作者机构：
School of Furniture and Art Design, Central South University of Forestry and TechnologySchool of Mechanical Engineering, Dongguan University of Technology
会议名称：
2019 International Conference on Educational Reform,Management Science and Sociology（ERMSS 2019）
会议时间：
2019-08-24
会议地点：
中国陕西西安
会议论文集名称：
Proceedings of 2019 International Conference on Educational Reform,Management Science and Sociology(ERMSS 2019)
关键词：
Hongmu cultural creative;Numerical Control
摘要：
This paper briefly analyses the status of hongmu cultural creative product design and the manufacture advantages of numerical control technology for it. Then the processing of numerical control technology applied is introduced through three specific design cases presented. This article tries to explore that the further improvement and development of hongmu cultural creative product design can be promoted by numerical control technology.
摘要：
Recent years has witnessed a boom in fog-assisted crowdsensing, which exploits powerful sensing capabilities of various mobile devices or vehicles distributed in large-scale areas to efficiently gather information and make better decisions. However, the fog-assisted crowdsensing system is totally open, which provides the opportunity for malicious individuals or organizations to launch different attacks. In order to cope with the security threats from participants, a blockchain-based crowdsensing framework is proposed, which helps check the authentication of submitted sensor data and resists record tempering. Moreover, a bitcoin-based reward delivery scheme is designed to prevent requesters from denying payments. The sensing capability differences between users are considered in our design. Through security analysis and simulations evaluation, the performance superiority of the proposed framework and reward delivery scheme is demonstrated, in terms of malicious behaviour detection, user utility and sensor data quality.
会议名称：
Photonics and Electromagnetics Research Symposium - Fall (PIERS - Fall)
会议时间：
DEC 17-20, 2019
会议地点：
Xiamen, PEOPLES R CHINA
会议主办单位：
[Zhai, Shengqing;Yang, Yusi;Lin, Lan] Tongji Univ, Dept Elect Sci & Technol, Shanghai, Peoples R China.^[Ou, Wenbo] Cent South Univ Forestry & Technol, Dept Automat, Changsha, Peoples R China.
会议论文集名称：
Progress in Electromagnetics Research Symposium
摘要：
There are many kinds of hepatic lesions which threaten human healthy by high incidence. Analyzing CT images is an effective method to evaluate the type of hepatic lesions. The original evaluation method is that medical personnel assessment by images, but abdominal CT images are complex, and the accuracy of the discrimination depends strongly on the subjective factors, knowledge level and experience of medical personnel. With the development of artificial intelligence technology, the hepatic lesions recognition technology based on deep visual feature learning can achieve a higher accuracy. While reducing the workload for doctors, this technology can assist doctors to diagnose the disease, so as to obtain more objective and accurate diagnosis results. The database established in this paper mainly includes three kinds of CT images: primary liver cancer, cirrhosis and normal liver. After the database pretreatment with image de-noise algorithms based on median filter and picture enhancement algorithms based on histogram equalization, the convolution neural network CaffeNet was taken as the main framework, and a large number of labeled liver CT images were used to train the network in the experimental process. Meanwhile, the network parameters were continuously updated according to the cost function. Traditional hepatic lesion recognition technology only depends on the surface features from the image, such as gray scale, statistical structure and texture. Deep learning, a tool with high precision and robustness, could be used to mine the hidden features in the huge database. Through testing the trained deep learning model, the average recognition accuracy of 96.67% and the average single sample recognition time of 1.28 seconds could be achieved perfectly. Therefore, there is no doubt that hepatic lesion recognition technology based on deep visual feature learning will greatly liberate doctors' physical strength and promote the development of pathology in the near future.
作者机构：
[Zheng, Xuan] Central South University Of Forestry and Technology, Changsha;410000, China;[Gao, Qin] Hubei University of Education, Wuhan;430000, China;[Zheng, Xuan] 410000, China
会议名称：
4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019
