• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.1
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• pp.46-53
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• 2023

In this paper, we evaluated the flexural performance of three types of reinforced concrete beams (SHCC-RB, SHCC-SB, SHCC-FRP) strengthened with ordinary steel rebar, very high strength (super strength) rebar, and FRP bars together with strain-hardening cement composite (SHCC). For this purpose, a series of beam specimens were manufactured and four-point load bending experiments were performed. As a result of the experiment, all specimens strengthened with SHCC exhibited tightly controlled flexural microcrakcs with the crack width of less than 100 ㎛. This is mostly due to the material properties of SHCC showing tensile strain hardening properties with multiple microcracks under uniaxial tension. The specimen SHCC-FRP showed lower initial cracking moment and yield flexural strength than SHCC-RB, whereas the maximum flexural strength of SHCC-FRP was superior to that of SHCC-RC. This is because the tensile strength of FRP bars is higher than that of ordinary steel reabr. The initial cracking moment of the beam specimen SHCC-SB was similar to that of SHCC-RB, but the yield flexural strength and maximum flexural strength of SHCC-SB were evaluated to be the highest.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.169-174
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• 2023

Currently, overall power usage is also increasing as power demand such as homes, offices, and factories increases. The increase in power use also raised interest in standby power as a change in awareness of energy saving appeared. Home and office devices are consuming power even in standby conditions. Accordingly, there is a growing need to reduce standby power, and it aims to have standby power of 1W or less. An intelligent outlet uses a near-field wireless network to connect to a home network and cut or reduce standby power of a lamp or appliance connected to an outlet. This research aims to develop a monitoring system and an intelligent outlet that can remotely monitor the amount of electricity used in a lighting lamp or a home appliance connected to an outlet using a short-range wireless network (Zigbee). Also, The intelligent outlet and monitoring system developed makes it possible for a user to easily cut off standby power by using a portable device. Intelligent outlets will not only reduce standby power but also be applicable to fire prevention systems. Devices that cut off standby power include intelligent outlets and standby power cutoff switches, so they will prevent short circuits and fires.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1244-1251
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• 2022

Marine accidents due to a loss of stability, have been gradually increasing over the last decade. Measures must be taken on the roll reduction of a ship. Amongst the measures, building an anti-roll tank in a ship is recognized as the most simple and effective way to reduce the roll motion. Therefore, this study aims to develop a computational model for a U-tube type anti-roll tank and to validate it by experiment. In particular, to validate the developed computational model, the height of the free surface in the tank was measured in the experiment. To develop a computational model, the mesh dependency test was carried out. Further, the effects of a turbulence model, time step size, and the number of iterations on the numerical solution were analyzed. In summary, a U-tube type anti-roll tank simulation had to be performed accurately with conditions of a realizable k-𝜖 turbulence model, 10^-2s time step size, and 15 iterations. In validation, the two cases of measured data from the experiment were compared with the numerical results. In the present study, STAR-CCM+ (ver. 17.02), a RANS-based commercial solver was used.

• Journal of Plant Biotechnology
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• v.49 no.4
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• pp.307-315
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• 2022

Artemisinin is a secondary metabolite of Artemisia annua that shows potent anti-malarial, anti-bacterial, antiviral, and anti-tumor effects. The supply of artemisinin depends on its content in Artemisia annua, in which various environmental factors can affect the plant's biosynthetic yield. In this study, the effects of different light-emitting diode (LED)-irradiation conditions were tested to optimize the germination and growth of Artemisia annua for the enhanced production of artemisinin. Specifically, the ratio between the red and blue lights in the irradiating LED was varied for investigation as follows: [Red : Blue] = [6 : 4], [7 : 3], and [8 : 2]. Furthermore, additional stress factors like UV-B-irradiation (1,395 ㎼/cm²), low temperature (4℃), and dehydration were also explored to induce hormetic expressions of ADS, CYP, and ALDH1, which are essential genes for the biosynthesis of artemisinin. Quantitative polymerase chain reaction (qPCR) was used to analyze the expression levels of the respective genes and their correlation with the specified conditions. [8 : 2] LED-irradiation was the most optimal among the tested conditions for the cultivation of Artemisia annua in terms of both fresh and dry weights post-harvest. For the production of artemisinin, however, [7 : 3] LED-irradiation with dehydration for six hours pre-harvest was the most optimal condition by inducing around twofold enhancement in the biosynthetic yield of artemisinin. As expected, a correlation was observed between the expression levels of the genes and the contents of artemisinin accumulated.

• Membrane Journal
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• v.33 no.4
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• pp.181-190
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• 2023

Wastewater purification is one of the most important techniques for controlling environmental pollution and fulfilling the demand for freshwater supply. Various technologies, such as different types of distillations and reverse osmosis processes, need higher energy input. Capacitive deionization (CDI) is an alternative method in which power consumption is deficient and works on the supercapacitor principle. Research is going on to improve the electrode materials to improve the efficiency of the process. A reverse electrodialysis (RED) is the most commonly used desalination technology and osmotic power generator. Among many studies conducted to enhance the efficiency of RED, MXene, as an ion exchange membrane (IEM) and 2D nanofluidic channels in IEM, is rising as a promising way to improve the physical and electrochemical properties of RED. It is used alone and other polymeric materials are mixed with MXene to enhance the performance of the membrane further. The maximum desalination performances of MXene with preconditioning, Ti₃C₂T_x, Nafion, and hetero-structures were respectively measured, proving the potential of MXene for a promising material in the desalination industry. In terms of osmotic power generating via RED, adopting MXene as asymmetric nanofluidic ion channels in IEM significantly improved the maximum osmotic output power density, most of them surpassing the commercialization benchmark, 5 Wm^-2. By connecting the number of unit cells, the output voltage reaches the point where it can directly power the electronic devices without any intermediate aid. The studies around MXene have significantly increased in recent years, yet there is more to be revealed about the application of MXene in the membrane and osmotic power-generating industry. This review discusses the electrodialysis process based on MXene composite membrane.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.14-14
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• 2023

산업발전에 따른 인구증가, 기후위기에 따른 가뭄 및 물 부족심화, 그리고 수질오염 등은 2015년 제79차 UN총회의 물 안보측면에서 국제사회의 물 분야 위기관리를 위해 2030년을 지속가능한 발전 목표(Sustainable Development Goals)로 하였다. 또한, 현재 물 산업은 빠르게 성장하고 있으며, 2016년 세계경제포럼(World Economic Forum) 의장 클라우스 슈밥(Klaus Schwab)부터 주창된 제4차 산업혁명로 인해 현재 물 산업의 패러다임 또한 급속히 변화하고 있다. 이는 컴퓨터를 기반으로 하는 CPS(Cyber Physical System) 및 DT(Digital Twin) 연계 분석방식의 혁신을 일컫는다. 2002년경에 DT의 기본개념이 제시되었고, 2006년경에는 Embedded System에서의 DT와 같은 개념으로 CPS의 용어가 등장했다. DT는 현실세계에 존재하는 사물, 시스템, 환경 등을 S/W시스템의 가상공간에 동일하게 모사(Virtualization) 및 모의(Simulation)할 수 있도록 하고, 모의결과를 가상시스템으로 현실세계를 최적화 체계 구현 기술을 말한다. DT의 6가지 기능은 ① 실제 데이터(Live Data), ② 모사, ③ 분석정보(Analytics), ④ 모의, ⑤ 예측(Predictions), ⑥ 자동화(Automation) 이다. 또한, CPS는 대규모 센서 및 액추에이터(Actuator)를 가지는 물리적 요소와 이를 실시간으로 제어하는 컴퓨팅 요소가 결합된 복합시스템을 말한다. CPS는 물리세계에서 발생하는 변화를 감지할 수 있는 다양한 센서를 통해 환경인지 기능을 수행한다. 센서로부터 수집된 정보와 물리세계를 재현 및 투영하는 고도화된 시스템 모델들을 기반으로 사이버 물리공간을 인지·분석·예측할 수 있다. CPS의 6가지 구성요소는 ① 상호 운용성(Interoperability), ② 가상화(Virtualization), ③ 분산화(Decentralization), ④ 실시간(Real-time Capability), ⑤ 서비스 오리엔테이션(Service Orientation), ⑥ 모듈화(Modularity)이다. DT와 CPS는 본질적으로 같은 목적, 내용, 그리고 결과를 만들어내고자 하는 같은 종류의 기술이라고 할 수 있다. CPS 및 DT는 물리세계에서 발생하는 변화를 감지할 수 있으며, 토양-지하수 센서를 포함한 관측기술을 통해 환경인지 기능을 수행한다. 지하수 관측기술로부터 수집된 정보와 물리세계를 재현 및 투영하는 고도화된 시스템 모델들을 기반으로 사이버 물리공간 및 디지털 트윈 공간을 인지·분석·예측할 수 있다. CPS 및 DT의 기본 요소들을 실현시키는 것은 양질의 데이터를 모니터링할 수 있는 정확하고 정밀한 1차원 연직 프로파일링 관측기술이며, 이를 토대로 한 수자원 관련 빅데이터의 증가, 빅데이터의 저장과 분석을 가능하게 하는 플랫폼의 개발이다. 본 연구는 CPS 및 DT 기반 토양수분-지하수 관측기술을 이용한 지표수-지하수 연계, 지하수 순환 및 관리, 정수 운영 및 진단프로그램 개발을 위한 토양수분-지하수 관측장치를 지하수 플랫폼 동시성과 디지털 트윈 시뮬레이터 시스템 개발 방향으로 제시하고자 한다.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.93-108
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• 2023

In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.381-386
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• 2023

Many existing test case generation researchers extract test cases from models. However, research on generating test cases from natural language requirements is required in practice. For this purpose, the combination of natural language analysis and requirements engineering is very necessary. However, Requirements analysis written in Korean is difficult due to the diverse meaning of sentence expressions. We research test case generation through natural language requirement definition analysis, C3Tree model, cause-effect graph, and decision table steps as one of the test case generation methods from Korean natural requirements. As an intermediate step, this paper generates test cases from C3Tree model-based decision tables using meta-modeling. This method has the advantage of being able to easily maintain the model-to-model and model-to-text transformation processes by modifying only the transformation rules. If an existing model is modified or a new model is added, only the model transformation rules can be maintained without changing the program algorithm. As a result of the evaluation, all combinations for the decision table were automatically generated as test cases.