• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.1-7
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building prediction methods for software size in fourth-generation languages and database projects. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset Ⅰ and Ⅱ, respectively. For the data set Ⅰ and Ⅱ, our proposed prediction method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Journal of Biosystems Engineering
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• v.17 no.3
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• pp.229-236
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• 1992

In an effort to localize the spray-dryer which is markedly used for drying food materials, a experiment was carried out with a wheel type atomizer locally designed and manufactured to evaluate the effect of rotational speed, feed rate and physical properties of liquid food material on the droplet size, and to develop a model to predict the droplet size sprayed at various operational conditions. The result are summarized as follows. 1. The frequency of droplet size sprayed from the atomizer at every treatment were similar to normal distributions. 2. Under the test conditions adopted in this study, that is, rotational speed of the atomizer ranging from 15,000 to 20,000 rpm (55.0 m/sec - 73.3 m/sec), feed rate from 14 to 37 kg/hr and viscosity of the material from 1.14 to 350 cP, the mean volume-surface dia. of droplets was decreased as increase in rotational speed and was not affected significantly by the feed rate and viscosity. 3. Through the dimensional analysis, a prediction model was developed as follows : $$\frac{Dvs}{r}=K[\frac{Q}{{\mu}r}]^a[\frac{rN^2}{g}]^b[\frac{{\rho}^2r^3g}{{\mu}^2}]^c[\frac{L}{r}]^d$$ and it was proved that the above model was better in degree of fitness than other models reported. 4. A prediction equation for the droplet size sprayed from the atomizer under the test was expressed as follows : $$\frac{Dvs}{r}=0.0215[\frac{Q}{{\mu}r}]^{0.06}[\frac{rN^2}{g}]^{0.3314}[\frac{{\mu}^2}{{\rho}^2r^3g}]^{0.0158}$$.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.41-60
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• 2004

A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases. becomes increasingly large over time. To mine information from this massive data. it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise. if we use only recent or part of a whole data to mine information or pattern. there can be loss of information. which may be useful. To avoid this problem. we suggest a method that efficiently accumulates information. in the form of rule sets. over time. It takes much smaller storage compared to traditional mining methods. These accumulated rule sets are used as prediction models in the future. Based on theories of ensemble approaches. combination of many prediction models. in the form of systematically merged rule sets in this study. is better than one prediction model in performance. This study uses a customer data set that predicts buying power of customers based on their information. This study tests the performance of the suggested method with the data set alone with general prediction methods and compares performances of them.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.

• Journal of Ocean Engineering and Technology
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• v.38 no.1
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1115-1122
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• 2011

Reinforced-Roadbed materials are usually composed of crushed stones. Repeated load application can induce deformation in the reinforced-roadbed layer so that it causes irregularity of track. Thus it is important to develop a prediction model of elastic modulus based on stress-strain relation under repeatitive load in order to investigate behavior of reinforced roadbed. The prediction model of elastic modulus of the material can be obtained from repeated triaxial test. However, a proper size of the sample for the test must be used. In this study, a large repeatitive triaxial test apparatus with the sample size of diameter of 30 cm and height of 60cm was adapted for performing test of the crushed stone reinforced-roadbed considering large particle size to get resilient modulus Mr. The obtained resilient modulus was compared to shear modulus obtained from mid size resonant column test. The sample size effect is somewhat large enough so that it is required to design a scale factor based on similarity law in order to use smaller samples for getting elastic modulus of the crushed stone reinforced-roadbed material. A scale factor could be obtained from this study.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.1
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• pp.63-70
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• 2010

In this paper, we study the structure of neural network for predicting defect size of steam generator tube. After extracting the features from the eddy current testing (ECT) signals, multi-layer neural networks are used to predict the defect size. In order to maximize the prediction performance for the defect size, we should carefully choose the structure of neural networks, especially the number of neurons in the hidden layer. In this paper, it is shown that, for the prediction of defect size, the number of neurons in the hidden layer can be efficiently determined by using cross-validation.

• Journal of the korean academy of Pediatric Dentistry
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• v.33 no.2
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• pp.253-261
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• 2006

Estimating the size of unerupted teeth is an essential aspect of orthodontic diagnosis and treatment planning in the mixed dentition. Several methods were introduced and used for the prediction. The most common methods among these would be Moyers probability chart and Tanaka and Johnston equations. These are currently used widely, but they were developed for Caucasians. Because there are clear racial differences in teeth size, the objectives of this study were to produce correlation coefficients between the combined mesiodistal widths of the permanent mandibular incisors and those of the canines and premolars for each quadrant, and prediction tables with regression equations, specifically for Korean. 178 young adults (70 women, 108 men, mean age 21.63 years) were selected from the College of Dentistry, Yonsei University, Seoul, Korea. The mesiodistal crown diameters of the permanent teeth were measured with calipers. Significant sexual dimorphism was found in tooth sizes. The correlation coefficients between the total mesiodistal width of the mandibular permanent incisors and those of the maxillary and mandibular canines and premolars were found to be between 0.52 and 0.64. The standard error of the estimatation was better (0.60) for women and the ${\gamma}^2$ values ranged from 0.27 to 0.41 for both sexes Prediction tables were prepared for Korean. This study showed larger canine and premolar diameters than Tanaka and Johnston's and Moyers' studies which might be due to the racial differences. Further investigations with a larger sample size will be needed for more representative data on the Korean population.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.815-829
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• 2010

In this paper, we propose a new $4{\times}4$ intra coding method by unidirectional prediction for improvement of intra-frame coding efficiency of H.264/AVC. There are $4{\times}4$, $8{\times}8$, and $16{\times}16$ intra prediction modes in the current H.264/AVC. For the $4{\times}4$ intra prediction, coding efficiency is achieved by accurate prediction with small block size in relatively complicated regions, and the $16{\times}16$ intra prediction method can predict more accurately compared to $4{\times}4$ intra prediction with only one directional information in relatively homogeneous regions. We propose a unidirectional $4{\times}4$ intra prediction method adopting a small-size prediction and one directional prediction approaches. In order to improve coding efficiency, the proposed method is conducted by $4{\times}4$ block and their prediction directions are all the same, resulting that we need to send only one directional information for each macroblock. For intra-frame coding setting, we achieve 10.47% and 1.57% coding efficiency in BD-bitrate for only $16{\times}16$ intra mode and $4{\times}4$, $16{\times}16$ intra mode, respectively.

• Korean Journal of Food Science and Technology
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• v.20 no.6
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• pp.827-833
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• 1988

The objectives of this investigation were to develop an improved analytical method and to review with respect to experimental parameters and thermo-physical properties influencing the freezing time prediction. The results indicate that the relationship between freezing time and product size is dependent on the surface heat transfer coefficient. As the magnitude of surface heat transfer coefficient decreases, the influence of product size on freezing time becomes more profound. But the freezing time does decrease slightly as the coefficients are increased to values greater than 150 $w/m^2^{\circ}C$. In addition, influence of thermo-physical properties on the freezing time prediction shown generally density, water content, specific heat and thermal conductivity, in order of % difference. Multiple linear regression equation for freezing time prediction were obtained with respect to 4 different food materials with varying thickness.