• Composites Research
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• v.20 no.3
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• pp.37-42
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• 2007

Tensile residual stress is occurred by difference of coefficients of thermal expansion between fiber and matrix is one of the serious problems in metal matrix composite(MMC). TiNi alloy fiber was used to solve the problem of the tensile residual stress as the reinforced material. TiNi alloy fiber improves the tensile strength of composite with occurring of compressive residual stress in the matrix by its shape memory effect. A hot press method was used to create the optimal fabrication condition for a Shape Memory Alloy(SMA) composite. The bonding effect of the matrix and the reinforcement within the SMA composite by the hot press method was strengthened by cold rolling. In addition, acoustic emission technique was used to quantify the microscopic damage behavior of cold rolled TiNi/A16061 shape memory alloy composite at low temperature. The damage degree for the specimen that underwent thermal shock cycles was also discussed.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.139-166
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• 2022

Ethical Issues increase when people engage in smart technological systems such as 5G, IoT, Cloud computing services and AI applications. Range of this research is comparison of various literacy concepts and its ethical issues in considering of 5G features and UX. 86 research papers and reports which have been published within the recent 5 years (2017-2022), relating the research subject, are investigated and analyzed. Two results show that various literacies can be grouped into four areas and that some of common issues among those areas as well as unique issues of each area are identified. Based on the literature analysis, an Operational Definition of Ethics-Literacy is presented and the model of ethics-literacy curriculum supporting ethical behavior of 5G information professionals is developed and suggested.

• Smart Structures and Systems
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• v.29 no.5
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• pp.661-675
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• 2022

In this paper, an electric vehicle drives with efficient control and low cost hardware using four quadrant DC converter with Permanent Magnet Direct Current (PMDC) motor fed by DC boost converter is presented. The main idea of this work is to improve the energy efficiency of the conversion chain of an electric vehicle by inserting a boost converter between the battery and the four quadrant-DC motor chopper assembly. Consequently, this method makes it possible to maintain the amplification gain of the 4 quadrant chopper constant regardless of the battery voltage drop and even in the presence of a fault in the battery. One of the most important control problems is control under heavy uncertainty conditions. The higher order sliding mode control technique is introduced for the adjustment of DC bus voltage and mechanical motor speed. To implement the proposed approach in the automotive field, experimental tests were carried out. The performances obtained show the usefulness of this system for a better energy management of an electric vehicle and an ideal control under different operating conditions and constraints, mostly at nominal operation, in the presence of a load torque, when reversing the direction of rotation of the motor speed and even in case of battery chamber failure. The whole system has been tested experimentally and its performance has been analyzed.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.279-288
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• 2018

In this paper, we present the results of educational innovation case study using U-Learning Box and Ubiquitous-based Test(UBT) system for 6 sample primary schools in Nepal. As Nepal is considered to be a developing country with electricity problem to the school, the U-Learning Box, consisting of a small and easy-to-use tablet PC for teacher and a small smart beam with its own battery was evaluated as the optimum solution to support continuous basic English and hygiene education for these schools. And UBT technology using tablet PC was used to evaluate and analyze basic English learning ability of the students, which helped us realized that it is necessary to improve the educational environment and develop suitable educational contents. We hope that the global educational innovation using U-Learning Box and UBT technology will become a successful model for global equality of educational opportunity project for developing countries including Nepal.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Smart Structures and Systems
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• v.32 no.1
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• pp.1-7
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• 2023

In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

• Smart Structures and Systems
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• v.32 no.1
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• Wind and Structures
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• v.36 no.3
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• pp.201-213
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• 2023

In this study, the effect of sea surface temperature (SST) on the distribution of vertical wind speed in the atmospheric boundary layer of coastal areas was analyzed. In general, coastal areas are known to be more susceptible to various meteorological factors than inland areas due to interannual changes in sea surface temperature. Therefore, the purpose of this study is to analyze the relationship between sea surface temperature (ERA5) and wind resource data based on the meteorological mast of Høvsøre, the test bed area of the onshore wind farm in the coastal area of Denmark. In addition, the possibility of coastal disasters caused by abnormal vertical wind shear due to changes in sea surface temperature was also analyzed. According to the analysis of the correlation between the wind resource data at met mast and the sea surface temperature by ERA5, the wind speed from the sea and the vertical wind shear are stronger than from the inland, and are vulnerable to seasonal sea surface temperature fluctuations. In particular, the abnormal vertical wind shear, in which only the lower wind speed was strengthened and appeared in the form of a nose, mainly appeared in winter when the atmosphere was near-neutral or stable, and all occurred when the wind blows from the sea. This phenomenon usually occurred when there was a sudden change in sea surface temperature within a short period of time.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.247-260
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• 2022

This paper investigates plans to expand distribution channels for books by installing a book vending machine at convenience stories for the vitalization of the publishing culture and industry. After establishing the meaning of convenience stories and the concept of a book vending machine, the researcher identified a need for the installation and management of a book vending machine at convenience stories. This plan will promote fairness and transparency in book sales through the convenience store system and push forward outsourcing between the publishing industry and the convenience store industry, the expansion of omnichannel service for consumers, and experiential values for clients to increase their customer satisfaction. The composition of the book vending machine will take the smart library form for its management with a universal design applied to a kiosk program. In the study, the researcher schematized this process and conducted a survey with 310 adults to search for practical management plans for a book vending machine. In the future, more research on the diversification of sales and distribution channels for publications such as the installation of a book vending machine at convenience stores will hopefully contribute to the growing amount of reading per capita and promotion of a reading environment for people according to the vitalization of the publishing culture and industry.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.