• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.136-146
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• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

• Journal of Power Electronics
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• v.4 no.4
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• pp.197-204
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• 2004

Two-stage Power-Factor-Correction (PFC) converters are the most common circuits for drawing sinusoidal and in phase current waveforms from an ac source with a good regulated output voltage. The first stage is a boost PFC converter with average-current-mode control for achieving the near-unity power factor and the second stage is a forward converter with voltage-mode control to regulate the output voltage. Stability analysis and design methods of two-stage PFC converters have previously been discussed using linear models. Recently, new nonlinear phenomena have been detected in pre-regulator boost PFC circuits and a new nonlinear model has been proposed for pre-regulated PFC converters. Therefore, investigation of two-stage PFC converters from the nonlinear viewpoint becomes important because the second stage DC/DC converter adds more complexity to the circuit. So, this paper introduces a study of the stability of two-stage PFC converters. A novel nonlinear model of two-stage PFC converters is proposed. Then, a stability analysis is made based upon this nonlinear model. The high correspondence between the simulated and experimental results confirms our analysis.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.727-735
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• 2008

Consider a clinical trial in which the main end-point is survival. Suppose after the start of the study an intermediate event occurs which may be influenced by a covariate(or treatment). In many clinical studies the occurrence of an intermediate event may change the survival distribution. This investigation develops two-stage model which, in the first stage, models the effect of covariate on the intermediate event and models the relationship between survival time and covariate as well as the intermediate event. In this paper, the two-stage model is presented in order to model intermediate event and a test based on this model is also provided. A numerical simulations are carried out to evaluate its overall significance level.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.694-700
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• 1998

PSC box bridge by MSS construction method may not be set at cross section at one step. Web and bottom flange(U member) would be set at first, top flange will be set later with a time lag. In this case, U member and top flange concrete have different strain history. As two different aged section behaves as the composite section, there would happen the redistribution of stress. This is come from time-dependent strain characteristics of concrete itself. In this study, two models are considered, one with considering the set time of cross section and the other without. By performing longitudinal analysis of two models on considering construction stage, the stress differences of two are compared. As the analysis results show a considerable differences in the stresses of cross section between two models, the set time of cross section is needed for rational design f PSC box girder bridge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.29-38
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• 2015

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.329-365
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• 2013

The purpose of this paper is to measure economy and environment efficiencies under fossil fuel and environment regulation by countries for 2000-2009. Distinguishing 83 countries with three groups of OECD, upper-middle, and low countries, we compare four models such as environment oriented, economy-oriented, environment-economy oriented, and two-stage types, which include a desirable output, GDP and an undesirable output, pollutant together in the production possibility set. OECD countries relatively showed high economy efficiency and low environment efficiency, whereas Non-OECD countries showed high environment efficiency and low economy efficiency. OECD countries reported a higher possibility to reduce fossil fuel and $CO_2$ emission.

• Computers and Concrete
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• v.19 no.2
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• pp.133-142
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• 2017

Two-stage concrete (TSC), also known as pre-placed aggregate concrete, is characterized by its unique placement technique, whereby the coarse aggregate is first placed in the formwork, then injected with a special grout. Despite its superior sustainability and technical features, TSC has remained a basic concrete technology without much use of modern chemical admixtures, new binders, fiber reinforcement or other emerging additions. In the present study, an experimental database for TSC was built. Different types of cementitious binders (single, binary, and ternary) comprising ordinary portland cement, fly ash, silica fume, and metakaolin were used to produce the various TSC mixtures. Different dosages of steel fibres having different lengths were also incorporated to enhance the mechanical properties of TSC. The database thus created was used to develop fuzzy logic models as predictive tools for the grout flowability and mechanical properties of TSC mixtures. The performance of the developed models was evaluated using statistical parameters and error analyses. The results indicate that the fuzzy logic models thus developed can be powerful tools for predicting the TSC grout flowability and mechanical properties and a useful aid for the design of TSC mixtures.

• International Journal of Fluid Machinery and Systems
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• v.6 no.3
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• pp.160-169
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• 2013

Two-stage turbocharging is an important way to raise engine power density, to realize energy saving and emission reducing. At present, turbine matching of two-stage turbocharger is based on MAP of turbine. The matching method does not take the effect of turbines' interaction into consideration, assuming that flow at high pressure turbine outlet and low pressure turbine inlet is uniform. Actually, there is swirl flow at outlet of high pressure turbine, and the swirl flow will influence performance of low pressure turbine which influencing performance of engine further. Three-dimension models of turbines with two-stage turbocharger were built in this paper. Based on the turbine models, mechanism of swirl flow at high pressure turbine outlet influencing low pressure turbine performance was studied and a two-stage radial counter-rotation turbine system was raised. Mechanisms of the influence of counter-rotation turbine system acting on low-pressure turbine were studied using simulation method. The research result proved that in condition of small turbine flow rate corresponding to engine low-speed working condition, counter-rotation turbine system can effectively decrease the influence of swirl flow at high pressure turbine outlet imposing on low pressure turbine and increases efficiency of the low-pressure turbine, furthermore increases the low-speed performance of the engine.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.11-25
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• 2024

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size f² of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.