해외 동향 - 중국 원자력의 성장

Hore-Lacy, Ian,Tarlton, Stephen 한국원자력산업회의 2014 원자력산업 Vol.34 No.3

중국에서는 향후 2년 내에 많은 신규 원자력발전소가 가동을 시작하여 현재 원자력 발전량의 2배를 기록할 것이다. 중국의 신규 원자력발전소 건설 계획이 세계 어느 국가보다도 대규모로 유지되고 있지만, 원자력 발전 용량의 증가는 2020년대에 점차 둔화될 것으로 예측된다.

Neural-based prediction of structural failure of multistoried RC buildings

Sirshendu Hore,Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Dana Bălas-Timar,Valentina E. Balas 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.58 No.3

Various vague and unstructured problems encountered the civil engineering/ designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

중국, 티베트, 좌파

찰리 호어(Charlie Hore),이정구(번역자) 책갈피 2020 마르크스21 Vol.- No.33

Fuzzy logic based improved Active and Reactive Power control operation of DFIG for Wind Power Generation

J.P. Mishra,Debirupa Hore,Asadur Rahman 전력전자학회 2011 ICPE(ISPE)논문집 Vol.2011 No.5

The fuzzy-controllers are designed to tune along with the conventional PI-controllers for the vector control of active and reactive power of a wind-turbine driven DFIG under varying wind speed operation to optimize the power generation at specified power-factor. Initially, stator-flux-oriented vector control scheme is implemented using tuned active and reactive power PI-controllers for the rotor-side-converter. Then the fuzzy-controllers are also tuned along with conventional PI-controllers for the generated active power to track more precisely the reference power at specified power-factor in both sub-synchronous and super-synchronous modes of operations. The grid-side-converter is controlled in grid-voltage-oriented reference frame using dc-link voltage PI-controller. Hysteresis current controlled based PWM switching of both rotor-side and grid-side converters ensure fast and accurate control of active and reactive power. Simulation results under varying wind conditions reveal that the additional fuzzy-controller improves the performance of variable speed wind power generating system using DFIG.

Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm

Chatterjee, Sankhadeep,Sarkar, Sarbartha,Hore, Sirshendu,Dey, Nilanjan,Ashour, Amira S.,Shi, Fuqian,Le, Dac-Nhuong Techno-Press 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm

Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Fuqian Shi,Dac-Nhuong Le 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multiobjective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLPFFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

Does Visitation Dictate Animal Welfare in Captivity? : A Case Study of Tigers and Leopards from National Zoological Park, New Delhi

Gupta, Avni,Vashisth, Saurabh,Sharma, Mahima,Hore, Upamanyu,Lee, Hang,Pandey, Puneet National Institute of Ecology 2022 Proceedings of NIE Vol.3 No.2

Zoological Parks house exclusive animal species, thus creating a source of education and awareness for visitors. Big cats like tigers and leopards are among the most visited species in zoos globally. However, they often display stressful or stereotypic behaviours. Such behaviours are influenced by multiple factors including visitors, animal history, and captive environment. To understand this impact, we investigated the behavioural response of tigers and leopards to visitation, captive, and biological factors. The behaviour of eight big cats housed in the National Zoological Park, New Delhi, was monitored using focal sampling technique during May and June 2019. We recorded the captive and biological factors and visitor density for the subjects. The study revealed high proportions of inactive and stereotypic behaviours amongst the species. Tigers and leopards were found to perform stereotypic behaviours for 22% and 28% of their time, respectively. Generalised Linear Models revealed a significant variation of stereotypy in association with the factors. Stereotypy was influenced by visitor density, age, sex, breeding history, coat colour, and enclosure design. Adults, males, white-coated, previously bred, and those housed in smaller and simple enclosures display more stereotypy than young, females, normal-coated, unbred, and those housed in larger and complex enclosures, respectively. A high density of visitors induced more stereotypic behaviours amongst the big cats. As providing entertainment and awareness amongst the public is one of the fundamental objectives of the zoo, visitors can not be avoided. Thus, we suggest providing appropriate enrichments that would reduce stereotypies and promote naturalistic behaviours.