DEAL: Differentially Private Auction for Blockchain based Microgrids Energy Trading

Wang

et al. 2020

Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

Section: Discussionmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Wang

et al. 2020

“…The actual street view picture (Figure 7) demonstrates that the traffic in this area is inconvenient, some cultural landscapes are not well protected, and the visual landscape is also blocked by high‐rise buildings 28,29 . Based on the actual scene and the evolution of cell constraints, it can be found that the cell automaton model has practical auxiliary guidance significance, and the future development of urban planning has huge potential 30,31 …”

Section: Ca Model Simulation and Evolutionmentioning

confidence: 99%

Construction of an intelligent cellular automata model for historic landscape islands in Shanghai and fusion simulation

Wei

Zhang

2020

Summary Historic landscape islands are a unique landscape phenomenon in the urbanization and modernization process in Shanghai. Shanghai has been fragmented and isolated at the landscape space level because of islanding, and its historic and cultural values have been broken and weakened. Therefore, it is urgent to implement fusion protection planning. Considering the islanding of urban historic landscape, cellular automata (CA) and neural network intelligent method were used to establish the intelligent CA model of Shanghai historic landscape islands and carry out computer simulation, expecting to provide a basis for studying urban historic landscapes in China via the intelligent evaluation of urban historic landscape islands and the establishment of an intelligent planning model. A technical path to alleviate landscape fragmentation and cultural value fracture of isolated islands was explored, so as to provide the latest theoretical basis for decision makers to manage historic landscape islands in Shanghai and offer reference for more scientific, intelligent, and efficient decision‐making.

“…With the coming of the 21st century, people have entered the information age, and computers have been more widely used in various fields including Cyber Physical Systems, 6 Public Opinion Polarization Process, 7 Blockchain, 8,9 Emergency Logistics Network 10 and multiobjective optimization problem, 11,12 and so on. Meanwhile, artificial algorithm has been widely used to all kind of fields including network structure optimization, 13,14 Malicious code detection, 15 Green Coal Production Problem, 16 mixed traffic flow model, 17 Road Network, 18 and so on.…”

Section: Introductionmentioning

confidence: 99%

Welding flame detection based on color recognition and progressive probabilistic Hough transform

Chen

Guo

et al. 2020

Summary Welders are widely used in machinery manufacturing and machining industry. In the process of electric welding, accidents are often caused by some unsafe behaviors. This paper focuses on the intelligent recognition method of welding flame, which can reduce the accidents of welding flame to a certain extent. Due to the difficulties in observing workers on job sites, developing an algorithm for intelligent identification of unsafe factors is significant. Firstly, Hue,Saturation and Value (HSV) color space with strong robustness is selected to segment the mark image with threshold value, and the region of interest in which the welding fame is located is segment; secondly, the Progressive Probabilistic Hough transform is used to detect the region of interest, judge the number of existing lines, and finally determine whether there is welding flame in this region. In this way, the welding flame can be better detected, which can reduce the workload of managers and is beneficial to the supervision of the management personnel.