• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.337-342
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• 2022

Hydrogen (H₂) gas is widely preferred for use as a renewable energy source owing to its characteristics such as environmental friendliness and a high energy density. However, H₂ can easily reverse or explode due to minor external factors. Therefore, H₂ gas monitoring is crucial, especially when the H₂ concentration is close to the lower explosive limit. In this study, metal oxide materials and their p-n heterojunctions were synthesized by a hydrothermal-assisted dip-coating method. The synthesized thin films were used as sensing materials for H₂ gas. When the H₂ concentration was varied, all metal oxide materials exhibited different gas sensitivities. The performance of the metal oxide gas sensor was analyzed to identify parameters that could improve the performance, such as the choice of the metal oxide material, effect of the p-n heterojunctions, and operating temperature conditions of the gas sensor. The experimental results demonstrated that a CuO/ZnO gas sensor with a p-n heterojunction exhibited a high sensitivity and fast response time (134.9% and 8 s, respectively) to 5% H₂ gas at an operating temperature of 300℃.

• Journal of Wetlands Research
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• v.24 no.2
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• pp.102-114
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• 2022

Global climate change can be solved only through international cooperation. Climate change can be caused by natural and anthropogenic causes. An important policy responding on the climate change is to reduce the emission of climate change-affecting substances caused by anthropogenic causes. This research was conducted to suggest the direction of Korea's LULUCF(Land Use-Land Use Change and Forestry) policy by comparing Germany's LULUCF policy, which is considered as a good case for establishing the EU's greenhouse gas reduction response policy. Germany's LULUCF policy concerns with various sectors for synergy effects, while Korea's LULUCF policy is biased towards the forest sector. Although Korea's LULUCF policy focuses on forests, basic research is still insufficient and the linkage with existing environmental policies is low. Therefore, Korea's LULUCF policy needs more expansion into many different sectors such as agricultural, environmental, and other fields.

• Geomechanics and Engineering
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• v.32 no.5
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• pp.483-494
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• 2023

High-speed trains became common nowadays due to the need for fast and safe mean to transport goods and people. However, the use of high-speed trains necessitates the examination of the critical speed, which is the train speed at which the maximum settlement of the railway track occurs. The critical speed and railway track settlement have been investigated considering only one train in previous studies. However, it is normal to have two adjacent trains moving at the same time. This paper aims to understand how the interference of two moving trains affects the settlement and critical speed of ballasted railway track. Calibrated three-dimensional finite element models of railway track subjected to one moving train and two moving trains have been developed to address the aim of the study. It is found that the interference dramatically increases the railway track settlement with a percentage increase ranges between 5 and 100%. It is also found that the percentage increase of the railway track settlement depends on the train speed and the distance between the moving trains. In addition, it is found that the thickness of the ballast layer and the stiffness of the subgrade have minor influence on the percentage increase of the settlement. Importantly, the results of this paper illustrate the importance of the interference of the moving trains on the dynamic response of the railway track. Thus, there is a need to consider the dynamic interaction between the adjacent moving trains in the design of railway track foundation.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Progress in Superconductivity and Cryogenics
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• v.10 no.4
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• pp.23-28
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• 2008

As a part of the plasma control system (PCS) for the first plasma campaign of KSTAR, seven sets of fast feedback control loop for the superconducting poloidal field magnet power supply (PF MPS) have been implemented. A special real-time digital communication interface has been developed for the simultaneous exchanges of the current/voltage data from the 7 sets of 12-thyristor power supplies in a 200 microsecond control cycle. Preliminary power supply tests have been performed before actual cooldown of the device. A $29mH/50m{\Omega}$ solenoid dummy has been fabricated for a series of single power supply tests. Connectivity and response speed of the plasma control system have been verified. By changing hardware cabling, this load was also used to estimate mutual inductance coupling effects of two geometrically adjacent solenoid coils on each power supply. After the cooldown was complete, each pair of the up/down symmetric PF coils has been serially connected and tested as part of the device commissioning process. Bipolar operation and longer pulse attempts have been investigated. The responses of the coils and power supplies corresponding to the plasma magnetic controls in plasma discharges are also analyzed for the future upgrades.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.2
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• pp.143-150
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• 2023

Underwater wireless communication is a challenging issue for realizing the smart aqua-farm and various marine activities for exploring the ocean and environmental monitoring. In comparison to acoustic and radio frequency technologies, the visible light communication is the most promising method to transmit data with a higher speed in complex underwater environments. To send data at a speedier rate, high-performance photodetectors are essentially required to receive blue and/or cyan-blue light that are transmitted from the light sources in a light-fidelity (Li-Fi) system. Here, we fabricated high-performance organic phototransistors (OPTs) based on P-type donor polymer (PTO2) and N-type acceptor small molecule (IT-4F) blend semiconductors. Bulk-heterojunction (BHJ) PTO2:IT-4F photo-active layer has a broad absorption spectrum in the range of 450~550 nm wavelength. Solution-processed OPTs showed a high photo-responsivity >1,000 mA/W, a large photo-sensitivity >10³, a fast response time, and reproducible light-On/Off switching characteristics even under a weak incident light. BHJ organic semiconductors absorbed photons and generated excitons, and efficiently dissociated to electron and hole carriers at the donor-acceptor interface. Printed and flexible OPTs can be widely used as Li-Fi receivers and image sensors for underwater communication and underwater internet of things (UIoTs).

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.939-946
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• 2018

In this work, we present simple schematics for a three-terminal graphene-based nanoelectromechanical switch with the vertical electrode, and we investigated their operational dynamics via classical molecular dynamics simulations. The main structure is both the vertical pin electrode grown in the center of the square hole and the graphene covering on the hole. The potential difference between the bottom gate of the hole and the graphene of the top cover is applied to deflect the graphene. By performing classical molecular dynamic simulations, we investigate the nanoelectromechanical properties of a three-terminal graphene-based nanoelectromechanical switch with vertical pin electrode, which can be switched by the externally applied force. The elastostatic energy of the deflected graphene is also very important factor to analyze the three-terminal graphene-based nanoelectromechanical switch. This simulation work explicitly demonstrated that such devices are applicable to nanoscale sensors and quantum computing, as well as ultra-fast-response switching devices.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.317-328
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• 2023

The soil behaviour can be represented by numerical modelling of element testing using diverse constitutive models. However, not all constitutive models allow the simulation of the stress-strain response at the critical state in granular soils with both contractive and dilative behaviour. Moreover, the accuracy of these models depends highly on the quality of the experimental data used for their calibration. This study addresses the modelling of the critical state behaviour of an alluvial natural soil from the Lower Tagus Valley (south of Portugal), known as TP-Lisbon sand, using the NorSand constitutive law. For this purpose, a series of numerical simulations of element testing was carried out using two algorithms performed in Visual Basic (VB) and Fast Lagrangian Analysis of Continua (FLAC). Moreover, this study presents the characterisation of of NorSand parameters from an accurate experimental programme based on triaxial and bender element testing. This experimental program allowed defining: (i) the critical state locus, (ii) the stress-dilatancy, and (iii) the soil elasticity of TP-Lisbon sand -all fundamental to calibrate the contractive and dilative behaviour of such alluvial soil. The results revealed a good agreement between experimental data and NorSand simulations using VB and FLAC. Therefore, this study showed that the quality of laboratory testing procedures and its good interpretation enables NorSand constitutive law to capture representatively the non-associated plastic strains, often expressed by the state parameter, allowing a representation of soil behaviour of alluvial soils within the critical state soil mechanics framework for different state parameters.

• Journal of IKEEE
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• v.27 no.4
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• pp.494-500
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• 2023

Image processing is currently used in various fields. Among them, autonomous vehicles, medical image processing, and robot control require fast image processing response speeds. To fulfill this requirement, hardware design for real-time processing is being actively researched. In addition to the size of the input image, the hardware processing speed is affected by the size of the inactive video periods that separate lines and frames in the image. In this paper, we design three different scaler structures based on the type of line memories, which is closely related to the inactive video periods. The structures are designed in hardware using the Verilog standard language, and synthesized into logic circuits in a field programmable gate array environment using Xilinx Vivado 2023.1. The synthesized results are used for frame rate analysis while comparing standard image sizes that can be processed in real time.