Fused deposition modeling (FDM), one of representative additive manufacturing (AM) technologies, has been widely used for fabricating functional parts with geometrical complexity. However, it has suffered from degrade...
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Fused deposition modeling (FDM), one of representative additive manufacturing (AM) technologies, has been widely used for fabricating functional parts with geometrical complexity. However, it has suffered from degraded part quality and low process reliability and controllability. Therefore, it is of much significance to develop a monitoring and diagnosis system for the FDM process to overcome such drawbacks. In this paper, a data-driven FDM process monitoring and diagnosis system is developed by using two types of sensors - an accelerometer and an acoustic emission (AE) sensor. A large number of experimental data, collected from the accelerometers and AE sensor under healthy and faulty process states, are processed to obtain a critical feature - a root mean square (RMS). The RMS values are then used for training the FDM process monitoring and diagnosis models based on a supportvectormachine (SVM) algorithm and a k-fold cross validation approach. In particular, the SVM-based models for the odd-and even-numbered layers of one FDM specimen are developed. For a real-time validation in a factory floor, the non-linear SVM-based models using the acceleration signals are used for the software development. The diagnosis accuracy is better than 87.5%, and an applicability of the models is verified.
For the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to...
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For the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to the employment and entrepreneurship management of college students. Based on deep learning technology, the deep neural network is constructed based on SVR and restrictive Boltzmann machine, namely, SVR-DBN, including theoretical derivation of model architecture, design and selection of model training algorithms, and the modeling steps and flow charts are given, and finally applied to the influence factor analysis. The multiangle comparison proves that the proposed depth model has excellent feature extraction ability and regression prediction. The results show that the algorithm has higher accuracy and has a 26% improvement over traditional algorithms. The research is of great significance to the improvement of the efficiency of employment and entrepreneurship management and the application of support vector machine algorithms.
A new microcell prediction model for mobile radio environment is presented in this paper. The popular support vector machine algorithm is used as an optimizing tool to build a model. In order to validate the model qua...
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A new microcell prediction model for mobile radio environment is presented in this paper. The popular support vector machine algorithm is used as an optimizing tool to build a model. In order to validate the model quality, extensive electric field strength measurements were carried out in the city of Belgrade, for two different test transmitter locations. The analysis of the model has shown that proposed model is fast, accurate (on the order of the local mean measurements uncertainty), reliable, and suitable for computer implementation.
BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE: To establi...
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BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE: To establish models for early prediction and intervention of HDP. METHODS: This study used the three types of risk factors and supportvectormachine (SVM) to establish prediction models of HDP at different gestational weeks. RESULTS: The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and >= 35 weeks, it reached more than 92%. CONCLUSION: Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.
The aim of this study is to perform machine learning based classification using the Novel Random Forest algorithm to predict the home environment for intelligent decision making comparison with supportvectormachine ...
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ISBN:
(纸本)9781665460712
The aim of this study is to perform machine learning based classification using the Novel Random Forest algorithm to predict the home environment for intelligent decision making comparison with supportvectormachine to measure accuracy, precision and recall. Materials and methods: A total no of 1599 samples are collected from the dataset available in UCI Repository. These samples are divided into 70% for the training dataset (n = 1119) and 30% for testing dataset (n = 480). Accuracy, Recall and Precision values are determined to evaluate the performance of the Novel random forest algorithm. Result: Novel Random forest classifier achieved accuracy of 96% whereas supportvectormachine achieved 78.50%. Precision obtained for the Novel Random forest classifier is 94.04% and 73.05% for supportvectormachine. Recall obtained for the Novel Random forest classifier is 94.13% and 78.52% for supportvectormachine. The significant value achieved is 0.025 (p (sic) 0.05). Conclusion: Novel Random Forest classifier predicts significantly better accuracy for a secure home environment for intelligent decision making and block chain technology when compared to supportvectormachine classifier.
The eyewitness message on twitter as a social network sensor aims to determine the classification process's performance. In the classification of flood disaster messages, preprocessing data is required before the ...
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ISBN:
(纸本)9781728199177
The eyewitness message on twitter as a social network sensor aims to determine the classification process's performance. In the classification of flood disaster messages, preprocessing data is required before the classification process is carried out. Preprocessing affects the resulting level of accuracy in the classification process. Stopword removal is part of preprocessing so that the effect of stopword removal on classification performance will be examined. supportvectormachine (SVM) is used to classify by weighting words using Term Frequency-Inverse Document Frequency (TF-IDF). The data taken from Twitter is 3000 data with 1000 data each for each label. The effect of the stopword on accuracy performance can be seen in several experiments that have been carried out. We had already conducted three different experiments, and the highest level of accuracy was 76.6%, 76.87%, and 77.87%. Based on the experiments that have been carried out, stopword is very influential on the accuracy generated by the classification of flood disaster messages on Twitter.
As a major statistical learning method in case of small sample, support vector machine algorithm (SVM) has some disadvantages in dealing with vast amounts of data, such as the memory overhead and slow training, we use...
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ISBN:
(纸本)9783037851951
As a major statistical learning method in case of small sample, support vector machine algorithm (SVM) has some disadvantages in dealing with vast amounts of data, such as the memory overhead and slow training, we use Multi-class supportvectormachine (MSVM) with Self-Organize Selective Fusion (SOSF) to optimize the multiple classifiers selectively, which can update the classification and self-adjust its classification performance, and eliminate some redundancy and conflicts, achieve the fusion of multiple classifiers selectively, and effectively solve the shortcoming of disturbances by the sub-samples distribution in large sample, and improve the training efficiency and classification efficiency.
The gait pattern, as well as the walking process itself, can be an indicator of the overall health of patients. For this reason, it is very important to accurately, clearly and quickly determine the affiliation of the...
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ISBN:
(纸本)9781728169491
The gait pattern, as well as the walking process itself, can be an indicator of the overall health of patients. For this reason, it is very important to accurately, clearly and quickly determine the affiliation of the gait pattern (healthy or pathological) and take appropriate measures if necessary. As anterior cruciate ligament injuries are common and may be undetectable, this study presents a classification of gait using a supportvectormachine (SVM) algorithm. The test data were taken from a Gait LAB laboratory;anterior posterior translation and internal external rotation were used as significant parameters. The classifier performance was evaluated using a confusion matrix. These results showed that the SVM algorithm can be successfully used in tasks of this type of classification.
We developed a real-time program utilizing the supportvectormachine (SVM) algorithm in conjunction with the Microsoft Kinect V2 sensor to analyze the proper execution of the conventional deadlift exercise. A user-de...
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Based on the research of predicting β-hairpin motifs in proteins,we apply Random Forest and support vector machine algorithm to predict β-hairpin motifs in ArchDB40 *** motifs with the loop length of 2 to 8 amino ac...
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Based on the research of predicting β-hairpin motifs in proteins,we apply Random Forest and support vector machine algorithm to predict β-hairpin motifs in ArchDB40 *** motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and the fixed-length pattern of 12 amino acids are *** using the same characteristic parameters and the same test method,Random Forest algorithm is more effective than supportvector *** addition,because of Random Forest algorithm doesn't produce overfitting phenomenon while the dimension of characteristic parameters is higher,we use Random Forest based on higher dimension characteristic parameters to predict β-hairpin *** better prediction results are obtained;the overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59,respectively.
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