• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.312-317
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• 2022

Piezoelectric energy harvesting has attracted increasing attention over the last decade as a means for generating sustainable and long-lasting energy from wasted mechanical energy. To develop self-powered wearable devices, piezoelectric materials should be flexible, stretchable, and bio-eco-friendly. This study proposed the fabrication of stretchable piezoelectric composites via dispersing perovskite-structured BaTiO₃ nanoparticles inside an Ecoflex polymeric matrix. In particular, the stretchable piezoelectric sensor array was fabricated via a simple and cost-effective spin-coating process by exploiting the piezoelectric composite comprising of BaTiO₃ nanoparticles, Ecoflex matrix, and stretchable Ag coated textile electrodes. The fabricated sensor generated an output voltage of ~4.3 V under repeated compressing deformations. Moreover, the piezoelectric sensor array exhibited robust mechanical stability during mechanical pushing of ~5,000 cycles. Finite element method with multiphysics COMSOL simulation program was employed to support the experimental output performance of the fabricated device. Finally, the stretchable piezoelectric sensor array can be used as a self-powered touch sensor that can effectively detect and distinguish mechanical stimuli, such as pressing by a human finger. The fabricated sensor demonstrated potential to be used in a stretchable, lead-free, and scalable piezoelectric sensor array.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.259-268
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• 2022

With prosperous usage of cloud services to improve management efficiency due to the explosive increase in data volume, various cryptographic techniques are being applied in order to preserve data privacy. In spite of the vast computing resources of cloud systems, decrease in storage efficiency caused by redundancy of data outsourced from multiple users acts as a factor that significantly reduces service efficiency. Among several approaches on privacy-preserving data deduplication over encrypted data, in this paper, the research results for improving efficiency of encrypted data deduplication using trusted execution environment (TEE) published in the recent USENIX ATC are analysed in terms of security and efficiency of the participating entities. We present a way to improve the stability of a key-managing server by integrating it with individual clients, resulting in secure deduplication without independent key servers. The experimental results show that the communication efficiency of the proposed approach can be improved by about 30% with the effect of a distributed key server while providing robust security guarantees as the same level of the previous research.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.185-193
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• 2022

This paper proposes an adaptive sliding mode control with radial basis function neural network(RBFNN) scheme to enhance the performance of position and attitude tracking control of quadrotor UAV. The RBFNN is utilized on the approximation of nonlinear function in the UAV dynmic model and the weights of the RBFNN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering problems, the sliding mode control term is adjusted by adaptive laws, which can enhance the robust performance of the system. The simulation results of the proposed control method confirm the effectiveness of the proposed controller which applied for a nonlinear quadrotor UAV is presented. Form the results, it's shown that the developed control system is achieved satisfactory control performance and robustness.

• Applied Chemistry for Engineering
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• v.34 no.5
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• pp.479-487
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• 2023

Copper is cost-effective and abundantly available as a biocidal coating agent for a wide range of material surfaces. Natural oxidation does not compromise the efficacy of copper, allowing it to maintain antimicrobial activity under prolonged exposure conditions. Furthermore, copper compounds exhibit a broad spectrum of antimicrobial activity against pathogenic yeast, both enveloped and non-enveloped types of viruses, as well as gram-negative and gram-positive bacteria. Contact killing of copper-coated surfaces causes the denaturation of proteins and damage to the cell membrane, leading to the release of essential components such as nucleotides and cytoplasm. Additionally, redox-active copper generates reactive oxygen species (ROS), which cause permanent cell damage through enzyme deactivation and DNA destruction. Owing to its robust stability, copper has been utilized in diverse forms, such as nanoparticles, ions, composites, and alloys, resulting in the creation of various coating methods. This mini-review describes representative coating processes involving copper ions and copper oxides on various material surfaces, highlighting the antibacterial and antiviral properties associated with different oxidation states of copper.

• Journal of Electrochemical Science and Technology
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• v.15 no.1
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• pp.198-206
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• 2024

Given the high theoretical capacity (1,675 mAh g^-1) and the inherent affordability and ubiquity of elemental sulfur, it stands out as a prominent cathode material for advanced lithium metal batteries. Traditionally, sulfur was sequestered within conductive porous carbons, rooted in the understanding that their inherent conductivity could offset sulfur's non-conductive nature. This study, however, pivots toward a transformative approach by utilizing diatom shell (DS, diatomite)-a naturally abundant and economically viable siliceous mineral-as a sulfur host. This approach enabled the development of a sulfurlayered diatomite/S composite (DS/S) for cathodic applications. Even in the face of the insulating nature of both diatomite and sulfur, the DS/S composite displayed vigorous participation in the electrochemical conversion process. Furthermore, this composite substantially curbed the loss of soluble polysulfides and minimized structural wear during cycling. As a testament to its efficacy, our Li-S battery, integrating this composite, exhibited an excellent cycling performance: a specific capacity of 732 mAh g^-1 after 100 cycles and a robust 77% capacity retention. These findings challenge the erstwhile conviction of requiring a conductive host for sulfur. Owing to diatomite's hierarchical porous architecture, eco-friendliness, and accessibility, the DS/S electrode boasts optimal sulfur utilization, elevated specific capacity, enhanced rate capabilities at intensified C rates, and steadfast cycling stability that underscore its vast commercial promise.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8051-8055
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• 2014

Background: A common genetic variant rs3757318, located in intron of C6orf97, was firstly identified to be associated with breast cancer (BC) risk by a genome-wide association (GWA) study. However, subsequent validation studies with different ethnicities have yielded conflicting results. Materials and Methods: We performed a meta-analysis to synthesize all available data for evaluating the precise effect of this variant on BC susceptibility. Results: A total of 8 articles containing 11 studies with 62,891 cases and 65,635 controls were included in this meta-analysis. When compared to the G allele, the rs3757318-A allele was significantly associated with BC risk with the pooled OR of 1.21 (95% CI=1.15 - 1.29, P<0.001) but with obvious between-study heterogeneity (P=0.040). Stratified analysis suggested that diversity of ethnicity along with control source may explain part of the heterogeneity. Similarly, significant associations were also identified in heterozygote, homozygote, dominant and recessive genetic models. Sensitivity and publication bias analyses indicated robust stability of our results. Conclusions: Our present meta-analysis demonstrated that the variant rs3757318 is associated with increased BC risk. Nevertheless, further studies are needed to clarify the underlying biological mechanisms.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.117-130
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• 2008

This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.51-64
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• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.