scholarly journals Research on Classification Method of Network Resources Based on Modified SVM Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao Zhang ◽  
Jingchao Hu ◽  
Yaodong Zhang
Keyword(s):  
Principal Component ◽  
Classification Method ◽  
Svm Classifier ◽  
Original Sample ◽  
Network Resources ◽  
Search Range ◽  
Network Resource ◽  
Svm Algorithm ◽  
Sample Data ◽  
Low Efficiency

According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.

scholarly journals Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248515
Author(s):  
Ke Luo ◽  
Yingying Jiao
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Wolf ◽  
Existing Problems ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Gray Wolf Optimizer ◽  
The Individual

The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

scholarly journals Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1970
Author(s):  
Jun-Kyu Park ◽  
Suwoong Lee ◽  
Aaron Park ◽  
Sung-June Baek
Keyword(s):  
Search Algorithm ◽  
Principal Component ◽  
Search Method ◽  
Reference Spectrum ◽  
Search Range ◽  
Fast Search ◽  
Input Spectrum ◽  
Computationally Intensive ◽  
Multiple Clusters ◽  
Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

scholarly journals Bioactive Phenolic Metabolites from Adriatic Brown Algae Dictyota dichotoma and Padina pavonica (Dictyotaceae)

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1187
Author(s):  
Ivana Generalić Mekinić ◽  
Vida Šimat ◽  
Viktorija Botić ◽  
Anita Crnjac ◽  
Marina Smoljo ◽  
...  
Keyword(s):  
Total Phenolics ◽  
Antioxidant Activities ◽  
Principal Component ◽  
Ultrasound Assisted Extraction ◽  
Antioxidant Power ◽  
Dictyota Dichotoma ◽  
Assisted Extraction ◽  
Sample Data ◽  
Ferric Reducing Antioxidant Power ◽  
The Individual

In this study, the influences of temperature (20, 40 and 60 °C) and extraction solvents (water, ethanol) on the ultrasound-assisted extraction of phenolics from the Adriatic macroalgae Dictyota dichotoma and Padina pavonica were studied. The extracts were analysed for major phenolic sub-groups (total phenolics, flavonoids and tannins) using spectrometric methods, while the individual phenolics were detected by HPLC. The antioxidant activities were evaluated using three methods: Ferric Reducing/Antioxidant Power (FRAP), scavenging of the stabile 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and Oxygen Radical Antioxidant Capacity (ORAC). The aim of the study was also to find the connection between the chemical composition of the extracts and their biological activity. Therefore, principal component analysis (PCA), which permits simple representation of different sample data and better visualisation of their correlations, was used. Higher extraction yields of the total phenolics, flavonoids and tannins were obtained using an alcoholic solvent, while a general conclusion about the applied temperature was not established. These extracts also showed good antioxidant activity, especially D. dichotoma extracts, with high reducing capacity (690–792 mM TE) and ORAC values (38.7–40.8 mM TE in 400-fold diluted extracts). The PCA pointed out the significant influence of flavonoids and tannins on the investigated properties. The results of this investigation could be interesting for future studies dealing with the application of these two algae in foods, cosmetics and pharmaceuticals.

Network resource mobilisation limitations and the alliance portfolio network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Miguel Bolivar ◽  
Ignacio Castro-Abancéns ◽  
Cristóbal Casanueva ◽  
Angeles Gallego
Keyword(s):  
Financial Performance ◽  
Curvilinear Relationship ◽  
Network Resources ◽  
Network Resource ◽  
Alliance Portfolio ◽  
Value Capture ◽  
Content Type ◽  
Network Parameters ◽  
Resource Mobilisation ◽  
Practical Implications

PurposeThe purpose of this study is to examine how access and mobilisation of network resources influence a firm's performance. It has been established that alliance portfolio (AP) network parameters shape the access to network resources; however, resource access understood as value creation differs from resource mobilisation understood as value capture. Hence, the paper contributes towards the comprehension of AP performance by examining the extent to which a firm's level of network resource mobilisation (NRM) plays a role in improving financial performance and how this strategy conditions the benefits obtained from a firm's AP.Design/methodology/approachThis study employs an interorganisational network approach to describe the APs of firms; subsequently, it examines how AP network parameters and resource mobilisation determine financial performance. To this end, sequential multiple regression models are applied to a sample from the Top International Airlines database, covering 135 portfolios that correspond to 1117 codeshare partnerships.FindingsThe analyses show that the NRM level has an inverted U-shaped relationship with revenue performance, thereby revealing the limitations and considerations in the strategic alliance strategy. In addition, the authors show how the resource mobilisation decision moderates the faculty of AP parameters to influence a firm's financial performance, thereby exposing the nuanced relationship between AP size, diversity and redundancy. The findings convey strategic and practical implications for managers regarding how to capture value from their APs.Practical implicationsThe findings suggest the need for NRM to form part of a firm's AP management capability, so that, by acquiring superior strategic knowledge in NRM, the firm is able to extract value from its AP through the optimal exploitation of complementary assets.Originality/valuePrevious research has highlighted the multidimensional nature of APs at the theoretical level; however, no simultaneous empirical analysis of various AP parameters has yet been produced. The research empirically analyses an AP network and how its parameters affect financial performance in the presence of a resource mobilisation strategy. Not only do the authors introduce the analysis of the curvilinear relationship between the level of NRM and a firm's performance, but also of its role in advancing the AP literature.

scholarly journals An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
John Sospeter ◽  
Di Wu ◽  
Saajid Hussain ◽  
Tesfanesh Tesfa
Keyword(s):  
Data Dissemination ◽  
Probabilistic Approach ◽  
Mobile Network ◽  
Delivery Ratio ◽  
Network Resources ◽  
Network Resource ◽  
V2v Communication ◽  
End To End Delay ◽  
End To End ◽  
Road Segments

Mobile network topology changes dynamically over time because of the high velocity of vehicles. Therefore, the concept of the data dissemination scheme in a VANET environment has become an issue of debate for many research scientists. The main purpose of VANET is to ensure passenger safety application by considering the critical emergency message. The design of the message dissemination protocol should take into consideration effective data dissemination to provide a high packet data ratio and low end-to-end delay by using network resources at a minimal level. In this paper, an effective and efficient adaptive probability data dissemination protocol (EEAPD) is proposed. EEAPD comprises a delay scheme and probabilistic approach. The redundancy ratio (r) metric is used to explain the correlation between road segments and vehicles’ density in rebroadcast probability decisions. The uniqueness of the EEAPD protocol comes from taking into account the number of road segments to decide which nodes are suitable for rebroadcasting the emergency message. The last road segment is considered in the transmission range because of the probability of it having small vehicle density. From simulation results, the proposed protocol provides a better high-packet delivery ratio and low-packet drop ratio by providing better use of the network resource within low end-to-end delay. This protocol is designed for only V2V communication by considering a beaconless strategy. the simulations in this study were conducted using Ns-3.26 and traffic simulator called “SUMO”.

A Discovery Technology Based on Distributed Resource Management Domain

2015 ◽  
Vol 713-715 ◽  
pp. 2195-2198
Author(s):  
Jun Li Mao ◽  
Xiang Luo ◽  
Xiao Zhen Wang ◽  
Chao Hong Yang
Keyword(s):  
Resource Management ◽  
Resource Selection ◽  
Resource Discovery ◽  
Network Resources ◽  
Network Resource ◽  
Management Domain ◽  
Network Resource Management ◽  
Long Time ◽  
Resource Organization ◽  
Resource Description

Resource discovery is the key of network resource management, which includes multiple aspects, such as resource description, resource organization, and resource discovery and resource selection. For a long time, communication network resourcehas been lack of unified and standardized description, causing users difficult to precisely find related resources in demand. This paper presents a distributed resource query methods based on management domain, including distributed resource query architecture, the basic process of resource discovery, update method,query methods and so on. The method of network resources makes use of collaborative queries to realize network resource discovery according to need.

scholarly journals Fault Diagnosis for Vehicle on-Board Equipments of High-Speed Railway Based on GA-ANFIS Model

2020 ◽  
Author(s):  
Linggang Kong ◽  
Shuo Li ◽  
Xinlong Chen ◽  
Hongyan Qin
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Principal Component ◽  
Weighting Factor ◽  
Anfis Model ◽  
High Speed Railway ◽  
Railway Line ◽  
Diagnosis Rate ◽  
Low Efficiency ◽  
Frequency Weighting

Vehicle on-board equipment is the most important train control equipment in high-speed railways. Due to the low efficiency and accuracy of manual detection, in this paper, we propose an intellectualized fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) network. Firstly, we collect the fault information sheets that are recorded by electrical personnel, using frequency weighting factor and principal component analysis (PCA) to realize the data extraction and dimension reduction; Then, in order to improve the fault diagnosis rate of the model, using genetic algorithm (GA) to optimize the parameters of the ANFIS network; Finally, using the fault data of a high-speed railway line in 2019 to test the model, the optimized ANFIS model can achieve 96% fault diagnosis rate for vehicle on-board equipments, which indicating the method is effective and accurate.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

scholarly journals An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

2021 ◽  
Author(s):  
Ze Xi Xu ◽  
Lei Zhuang ◽  
Meng Yang He ◽  
Si Jin Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Edit Distance ◽  
Virtual Network ◽  
Cost Ratio ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Graph Edit Distance ◽  
Network Resources ◽  
Network Resource ◽  
Distance Method ◽  
Edge Based

Abstract Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

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