• Journal of Gastric Cancer
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• v.18 no.2
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• pp.142-151
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• 2018

Purpose: The modification of the cancer classification system aimed to improve the classical anatomy-based tumor, node, metastasis (TNM) staging by considering tumor biology, which is associated with patient prognosis, because such information provides additional precision and flexibility. Materials and Methods: We previously developed an mRNA expression-based single patient classifier (SPC) algorithm that could predict the prognosis of patients with stage II/III gastric cancer. We also validated its utilization in clinical settings. The prognostic single patient classifier (pSPC) differentiates based on 3 prognostic groups (low-, intermediate-, and high-risk), and these groups were considered as independent prognostic factors along with TNM stages. We evaluated whether the modified TNM staging system based on the pSPC has a better prognostic performance than the TNM 8th edition staging system. The data of 652 patients who underwent gastrectomy with curative intent for gastric cancer between 2000 and 2004 were evaluated. Furthermore, 2 other cohorts (n=307 and 625) from a previous study were assessed. Thus, 1,584 patients were included in the analysis. To modify the TNM staging system, one-grade down-staging was applied to low-risk patients according to the pSPC in the TNM 8th edition staging system; for intermediate- and high-risk groups, the modified TNM and TNM 8th edition staging systems were identical. Results: Among the 1,584 patients, 187 (11.8%), 664 (41.9%), and 733 (46.3%) were classified into the low-, intermediate-, and high-risk groups, respectively, according to the pSPC. pSPC prognoses and survival curves of the overall population were well stratified, and the TNM stage-adjusted hazard ratios of the intermediate- and high-risk groups were 1.96 (95% confidence interval [CI], 1.41-2.72; P<0.001) and 2.54 (95% CI, 1.84-3.50; P<0.001), respectively. Using Harrell's C-index, the prognostic performance of the modified TNM system was evaluated, and the results showed that its prognostic performance was better than that of the TNM 8th edition staging system in terms of overall survival (0.635 vs. 0.620, P<0.001). Conclusions: The pSPC-modified TNM staging is an alternative staging system for stage II/III gastric cancer.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1295-1302
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• 2013

The structural deformed shape is important information to structural analysis. If the sufficient measuring points are secured at the structural monitoring system, reasonable and accurate structural deformation shapes can be obtained and structural analysis is possible using this deformation. However, the accurate estimation of the global structural shapes might be difficult if sufficient measuring points are not secure under cost limitations. In this study, SFSM-LS algorithm, the economic and effective estimation method for the structural deformation shapes with limited displacement measuring points is developed and suggested. In the suggested method, the global structural deformation shape is determined by the superposition of the pre-investigated structural deformed shapes obtained by preliminary FE analyses, with their optimum weight factors which lead minimization of the estimate errors. 2-span continuous bridge model is used to verify developed algorithm and parametric studies are performed. By the parametric studies, the characteristics of the estimation results obtained by the suggested method were investigated considering essential parameters such as pre-investigated structural shapes, locations and numbers of displacement measuring points. By quantitative comparison of estimation results with the conventional methods such as polynomial, Lagrange and spline interpolation, the applicability and accuracy of the suggested method was validated.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1840-1848
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• 2017

Linear wireless sensor networks typically construct a network topology with a high reliability through sequential 1:1 mapping among sensor nodes, so that they are used in various surveillance applications of major national infrastructures. Most existing techniques for identifying sensor nodes in those networks are using GPS, AOA, and RSSI mechanisms. However, GPS or AOA based node identification techniques affect the size or production cost of the nodes so that it is not easy to construct practical sensor networks. RSSI based techniques may have a high deviation regrading location identification according to propagation environments and equipment quality so that complexity of error correction algorithm may increase. We propose a timestamps based sequential localization algorithm that uses transmit and receive timestamps in a message between sensor nodes without using GPS, AOA, and RSSI techniques. The algorithms for distance measurement between each node are expected to measure distance within up to 1 meter in case of an crystal oscillator of 300MHz or more.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.32-40
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• 2010

In this work, the method of acquiring the pupil function of optical system is proposed. The wavefront aberration and the intensity distribution of pupil can be analysed with the pupil function. This system can be adopted to the manufacturing line of optical pickup directly and also has good performance to analysing various property of optical instrument. It is one kind of inverse problem to get pupil functions by 3D beam data. The extended Nijboer-Zernike(ENZ) approach recently proposed by Netherlands research group is adopted to accompany to solve these inverse problem. The ENZ approach is one of a aberration retrieval method for which numerous approaches are available. But this approach is new in the sense that it use the highly efficient representation of pupil functions by means of their Zernike coefficients. These coefficients are estimated by using matching procedure in the focal region the theoretical 3D intensity distribution and measured 3D intensity distribution. The algorithm that can be applied more general circumstance such as high-numerical aperture instrument is developed by modifying original ENZ approach. By these scheme, MS windows based GUI program is developed and the good performance is verified with generated 3D beam data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.146-170
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• 2017

Joint channel assignment and routing is a well-known problem in multi-radio wireless mesh networks for which optimal configurations is required to optimize the overall throughput and fairness. However, other objectives need to be considered in order to provide a high quality service to network users when it deployed with high traffic dynamic. In this paper, we propose a re-configuration optimization model that optimizes the network throughput in addition to reducing the disruption to the mesh clients' traffic due to the re-configuration process. In this multi-objective optimization model, four objective functions are proposed to be minimized namely maximum link-channel utilization, network average contention, channel re-assignment cost, and re-routing cost. The latter two objectives focus on reducing the re-configuration overhead. This is to reduce the amount of disrupted traffic due to the channel switching and path re-routing resulted from applying the new configuration. In order to adapt to traffic dynamics in the network which might be caused by many factors i.e. users' mobility, a centralized heuristic re-configuration algorithm called State-Aware Joint Routing and Channel Assignment (SA-JRCA) is proposed in this research based on our re-configuration model. The proposed algorithm re-assigns channels to radios and re-configures flows' routes with aim of achieving a tradeoff between maximizing the network throughput and minimizing the re-configuration overhead. The ns-2 simulator is used as simulation tool and various metrics are evaluated. These metrics include channel-link utilization, channel re-assignment cost, re-routing cost, throughput, and delay. Simulation results show the good performance of SA-JRCA in term of packet delivery ratio, aggregated throughput and re-configuration overhead. It also shows higher stability to the traffic variation in comparison with other compared algorithms which suffer from performance degradation when high traffic dynamics is applied.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.22-31
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• 2007

The proposed algorithm in this paper is based on the wavelet decomposition and the energy computation of composed blocks so the amount of calculation and complexity is minimized by adaptively replacing the DWT coefficients and managing the resources effectively. We are now living in the world of a lot. of multimedia applications for many digital electric appliances and mobile devices. Among so many multimedia applications, the digital image compression is very important technology for digital cameras to store and transmit digital images to other sites and JPEG2000 is one of the cutting edge technology to compress still images efficiently. The digital cm technology is mainly using the digital image compression features so that those images could be efficiently saved locally and transferred to other sites without any losses. JPEG2000 standard is applicable for processing the digital images usefully to keep, send and receive through wired and/or wireless networks. The discrete wavelet transform (DWT) is one of the main differences to the previous digital image compression standard such as JPEG, performing the DWT to the entire image rather than splitting into many blocks. Several digital images m tested with this method and restored to compare to the results of conventional DWT which shows that the proposed algorithm get the better result without any significant degradation in terms of MSE & PSNR and the number of zero coefficients when the energy based adaptive DWT is applied.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Korean Journal of Optics and Photonics
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• v.28 no.4
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• pp.166-171
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• 2017

Hydrogen gas is a green energy sources because it features no emission of pollutants during combustion. But hydrogen gas is very dangerous, being flammable and very explosive. Hydrogen gas detection is very important for the safety of a nuclear power plant. Hydrogen gas is generated by oxidation of nuclear fuel cladding during a critical accident, and leads to serious secondary damage in the containment building. This paper discusses the development of a Raman lidar system for remote detection and measurement of hydrogen gas. A small, portable Raman lidar system was designed, and a measurement algorithm was developed to quantitatively measure hydrogen gas concentration. To verify the capability of measuring hydrogen gas with the developed Raman lidar system, experiments were carried out under daytime outdoor conditions by using a gas chamber that can adjust the hydrogen gas density. As results, our Raman lidar system is able to measure a minimum density of 0.67 vol. % hydrogen gas at a distance of 20 m.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.