Landscape Design Talents' Knowledge and Ability Structure Based on Industry Demands

Knowledge transference and ability training are 2 standing points and starting points for the industry demands-oriented talent cultivation of modern higher education.In terms of landscape design knowledge,the landscape design industry requires 3 major knowledge modules,namely basic knowledge and theories,professional knowledge,and relevant knowledge;in terms of ability,it also requires 3 major ability modules,namely computer-aided design(CAD),design performance ability and ability of making conception,and each of them includes many specific knowledge points and ability points.It is important for such teaching processes as real question and proposition teaching,studio teaching and project-led teaching to propose a dual-track talent cultivation scheme focusing on both theoretical knowledge and practical ability according to the needs of landscape design industry.Moreover,it is imperative to arrange relevant courses and curriculum group that reflect knowledge and ability demands,as well as practicing courses or comprehensive design projects based on ability structure.

The Manifestation Range of Innately Good Knowledge and Ability, and the Danger of Separation： On Zhuzi＇s Ques#＇on about Understanding Words

Zhuzi （Zhu Xi）, Zhang Nanxuan and Lu Donglai continued a discussion begun by Hu Wufeng and his disciples on the subject of＂knowing the form of benevolence,＂ and ＂seeking for a true mind in an absent one.＂ One result of their discussion was to make people realize that innately good knowledge and ability are not only manifested in loving one＇s parents and respecting one＇s elders, but also in the simple acts of drinking when thirsty and eating when hungry. This generated the idea of ＂manifestation range of innately good knowledge and ability.＂ However, another conclusion of this discussion claimed that if the desire to drink and eat or the king of Qi＇s grudging an ox are included in this range, there would be a danger of viewing innately good knowledge and ability merely as inborn human nature or instinct. This discussion reveals an unsteady relationship between innately good knowledge and ability and the feeling of commiseration, which are sometimes united and sometimes separate.

Knowledge-based Convolutional Neural Networks for Transformer Protection

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.