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285 results on '"LU, Shihua"'

24. From the Cultural Revolution to the COVID-19: a Redefinition of Homeland

Author

Lu, Shihua

Abstract

In this essay, I will approach this duality of Chineseness, both being a geographic area that 1.4 billion of people belong to, and being a political regime, from different angles and present how it has been the source of emotional struggles for Chinese people. This essay is generally divided into two parts which separately show how both the non-Chinese world and Chinese people fail to separate the dual aspects (namely, China being a nation state does not coincide with it being one’s homeland) of China over time.

Published
2022
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27. Using random forests to predict passengers' thermal comfort in underground train carriages.

Author

Huang, Kangkang, Lu, Shihua, Li, Xinjun, and Chen, Weiwei

Subjects
SUBWAYS, THERMAL comfort, RANDOM forest algorithms, STANDARD deviations, SUPPORT vector machines, DECISION trees
Abstract

This research developed an intelligent ensemble machine learning prediction model for the thermal comfort of passengers inside the compartment of the subway. Data sources used for data-driven modelling were obtained from on-site measurements and passengers' questionnaires in the compartments of the Nanjing subway. The four models were established using methodologies of Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT) in machine learning, respectively. The performance of the RF method was compared with DT, LR and SVM in terms of conventional statistical metrics, namely, Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficients squares (R2). Thermal Sensation Vote with the seven-level indicator (TSV-7) and Thermal Sensation Vote with the three-level indicator (TSV-3) were employed to obtain passengers' thermal comfort and evaluate the models' predictions. In this study, the R2 value of the RF model is 0.6527 and 0.6607 for TSV-7 and TSV-3, which shows higher accuracy than DT, LR and SVM models in predicting the two kinds of Thermal Sensation Vote (TSV). The results show that the predictive performance of the proposed RF model is outstanding, and it can predict the TSV value of passengers inside the compartment of the subway more efficiently. [ABSTRACT FROM AUTHOR]

Published
2023
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33. Ant-Colony-Algorithm-Based Intelligent Transmission Network Planning

Author

Yuan, Jingzhong, Guo, Jia, Xie, Jinghai, Lu, Shihua, Su, Dongyu, Sun, Mi, and Shah, Mohd Asif

Abstract

Background: The efficiency of wireless sensor networks is limited by limitations in energy supply. Efficient routing strategies should be designed to save and balance the energy consumption of each node in a wireless sensor network. Aim: In this study, a transmission network based on an ant colony algorithm was proposed to meet the power load demands of a city. Objective: Based on the chaos ant colony algorithm, using a combination of wireless sensor network and node residual energy factors, a neighbor selection strategy was proposed. Results: The optimal result was 1896, and additional lines were: N14-15= 2, N4-16= 1, N5-12= 2, N7-13= L, N6-14= 1, N7-8. The coding method of solving transmission network planning based on multi-stage and multi-dimensional control variables was employed to decompose each control variable into two variables. The sum of total weight and non-zero bits was transformed into high-dimensional variables in state transition probability. Conclusion: The key analysis showed that the ant colony algorithm, as a simulated evolutionary algorithm, is an efficient internal heuristic method.

Published
2023
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36. An Intelligent Digital Secretary for Design of Electric Power Engineering

Author

Xie Jinghai, Shaorong Wang, Yiqi Lu, Chuye Hu, Ying Xu, and Lu Shihua

Subjects
Power transmission, Artificial neural network, business.industry, Computer science, Reliability (computer networking), media_common.quotation_subject, Automation, Industrial engineering, Electric power, Engineering design process, business, Function (engineering), Feature learning, media_common
Abstract

In order to improve the digital level of design of electric power engineering and ensure the accuracy and reliability, this paper proposes an intelligent digital secretary. It clarifies the construction ideas and applications of this secretary from three aspects: the architecture, functions and intelligent business services. In intelligent business services, this paper proposes for the first time the application of feature learning based on Auto-encoder for electric power engineering design. Then, the feature parameters learned by Auto-encoder are trained through BP neural network to get the values that meet design standards. Furthermore, this paper constructs the loss function based on the optimal bias to get the results more in line with actual design requirements. Automated design algorithm based on the error model is also provided in this paper. The proposed intelligent digital secretary provides a feasible idea for realizing the leap from current manual design mode to digital design mode.

Published
2020

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