• Title/Summary/Keyword: 효율성 향상

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Ammonia Decomposition over Ni Catalysts Supported on Zeolites for Clean Hydrogen Production (청정수소 생산을 위한 암모니아 분해 반응에서 Ni/Zeolite 촉매의 반응활성에 관한 연구)

  • Jiyu Kim;Kyoung Deok Kim;Unho Jung;Yongha Park;Ki Bong Lee;Kee Young Koo
    • Journal of the Korean Institute of Gas
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    • v.27 no.3
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    • pp.19-26
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    • 2023
  • Hydrogen, a clean energy source free of COx emissions, is poised to replace fossil fuels, with its usage on the rise. Despite its high energy content per unit mass, hydrogen faces limitations in storage and transportation due to its low storage density and challenges in long-term storage. In contrast, ammonia offers a high storage capacity per unit volume and is relatively easy to liquefy, making it an attractive option for storing and transporting large volumes of hydrogen. While NH3 decomposition is an endothermic reaction, achieving excellent low-temperature catalytic activity is essential for process efficiency and cost-effectiveness. The study examined the effects of different zeolite types (5A, NaY, ZSM5) on NH3 decomposition activity, considering differences in pore structure, cations, and Si/Al-ratio. Notably, the 5A zeolite facilitated the high dispersion of Ni across the surface, inside pores, and within the structure. Its low Si/Al ratio contributed to abundant acidity, enhancing ammonia adsorption. Additionally, the presence of Na and Ca cations in the support created medium basic sites that improved N2 desorption rates. As a result, among the prepared catalysts, the 15 wt%Ni/5A catalyst exhibited the highest NH3 conversion and a high H2 formation rate of 23.5 mmol/gcat·min (30,000 mL/gcat·h, 600 ℃). This performance was attributed to the strong metal-support interaction and the enhancement of N2 desorption rates through the presence of medium basic sites.

Performance Evaluation of Absorbent Solution for Draw Solute Recovery in Forward Osmosis Desalination Process (정삼투식 담수공정의 유도용질 회수를 위한 흡수용액 성능 평가)

  • Kim, Young;Lee, Jong Hoon;Lee, Kong Hoon;Kim, Yu-Chang;Oh, Dong Wook;Lee, Jungho
    • Korean Chemical Engineering Research
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    • v.51 no.2
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    • pp.240-244
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    • 2013
  • Although forward osmosis desalination technology has drawn substantial attention as a next-generation desalination method, the energy efficiency of its draw solution treatment process should be improved for its commercialization. When ammonium bicarbonate is used as the draw solute, the system consists of forward-osmosis membrane modules, draw solution separation and recovery processes. Mixed gases of ammonia and carbon dioxide generated during the draws solution separation, need to be recovered to re-concentrate ammonium bicarbonate solution, for continuous operation as well as for the economic feasibility. The diluted ammonium bicarbonate solution has been proposed as the absorbent for the draw solution regeneration. In this study, experiments are conducted to investigate performance and features of the absorption corresponding to absorbent concentration. It is concluded that ammonium bicarbonate solution can be used to recover the generated ammonia and carbon dioxide. The results will be applied to design and operation of pilot-scale forward-osmosis desalination system.

Pre-Service Chemistry Teacher's Designing and Implementing Inquiry-Based Science Instruction that Emphasizes Argumentation and Writing: Focus on Ways to Overcome Difficulties (예비 화학 교사의 논의와 글쓰기가 강조된 탐구 중심 과학 수업 계획과 수행: 어려움과 극복과정을 중심으로)

  • Bang, AeRee;Choi, Aeran
    • Journal of the Korean Chemical Society
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    • v.60 no.5
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    • pp.342-352
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    • 2016
  • The purpose of this study was to investigate inquiry-based science instruction developed and implemented by a pre-service chemistry teacher regarding the difficulties that she encountered and the ways how she tried to solve out problems. Main data of this study were pre-service teacher reflections that were written after developing both each lesson plan and the whole 10 lesson plans, and after implementing both each lesson and the whole classes. Supplemental data were lesson plans, class audio recordings, and student written journals. The pre-service teacher learned that she was lack of science content knowledge and understanding of students’ understandings. Also she had difficulties of developing inquiry-based science lesson plans, managing classrooms, and guiding students to engage in science inquiry. In order to overcome the difficulties, she asked for advice to experienced teachers, studied science concepts using textbooks and internet resources, provided detailed and concrete guidance for student argumentation and writing.

Efficient High-Speed Intra Mode Prediction based on Statistical Probability (통계적 확률 기반의 효율적인 고속 화면 내 모드 예측 방법)

  • Lim, Woong;Nam, Jung-Hak;Jung, Kwang-Soo;Sim, Dong-Gyu
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.3
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    • pp.44-53
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    • 2010
  • The H.264/AVC has been designed to use 9 directional intra prediction modes for removing spatial redundancy. It also employs high correlation between neighbouring block modes in sending mode information. For indication of the mode, smaller bits are assigned for higher probable modes and are compressed by predicting the mode with minimum value between two prediction modes of neighboring two blocks. In this paper, we calculated the statistical probability of prediction modes of the current block to exploit the correlation among the modes of neighboring two blocks with several test video sequences. Then, we made the probable prediction table that lists 5 most probable candidate modes for all possible combinatorial modes of upper and left blocks. By using this probability table, one of 5 higher probable candidate modes is selected based on RD-optimization to reduce computational complexity and determines the most probable mode for each cases for improving compression performance. The compression performance of the proposed algorithm is around 1.1%~1.50%, compared with JM14.2 and we achieved 18.46%~36.03% improvement in decoding speed.

Improvement of Photoelectrochemical Properties through Activation Process of p-type GaN (p-type GaN의 Activation을 통한 광전기화학적 특성 향상)

  • Bang, Seung Wan;Kim, Haseong;Bae, Hyojung;Ju, Jin-Woo;Kang, Sung-Ju;Ha, Jun-Seok
    • Journal of the Microelectronics and Packaging Society
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    • v.24 no.4
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    • pp.59-63
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    • 2017
  • The n-type GaN semiconductor has excellent properties as a photoelectrode, but it has disadvantage that its reliability is deteriorated due to the photocorrosion because the oxygen reaction occurs on the surface. For this reason, there are fundamental attempts to avoid photocorrosion reaction of GaN surfaces by using the p-type GaN as a photoelectrode where hydrogen generation reaction occurs on the surface. However, p-type GaN has a problem of low efficiency because of its high resistivity and low hole mobility. In this study, we try to improve the photocurrent efficiency by activation process for the p-type GaN. The p-type GaN was annealed for 1 min. at $500^{\circ}C$ in $N_2$ atmosphere. Hall effect measurement system was used for the electrical properties and potentiostat (PARSTAT4000) was used to measure the photoelectrochemical (PEC) characteristics. Consequently, the photocurrent density was improved more than 1.5 times by improving the activation process for the p-type GaN. Also, its reliability was maintained for 3 hours.

Construction of Faster R-CNN Deep Learning Model for Surface Damage Detection of Blade Systems (블레이드의 표면 결함 검출을 위한 Faster R-CNN 딥러닝 모델 구축)

  • Jang, Jiwon;An, Hyojoon;Lee, Jong-Han;Shin, Soobong
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.23 no.7
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    • pp.80-86
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    • 2019
  • As computer performance improves, research using deep learning are being actively carried out in various fields. Recently, deep learning technology has been applying to the safety evaluation for structures. In particular, the internal blades of a turbine structure requires experienced experts and considerable time to detect surface damages because of the difficulty of separation of the blades from the structure and the dark environmental condition. This study proposes a Faster R-CNN deep learning model that can detect surface damages on the internal blades, which is one of the primary elements of the turbine structure. The deep learning model was trained using image data with dent and punch damages. The image data was also expanded using image filtering and image data generator techniques. As a result, the deep learning model showed 96.1% accuracy, 95.3% recall, and 96% precision. The value of the recall means that the proposed deep learning model could not detect the blade damages for 4.7%. The performance of the proposed damage detection system can be further improved by collecting and extending damage images in various environments, and finally it can be applicable for turbine engine maintenance.

The Relationship between Structured Time Usage and Quality of Life for University Students : Centered on Health-related University Students (대학생들의 시간 사용과 삶의 질의 관계 : 보건계열 대학생을 중심으로)

  • Sim, Kyoung-Bo;Kwag, Sung-Won
    • The Journal of Korean society of community based occupational therapy
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    • v.9 no.3
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    • pp.31-39
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    • 2019
  • Objective : The purpose of this study is to identify time management among college students and provide them with a basic resource for planning and educating them on efficient time management methods for improving the quality of life of all clients who receive occupational therapy services, including non-disabled people. Methods : This study is a survey of four-year university students using the questionnaire to know the relationship between structured time usage and quality of life. A total of 142 questions, excluding eight missing questionnaires, was compared and analyzed with using the general characteristics, time structure questionnaires (time structure questionnaires) and World Health Organization (WHOQOL-BREF). Results : The results of this study showed that there was a significant correlation between quality of life and structured time management, and between the sub-items of structured time management; sense of purpose, structured daily life, persistence, past direction and sleep intensity. Also, factors that affect the quality of life were sleep (β=.214), structured daily life (β=.203), a sense of purpose(β=.343), and past direction(β=.244) appeared in order. Conclusion : In the case of university student who is working within structured time, there were difficulties in showing satisfactory quality of life. These factors of poor quality of life were analyzed to affect structured daily life, sense of purpose, past direction, and sleep level.

The Competence and Satisfaction on Inventory Management of the Operating Room Nurses (수술실 간호사의 물품관리 업무역량과 업무만족도 연구)

  • Son, Jeong-Sook;Choi, kyung-Sook;Kim, Hyun-Joo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.6
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    • pp.449-458
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    • 2016
  • The study verified the work capacity (job performance, perception, knowledge) and satisfaction on managing the inventory of operating room nurses working in a more than 700-bed hospital. The purpose of this study was to provide basic data of education for work capacity and satisfaction and quality insurance by analyzing the scores between the three different operating rooms and the correlation between the work capacity and satisfaction by investigating the related factors. This study presented a structured and self-administered questionnaire to 181 nurses who had been working in the operating room more than six months. The mean and standard deviation of the job performance, perception, knowledge, and satisfaction were 4.2(${\pm}0.56$), 3.4(${\pm}0.76$), 3.5(${\pm}0.40$), and 3.4(${\pm}0.55$), respectively. The work capacity and satisfaction of each group did not show a statistically significant difference. The correlations between the job performance and knowledge, knowledge and perception, perception and satisfaction were positive (r=.627, p<0.01), (r=.663, p<0.01) and (r=.485, p<0.01), respectively. Among the factors related to the general characteristics of operating room nurses, only age significantly affected their job performance. This study provides basic data on the maintenance and improvement of their competence and satisfaction by being served as a resource for sustainable human resources management and training, and efficient management of the communication channel between hospitals.

Nanophase Catalyst Layer for Direct Methanol Fuel Cells

  • Chang Hyuk;Kim Jirae
    • Journal of the Korean Electrochemical Society
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    • v.4 no.4
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    • pp.172-175
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    • 2001
  • Nanophase catalyst layer for direct methanol fuel cell has been fabricated by magnetron sputtering method. Catalyst metal targets and carbon were sputtered simultaneously on the Nafion membrane surface at abnormally higher gas (Ar/He mixture) pressure than that of normal thin film processing. They could be coated as a novel structure of catalyst layer containing porous PtRu or Pt and carbon particles both in nanometer range. Membrane electrode assembly made with this layer led to a reduction of the catalyst loading. At the catalyst loading of 1.5mg $PtRu/cm^2$ for anode and 1mg $Pt/cm^2$ for cathode, it could provide $45 mW/cm^2$ in the operation at 2 M methanol, 1 Bar Air at 80"C. It is more than $30\%$ increase of the power density performance at the same level of catalyst loading by conventional method. This was realized due to the ultra fine particle sizes and a large fraction of the atoms lie on the grain boundaries of nanophase catalyst layer and they played an important role of fast catalyst reaction kinetics and more efficient fuel path. Commercialization of direct methanol fuel cell for portable electronic devices is anticipated by the further development of such design.

Band Selection Using L2,1-norm Regression for Hyperspectral Target Detection (초분광 표적 탐지를 위한 L2,1-norm Regression 기반 밴드 선택 기법)

  • Kim, Joochang;Yang, Yukyung;Kim, Jun-Hyung;Kim, Junmo
    • Korean Journal of Remote Sensing
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    • v.33 no.5_1
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    • pp.455-467
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    • 2017
  • When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.