• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.43-49
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• 2021

A small infrared image homing system is a tracking system that has an infrared image sensor that identifies a target through the day and night infrared image processing of the target on the ground and searches for and detects the target with respect to the main target. This paper describes the development of a board equipped with a high-speed CPU and FPGA (Field Programmable Gate Array) to identify target through real-time image processing by acquiring target information through infrared image. We propose a CPU-FPGA combining architecture for CPU and FPGA selection and video signal processing, and also describe a controller design using FPGA to control infrared sensor.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.617-627
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• 2021

Underwater Acoustic Channels (UAC) have inherent sparse characteristics. The traditional adaptive equalization techniques do not utilize this feature to improve the performance. In this paper we consider the Variable Adaptive Subgradient Projection (V-ASPM) method to derive a new sparse equalization algorithm based on the Minimum Symbol Error Rate (MSER) criterion. Compared with the original MSER algorithm, our proposed scheme adds sparse matrix to the iterative formula, which can assign independent step-sizes to the equalizer taps. How to obtain such proper sparse matrix is also analyzed. On this basis, the selection scheme of the sparse matrix is obtained by combining the variable step-sizes and equalizer sparsity measure. We call the new algorithm Sparse-Control Proportional-MSER (SC-PMSER) equalizer. Finally, the proposed SC-PMSER equalizer is embedded into a turbo receiver, which perform turbo decoding, Digital Phase-Locked Loop (DPLL), time-reversal receiving and multi-reception diversity. Simulation and real-field experimental results show that the proposed algorithm has better performance in convergence speed and Bit Error Rate (BER).

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• 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.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1068-1074
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• 2009

The progress is widely used as a critical index for successful construction project management. In spite of the importance of progress measurement, the excessive management effort to collect and maintain detailed data has been highlighted as a major barrier to measurement of highly accurate progress. In order to reduce the required workload and to enhance accuracy, several researches have been conducted. These researches can be categorized into two groups. First group focuses on automated data collection utilizing advanced technologies only for limited construction tasks. The second group is a research area where the standard progress measurement methodologies encompassing entire construction tasks are investigated. Topics include the adjusting the level of details, standardizing work processes, and applying flexible WBS. However, the techniques for automated data collection are not fully investigated yet in the second group. Combining these two research areas can provide a solution for more effective progress management in terms of enhancing accuracy and optimizing workload. However, there has been no comprehensive research addressing these two research groups in an integrated manner. In this context, the purpose of this paper is to propose a methodology that identifies the most suitable measurement method and data acquisition technology (e.g., GPS, RFID, etc.) for entire construction tasks of a project. The proposed methodology in this paper will be able to facilitate the selection process of data acquisition technologies for entire construction tasks of a project and to support the overall enhancement of automated progress management.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.593-608
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• 2023

This study attempted to classify the types of perceptions of the new Hanbok, focusing on female college students in their 20s, and to analyze the characteristics of each type. This study was conducted with the Q methodology and analyzed with the QUANL pc program. The perceptions of the new Hanbok among female college students in their 20s were classified into four types: Type 1: modern/practical, Type 2: convenient/practical, Type 3: temporary/accessible, and Type 4: convenient/traditional. Type 1 emphasized the selection of Western clothing fabric, matching zipper/buttons, and achromatic colors. Type 2 highlighted a design that can be worn as daily clothing by combining materials that are convenient for laundering. Type 3 stressed the operation of new Hanbok rental stores so that it is easy to access and accept new Hanboks in old palaces and Hanok villages. Type 4 required the development of a design that reflects the elements of the traditional Hanbok and is convenient for activity and laundering.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.367-374
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• 2013

Underground infrastructure systems provide essential public services and goods through buried structures including water and sewer, gas and petroleum, power and communication pipelines. The majority of existing underground infrastructure systems was installed in green field areas prior to development of complex urban built environments. Currently, there is a global trend to escalate major demand for underground infrastructure system renewal and new installation while minimizing disruption and maintaining functions of existing superstructures. Therefore, Engineers and utility owners are rigorously seeking technologies that minimize environmental, social, and economic impact during the renewal and installation process. Trenchless technologies have proven to be socially less disruptive, more environmentally friendly, energy conservative and economically viable alternative methods. All of those benefits are adequate to enhance overall sustainability. This paper describes effective sustainable solutions using trenchless technologies. Sustainability is assessed by a comparison between conventional open cut and trenchless technology methods. Sustainability analysis is based on a broad perspective combining the three main aspects of sustainability: economic; environmental; and social. Economic includes construction cost, benefit, and social cost analysis. Environmental includes emission estimation and environmental quality impact study. Social includes various social impacts on an urban area. This paper summarizes sustainable trenchless technology solutions and presents a sustainable construction method selection process in a proposed framework to be used in urban underground infrastructure capital improvement projects.

• Korean Journal of Poultry Science
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• v.7 no.1
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• pp.31-42
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• 1980

In order to identify the best superior cross combination of breeder stocks for broiler production, combining ability test and analyses of phenotypic performances for parent stocks were examined on records of 1,440 broiler chicken which were produced from 4 parental strains and 3 maternal strains at Hanhyup Poultry Breeding Farm from September 28, 1978 to January 5, 1979. The results obtained were as follows; 1. There was not found heterosis effect in viability but it seems to be desirable to select Hubbard strain in paternal line to improve viability. 2. As the paternal and maternal lines, selection of Ross strain showed the best paternal and maternal performance and the best general combining ability in body weight at 8 weeks of age is expected to be able to improve body weight of it s crossbred And the most superior cross combinations based on the specific combining ability and performance of rack crossbred were identified as Hubbard x Ross ana Ross x Hypeco crossbreds. 3. The best paternal and maternal lines on the smallest feed consumption for 8 weeks were Hubbard and Ross strains, and Hypeco strain, respectively. Especially Hubbard x Hypeco cross combination was proved as the smallest feed consumption compared with other cross combinations. 4. In feed requirement per Kg body weight increase, Hubbard strain for paternal line, Hypeco strain for naternal line, and cross combinations of Hubbard x Hypeco, Hubbard x Ross and Ross x Hypeco were certified as the most superiors. 5. Also superior cross combinations of Hubbard x Hypeco and Hubbard x Ross earned the most profit per bird through economic analysis. According to results as shown above, this experiment seems to be able to reach a such conclusion that production of superior cross combinations Hubbard x Ross, Hubbard x Hypeco and Ross x Hypeco through selection of Ross and Hubbard strains to paternal line and Hypeco and Ross strains for maternal line may become to considerable improvement for important economic characters of broiler; viability, body weight, feed consumption and feed requirement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12A
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• pp.1319-1332
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• 2004

Multicarrier direct sequence code-division multiple access CDMA(MC DS-CDMA) is an attractive technique for achieving high data rate transmission even if the potentially large peak-to-average power ratio(PAPR) is an important factor for its application. On the other hand, code select CDMA(CS-CDMA) is an attractive technique with constant amplitude transmission of multicode signal irregardless of subchannels by introducing code selection method. In this paper we propose a new multiple access scheme based on the combination of MC DS-CDMA and CS-CDMA. Proposed scheme, which we called MC CS-CDMA, includes the sutclasses of MC DS-CDMA and CS-CDMA as special cases. The performance of this system is investigated for multipath Sequency selective fading channel and maximal ratio combining with rake receiver. In addition the PAPR of proposed system is compare with that of both MC BS-CDMA and CS-CDMA. Simulation results show that proposed system improves PAPR reduction than MC DS-CDMA at the expense of the complexity of receiver and the number of available non. Also, the numerical result shows that the proposed system is better performance than MC DS-CDMA due to the increasing processing gain and the number of time diversity gain.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.191-200
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• 2017

A robust parameter set (ROPS) selection framework for an unsteady flow model was developed by combining Pareto optimums obtained by outcomes of model calibration using multi-site observations with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem was solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations to obtain Pareto solutions. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was proposed and the related coefficients were optimized as model parameters. By applying the MRA which is a decision criterion, the Pareto solutions were ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation was determined as the only ROPS. It was found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations varies considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models.