• Korean Journal of Microbiology
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• v.37 no.4
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• pp.312-316
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• 2001

To improve the ethanol fermentability, four Saccharomyces yeast strains with efficient ethanol fermentability were subjected to rare-mating and protoplast fusion. Using these 4 strains, 5 different combinations of mating-pair or fusion-pair were constructed and their hybrids or fusants were obtained. From the statistical analysis of the results of the ethanol fermentation by the hybrids of the different mating-pair or fusion-pair, no difference was found in ethanol production, but [S. kluveri $khl{\times}S$ cerevisiae cp3] pair was shown to be the best combination which can produce high cell-viability. In fact, the clone No. 3 of the [S. kluveri $khl{\times}S$ cerevisiae cp3] pair was selected as the best strain which produced ethanol of 10.11% (w/v) or 12.81% (v/v) from 25% (w/v) glucose at $33^{\circ}C$ for 3 days with the residual sugar of 3.53% (w/v), viability of 62.65%, fermentation efficiency of 92.2%.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2309-2312
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• 2014

Esophageal squamous cell carcinoma (ESCC) is the most common histologic subtype of esophageal cancer and is characterized by a poor prognosis. Determining gene changes in ESCCs should improve understanding of putative risk factors and provide potential targets for therapy. We sequenced about 55 million pair-end reads from a pair of adjacent normal and ESCC samples to identify the gene expression level and gene fusion. Sanger sequencing was used to verify the result. About 17 thousand genes were expressed in the tissues, of which approximately 2400 demonstrated significant differences between tumor and adjacent non tumor tissue. GO and KEGG pathway analysis revealed that many of these genes were associated with cellular adherence and movement, simulation responses and immune responses. Notably we identified and validated one fusion gene, HLA-E and HLA-B, located 1 MB apart. We also identified thousands of remarkably expressed transcripts. In conclusion, a novel fusion gene HLA-E and HLA-B was identified in ESCC via whole transcriptome sequencing, which would be a biomarker for ESCC diagnosis and target for therapy, shedding new light for better understanding of ESCC tumorigenesis.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.111-118
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• 2023

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.5
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• pp.111-116
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• 2018

Thermal fusion bonding is a method to enclose open microchannels fabricated on polymer chips for use in lab-on-a-chip (LOC) devices. Polymethyl methacrylate (PMMA) is utilized in various biomedical-microelectromechanical systems (bio-MEMS) applications, such as medical diagnostic kits, biosensors, and drug delivery systems. These applications utilize PMMAs biochemical compatibility, optical transparency, and mold characteristics. In this paper, we elucidate both the conformational entanglement of PMMA molecules at the contact interfacial regime, and the qualitative nature of the thermal fusion bonding phenomena through systematic molecular dynamics simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.38-46
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• 2008

A distributed system for tracking multiple targets with a pair of multifunction radars is proposed and implemented. The system performs track-to-track association and track-to-track fusion at the fusion center to form fused tracks. The association and fusion are performed using target state information linked via communication nodes from a radar at a remote location. Many factors can affect the track-to-track association and fusion performances. They include delays in data transmission buffer of the remote radar, the error in estimating time-stamp of the remote radar, and the gating in track-to-track association. The effects on association and fusion performances due to these factors are investigated through extensive numerical simulations.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.4
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• pp.212-222
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• 2003

The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. A stereo matching has been an important tool for reconstructing three dimensional terrain. However, there exist many factors causing stereo matching error, such as occlusion, no feature or repetitive pattern in the correlation window, intensity variation, etc. Among them, occlusion can be only resolved by true multi-image stereo. In this paper, we present multi-image stereo method using DEM fusion as one of efficient and reliable true multi-image methods. Elevations generated by all pairs of images are combined by the fusion process which accepts an accurate elevation and rejects an outlier. We propose three fusion schemes: THD(Thresholding), BPS(Best Pair Selection) and MS(Median Selection). THD averages elevations after rejecting outliers by thresholding, while BPS selects the most reliable elevation. To determine the reliability of a elevation or detect the outlier, we employ the measure of self-consistency. The last scheme, MS, selects the median value of elevations. We test the effectiveness of the proposed methods with a quantitative analysis using simulated images. Experimental results indicate that all three fusion schemes showed much better improvement over the conventional binocular stereo in natural terrain of 29 Palms and urban site of Avenches.