• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.3
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• pp.43-52
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• 1984

This paper is concerned with the strain characteristics of the time effect on the remoulded saturated day sampled from the downstream of the Yeongsan river, and the constitutive equation that can generally explain time-dependent behaviors of norma1ly consolidated clay. This paper examines whether or not the afore-said constitutive equation can be applied to the remoulded Mooan-clay. Throughout this study, the conclusions obtained are as follows. 1. Throughout the isotropic consolidation test for 7 days and the isotropic relaxation test, the existence of the static and dynamic yielding surfaces is confirmed respectively. 2. The characteristics of time effect of the deformation, namely, the existence of a unique stress-strain-time relation, is conformed from the experimental result on the Mooan-clay. 3. The prodictions of the stress path and the strain on the Cam-clay theory is not consistent with those observed during the experiments. 4. Constitutive equation(2-3-12) obtained by applying Cam-clay theory to Perzyna's elastic-viscoplasticity theory can explain the behavior of pore water pressure during isotropic stress relaxation, concerned with time dependency under undrained condition. The equation can also explain the results of the undrained triaxial compression test for the clay with different strain rate under the same or different consolidation history. 5. This constitutive equation has eight material parameters which can be determined from triaxial compression tests.

• Journal of the Korean Wood Science and Technology
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• v.27 no.1
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• pp.37-49
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• 1999

The effect of fire-retardant treatment and redrying on the mechanical properties of radiata pine sapwood were evaluated. Small, clear specimens were treated with three different fire-retardant(FR) chemicals, borax-boric acid(BRX), minalith(MIN), and pyresote(PYR), with target retentions of 30 and 60kg/$m^3$, and then redried at maximum dry-bulb temperature of $25^{\circ}C$, $60^{\circ}C$, $80^{\circ}C$ or $110^{\circ}C$. Each specimen, including untreated and water-treated controls, was tested in static bending and in compression parallel to grain. The extent of strength reduction was dependent on the type of FR chemicals, retention, and redrying temperature, and a highly significant interaction existed between FR treatment and redrying temperature. Modulus of rupture(MOR) and work to maximum load(WML) were significantly decreased by FR treatment and redrying. None of three FR chemicals adversely affect modulus of elasticity (MOE) and maximum crushing strength(MCS). MOE of BRX treatment and MCS of both BRX and PYR treatment increased significantly compared to untreated controls. No significant differences existed between retention levels except for MOE and MCS of some combinations of FR chemicals and redrying temperatures. Although MOE and MCS was not significantly affected by any of the redrying temperatures, these properties were generally decreased with the increase in redrying temperature. The significant reduction in MOR and WML was observed in BRX treatment when dried at temperatures of $60^{\circ}C$ and above, and in MIN and PYR treatment when dried at temperatures of $80^{\circ}C$ and above. Consequently, BRX-treated radiata pine should not be redried at temperatures >$60^{\circ}C$, and MIN- and PYR-treated radiata pine should not be redried at temperatures > $80^{\circ}C$ where bending strength and energy-related properties are important design considerations.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.17-23
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• 2010

Since the relatively stiff binder shows a higher tensile strength as well as higher rutting resistance, it is believed that the binder stiffness is an important factor for rutting and tensile strength of asphalt mixtures. The typical tensile property is measured by indirect tensile strength (ITS) test at $25^{\circ}C$ and the rutting resistance is most widely measured by wheel tracking (WT) test at $60^{\circ}C$. The deformation strength ($S_D$) is newly developed property to estimate rut resistance of asphalt concretes at $60^{\circ}C$. The ITS and $S_D$ are very simple to measure by static test techniques, but the WT is measured by repeated loading procedure which requires relatively longer time and more efforts. Since these three properties are highly dependent upon the binder stiffness, it may be possible to estimate one property from another. Therefore, this study investigate the possibility of estimating the rutting characteristics (measured by WT test) by ITS or $S_D$ test, and the ITS by $S_D$. Because of binder stiffness effect, in the WT estimation by ITS, a tendency was observed for the higher ITS mixture to have the lower rut depth, giving $R^2{\fallingdotseq}$0.6, on the average. The ITS estimation by $S_D$ showed $R^2{\fallingdotseq}$0.64, and the WT estimation by SD showed $R^2{\fallingdotseq}$0.84, which is highest correlation among the three. Therefore, it was concluded that there is relatively good possibility of estimating WT result by $S_D$, and even though $R^2$ is somewhat low, there is some correlation between WT and ITS.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.1
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• pp.40-49
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• 1999

A laboratory flume experiment, using turbulence-generating acryl tank and natural sediments, was conducted to investigate the effects of salinity, concentration of suspended sediment, turbulence and clay minerals on the flocculation and settling of fine-grained suspended sediments. While experiments were run, a sequence of water samples were taken near the bottom of the tank to analyze the variations of size distribution and relative contents of clay minerals. The results of the salinity experiment indicate that median settling velocity ($W_{50}$) increases linearly with salinity. Different settling processes of suspended sediments under variable concentrations appear to be predictable, depending upon the range of the suspension concentration. At concentrations less than 200 mg/l, $W_{50}$ is rarely varied with concentration probably because of the individual--grain settling mode. In the range of 200 to 13,000 mg/l show $W_{50}$ and concentration a good relationship following an empirical formula: $W_{50}=0.45C^{0.44}$. This relationship, however, no longer holds in concentrations exceeding 13,000 mg/l; instead, a more or less reverse one is shown. This result suggests an effect of hindered settling. The turbulence effect is somewhat different from that of concentration. Turbulence accelerates the flocculation and settling susepended sediments at low concentration (200 mg/l), whereas at high concentration turbulence breaks floes down and impedes the settling. Size distribution of suspended sediments sampled near the bottom of the tank tend to be more negatively skewed and leptokurtic in turbulent conditions compared to those in static conditions. The clay mineral analysis from the sequential water samples shows that over time the content of smectite decreases most rapidly with illite remaining concentrated in suspension. This means that smectite, among other clay minerals, plays the most effective role in the flocculation of fine-grained sediment in saline water.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.25-30
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• 1998

Time-lapse crosswell seismic data, recorded before and after the cavity filling, showed that the filling increased the velocity at a known cavity zone in an old mine site in Inchon area. The seismic response depicted on the tomogram and in conjunction with the geologic data from drillings imply that the size of the cavity may be either small or filled by debris. In this study, I attempted to evaluate the filling effect by analyzing velocity measured from the time-lapse tomograms. The data acquired by a downhole airgun and 24-channel hydrophone system revealed that there exists measurable amounts of source statics. I presented a methodology to estimate the source statics. The procedure for this method is: 1) examine the source firing-time for each source, and remove the effect of irregular firing time, and 2) estimate the residual statics caused by inaccurate source positioning. This proposed multi-step inversion may reduce high frequency numerical noise and enhance the resolution at the zone of interest. The multi-step inversion with different starting models successfully shows the subtle velocity changes at the small cavity zone. The inversion procedure is: 1) conduct an inversion using regular sized cells, and generate an image of gross velocity structure by applying a 2-D median filter on the resulting tomogram, and 2) construct the starting velocity model by modifying the final velocity model from the first phase. The model was modified so that the zone of interest consists of small-sized grids. The final velocity model developed from the baseline survey was as a starting velocity model on the monitor inversion. Since we expected a velocity change only in the cavity zone, in the monitor inversion, we can significantly reduce the number of model parameters by fixing the model out-side the cavity zone equal to the baseline model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.117-129
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• 2023

Organizations must seek change in order to adapt to environmental changes and achieve better performance. However, despite this obvious statement, empirical analysis has been almost non-existent due to the difficulty of manipulating organizational performance or change. In this study, we overcame these limitations and analyzed the causes and effects of organizational change by assuming a professional sports team as a venture company, which is relatively easy to objectively measure and evaluate organizational change or performance. We systematically collected and preprocessed traditional and advanced metrics of National Basketball Association (NBA) statistics along with preprocessed trade data from eight years of regular seasons (2014~2015-2021~2022) to analyze our research model. Assessment of process macro model 7 derives the following empirical result. The results of the empirical analysis depict that NBA teams with low organizational performance in the previous season are more likely to make organizational changes through player trades to improve performance. Into the bargain player trades mediate the static relationship between the winning percentage in the previous season and the winning percentage in the current season. However, the indirect effect of a team's previous season's performance on player trades appears to vary depending on the current situations and context of each NBA team. Teams that made the playoffs in the previous season tend to make fewer trades than teams that did not and the previous season's performance is highly correlated with the current season's performance. On the other hand, teams that did not make the playoffs in the previous season tend to make a relatively larger amount of player trades in total, and the mediating effect of trades vanishes in this case. In other words, teams that did not make the playoffs in the previous season experience a larger change in performance due to trades than teams that made the playoffs, even if they achieved the same winning percentage. This empirical analysis of the inverse relationship between organizational change and the performance of professional sports teams has both theoretical and practical implications in the field of sports industry and management by analyzing the fundamentals of organizational change and the performance of professional sports teams.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Dental Rehabilitation and Applied Science
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• v.18 no.3
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• pp.145-155
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• 2002

The results of the present feasibility study are summarized as follows, 1. The three unit bridge of knitted material and UD fibre reinforcement has both the rigidity and the strength against a vertical occlusal load of 75N. 2. Stress concentration at the junctional area between the bridge and the abutments, i.e. between the pontic and the knitted caps was observed. In the case of the bridge with reinforcement straps, it was partly shown that the concentration problem could be improved by simply increasing the fillet size at the area. Further refining in the surface of the junctional area will be needed to ensure a further improvement in the stress distribution. This will require some trade off in the level of the stress and the available space. A parametric study will help to decide the appropriate size of the fillet. 3. Design refinement is a must to improve the stress distribution and realize the most favourable shape in terms of fabrication. The current straight bar with a constant cross section area can be redesigned to a tapered shape. The curve from the dental arch should also be placed on the pontic design. In accordance with design refinement, the resistance of the bridge frame to other load cases should be evaluated. 4. Although not included in the present feasibility study, it is estimated that bridges of the anterior teeth can be made strong enough with the knitted material without further reinforcement using unidirectional materials. In this regard, a feasibility study on design concepts and stress analysis for 3, 4, 5 unit bridge is suggested. 5. Two types of bridge were analysed in terms of fatigue. The safe life design concept, i.e. fatigue design concept, looks reasonable for the bridge where if cracks should form and propagate there is virtually nothing a dentist to do. The bridge must be designed so that no crack will be initiated during the life span. In the case of crowns, however, if constructed with composite resin with knitted materials, it might be possible to repair them, which in general is impossible for crowns of PFM or of metal. Therefore for composite resin crowns, a damage tolerance design concept can be applied and reasonably higher operational stresses can be allowed. In this case, of course, a periodic inspection program should be established in parallel. 6. Parts of future works in terms of structural viewpoint which need to be addressed are summarized as the following: 1) To develop processing technology to accommodate design concepts; 2) More realistic modelling of the bridge and analysis-geometry and loading condition. Thickness variation in the knitted material, taper in the pontic, design for anterior tooth bridge, the effect of combined loads, etc, will need to be included; 3) To develop appropriate design concepts and design goals for the fibre composite FPD aiming at taking the best advantage of knitted materials, including the damage tolerance design concept; 4) To develop testing method and perform test such as static ultimate load test, fatigue test, repair test, etc, as necessary.