DeepImpact: a deep learning model for whole body vibration control using impact force monitoring

Ali, Danish; Frimpong, Samuel

doi:10.1007/s00521-020-05218-6

Cited by 13 publications

(5 citation statements)

References 70 publications

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“…Generally speaking, active control is a vibration reduction control technology that uses other external forces to effectively control the vibration reduction of buildings by using different external vibration reduction control forces. Its working principle consists of four factors [20]:…”

Section: Recognition Of Vibration Pattern Of Environmental Protection...mentioning

confidence: 99%

Seismic Performance and Vibration Control of Rapid Construction Environmental Protection Wall Based on Artificial Intelligence

Jie

Zhou

Liu

et al. 2022

International Transactions on Electrical Energy Systems

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With the continuous increase in building types, people's demand for buildings is increasing. In addition to focusing on the work of the building itself, high requirements are also placed on the safety, stability, and durability of the building. Especially in other earthquake-prone areas, it is even more necessary to strengthen seismic isolation research to reduce vibration. Rapid construction of environmental protection walls pays attention to environmental protection and efficiency during construction, which can effectively solve problems in the construction process, but the seismic resistance of rapid construction environmental protection walls is unknown. Therefore, this paper used artificial intelligence technology to study the seismic performance of rapid construction environmental protection walls and analyzed its vibration control. This paper firstly established the seismic performance analysis model of the environmental protection wall, used the finite element method to construct the environmental protection wall model, and then used the artificial intelligence algorithm to analyze the seismic performance of the rapid construction environmental protection wall. The experimental results showed that the peak load of the rapid construction environmental protection wall was about 250 KN, and the seismic performance was good.

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Section: Recognition Of Vibration Pattern Of Environmental Protection...mentioning

confidence: 99%

Seismic Performance and Vibration Control of Rapid Construction Environmental Protection Wall Based on Artificial Intelligence

Jie

Zhou

Liu

et al. 2022

International Transactions on Electrical Energy Systems

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“…However, in the case of froth flotation, the input-output correlations are expected to be complex (presumably highly nonlinear), thus rendering the predictions of ANN models inaccurate-as reflected by the R 2 values shown in Table 4. ANN predictions can be improved by combining the model with generic programming algorithms [19] or using the hybrid neural fuzzy models [17] or may be with using deep learning algorithms, which is an advancement of the ANN algorithm [38,57].…”

Section: Discussion On the Models' Prediction Performancementioning

confidence: 99%

“…A search was conducted to optimize the hyperparameter for the neural network model through trial and error. The goal was to find the optimum number of hidden neurons in an attempt to optimize the single hidden layer neural network architecture [31,38,53]. The optimization results showed that the performance of the neural network improved as the number of neurons in the single hidden layer increased, then the perfor- The training dataset was used to develop a single hidden layer feed-forward neural network model with eight input variables and eight response variables.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Experimental and Machine Learning Studies on Chitosan-Polyacrylamide Copolymers for Selective Separation of Metal Sulfides in the Froth Flotation Process

Monyake

Han

Ali

et al. 2023

Colloids and Interfaces

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The froth flotation process is extensively used for the selective separation of valuable base metal sulfides from uneconomic associated minerals. However, in this complex multiphase process, various parameters need to be optimized to ensure separation selectivity and peak performance. In this study, two machine learning (ML) models, artificial neural network (ANN) and random forests (RF), were used to predict the efficiency of in-house synthesized chitosan-polyacrylamide copolymers (C-PAMs) in the depression of iron sulfide minerals (i.e., pyrite) while valuable base metal sulfides (i.e., galena and chalcopyrite) were floated using nine flotation variables as inputs to the models. The prediction performance of the models was rigorously evaluated based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results showed that the RF model was able to produce high-fidelity predictions of the depression of pyrite once thoroughly trained as compared to ANN. With the RF model, the overall R2 and RMSE values were 0.88 and 4.38 for the training phase, respectively, and R2 of 0.90 and RMSE of 3.78 for the testing phase. As for the ANN, during the training phase, the overall R2 and RMSE were 0.76 and 4.75, respectively, and during the testing phase, the R2 and RMSE were 0.65 and 5.42, respectively. Additionally, fundamental investigations on the surface chemistry of C-PAMs at the mineral–water interface were conducted to give fundamental insights into the behavior of different metal sulfides during the flotation process. C-PAM was found to strongly adsorb on pyrite as compared to galena and chalcopyrite through zeta potential, X-ray photoelectron spectroscopy (XPS), and adsorption density measurements. XPS tests suggested that the adsorption mechanism of C-PAM on pyrite was through chemisorption of the amine and amide groups of the polymer.

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“…The main function of the SCADA system is to collect the information contained in each control point scattered in the field, and then transmit it to the central processing computer through the network. At this time, the operator can view all information in real-time and monitor all production processes or devices [25]. Hence, the main components of the SCADA system include the control center, communication facilities and field device.…”

Section: Scada System Componentsmentioning

confidence: 99%

Intelligent Control of Cabin Environment Using Computational Fluid Dynamics for Intelligent Manufacturing

Wang¹,

Zeng²

2022

Fluid Dynamics &Amp; Materials Processing

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An efficient and versatile intelligent algorithm is developed for the control of the cabin environment of wind power generators. The method can be used to monitor and solve wind power generation problems at the same time. It also provides several advantages with respect to other traditional methods which imply significant workload and maintenance personnel. The functional requirements of the intelligent control system are analyzed, and a control algorithm for the stepping motor is selected and evaluated. Through the comparative analysis of the active power and internal temperature curve for three kinds of output power of the prototype, it is proved that the environmental intelligent control system greatly improves the operation efficiency, solves typical problems in the ventilator room environment, and provides a solid theoretical basis for further research in this field.

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DeepImpact: a deep learning model for whole body vibration control using impact force monitoring

Cited by 13 publications

References 70 publications

Seismic Performance and Vibration Control of Rapid Construction Environmental Protection Wall Based on Artificial Intelligence

Seismic Performance and Vibration Control of Rapid Construction Environmental Protection Wall Based on Artificial Intelligence

Experimental and Machine Learning Studies on Chitosan-Polyacrylamide Copolymers for Selective Separation of Metal Sulfides in the Froth Flotation Process

Intelligent Control of Cabin Environment Using Computational Fluid Dynamics for Intelligent Manufacturing

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