• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.166-171
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• 2023

Efficient energy management is a very important factor in sensor networks with limited resources, and cluster techniques have been studied a lot in this respect. However, a problem may occur in which energy use of the cluster header is concentrated, and when the cluster header is not evenly distributed over the entire area but concentrated in a specific area, the transmission distance of the cluster members may be large or very uneven. The transmission distance can be directly related to the problem of energy consumption. Since the energy of a specific node is quickly exhausted, the lifetime of the sensor network is shortened, and the efficiency of the entire sensor network is reduced. Thus, balanced energy consumption of sensor nodes is a very important research task. In this study, factors for balanced energy consumption by cluster headers and sensor nodes are analyzed, and a balancing distribution clustering method in which cluster headers are balanced distributed throughout the sensor network is proposed. The proposed cluster method uses multi-hop routing to reduce energy consumption of sensor nodes due to long-distance transmission. Existing multi-hop cluster studies sets up a multi-hop cluster path through a two-step process of cluster setup and routing path setup, whereas the proposed method establishes a hierarchical cluster routing path in the process of selecting cluster headers to minimize the overhead of control messages.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.5
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• pp.1-8
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• 2009

Photobioreactor (PBR) that houses and cultivates microalgae providing a suitable environment for its growth, such as light, nutrients, CO2, heat, etc. is now getting more popular in the last decade. Among the many types of PBRs, the bubble column type is very attractive because of its simple construction and easy operation. However, despite the availability of these PBRs, only a few of them can be practically used for mass production. Many limitations still holdback their use especially during their scale-up. To enlarge the culture volume and productivity while supplying optimum environmental conditions, various PBR structures and process control are needed to be investigated. In this study, computational fluid dynamics (CFD) was economically used to design a bubble-column type PBR taking the place of field experiments. CFD is a promising technique which can simulate the growth and production of microalgae in the PBR. To study bubble column PBR with CFD, the most important factor is the possibility of realizing bubble. In this study, multi-phase models which are generally used to realize bubbles were compared by theoretical approaches and comparing in a 2D simulation. As a result, the VOF (volume of fluid) model was found to be the most effective model to realize the bubbles shape as well as the flow inside PBR which may be induced by bubble injection. Considering the accuracy and economical efficiency, 0.005 second time step size was chosen for 2.5 mm mesh size. These results will be used as criteria for scale-up in the PBR simulation.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.141-150
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• 2011

Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.20-29
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• 1991

A fundamental assumption in conventional linear predictive coding (LPC) analysis procedure is that the input to an all-pole vocal tract filter is white process. In the case of periodic inputs, however, a pitch bias error is introduced into the conventional LP coefficient. Multi-pulse (MP) LP analysis can reduce this bias, provided that an estimate of the excitation is available. Since the prediction error of conventional LP analysis can be modeled as the sum of an MP excitation sequence and a random noise sequence, we can view extracting MP sequences from the prediction error as a classical detection and estimation problem. In this paper, we propose an algorithm in which the locations and amplitudes of the MP sequences are first obtained by applying a likelihood ratio test (LRT) to the prediction error, and LP coefficients free of pitch bias are then obtained from the MP sequences. To verify the performance enhancement, we iterate the above procedure with adaptive threshold at each step.

• Journal of Microbiology and Biotechnology
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• v.19 no.1
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• pp.65-71
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• 2009

Microbial oxidoreductive systems have been widely used in asymmetric syntheses of optically active alcohols. However, when reused in multi-batch reaction, the catalytic efficiency and sustainability of non-growing cells usually decreased because of continuous consumption of required cofactors during the reaction process. A novel method for NADPH regeneration in cells was proposed by using pentose metabolism in microorganisms. Addition of D-xylose, L-arabinose, or D-ribose to the reaction significantly improved the conversion efficiency of deracemization of racemic 1-phenyl-1,2-ethanediol to (S)-isomer by Candida parapsilosis cells already used once, which afforded the product with high optical purity over 97%e.e. in high yield over 85% under an increased substrate concentration of 15 g/l. Compared with reactions without xylose, xylose added to multi-batch reactions had no influence on the activity of the enzyme catalyzing the key step in deracemization, but performed a promoting effect on the recovery of the metabolic activity of the non-growing cells with its consumption in each batch. The detection of activities of xylose reductase and xylitol dehydrogenase from cell-free extract of C. parapsilosis made xylose metabolism feasible in cells, and the depression of the pentose phosphate pathway inhibitor to this reaction further indicated that xylose facilitated the NADPH-required deracemization through the pentose phosphate pathway in C. parapsilosis. moreover, by investigating the cofactor pool, the xylose addition in reaction batches giving more NADPH, compared with those without xylose, suggested that the higher catalytic efficiency and sustainability of C. parapsilosis non-growing cells had resulted from xylose metabolism recycling NADPH for the deracemization.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4067-4070
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• 2014

Background: The Chinese Hui ethnic group has diverse origins, including Arab, Persian, Central Asian, and Mongol. The standardized mortality rate of gastric cancer in the Hui population is higher than the overall Chinese population. In this study, we investigated whether COX-2-765G>C polymorphism, an extensively studied polymorphism, contributes to gastric cancer and its precursor lesions (GPL) in the Chinese Hui ethnic group. Materials and Methods: COX-2-765G>C polymorphism was determined by pyrosequencing in 100 gastric cancer cases, 102 gastric cancerand its precursor lesions cases and 105 controls. Data were statistically analyzed using Chi-square tests and logistic regression models. Results: Among the Chinese Hui ethnic group COX-2-765 C allele carriers were at increased risk for gastric cancer (OR=1.977, 95%CI=1.104-3.541). We also found an interaction between COX-2 -765 C carriers and Helicobacter pylori infection and eating pickled vegetables. Conclusions: Our findings suggest a multi-step process of gene-environment interaction contributes to gastric carcinogenesis.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2201-2205
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• 2013

Colorectal cancer (CRC) is one of the most common cancers in many parts of the world. Its development is a multi-step process involving three distinct stages, initiation that alters the molecular message of a normal cell, followed by promotion and progression that ultimately generates a phenotypically altered transformed malignant cell. Reports have suggested an association of the phosphoinositide-3-kinase (PI3K)/Akt pathway with colon tumorigenesis. Activation of Akt signaling and impaired expression of phosphatase and tensin homolog (PTEN) (a negative regulator of Akt) has been reported in 60-70% of human colon cancers and inhibitors of PI3K/Akt signaling have been suggested as potential therapeutic agents. Around 80% of human colon tumors possess mutations in the APC gene and half of the remainder feature ${\beta}$-catenin gene mutations which affect downstream signaling of the PI3K/Akt pathway. In recent years, there has been a great focus in targeting these signaling pathways, with natural and synthetic drugs reducing the tumor burden in different experiment models. In this review we survey the role of PI3K/Akt/mTOR and Wnt signaling in CRC.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.25-31
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• 2007

For areas such as the Korean Peninsula, which have moderate seismic activity but no available records of strong ground motion, synthetic seismograms can be used to evaluate ground motion without waiting for a strong earthquake. Such seismograms represent the estimated ground motions expected from a set of possible earthquake scenarios. Local site effects are especially important in assessing the seismic hazard and possible ground motion scenarios for a specific fault. The earthquake source and rupture dynamics can be described as a two-step process of rupture initiation and front propagation controlled by a frictional sliding mechanism. The seismic wavefield propagates through heterogeneous geological media and finally undergoes near-surface modulations such as amplification or deamplification. This is a complex system in which various scales of physical phenomena are integrated. A unified approach incorporates multi-scale problems of dynamic rupture, radiated wave propagation, and site effects into an all-in-one model using a three-dimensional, fourth-order, staggered-grid, finite-difference method. The method explains strong ground motions as products of complex systems that can be modified according to a variety of fine-scale rupture scenarios and friction models. A series of such deterministic earthquake scenarios can shed light on the kind of damage that would result and where it would be located.