• BioChip Journal
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• v.12 no.4
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• pp.268-279
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• 2018

Flower-shaped organic-inorganic hybrid nanostructures, termed nanoflowers, have received considerable recent attention as they possess greatly enhanced activity, stability, durability, and even selectivity of entrapped organic biomolecules, which are much better than those from the conventional methods. They can be synthesized simply via co-incubation of organic and inorganic components in aqueous buffer at room temperature and yield hierarchical nanostructures with large surface-to-volume ratios, allowing for low-cost production by easy scale-up, as well as the high loading capacity of biomolecules without severe mass transfer limitations. Since a pioneering study reported on hybrid nanoflowers prepared with protein and copper sulfate, many other organic and inorganic components, which endow nanoflowers with diverse functionalities, have been employed. Thanks to these features, they have been applied in a diverse range of areas, including biosensors and biocatalysis. To highlight the progress of research on organic-inorganic hybrid nanoflowers, this review discusses their synthetic methods and mechanisms, structural and biological characteristics, as well as recent representative applications. Current challenges and future directions toward the design and development of multi-functional nanoflowers for their widespread utilization in biotechnology are also discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.208-214
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• 2021

Recently, much of the intelligent security scenario and criminal investigation demands for matching photo and non-photo. Existing face recognition system can not sufficiently guarantee these needs. In this paper, we propose an algorithm to improve the performance of heterogeneous face recognition systems by reducing the different modality between sketches and photos of the same person. The proposed algorithm extracts each image's texture features through texture descriptors (gray level co-occurrence matrix, multiscale local binary pattern), and based on this, generates a transformation matrix through eigenfeature regularization and extraction techniques. The score value calculated between the vectors generated in this way finally recognizes the identity of the sketch image through the score normalization methods.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Smart Media Journal
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• v.13 no.3
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• pp.53-61
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• 2024

This paper presents a novel M-shaped encoder-decoder architecture for skin lesion segmentation, achieving better performance than existing approaches. The proposed architecture utilizes the left and right legs to enable multi-scale feature extraction and is further enhanced by integrating an attention module within the skip connection. The image is partitioned into four distinct patches, facilitating enhanced processing within the encoder-decoder framework. A pivotal aspect of the proposed method is to focus more on critical image features through an attention mechanism, leading to refined segmentation. Experimental results highlight the effectiveness of the proposed approach, demonstrating superior accuracy, precision, and Jaccard Index compared to existing methods

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.15-29
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• 2024

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.783-791
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• 2000

A multidimensional representation for precipitation, given In the theory proposed by E. Waymire et al. (1984), is used for simulating rainfall in space and time. The model produces moving storms with realistic meso-scale meteorological features in time and space. The first- and second-order statistics derived from observed JX)int gauge data were used to estimate the model parameters based on the Nelder-Mead algorithm of optimization. Then twelve-year traces of rainfall intensities at fixed gage stations were generated at intervals of 1 hours. First- and second-order statistics are evaluated from the above series, which are used for estimating the parameters of one dimensional model of temporal rainfall at a point. As a result from the comparisons of one dimensional model parameters used observed and generated data from multidimensional model, we found that the multidimensional rainfall model generated visually realistic spatial patterns of rainfall as well as realistic temporal hyetographs of rainfall at a point. point.

• KSBB Journal
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• v.17 no.4
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• pp.326-332
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• 2002

The quality of biopharmaceutical proteins is strongly affected by a manufacturing process employed to produce Et, and thus validation of the manufacturing bioprocess is a very important issue. Chromatography is probably the most widely used bioprocess unit operation for protein purification. In this study, a miniaturized automatic chromatography system was designed and constructed for scale-down studies for process chromatography validation. This system, named MiniValChrom, has the following features: automatic and repeated operation, flexible sequences and intervals among the steps, on-line and real-time monitoring and control, method files savings, etc. Using the MiniValChrom, we peformed a case study of an abbreviated experiment to estimate chromatographic resin lifetime. BSA (bovine serum albumin) and Cibacron Blue 3G-A were used as the model protein and the resin, respectively. The resin deterioration was evaluated by determining and monitoring the HETP and NTP values from the chromatograms every 5 cycles. It was observed that the HETP and the NTP values were changed by 9% after 15 cycles. The resin lifetime validation could be completed by repeating this experiment until the HETP value reached a predetermined value. The MiniValChrom's concept and the protocol suggested in this study can serve as a rapid and economical tool for the validation studies of bioprocess chromatography system.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.65-73
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• 2016

Peer-to-Peer (P2P) systems with super-peer overlay networks show combined advantages of both hybrid and pure P2P systems. Super-peer is a special peer acting as a server to a cluster of generic peers. Organizing a super-peer network is one of important issues for P2P systems with super-peer networks. Conventional P2P systems are based on two-level hierarchies of peers. One is a layer for generic peers and the other is for super-peers. And it is usual that super-peer networks have forms of random graphs. However, for accommodating a large-scale collection of generic peers, the super-peer network has also to be extended. In this paper, we propose a scheme of hierarchically constructing super-peer networks for large-scale P2P systems. At first, a two-level tree, called a simple super-peer network, is proposed, and then a scheme of generalizing and then extending the simple super-peer network to multi-level super-peer network is presented to construct a large-scale super-peer network. We call it an extended super-peer network. The simple super-peer network has several good features, but due to the fixed number of levels, it may have a scalability problem. Thus, it is extended to k-level tree of a super-peer network, called extended super-peer network. It shows good scalability and easy management of generic peers for large scale P2P system.

• Journal of the Korean Vacuum Society
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• v.13 no.1
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• pp.39-45
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• 2004

Ultraviolet-nanoimprint lithography (UV-NIL) is a promising method for cost-effectively defining nanoscale structures at room temperature and low pressure. A 5${\times}$5${\times}$0.09 in. quartz stamp is fabricated using the etch process in which a Cr film was employed as a hard mask for transferring nanostructures onto the quartz plate. FAS(Fluoroalkanesilane) is used as a material for anti-adhesion surface treatment on the stamp and a thin organic film to improve adhesion on a wafer is formed by spin-coating. The low viscosity resin droplets with a nanometer scale volume are dispensed on the whole area of the coated wafer. The UV-NIL experiments have been performed using the EVG620-NIL. 370 nm - 1 m features on the stamp have been transferred to the thin resin layer on the wafer using the multi-dispensing method and UV-NIL process. We have measured the imprinted patterns and residual layer using SEM and AFM to evaluate the potential of the process.