• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.22 no.4
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• pp.32-40
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• 1986

A model experiment of purse seine by the circulating water tank was carried out on the changes of net shape and the tension of purseline under operation in two layer current. In the circular tank, the two layer current was made by cutting off the current of upper layer and producing the bottom current by the equipment shown in Fig. 1. The model experiment of purse sein was made on a reduced scale 1 :400, and the experiment was carried out according to the Tauti's model law. When the bottom current of O. 5 knot flows to lower part of three-eighths of net, following results are derived. The depth of sinkerline reached only about 80% of that of no current set. The horizontal shift of sinker line caused by the bottom current is maximized in tight set. The enclosed area by the floatIine immediately after the completion of set net is 61. 5% in tight set, 50. 0 % in loose set and 54. 1 % in lateral set of those in the case of no current. In the first half period of pursing, the tension of the purseline is enhenced by the bottom current and the pattern of increasing is irregular in the tension curves.

• Kyungpook Mathematical Journal
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• v.58 no.1
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• pp.37-46
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• 2018

The objective of the present manuscript is to obtain a moderated theorem proceeding with absolute Riesz summability ${\mid}{\bar{N}},p_n,{\gamma};{\delta}{\mid}_k$ by applying almost increasing sequence for infinite series. Also, a set of reduced and well-known factor theorems have been obtained under suitable conditions.

• Elastomers and Composites
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• v.36 no.1
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• pp.14-21
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• 2001

To increase the properties of EVA foam such as tensile strength, rebound resilience, and compression set, ethylene-1-butene copolymer (EtBC) was blended with EVA. After that crosslink characteristics of the blends and cell structures and mechanical properties of the foam were studied. As the amount of EtBC increased in EVA/EtBC blends, torque values of oscillating disc rheometer(ODR) increased and the foaming ratio decreased because the viscosity and crosslink density of EVA/EtBC blends increased. Foaming ratio and cell size of the foam increased by increasing the amount of foaming agent. When compared the mechanical properties of the foam which have same densities, tensile strength, rebound resilience, and compression set properties of the foam were improved by increasing the amount of EtBC in the EVA/EtBC blends.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Journal of the Korean Wood Science and Technology
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• v.39 no.4
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• pp.311-317
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• 2011

The mechanical properties of heat-compressed Radiata pine (Pinus radiata D.Don) by compression set were investigated. Heat-compression condition was temperature at $180^{\circ}C$ and press time for 60 minutes. The mechanical properties of heat-compressed wood increased with increasing compression set. Increase of the specific gravity has led to increase in mechanical properties. The maximum compression set of Radiata pine was investigated approximately 65%. It was almost same result with porosity 68% of Radiata pine in specific gravity 0.48.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.95-101
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• 1999

In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

• East Asian Economic Review
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• v.22 no.1
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• pp.75-114
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• 2018

A two-part model is estimated to see if increasing returns and comparative advantage are empirically equivalent in explaining intra-industry trade. The model has separate mechanisms for determining the occurrence and the extent of intra-industry trade. Estimation is based on an augmented Grubel-Lloyd index derived from the data set on SITC 7 goods at the 3-digit SITC (Revision 4) for country pairs in which Korea is fixed as a source country. Estimation results show that both increasing returns and comparative advantage can explain the occurrence and the extent of intra-industry trade.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.159-164
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• 2009

The water droplet motion and the interaction between the droplets in a PEMFC air flow channel with multiple pores, through which water emerges, is studied numerically by solving the equations governing the conservation of mass and momentum. The liquid-gas interface is tracked by a level set method which is based on a sharp-interface representation for accurately imposing the matching conditions at the interface. The method is modified to implement the contact angle conditions on the walls and pores. The dynamic interaction between the droplets growing on multiple pores while keeping the total water flow rate through pores constant is investigated by conducting the computations until the droplet motion exhibits a periodic pattern. The numerical results show that the droplet merging caused by increasing the number of pores is not effective for water removal and that the contact angle of channel wall strongly affects water management in the PEMFC air flow channel.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.231-237
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• 2015

The pin-gear drive is a special form of fixed-axle gear mechanism. A large wheel with cylindrical pin teeth is called a pinwheel. As pinwheels are rounded, they have a simple structure, easy processing, low cost, and easy overhaul compared with general gears. They are also suitable for low-speed, heavy-duty mechanical transmission and for occasions with more dust, poor lubrication, etc. This paper introduces a novel slewing ring bearing with an external pinwheel gear set (e-PGS). First, we consider the exact cam pinion profile of the e-PGS with the introduction of a profile shift. Then, the contact stresses are investigated to determine the characteristics of the surface fatigue by varying the shape design parameters. The results show that the contact stresses of the e-PGS can be lowered significantly by increasing the profile shift coefficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.