• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

• Computers and Concrete
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• v.16 no.6
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• pp.825-846
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• 2015

In this study, shear tests on steel fiber reinforced-prestressed concrete (SFR-PSC) members were conducted with test parameters of the concrete compressive strength, the volume fraction of steel fibers, and the level of effective prestress. The SFR-PSC members showed higher shear strengths and stiffness after diagonal cracking compared to the conventional prestressed concrete (PSC) members without steel fibers. In addition, their shear deformational behavior was measured using the image-based non-contact displacement measurement system, which was then compared to the results of nonlinear finite element analyses (NLFEA). In the NLFEA proposed in this study, a bi-axial tensile behavior model, which can reflect the tensile behavior of the steel fiber-reinforced concrete (SFRC) in a simple manner, was introduced into the smeared crack truss model. The NLFEA model proposed in this study provided a good estimation of shear behavior of the SFRPSC members, such as the stiffness, strengths, and failure modes, reflecting the effect of the key influential factors.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.187-198
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• 2011

User authentication refers to user identification based on something a user knows, something a user has, something a user is or something the user does; it can also take place based on a combination of two or more of such factors. With the increasingly diverse risks in online environments, user authentication methods are also becoming more diversified. This research analyzes user authentication methods being used in various online environments, such as web portals, electronic transactions, financial services and e-government, to identify the characteristics and issues of such authentication methods in order to present a user authentication level system model suitable for different online services. The results of our method are confirmed through a risk assessment and we verify its safety using the testing method presented in OWASP and NIST SP800-63.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.55-62
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• 2017

The SRP (Solar Radiation Pressure) model has always been an issue in the dynamic GPS (Global Positioning System) orbit determination. The widely used CODE (Center for Orbit Determination in Europe) model and its variants have nine parameters to estimate the solar radiation pressure from the Sun and to absorb the remaining forces. However, these parameters show a very high correlation with each other and, therefore, only several of them are estimated at most of the IGS (International GNSS Service) analysis centers. In this study, we attempted to numerically verify the correlation between the parameters. For this purpose, a bi-directional, multi-step numerical integrator was developed. The correlation between the SRP parameters was analyzed in terms of post-fit residuals of the orbit. The integrated orbit was fitted to the IGS final orbit as external observations. On top of the parametric analysis of the SRP parameters, we also verified the capabilities of orbit prediction at later time epochs. As a secondary criterion for orbit quality, the positional discontinuity of the daily arcs was also analyzed. The resulting post-fit RMSE (Root-Mean-Squared Error) shows a level of 4.8 mm on average and there is no significant difference between block types. Since the once-per-revolution parameters in the Y-axis are highly correlated with those in the B-axis, the periodic terms in the D- and Y-axis are constrained to zero in order to resolve the correlations. The 6-hr predicted orbit based on the previous day yields about 3 cm or less compared to the IGS final orbit for a week, and reaches up to 6 cm for 24 hours (except for one day). The mean positional discontinuity at the boundary of two 1-day arcs is on the level of 1.4 cm for all non-eclipsing satellites. The developed orbit integrator shows a high performance in statistics of RMSE and positional discontinuity, as well as the separations of the dynamic parameters. In further research, additional verification of the reference frame for the estimated orbit using SLR is necessary to confirm the consistency of the orbit frames.

• Steel and Composite Structures
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• v.48 no.5
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• pp.547-564
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• 2023

This study investigates the effect of bolt preloading on the rotational stiffness of stainless steel end-plate connections. An experimental programme incorporating 11 full-scale joint specimens are carried out comparing the behaviours of fully pre-tensioned (PT) and snug-tightened (ST) flush/extended end-plate connections, made of austenitic or lean duplex stainless steels. It is observed from the tests that the presence of bolt preloading leads to a significant increase in the rotational stiffness. A parallel finite element analysis (FEA) validated against the test results demonstrates that the geometric imperfection of end-plate has a strong influence on the moment-rotation response of preloaded end-plate connections, which is crucial to explain the observed "two-stage" behaviour of these connections. Based on the data obtained from the tests and FE parametric study, the performance of the Eurocode 3 predictive model is evaluated, which exhibits a significant deviation in predicting the rotational stiffness of stainless steel end-plate connections. A modified bi-linear model, which incorporates three key properties, is therefore proposed to enable a better prediction. Finally, the effect of bolt preloading is demonstrated at the system (structure) level considering the serviceability of semi-continuous stainless steel beams with end-plate connections.

• Korean Journal of School Psychology
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• v.17 no.1
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• pp.17-38
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• 2020

This study examined the factor structure of the Child Behavior Checklist Dysregulation Profile(CBCL-DP) for school-aged children in Korea identified differences in the level of maladjustment and problematic behaviors between the clinical group which had characteristics of CBCL-DP and the control group which did not. Confirmative factor analysis was performed on three alternative models from the literature to determine which was the most appropriate factor structure for the CBCL-DP. The result showed that the bi-factor model fit the sample data better than both the one and second-factor models. To confirm that the bi-factor model was the most appropriate factor structure, regression paths with relevant variables examined. The showed that CBCL-DP with the bi-factor model was associated with executive function difficulty as reported by parents and with school adjustment and all sub-factors of strength and difficulty as reported by teachers. The results also showed that this model had a different relationship with anxiety/depression, aggressive behavior, and attention problems than the other models. The clinical group was shown to have more executive function difficulty, worse adjustment of school life and to be less likely to engage in desired behaviors than the control group. These results indicate the CBCL-DP is more related to negative outcomes than any other factor, and that the bi-factor model was found to best fit the sample data, consistent with other studies. The early discovery of CBCL-DP can be used to provide interventions for high-risk children who exhibit emotional and behavioral problems, making its detection a significant diagnostic tool. The implications of these result, the limitations of this study, and areas for future research are discussed in this paper.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2795-2798
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• 2012

Objective: This study is conducted to evaluate the effects of anti-HER-2${\times}$anti-CD3 bi-specific antibodies(BsAb) on HER-2/neuover-expressing human colorectal carcinoma cells. Methods: Growth was assessed by MTT assays after exposure of HCT-116 cells to Herceptin, anti-CD3 and BsAb antibodies. Immunocytochemistry was applied to test the HER-2 level of HCT-116. In a nude mouse model, HER-2${\times}$CD3 BsAb was combined with effector cells (peripheral blood lymph cells from normal human being) for observations on in Vivo growth of tumors. Results: Compared with the control group, using effector cells combined with anti-CD3 McAb, Herceptin or HER2${\times}$CD3 BsAb, tumor cell growth in vitro and in vivo was significantly inhibited (P<0.05), most remarkably in the HER2${\times}$CD3 BsAb case. The growth of xenografts with HER2${\times}$CD3 BsAb combined with effector cells was also significantly inhibited when compared with the anti-CD3 McAb or Herceptin groups (P<0.05). Conclusion: HER-2/neu might be a useful target for immunotherapy in colorectal carcinoma, anti-HER2${\times}$anti-CD3 BsAb exerting clear anti-tumor effects.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.87-108
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• 2017

Objective: The purpose of this study was to precede the acceptance study based on automation steps and user experience that was lacked in the past study on the core technology of autonomous vehicle, ADAS. The first objective was to construct the acceptance model of ADAS technology that is the core technology, and draw factors that affect behavioral intention through user experience-based evaluation by applying driving simulator. The second one was to see the change of factors on automation step of autonomous vehicle through the UX/UA score. Background: The number of vehicles with the introduction of ADAS is increasing, and it caused change of interaction between vehicle and driver as automation is being developed on the particular drive factor. For this reason, it is becoming important to study the technology acceptance on how driver can actively accept giving up some parts of automated drive operation and handing over the authority to vehicle. Method: We organized the study model and items through literature investigation and the scenario according to the 4 stages of automation of autonomous vehicle, and preceded acceptance assessment using driving simulator. Total 68 men and woman were participated in this experiment. Results: We drew results of Performance Expectancy (PE), Social Influence (SI), Perceived Safety (PS), Anxiety (AX), Trust (T) and Affective Satisfaction (AS) as the factors that affect Behavioral Intention (BI). Also the drawn factors shows that UX/UA score has a significant difference statistically according to the automation steps of autonomous vehicle, and UX/UA tends to move up until the stage 2 of automation, and at stage 3 it goes down to the lowest level, and it increases a little or stays steady at stage 4. Conclusion and Application: First, we presented the acceptance model of ADAS that is the core technology of autonomous vehicle, and it could be the basis of the future acceptance study of the ADAS technology as it verifies through user experience-based assessment using driving simulator. Second, it could be helpful to the appropriate ADAS development in the future as drawing the change of factors and predicting the acceptance level according to the automation stages of autonomous vehicle through UX/UA score, and it could also grasp and avoid the problem that affect the acceptance level. It is possible to use these study results as tools to test validity of function before ADAS offering company launches the products. Also it will help to prevent the problems that could be caused when applying the autonomous vehicle technology, and to establish technology that is easily acceptable for drivers, so it will improve safety and convenience of drivers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1115-1122
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• 2013

Recently the demands for traffic information tend to increase, and travel time might one of the most important traffic information. To effectively estimate exact travel time, highly reliable traffic data collection is required. BIS(Bus Information System) data would be useful for the estimation of the route travel time because BIS is collecting data for the bus travel time on the main road of the city on real-time basis. Traditionally use of BIS data has been limited to the realm of bus operating but it has not been used for a variety of traffic categories. Therefore, this study estimates a route travel time on road networks in urban areas on the basis of real-time data of BIS and then eventually constructs regression models. These models use an explanatory variable that corresponds to bus travel time excluding service time at the bus stop. The results show that the coefficient of determination for the constructed regression model is more than 0.950. As a result of T-test performance with assistance from collected data and estimated model values, it is likely that the model is statistically significant with a confidence level of 95%. It is generally found that the estimation for the exact travel time on real-time basis is plausible if the BIS data is used.