Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of ...
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Natural fiber-based nanocomposite is an innovative material created recently, widely used in various industries for its exceptional strength-to-weight ratio and lightweight properties. The study examined the impact of...
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Machine tool chatter adversely affects tool life and surface quality, making early detection essential in machining processes. However, vibration signals collected during machining are often contaminated by noise, hin...
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Machine tool chatter adversely affects tool life and surface quality, making early detection essential in machining processes. However, vibration signals collected during machining are often contaminated by noise, hindering accurate chatter prediction. This study presents an advanced chatter detection framework integrating Adaptive Threshold Wavelet De-noising (ATWD), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Hilbert–Huang Transforms (HHT), and an Adaptive Probabilistic Neural Network (APNN). The novelty of this work lies in the introduction of an adaptive noise term, β2E2(η(t)), within the ICEEMDAN process, which mitigates mode mixing and ensures precise resonance frequency identification. The ATWD dynamically adjusts noise thresholds based on signal decomposition levels, achieving significant noise suppression for non-stationary signals while preserving critical chatter features. Using the Normalised Energy Rati;(NER), the most responsive Intrinsic Mode Functions (IMFs) are selected for feature analysis, leading t;improved signal decomposition. The APNN further enhances classification accuracy by dynamically adjusting network parameters, outperforming traditional PNN classifiers. Comparative analysis demonstrates that the proposed APNN achieves a classification accuracy of 99.5%, representing a substantial improvement over baseline methods. Internal turning experiments using a flexible boring bar validate the proposed methodology, showcasing its practical effectiveness and reliability. The integration of ATWD, ICEEMDAN–HHT fusion, and APNN provides a novel solution for chatter detection, offering significant advancements in noise filtering, feature extraction, and real-time classification accuracy. This methodology is particularly suited for challenging machining environments and Industry 4.0 applications, where precise and rapid chatter detection enhances tool life, reduces production costs, and
Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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Industrial and human waste releases sulphur and fluoride metals into the water sources, polluting them and endangering humans and the environment. There are various methods available for removals of the pollutants fro...
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Industrial and human waste releases sulphur and fluoride metals into the water sources, polluting them and endangering humans and the environment. There are various methods available for removals of the pollutants from the water. The membrane filtering and Chemical precipitation are more expensive, useless at low metal concentrations, and produce vast amounts of sludge and toxic by products that must be disposed of when treating big volumes of water. Alternative the wastewater treatment using the biosorption technology is environmentally friendly method. These methods are cheaper, more accessible, and reusable than traditional ones. Biomass can be treated physically and chemically treated before usage. The other parameters included the contact time, agitation speed, adsorbent dosage, pH, and temperature also affects the biosorption performances. After the removal of sulphur and fluorides from water, the bio-sorbent can be regenerated and reused to save money. Wastewater pollution removal using adsorption method is generally accepted. In the present scenario, the magnetic biosorbents and nanoparticles biosorbents are used for wastewater treatment have been developed. The magnetic biosorbents are attractive because to their various active sites, large specific surface area, easy separation, and lower cost. Chemical activations like acid, alkali, and salt improve biosorbents adsorption. In this observe, the various biosorbents have been developed for their better performance on defluoridation and desulphuridation. Due to different scientific barriers in the biosorption procedures those obstruct its commercialization;there has been a gradually rising consideration in this area of study. Of late more consideration is being paid in the formation of cost-effective adsorbents using various agricultural wastes, plant biomass, bacteria, algae and fungi. This review article highlights the applications of different biosorbents, sorption isotherm, and kinetics. In addition, the
The most prevalent sense of impairment in the world, hearing loss hinders learning and communication. The best way to address this issue is to use electroencephalograms (EEGs) to detect hearing loss early and accurate...
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The most prevalent sense of impairment in the world, hearing loss hinders learning and communication. The best way to address this issue is to use electroencephalograms (EEGs) to detect hearing loss early and accurately. The most relevant modality for hearing loss among the several EEG control signals is the auditory evoked potential (AEP), which is generated by the brain in response to an auditory input. Consequently, a new approach is suggested in this study to ascertain the degree of hearing sensitivity based on the AEP response. In this paper, a Fused Perceptron with Chi Squared Multi-Kernel based Extreme Learning (FPCSMK-EL) method is presented for more accurate detection of hearing sensitivity levels. Preprocessing, feature extraction, and classification are the three distinct processes carried out by the suggested FPCSMK-EL approach. Initially, the Fusion procedure known as Fast Independent Component Analysis (FICA) and Empirical Mode Decomposition (EMD) are used to preprocess the EEG signal in order to minimize the error rate during the identification process by eliminating artifacts. After that, Haar Wavelet Multilayer Perceptron and Kullback–Leibler Divergent Chi-Square Model are applied to extract the relevant EEG features from AEP. Here, Haar Wavelet Multilayer Perceptron is employed to determine the threshold factor for hearing (i.e., parameter modeling) and Kullback–Leibler Divergent Chi-Square Model is used to extract the features (i.e., non-parameter modeling) with minimum time. Through the extraction of relevant EEG features, overhead incurred in the hearing sensitivity level detection is reduced. Lastly, Multi-kernel extreme learning machine classifier model is applied to perform classification based on the relevant features of EEG signal for detecting hearing sensitivity level with higher accuracy. A MATLAB simulation tool with a variety of performance measures is used to analyze the proposed FPCSMK-EL method. The proposed FPCSMK-EL method has an
computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other tra...
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computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.
This experimentation conducts for the optimization of Polycarbonate(PC) material 3D printing factors. The printing is done on the Flash Forge Guider 2 FDM equipment, employing the Taguchi methodology. The factors cons...
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Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challengin...
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Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective ***,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective *** paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these *** network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation ***,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information *** order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target ***,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter ***,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and *** and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon *** results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
Fe-based alloys have recently become well-known for their ability to resist wear and corrosion. Iron, the primary component of alloys, is a cheaper option than alloys made from Co and Ni. There is a need for affordabl...
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