• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3609-3615
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• 2014

This study examined the difference in the item characteristics in multiple-choice aptitude tests focusing on the response position, response length and question types. A university aptitude test consisting of 80 questions was used for this study. The subjects were 3120 senior high school students from 80 schools nation-wide (liberal arts-1650, natural sciences-1467 patients). The results suggest that item prediction is higher for numbers 2 and 3 (located in the middle) than numbers 1 and 4. The item discrimination was higher for pick-the-'wrong'-items than pick-the-'right'-items. In addition, longer choices are preferred. The suggestions for future research are provided based on these findings.

• Information Systems Review
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• v.9 no.1
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• pp.67-84
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• 2007

In diffusing an information systems(IS), the provider of the IS can be more effective if they can identify user groups who can adopt the system early. By focusing on the user groups, system providers can encourage them to adopt the IS. After the early adopters adopt an IS, the diffusion of the system to other groups can be easier by early adopters' voluntary advertisement and help in adopting the IS. Instead of discrete choice methods which are usually used for this purpose, we suggest a decision tree method. Compared to discrete choice methods, this method is more accurate for prediction and can easily identify non-linear segments of groups. By testing the data of adopters of an IS in agricultural business, we show the excellence of this method in identifying target groups to focus on. This method would help system providers to diffuse their systems by starting from early adopters.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1281-1284
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• 2012

Background: Platinum-hased chemotherapy for advanced non-small cell lung cancer (NSCLC) is still considered the first choice, presenting a modest survival advantage. However, the patients eventually experience disease progression and require second-line therapy. While there are reliable predictors to identify patients receiving first-line chemotherapy, very little knowledge is available about the prognostic factors in patients who receive second-line treatments. The present study was therefore performed. Methods: We retrospectively reviewed 107 patients receiving second-line treatments from August 2002 to March 2012 in the Dicle University, School of Medicine, Department of Medical Oncology. Fourteen potential prognostic variables were chosen for analysis in this study. Univariate and multivariate analyses were conducted to identify prognostic factors associated with survival. Result: The results of univariate analysis for overall survival (OS) were identified to have prognostic significance: performance status (PS), stage, response to first-line chemotherapy response to second-line chemotherapy and number of metastasis. PS, diabetes mellitus (DM), response to first-line chemotherapy and response to second-line chemotherapy were identified to have prognostic significance for progression-free survival (PFS). Multivariate analysis showed that PS, response to first-line chemotherapy and response to second-line chemotherapy were considered independent prognostic factors for OS. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. Conclusion: In conclusion, PS, response to first and second-line chemotherapy were identified as important prognostic factors for OS in advanced NSCLC patients who were undergoing second-line palliative treatment. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. It may be concluded that these findings may facilitate pretreatment prediction of survival and can be used for selecting patients for the correct choice of treatment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.173-185
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• 1999

The purpose of this study is to develope a forecasting model to implement short-term Congestion Management Program (CMP) based on TDM strategies in Seoul. The CMP is composed of three elements: 1) setting a goal of short-term traffic management. 2) developing a model to forecast the impacts of TDM alternatives, and 3) finding TDM measures to achieve the goal To Predict the impacts of TDM alternatives, a model called SECOMM (SEoul COngestion Management Model) is developed. The model assumes that trip generation and distribution are not changing in a short term, and that only mode split and traffic assignment are affected by TDM. The model includes the parameter values calibrated by a discrete mode choice model, and roadway and transit networks with 1,020 zones. As a TDM measure implement, it affects mode choice behavior first and then the speeds of roadway network. The chanced speed again affects the mode choice behavior and the roadway speeds. These steps continue until the network is equilibrated. The study recommends that CMP be introduced in Seoul, and that road way conditions be monitored regularly to secure the prediction accuracy of SECOMM. Also, TDM should be the major Policy tools in removing short-term congestion problems in a big city.

• Water for future
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• v.28 no.2
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• pp.125-136
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• 1995

The propose of this study is improve the uncertainty of parameter choice of tank model by the trials and errors method. The real time prediction of parameter by using the Kalman filter is practiced to get the effective prediction algorithm of low flow runoff. Even though the total discharge of runoff through the orifice of each tank should be similar to the observed discharge, the tank model which can show the various basin characteristic is influenced by the runoff circumstances. As a result of the real-time estimation of the tank model parameter by the state-space type of Kalman filter, the variation of runoff circumstances is static when the convergence of observed value and estimated value keeps the ficed high point. The parameter of tank model which is estimated by Kalman filter shows good result for low flow and reasonable adaptability where flow change abruptly. The Kalman filter method is proved to give better result than Automatic structure estimation method.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.255-261
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• 2003

Tunnelling in poor and heterogeneous ground is a difficult task. Even with a good geological investigation, uncertainties with respect to the local rock mass structure will remain. Especially for such conditions, a reliable short-term prediction of the conditions ahead and outside the tunnel profile are of paramount importance for the choice of appropriate excavation and support methods. The information contained in the absolute displacement monitoring data allows a comprehensive evaluation of the displacements and the determination of the behaviour and influence of an anisotropic rock mass. Case histories and with numerical simulations show, that changes in the displacement vector orientation can indicate changing rock mass conditions ahead of the tunnel face (Schubert & Budil 1995, Steindorfer & Schubert 1997). Further research has been conducted to quantify the influence of weak zones on stresses and displacements (Grossauer 2001). Sellner (2000) developed software, which allows predicting displacements (GeoFit$\circledR$). The function parameters describe the time and advance dependent deformation of a tunnel. Routinely applying this method at each measuring section allows determining trends of those parameters. It shows, that the trends of parameter sets indicate changes in the stiffness of the rock mass outside the tunnel in a similar way, as the displacement vector orientation does. Three-dimensional Finite Element simulations of different weakness zone properties, thicknesses, and orientations relative to the tunnel axis were carried out and the function parameters evaluated from the results. The results are compared to monitoring results from alpine tunnels in heterogeneous rock. The good qualitative correlation between trends observed on site and numerical results gives hope that by a routine determination of the function parameters during excavation the prediction of rock mass conditions ahead of the tunnel face can be improved. Implementing the rules developed from experience and simulations into the monitoring data evaluation program allows to automatically issuing information on the expected rock mass quality ahead of the tunnel.

• Animal Bioscience
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• v.37 no.8
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• pp.1333-1344
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• 2024

Objective: Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. Methods: A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results: Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. Conclusion: The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.