Intelligent learning system based on personalized recommendation technology
With the continuous development of networks, web-based e-learning is changing the way people acquire knowledge. An increasing number of learners are eager to acquire more knowledge through personalized and intelligent means. Based on content ...
Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network
The gear cracks of gear box are one of most common failure forms affecting gear shaft drive. It has become significant for practice and economy to diagnose the situation of gearbox rapidly and accurately. The extracted signal is filtered first to ...
Robust guaranteed cost control for continuous-time uncertain Markov switching singular systems with mode-dependent time delays
The guaranteed cost control problem for mode-dependent time-delay Markov switching singular systems with norm-bounded uncertain parameters is discussed. Based on delay-dependent linear matrix inequalities, sufficient conditions which ensure the ...
Emotion recognition based on physiological signals using brain asymmetry index and echo state network
This paper proposes a method to evaluate the degree of emotion being motivated in continuous music videos based on asymmetry index (AsI). By collecting two groups of electroencephalogram (EEG) signals from 6 channels (Fp1, Fp2, Fz and AF3, AF4, Fz)...
Iterative learning control for linear generalized distributed parameter system
In this paper, we use the iterative learning control algorithm to deal with generalized distributed parameter system with parabolic type which described by generalized partial differential equation. Because of the particularity of the generalized ...
Civil engineering supervision video retrieval method optimization based on spectral clustering and R-tree
The civil engineering supervision video provides the effective method to improve the quality of civil engineering supervision, but its usual retrieval by B+ tree can’t show the efficient performance to meet the real requirements. This paper uses ...
Design of deep learning accelerated algorithm for online recognition of industrial products defects
With the defects of LED chip as the research object, in LED chip defect recognition, an efficient and scalable parallel algorithm is critical to the deep model using a large data set training. As the implementation of parallel in multi-machine is ...
Forest fire forecasting using ensemble learning approaches
Frequent and intense forest fires have posed severe challenges to forest management in many countries worldwide. Since human experts may overlook important signals, the development of reliable prediction models with various types of data generated ...
The prediction model of worsted yarn quality based on CNN–GRNN neural network
It is key indexes of worsted yarn quality such as worsted yarn strength index, etc., and it can well control worsted yarn quality by predicting yarn strength index, etc. Generally, it is generally used to predict yarn strength indexes such as ...
Multiple order semantic relation extraction
In order to get more comprehensive and accurate knowledge from the Semantic Web, it is essential to design an effective method to extract semantic data from the web and documents. However, as a crucial topic of semantic information extraction, ...
Spark-based intelligent parameter inversion method for prestack seismic data
Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are ...
Hybrid optimization algorithms by various structures for a real-world inverse scheduling problem with uncertain due-dates under single-machine shop systems
This paper investigates the single-machine inverse scheduling problem with adjusted due-dates (SISPAD) which has a strong background in practical industries. In the SISPAD, the parameters values are uncertain, and the objective is to obtain the ...
Constructive function approximation by neural networks with optimized activation functions and fixed weights
Our purpose in this paper is to construct three types of single-hidden layer feed-forward neural networks (FNNs) with optimized piecewise linear activation functions and fixed weights and to present the ideal upper and lower bound estimations on ...
A new method of online extreme learning machine based on hybrid kernel function
Computational complexity and sample selection are two main factors that limited the performance of online sequential extreme learning machines (OS-ELMs). This paper proposes a new model that introduces the concept of hybrid kernel and sample ...
Agent–cellular automata model for the dynamic fluctuation of EV traffic and charging demands based on machine learning algorithm
Electric vehicles (EV) comprise one of the foremost components of the smart grid and tightly link the power system with the road network. Spatial and temporal randomness in electric charging distribution will exert negative impacts on power grid ...
Seismic performance evaluation of existing RC structures based on hybrid sensing method
The properties of structures such as stiffness and damping ratio are not a constant but varying in time due to material deterioration such as strength degradation, corrosion, crack and fatigue. The seismic performance of an existing structure is ...
Open-circuit fault detection for three-phase inverter based on backpropagation neural network
To realize real-time fault detection in power devices and enhance reliability of inverter circuits, this paper proposes a detection method based on Park’s transform algorithm and neural network. Park’s transform is applied to obtain the three-...
ACCP: adaptive congestion control protocol in named data networking based on deep learning
Named data networking (NDN) is a novel network architecture which adopts a receiver-driven transport approach. However, NDN is the name-based routing and source uncontrollability, and network congestion is inevitable. In this paper, we propose an ...
A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information
With rapidly increasing information on the Internet, it can be more difficult and time consuming to find what one really wants, especially in e-commerce. Systems and methods based on machine learning are emerging to generate recommendations based ...
A novel temporal protein complexes identification framework based on density–distance and heuristic algorithm
The construction of dynamic protein–protein interaction networks is affected by cell tissue and its biological function, and the identification of protein complexes is important for understanding biological functions. This paper presents a new ...
Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm
In the future, big data will become an efficient and useful means for improving urban planning, and machine learning can take city as a simplified and efficient system. We take full advantage of the benefits of new technology, but also clarify ...
Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model
Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and ...
Machine learning-based evolution model and the simulation of a profit model of agricultural products logistics financing
An agricultural products logistics and financial warehousing business mainly involves a tripartite of agricultural production and processing enterprises, third-party logistics enterprises and financial institutions and enables the three parties to ...
Deep learning of system reliability under multi-factor influence based on space fault tree
For the fault tree analysis, a basic event probability is often complicated. The probability is not constant and even can be represented by function. In order to analyze the system reliability and related characteristics, we represent the ...
Uncertainties in the friction moment of rolling bearings based on the Bayesian theory and robust theory
A method combining median estimation with Huber M estimation based on robust theory is proposed to establish prior distributions for Bayesian methods, and the posterior distribution is induced according to Bayesian methods. Uncertainties in the ...
Research on partial fingerprint recognition algorithm based on deep learning
Fingerprint recognition technology is widely used as a kind of powerful and effective authentication method on various mobile devices. However, most mobile devices use small-area fingerprint scanners, and these fingerprint scanners can only obtain ...
Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks
As one of the most important medical field subjects, adverse drug reaction seriously affects the patient’s life, health, and safety. Although many methods have been proposed, there are still plenty of important adverse drug reactions unknown, due ...
Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis
Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine ...
Decision function with probability feature weighting based on Bayesian network for multi-label classification
The multi-label classification problem involves finding a multi-valued decision function that predicts an instance to a vector of binary classes. Two methods are widely used to build multi-label classifiers: the binary relevance method and the ...