MFNN: Position and Attitude Measurement Neural Network Based on Multi-Feature Fusion

Man, Jiabao; Li, Gen; Meng, Xiaofeng; Lei, Yutian; Lu, Wen; Gao, Xinbo

doi:10.1109/access.2019.2933878

Cited by 4 publications

(2 citation statements)

References 26 publications

(23 reference statements)

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“…e economic measurement algorithm of strategic emerging industries based on multifeatures is, as a result, an algorithm that is based on the fusion of multiple features. Because different angles are represented by a variety of features, we should be competent to accomplish a higher level of precision if we use this algorithm rather than an alternative feature algorithm [27]. As the angle grows, naturally, so do the benefits to which we can look forward.…”

Section: Research Status and Backgroundmentioning

confidence: 99%

Economic Efficiency Measurement Algorithm of Strategic Emerging Industries Based on Multifeature Fusion

Jing

2022

Mobile Information Systems

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The steady and healthy development of strategic emerging industries (SEI) can enable the Republic of China to smoothly transform its economic progression mode and realize the upgradation and optimization of its industrial organization. In this way, China can take the initiative in the process of globalization. The strategic emerging industry economy has long argued for the influence of resilient firm dynamic aptitudes on innovative product growth. This study takes strategic emerging industrial economies as an important part and establishes a comprehensive evaluation index system for economic efficiency of SEI in the Republic of China from the perception of interorganizational interactions and entrepreneurial coordination features. We use the Random Forest (RF) algorithm to perform multifeature fusion and establish an economic efficiency measurement algorithm for SET through survey data from several Chinese companies. We find, in addition, that entrepreneurial coordination affects the firms’ inclination and aptitude to exploit relationship profits and by this means significantly strengthening the effects of vertical interactions however weakening the effects of horizontal interactions. When compared with the BP and MLP approaches, the suggested approach has achieved 86.98% accuracy, while the other has 86.02% and 85.75%, respectively.

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Section: Research Status and Backgroundmentioning

confidence: 99%

Economic Efficiency Measurement Algorithm of Strategic Emerging Industries Based on Multifeature Fusion

Jing

2022

Mobile Information Systems

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“…However, the high cost of navigation systems will hamper the spread of mobile robots in practical applications. Vision-based related research has become a hotspot in recent years with the characteristics of low-cost and the ability to obtain rich environmental information [9] , [10] , [11] , [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

Scene perception based visual navigation of mobile robot in indoor environment

2021

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Only vision-based navigation is the key of cost reduction and widespread application of indoor mobile robot. Consider the unpredictable nature of artificial environments, deep learning techniques can be used to perform navigation with its strong ability to abstract image features. In this paper, we proposed a low-cost way of only vision-based perception to realize indoor mobile robot navigation, converting the problem of visual navigation to scene classification. Existing related research based on deep scene classification network has lower accuracy and brings more computational burden. Additionally, the navigation system has not yet been fully assessed in the previous work. Therefore, we designed a shallow convolutional neural network (CNN) with higher scene classification accuracy and efficiency to process images captured by a monocular camera. Besides, we proposed an adaptive weighted control (AWC) algorithm and combined with regular control (RC) to improve the robot’s motion performance. We demonstrated the capability and robustness of the proposed navigation method by performing extensive experiments in both static and dynamic unknown environments. The qualitative and quantitative results showed that the system performs better compared to previous related work in unknown environments.

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