Study on vibration and noise influence for optimization of garden mower

Chen, Yimin; Dong, Li; Cengiz, Korhan; Sharma, Amit

doi:10.1515/nleng-2021-0034

Cited by 29 publications

(19 citation statements)

References 24 publications

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“…Different layers will extract different features. In order to ensure the optimal classification performance of the hidden layer, the training data and test data in the experimental data are divided according to the ratio of 4 : 1 and input into the convolutional neural network for solution optimization [20].…”

Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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“…e statistics of the three datasets are listed in Table 1. PPI is the biological protein network, the network contains text information and node category information, and experiments use the associated user mining algorithm to perform protein retrieval tasks in protein networks [16]. Flickr is a well-known photosharing site whose users form relationships in social networks, and the website provides the user's tag information.…”

Section: Results' Analysismentioning

confidence: 99%

Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Wang¹,

Zhang²,

Zhang³

2022

Security and Communication Networks

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To explore how related users can optimize the network mining algorithm, the author proposes a related user mining algorithm based on the fusion of user attributes and user relationships. This method recommends key technical problems and solutions based on information represented by multi-information fusion and explores research on associated user network data mining algorithms. Research has shown that the associated user network data mining algorithm based on multi-information fusion is 65% higher than previous methods. AUMA-MRL has good performance under different network overlaps. Also, since the node embedding of the AUMA-MRL algorithm is obtained by neighborhood sampling, for new nodes in the network, the algorithm can quickly obtain the new node embedding, as well as the similarity vector between the new node and the rest of the nodes in the network, therefore, the associated users of newly added nodes in the network can be quickly mined, and the robustness of the mining algorithm of associated users in the network is enhanced. Compared with the existing classical algorithms, the recall rate of the proposed algorithm is increased by 17.5% on average, which can effectively mine the associated users in the network.

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“…In this process, due to the smooth start and stepless speed change characteristics of the frequency converter, the failure risk of the makeup pump and the pressure impact on the pipe network system are reduced. Figure 4 shows the automatic control of the water supply system of a heat supply network [17]. After the control system is started, the makeup pump is in manual mode by default.…”

Section: Adjustment Of a Frequency Converter In The Automaticmentioning

confidence: 99%

Application of a Frequency Converter in Electric Automatic Control of Central Heating

Yin¹,

Zhang²

2022

Journal of Control Science and Engineering

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In order to improve the efficiency of a central heating system’s control system, this paper puts forward the application scheme of a frequency converter in central heating electrical automation control. In the central heating electrical automation control system, the frequency converter plays an important role. It can regulate and control the relevant equipment, realize the electrical automation of the heating system under the action of the frequency converter AC motor, and effectively adjust the performance and speed, so as to achieve the purpose of energy saving. The experimental results show that the temperature control strategy using a frequency converter automatic compensation method reduces the energy waste caused by manual regulation to a certain extent. When the speed of a water pump motor decreases to 4/5 of the original, the power consumption will decrease to about 52% of the original and save about 48% of the power; When the speed drops by 3/5, the power consumption is about 22% of the original, saving about 78% of the power. Conclusion. This technology can fully realize the demand of electrical automatic control of the heating system, greatly improve the stability and safety of the heating system, have good energy-saving effect, and greatly save energy consumption. The frequency converter has a remarkable effect in ensuring heating quality and improving heating efficiency in the heating industry.

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Study on vibration and noise influence for optimization of garden mower

Cited by 29 publications

References 24 publications

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Application of a Frequency Converter in Electric Automatic Control of Central Heating

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