• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.4
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• pp.446-455
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• 2017

The improvement of resistance performance for the 4.99 ton class fishing boats was shown. The 4.99 ton fishing boats are the most commonly used one in the Korean coastal region. The evaluation of resistance performance was estimated by the Computational Fluid Dynamics (CFD) analysis. The CFD simulation was performed by the validation for various types of bow shapes on the hull. The optimized hull form from the simulation was selected and showed the best resistance performance. This hull type was tested on the towing tank in the National Institute of Fisheries Science (NIFS). The effective horsepower (EHP) was estimated by the resistance test on the towing tank with the bare hull condition. The drag force on the three service speed conditions was obtained for the resistance analysis to power prediction. The measured drag forces are compared with the results from the CFD simulation with one another. As results of the model tests, it was confirmed that the shape of the bow is an important factor in the resistance performance. The effective horsepower decreased about 30% in comparison with the conventional hull form. Also, the resistance performance improved the reduction of required horsepower, which especially contributed to the energy-saving for the fisheries industry. In the CFD analysis, the resistance performance improved slightly. In this case, the ratio of the residual resistance ($C_R$) in the total resistance ($C_T$) was high. Therefore, the CFD analysis was not enough to satisfy with reflection for the free surface and wave form in the CFD procedure. Both model test and CFD calculation in this study can be applied to the initial design process for the coastal fishing vessel.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.59-75
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• 2006

One of the most challenging and encouraging applications of state-of-the-art technology is the field of traffic control systems. It combines techniques from the areas of telecommunications and computer science to establish traffic information and various assistance services. The support of the system requires a moving objects database system (MODB) that stores moving objects efficiently and performs spatial or temporal queries with time conditions. In this paper, we propose schemes to distribute an index nodes of trajectory based on spatio-temporal proximity and the characteristics of moving objects. The scheme predicts the extendible MBB of nodes of index through the prediction of moving object, and creates a parallel trajectory index. The experimental evaluation shows that the proposed schemes give us the performance improvement by 15%. This result makes an improvement of performance by 50% per one disk.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.44-53
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• 2010

The H.264/AVC has been designed to use 9 directional intra prediction modes for removing spatial redundancy. It also employs high correlation between neighbouring block modes in sending mode information. For indication of the mode, smaller bits are assigned for higher probable modes and are compressed by predicting the mode with minimum value between two prediction modes of neighboring two blocks. In this paper, we calculated the statistical probability of prediction modes of the current block to exploit the correlation among the modes of neighboring two blocks with several test video sequences. Then, we made the probable prediction table that lists 5 most probable candidate modes for all possible combinatorial modes of upper and left blocks. By using this probability table, one of 5 higher probable candidate modes is selected based on RD-optimization to reduce computational complexity and determines the most probable mode for each cases for improving compression performance. The compression performance of the proposed algorithm is around 1.1%~1.50%, compared with JM14.2 and we achieved 18.46%~36.03% improvement in decoding speed.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.6
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• pp.151-159
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• 2006

A one-dimensional numerical analysis is carried out to investigate the effects of inlet gas humidities, inlet gas pressures, and thicknesses of membrane on the performance of a proton exchange membrane fuel cell. It is found that the relative humidity of inlet gases at anode and cathode sides has a significant effect on the fuel cell performance. Especially, the desirable fuel cell performance occurs at low relative humidity of the cathode side and at high humidity of the anode side. In addition, an increase in the pressure ranging from 1 atm to 4 atm at the cathode side results in a significant improvement in the fuel cell performance due to the convection effect by a pressure gradient toward the anode side, and with decreasing the thickness of membrane, the fuel cell performance is enhanced reasonably.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.154-164
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• 2017

This work conducts a validation study for the XH-59A helicopter using a rigid coaxial rotor system in order to establish the techniques of the conceptual design and performance analysis for the lift-offset compound rotorcraft. As a tool for conceptual design and performance analysis, NDARC (NASA Design and Analysis of Rotorcraft) is used for the present study. An assumed mission profile is considered for the conceptual design of the XH-59A. As a validation result of the design, the dimensions and weight of the XH-59A are appropriately designed when compared to the target values since the relative error is less than 0.5%. Then, performance analyses are conducted for the designed XH-59A model with and without auxiliary propulsion in hover and forward flight conditions. The present analyses show good validity since the prediction results compare well with both the flight test and previous analyses. Therefore, the techniques for the conceptual design and performance analysis of the lift-offset compound helicopter are overall considered to be appropriately established. In addition, this study investigates the influence of the lift-offset on the rotor effective lift-to-drag ratio of the XH-59A helicopter with auxiliary propulsion. As a result, the improvement of the rotor effective lift-to-drag ratio can be obtained by appropriately increasing the lift-offset in high-speed flight.

• Journal of Korea Multimedia Society
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• v.11 no.7
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• pp.956-965
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• 2008

H.264/AVC is the newest video coding standard of ITU-T VCEG and the ISO/IEC MPEG, offering a significant performance improvement over previous video coding standards. However, the computational complexity of H.264/AVC is drastically increased because of new technologies such as intra prediction, variable block size, quarter-pels motion estimation/compensation, etc. In this paper, we propose a fast intra prediction scheme which has two step processing. The first step is a fast block size decision which can be calculated only in one block without considering all cases of $4{\times}4$ block and $16{\times}16$ block. The complexity of the intra prediction can be reduced by using boundary difference values of macroblock. After selecting the block size, we can make mode decision using the neighbouring reference pixels and representative pixels of the block in the second step. The experimental results show that the proposed algorithm saved on the average 41.5% encoding time without any significant PSNR losses.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.837-844
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• 2010

Prediction of the prosodic phrase boundary is one of the most important natural language processing tasks. We propose, for the natural prediction of the Korean prosodic phrase boundary, a statistical approach incorporating efficient learning features. These new features reflect the factors that affect generation of the prosodic phrase boundary better than existing learning features. Notably, moreover, such learning features, extracted according to the hand-crafted prosodic phrase boundary prediction rule, impart higher accuracy. We developed a statistical model for Korean prosodic phrase boundaries based on the proposed new features. The results were 86.63% accuracy for three levels (major break, minor break, no break) and 81.14% accuracy for six levels (major break with falling tone/rising tone, minor break with falling tone/rising tone/middle tone, no break).

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.