• Smart Media Journal
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• v.13 no.5
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• pp.19-25
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• 2024

The biggest type of behavior that prevents people with developmental disabilities from entering society is aggressive behavior. Aggressive behavior can pose a threat not only to the personal safety of the person with a developmental disability, but also to the physical safety of others. In this study, we propose a wearable system using a low-power processor. The proposed system uses an IMU (Inertial Measurement Unit) to analyze user behavior, and when attack behavior is not detected for a certain period of time through an LED array attached to the developed system, an interesting LED is displayed. By expressing patterns, we provide behavioral intervention through compensation to people with developmental disabilities. In order to implement a system that must be worn for a long time in a power-limited environment, we present a method to optimize performance and energy consumption across all stages, from data preprocessing to AI model application.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.565-575
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• 2009

In recent years, there have been numerous explosion-related accidents due to military and terrorist activities. Such incidents caused not only damages to structures but also human casualties, especially in urban areas. To protect structures and save human lives against explosion accidents, better understanding of the explosion effect on structures is needed. In an explosion, the blast load is applied to concrete structures as an impulsive load of extremely short duration with very high pressure and heat. Generally, concrete is known to have a relatively high blast resistance compared to other construction materials. However, normal strength concrete structures require higher strength to improve their resistance against impact and blast loads. Therefore, a new material with high-energy absorption capacity and high resistance to damage is needed for blast resistance design. Recently, Ultra High Strength Concrete(UHSC) and Reactive Powder Concrete(RPC) have been actively developed to significantly improve concrete strength. UHSC and RPC, can improve concrete strength, reduce member size and weight, and improve workability. High strength concrete are used to improve earthquake resistance and increase height and bridge span. Also, UHSC and RPC, can be implemented for blast resistance design of infrastructure susceptible to terror or impact such as 9.11 terror attack. Therefore, in this study, the blast tests are performed to investigate the behavior of UHSC and RPC slabs under blast loading. Blast wave characteristics including incident and reflected pressures as well as maximum and residual displacements and strains in steel and concrete surface are measured. Also, blast damages and failure modes were recorded for each specimen. From these tests, UHSC and RPC have shown to better blast explosions resistance compare to normal strength concrete.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.4
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• pp.83-89
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• 2020

For the past few decades, as part of efforts to protect the environment where fossil fuels, which have been a key energy resource for mankind, are becoming increasingly depleted and pollution due to industrial development, ecofriendly secondary batteries, hydrogen generating energy devices, energy storage systems, and many other new energy technologies are being developed. Among them, the lithium-ion battery (LIB) is considered to be a next-generation energy device suitable for application as a large-capacity battery and capable of industrial application due to its high energy density and long lifespan. However, considering the growing battery market such as eco-friendly electric vehicles and drones, it is expected that a large amount of battery waste will spill out from some point due to the end of life. In order to prepare for this situation, development of a process for recovering lithium and various valuable metals from waste batteries is required, and at the same time, a plan to recycle them is socially required. In this study, we introduce a nanoscale pattern transfer printing (NTP) process of Li2CO3, a representative anode material for lithium ion batteries, one of the strategic materials for recycling waste batteries. First, Li2CO3 powder was formed by pressing in a vacuum, and a 3-inch sputter target for very pure Li2CO3 thin film deposition was successfully produced through high-temperature sintering. The target was mounted on a sputtering device, and a well-ordered Li2CO3 line pattern with a width of 250 nm was successfully obtained on the Si substrate using the NTP process. In addition, based on the nTP method, the periodic Li2CO3 line patterns were formed on the surfaces of metal, glass, flexible polymer substrates, and even curved goggles. These results are expected to be applied to the thin films of various functional materials used in battery devices in the future, and is also expected to be particularly helpful in improving the performance of lithium-ion battery devices on various substrates.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of the Korea Organic Resources Recycling Association
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• v.26 no.4
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• pp.53-61
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• 2018

Anaerobic digestion (AD) is one of the most widely used process that can convert the organic fraction of waste activated sludge (WAS) into biogas. However, most researched actual methane yields of anaerobic digester (AD) on lab scale is lower than theoretical ones. Bioelectrochemical, anaerobic digester was used to increase methane yield from waste activated sludge. The influence of anaerobic digestion sludge and raw sludge mixing ratio (3:7, 5:5) on methane yield and organic matter removal efficiency were explored. As a result, when the mixing ratio of bioelectrochemical anaerobic sludge was 5:5 compared with 3:7, the highest methane yields were 294.2 mL $CH_4/L$ (0.63 times increase) and 52.5% (7.5% increase), the bioelectrochemical anaerobic digester(5:5) was more stable in the pH, t otal alkalinity and VFAs, respectively. These results showed that the increase in the mixing ratio of anaerobic digestion sludge was found to be effective for maintaining the stable performance of bioelectrochemical anaerobic digester.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.33-40
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• 2021

In this study, the physico-chemical and biological characteristics of anaerobic granular sludge at different heights of a full-scale UASB reactor (UASBr) were investigated. Granular sludge was taken from 1 m, 2 m, and 3 m above the ground level in the UASBr. The morphological analysis showed that the upper part had bigger granules and the lower part had rounder granules. The ANOVA test confirmed that the mean size and the roundness of the granules had statistically significant difference along the height at 95% confidence level, and there was a significant negative linear relationship between the size and roundness (r=-0.40, p<0.05). A SMA test using acetic, propionic and butyric acids showed that granules from 2 m height had the highest specific methanogenic activity. The EPS contents were also unequal to each height, with 2 m showing the highest content. These findings could be helpful to understand the different characteristics of stratified anaerobic granules in full-scale UASBr and maintain the reactor performance.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.13-22
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• 2008

The initiation and growth processes of cyclic ice body in porous systems are affected by the thermo-physical and mass transport properties, as well as gradients of temperature and chemical potentials. Furthermore, the diffusivity of deicing chemicals shows significantly higher value under cyclic freeze-thaw conditions. Consequently, the disintegration of concrete structures is aggravated at marine environments, higher altitudes, and northern areas. However, the properties of cyclic freeze-thaw with crack growth and the deterioration by the accumulated damages are hard to identify in tests. In order to predict the accumulated damages by cyclic freeze-thaw, a regression analysis by the response surface method (RSM) is used. The important parameters for cyclic freeze-thawdeterioration of concrete structures, such as water to cement ratio, entrained air pores, and the number of cycles of freezing and thawing, are used to compose the limit state function. The regression equation fitted to the important deterioration criteria, such as accumulated plastic deformation, relative dynamic modulus, or equivalent plastic deformations, were used as the probabilistic evaluations of performance for the degraded structural resistance. The predicted results of relative dynamic modulus and residual strains after 300 cycles of freeze-thaw show very good agreements with the experimental results. The RSM result can be used to predict the probability of occurrence for designer specified critical values. Therefore, it is possible to evaluate the life cycle management of concrete structures considering the accumulated damages due to the cyclic freeze-thaw using the proposed prediction method.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Applied Chemistry for Engineering
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• v.24 no.3
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• pp.219-226
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• 2013

Organic semiconductor-based soft electronics has potential advantages for next-generation electronics and displays, which request mobile convenience, flexibility, light-weight, large area, etc. Organic field-effect transistors (OFET) are core elements for soft electronic applications, such as e-paper, e-book, smart card, RFID tag, photovoltaics, portable computer, sensor, memory, etc. An optimal multi-layered structure of organic semiconductor, insulator, and electrodes is required to achieve high-performance OFET. Since most organic semiconductors are self-assembled structures with weak van der Waals forces during film formation, their crystalline structures and orientation are significantly affected by environmental conditions, specifically, substrate properties of surface energy and roughness, changing the corresponding OFET. Organo-compatible insulators and surface treatments can induce the crystal structure and orientation of solution- or vacuum-processable organic semiconductors preferential to the charge-carrier transport in OFET.