Visible to the public Biblio

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2022-03-10
Qin, Shuangling, Xu, Chaozhi, Zhang, Fang, Jiang, Tao, Ge, Wei, Li, Jihong.  2021.  Research on Application of Chinese Natural Language Processing in Constructing Knowledge Graph of Chronic Diseases. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). :271—274.
Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.
2022-03-01
Wang, Weidong, Zheng, Yufu, Bao, Yeling, Shui, Shengkun, Jiang, Tao.  2021.  Modulated Signal Recognition Based on Feature-Multiplexed Convolutional Neural Networks. 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). 2:621–624.
Modulated signal identification plays a crucial role in both military reconnaissance and civilian signal regulation. Traditionally, modulated signal identification is based on high-order statistics, but this approach has many drawbacks. With the development of deep learning, its advantages are fully exploited by combining it with modulated signals to avoid the complex process of computing a priori knowledge while having good fault tolerance. In this paper, ten digital modulated signals are classified and recognized, and improvements are made on the basis of convolutional neural networks, using feature reuse to increase the depth of the convolutional layer and extract signal features with better results. After experimental analysis, the recognition accuracy increases with the rise of the signal-to-noise ratio, and can reach 90% and above when the signal-to-noise ratio is 30dB.
2020-07-30
Jiang, Tao, Hu, Shuijing.  2019.  Intellectual Property Protection for AI-Related Inventions in Japan. 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). :286—289.
To increase the possibility of patent entitled of artificial intelligence related inventions at the Japanese patent office, this paper analyzes the Japanese patent act and patent examination guidelines. The approach for assessing whether a computer related invention belongs to a eligible subject-matter includes two steps. The first step is whether a computer related invention meets the definition of an "invention" that is "creation of a technical idea utilizing the laws of nature" . The second step is whether a computer related invention meets "idea based on the standpoint of software" . From the perspective of patent analysis, Japan's artificial intelligence technology is leading the world, second only to the United States. In this field, the Japanese patent office is one of the most important intellectual property offices, and its legislation and practice of patent eligibility review for artificial intelligence related inventions have an important impact on the world.