Multi-sensor state estimation over lossy channels using coded measurements

Sui, Tianju; Marelli, Damián; Sun, Xi‐Ming; Fu, Minyue

doi:10.1016/j.automatica.2019.108561

Cited by 86 publications

(47 citation statements)

References 31 publications

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“…Multiobjective Ant Colony Secure Routing Model. Traditional optimization algorithms by themselves do not solve multiobjective optimization problems [18][19][20][21]. Multiple objective functions can be transformed into but one objective problem by the form of the weighting method or constraint method, which is handled by the traditional solution method.…”

Section: Optimization Strategy Of Ant Colonymentioning

confidence: 99%

Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

Wang

2021

Journal of Sensors

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As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

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Section: Optimization Strategy Of Ant Colonymentioning

confidence: 99%

Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

Wang

2021

Journal of Sensors

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“…This technology can effectively use the Internet's big data foundation and implement parallel distributed image processing training on computer hardware. The limitation of the traditional neural network method for image data processing is that it generally adopts the method of using the CPU to calculate on the computer hardware [27,28].…”

Section: Multiview Recognitionmentioning

confidence: 99%

RPA and Artificial Intelligence in Budget Management Based on Multiperspective Recognition Based on Network Communication Integration

Luo¹,

Luo

2021

Wireless Communications and Mobile Computing

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Nowadays, RPA robots are increasingly used in daily office tasks such as finance and human resources. They play an increasingly important role in realizing office automation, which can improve work efficiency and reduce labor costs. In order to improve the efficiency of budget management and save human resources, this paper conducts related research based on the multiview recognition technology of network communication integration, combined with RPA in artificial intelligence technology. In the method part, this article introduces the mode of network communication integration and the principles that should be followed, as well as the related processes of RPA. In the algorithm, this paper introduces an integrated algorithm based on ELM. In the experimental part, this article predicts the performance of each model, compares identification functions with different signal-to-voice signals, and compares timing functions on different signal-to-voice signals, periodic transmission mode indicators, recognition rates of different kernel functions, and comparison of average recognition rates and multiview recognition rate comprehensive analysis of these multiple aspects. Under the same conditions, the recognition rate of some angles is lower than other angles; 0 degrees, 18 degrees, 126 degrees, and 180 degrees are slightly lower than other angles, which will affect the average recognition rate of the entire recognition. But for multiview gait features, considering the influence of each angle on the recognition rate, the characteristics of each angle are merged together, so that the recognition rate is significantly higher than the average recognition rate of 11 angles. It can be seen that multiview recognition based on network communication integration does have obvious effects on RPA and artificial intelligence in budget management and can improve the efficiency of budget management. The multiperspective recognition technology designed in this study can realize modernization and digitization in budget management.

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“…Due to the limitations of professional knowledge, farmers often cannot judge or misjudge some crop diseases [4]. In addition, because some crop diseases are not obvious or the symptoms are similar at the beginning of the disease, even professional technicians are difficult to identify them accurately [5]. When the symptoms are clearly identifiable, they miss the best prevention time, which makes it difficult to obtain accurate and timely control of crop diseases.…”

Section: Introductionmentioning

confidence: 99%

Research Progress of Crop Disease Image Recognition Based on Wireless Network Communication and Deep Learning

2021

Wireless Communications and Mobile Computing

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The traditional digital image processing technology has its limitations. It requires manual design features, which consumes manpower and material resources, and identifies crops with a single type, and the results are bad. Therefore, to find an efficient and fast real-time disease image recognition method is very meaningful. Deep learning is a machine learning algorithm that can automatically learn representative features to achieve better results in areas of image recognition. Therefore, the purpose of this paper is to use deep learning methods to identify crop pests and diseases and to find efficient and fast real-time image recognition methods of disease. Deep learning is a newly developed discipline in recent years. Its purpose is to study how to actively obtain a variety of feature representation methods from data samples and rely on data-driven methods, a series of nonlinear transformations are applied to finally collect the original data from specific to abstract, from general to specified semantics, and from low-level to high-level characteristic forms. This paper analyzes the classical and the latest neural network structure based on the theory of deep learning. For the problem that the network based on natural image classification is not suitable for crop pest and disease identification tasks, this paper has improved the network structure that can take care of both recognition speed and recognition accuracy. We discussed the influence of the crop pest and disease feature extraction layer on recognition performance. Finally, we used the inner layer as the main structure to be the pest and disease feature extraction layer by comparing the advantages and disadvantages of the inner and global average pooling layers. We analyze various loss functions such as Softmax Loss, Center Loss, and Angular Softmax Loss for pest identification. In view of the shortcomings of difficulty in loss function training, convergence, and operation, making the distance between pests and diseases smaller and the distance between classes more greater improved the loss function and introduced techniques such as feature normalization and weight normalization. The experimental results show that the method can effectively enhance the characteristic expression ability of pests and diseases and thus improve the recognition rate of pests and diseases. Moreover, the method makes the pest identification network training simpler and can improve the pest and disease recognition rate better.

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Multi-sensor state estimation over lossy channels using coded measurements

Cited by 86 publications

References 31 publications

Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

RPA and Artificial Intelligence in Budget Management Based on Multiperspective Recognition Based on Network Communication Integration

Research Progress of Crop Disease Image Recognition Based on Wireless Network Communication and Deep Learning

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