The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approac...The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approach,developed by the author since the 1980s,is based on particular and general comparative concepts-concepts of practical mind and categories of pure mind.Therefore,the key element of the concept is the category of"particular and general",which fundamentally differs from the traditional category of"part and whole".This allows for the description of both structural and functional aspects of complex systems not only at the interdisciplinary but also at the transdisciplinary level.The primary categories of thought-Identity,Difference,Correlated,Opposite,and others-are regarded as universal notions that connect levels of reality and ensure the integration of individual sciences.Unlike contemporary transdisciplinary concepts based on Basarab Nicolescu's logic of the included middle and Edgar Morin's dialogics,the author's theory is built on the ultimate general Hegelian notion of"concrete identity"and its differentiation into a multitude of"concrete differences"-comparative concepts.As a result,a unique philosophical language has been developed,presented within the framework of the Philosophical Matrix as a system of categories of pure mind capable of describing the dynamics and wholeness of complex processes at the transdisciplinary level.The article is intended for researchers interested in the philosophical foundations of transdisciplinarity,the theory of complexity,and the development of universal categories of thought.展开更多
In addition to its relevance to the history of education, the study of changes in curriculum design also provides insights into changes in educational attitudes. This paper examines the historical evolution of the Cha...In addition to its relevance to the history of education, the study of changes in curriculum design also provides insights into changes in educational attitudes. This paper examines the historical evolution of the Changshi curriculum in China's mainland, explains the concept of Changshi and its different understandings in Changshi or general knowledge courses,and then applies the concept to the narration and classification of history. It also includes a brief discussion on related issues.展开更多
Deep learning based transformer protection has attracted increasing attention.However,its poor generalization abilities hinder the application of deep learning in the power system owing to the limited training samples...Deep learning based transformer protection has attracted increasing attention.However,its poor generalization abilities hinder the application of deep learning in the power system owing to the limited training samples.In order to improve its generalization abilities,this paper proposes a knowledge-based convolutional neural network(CNN)for the transformer protection.In general,the power experts can reliably discriminate between faulty transformers and healthy transformers only through the unsaturated parts of equivalent magnetization curve(voltage of magnetizing branch-differential current curve)but deep learning intends to focus on the combined features of saturated and unsaturated parts.Inspired by the identification process of power experts,CNN adopted a specially designed loss function in this paper which is used to identify the running states of power transformers.Specifically,the presented Restrictive Weight Sparsity substitutes a special regularization term for the common LI regularization.The presented Adaptive Sample Weight Adjustment endows the softmax loss of each sample with the optimizable weight the softmax loss of each sample with the optimizable weights to increase the impact of more-difficult-to-identify cases on the training process.With the modified loss function,the knowledge is abstractly introduced into the training process of CNN so as to successfully imitate the identification process of power experts.Accordingly,the proposed knowledge-based CNN will pay more attention to the unsaturated parts of equivalent magnetization curve even if only limited samples are included in the training process.The results of simulations and dynamic model experiments reveal that the knowledge-based CNN exhibits an improved generalization ability and the knowledge-based deep learning algorithm is a promising research direction.展开更多
文摘The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approach,developed by the author since the 1980s,is based on particular and general comparative concepts-concepts of practical mind and categories of pure mind.Therefore,the key element of the concept is the category of"particular and general",which fundamentally differs from the traditional category of"part and whole".This allows for the description of both structural and functional aspects of complex systems not only at the interdisciplinary but also at the transdisciplinary level.The primary categories of thought-Identity,Difference,Correlated,Opposite,and others-are regarded as universal notions that connect levels of reality and ensure the integration of individual sciences.Unlike contemporary transdisciplinary concepts based on Basarab Nicolescu's logic of the included middle and Edgar Morin's dialogics,the author's theory is built on the ultimate general Hegelian notion of"concrete identity"and its differentiation into a multitude of"concrete differences"-comparative concepts.As a result,a unique philosophical language has been developed,presented within the framework of the Philosophical Matrix as a system of categories of pure mind capable of describing the dynamics and wholeness of complex processes at the transdisciplinary level.The article is intended for researchers interested in the philosophical foundations of transdisciplinarity,the theory of complexity,and the development of universal categories of thought.
基金funded by the Research on the Education for Scientific Competencies sub-project of the key project Research on the Connection Mechanism and Curriculum System of the Comprehensive Competencies Training of Ordinary High School Students launched by the National Center for School Curriculum and Textbook Development of the Ministry of Education
文摘In addition to its relevance to the history of education, the study of changes in curriculum design also provides insights into changes in educational attitudes. This paper examines the historical evolution of the Changshi curriculum in China's mainland, explains the concept of Changshi and its different understandings in Changshi or general knowledge courses,and then applies the concept to the narration and classification of history. It also includes a brief discussion on related issues.
基金supported in part by the National Natural Science Foundation of China(No.51877167).
文摘Deep learning based transformer protection has attracted increasing attention.However,its poor generalization abilities hinder the application of deep learning in the power system owing to the limited training samples.In order to improve its generalization abilities,this paper proposes a knowledge-based convolutional neural network(CNN)for the transformer protection.In general,the power experts can reliably discriminate between faulty transformers and healthy transformers only through the unsaturated parts of equivalent magnetization curve(voltage of magnetizing branch-differential current curve)but deep learning intends to focus on the combined features of saturated and unsaturated parts.Inspired by the identification process of power experts,CNN adopted a specially designed loss function in this paper which is used to identify the running states of power transformers.Specifically,the presented Restrictive Weight Sparsity substitutes a special regularization term for the common LI regularization.The presented Adaptive Sample Weight Adjustment endows the softmax loss of each sample with the optimizable weight the softmax loss of each sample with the optimizable weights to increase the impact of more-difficult-to-identify cases on the training process.With the modified loss function,the knowledge is abstractly introduced into the training process of CNN so as to successfully imitate the identification process of power experts.Accordingly,the proposed knowledge-based CNN will pay more attention to the unsaturated parts of equivalent magnetization curve even if only limited samples are included in the training process.The results of simulations and dynamic model experiments reveal that the knowledge-based CNN exhibits an improved generalization ability and the knowledge-based deep learning algorithm is a promising research direction.
