Crusher is an important production equipment for the crushing production of metal minerals. The crusher plays an important role in the production of the mine, which not only directly affects the entire production line...
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ISBN:
(纸本)9781728158556
Crusher is an important production equipment for the crushing production of metal minerals. The crusher plays an important role in the production of the mine, which not only directly affects the entire production line, but also causes major economic losses and even accidents caused by machine damage. To ensure the safe operation of the equipment, reduce equipment maintenance costs and increase equipment utilization, this paper proposed an ASO-BP neural network optimization model. By improving the BP neural network model with atom search algorithm, through the analysis of the six failure tests of the crusher, the proposed ASO-BP neural network can improve the accuracy of crusher fault diagnosis. By improving the BP neural network, the speed and accuracy of crusher fault diagnosis are improved, which is of great significance for the safe operation and production management of the crusher.
Crusher is an important production equipment for the crushing production of metal minerals. The crusher plays an important role in the production of the mine, which not only directly affects the entire production line...
详细信息
Crusher is an important production equipment for the crushing production of metal minerals. The crusher plays an important role in the production of the mine, which not only directly affects the entire production line, but also causes major economic losses and even accidents caused by machine damage. To ensure the safe operation of the equipment, reduce equipment maintenance costs and increase equipment utilization, this paper proposed an ASO-BP neural network optimization model. By improving the BP neural network model with atom search algorithm, through the analysis of the six failure tests of the crusher, the proposed ASO-BP neural network can improve the accuracy of crusher fault diagnosis. By improving the BP neural network, the speed and accuracy of crusher fault diagnosis are improved, which is of great significance for the safe operation and production management of the crusher.
The feature selection process aims to obtain the vital information contained in the dataset. Determining the high-impact features has a key role in improving the classification process, applied in many scientific and ...
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ISBN:
(纸本)9781665426329
The feature selection process aims to obtain the vital information contained in the dataset. Determining the high-impact features has a key role in improving the classification process, applied in many scientific and medical fields within our study. This paper proposes a hybrid SD-BASO algorithm between the statistical dependence (SD) technique and the binary atomsearch optimization (BASO) algorithm. This algorithm depends on a proposed fitness function through which the essential features that affect the classification process are obtained. The experimental results on the datasets showed that the proposed algorithm, which we refer to as SD-BASO, is superior to the classical algorithm in terms of accuracy in the results and the number of features selected.
Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic searchalgorithm for feature selection ...
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Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic searchalgorithm for feature selection is ***,texture features of five scales and eight directions in the face region are extracted by Gabor wavelet *** statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor ***,a new hybrid feature selection algorithm chaotic improved atomsearch optimisation with simulated annealing(CIASO-SA)is presented,which is based on an improved atomic searchalgorithm and the simulated annealing ***,the CIASO-SA algorithm introduces a chaos mechanism during atomic initialisation,significantly improving the convergence speed and accuracy of the ***,a support vector machine(SVM)is used to get classification results of the age *** verify the performance of the proposed algorithm,face images with three resolutions in the Adience dataset are *** the Gabor real part fusion feature at 48�48 resolution,the average accuracy and 1-off accuracy of age classification exhibit a maximum of 60.4%and 85.9%,*** results prove the superiority of the proposed algorithm over the state-of-the-art methods,which is of great referential value for application to the mobile terminals.
The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT...
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The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst *** drones are naturally mobile,it undergoes frequent topological *** alterations in the topology cause route election,stability,and scalability problems in *** is considered as an effective method to transmit the images in IoD *** current study introduces an atomsearch Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)*** key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by *** presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction *** addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD *** process enables the secure transmission of images to the ground *** order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different *** comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.
An adsorption chiller system is one of the most promising technologies that utilize waste thermal energy to simultaneously produce cooling and potable water. However, the energy utilization optimization and detection ...
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An adsorption chiller system is one of the most promising technologies that utilize waste thermal energy to simultaneously produce cooling and potable water. However, the energy utilization optimization and detection of desiccant's sorption capacity degradation are two unresolved issues that have severely impeded the devel-opment and commercial applications of adsorption chiller technologies. This study is pioneered to develop a digital twin platform specifically designed for an experimental four-bed two-evaporator adsorption chiller system prototype. Leveraging this platform, system monitoring, performance prediction, and optimization functions are achieved. Relying on the monitoring function, the digital twin can detect the capacity degradation of desiccant -coated heat exchangers. By employing the prediction and optimization functions, the application performance of the adsorption chiller system under varying ambient and load conditions can be simulated and optimized under real-time operating conditions. Additionally, this work projects a first-time experimental parametric study analysis for a four-bed two-evaporator adsorption chiller system prototype under a heat recovery scheme that considers fourteen operating parameters. Key results revealed that COPth reaches 0.68 when the cycle time is 2240 s. Case studies also showed that the adsorption chiller system can yield significant energy-saving perfor-mance for climatic conditions in Malaysia and Saudi Arabia. The proposed digital twin optimization method demonstrates that COPth is enhanced by 8.5 %, 9.5 %, and 8.5 %, respectively. In contrast to the conventional method, optimizing the adsorption chiller's performance through the digital twin platform enables a reduction of the annual electricity consumption by up to 10.3 %.
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