• Journal of computational fluids engineering
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• v.10 no.2
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• pp.38-47
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• 2005

Substructuring methods are often used in finite element structural analyses. In this study a multi-level substructuring(MLSS) algorithm is developed and proposed as a possible candidate for finite element fluid solvers. The present algorithm consists of four stages such as a gathering, a condensing, a solving and a scattering stage. At each level, a predetermined number of elements are gathered and condensed to form an element of higher level. At the highest level, each sub-domain consists of only one super-element. Thus, the inversion process of a stiffness matrix associated with internal degrees of freedom of each sub-domain has been replaced by a sequential static condensation of gathered element matrices. The global algebraic system arising from the assembly of each sub-domain matrices is solved using a well-known iterative solver such as the conjugare gradient(CG) or the conjugate gradient squared(CGS) method. A time comparison with CG has been performed on a 2-D Poisson problem. With one domain the computing time by MLSS is comparable with that by CG up to about 260,000 d.o.f. For 263,169 d.o.f using 8 x 8 sub-domains, the time by MLSS is reduced to a value less than $30\%$ of that by CG. The lid-driven cavity problem has been solved for Re = 3200 using the element interpolation degree(Deg.) up to cubic. in this case, preconditioning techniques usually accompanied by iterative solvers are not needed. Finite element formulation for the incompressible flow has been stabilized by a modified residual procedure proposed by Ilinca et al.[9].

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.249-257
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• 2022

This paper presents an improved computational framework for the direct-solution-based finite element tearing and interconnecting (FETI) algorithm. The FETI-local algorithm is further improved herein, and localized Lagrange multipliers are used to define the interface among its subdomains. Selective inverse entry computation, using a property of the Boolean matrix, is employed for the computation of the subdomain interface stiffness and load, in which the original FETI-local algorithm requires a full matrix inverse computation of a high computational cost. In the global interface computation step, the original serial computation is replaced by a parallel multi-frontal method. The performance of the improved FETI-local algorithm was evaluated using a numerical example with 64 million degrees of freedom (DOFs). The computational time was reduced by up to 97.8% compared to that of the original algorithm. In addition, further stable and improved scalability was obtained in terms of a speed-up indicator. Furthermore, a performance comparison was conducted to evaluate the differences between the proposed algorithm and commercial software ANSYS using a large-scale computation with 432 million DOFs. Although ANSYS is superior in terms of computational time, the proposed algorithm has an advantage in terms of the speed-up increase per processor increase.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.1
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• pp.39-55
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• 1993

Modal Analysis is the process of characterizing the dynamic properties of an elastic structure by identifying its modes of vibration. A mode of vibration is a global property of an elastic structure. That is, a mode has a specific natural frequency and damping factor which can be identified from response data at practically any point on a structure, and it has a characteristic mode shape which identifies the mode spatially over the entire structure. Modal testing is able to be performed on structural and mechanical structure in an effort to learn more about their elastic behavior. Once the dynamic properties of a structure are known its behavior can be predicted and therefore controlled or corrected. Resonant frequencies, damping factors and mode shape data can be used directly by a mechanical designer to pin point weak spots in a structure design, or this data can also be used to confirm or synthesize equations of motion for the elastic structure. These differential equations can be used to simulate structural response to know input forces and to examine the effects of pertubations in the distributed mass, stiffness and damping properties of the structure in more detail. In this paper the measurement of transfer functions in digital form, and the application of digital parameter identification techniques to identify modal parameters from the measured transfer function data are discussed. It is first shown that the transfer matrix, which is a complete dynamic model of an elastic plate structure can be written in terms of the structural modes of vibration. This special mathematical form allows one to identify the complete dynamics of the structure from a much reduced set of test data, and is the essence of the modal approach to identifying the dynamics of a structure. Finally, the application of transfer function models and identification techniques for obtaining modal parameters from the transfer function data are discussed. Characteristics on vibration response of elastic plate structure obtained from the dynamic analysis by Finite Element Method are compared with results of modal analysis.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.333-355
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• 2006

Network generation methods to visualize bibliometric data for examining the intellectual structure of knowledge domains are investigated in some detail. Among the four methods investigated in this study, pathfinder network algorithm is the most effective method in representing local details as well as global intellectual structure. The nearest neighbor graph, although never used in bibliometic analysis, also has some advantages such as its simplicity and clustering ability. The effect of input data preparation process on resulting intellectual structures are examined, and concluded that unlike MDS map with clusters, the network structure could be changed significantly by the differences in data matrix preparation process. The network generation methods investigated in this paper could be alternatives to conventional multivariate analysis methods and could facilitate our research on examining intellectual structure of knowledge domains.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.11
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• pp.45-55
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• 1999

The small signal analyzer for the stationary drift-diffusion equation is developed. The slotboom variables of the potential, electron and hole concentrations for the response of applied small signal are defined and the stationary drift-diffusion equation is linearlized on DC operation point by $S^3A$ method. Frontal solver, which is used to solve the global matrix, progresses the accuracy of the solution in high frequency and minimizes the requirement of the memory. The simulations are executed on the structure of 3 dimensional N'P junction diode and 2 dimensional n-MOSFET to verify the proposed algorithm. The average relative errors of the conductance and the capacitance compared with MEDICI are about 26% and 0.67 for N'P junction diode and 7.75% and 2.24% for n-MOSFET. The simulation by the proposed algorithm can analyze the stationary drift-diffusion equation for applied small signal in high frequency region about 100GHz.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.220-227
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• 2018

The most widely used method for constructing an inertial navigation system (INS)/global navigation satellite system (GNSS) coupling system is to construct an integrated navigation system using a Kalman filter. However, depending on the trajectory, non-observable state variables may be generated. In this case, the state variables are not estimated. To solve this problem, a integrated navigation system is constructed and then an observability analysis is performed. In this paper, a 24th order position-matched Kalman filter is defined to design an INS/GNSS integrated navigation system for vehicles moving with a large pitch angle change. To verify the appropriateness of the error state variables applied to the Kalman filter, an observability analysis was performed. The trajectory was divided into five segments, and the piece-wise constant system (PWCS) was assumed for each segment, and the results were analytically analyzed. The analytical results and the simulation results confirm that the error state parameters of the Kalman filter are well-designed to the estimation side.

• Annals of Laboratory Medicine
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• v.38 no.6
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• pp.569-577
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• 2018

Background: The increasing prevalence of drug-resistant tuberculosis (TB) infection represents a global public health emergency. We evaluated the usefulness of a newly developed multiplexed, bead-based bioassay (Quantamatrix Multiplexed Assay Platform [QMAP], QuantaMatrix, Seoul, Korea) to rapidly identify the Mycobacterium tuberculosis complex (MTBC) and detect rifampicin (RIF) and isoniazid (INH) resistance-associated mutations. Methods: A total of 200 clinical isolates from respiratory samples were used. Phenotypic anti-TB drug susceptibility testing (DST) results were compared with those of the QMAP system, reverse blot hybridization (REBA) MTB-MDR assay, and gene sequencing analysis. Results: Compared with the phenotypic DST results, the sensitivity and specificity of the QMAP system were 96.4% (106/110; 95% confidence interval [CI] 0.9072-0.9888) and 80.0% (72/90; 95% CI 0.7052-0.8705), respectively, for RIF resistance and 75.0% (108/144; 95% CI 0.6731-0.8139) and 96.4% (54/56; 95% CI 0.8718-0.9972), respectively, for INH resistance. The agreement rates between the QMAP system and REBA MTB-MDR assay for RIF and INH resistance detection were 97.6% (121/124; 95% CI 0.9282-0.9949) and 99.1% (109/110; 95% CI 0.9453-1.0000), respectively. Comparison between the QMAP system and gene sequencing analysis showed an overall agreement of 100% for RIF resistance (110/110; 95% CI 0.9711-1.0000) and INH resistance (124/124; 95% CI 0.9743-1.0000). Conclusions: The QMAP system may serve as a useful screening method for identifying and accurately discriminating MTBC from non-tuberculous mycobacteria, as well as determining RIF- and INH-resistant MTB strains.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.139-148
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• 2020

The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.