• Progress in Medical Physics
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• v.29 no.4
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• pp.137-142
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• 2018

This study aimed to evaluate and verify a process for correcting the extended source-to-imager distance (SID) in portal dosimetry (PD). In this study, eight treatment plans (four volumetric modulated arc therapy and four intensity-modulated radiation therapy plans) at different treatment sites and beam energies were selected for measurement. A Varian PD system with portal dose image prediction (PDIP) was used for the measurement and verification. To verify the integrity of the plan, independent measurements were performed with the MapCHECK device. The predicted and measured fluence were evaluated using the gamma passing rate. The output ratio was defined as the ratio of the absolute dose of the reference SID (100 cm) to that of each SID (120 cm or 140 cm). The measured fluence for each SID was absolutely and relatively compared. The average SID output ratios were 0.687 and 0.518 for 120 SID and 140 SID, respectively; the ratio showed less than 1% agreement with the calculation obtained by using the inverse square law. The resolution of the acquired EPIDs were 0.336, 0.280, and 0.240 for 100, 120, and 140 SID, respectively. The gamma passing rates with PD and MapCHECK exceeded 98% for all treatment plans and SIDs. When autoalignment was performed in PD, the X-offset showed no change, and the Y-offset decreased with increasing SID. The PD-generated PDIP can be used for extended SID without additional correction.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of Korean Society of Rural Planning
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• v.25 no.3
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• pp.59-66
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• 2019

This study saw developed to build a landscape monitoring methodology by simulation of landscape effect prediction. A Visual landscape planning and management system has been introduced and implemented by each ministry so as to solve the problems of visual landscape destruction due to recognition on the value of natural landscape of beautiful territory and various development projects. At present, this system emphasizes the importance of the visual and perceptual aspect of the landscape however, there is a lack of techniques required for comprehensively predicting, evaluating, and managing it. Furthermore, sustainable landscape management after the completion of development projects has been inadequately carried out, as the focus has been only on consultation in the planning process of the development project in institutional performance. The viewpoint for judging the change in the visual landscape of the development plan and development project should be selected as the effective point where the development project is expected to result in a remarkable landscape change. As for the method of selecting effective viewpoints, the main viewpoints are selected by analyzing the visible area of the target viewpoint. When selecting the viewpoint centered on the viewpoint target, it was judged that it is possible to reduce the procedure of selecting and checking the existing preliminary viewpoints and widening the effective visible range. The proposed visual landscape monitoring is expected to be able to solve the existing institutional problems, and to be used when the implementers and authors of the development projects review the effects on the landscape.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.9
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• pp.617-628
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• 2019

The currently considered pyrotechnic separation nut is separated through the complicated process, because it has many internal moving parts and two variable-volume chambers connected by the vent hole. Therefore, it has many design parameters. Some of these are the contact angles between internal moving parts, the masses of the internal moving parts, the inner diameter of the push rod protrusion, the initial volumes of the chambers, the mass of the explosive charge, and the diameter of the vent hole. To improve the pyrotechnic separation nut, it is necessary to understand how the behavior of the separation nut is changed according to design parameters. In this point of view, parametric studies are carried out using the previously proposed prediction model for pyrotechnic separation nut behaviors. In each case, the parameter of the interest is changed while the others are kept unchanged. From the results, it is investigated how each design parameter influences the separation behavior.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.10
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• pp.1483-1490
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• 2019

Objective: To explore the molecular mechanisms of fat metabolism and deposition in pigs, an experiment was conducted to identify hepatic mRNAs and miRNAs expression and determine the potential interaction of them in two phenotypically extreme pig breeds. Methods: mRNA and miRNA profiling of liver from 70-day Jinhua (JH) and Landrace (LD) pigs were performed using RNA sequencing. Blood samples were taken to detect results of serum biochemistry. Bioinformatics analysis were applied to construct differentially expressed miRNA-mRNA network. Results: Serum total triiodothyronine and total thyroxine were significantly lower in Jinhua pigs, but the content of serum total cholesterol (TCH) and low-density lipoprotein cholesterol were strikingly higher. A total of 467 differentially expressed genes (DEGs) and 35 differentially expressed miRNAs (DE miRNAs) were identified between JH and LD groups. Gene ontology analysis suggested that DEGs were involved in oxidation-reduction, lipid biosynthetic and lipid metabolism process. Interaction network of DEGs and DE miRNAs were constructed, according to target prediction results. Conclusion: We generated transcriptome and miRNAome profiles of liver from JH and LD pig breeds which represent distinguishing phenotypes of growth and metabolism. The potential miRNA-mRNA interaction networks may provide a comprehensive understanding in the mechanism of lipid metabolism. These results serve as a basis for further investigation on biological functions of miRNAs in the porcine liver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• Journal of the Korean Geosynthetics Society
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• v.18 no.3
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• pp.55-67
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• 2019

Deep soft ground in mouth of Nackdong river requires to be analysed with prediction method concerning characteristics of secondary consolidation from the beginning because it causes excessive settlement due to time-dependant secondary consolidation characteristics. This study investigated characteristics of extended settlement by conducting one-dimensional theory, elasto-plastic model and visco-elasto-plastic model as well as analyzing long-term measuring data observed over 2,000 days. According to one-dimensional theory and elasto-plastic model, there is not definite correlation between height of embankment and depth of soft ground while visco-elasto-plastic model showed similar result of settlement to that of long-term measuring data. Consequently it is suggested that applying visco-elasto-plastic model to developing deep underground place as studied area on predicting extended settlement before construction prevents economic loss and delay during process by preparing secondary consolidation characteristics.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.105-110
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• 2021

Radiation shielding of affinity material, which is widely used in medical institutions, is made in sheet form and is mainly applied to apron. Shielding performance is presented based on lead equivalent, and is presented as 0.25-0.50mmPb. In the case of shielding materials where lead is used as the main material, the shielding performance can be adjusted by thickness due to the excellent machinability of lead. However, eco-friendly shielding sheets are difficult to control shielding performance based on thickness criteria as shielding performance varies depending on the content of shielding materials, the properties of polymeric materials that are base materials, and the technical differences in the process. In this study, shielding sheets were manufactured based on thickness to solve these problems and the shielding performance was compared in this study. As a result, it was shown that the laminated structure shielding sheet was more effective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.161-167
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• 2020

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.

• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.5
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• pp.65-76
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• 2020

On the initial design process of a scramjet vehicle such as the trajectory prediction, it is inevitable to estimate the aerodynamic performance of a three-dimensional effect. Despite the necessity of intensive computing for the three-dimensional model, it is inefficient in predicting a wide range of aerodynamic performance. In this study, an engineering model for aerodynamic performance was developed based on two-dimensional computational fluid analysis and linearized supersonic inviscid flow theory. Correspondingly, the three-dimension aerodynamic performance relations are presented based on the two-dimensional results. And the additional three-dimensional computation was performed to evaluate the adequacy for the extended relations.