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Jo, Gyuha,Choi, Ilyoung,Ahn, Hyungmin,Park, Moon Jeong The Royal Society of Chemistry 2012 Chemical communications Vol.48 No.33
<P>We present a facile synthetic route toward binder-free, highly-dispersed Ge nanoparticles in carbon matrices using one-step pyrolysis of self-assembled Ge–polymer hybrids. 3-Dimensionally arranged Ge–carbon exhibits remarkably enhanced cycling properties and rate capability compared with carbon sheathed Ge lacking organization.</P> <P>Graphic Abstract</P><P>Binder-free GeNPs–carbon hybrid anodes with highly reversible capacities and rate capability were achieved by one-step pyrolysis of self-assembled GeNPs–polymer hybrids. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c2cc30294b'> </P>
( Myeongsoo Lee ),( Jinhyuk Hong ),( Hyungmin Jo ) 한국교육공학회 2014 한국교육공학회 학술대회발표자료집 Vol.2014 No.2
A simulation facility, ``plant-referenced simulator``, shall be used to meet the lawful requirements for applicants for Reactor operator (RO) or Senior reactor operator (SRO) licenses. Korea Hydro Nuclear Power (KHNP) has developed and used the full-scope simulators and other training facilities by using the modeling and simulation (M&S) technology. This paper describes current status of KHNP``s applications, full-scope simulator, compact(part task) simulator, nuclear plant analyzer (NPA), etc., by using modeling and simulation (M&S) technology for the operator and staff trainings in nuclear power plants (NPPs). Though the full-scope training simulators and other facilities are useful for the operator and non-operating staff trainings of NPPs, it cannot be too strongly emphasized that the importance of the safety culture included an administrative system to support high safety standards for emergency response.
( Payam Hosseinzadeh Kasani ),( Seung Min Oh ),( Yo Han Choi ),( Sang Hun Ha ),( Hyungmin Jun ),( Kyu Hyun Park ),( Han Seo Ko ),( Jo Eun Kim ),( Jung Woo Choi ),( Eun Seok Cho ),( Jin Soo Kim ) 한국축산학회(구 한국동물자원과학회) 2021 한국축산학회지 Vol.63 No.2
The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.