• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.583-604
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• 2017

The most popular regression model for the analysis of time-to-event data is the Cox proportional hazards model. While the model specifies a parametric relationship between the hazard function and the predictor variables, there is no specification regarding the form of the baseline hazard function. A critical assumption of the Cox model, however, is the proportional hazards assumption: when the predictor variables do not vary over time, the hazard ratio comparing any two observations is constant with respect to time. Therefore, to perform credible estimation and inference, one must first assess whether the proportional hazards assumption is reasonable. As with other regression techniques, it is also essential to examine whether appropriate functional forms of the predictor variables have been used, and whether there are any outlying or influential observations. This article reviews diagnostic methods for assessing goodness-of-fit for the Cox proportional hazards model. We illustrate these methods with a case-study using available R functions, and provide complete R code for a simulated example as a supplement.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.40-51
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• 2012

Online discussion bulletin in Korea is not only a specific place where user exchange opinions but also a public sphere through which users discuss and form public opinion. Sometimes, there is a heated debate on a topic and any article becomes a political or sociological issue. In this paper, we propose how to analyze the popularity of articles by collecting the information of articles obtained from two well-known discussion forums such as AGORA and SEOPRISE. And we propose a prediction model for the article popularity by applying the characteristics of subject articles. Our experiment shown that the popularity of 87.52% articles have been saturated within a day after the submission in AGORA, but the popularity of 39% articles is growing after 4 days passed in SEOPRISE. And we observed that there is a low correlation between the period of popularity and the hit count. The steady increase of the hit count of an article does not necessarily imply the final hit count of the article at the saturation point is so high. In this paper, we newly propose a new prediction model called 'baseline'. We evaluated the predictability for popular articles using three models (SVM, similar matching and baseline). Through the results of performance evaluation, we observed that SVM model is the best in F-measure and precision, but baseline is the best in running time.

• The Journal of Information Systems
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• v.17 no.2
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• pp.73-94
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• 2008

Knowledge is a fundamental assets, therefore, the ability to create, acquire, integrate, and share knowledge has emerged as a fundamental organizational capability(Sambamurthy and Subramani, 2005). This apaper reports the results of an empirical study investigating the factors of acceptance and the moderating role of resistance in Knowledge Management Systems(KMS). The research model is based on the theory of planned behavior(TPB) and technology acceptance model(TAM). It includes the perceived usefulness instead of attitude, subjective norm, perceived behavior control and intention of acceptance of KMS. Also, three external variables namely task-technology fit, organizational support, and perceived rewards are added. In the research model, all hypothrses of the baseline model and the moderating effects of resistance were found to be significant. The authors also of fred several implications based chi the findings.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.9
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• pp.884-891
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• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.25-33
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• 2001

This paper suggests a generalized logit model with mixed effects for analysing frequency data in multi-contingency table. In this model nominal response variable is assumed to be polychotomous. When some factors are fixed but condisered as ordinal and others are random, this paper shows how to use baseline-category logits to incoporate the mixed-effects of those factors into the model. A numerical algorithm was used to estimate model parameters by using marginal log-likelihood.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.17-23
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• 2023

Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• Journal of Periodontal and Implant Science
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• v.53 no.2
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• pp.120-134
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• 2023

Purpose: We investigated whether repeated irradiation with light-emitting diodes (LEDs) at a combination of 470 nm and 525 nm could suppress the progression of experimental periodontitis. Methods: A experimental periodontitis model was established in the second, third, and fourth premolars of the mandible in beagle dogs for 2 months. The spontaneous progression of periodontitis was monitored under the specified treatment regimen for 3 months. During this period, the animals were subjected to treatments of either plaque control only (control) or plaque control with LED application (test) at 2-week intervals. The clinical parameters included the probing pocket depth (PPD), gingival recession (GR), and the clinical attachment level (CAL). Histomorphometric analysis was performed using measurements of the length of the junctional epithelium, connective tissue (CT) zone, and total soft tissue (ST). Results: There were significant differences in PPD between the control and test groups at baseline and 12 weeks. When the change in PPD was stratified based on time intervals, it was shown that greater differences occurred in the test group, with statistical significance for baseline to 12 weeks, 6 to 12 weeks, and baseline to 6 weeks. There was no significant difference in GR between the control and test groups at any time points. Likewise, no statistically significant differences were found in GR at any time intervals. CAL showed a statistically significant difference between the control and test groups at baseline only, although significant differences in CAL were observed between baseline and 12 weeks and between 6 and 12 weeks. The proportion of CT to ST was smaller for both buccal and lingual areas in the control group than in the test group. Conclusions: Repeated LED irradiation with a combination of 470-nm and 525-nm wavelengths may help suppress the progression of periodontal disease.

• Korean Journal of Construction Engineering and Management
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• v.1 no.2 s.2
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• pp.89-97
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• 2000

This research has been Initiated to provide an effective management tool for budget control of the public projects using EVMS. Barriers to implementing the tool for the domestic public projects are identified : no PMB (Performance Measurement Baseline) for budget control, management by BOQ(bill of quantity), no systematic planning and control. To eliminate these barriers, an integrated cost and schedule management process model using EVMS is proposed. This model is composed of six sub processes : organizing, scheduling, budget allocating, establishing PMB, managerial analysis, change incorporation.

• Journal of computational fluids engineering
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• v.20 no.3
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• pp.35-40
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• 2015

Centrifugal pumps consume considerable amounts of energy in various industrial applications. Therefore, improving the efficiency of pumps machine is a crucial challenge in industrial world. This paper presents numerical investigation of flow characteristics in volutes of centrifugal pumps in order to compare the energy consumption. A wide range of volumetric flow rate has been investigated for each case. The standard k-${\varepsilon}$ is adopted as the turbulence model. The impeller rotation is simulated employing the Multi Reference Frames(MRF) method. First, two different conventional design methods, i.e., the constant angular momentum(CAM) and the constant mean velocity (CMV) are studied and compared to a baseline volute model. The CAM volute profile is a logarithmic spiral. The CMV volute profile shape is an Archimedes spiral curve. The modified volute models show lower head value than baseline volute model, but in case of efficiency graph, CAM curve has higher values than others. Finally for this part, CAM curve is selected to be used in the simulation of different cross-section shape. Two different types of cross-section are generated. One is a simple rectangular shape, and the other one is fan shape. In terms of different cross-section shape, simple rectangular geometry generated higher head and efficiency. Overall, simulation results showed that the volute designed using constant angular momentum(CAM) method has higher characteristic performances than one by CMV volute.