• Imaging Science in Dentistry
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• v.34 no.1
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• pp.1-5
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• 2004

Purpose: To evaluate the diagnostic accuracy of occlusal and proximal caries detection using CCD, CMOS, PSP and film system. Materials and Methods : 32 occlusal and 30 proximal tooth surfaces were radiographed under standardized conditions using 3 digital systems; CCD (CDX-2000HQ, Biomedysis Co., Seoul, Korea), CMOS (Schick, Schick Inc., Long Island, USA), PSP (Digora/sup (R)/FMX, Orion Co./Soredex, Helsinki, Finland) and I film system (Kodak Insight, Eastman Kodak, Rochester, USA). 5 observers examined the radiographs for occlusal and proximal caries using a 5-point confidence scale. The presence of caries was validated histologically and radiographically. Diagnostic accuracy was evaluated using ROC curve areas (Az). Results: Analysis using ROC curves revealed the area under each curve which indicated a diagnostic accuracy. For occlusal caries, Kodak Insight film had an Az of 0.765, CCD one of 0.730, CMOS one of 0.742 and PSP one of 0.735. For proximal caries, Kodak Insight film had an Az of 0.833, CCD one of 0.832, CMOS one of 0.828 and PSP one of 0.868. No statistically significant difference was noted between any of the imaging modalities. Conclusion: CCD, CMOS, PSP and film performed equally well in the detection of occlusal and proximal dental caries. CCD, CMOS and PSP-based digital images provided a level of diagnostic performance comparable to Kodak Insight film.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.9-17
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• 2013

In this paper, a pseudorange correction regeneration algorithm was developed to improve the positioning accuracy of DGPS using multi-reference stations, and the optimal minimum number of reference sites was determined by trying out different numbers of reference. This research was conducted using from two to five sites, and positioning errors of less than 1 m were obtained when pseudorange corrections are collected from at least four reference stations and interpolated as the pseudorange correction at the rover. After determining the optimal minimum number of reference stations, the pseudorange correction regeneration algorithm developed was tested by comparison with the performance of other algorithms. Our approach was developed based on an exponential model. If pseudorange corrections are regenerated using an exponential model, the effect of a small difference in the baseline distance can be enlarged. Therefore, weights can be applied sensitively even when the baseline distance differs by a small amount. Also weights on the baseline distance were applied differently by assigning weights depending on the difference of the longest and shortest baselines. Through this method, the positioning accuracy improved by 19% compared to the result of previous studies.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.800-805
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• 2013

Incremental forming of sheet metal is a modern method of forming sheet metal, where parts can be formed without the use of dedicated dies. Existing experimental configurations for incremental forming can be broadly classified into two categories, i.e., negative and positive forming. In this paper, forming qualities such as shape accuracy and surface roughness of Al 1050 material were discussed for different forming methods. The formed and the corresponding opposing surfaces were measured with a three-dimensional scanner and a surface roughness tester. It was found that in terms of shape accuracy, the best opposing surface was obtained with positive forming, whereas the worst formed surface was obtained with negative forming; furthermore, the opposing surface is always better than the formed surface, regardless of the forming method used.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.417-428
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• 2018

In this paper, various model reduction methods were assessed using a shear frame, plane and space truss structures. Each of the structures is one-dimensional, two-dimensional and three-dimensional, respectively. Three scenarios of poor, better, and the best were considered for each of the structures in which 25%, 40%, and 60% of the total degrees of freedom (DOFs) were measured in each of them, respectively. Natural frequencies of the full and reduced order structures were compared in each of the numerical examples to assess the performance of model reduction methods. Generally, it was found that system equivalent reduction expansion process (SEREP) provides full accuracy in the model reduction in all of the numerical examples and scenarios. Iterated improved reduced system (IIRS) was the second-best, providing acceptable results and lower error in higher modes in comparison to the improved reduced system (IRS) method. Although the Guyan's method has very low levels of accuracy. Structures were classified with the excitation frequency. High-frequency structures compared to low-frequency structures have been poor performance in the model reduction methods (Guyan, IRS, and IIRS).

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.245-251
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• 2009

Numerical algorithms for solving the incompressible Navier-Stokes equations using P2P1 finite element are compared regarding the eigenvalues of matrices. P2P1 element allocates pressure at vertex nodes and velocity at both vertex and mid nodes. Therefore, compared to the P1P1 element, the number of pressure variables in the P2P1 element decreases to 1/4 in the case of two-dimensional problems and to 1/8 in the three-dimensional problems. Fully-implicit-integrated, semi-implicit- integrated and semi-segregated finite element formulations using P2P1 element are compared in terms of elapsed time, accuracy and eigenvlue distribution (condition number). For the comparison,they have been applied to the well-known benchmark problems. That is, the two-dimensional unsteady flows around a fixed circular cylinder and decaying vortex flow are adopted to check spatial accuracy.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.101-113
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• 2015

The Monte-Carlo (MC) technique is very efficient solution for statistical problem. Various MC methods can easily be applied to statistical circuit performance analysis. Recently, as the number of process parameters and their impact, has increasingly affected circuit performance, a sufficient sample size is required in order to consider high dimensionality, profound nonlinearity, and stringent accuracy requirements. Also, it is important to identify the performance of circuit as soon as possible. In this paper, Fast MC method is proposed for efficient analysis of circuit performance. The proposed method analyzes performance using enhanced-precision Latin Hypercube Sampling Monte Carlo (LHSMC). To increase the accuracy of the analysis, we calculate the effective dimension for the low discrepancy value on critical parameters. This will guarantee a robust input vector for the critical parameters. Using a 90nm process parameter and OP-AMP, we verified the accuracy and reliability of the proposed method in comparison with the standard MC, LHS and Quasi Monte Carlo (QMC).

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.117-124
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• 2005

Splitting algorithms of the incompressible Navier-Stokes equations using P1P1/P2P1 finite element formulation are newly proposed. P1P1 formulation allocates velocity and pressure at the same nodes, while P2P1 formulation allocates pressure only at the vertex nodes and velocity at both the vertex and mid nodes. For comparison of the elapsed time and accuracy of the two methods, they have been applied to the well-known benchmark problems. The three cases chosen are the two-dimensional steady and unsteady flows around a fixed cylinder, decaying vortex, and impinging slot jet. It is shown that the proposed P2P1 semi-splitting method performs better than the conventional P1P1 splitting method in terms of both accuracy and computation time.

• Structural Engineering and Mechanics
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• v.35 no.1
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• pp.53-65
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• 2010

The rigid body inertia properties of a structure including the mass, the center of gravity location, the mass moments and principal axes of inertia are required for structural dynamic analysis, modeling of mechanical systems, design of mechanisms and optimization. The analytical approaches such as solid or finite element modeling can not be used efficiently for estimating the rigid body inertia properties of complex structures. Several experimental approaches have been developed to determine the rigid body inertia properties of a structure via Frequency Response Functions (FRFs). In the present work two experimental methods are used to estimate the rigid body inertia properties of a frame. The first approach consists of using the amount of mass as input to estimate the other inertia properties of frame. In the second approach, the property of orthogonality of modes is used to derive the inertia properties of a frame. The accuracy of the estimated parameters is evaluated through the comparison of the experimental results with those of the theoretical Solid Work model of frame. Moreover, a thorough discussion about the effect of accuracy of measured FRFs on the estimation of inertia properties is presented.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.