• Mass Spectrometry Letters
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• v.1 no.1
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• pp.21-24
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• 2010

The development of clinical biomarkers involves discovery, verification, and validation. Recently, multiple reaction monitoring (MRM) coupled with stable isotope dilution mass spectrometry (IDMS) has shown considerable promise for the direct quantification of proteins in clinical samples. In particular, multiple biomarkers have been tracked in a single experiment using MRM-based MS approaches combined with liquid chromatography. We report here a highly reproducible, quantitative, and dynamic MRM system for validating multi-biomarker proteins using Nanoflow HPLC-Microfluidics Chip/Triple-Quadrupole MS. In this system, transitions were acquired only during the retention window of each eluting peptide. Transitions with the highest MRM-MS intensities for the five target peptides from colon cancer biomarker candidates were automatically selected using Optimizer software. Relative to the corresponding non-dynamic system, the dynamic MRM provided significantly improved coefficients of variation in experiments with large numbers of transitions. Linear responses were obtained with concentrations ranging from fmol to pmol for five target peptides.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5282-5300
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• 2017

Multi-hop cellular networks (MCNs) reduce the transmit power and improve the system performance. Recently, several research studies have been conducted on MCNs. The mobile relay selection scheme is a rising issue in the design of MCNs that achieves these advantages. The conventional opportunistic relaying (OR) is performed on the single factor for maximum signal-to-interference-plus-noise ratio (SINR). In this paper, a comprehensive OR scheme based on Bi-Target is proposed to improve the system throughput and reduce the relay handover by constraining the amount of required bandwidth and SINR. Moreover, the proposed algorithm captures the variability and the mobility that makes it more suitable for dynamic real scenarios. Numerical and simulation results show the superiority of the proposed algorithm in both enhancing the overall performance and reducing the handover.

• Bulletin of the Korean Chemical Society
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• v.33 no.1
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• pp.69-75
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• 2012

Five-channel electrochemical chips were fabricated based on the Micro-electromechanical System (MEMS) technology and were used as platforms to develop DNA arrays. Different kinds of thiolated DNA strands, whose sequences were related to white spot syndrome virus (WSSV) gene, were separately immobilized onto different working electrodes to fabricate a combinatorial biosensor system. As a result, different kinds of target DNA could be analyzed on one chip via a simultaneous recognition process using potassium ferricyanide as an indicator. To perform quantitative target DNA detection, a limit of 70 nM (S/N=3) was found in the presence of 600 nM coexisting noncomplementary ssDNA. The real samples of loop-mediated isothermal amplification (LAMP) products were detected by the proposed method with satisfactory result, suggesting that the multichannel chips had the potential for a high effective microdevice to recognize specific gene sequence for pointof-care applications.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.621-629
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• 2001

In this thesis a wireless data transmission system has been proposed and analysed that uses the multi-carrier technique with the adaptive modulation scheme. In general, the OFDM(Orthogonal Frequency Division Multiplexing) system is assigning a same amount of information to all sub-carriers in a wireless data transmission. In the proposed system, the different amount of information is assigned to each sub-carrier depending on the state of channel and the target probability of error of system. With the proposed scheme, the transmission rate can be maximized with the fixed power and the required power to transmit the information can be minimized with the target probability of error of system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.11-22
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• 2000

The main objective of this research is to analyze an order point and an order quantity of a distribution center and each branch to attain a target service level in multi-level inventory distribution system. In case of product item, we use the item with low volume of average monthly demand. Under the continuous review method, the distribution center places a particular order quantity to an outside supplier whenever the level of inventory reaches an order point, and receives the order quantity after elapsing a certain lead time. Also, each branch places an order quantity to the distribution center whenever the level of inventory reaches an order point, and receives the quantity after elapsing a particular lead time. When an out of stock condition occurs, we assume that the item is backordered. For considering more realistic situations, we use generic type of probability distribution of lead times. In the variable lead time model, the actually achieved service level is estimated as the expected service level. Therefore, this study focuses on the analysis of deciding the optimal order point and order quantity to achieve a target service level at each depot as a expected service level, while the system-wide inventory level is minimized. In addition, we analyze the order level as a maximum level of inventory to suggest more efficient way to develop the low demand item model.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.416-422
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• 2021

Due to the large differences in human activity within classes, the large similarity between classes, and the problems of visual angle and occlusion, it is difficult to extract features manually, and the detection rate of human behavior is low. In order to better solve these problems, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multi-object recognition and localization through a second-order detection network, and replaces the original feature extraction module with Dense-Net, which can fuse multi-level feature information, increase network depth and avoid disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects, and enhancing the network detection accuracy under multiple objects. During the experiment, the improved Faster R-CNN method in this article has 84.7% target detection result, which is improved compared to other methods, which proves that the target recognition method has significant advantages and potential.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• Progress in Medical Physics
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• v.21 no.2
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• pp.153-164
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• 2010

To determine the clinical target volumes considering vascularity and cellularity of tumors, the software was developed for mapping of the analyzed biological clinical target volumes on anatomical images using regional cerebral blood volume (rCBV) maps and apparent diffusion coefficient (ADC) maps. The program provides the functions for integrated registrations using mutual information, affine transform and non-rigid registration. The registration accuracy is evaluated by the calculation of the overlapped ratio of segmented bone regions and average distance difference of contours between reference and registered images. The performance of the developed software was tested using multimodal images of a patient who has the residual tumor of high grade gliomas. Registration accuracy of about 74% and average 2.3 mm distance difference were calculated by the evaluation method of bone segmentation and contour extraction. The registration accuracy can be improved as higher as 4% by the manual adjustment functions. Advanced MR images are analyzed using color maps for rCBV maps and quantitative calculation based on region of interest (ROI) for ADC maps. Then, multi-parameters on the same voxels are plotted on plane and constitute the multi-functional parametric maps of which x and y axis representing rCBV and ADC values. According to the distributions of functional parameters, tumor regions showing the higher vascularity and cellularity are categorized according to the criteria corresponding malignant gliomas. Determined volumes reflecting pathological and physiological characteristics of tumors are marked on anatomical images. By applying the multi-functional images, errors arising from using one type of image would be reduced and local regions representing higher probability as tumor cells would be determined for radiation treatment plan. Biological tumor characteristics can be expressed using image registration and multi-functional parametric maps in the developed software. The software can be considered to delineate clinical target volumes using advanced MR images with anatomical images.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.526-532
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• 2014

In this paper, we present and analyze a LQ (Linear Quadratic) inverse optimal state-consensus protocol for continuous-time multi-agent systems with undirected graph topology. By Lyapunov analysis of the state-consensus error dynamics, we show the sufficient conditions on the algebraic connectivity of the graph to guarantee LQ inverse optimality and closed-loop stability. A more relaxed stability condition is also provided in terms of the algebraic connectivity. Finally, a formation control protocol for multiple mobile robots is proposed based on the target LQ inverse optimal consensus protocol, and the simulation results are provided to verify the performance of the proposed LQ inverse formation control method.