By using positive operator-valued measure, I present a new tripartite scheme for realizing quantum state sharing of an arbitrary unknown single-qubit pure state in either distant agent's place. In this scheme, the sender averagely partitions its unknown single-qubit state with a Greenberger-Horne-Zeilinger (GHZ)-basis measurement. Then by performing a proper positive operator-valued measure, one agent can recover the splitter's state in a probabilistic manner provided that he/she gets another one's help. Moreover, the total success probability of the scheme is also worked out. Finally, I concisely generalize the tripartite QSTS scheme to a multiparty case.

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With the homogenization of product function and performance, the design technology for product appearance quality has been increasingly valued by academia and industry and has become an effective technical way to meet the continuously growing diversified and personalized needs of consumers. The appearance quality attribute of a product can be characterized or described by its appearance image. Data-driven product appearance image design is based on the quantitative data of product appearance and consumer emotional needs and completes the product appearance through computer-aided design technology and intelligent algorithms. Design innovation can help companies quickly respond to consumers’ emotional needs and effectively improve design quality and product competitiveness. When visual objects are disturbed in complex scenes, the issues such as how the human brain coordinates multisensory information processing and what neural processing mechanisms follow are still unclear. In this paper, a visual object recognition experiment in a complex scene was designed and the brain activation signals of three modalities of noise, added audio-visual (AVd), single visual noise and noise (Vd), and single-audio (A), were recorded. The properties and neural processing mechanisms of multisensory modulation of auditory stimuli during noisy image recognition were explored. Using the conjunction method combined with the classic “max criterion” rule, it was found that only when a certain amount of noise was added to the visual stimulus, the integration area changed. The product appearance has a decisive influence on the user’s product perceptual attribute preference and greatly affects the consumer’s satisfaction. The importance of product appearance image design is increasingly prominent. In addition, pattern analysis of brain activation signals confirmed that semantically consistent sounds can facilitate the recognition of noisy images and this facilitation shows a certain category selectivity when subdivided into categories. Using the analysis method of functional connectivity, a functional connectivity network containing nodes at different integration levels was constructed to explore the overall characteristics and processing patterns of the multisensory network. Through the analysis of the network connection relationship, it is found that the prefrontal cortex, STS, and lateral occipital lobe are the nodes with more aggregation in the network, and their functions are similar to the hub in the network. The brain functional network was constructed, and functional connectivity was used to explore the connection characteristics of the network and the multisensory modulation mechanism between different processing levels of the brain.

Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group.

Put forward a kind of association rules mining method based on fuzzy genetic algorithm, this approach by building a mining model, the association rules and fuzzy genetic algorithm fuses in together, and then given to the fitness function of the mining space, and uses threshold to limit the fuzzy genetic algorithm will cross distribution and compile the fitness function, the improved method excavation stability is strong, the mining accuracy is high. The clustering analysis method of multidimensional fuzzy genetic algorithm mapping association network is studied, and the multidimensional module layout target is analyzed by using fuzzy hierarchical analysis technology and improved genetic algorithm combined with the clustering target of each angle, and the module division of each angle is realized. The main structure of the product is constructed with process model as the integration framework, style as the organization form, and feature list as the expression mechanism. The product characteristics based on fuzzy genetic algorithm are studied, the main structure configuration design process model mapping relation analysis, combined with the main structure of the joint model, together to achieve the fuzzy genetic algorithm (GA) variant design of fine-grained axiomatic mode, based on the associated network building and integration of new product design process, product structure of multidimensional optimization problem is solved.