• Journal of Urban Science
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• v.11 no.2
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• pp.19-24
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• 2022

Conventional RC beams for crossing small and medium-sized rivers do not have a cross-sectional area, so the floating debris is accumulated and disasters such as damage to bridges occur. To improve this, the PSC method was invented. However, this also had problems such as transverse curvature, increase in dead weight due to cross-sectional shape, and negative moment generated during serialization, so it was necessary to develop a new type of girder. Therefore, it was intended to propose a LIT(Leton Interaction Thrust) girder bridge that is safer and has better performance than the conventional PSC girder with improved section efficiency. Unlike existing girder bridges, the LIT girder has the feature that the change in the strands of the entire girder occurs only in the vertical direction when the first tension is applied because the tendon arrangement is symmetrical by applying the raised portion. In addition, slab continuation generates a secondary moment that is advantageous to the continuous point, effectively controlling the negative moment and preventing the corrosion of the tendon. The dimensions of the cross section were determined, and the arrangement of the strands was designed to conduct structural analysis and detailed analysis. As a result of the structural analysis, the stress of the girder showed results within the allowable compressive stress, and the deflection showed the result within the allowable deflection. showed results. In addition, a detailed analysis was performed to examine the stress distribution around the girder body and the anchorage area and the stress distribution of the embossed portion, and as a result, the stress of the girder body due to the tension force showed a stable level.

• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.79-90
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• 2007

Behaviors of instrumented steel sheet piles which are installed in sand ground by vibratory hammer were investigated. Especially, stresses acting on the pile during vibratory driving, efficiency factor which reflects differences between theoretical driving force and actually delivered acting force, justifiability of rigidity of steel sheet pile, dynamic resistance characteristics of soil and penetration characteristics of sheet pile were analysed. According to the field test results it is justifiable that steel sheet pile behaves as a rigid body during vibratory driving. And it can be seen that maximum stress acting on sheet pile section is far less than tensile strength of the material. Value of the maximum section force at sheet pile head was 72% of that estimated from theoretical equation. Magnitudes of displacement amplitudes computed from displacement-time history curve corresponding to four penetration depths were in the range of 16 $\sim$ 75% of that specified by manufacturer.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.37-44
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• 2009

Recently, as the quality of multimedia data gets higher, multimedia servers require larger storage capacity and higher I/O bandwidth. In these large scale multimedia servers, the load-unbalance problem among disks due to the difference in access frequencies to multimedia objects according to their popularities significantly affects the system performance. To address this problem, many data replication schemes have been proposed. In this paper, we propose a novel data migration/replication scheme to provide better storage efficiency and performance than the dynamic data replication scheme which is typical data replication scheme employed in multimedia servers. This scheme can reduce the additional storage space required for replication, which is a major defect of replication schemes, by decreasing the number of copies per object. The scheme can also increase the number of concurrent users by increasing the caching effect due to the reduced lengths of the intervals among requests for each object.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.925-934
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• 2017

R-tree is index structure which is frequently used for handling spatial data. However, if the number of dimensions increases, or if only partial dimensions are used for searching the certain data according to user preference, the time for indexing is greatly increased and the efficiency of the generated R-tree is greatly reduced. Hence, it is not suitable for the multidimensional data, where dimensions are continuously increasing. In this paper, we propose a multidimensional hash index, a new multidimensional index structure based on a hash index. The multidimensional hash index classifies data into buckets of euclidean space through a hash function, and then, when an actual search is requested, generates a hash search tree for effective searching. The generated hash search tree is able to handle user preferences in selected dimensional space. Experimental results show that the proposed method has better indexing performance than R-tree, while maintaining the similar search performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• Journal of Integrative Natural Science
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• v.16 no.3
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• pp.81-95
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• 2023

Colorectal cancer is one of the most common types of cancer worldwide, ranking third after lung and breast cancer in terms of global prevalence. With an expected 1.93 million new cases and 935,000 deaths in 2020, it is more prevalent in males than in women. Evidence has shown that during the later stages of colon cancer, STAT1 promotes tumor progression by promoting cell survival and resistance to chemotherapy. Recent studies have shown that inhibiting STAT1 pathway leads to a reduction in tumor cell proliferation and growth, and can also promote apoptosis in colon cancer cells. One of the recent approaches in the field of drug discovery is drug repurposing. In drug repurposing approach we have virtually screened FDA database against STAT1 protein and their interactions have been studied through Molecular docking. Cross docking was performed with the top 10 compounds to be more specific with STAT1 comparing the affinity with STAT2, STAT3, STAT4, STAT5a, STAT5b and STAT6. The drugs that showed higher affinity were subjected to Conceptual - Density functional theory. Besides, the Molecular dynamic simulation was also carried out for the selected leads. We also validated in-vitro against colon cancer cell lines. The results showed mainly Acetyldigitoxin has shown better binding to the target. From this study, we can predict that the drug Acetyldigitoxin has shown noticeable inhibitory efficiency against STAT1, which in turn can also lead to the reduction of tumor cell growth in colon cancer.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.2
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• pp.115-124
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• 2024

To detect and prevent structural damage caused by various loads on marine structures and ships, structural health monitoring procedure is essential. Estimating loads acting on the structures which are measured by sensors that are mounted properly are crucial for structural health monitoring. However, attaching an excessive number of sensors to the structure without consideration can be inefficient due to the high costs involved and the potential for inducing structural instability. In this study, we introduce a method to determine the optimal number of sensors and their optimized locations for strain measurement sensors, allowing for accurate load estimation throughout the structure using model expansion method. To estimate the loads exerted on the entire structure with minimal sensors, we construct a strain-load interpolation matrix using the strain mode shapes of the finite element (FE) model and select the optimal sensor locations by applying D-Optimal Design and the row exchange algorithm. Finally, we estimate the loads exerted on the entire structure using the model expansion method. To validate the proposed method, we compare the results obtained by applying the optimal sensor placement and model expansion method to an FE model subjected to arbitrary loads with the loads exerted on the entire FE model, demonstrating efficiency and accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.115-121
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• 2009

The paper presents a signal-based damage detection algorithm of ARX model using dynamic acceleration data. An ARX model correlates acceleration data measured at two locations in a structure by considering those two sets of data as input and output signals. For detecting damage, the error between the measured data and the predicted response from the defined ARX model is computed in time and used for a statistical evaluation. A normal distribution function from the error in time is constructed and its statistical characteristic values are used for the evaluation of damage. By comparing the normal distribution functions before and after damage, three different types of damage indices are proposed. The efficiency and limitation of the proposed algorithm with the statistical evaluation of damage indices have been examined and discussed through laboratory experiments.