Extract Energy Features Research Articles

An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.

Research on Aging State Detection Method of Rubber O-ring Sealing Structure

The rubber seal structure is used extensively in mechanical devices, long-term use may bring major safety hazards and economic losses. In order to ensure the healthy operation of machinery and equipment, it is necessary to test the aging condition of the rubber sealing structure nondestructive online. In order to address the problem of unsteady vibration signals, a complex nonstationary signal is degraded into the sum of multiple product functions by means of global local mean decomposition, and then the energy features in these PF components are extracted, and the first three components with the largest energy eigenvalues are selected to form a sensitive feature vector set. Overall, the results of the search data show that the proposed method can efficiently extract energy features from a large set of original features, and the first three largest energy features can be selected to form a sensitive feature set, which can provide effective data information for the detection of the aging state of Rubber O-ring Sealing Structure (ROSS) in the next step.

Feature Extraction on the Difference of Plant Stem Structure Based on Ultrasound Energy

Plant growth is closely related to the structure of its stem. The ultrasonic echo signal of the plant stem carries much information on the stem structure, providing an effective means for analyzing stem structure characteristics. In this paper, we proposed to extract energy features of ultrasonic echo signals to study the structure of the plant stem. Firstly, it is found that there are obvious different ultrasonic energy changes in different kinds of plant stems whether in the time domain or the frequency domain. Then, we proposed a feature extraction method, density energy feature, to better depict the interspecific differences of the plant stems. In order to evaluate the extracted 24-dimensional features of the ultrasound, the information gain method and correlation evaluation method were adopted to compute their contributions. The results showed that the mean density, an improved feature, was the most significant contributing feature in the four living plant stems. Finally, the top three features in the feature contribution were selected, and each two of them composed as 2-D feature maps, which have significant differentiation of the stem species, especially for grass and wood stems. The above research shows that the ultrasonic energy features of plant stems can provide a new perspective for the study of distinguishing the structural differences among plant stem species.

A Novel Fault Detection and Classification Strategy for Photovoltaic Distribution Network Using Improved Hilbert–Huang Transform and Ensemble Learning Technique

Due to the increased integration of distributed generations in distributed networks, their development and operation are facing protection challenges that traditional protection systems are incapable of addressing. These problems include variations in the fault current during various operation modes, diverse distributed network topology, and high impedance faults. Therefore, appropriate and reasonable fault detection is highly encouraged to improve the protection and dependability of the distributed network. This paper proposes a novel technique that employs an improved Hilbert–Huang Transform (HHT) and ensemble learning techniques to resolve these challenges in a photovoltaic distributed network. First, improved HHT is utilized to extract energy features from the current signal. Second, variational mode decomposition (VMD) is applied to extract the intrinsic mode function from the zero component of the current signal. Then, the acquired energy feature and intrinsic mode function are input to the ensemble learning technique for fault detection and classification. The proposed technique is implemented using MATLAB software environment, including a classification learner app and SIMULINK. An evaluation of the results is conducted under normal connected mode (NCM) and island mode (ISM) for radial and mesh-soft normally open point (SNOP) configurations. The accuracy of the ensemble bagged trees technique is higher when compared to the narrow-neural network, fine tree, quadratic SVM, fine-gaussian SVM, and wide-neural network. The presented technique depends only on local variables and has no requirements for connection latency. Consequently, the detection and classification of faults using the proposed technology are reasonable. The simulation results demonstrate that the proposed technique is superior to the neural network and support vector machine, achieving 100%, 99.2% and 99.7% accurate symmetrical and unsymmetrical fault detection and classification throughout NCM, ISM, and dynamic operation mode, respectively. Moreover, the developed technique protects DN effectively in radial and mesh-SNOP topologies. The suggested strategy can detect and classify faults accurately in DN with/without DGs. Additionally, this technique can precisely detect high and low impedance faults within 4.8 ms.

Processing of echocardiographic images using segmentation, feature extraction and classification for detection of heart abnormality

This article is mainly focused to accurately detecting any abnormality of heart if present using echocardiographic image of the patient. Heart abnormalities are now a days very common not only in India but all over the globe irrespective of age and gender. The detection of abnormality is achieved by using Artificial neural network (ANN) Classifier. However, processing of the image is achieved through preprocessing, segmentation, feature extraction and then achieving classification. Processing of image for removal of noise and enhancement is carried out as Preprocessing of image followed by segmentation. The most significant processing task is segmentation which is discussed in detail and preferable algorithm which overcomes the drawbacks and limitations of previous algorithms is proposed. This algorithm is a solution to all problems faced in previous algorithms. carried out using different techniques, three different segmentation techniques are discussed where algorithm proposed Reaction Diffusion Level Set Segmentation (RDLSS) is better than other three methods also overcome the problems faced in previous algorithms, then feature extraction is done to extract energy features where the novelty of the research is use of symlet, Debauches and Bio orthogonal filters for feature extraction and these features are used to classify the images as normal or abnormal using ANN classifier. The ANN classifier is effective and efficient resulting in accuracies of greater than 98%. The results are also clinically validated by doctors.

Energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method

Internal leakage is one of themost common faultsinhydraulic cylinders, and seal wear is the main factor in internal leakage. However, it is difficult to detect seal wear and internal leakage in hydraulic cylinder using present approaches due to the complex hydraulic system. Therefore, an intelligent fault diagnosis method based energy features fusion is proposed to detect seal wear and internal leakage. First, computational fluid dynamics(CFD) technology was adopted to analyze the flow field in the internal leakage area of hydraulic cylinder, and it was found that energy features of pressure signal are related to internal leakage. Then, wavelet packet transform is appliedto extract energy features of pressure signal. Finally,energy featuresis decomposed into statistics by multivariate statisticstheory. Statistics are used to detect piston seal wear and internal leakage.The proposed method creativelystudiesseal wear and internal leakage from the perspective of flow field analysis, which doesnot requirealarge number of faultsamplesand complicated parametersoptimization. Experimental investigations are performed to validate the performance of the proposed approach. It is shown that the proposed method has much more robustness and accuracy than several classical fault diagnosis methods. The study does provide an effective way to detect seal wear and internal leakage in hydraulic cylinder.

A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

Electroencephalogram (EEG) signal processing is a very important module in the brain-computer interface system. As an important physiological feature of the human body, EEG signals are closely related to the functional state of the cerebral nervous system. However, the EEG signals collected on the scalp are generally weak and inevitably subject to various noise interferences. In order to remove artifacts from the EEG in brain computer interfaces (BCIs), a fusion algorithm for EEG signal preprocessing is proposed. The fusion method includes the following steps: firstly, the raw EEG signals are separated into a set of statistics independent components (ICs) by the improved FastICA algorithm. Then, each independent component is decomposed into a series of intrinsic mode functions (IMFs) by using the improved empirical mode decomposition method (EMD). Many IMFs with high-frequency noise are deleted. The rest of the IMFs are reconstructed. Furthermore, artifacts are further eliminated by iterative process of the improved FastICA algorithm, and then, the EEG signals are reconstructed again by inverse ICA. Finally, the cleaned EEG signal was obtained. The comparative experiment shows that the EMD-ICA fusion algorithm not only accurately eliminates the artifact components but also better retains the local characteristics of the raw EEG. Continuous wavelet transform was used to extract energy features of μ rhythm and β rhythmic to represent the characteristics of EEG signals under different motor imageries. These two features are normalized and used as the input data of the convolutional neural network (CNN) designed by the paper, and the two kinds of features are learned by CNN, and then, the two-classification problem of motor imagery EEG signals is completed. The experimental results show that the average classification accuracy and kappa value of the proposed method are higher than those of SVM and SAE for most subjects.

Breakdown Voltage Prediction for Sphere and Semispheroid Geometries With Gaussian Process Regression-Based Model Under the Application of Lightning Impulses of Both Polarities

The design of high-voltage (HV) systems is principally dependent on the discharge voltage of their insulation. Sphere geometry and semispheroid geometry are extremely important in HV systems, such as ground rods and gas-insulated substations (GISs). Hence, in this work, a machine learning algorithm is proposed to develop a model to predict the discharge characteristics of air for sphere and semispheroid geometries. Finite element method (FEM) simulations have been performed to extract different electric fields and energy features of air gaps in the range of 5-40 mm under lightning impulses of both polarities. While developing the model, these features along with gap lengths are considered. The features have been used for training a machine learning algorithm based on the Gaussian process regression (GPR) to develop the model. The outcomes received from the model are ratified with measured experimental data. A good comparison between the two establishes the fidelity of the novel model. The proposed methodology is also compared with the other state-of-the-art techniques and found good. Remarkable performance has been acquired for other gap geometries as well.

Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm

In the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for feature extraction of vibration signals, which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals, and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition. The features are visualized by the K-Means clustering method, and the results show that the extracted energy features can accurately distinguish the different states of the bearing. Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algorithm is proposed to identify the bearing faults. Compared with the Particle Swarm Algorithm, Beetle Algorithm can quickly find the error extreme value, which greatly reduces the training time of the model. At last, two experiments are conducted, which show that the accuracy of the model can reach more than 95%, and the model has a certain anti-interference ability.

Research on the Application of Wireless Wearable Sensing Devices in Interactive Music

Wireless wearable devices can greatly assist and promote the artistic presentation of interactive music and have attracted the attention of more and more composers, musicians, dancers, and visual artists. It can pick up data information in real time, integrate with performers, and provide immersive performance experience. It not only builds a bridge between subjective feeling and spiritual perception for performers and audience but also enables audience to directly observe art information better. This paper mainly introduces the development process of a wearable sensor system designed for monitoring interactive music movement. Firstly, an interactive music motion model is established according to the principle of human body kinematics, and the experimental scheme of measuring the swaying angle of interactive music with a single sensor device is standardized. A multisensor fusion algorithm is proposed to estimate the swing angle of interactive music. Based on the “cost‐incentive” emotional model, the wireless wearable device and interactive music model are regarded as continuous variables determined by the emotional effect value and the incentive value. Extract energy, rhythm, harmony, time domain, and spectrum features of interactive music of wireless wearable devices, and reduce the dimension of a music feature space through principal component analysis, spatial projection, and relief feature selection. Finally, the practicability of the system and the accuracy of the algorithm are verified by experiments. The recognition rate of wireless wearable devices and interactive music realized based on this algorithm was improved.

A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM

Considering frequency domain energy distribution differences of bearing vibration signal in the different failure modes, a rolling bearing fault pattern recognition method is proposed based on orthogonal wavelet packet decomposition and Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The orthogonal three-layer wavelet packet decomposition is used to obtain wavelet packet decomposition coefficients from low frequency to high frequency. Rolling bearing raw vibration signals are firstly decomposed into the wavelet signals of different frequency bands, then different frequency band signals are reconstructed respectively to extract energy features, which form feature vectors as the model input of GMM-HMM. A large number of samples are trained to get model parameters for different bearing faults, then several groups of test data are adopted to verify GMM-HMMs so different fault types of rolling bearings are recognized. By calculating the current state appearance probability of monitoring data in GMM-HMMs, different failure patterns are recognized and evaluated from the maximum probability. Similarly, we establish GMM-HMMs for different grade fault samples and evaluated the performance degradation state. Test results show that the proposed fault diagnosis approach can identify accurately the fault pattern of rolling bearings and evaluate performance degradation of bearings.

Exploring Macroscopic and Microscopic Fluctuations of Elicited Facial Expressions for Mood Disorder Classification

In the clinical diagnosis of mood disorder, a large proportion of patients with bipolar disorder (BD) are misdiagnosed as having unipolar depression (UD). Generally, long-term tracking is required for patients with BD to conduct an appropriate diagnosis by using traditional diagnosis tools. A one-time diagnosis system for facilitating diagnosis procedures is thus highly desirable. Accordingly, in this study, the facial expressions of patients with BD, patients with UD, and healthy controls elicited by emotional video clips were used for conducting mood disorder classification; the classification was performed by exploring the temporal fluctuation characteristics among the three groups. First, macroscopic facial expressions characterized by action units (AUs) were applied for describing the temporal transformation of muscles. Modulation spectrum analysis was applied to extract short-term intensity variations in the AUs. An interval-based multilayer perceptron (MLP) neural network was then used to classify mood disorder on the basis of the detected AU intensities. Moreover, motion vectors (MVs) were employed to describe subtle changes in facial expressions in the microscopic view. Eight basic orientations of MV change were considered for representing microfluctuation. Wavelet decomposition was then applied to extract entropy and energy features in different frequency bands. A long short-term memory model was finally used to model long-term variations for conducting mood disorder classification. A decision-level fusion approach was conducted on the combined results of macroscopic and microscopic facial expressions. For evaluating the described methods, the facial expressions elicited from the 36 subjects (12 from each of the BD, UD, and control groups) were used in 12-fold cross-validation experiments. Approaches for macroscopic and microscopic expressions achieved classification accuracies of 63.9 and 66.7 percent, respectively, and the accuracy of the fusion approach reached 72.2 percent. The results indicate that macroscopic and microscopic view descriptors are complementary to each other and helpful for conducting mood disorder classification.

Validation and physiological association of changes in magnetic resonance imaging radiomic features in response to androgen deprivation therapy in patients with high-risk prostate cancer.

43 Background: Biochemical response to neoadjuvant androgen deprivation therapy (nADT) predicts relapse-free and overall survival for prostate cancer (PCa). Radiomic features change in response to nADT, showing potential as prognostic imaging biomarkers. Using multi-parametric MRI (mpMRI) before and after nADT, we aim to validate these changes, evaluate reproducibility and assess their association with previously described ADT-induced reductions in quantitative blood flow (BF) and blood volume (BV) imaging parameters. Methods: 20 patients with high-risk PCa received 4 mpMRI scans, 2 pre-nADT and 2 after 3 months therapy. T2-weighted sequences were used to define tumour regions-of-interest (ROI) which were subtracted from the prostate outline to form the benign prostate ROI. Established kinetic models were applied to dynamic contrast susceptibility MR sequences to derive changes in BF and BV in both ROI. Pyradiomics was used to extract homogeneity and energy features from both ROI. A paired student T-test compared feature values pre- and post-nADT. An inter-class correlation coefficient (ICC) calculated using mean values from consecutive scans assessed reproducibility. Feature changes and physiological parameters were correlated using Pearson’s coefficient. Results: In response to nADT, homogeneity and energy values significantly increased in benign prostate (p < 0.001) and decreased in tumour (p < 0.005). Both features exhibited high reproducibility pre- and post-nADT; all ICC ≥0.89. The reduction in tumour homogeneity and energy showed a positive association with the decline in BF and BV induced by ADT; all r 0.69-0.78, all p < 0.001. No correlation was seen in benign prostate. Conclusions: Energy and homogeneity radiomic features in benign and malignant prostate are reproducible and show significant reciprocal change in response to nADT. Validation of these changes and the strong association in tumour with ADT-induced physiological effects confirms their potential as prognostic biomarkers in high-risk PCa.

Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion

Aiming at the problem of gear fault diagnosis, in order to effectively extract the features and improve the accuracy of gear fault diagnosis, the method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion is proposed in this research. The proposed wavelet-packet independent component analysis feature extraction method can effectively combine the advantages of wavelet packet and independent component analysis methods and acquire more comprehensive feature information. Besides, the proposed kernel-function-fusion support vector machine can well integrate the advantage characteristics of each kernel function. The energy features of wavelet packet coefficients are acquired with four-layer wavelet packet decomposition and then the extracted energy features are further optimized by the independent component analysis method. The kernel-function-fusion support vector machine method is adopted to realize the gear fault diagnosis. Two kernel function models with the best self-classification accuracy are employed to serve the gear fault diagnosis corporately. The test samples are primarily classified by the main kernel function model, and then some samples are selected to be reclassified with the other kernel function model. Finally, the two kernel function models cooperate to determine the type of test samples. The comparison investigations demonstrate that the proposed method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion achieves very high diagnosis accuracy.

A Novel Approach for Design and Analysis of Diabetic Retinopathy Glaucoma Detection Using Cup to Disk Ration and ANN

Glaucoma is an eye disease which damages the optic nerve of the eye and becomes severe over a period of time. It is caused due to buildup of pressure inside the eye. Glaucoma tends to be inherited and may not show up until later in life. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. It is caused due to buildup of pressure inside the eye. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. The early detection of glaucoma is an important in human life in order to enable appropriate monitoring, treatment and to minimize the risk of irreversible visual field loss. Although advances in ocular imaging offer the potential for earlier diagnosis, the best method is to involve a combination of information from structural and functional tests. In this proposed method both structural and energy features are considered, then analyzed to classify as glaucomatous image. Energy distribution over cup to disk ration were applied to find these important texture energy features. Finally extracted energy features are applied to Multilayer Perceptron (MLP) and Back Propagation (BP) neural network for effective classification by considering normal subject's extracted energy features. Naive Bayes classifies the images in the database with the accuracy of 89.6%. MLP-BP Artificial Neural Network (ANN) algorithm classifies the images in the database with the accuracy of 90.6%.

Online Signature Verification Based on DCT and Sparse Representation.

In this paper, a novel online signature verification technique based on discrete cosine transform (DCT) and sparse representation is proposed. We find a new property of DCT, which can be used to obtain a compact representation of an online signature using a fixed number of coefficients, leading to simple matching procedures and providing an effective alternative to deal with time series of different lengths. The property is also used to extract energy features. Furthermore, a new attempt to apply sparse representation to online signature verification is made, and a novel task-specific method for building overcomplete dictionaries is proposed, then sparsity features are extracted. Finally, energy features and sparsity features are concatenated to form a feature vector. Experiments are conducted on the Sabancı University's Signature Database (SUSIG)-Visual and SVC2004 databases, and the results show that our proposed method authenticates persons very reliably with a verification performance which is better than those of state-of-the-art methods on the same databases.

An Energy Computing Method Inspired from Visual Cognitive Function for Dynamic Behavioural Detection in Video Frames

The brain visual cortical simple cells have strong response to notable edges with directivity and contrast of light and dark, as well as the non-classical receptive fields of the neurons in visual cortex that have inhibition function to small light-spot stimulation. Because of this property, human vision system contrast sensitivity tends to dynamic videos. This paper, based on biological visual features, constructs an energy-computing model for dynamic video behaviors analysis, and designs computing methods for strengthening selectivity to directions of edges and inhibiting energy of non-significant areas in the images. The experiment is conducted on 30,000 frames of dynamic behaviors in video and shows 90% accuracy, which proves that the proposed method is capable to simulate the function of visual cortex simple cells, i.e. the enhancement to directional selection, and the inhabitation function of non-classical receptive fields, as well as extract energy features of dynamic behaviors in video. This contributes a choice for computer image processing and improves the understanding of machine vision.

A Study on Spiral Bevel Gear Fault Detection Using Artificial Neural Networks and Wavelet Transform

Based on normal and defective gears of spiral bevel gear pair test, a study is represented to develop the performance of gear fault detection with artificial neural networks and wavelet transform. In order to research the relevant studies of gear failures, a gear fault test rig is designed and constructed, with which vibration test are processed for collecting the signals of a gearbox from this rig. The noise is removed from the original time-domain vibration signals by application of wavelet analysis threshold technique. The extracted energy features from those preprocessed signals are implemented by the wavelet transform, which are used as inputs to the artificial neural networks for two-pattern (normal or fault) recognition. The results show that the represented recognition accuracy of the ANN and WT method for gear fault diagnosis is 100% that is much higher compared with the results of application of ANN separately.

An optimal kernel feature extractor and its application to EEG signal classification

An optimal nonlinear feature extractor for extracting energy features under two different kinds of patterns is proposed. It carries out the simultaneous diagonalization of two signal covariance matrices in a high-dimensional kernel transformed space, and thus promises to find features which are more discriminant, especially when the original data have nonlinear structures. Two operations, whitening transform and projection transform, are involved in kernel spaces. The mechanism of the feature extractor and its effectivity are shown with simulation data and the classification task of real electroencephalographic (EEG) signals.