• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.119-125
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• 2015

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical dosage. When the strange substance is contained in the sludge, however, the attenuation of ultrasonic wave could be increased or not be transmitted to the receiver. At that case, the value of concentration meter is higher than the actual density value or vibrated up and down. It has also been difficult to automate the residuals treatment process according to the problems as sludge attachment or damage of a sensor. Multi-beam ultrasonic concentration meter has been developed to solve these problems, but the failure of the ultrasonic beam of a specific concentration measurement value degrade the performance of the entire system. This paper proposes the method to improve the accuracy of sludge concentration rate by choosing reliable sensor values and learning them by proposed algorithm. The prediction algorithm is chosen as neuro-fuzzy model, which is tested by the various experiments.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.479-487
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• 2004

Biopotential signals have been used as command in systems using electrical stimulation of motor nerves to restore movement after an injury to the central nervous system (CNS). In order to use the voluntary EMG (electromyography) among the biopotentials as a control signal for the electrical stimulation of the same muscle for CNS injury patients, it is necessary to remove M-wave of having high magnitude from raw data. We designed an optimal filter for removing the M-wave and preserving the voluntary EMG and showed that the optimal filter is eigen filter. We also proved that the previous method using the prediction error filter(PEF) is a suboptimal filtering in the sense of preserving the voluntary EMG. On basis of the data obtained from a model for M-wave and voluntary EMG and from actual CNS injury patients, with false-positive rate analysis, the proposed adaptive filter showed a very promising performance in comparison with previous method.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.49-58
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• 2008

Generally, steel corrosion occurs in concrete structures due to carbonation in down-town area and underground site and it propagates to degradation of structural performance. In general diagnosis and inspection, only carbonation depth in sound concrete is evaluated but unsound concrete such as joint and cracked area may occur easily in a concrete member due to construction process. In this study, field survey of carbonation for RC columns in down-town area is performed and carbonation depth in joint and cracked concrete including sound area is measured. Probability of durable failure with time is calculated through probability variables such as concrete cover depth and carbonation depth which are obtained from field survey. In addition, service life of the structures is predicted based on the intended probability of durable failure in domestic concrete specification. It is evaluated that in a RC column, various service life is predicted due to local condition and it is rapidly decreased with insufficient cover depth and growth of crack width. It is also evaluated that obtaining cover depth and quality of concrete is very important because the probability of durable failure is closely related with C.O.V. of cover depth.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.149-163
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• 2015

In this study, we conducted study on the confirmation of thermal-hydraulic safety for Mast assembly with Computational Fluid Dynamics(CFD) analysis. Before performing the natural convection analysis for the Mast assembly by using CFD code, we validated the CFD code against two benchmark natural convection data for the evaluation of turbulence models and confirmation of its applicability to the natural convection flow. From the first benchmark test which was performed by Betts et al. in the simple rectangular channel, we selected standard k-omega turbulence model for natural convection. And then, calculation performance of CFD code was also investigated in the sub-channel of rod bundle by comparing with PNL(Pacific Northwest Laboratory) experimental data and prediction results by MATRA and Fluent 12.0 which were performed by Kwon et al.. Finally, we performed main natural convection analysis for fuel assembly inside the Mast assembly by using validated turbulence model. From the calculation, we observed stable natural circulation flow between the mast assembly and pool side and evaluated the thermal-hydraulic safety by calculating the departure from nucleate boiling ratio.

• Journal of the Korea Institute of Building Construction
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• v.14 no.2
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• pp.118-126
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• 2014

The establishment of the technology for evaluating friction resistance and pipe pressure and the relation of the fluid characteristics and pumpability of concrete is essential for concrete pumping performance for the rapid construction of super-tall buildings. In this study, a quantitative evaluation of concrete fluid characteristics and surface friction resistance was performed, applying different concrete mix proportions and pumping conditions. To achieve this, we developed a temporary horizontal pumping evaluation system to measure pipe pressure and surface friction characteristics, and performed an experiment to investigate the relations between concrete rheology characteristics and friction resistance in pipe. The experiment found that in terms of the rheology characteristics, plastic viscosity was reduced remarkably after pumping. As well, high regression between the surface friction and pressure gradient was confirmed. This means that it is possible to evaluate the friction resistance between concrete and pipe by means of a pumping system that includes a frictional resistance testing pipe. In addition, high regression between the plastic viscosity of concrete after pumping and friction resistance coefficient was confirmed. Finally, it is considered that pumping pressure can be predicted using the friction resistance coefficient derived in this study, and it has high regression.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.995-1007
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• 2008

Studies on modeling the rainfall-runoff relationship which shows nonlinear trend strongly use artificial neural networks theory not only for the prediction but also for the characteristics analysis of the data used by pattern classification. For the pattern classification, the results from Self-Organizing Map (SOM) mention that the map size and array for the SOM training have significantly influenced on the SOM performance. Since there is no deterministic method or theoretical equation to determine the number of rows and columns for the map size, hexagonal array is generally used for the map array. Therefore, this study present a determination of the optimized map structure for the rainfall-runoff analysis in Naju station considering the map size and array simultaneously which can represent the classified characterization of rainfall-runoff relationship. The result showed that the map size of 20$\times$16 hexagonal array with 8-clustered patterns was selected as an appropriate map structure for rainfall-runoff analysis in Naju station.