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Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions...
Clustering algorithm is often used to analyze the communication data for network intrusion detection system. However, network communication data are mixed, e.g., numerical and categorical data. So, at first, this paper put forward a method for representing the cluster center (prototype) of mixed-type data. Then respectively in combination with the continuity characteristic of the numerical attributes...
Hyperspectral remote-sensing image has high data dimensionality and a small amount of labeled pixels, which causes the curse of dimensionality phenomenon. Therefore, feature extraction is needed ahead of recognition for reducing dimensionality and improving classification accuracy. A novel multiclass feature extraction method, i.e., M-ary discriminant analysis (M-ary DA), is presented for solving...
Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different...
Fault diagnosis is an important procedure to ensure the equipment efficiency and stability. The diagnosis process is actually a pattern recognition process, and usually, the fault samples are lack of tags of fault types. In this case, the non-supervised learning method is more available, and kernel clustering is one of the most effective methods. In this paper, a novel electromagnetic particle swarm...
With increasing complexity of today's automotive combustion engines, end-of-line (EOL) testing has become an important method to test assembled engines for production faults. Several hundered measurement signals are evaluated for every EOL test, up to 100% of production volume. The difficulty of finding and maintaining accurate test limits makes EOL testing of complex products interesting as a machine...
A new highly accurate and efficient coordinate transformation algorithm is proposed for the evaluation of the self-coupling in the Method of Moments (MoM), which produces usually the strongest contributions to the MoM system matrices. The new algorithm provides an effective solution to remove the singularity due to the Green's function inside the self-couplings. Moreover, the new solution reduces...
The Beck Depression Inventory (BDI), a self-report questionnaire consisting of 21 question items, has been the most extensively used for depression assessment. The problem of interest here is to identify a subset of questions in the BDI that are most predictive of depression and can reveal gender differences between depression profiles. We investigate feature selection techniques to select a subset...
Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
This paper proposes a prediction system to make a forecast of temperature which is based on support vector machines. The system uses a database which provides information about weather parameters such as pressure, temperature, wind speed, etc. In order to adapt the input data, the present proposal has applied a pre-processing method before the prediction phases start. The best testing results have...
Playback attack detection (PAD) is essentially a binary classification task which is used to identify the authentic recordings from the playback recordings. For PAD problem, the difference of the acoustic feature between the authentic and playback recordings mainly comes from the recording channel and the ambient noise. Motivated by the excellent performance of the Gaussian Mixture Model-Universal...
Myocardial infarct cause myocardial tissue disease increasing the probability to have arrhythmias such as ventricular fibrillation, these cardiac problems are cause of death. This paper implement a support vector machine classifier trained with the JTp/JT, Tpe/JTp and Tpe/JT intervals ratios extracted from ECG signals of healthy patients and patients with post myocardial infarct diagnosis. The accuracy...
Linux container virtualisation is gaining momentum as lightweight technology to support cloud and distributed computing. Applications relying on container architectures might at times rely on inter-container communication, and container networking solutions are emerging to address this need. Containers can be networked together as part of an overlay network, or with actual links from the container...
In this paper, two variants of the fractional powers of generalized Hankel-Clifford transformation defined. These transformations are extended to certain spaces of generalized functions and prove several results on inversion, uniqueness, boundedness and analyticity. The operational calculus of these transformations is developed and then applied to solve certain class of partial differential equations...
In this paper, two variants of the fractional powers of generalized Hankel-Clifford transformation defined. These transformations are extended to certain spaces of generalized functions and prove several results on inversion, uniqueness, boundedness and analyticity. The operational calculus of these transformations is developed and then applied to solve certain class of partial differential equations...
In most document archiving systems, one of the main fields is to identify the category of documents. In most case, determination of the document category in archiving tasks requires the application of classification model, which have had successes in improving documents processing. However, concerns exploding the frequency of use of documents in many office managers have driven increasing interests...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
In Linux, Sysfs entries are created to let the kernel export information to user space processes as well as to take in user input. The entries go through the File System to locate the show and store functions that are registered for it. Although this method is a good way to give inputs from user to the kernel space while restricting access, it is a slower method as it has to go through the file system...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
In the procedure of China's market-oriented reforms of interest rates, the interest-rate risk becomes increasingly apparent. Analyzing the existing studies and the interbank offered rate trend, this paper finds that LS-SVM, which is short for Least Squares Support Vector Machines, is excel in nonlinear data approximation, which is suitable for interest rate forecasting. Firstly, the paper established...
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