• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.116-123
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• 2022

This study analyzes the Mechanical properties of a medical bone plate by 3D printing. With the recent development of 3D printing technology, it is being applied in various fields. In particular, in the medical field, the use of 3D printing technology, which was limited to the existing orthosis and surgical simulation, has recently been used to replacement bones lost due to orthopedic implants using metal 3D printing. The field of application is increasing, such as replacement. However, due to the manufacturing characteristics of 3D printing, micro pores are generated inside the metal printing output, and it is necessary to reduce the pores and the loss of mechanical properties through post-processing such as heat treatment. Accordingly, the purpose of this study is to analyze the change in mechanical performance characteristics of medical metal plates manufactured by metal 3D printing under various conditions and to find efficient metal printing results. The specimen to be used in the experiment is a metal plate for trauma fixation applied to the human phalanx, and it was manufactured using the 'DMP Flex 100(3D Systems, USA), a metal 3D printer of DMLS (Direct Metal Laser Sintering) method. It was manufactured using the PBF(Powder Bed Fusion) method using Ti6Al4V ELI powder material.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.37-43
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• 2022

The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.639-655
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• 2023

Recently, there has been a growing demand for underground space, leading to the utilization of earth retaining walls for deep excavations. Earth retaining walls are structures that are susceptible to displacement, and their measurement and management are carried out in accordance with the standards established by the Ministry of Land, Infrastructure, and Transport. However, managing displacement through measurement can be considered similar to post-processing. Therefore, in this study, we not only predicted the horizontal displacement of a retaining wall with ground anchors installed using machine learning, but also analyzed the impact of the prediction model based on data scaling and data splitting methods while learning measurement data using machine learning. Custom splitting was the most suitable method for learning and outputting measurement data. Data scaling demonstrated excellent performance, with an error within 1 and an R-squared value of 0.77 when the anchor tensile force and water pressure were standardized. Additionally, it predicted a negative displacement compared to a model that without scaling.

• Journal of radiological science and technology
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• v.40 no.2
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• pp.197-203
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• 2017

The purpose of this study was to measure signal to noise ratio (SNR) according to change of equalization from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 87 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p < 0.05). In SNR results, with the quality of distributions in the order of original chest image, original chest image heart shadow and equalization chest image, equalization chest image heart shadow(p < 0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the histogram equalization chest image.

• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.391-397
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• 2014

Wide Area Differential GNSS(WA-DGNSS) was developed in order to improve the accuracy and integrity performance of GNSS. In this paper, overall structure of Pseudolite-Based Augmentation System(PBAS) and experimental methods which enables the post-processing test with commercial receiver will be described. For generating augmenting message, GPS measurement collected from five NDGPS reference stations were processed by reference station S/W and master station S/W. The accuracy of augmenting message was tested by comparing SP3, IONEX data. In the test, RF signal of user was collected and correction data were generated. After that, RF signal was broadcasted with pseudolite signal. Test was conducted using three commercial receiver and the performance was compared with MSAS and standalone user. From the position output of each receiver, it was shown that improved position was obtained by applying augmenting message.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.341-341
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• 2016

The discovery of light emission in nanostructured silicon has opened up new avenues of research in nano-silicon based devices. One such pathway is the application of silicon quantum dots in advanced photovoltaic and light emitting devices. Recently, there is increasing interest on the silicon quantum dots (c-Si QDs) films embedded in amorphous hydrogenated silicon-nitride dielectric matrix (a-SiNx: H), which are familiar as c-Si/a-SiNx:H QDs thin films. However, due to the limitation of the requirement of a very high deposition temperature along with post annealing and a low growth rate, extensive research are being undertaken to elevate these issues, for the point of view of applications, using plasma assisted deposition methods by using different plasma concepts. This work addresses about rapid growth and single step development of c-Si/a-SiNx:H QDs thin films deposited by RF (13.56 MHz) and ultra-high frequency (UHF ~ 320 MHz) low-pressure plasma processing of a mixture of silane (SiH4) and ammonia (NH3) gases diluted in hydrogen (H2) at a low growth temperature ($230^{\circ}C$). In the films the c-Si QDs of varying size, with an overall crystallinity of 60-80 %, are embedded in an a-SiNx: H matrix. The important result includes the formation of the tunable QD size of ~ 5-20 nm, having a thermodynamically favorable <220> crystallographic orientation, along with distinct signatures of the growth of ${\alpha}$-Si3N4 and ${\beta}$-Si3N4 components. Also, the roles of different plasma characteristics on the film properties are investigated using various plasma diagnostics and film analysis tools.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.283-288
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• 2021

Due to the 4th Industrial Revolution and the Digital Revolution, many changes are being made in the manufacturing method and structure in the field of plastic arts and crafts. Therefore, in this study, we tried to present a new work method suitable for this era by effectively utilizing the digital technology called 3D printer. For this study, first of all, the theoretical background of 3D printing technology was understood, and prior studies on the use cases of 3D printing technology were summarized. Based on this, three types of craft molds were produced using a 3D printer. As a result of the study, there were characteristics that appear respectively depending on the thickness or overlapping of the craft molds using 3D printing technology. First, in the case of the thickness of the craft molds, the thinner the strength, the weaker the strength, but there was an advantage in that it was easier to take out the contents of the molds. However, it was determined that the thick craft molds was stable to contain the dense and heavy material. Second, in the case of overlapping of craft molds, the advantages of both thin and thick molds were obtained as a result of using a double-layered molds. However, there was a disadvantage that the surface of the contents taken out was not smooth, so that post-processing was necessary. In future research, I hope to deal with the material of the filament used in 3D printers.

• Journal of Ocean Engineering and Technology
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• v.31 no.1
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• pp.69-79
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• 2017

This paper presents the operations of a multi-beam echo sounder (MBES) installed on the deep-sea remotely operated vehicle (ROV) Hemire. Hemire explored hydrothermal vents in the Forecast volcano located near the Mariana Trench in March of in 2006. During these explorations, we acquired profiling points on the routes of the vehicle using the MBES. Information on the position, depth, and attitude of the ROV are essential to obtain higher accuracy for the profiling quality. However, the MBES installed on Hemire does not have its own position and depth sensors. Although it has attitude sensors for roll, pitch, and heading, the specifications of these sensors were not clear. Therefore, we had to merge the high-performance sensor data for the motion and position obtained from Hemire into the profiling data of the MBES. Then, we could properly convert the profiling points with respect to the Earth-fixed coordinates. This paper describes the integration of the MBES with Hemire, as well as the coordinate conversion between them. Bathymetric maps near the summit of the Forecast volcano were successfully collected through these processes. A comparison between the bathymetric maps from the MBES and those from the Onnuri Research Vessel, the mother ship of the ROV Hemire for these explorations, is also presented.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.17-24
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• 2023

A post-processing technique for the measurement signal of a solenoid-type sensor is introduced. The solenoid-type sensor nondestructively evaluates an external tendon of prestressed concrete using the total flux leakage (TFL) method. The TFL solenoid sensor consists of primary and secondary coils. AC electricity, with the shape of a sinusoidal function, is input in the primary coil. The signal proportional to the differential of the input is induced in the secondary coil. Because the amplitude of the induced signal is proportional to the cross-sectional area of the tendon, sectional loss of the tendon caused by ruptures or corrosion can be identified by the induced signal. Therefore, it is important to extract amplitude information from the measurement signal of the TFL sensor. Previously, the amplitude was extracted using local maxima, which is the simplest way to obtain amplitude information. However, because the sampling rate is dramatically decreased by amplitude extraction using the local maxima, the previous method places many restrictions on the direction of TFL sensor development, such as applying additional signal processing and/or artificial intelligence. Meanwhile, the proposed method uses amplitude demodulation to obtain the signal amplitude from the TFL sensor, and the sampling rate of the amplitude information is same to the raw TFL sensor data. The proposed method using amplitude demodulation provides ample freedom for development by eliminating restrictions on the first coil input frequency of the TFL sensor and the speed of applying the sensor to external tension. It also maintains a high measurement sampling rate, providing advantages for utilizing additional signal processing or artificial intelligence. The proposed method was validated through experiments, and the advantages were verified through comparison with the previous method. For example, in this study the amplitudes extracted by amplitude demodulation provided a sampling rate 100 times greater than those of the previous method. There may be differences depending on the given situation and specific equipment settings; however, in most cases, extracting amplitude information using amplitude demodulation yields more satisfactory results than previous methods.