Multi-system fusion based on deep neural network and cloud edge computing and its application in intelligent manufacturing

Fan, Linyuan; Zhang, Liang

doi:10.1007/s00521-021-05735-y

Cited by 17 publications

(12 citation statements)

References 25 publications

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“…In addition, it is impossible to know in advance when the user will speak to the robot, whether he is talking to the robot, and the duration of the speech. Therefore, the problems of noise interference, when to collect the user's voice information, and the collection time must be solved first [ 18 ]. These problems can be solved by voice activity detection (voice activity detection, VAD) technology.…”

Section: Methodsmentioning

confidence: 99%

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

Yan

Jun-hui

2022

Computational Intelligence and Neuroscience

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In order to cope with the challenges that operators face after the impact of diversified social channels in the field of interactive services, we actively build a new generation of intelligent interactive systems based on artificial intelligence technology, a semantic understanding, and intention recognition, covering rich media content, omnichannel coverage, high-frequency knowledge updates, consistent service response, high-quality, and low-cost intelligent interactive solutions is proposed. The solution provides overall business modeling for scenario design and a scenario-based knowledge expression system, with the function of fragmented knowledge processing. With complete text and voice information, combined with pictures, text, audio, video, and other multimedia, we intelligently interact with users, allowing users to obtain required information and solve problems in a pleasant and relaxing interaction. Therefore, the research and exploration of the intelligent interactive system architecture based on artificial intelligence is a useful practice and strong support for operators to redefine the connotation and elements of “smart service” in the process of building “smart operation.” Through repeated tests, it can be seen that the language similarity has reached 0.75549, which is very close to 1.0000. It can be seen that the design of this platform has been successful.

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Section: Methodsmentioning

confidence: 99%

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

Yan

Jun-hui

2022

Computational Intelligence and Neuroscience

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“…In the end, their work has improved the manufacturing industry by nearly 15%. Although his research is very forward-looking, it is still difficult to apply in practice [3,4].…”

Section: Significance Interactive Installation Art Has Been Formedmentioning

confidence: 99%

The Intelligent Integration of Interactive Installation Art Based on Artificial Intelligence and Wireless Network Communication

Gao

Lin

2021

Wireless Communications and Mobile Computing

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With the development of technology and the times, the development of new media technology and interactive installation art has slowly entered the vision of our audience. It is simply “silent art.” The public no longer “retires” like the traditional one, but participates in it and swims with the artists in the world of art. This article is aimed at studying the application of artificial intelligence and wireless network communication to the application of interactive installation art. Through the optimization of various communication equipment and the continuous advancement of various algorithms, we can strengthen the communication and connection between our interactive installation art. This article proposes that with the addition of artificial intelligence and wireless network communication, the interaction between artists and audiences may be more fun, so that we can be more colorful in our lives. The experimental results in this article show that when performing wireless network communication, the communication delay rate of the intelligent algorithm with artificial intelligence is much lower than that of the one without it, which shows that they can better transmit information to the control end. When affected by the outside world, the bit error rate of wireless network communication will increase, however, the artificial intelligence algorithm is added to his impact range, and his bit error rate increase is obviously not so high. In the process of wireless network communication, the improved algorithm is definitely better than the nonimproved algorithm in terms of energy consumption, communication delay, and bit error rate. Through the enhancement of signals and the selection of materials for communication equipment, these are all in continuous progress, and in this respect, are in continuous exploration. Compared with other algorithms, the ml algorithm has improved positioning accuracy by about 70%, 65%, and 30%. Increasing the number of nodes in the transmission signal can greatly reduce the number of hops between nodes, correspondingly reducing the hop distance error, correspondingly reducing the distance estimation error, and improving the positioning accuracy. It can solve the technical barriers of interactive installation art faster.

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“…Ramírez et al developed a new fuzzy controller and applied it to the problem of nickel recovery, minimizing fuel consumption and environmental pollution [ 38 ]. Fan and Zhang set up experiments to conduct multisystem fusion of neural networks, combined with big data and artificial intelligence, to verify the efficient operation of neural networks in multisystem fusion [ 39 ]. Using the method of mathematical analysis and big data fitting, the collected data are classified to improve the comprehensive work efficiency of the system.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

Tang

2022

Computational Intelligence and Neuroscience

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With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today’s manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

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Multi-system fusion based on deep neural network and cloud edge computing and its application in intelligent manufacturing

Cited by 17 publications

References 25 publications

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

The Intelligent Integration of Interactive Installation Art Based on Artificial Intelligence and Wireless Network Communication

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

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