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Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network
Get PDFRecently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, these vectors are segmented according to the positions of transition words in sentences. Thirdly, the most significant feature of each local segment is extracted using max pooling mechanism, and then the different aspects of features can be extracted. Specifically, the relative sequence of these features is preserved. Finally, after processed by the dropout algorithm, the softmax classifier is trained for sentiment classification. Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods, especially for datasets with transition sentences
Research on Trust Model in Container-Based Cloud Service
Get PDFContainer virtual technology aims to provide program independence and resource sharing. The container enables flexible cloud service. Compared with traditional virtualization, traditional virtual machines have difficulty in resource and expense requirements. The container technology has the advantages of smaller size, faster migration, lower resource overhead, and higher utilization. Within container-based cloud environment, services can adopt multi-target nodes. This paper reports research results to improve the traditional trust model with consideration of cooperation effects. Cooperation trust means that in a container-based cloud environment, services can be divided into multiple containers for different container nodes. When multiple target nodes work for one service at the same time, these nodes are in a cooperation state. When multi-target nodes cooperate to complete the service, the target nodes evaluate each other. The calculation of cooperation trust evaluation is used to update the degree of comprehensive trust. Experimental simulation results show that the cooperation trust evaluation can help solving the trust problem in the container-based cloud environment and can improve the success rate of following cooperation
A MPTCP Scheduler for Web Transfer
Get PDFMultipath TCP (MPTCP) is the most significant extension of TCP that enables transmission via multiple paths concurrently to improve the resource usage and throughput of long flows. However, due to the concurrent multiple transfer (CMT) in short flow trans-mission, the congestion window (cwnd) of each MPTCP subflow is too small and it may lead to timeout when a single lost packet cannot be recovered through fast retransmission. As a result, MPTCP has even worse performance for short flows compared to regular TCP. In this paper, we take the first step to analyze the main reason why MPTCP has the diminished performance for short flows, and then we propose MPTCP-SF, which dynamically adjusts the number of subflows for each flow . In particular, MPTCP-SF firstly analyzes the distribution characteristics of the web objects to extract two thresholds to be used for classifying each flow. After eceiving each new ACK, MPTCP-SF periodically counts the data being sent based on per-flow and uses the threshold to classify the we blows. Finally, MPTCP-SF dynamically switches path scheduling model for different classification flows. We conduct extensive experiments in NS3 to evaluate its e fficiency. Our evaluation proves that MPTCP-SF decreases the completion time of short flows by over 42.64% com-pared to MPTCP, and the throughput achieved by MPTCP-SF in transmitting long flows is about 11.11% higher than that of MPTCP in a WLAN/LTE wireless network. The results successfully validate the improved performance of MPTCP-SF
Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data
Get PDFGarlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices. The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model, SVM model and ARIMA-SVM model. The experimental results show that: (1) Garlic price is affected by many factors but the most is the supply and demand relationship; (2) The SVM model has a good effect in dealing with the nonlinear relationship of garlic prices; (3) The ARIMA-SVM hybrid model is better than the single ARIMA model and SVM model on the accuracy of garlic price prediction, it can be used as an effective method to predict the short-term price of garlic
Seed Selection for Data Offloading Based on Social and Interest Graphs
Get PDFThe explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph is generated to indicate the social relationships of users. Moreover, a contact-interest graph is developed on the basis of the social ties and individual interests of users. Corresponding on different graphs, three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data. We evaluate the performance of our algorithms by the trace data of real-word mobility. It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account
Research on Arterial Stiffness Status in Type 2 Diabetic Patients Based on Pulse Waveform Characteristics
Get PDFFor patients with type 2 diabetes, the evaluation of pulse waveform characteristics is helpful to understand changes in arterial stiffness. However, there is a lack of comprehensive analysis of pulse waveform parameters. Here, we aimed to investigate the changes in pulse waveform characteristics in patients with type 2 diabetes due to increased arterial stiffness. In this study, 25 patients with type 2 diabetes and 50 healthy subjects were selected based on their clinical history. Age, height, weight, blood pressure, and pulse pressure were collected as the subjects’ basic characteristics. The brachial-ankle pulse wave velocity (baPWV) was collected as an index of arterial stiffness. Parameters of time [the pulse wave period (T), the relative positions of peak point (T1) and notch point (T2), and pulse wave time difference between upper and lower limbs (T3)] and area [the total waveform area (A), and the areas of the waveform before (A1) and after (A2) the notch point] were extracted from the pulse wave signals as pulse waveform characteristics. An independent sample t-test was performed to determine whether there were significant differences between groups. Pearson’s correlation analysis was performed to determine the correlations between pulse waveform parameters and baPWV. There were significant differences in T3, A, A1, and A2 between the groups (p3, A, A1, and A2 (p3, A, A1, and A2) could be used as indices of arterial stiffness in patients with type 2 diabetes
Determination of the Normal Contact Stiffness and Integration Time Step for the Finite Element Modeling of Bristle-Surface Interaction
Get PDFIn finite element modeling of impact, it is necessary to define appropriate values of the normal contact stiffness, Kn, and the Integration Time Step (ITS). Because impacts are usually of very short duration, very small ITSs are required. Moreover, the selection of a suitable value of Kn is a critical issue, as the impact behavior depends dramatically on this parameter. In this work, a number of experimental tests and finite element analyses have been performed in order to obtain an appropriate value of Kn for the interaction between a bristle of a gutter brush for road sweeping and a concrete surface. Furthermore, a suitable ITS is determined. The experiments consist of releasing a steel bristle that is placed vertically at a certain distance from a concrete surface and tracking the impact. Similarly, in the finite element analyses, a beam is modeled in free fall and impacting a surface; contact and target elements are attached to the beam and the surface, respectively. The results of the experiments and the modeling are integrated through the principle of conservation of energy, the principle of linear impulse and momentum, and Newton’s second law. The results demonstrate that, for the case studied, Kn and the impact time tend to be independent of the velocity just before impact and that Kn has a very large variation, as concrete is a composite material with a rough surface. Also, the ratio between the largest height of the bristle after impact and the initial height tends to be constant
Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network
Get PDFPathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of less than 5%. The experimental result indicates the effectiveness of this approach, and is useful for providing an objective basis for evaluating the malignancy of breast tumors
Instability Analysis of Strike-Slip Fault Based on Cusp Catastrophe Model
Get PDFThe distribution of many active faults in western China is an important reason for the frequent earthquakes. With the rapid development of the western region, many major projects have been built there and the existence of active faults is bound to have an influence on the safety of the engineering structure. Therefore, it is of great significance to study the mechanism of fault slip instability for evaluating the geological stability of the region and for the site selection of major projects. In this paper, cusp catastrophe theory is used to establish a cusp catastrophe model with general softened form of strike-slip faults on the basis of strike-slip faults. In this model, the influence of the softening property of fault zone on fault instability is considered. Based on this model, the conditions of slip instability of strike-slip faults are derived and further the half-slip distance, far-field displacement and energy release equation of sliding-slip fault are revealed. The influences of the system stiffness ratio and the softening property of the fault zone on the half-wave displacement, the far-field displacement and the energy release are shown. Which lays a good foundation for further research on active fault-induced earthquake mechanism
Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool
Get PDFThis paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features. From the results obtained, C-SVC is the best model than ν-SVC and it can be able to give 94.5% classification accuracy for face milling of special steel alloy 42CrMo4