• Smart Structures and Systems
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• v.29 no.3
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• pp.457-473
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• 2022

A high-frequency vibration control method is proposed in this paper for Pumped Storage Power Plants (PSPPs) using Eddy Current Tuned Mass Damper (EC-TMD), based on which a new type of EC-TMD device is designed. The eddy current damper parameters are optimized by numerical simulation. On this basis, physical simulation model tests are conducted to compare and study the effect of structural performance with and without damping, different control strategies, and different arrangement positions of TMD. The test results show that EC-TMD can effectively reduce the control effect under high-frequency vibration of the plant structure, and after the additional damping device forms EC-TMD, the energy dissipation is further realized due to the intervention of eddy current damping, and the control effect is subsequently improved. The Multi-Tuned Mass Damper (MTMD) control strategy broadens the tuning band to improve the robustness of the system, and the vibration advantage is more obvious. Also, some suggestions are made for the placement of the dampers to promote their application.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• The Korean Journal of Franchise Management
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• v.6 no.1
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• pp.1-20
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• 2015

Home Meal Replacement (HMR) products are ready-to-eat or pre-cooked food products that are consumed at daily home. HMR market has grown rapidly due to societal changes: increases in female social activities, silver population, and one-person households. Consumption channels of HMR can be classified into take-out, delivery, and retail. In Korean HMR market, retail sector is largely growing, but companies are focusing their business on the home delivery sector. Moreover, franchise companies are expanding their areal coverage in the HMR market based on their multi-unit strategy. However, more research on the HMR market is needed as existing studies are limited in conceptualization, classification, and processed food from malls or home-shopping channels. Therefore, we conducted the efficiency analysis on Gukseonsaeng, one of franchises that applied the take-out channel, using DEA method. According to the research on 29 franchisees of Gukseonsaeng, 77.9% of input appeared inefficient for technical efficiency, while 53.3% of input appeared inefficient for scale efficiency. Thus, we found that franchises of Gukseonsaeng are structured in increasing returns to scale (IRS), so enhancing efficiency by expanding scales need to be implemented.

• Journal of Digestive Cancer Research
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• v.11 no.2
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• pp.85-92
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• 2023

Colorectal cancer (CRC) is one of the most prevalent cancers and is the leading cause of cancer-related mortality worldwide. Based on the current screening guidelines by the American Cancer Society and Korean multi-society expert committee, CRC screening is recommended in asymptomatic adults starting at the age of 45 years. Fecal immunochemical test-based screening programs reduce the development of CRC and related mortality in the general population. However, this most popular CRC screening strategy demonstrates a crucial limitation due to modest diagnostic accuracy. Colonoscopy may be considered as an alternative primary method for CRC screening; however, its implementation can still be challenging due to concerns regarding invasiveness, low adherence, cost-effectiveness, and quality assurance. To overcome the limitations of current screening tests, innovative noninvasive tests for CRC screening have been developed with advances in molecular biology, genetics, epigenetics, and microbiomics for detecting CRC, which may enhance the approach to CRC screening and diagnosis in clinical practice in the near future. This review explores the emerging screening methods and discusses their potential for integration into current practice.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.564-570
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• 2019

Although some studies have been attempted to utilize 3D spatial information for fire safety and disaster management, it is still not enough to apply it to actual work. Especially, in case of multi-use facilities, many facilities are more vulnerable to rapid response in the event of a disaster due to complexity of facilities, diversity of usage, and specificity of users. In this paper, we propose a method to develop a 3D safety status information platform that combines 3D spatial information and time - varying safety status information for efficient disaster management of multi-use facilities. In detail, first, we analyze the use cases of existing disaster management platform and the needs of business users. Second, based on the analyzed results, target facilities were selected and possible scenarios were created. Finally, we developed platform architecture design and service development strategy. The research results will be used as a basis for future 3D safety status information platform development. This will contribute to improving the safety of multi-use facilities and minimizing damage to disaster vulnerable groups.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1666-1689
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• 2021

The red line of Permanent Basic Farmland is the most important part in the "three-line" demarcation of China's national territorial development plan. The scientific and reasonable delineation of the red line is a major strategic measure being taken by China to improve its ability to safeguard the practical interests of farmers and guarantee national food security. The delineation of Permanent Basic Farmland zoning (DPBFZ) is essentially a multi-objective optimization problem. However, the traditional method of demarcation does not take into account the synergistic development goals of conservation of cultivated land utilization, ecological conservation, or urban expansion. Therefore, this research introduces the idea of artificial immune optimization and proposes a multi-objective model of DPBFZ red line delineation based on a clone selection algorithm. This research proposes an objective functional system consisting of these three sub-objectives: optimal quality of cropland, spatially concentrated distribution, and stability of cropland. It also takes into consideration constraints such as the red line of ecological protection, topography, and space for major development projects. The mathematical formal expressions for the objectives and constraints are given in the paper, and a multi-objective optimal decision model with multiple constraints for the DPBFZ problem is constructed based on the clone selection algorithm. An antibody coding scheme was designed according to the spatial pattern of DPBFZ zoning. In addition, the antibody-antigen affinity function, the clone mechanism, and mutation strategy were constructed and improved to solve the DPBFZ problem with a spatial optimization feature. Finally, Tongxu County in Henan province was selected as the study area, and a controlled experiment was set up according to different target preferences. The results show that the model proposed in this paper is operational in the work of delineating DPBFZ. It not only avoids the adverse effects of subjective factors in the delineation process but also provides multiple scenarios DPBFZ layouts for decision makers by adjusting the weighting of the objective function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.518-521
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• 2005

The Structures or buildings nowadays draw more complexity in design due to space limitation and other factor that affect the height and dimensions, that results to instability. So the various methods have been carried out to improve the safety factor from an earthquake or a boom until recently. But, it is very hard to get model precisely because these structures are the non-linear and multi-variable systems. For this reason, we developed the active control system that is applied the adaptive control method on the U type Tuned Liquid Damper(TLD) passive control system. It is proven that the proposed active control strategy of the plate carrying U type TLD system is the more effective control method to suppress the vibration of the structure. The entire hybrid control system is composed of the actuator acted in the opposite direction of the TLD system's motion direction and the active control device with an air pressure adjuster. This paper proposed the adaptive control methods to improve the problem of U type TLD system which is used widely for the passive control of the building. And it is proved by the simulation. In advanced, it is developed the pressure control method that is improved the hybrid controller's performance by using air chamber pressure controller. These methods take the advantage of the decrease of the maximum displacement by using the controller as soon as the impact is loaded. This is a very important element for the safety design and economic design of structures.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.41-54
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• 2020

This study was conducted to provide basic data for a transparent and fair bidding system by identifying problems and suggesting improvement measures through an analysis of the bidding status for construction projects and service-related landscaping of multi-family housing. To this end, we used the data from the "Multi-Family Housing Management Information System (K-apt)" that provides the history of apartment maintenance, bidding information, and the electronic bidding system to examine the winning bid status and amount, along with the size and trends of the winning bids by year, and the results of the selection of operators by construction type. As a result, it was found that out of the total number of successful bids (36,831), 4.4% (16,631) were in the landscaping business, and the average winning bid value was found to be about 24 million won. According to the data, 73% of the landscaping cases were valued between 3 million won and 30 million won, and 58.6% of the cases were in the field of "pest prevention and maintenance". 36% of the total number of bids were awarded from February to April, with "general competitive bidding" accounting for 59.8% of the bidding methods. As for the method of selecting the winning bidder, 55% adopted the "lowest bid" and "electronic bidding method," and 45% adopted the "qualification screening system" and "direct bidding method." As an improvement to the problems derived from the bidding status data, the following are recommended: First, the exception clause to the current 'electronic bidding method' application regulations must be minimized to activate the electronic bidding method so that a fair bidding system can be operated. Second, landscaping management standards for green area environmental quality of multi-family housing must be prepared. Third, the provisions for preparing design books, such as detailed statements and drawings before the bidding announcement, and calculating the basic amount shall be prepared so that fair bidding can be made by specifying the details of the project concretely and objectively must be made. Fourth, for various bidding conditions in the 'business operator selection guidelines', detailed guidelines for each condition, not the selection, need to be prepared to maintain fairness and consistency. These measures are believed to beuseful in the fair selection of landscaping operators for multi-family housing projects and to prepare objective and reasonable standards for the maintenance of landscaping facilities and a green environment.