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This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine...
In data mining, link prediction for the networks is one of the areas of greatest interest today. Research achievements of link prediction problem can be applied in many fields such as study genetically transferred diseases, online marketing, e-commerce services, discover the structure of criminal networks, friend request in social networks … However, most of researchers focused on predicting the existence...
This paper presents an interfacing system of a photovoltaic (PV) array with an electrical grid based on a dual cascaded inverter. The PV array is connected to the main inverter through a boost converter, for maximum power extraction, while the dc-side of the auxiliary inverter is connected to a capacitor bank. The main and auxiliary inverters are controlled to deliver the harvested maximum power from...
With the development of deep sequencing technology, isomiRs (isoform of miRNA) are consistently observed in a variety of cell types, tissues, and different cell development stages. miRNA isoforms as the products of miRNA genes, are variants which are different from mature miRNAs in length and position. Recently, many studies emphasized on isomiR and found its subtypes are differentially expression...
According to the noise and overlapping characteristics of agricultural irrigation water quality monitoring data for the comprehensive evaluation may bring about the boundary fuzzy problem. This paper proposes an improved Genetic Algorithm (GA) to avoid premature convergence, the global optimal solution of the function of the Projection Pursuit (PP) function is used as the comprehensive evaluation...
Degradation prediction is important for safety related products to avoid failures. When the degradations of multiple parameters of a product is taken into account, traditional univariate degradation prediction method is not applicable, especially when the parameters are correlated. To cope with this problem, Mahalanobis distance is proposed, to combine multiple parameters into one unified index. Then...
Mechanical characteristics, including the displacement curves of the movable contacts and coil current curves are the most common routine monitoring objects of high voltage circuit breakers to evaluate the machines' condition. Generally, a high-performance mechanical characteristic tester has the ability to offer dozens of parameters consisting of stroke, speed, magnitude of current and so on. Besides,...
In the context of the educational quality evaluation measured through standardized tests, this article aims to select the context variables that have a greater contribution in the differentiation of the categories of the 2015 SIMCE math score, for eighth grade students of the region of La Araucanía, Chile. Based on a cross-sectional research, a supervised classification design was implemented, defining...
In order to solve the classification prediction of dust pollution at different altitudes, the least square support vector machine(LS-SVM) and BP neural network is used to construct the distribution model. Built by LS-SVM, the accuracy of the model was verified by BP neural network with the realtime dust pollution data of different high monitored by Unmanned aerial vehicles. The data analysis shows...
In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the...
In today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sentiment analysis is simply to determine whether the emotion is included or not, and to determine whether the...
For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved...
Two GF-1 WFV images on August 3, 2015 and October 2, 2015 were selected to extract the cultivated area of paddy rice in Jianhu county of Jiangsu Province. Vegetation indexes were extracted from the original spectrum data in order to extract paddy rice area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy...
This paper compares two torque control methodologies, namely Field Oriented and Model Predictive ones, for monitoring Interior Permanent Magnet Motor (IPMM) over a wide speed range. IPMM magnetic saturation and cross coupling effects are taken into consideration, increasing significantly the precision of the developed controllers. Maximum Torque per Ampere (MTPA) and Field Weakening (FW) operating...
This paper presents a mathematical model to predict the half-hourly load in one year based on solar energy index, holiday index and weather index. The weather index and the holiday index are taken as the eigenvalues of the Support Vector Machine (SVM), Elman neural network (Elman), and BP artificial neural network (BP neural network), and then the final optimal model is selected through a comparison...
To assess the multi-state of a rolling bearing more effectively and simultaneously, a unified assessment method is proposed based on chaos fruit fly optimization algorithm hyper-sphere support vector machine (CFOA-HSVM) two measures combination. Aiming to the blindness of parameters selection for HSVM, multiple parameters of HSVM can be searched the optimal values using chaos theory combined with...
In this paper, a novel spectral-spatial very high resolution images shadow detection algorithm based on random walker is proposed. First, a set of training samples is obtained by an improved Otsu based thresholding method automatically. Then, a widely used pixel-wise classifier, i.e., the Support Vector Machine (SVM), is applied to obtain an initial binary classification map. Finally, the initial...
In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare...
This paper proposes a new approach for contextual feature extraction from superpixels in aerial urban scenes. Our method extracts features with many levels of context from superpixels by exploiting different layers of a pre-trained convolutional neural network. Experimental results show the effectiveness of the proposed approach, which outperforms traditional methods based on handcrafted feature extraction...
The keypoints detection, matching and tracking based online tracking algorithm, which is called CMT (Clustering of the Static-Adaptive Correspondences for Deformable Object Tracking), is robust and accurate for deformable object tacking. However, its optical flow tracker is error-prone when active points run outside the scope of the target. Worse still, the computational complexity of CMT greatly...
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