describes the development of pipe measurement system including system concepts, measuring procedures, system calibration, and its accuracy and productivity. And also, the possibility and things to be improved for application in shipyard are discussed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.173-178
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• 1998

The strategy presented in this paper is based on modifying the past patterens and adjusting the content of the driving patterns by a new algorithm. Learning happens during the driving procedure of a mobile vehicle. The purpose of this paper is to solve the problem how to realize the hardware neurocomputer by back propagation (BP) neural network learning on-line.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.845-848
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• 1993

This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.