Writing Miracles and Denominational Establishment: On the Belief Narratives of Quanzhen Daoism

ZHANG, Shuqing DAOS(The Daesoon Academy of Sciences) 2024 Journal of Daesoon Thought and the Religions of Ea Vol.3 No.2

This article focuses on the image of the ancestor of Quanzhen Daoism from a narrative perspective and also evaluates the influence of this image on the development of Quanzhen Daoism in terms of belief, genealogy, and the compilation of sacred history. Quanzhen Daoism has a rich tradition of narrating and writing its history. In fact, narrating history is actually a form of constructing history. From the recounting of events such as the birth of the founder of the religion, Wang Chongyang (王重陽,1112–1171), his conversion to Daoism, his practice and preaching, and his ‘ascent to immortality’ in Quanzhen historical hagiographies, readers can observe the recording of miracles as a narrative feature. The narratives of religious texts differ from ordinary historical narratives in that the former maintain the core concern of simultaneously promoting belief in miracles and strengthening the religious lineage of the respective tradition. Therefore, exploring the relationship between the narrative of the image of the ancestor and the development of the Quanzhen Sect, along with the establishment of beliefs, is the starting point of this article.

Integrated fault location method for distribution networks based on IACO‑PS

Shuqing Zhang,Xiaowen Zhang,Anqi Jiang,Liguo Zhang,Mingliang Li 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.1

This paper develops a new hybrid method based on an improved ant colony optimization algorithm that incorporates pattern search (IACO-PS) for determining the location of faults in a distribution network. The performance of the conventional ant colony optimization (ACO) algorithm is improved using the opposite-based learning strategy to generate the initial population and adding a weight coefficient into the pheromone update mechanism to dynamically adjust the pheromone volatilization factor. The hybrid IACO-PS algorithm combines the individual strengths of ACO and PS. In addition, the fitness function is constructed by counting the false and missing fault information into the fault variable. In optimizing benchmark function experiments, the proposed hybrid IACO-PS presents a superior performance when compared to other improved versions of ACO. The effectiveness of the proposed approach is corroborated by tests performed on an IEEE 134-bus network. Simulation results show that the proposed hybrid IACO-PS method can determine the location of a fault even in the presence of fault distortion. In addition, it is immune to noise and data loss errors. Finally, the method proposed in this paper significantly outperforms other published fault location methods, and it can accurately locate faults and identify the type of distortion.

A Hybrid Strategy for Fine-Grained Sentiment of Microblog

Ouyang Chunping,Luo Lingyun,Zhang Shuqing,Yang Xiaohua 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6

Currently, most sentiment analysis of microblog has been focused on coarse-grained sentiment analysis, but fine-grained sentiment is better for reflecting the opinion of the public when they are facing the social focus. Therefore, a hybrid strategy which is a combination of Naïve Bayesian and two-layer CRFs is put forward, which has been applied to the fine-grained sentiment analysis of Chinese microblog. First, microblog is classified into two types: sentiment and non-sentiment by using Naïve Bayesian classification algorithm. And then the first-layer CRFs model is built for the topic emotional sentence. Finally CRFs algorithm is used again to do multi-classification to assign a specific sentiment category. Experimental results show that a good result in sentiment identification based on the combination of Naïve Bayesian and CRFs, and also show the advantage of the combination of Naïve Bayesian and CRFs interrelated with emotional sentence extraction based on CRFs.

Estimation of semi-rigid joints by cross modal strain energy method

Shuqing Wang,Min Zhang,Fushun Liu 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.6

We present a semi-rigid connection estimation method by using cross modal strain energy method. While rigid or pinned assumptions are adopted for steel frames in traditional modeling via finite element method, the actual behavior of the connections is usually neither. Semi-rigid joints enable connections to be modeled as partially restrained, which improves the quality of the model. To identify the connection stiffness and update the FE model, a newly-developed cross modal strain energy (CMSE) method is extended to incorporate the connection stiffness estimation. Meanwhile, the relations between the correction coefficients for the CMSE method are derived, which enables less modal information to be used in the estimation procedure. To illustrate the capability of the proposed parameter estimation algorithm, a four-story frame structure is demonstrated in the numerical studies. Several cases, including Semi-rigid joint(s) on single connection and on multi-connections, without and with measurement noise, are investigated. Numerical results indicate that an excellent updating is achievable and the connection stiffness can be estimated by CMSE method.

Estimation of semi-rigid joints by cross modal strain energy method

Wang, Shuqing,Zhang, Min,Liu, Fushun Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.6

We present a semi-rigid connection estimation method by using cross modal strain energy method. While rigid or pinned assumptions are adopted for steel frames in traditional modeling via finite element method, the actual behavior of the connections is usually neither. Semi-rigid joints enable connections to be modeled as partially restrained, which improves the quality of the model. To identify the connection stiffness and update the FE model, a newly-developed cross modal strain energy (CMSE) method is extended to incorporate the connection stiffness estimation. Meanwhile, the relations between the correction coefficients for the CMSE method are derived, which enables less modal information to be used in the estimation procedure. To illustrate the capability of the proposed parameter estimation algorithm, a four-story frame structure is demonstrated in the numerical studies. Several cases, including Semi-rigid joint(s) on single connection and on multi-connections, without and with measurement noise, are investigated. Numerical results indicate that an excellent updating is achievable and the connection stiffness can be estimated by CMSE method.

A new hybrid method for bearing fault diagnosis based on CEEMDAN and ACPSO-BP neural network

Shanshan Song,Shuqing Zhang,Wei Dong,Xiaowen Zhang,Wei Ma 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.11

As an important part of rotating machinery, the failure of bearings will cause serious vibration and noise of mechanical equipment, which will affect the normal operation of the equipment and even lead to economic losses and casualties. To accurately and efficiently diagnose the working state and fault category of bearings, a new fault diagnosis method for rolling bearings based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), weighted permutation entropy (WPE) and adaptive chaotic particle swarm optimization back propagation (BP) neural network (ACPSO-BP) was proposed. CEEMDAN and WPE were used to extract fault features and optimize the feature vector by mean domain specification principles. ACPSO optimizes the convergence speed and recognition accuracy of the BP neural network by introducing an adaptive tent mapping interval. The experimental results on bearing data from Western Reserve University and actual wind turbine data show that the proposed diagnosis method can achieve high fault recognition accuracy with a small number of training samples.

Opinion Objects Identification and Sentiment Analysis

Ouyang Chunping,Liu Yongbin,Zhang Shuqing,Yang Xiaohua 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6

Sentiment analysis of reviews has been the focus of recent research, which also has been attempted in different domains such as product reviews, movie reviews, and customer feedback reviews. Most sentiment analysis of reviews focused on extracting overall evaluation for a single product which makes difficult for a customer to know all the features of product and make a decision. Thus, mining this data, identifying the user opinions about different features and classify them is an important task. This paper is devoted to identify opinion object from short comments, and analyze sentiment of product based on features-level. CRFs model based on word embedding feature is adopted by identifying opinion object, which obtains a satisfied results. In addition, calculate rules based on syntax parsing are proposed to accomplish features-level sentiment analysis which extracts user’s opinion on many aspects. Experimental results using short comments of movies show the effectiveness of our approach.

3D PIC Method for Modeling and Simulating the Beam Transport System of a Linear Induction Accelerator

Yang Changhong,Meng Lin,Liu Da Gang,Zhang Kaizhi,Liao Shuqing,Dai Zhiyong 한국물리학회 2011 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.59 No.61

Firstly, the method of combining the finite difference time domain with particle-in-cell code is used for simulating and modeling a accelerated section of the LIA, and on this basis, the numerical simulation of the solenoid coils and steering coils in accelerated section are completed by writing independent calculation module of magnetic components ,then on the platform of the MPICH2 message passing system, a proper method for parallel computing of more accelerate sections is provided and solved LIA's great scale problems. Finally, the 18 accelerated sections was simulated by the software, and compared with the envelope diagram of documents, which proved the correctness of the method used.

Application of Neural Network Based on Real-Time Recursive Learning and Kalman Filter in Flight Data Identification

Yao Li,Haiqing Si,Yitong Zong,Xiaojun Wu,Peihong Zhang,Hongyin Jia,Shuqing Xu,Dayong Tang 한국항공우주학회 2021 International Journal of Aeronautical and Space Sc Vol.22 No.6

The process of obtaining flight data from flight test is complex and costly, which makes it difficult to identify aerodynamic parameters. Therefore, Cessna172 flight simulator was used for flight data extraction, which ensures the convenience, efficiency and economy of the test. To obtain aerodynamic model, based on the idea of machine learning, a recurrent neural network was used to process multi-dimensional nonlinear flight test data, and a real-time recursive learning algorithm was proved to be suitable for dynamic training. Due to the large amount of state parameter data generated by aircraft, which will cause the real-time recursive learning algorithm to train slowly. So, Kalman filter algorithm was introduced for system identification. Considering validity analysis, the comparative verification method was used to verify system identification model. Results show that the aircraft aerodynamic and aerodynamic moment models have good applicability and can be popularized and applied.