Named Entity Recognition (NER) is one of the contents of Knowledge Extraction (KE) that transforms data into knowledge representation. However, Chinese NER faces the problem of lacking clear word boundaries that limit the effectiveness of the KE. Although the flat lattice Transformer (FLAT) framework, which converts lattice structure into a flat structure including a set of spans, can effectively improve this problem and obtain advanced results, there still exist the problems of insensitivity to entity importance weights and insufficient feature learning. This paper proposes a weighted flat lattice Transformer architecture for Chinese NER, namely WFLAT. The WFLAT first adds a weight matrix into self-attention calculation, which can obtain finer-grained partitioning of entities to improve experimental performance, and then adopts a multi-layer Transformer encoder with each layer using a multi-head self-attention mechanism. Extensive experimental results on benchmarks demonstrate that our proposed KE model can obtain state-of-the-art performance for the Chinese NER task.

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

This paper proposes innovative methodologies of building a Cloud Computing Platform to help industrial users to reduce product EMC compliance cost and speed up compliance procedure. Computer Guided EMC Compliance Concept and methodology are introduced with embodiments. A novel Cloud Computing Platform composed of Computing-View-Controller units is presented to guide industrial user effectively modifying product to approach EMC compliance. The Platform is easy to use for industrial users without EMC expertise, with affordable price - pay for the usage time of Cloud Computing. The solution of using a Smart Phone (or any kind of smart device with Web browser) as terminal device working with Platform is introduced to assist industrial users to do EMC tests in workshop or an open site and quickly approach EMC compliance. The successful application of Computer Guided EMC Compliance proposed in this paper will introduce a new way of combining CAD software with Cloud Computing.

Appliance grounding, cooperating with over-current protection device, can prevent the appliance user from electric shock. But in the real world the appliance sometimes is not well-grounded. This paper proposes an innovative technology of "surface leakage protection" (SLP) which can be a good substitute of appliance grounding for electrical safety purpose. A SLP device detects electrical leakage at the surface of an appliance and removes the power supply immediately from appliance in case the leakage occurs, no appliance grounding is required. The principle of SLP is discussed, and the schematic diagram of the SLP is given with embodiment. The neutral line identification of SLP is also discussed to ensure a reliable SLP performance. The study and application embodiment indicate the SLP can effectively protect appliance user from electric shock in case of grounding-fault, and the building cost of a SLP device is similar to a Residual Current Device (RCD), easy to be widely employed.