• Smart Structures and Systems
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• v.31 no.5
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• pp.531-543
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• 2023

Base isolation is one of the most widely implemented and well-known technique to reduce structural vibration and damages during an earthquake. However, while the base-isolated structure reduces storey drift significantly, it also increases the base drifts causing many practical problems. This study proposes the use of Shape Memory Alloys (SMA) wires for the reduction in base drift while controlling the overall structure vibrations. A multi-degree-of-freedom (MDOF) structure along with base isolators and Shape-Memory-Alloys (SMA) wires in diagonal is tested experimentally and analytically. The isolation bearing considered in this study consists of laminates of steel and silicon rubber. The performance of the proposed structure is evaluated and studied under different loadings including harmonic loading and seismic excitation. To assess the seismic performance of the proposed structure, shake table tests are conducted on base-isolated MDOF frame structure incorporating SMA wires, which is subjected to incremental harmonic and historic seismic loadings. Root mean square acceleration, displacement and drift are analyzed and discussed in detail for each story. To better understand the structure response, the percentage reduction of displacement is also determined for each story. The result shows that the reduction in the response of the proposed structure is much better than conventional base-isolated structure.

• Smart Structures and Systems
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• v.29 no.5
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Smart Structures and Systems
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• v.31 no.6
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• pp.561-571
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• 2023

Ensuring the safety of a structure necessitates that repairs are carried out based on accurate inspections and records of damage information. Traditional methods of recording damage rely on individual paper-based documents, making it challenging for inspectors to accurately record damage locations and track chronological changes. Recent research has suggested the adoption of building information modeling (BIM) to record detailed damage information; however, localizing damages on a BIM model can be time-consuming. To overcome this limitation, this study proposes a method to automatically localize damages on a BIM model in real-time, utilizing consecutive images and measurements from an inertial measurement unit in close proximity to damages. The proposed method employs a visual-inertial odometry algorithm to estimate the camera pose, detect damages, and compute the damage location in the coordinate of a prebuilt BIM model. The feasibility and effectiveness of the proposed method were validated through an experiment conducted on a campus building. Results revealed that the proposed method successfully localized damages on the BIM model in real-time, with a root mean square error of 6.6 cm.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.88-95
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• 2022

Vacuum simulation is associated with the prediction and calculation of how materials, pumps and systems will perform using mathematical equations. In this investigation, three different high vacuum systems were simulated and estimated with each vacuum characteristics by VacTran simulator. In each of modelled vacuum systems, selection of gas loads into vessel, combination of rough and high vacuum pumps and dimension of conductance elements were proposed as system variables. In pump station model, the pumping speed to pressures by the combination of root pump was analyzed under the variations of vessel volume. In this study, the effects of outgassing dependent on vessel materials was also simulated and aluminum vessel was estimated to optimum materials. It was obtained from the modelling with diffusion pump that the diameter, length of 50×250[mm]roughing line was characterized as optimum variables to reach the ultimate pressure of 10E-7[torr]. Optimum design factors for vacuum characteristics of modelled vacuum system were achieved by VacTran simulator. Feasibility of VacTran as vacuum simulator was verified and applications of VacTran in high tech process expected to be increased.

• Smart Structures and Systems
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• v.32 no.5
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• pp.297-308
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• 2023

Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges.

• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.43-52
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• 2024

Investigating ship motion under real conditions is vital for evaluating the seakeeping performance, particularly in the design process stage. This study examined the influence of the principal particulars of a trawler on its seakeeping performance. The wave conditions in the Bering Sea are investigated using available data. The length-to-beam (L/B) and beam-to-draft (B/T) ratios of the ship are changed by 10% for the numerical simulation. The response amplitude operator (RAO) motion, root mean square (RMS) value and sensitivity analysis are calculated to evaluate the influence of the trawler dimensions on ship motions. The peak RAO motion affected the ship motions noticeably because of the resonance at the natural frequency. The L/B and B/T ratios are important geometric parameters of a ship that significantly influence its RMS motion, particularly in the case of roll and pitch. The change in the B/T ratio has a good seakeeping performance based on a comparison of the roll and pitch with the seakeeping criteria. The present results provide insights into the seakeeping performance of ships due to the influence of the principal dimensions in the design stage.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.4
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• pp.53-61
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• 2022

Growing agricultural products in greenhouses controlled by creating suitable climatic conditions and root zone of crop has been an important research and application subject. Appropriate environmental conditions in greenhouse are necessary for optimum plant growth improved crop yields. This study aimed to establish web-based remote monitoring system which monitors crops growth environment and status of crop on a real-time basis by applying to greenhouses IT technology connecting greenhouse equipment such as temperature sensors, soil sensors, crop sensors and camera. The measuring items were air temperature, relative humidity, solar radiation, CO₂ concentration, EC and pH of nutrient solution, medium temperature, EC of medium, water content of medium, leaf temperature, sap flow, stem diameter, fruit diameter, etc. The developed greenhouse monitoring system was composed of the network system, the data collecting device with sensors, and cameras. Remote monitoring system was implemented in a server/client environment. Information on greenhouse environment and crops is stored in a database. Items on growth and environment is extracted from stored information, could be compared and analyzed. So, A integrated monitoring system for smart greenhouse would be use in application practice and understanding the environment and crop growth for smart greenhouse management. sap flow, stem diameter and pant-water relations

• Journal of the HCI Society of Korea
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• v.10 no.2
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• pp.13-19
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• 2015

Display technology has recently made enormous progress. In particular, display companies are competing each other to develop flexible display. Curved display, as a precursor of flexible display, are now used for smart phones and TVs. Curved monitors have been just introduced in the market, and are used for office work or entertainment. The aim of the current study was to investigate whether the curvature of a 42" multi-monitor affects postural control when it is used for entertainment purpose. The current study used two curvature levels (flat and 600mm). Ten college students [mean(SD) age = 20.9 (1.5)] with at least 20/25 visual acuity, and without color blindness and musculoskeletal disorders participated in this study. In a typical VDT environment, each participant played a car racing video game using a steering wheel and pedals for 30 minutes at each curvature level. During the video game, a pressure mat on the seat pan measured the participant's COP (Center of Pressure), and from which four measures (Mean Velocity, Median Power Frequency, Root-Mean-Square Distance, and 95% Confidence Ellipse Area) were derived. A larger AP (Anterior-Posterior) RMS distance was observed in the flat condition, indicating more forward-backward upper body movements. It can be partly due to more variability in visual distance across display, and hence longer ocular accommodation time in the case of the flat display. In addition, a different level of presence or attention between two curvature conditions can lead to such a difference. Any potential effect of such a behavioral change by display curvature on musculoskeletal disorders should be further investigated.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.