Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson’s ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

In this study, chemical properties and functional ingredients of ginger and ginger pomace discarded after juice were analyzed. Ginger and ginger pomace were subjected to hot air drying, steaming, followed by hot air drying, soaking in vitamin C for 1 hour and 3 hours. When soaked in vitamin C for 3 hours, the moisture content was highest at 9.2% for ginger and 7.3% for ginger pomace. Among inorganic ingredients, the potassium (K) content was high at 2,633.6 mg% in hot air-dried ginger after steaming and at 1,584.3 mg% in ginger pomace. Total flavonoid content of ginger pomace was high at 67.3 mg/g when soaked in vitamin C for 3 hours. Gingerol content was the highest at 9.8 mg/g when ginger was dried with hot air. It was 10.5 mg/g in ginger pomace. After ginger pomace was steamed and dried with hot air, shogaol content was as high as 2.0 mg/g.

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

As the demand for lithium-ion batteries for electric vehicles is increasing, it is important to recover valuable metals from waste lithium-ion batteries. In this study, the effects of gas flow rate and hydrogen partial pressure on hydrogen reduction of NCM-based lithium-ion battery cathode materials were investigated. As the gas flow rate and hydrogen partial pressure increased, the weight loss rate increased significantly from the beginning of the reaction due to the reduction of NiO and CoO by hydrogen. At 700 °C and hydrogen partial pressure above 0.5 atm, Ni and Li2O were produced by hydrogen reduction. From the reduction product and Li recovery rate, the hydrogen reduction of NCM-based cathode materials was significantly affected by hydrogen partial pressure. The Li compounds recovered from the solution after water leaching of the reduction products were LiOH, LiOH·H2O, and Li2CO3, with about 0.02 wt% Al as an impurity.

대용량 화학 및 청정에너지의 운반체인 수소는 석유화학 산업 및 연료전지 등에서 많이 활용되는 중요한 산업용 기체이다. 특히 수소는 주로 증기개질 및 가스화를 통해 화석 연료에서 생성되며 부산물로 이산화탄소가 발생한다. 따라서 고 순도 수소를 얻기 위해서는 이산화탄소를 제거해야 한다. 본 총설에서는 배러 단위[1 Barrer = 10−10 cm3 (STP) × cm / (cm2 × s × cmHg)]로 보고된 이산화탄소로부터 수소를 분리하는 프리스탠딩 고분자 분리막 및 혼합매질 분리막에 초점을 맞추었 다. 최근 보고된 다양한 논문들을 분석하여 분리막의 구조, 형태, 상호 작용 및 제조 방법에 대해 논의하고 구조-물성 관계를 이해하여 향후 더 나은 분리막 소재를 찾는 데 도움이 되고자 한다. 다양한 분리막의 성능 및 특성 검토를 통해 수소/이산화 탄소 분리에 대한 Robeson 성능 한계선을 제시하고, 가교, 혼합 및 열처리 등의 기술을 사용하여 분리 특성을 개선하는 다양 한 혼합매질 분리막에 대해 논의하였다.

높은 안전성과 견고한 기계적 특성을 가진 고체상 슈퍼커패시터는 차세대 에너지 저장 장치로서 세계적 관심을 끌고 있다. 슈퍼커패시터의 전극으로서 경제적인 탄소 기반 전극이 많이 사용되는데 수계 전해질을 도입하는 경우 소수성 표 면을 가진 탄소 기반 전극과의 계면 상호성이 좋지 않아 저항이 증가한다. 이와 관련하여 본 연구에서는 전극 표면에 산소 플라즈마 처리를 하여 친수화된 전극과 수계 전해질 사이의 향상된 계면 성질을 기반으로 더 높은 전기화학적 성능을 얻는 방법을 제시한다. 풍부해진 산소 작용기들로 인한 표면 친수화 효과는 접촉각 측정을 통해 확인하였으며, 전력과 지속시간을 조절함으로써 친수화 정도를 손쉽게 조절할 수 있음을 확인하였다. 수계 전해질로 PVA/H3PO4 고체상 고분자 전해질막을 사 용하였으며 프레싱하여 전극에 도입하였다. 15 W의 낮은 전력으로 5초간 산소 플라즈마 처리를 시행하는 것이 최적 조건이 었으며 슈퍼커패시터의 에너지 밀도가 약 8% 증가하였다.

The purpose of this study is to analyze the eco-friendly design characteristics of contemporary children’s collections. Photos from FirstviewKorea were utilized for analysis; 29 brands were selected that included children’s clothing collections featuring eco-friendly characteristics from 2007 to 2018. The results are as follows. First, naturalness was the most frequent characteristic of environmentally friendly children’s collections. It was not conveyed in an eccentric way in any season, showed a relatively uniform distribution, and was seen in various ways, including printed on the fabric and expressed in appliqués and embroidery. Second, handcrafted features frequently changed according to seasonal trends. Various methods such as beading, embroidery, applique, sewing techniques, and handbags were used, which enhanced manual workability, discrimination from other designs. Third, traditionality is divided into the characteristics of ethnicity and revivalism. National traditions were expressed in the clothing and reflected the current generation while connecting to the past. Fourth, simplicity appeared in classic designs such as simple silhouettes, sparse decoration, natural colors, and comfortable dress length that is not tight on the body. Simplicity was not a frequent feature due to the characteristics of the children’s clothing collections. Fifth, playfulness functioned to enhance the children’s clothing’s wear frequency. Although it was the least frequent of all the characteristics, it seemed to increase the design fun and the clothing’s value by fusing with other characteristics such as handcraftedness and naturalness.

본 연구는 잘피 추출물의 화장품 소재로서 활용가능성을 평가하기 위해 건조된 잘피 전체, 뿌리, 잎·줄기를 70% 에탄올로 추출하여 항균활성을 확인하였다. 잘피 추출물의 항균활성은 Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, Propionibacterium acnes 균주로 disc diffusion 방법을 통해 생육저해환(clear zone) 을 측정하였다. 그 결과 잘피 뿌리 추출물처리군에서 Staphylococcus epidermidis의 clear zone이 13.00±0.50mm로 확인되었고, Staphylococcus aureus, Propionibacterium acnes, Pseudomonas aeruginosa의 clear zone은 각각 11.75±0.25mm, 12.00±0.50mm, 12.25±0.25mm로 확인되어 잘피 추 출물의 항균 효능을 확인할 수 있었다.

이 연구에서는 식빵의 표준 묘사분석 절차를 확립하였으며, 그 결과, 식빵에서 23가지의 특성 묘사 용어를 선정하고 개발하였으며 평가 절차를 확립하였다. 개발된 표준 묘사분석 방법을 이용하여 숙성온도와 배합비가 다른 4종류의 식빵에 대해 관능적 특성을 평가한 결과, 4종류의 식빵 간에는 23개의 관능적 특성에서 모두 유의전인 차이(p<0.05)가 있었다. 따라서 이 실험에서 개발된 식빵의 특성 용어와 그 정의 및 평가 기술이 종류가 다른 식빵의 관능적 특성의 차이를 잘 설명탈 수 있음을 알 수 있었다. 앞으로 이와 같은 연구 결과가 식빵의 품질향상에 기여할 수 있으려면, 원료와 제조 방법에 따른 식빵의 품질평가에 묘사 분석이 적용되고, 이 결과와 소비자 기호도 검사 또는 기계적 측정 결과와의 상관관계를 분석하는 연구가 지속적으로 이루어져야겠다.

본 연구는 한․일 양 언어의 공간 형용사의 의미적․형태적 유사성을 비교하고 그것이 어떤 의미 속성을 가지며 어떻게 의미 전이하는지를 밝히는 데 있다. 이를 위해서 대립어로 이루어진 7쌍의 공간 형용사를 선택한 후, 한국어의 ‘멀다/가깝다’와 일본어의 ‘遠い/近い'를 가지고 그것의 의미 속성과 의미 전이에 대해 분석하였다. 연구 결과 ‘멀다/가깝다’와 ‘遠’い/近’い’는 서로 완전히 동일한 의미 속성을 가지지 않을 뿐만 아니라 의미 전이에 있어서도 일치성을 보이지 않았다. 하지만 본 연구에서는 사전적 의미만을 가지고 의미 속성을 밝혔다는 점에서 한계를 보여준다. 앞으로는 공간 형용사의 관용어 대응, 다의 구조, 연어 대응에 관한 다양한 측면에서의 연구가 이루어져야 할 것이다.