• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.513-518
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• 2022

Electron beam quality assurance (QA) should be done regularly for accurate radiation therapy. However, QA tools used in clinical practice are designed mainly for X-rays. So, a dosimeter for electron beam QA is required. Therefore, in this study, the electron beam detection performance was measured by using a thorium bromide material as an electron beam sensor. In addition, it was evaluated whether it could be applied with an electron beam QA dosimeter. Reproducibility, linearity, and dose rate dependence were evaluated at 6 MeV and 9 MeV energies. As a result of reproducibility, it showed a maximum output change of 0.92% at 6 MeV and 1.15% at 9 MeV. The linearity result evaluation and determination coefficient were presented as 0.9998. As a result of dose rate dependence evaluation, relative standard deviation 0.51% at 6 MeV and relative standard deviation 1.07% at 9 MeV were presented. The manufactured TlBr sensor shows the ability to detect radiation that meets the criteria for evaluation of reproducibility, linearity, and dose rate dependence. These results mean that the TlBr dosimeter is applicable as an electron beam QA dosimeter.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1124-1128
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• 2021

As regulations of emissions from ships become more stringent, electric propulsion systems have been increasingly used to solve this problem in vessels ranging from large merchant ships to small and medium-sized ships. Methods for improving the efficiency of the electric propulsion system include the improvement of power sources; the use of a system linked to environmentally friendly power sources, such as batteries, fuel cells, and solar power; and the development of hardware and control methodology for rectifiers, power conversion devices, and propulsion motors. The method using a phase-shifting transformer with diodes has been widely used for rectification. Power semiconductor devices with grid connection to an environmentally friendly power source using DC distribution, a variable speed power source, and the application of small and medium-sized electric propulsion systems have been developed. Accordingly, the demand for active front-end (AFE) rectifiers is increasing. In this study, a method using a neural network rather than a conventional proportional-integral controller was proposed to control the AFE rectifier. Tested controller data were used to design a neural network controller trained through MATLAB/Simulink. The neural network controller was applied to a rectification system designed using PSIM software. The results indicated the effectiveness of improving the waveform and power factor DC output stage according to the load variation. The proposed system can be applied as a rectification system for small and medium-sized environmentally friendly ships.

• Composites Research
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• v.35 no.6
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• pp.378-393
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• 2022

As an emerging power generation technology, triboelectric nanogenerators (TENGs) have received increasing attention due to their boundless promise in energy harvesting and self-powered sensing applications. The recent rise of soft robotics has sparked widespread enthusiasm for developing flexible and soft sensors and actuators. TENGs have been regarded as promising power sources for driving actuators and self-powered sensors, providing a unique approach for the development of soft robots with soft sensors and actuators. In this review, TENG-based soft robots with different morphologies and different functions are introduced. Among them, the design of biomimetic soft robots that imitate the structure, surface morphology, material properties, and sensing/generating mechanisms of nature has greatly benefited in improving the performance of TENGs. In addition, various bionic soft robots have been well improved compared to previous driving methods due to the simple structure, self-powering characteristics, and tunable output of TENGs. Furthermore, we provide a comprehensive review of various studies within specific areas of TENG-enabled soft robotics applications. We first explore various recently developed TENG-based soft robots and a comparative analysis of various device structures, surface morphologies, and nature-inspired materials, and the resulting improvements in TENG performance. Various ubiquitous sensing principles and generation mechanisms used in nature and their analogous artificial TENG designs are demonstrated. Finally, biomimetic applications of TENG enabled in tactile displays as well as in wearable devices, artificial electronic skin and other devices are discussed. System designs, challenges and prospects of TENGs-based sensing and actuation devices in the practical application of soft robotics are analyzed.

• Korean Journal of Construction Engineering and Management
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• v.23 no.5
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• pp.65-76
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• 2022

The New Excellent Technology (NET) designation system, introduced in 1989 for the purpose of promoting the development of domestic construction technology and enhancing national competitiveness, reviews the statement of construction cost of new technologies. And the cost reduction effect such as design, construction, and maintenance cost and the effect of reducing the construction duration are evaluated as an evaluation criteria of economic feasibility. However, in this evaluation process, differences of opinion between the institution of construction cost estimating standard management and the new technology developer about unique technologies frequently occur. In addition it is difficult to objectively compare the construction duration with existing similar technologies because there is no information on productivity as the current cost estimating standards for new technologies only present the required amount per unit quantity. In this study, the current state of cost estimating criteria review procedure, evaluation criteria, and cost estimating standards establishment method were analyzed when screening for the designation of a new construction technologies, and compared with overseas cost estimating standards, measures to improve the cost estimating standards of current construction new technologies were suggested. Through the improved cost estimating standards of this study, it is expected that cost information on new technologies will be provided to clients in more detail than the current ones, and the availability and applicability of new construction technologies would be improved by simplifying the construction cost calculation process more.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.57-62
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• 2022

Recently, the tracking radar continues research to develop a new operation algorithm that can acquire and track a target in various environments. In general, modeling similar to the real target and environment is used to develop a new operation algorithm, but there is a limit to modeling the real environment. In this paper, a RAW data storage device was developed to efficiently develop a new operation algorithm required for the tracking radar using millimeter wave to acquire and track the target. The RAW data storage equipment is designed so that the signal processing device of the tracking radar using millimeter wave can save the RAW data output from 8 channels to OOOMSPS. RAW data storage equipment consists of data acquisition equipment and data storage equipment. The data acquisition equipment was implemented using a commercial Xilinx KCU 105 Evaluation KIT capable of high-speed data communication interface, and the data storage equipment was implemented by applying a computer compatible with the commercial Xilinx KCU 105 Evaluation KIT. In this paper, the performance of the implemented RAW data storage equipment was verified through repeated interlocking tests with the signal processing device of the millimeter wave tracking radar.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.29-41
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• 2010

Due to the recent technological advance, new types of AS/RS which can handle containers are being developed, and it is expected that they will be applied to related industries before long. Some companies and institutes in our country have constructed pilot systems for high-density-high-stacking systems and tested them to develop AS/RS-typed warehouses for containers. Along with this kind of construction efforts, development of rules to operate such systems efficiently and safely is also important. When outward-bound shipment is scheduled in container port, re-marshalling which rearranges containers in the yard to make shipment easy is conducted. In this paper, operating rules for the re-marshalling as well as simulation experiments to evaluate the performance of the rules are presented. We suggested two kinds of alternative sets of operating rules for re-marshalling and described the relevant logics corresponding to all possible cases for each alternative of operating rules. Through various simulation experiments, we found that each alternative has the merits and demerits at the same time and we could not say the one is always superior to the other. As a useful strategy, changing the applying operating rule is recommended from moment to moment depending on the expected number of operations at the landside input/output position.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.45-54
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• 2023

Under combat simulation environment when inputting the detection performance data of the real system into the simulated object the given data affects the simulation analysis result. ACQUIRE-Target Task Performance Metric (TTPM)-Target Angular Size (TAS) model is used as a target acquisition model to simulate the detection ability of entities in the main combat simulation tool. This model estimates the decomposition curve of the object sensor and output the detection distance according to the target type. However, it is not easy to apply the performance of the new detection object that the user wants to input to the target acquisition model. Users want to input the detection distance into the target acquisition model, but the target acquisition model requires sensor decomposition curve data according to encounter conditions. In this paper, we propose a method of inversely deriving the sensor decomposition curve data of the target acquisition model by taking the detection distance to the target as an input. Here, the sensor decomposition curve data simultaneously satisfies each detection distance for three types of targets: personnel, ground vehicles, and aircraft. Finally, the detection distance of various reconnaissance equipment is applied to the detection object, and the detection effect according to the reconnaissance equipment is analyzed.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.37-46
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• 2007

In the practice of preloading method for soft ground improvement, field engineers need information of ground properties, construction works and field monitoring on ground behaviors of the site. So, integrating all these informations into one database can provide more efficient way for managing and utilizing the data for construction management. In this study, integrated system for construction management of ground improvement sites under preloading is developed. The developed system consists of database (DB) and application program. The database contains all collected data in a construction site and processed data in the system with their geographic information. All informations in the database are standardized from the result of data characterization. Application program performs various functions on managing and utilizing information in the database; pre- and post- data processing with graphic visualization of output, spatial data interpolation, and prediction of ground behavior using field measuring data. And by providing integrating informations and predictions over entire project area with comprehensible visual displays, the applicability and effectiveness of the developed system for construction management were confirmed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.83-89
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• 2023

The clustering technique using RF fingerprint extracts the characteristic signature of the transmitters which are embedded in the transmission waveforms. The output of the RF-Fingerprint feature extraction algorithm for clustering identical DMR(Digital Mobile Radios) is a high-dimensional feature, typically consisting of 512 or more dimensions. While such high-dimensional features may be effective for the classifiers, they are not suitable to be used as inputs for the clustering algorithms. Therefore, this paper proposes a dimension reduction algorithm that effectively reduces the dimensionality of the multidimensional RF-Fingerprint features while maintaining the fingerprinting characteristics of the DMRs. Additionally, it proposes a clustering algorithm that can effectively cluster the reduced dimensions. The proposed clustering algorithm reduces the multi-dimensional RF-Fingerprint features using t-SNE, based on KL Divergence, and performs clustering using Density Peaks Clustering (DPC). The performance analysis of the DMR clustering algorithm uses a dataset of 3000 samples collected from 10 Motorola XiR and 10 Wintech N-Series DMRs. The results of the RF-Fingerprinting-based clustering algorithm showed the formation of 20 clusters, and all performance metrics including Homogeneity, Completeness, and V-measure, demonstrated a performance of 99.4%.