• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2718-2731
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• 2024

To ensure the operational safety of nuclear power plants, we present a Quadruped Inspection Robot that can be used for many types of steam generators. Since the Inspection Robot relies on the Holding Modules to grip the tube-sheet, it can be regarded as a hybrid robot with variable configurations, switching between 4-RRR-RR, 3-RRR-RR, and two types of 2-RRR-RR, and the variable configurations bring a great challenge to dimensional synthesis. In this paper, the kinematic model of the Inspection Robot in multiple configurations is established, and the analytical solution is given. The workspace mapping is analyzed by the solution-space, and the workspace of multiple configurations is decomposed into the workspace of 2-RRR to reduce the analysis complexity, and the workspace calculation is simplified by using the envelope rings. The optimization problem of the manipulator is transformed into the calculation of the shortest contraction length of the swing leg. The switching performance of the Inspection Robot is evaluated by stride-length, turning-angle, and workspace overlap-ratio. The performance indexes are classified and transformed based on the proportions and variation trends of dimensional parameters to reduce the number of optimization objective functions, and Pareto optimal solutions are obtained using an intelligent optimization algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.1
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• pp.131-138
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• 2006

Structural bracing concept equipped with a new and efficient friction based energy dissipation device is referred to Friction SliP Brace (FSB) where the behavior of the brace components is elastic until the axial resistant force in the brace exceeds the friction force developed at the frictional interface of the device. In this study, the FSB concept is modified and new type of hybrid energy dissipation device, the Active Friction SliP Braces (AFSB), is described. The FSB is by far improved in the AFSB by inclusion of an active clamping mechanism on the friction interface. The clamping action regulated by the developed algorithm is altered during the response of the building. The results indicate that the action of dissipating vibrational energy in the AFSB impacts on the response at later cycles by keeping the drift amplitudes at much lower levels, revealing overshooting problem due to its early slippage. Providing predetermined constant incremental strengths to the building by AFSB medium improves response by reducing drift amplitudes and base shear under small and medium amplitude ground accelerations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.5 s.57
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• pp.121-132
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• 2009

This paper introduces an experimental verification and a field application of the proposed technique using the combination of FEM and HGA about the loss prestressing force of an exteranl tendon by above same authors. The vibration tests have been conducted by using a laboratory models and the externally prestressed tendon at the field and the natural frequencies are extracted from the vibration tests. The proposed technique based on the extracted natural frequencies is applied. It is seen that the errors in the tension and lost prestressing force by proposed technique are about 4% from a laboratory model test. For the model verification at field, exact modeling has beem made with Rayleigh damping. It is seen that the error in the tension by proposed technique is less than 1% and the estimated lost prestressing force converges less than the exact value.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.1-11
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• 2007

H.264/AVC uses variable block sizes to achieve significant coding gain. It has 7 different coding modes having different motion compensation block sizes in Inter slice, and 2 different intra prediction modes in Intra slice. This fine-tuned new coding feature has achieved far more significant coding gain compared with previous video coding standards. However, extremely high computational complexity is required when rate-distortion optimization (RDO) algorithm is used. This computational complexity is a major problem in implementing real-time H.264/AVC encoder on computationally constrained devices. Therefore, there is a clear need for complexity reduction algorithm of H.264/AVC such as fast mode decision. In this paper, we propose a fast mode decision with early $P8\times8$ mode rejection based on block size activity using large block history map (LBHM). Simulation results show that without any meaningful degradation, the proposed method reduces whole encoding time on average by 53%. Also the hybrid usage of the proposed method and the early SKIP mode decision in H.264/AVC reference model reduces whole encoding time by 63% on average.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.4
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• pp.94-104
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• 2008

In this paper, we propose a new architecture of multiplier-and-accumulator (MAC) for high speed multiplication and accumulation arithmetic. By combining multiplication with accumulation and devising a hybrid type of carry save adder (CSA), the performance was improved. Since the accumulator which has the largest delay in MAC was removed and its function was included into CSA, the overall performance becomes to be elevated. The proposed CSA tree uses 1's complement-based radix-2 modified booth algorithm (MBA) and has the modified array for the sign extension in order to increase the bit density of operands. The CSA propagates the carries by the least significant bits of the partial products and generates the least significant bits in advance for decreasing the number of the input bits of the final adder. Also, the proposed MAC accumulates the intermediate results in the type of sum and carry bits not the output of the final adder for improving the performance by optimizing the efficiency of pipeline scheme. The proposed architecture was synthesized with $250{\mu}m,\;180{\mu}m,\;130{\mu}m$ and 90nm standard CMOS library after designing it. We analyzed the results such as hardware resource, delay, and pipeline which are based on the theoretical and experimental estimation. We used Sakurai's alpha power low for the delay modeling. The proposed MAC has the superior properties to the standard design in many ways and its performance is twice as much than the previous research in the similar clock frequency.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.

• Journal of KIISE
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• v.42 no.3
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• pp.367-378
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• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.444-454
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• 2003

An analysis of heart movement is to estimate a role which supplies blood in human body. We have constructed a left ventricle myocardium model and mathematically evaluated the motion of myocardium. The myocardial motility was visualized using some parameters about cardiac motion. We applied the myocardium model in the gated myocardium SPECT image that showed a cardiac biochemical reaction, and analyzed a motility between the gated myocardium SPECT image and the myocardium model. The myocardium model was created of the based on three dimensional super-ellipsoidal model that was using the sinusoidal function. To express a similar form and motion of the left ventricle myocardium, we calculated parameter functions that gave the changing of motion and form. The LSF algorithm was applied to the myocardium gated SPECT image data and the myocardium model, and finally created a fitting model. Then we analyzed a regional motility direction and size of the gated myocardium SPECT image that was constructed on a fitting model. Furthermore, we implemented the Bull's Eye map that had evaluated the heart function for presentation of regional motility. Using myocardium's motion the evaluation of cardiac function of SPECT was estimated by a contraction ability, perfusion etc. However, it is not any estimation about motility. So, We analyzed the myocardium SPECT's motility of utilizing the myocardium model. We expect that the proposed algorithm should be a useful guideline in the heart functional estimation.