Sampling Strategy Analysis of Machine Learning Models for Energy Consumption Prediction

Wu, Zeqing; Chu, Weishen

doi:10.1109/sege52446.2021.9534987

Cited by 31 publications

(18 citation statements)

References 10 publications

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“…The emotional features involved in this chapter are visual signal features and four physiological signal features. After feature extraction [ 30 – 32 ], the visual signal features and four physiological signal features are combined in series to form four multimodal features.…”

Section: Methodsmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

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Section: Methodsmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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“…This study also compared the proposed bearing fault diagnosis method with some traditional algorithms [ 28 , 29 ] including support vector machine, BP neural network, and WPT—convolutional neural network. Finally, the effectiveness of the proposed diagnosis method was verified through the comparison.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Feng

2022

Sensors

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Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.

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“…As a result, investigating an effective unbalanced data classification technology [7] is extremely important. K-nearest neighbor (KNN) method has become one of the most famous algorithms in the field of pattern recognition [8][9][10] and statistics because of its simple algorithm, easy realization, no need to estimate parameters, and high classification accuracy, and it is also one of the earliest nonparametric algorithms applied to automatic text classification in machine learning [11,12]. However, KNN method needs to store all the training sample data in the process of calculating the nearest neighbor of each sample to be tested, which leads to a large number of similarity calculations for classification and significantly increases the complexity of classification calculation with the increase of sample data set [13], thus reducing the classification efficiency.…”

Section: Introductionmentioning

confidence: 99%

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Ji¹

2022

Scientific Programming

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The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.

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Sampling Strategy Analysis of Machine Learning Models for Energy Consumption Prediction

Cited by 31 publications

References 10 publications

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

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