Feature Vector Research Articles

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

ABSTRACT Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL prediction. However, a single model cannot effectively extract state information and obtain accurate prediction results, and its generalisation is not stable under the condition of small sample data. Therefore this paper proposes an intelligent hybrid deep learning model for achieving accurate RUL prediction of rolling bearings. Firstly, the one-dimensional vibration signal is transformed into the corresponding two-dimensional time-frequency diagram via Continuous Wavelet Transform (CWT). Secondly, the diagram is input into a Multilayer Perceptron (MLP) consisting of a basic three-layer feed-forward network to obtain a one-dimensional feature vector. And lastly, the obtained feature vector is input into an integrated model based on Deep Autoregressive and Transformer to produce the probability distribution and obtain the prediction results of rolling bearing RUL. Extensive experiments on two rolling bearing datasets show that the proposed model outperforms six other comparative models in extracting bearing fault features and predicting bearing RUL, which demonstrates that the proposed model can effectively extract bearing fault features and accurately predict bearing remaining useful life.

Combined wavelet transforms and neural network feed-forward model for ECG peak detection and classification

We have focused on development of a combined approach for electrocardiogram (ECG) signal filtering and various ECG peak detection. The filtering model is based on the combination of wavelet transform and neural network where after computing the wavelet coefficients the neural network feed-forward model is used to update the weights. The filtered signal is processed through the convolution layers and bidirectional long short-term memory (Bi-LSTM) architecture to perform the ECG peak detection. Further, we apply a combined feature extraction strategy where wavelet transform and morphological feature are extracted to classify the ECG beats as classify 5 different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC) to examine the heart condition. The feature extraction phase uses wavelet transform, morphological features and high-order statistics to generate the robust features. The obtained feature vector is processed through the principal component analysis (PCA) module to reduce the dimension of feature vector. These features are trained by using support vector machine (SVM) and k-nearest neighbor (KNN) supervised model. The proposed approach is tested on publicly available MIT-BIH dataset where performance analysis shows that the proposed approach obtained average precision, sensitivity and error as 99.98%, 99.96%, and 0.101 which outperforms the existing filtering and peak detection schemes.

An Effective Algorithm Based on Sequence and Property Information for N4-methylcytosine Identification in Multiple Species

Abstract: N4-methylcytosine (4mC) is one of the most important epigenetic modifications, which plays a significant role in biological progress and helps explain biological functions. Although biological experiments can identify potential 4mC sites, they are limited due to the experimental environment and labor-intensive process. Therefore, it is crucial to construct a computational model to identify the 4mC sites. Some computational methods have been proposed to identify the 4mC sites, but some problems should not be ignored, such as those presented as follows: (1) a more accurate algorithm is required to improve the prediction, especially for Matthew’s correlation coefficient (MCC); (2) easier method is needed for clinical research to design medicine or treat disease. Considering these aspects, an effective algorithm using comprehensible encoding in multiple species was proposed in this study. Since nucleotide arrangement and its property information could reflect the sequence structure and function, several feature vectors have been developed based on nucleotide energy information, trinucleotide energy information, and nucleotide chemical property information. Besides, feature effect has been analyzed to select the optimal feature vectors for multiple species. Finally, the optimal feature vectors were inputted into the CatBoost algorithm to construct the identification model. The evaluation results showed that our study obtained the highest MCC, i.e., 2.5%~11.1%, 1.4%~17.8%, 1.1%~7.6%, and 2.3%~18.0% higher than previous models for the A. thaliana, C. elegans, D. melanogaster, and E. coli datasets, respectively. These satisfactory results reflect that the proposed method is available to identify 4mC sites in multiple species, especially for MCC. It could provide a reasonable supplement for biological research.

Folk art classification using support vector machine

<p dir="ltr">Tremendous amounts of effort have been carried out every year by the governments of all the countries to preserve art and culture. Art in the form of paintings, artifacts, music, dance, and cuisines of every country has the utmost importance. The study of Tribal arts provides deep insight into our history and acts as a milestone in the roadmap of our future. This paper focuses on three popular folk arts namely: Gond, Manjusha, and Warli. 300 images of each artwork have been collected from various online repositories. To generate a robust system, data augmentation is applied which results in 7510 images. A feature vector based on a generalized co-occurrence matrix, local binary pattern, HSV histogram, and canny edge detector is constructed and classification is performed using a linear support vector machine. 10- fold cross-validation produces 99.8% accuracy.</p>

A proposed semantic keywords search engine for Indonesian Qur’an translation based on word embedding

Obtaining relevant information from the Holy Qur’an can be really challenging for people who cannot speak Arabic, such as the Indonesian people. One technology implementation which is commonly used to tackle this problem is to develop a search engine application for Al-Qur’an verses. This paper proposes a search engine based on semantic representation keywords for the Indonesian translation of the Al-Qur’an which consists of 3 phases i.e., data preparation, document representation, and search engine development. In the first stage, the Al-Qur’an dataset was built using the official translation of the Al-Qur’an from the Ministry of Religion and then enriched with the Wikipedia corpus. The second phase is document representation which produces feature vectors by utilizing the Word2Vec algorithm. Finally, the development of a search engine that can find the most relevant verses by calculating the cosine similarity between the document and the keywords. It was found that the proposed search engine succeeded in exceeding the performance of ordinary search engines by finding wider information due to the use of semantic keywords. Apart from that, the proposed search engine succeeded in maintaining the relevance of search results by achieving precision and recall levels of 98.7% and 97.3% respectively.

Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection

Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection

A novel and convenient lying cow identification method based on YOLOX and CowbodyNet: A study with applications in a barn

The lying time of cows is a key indicator of their health and comfort. The ability to automatically recognize the lying posture of cows while simultaneously realizing individual cow identification can play an important role in improving cow welfare, increasing milk yield, detecting cow diseases in a timely manner and enabling precision dairy farming management. In this paper, a method of individual identification for lying dairy cows in a barn based on YOLOX and a feature extraction network named CowbodyNet is proposed. When new cows join the herd, there is no need to collect a large number of images to retrain the model. It is very convenient to collect several images of newly added cows lying and store them in the database. First, the low-light images collected at night are enhanced by the multiscale retinex with chromaticity preservation (MSRCP) algorithm to improve the image quality. Then, the YOLOX target detection algorithm is applied to detect and segment cows in the lying posture. Following this, the segmented images of lying cows are input into CowbodyNet to generate feature vectors, which are used to construct a feature vector database. Subsequently, the Euclidean distances between the feature vector of a cow to be identified and the feature vectors in the database are calculated to determine the identification result. The proposed method achieves 94.43% lying cow identification accuracy on a data set containing top-view images of 72 cows. Finally, the individual cow detection and identification model is successfully deployed on the Jetson Xavier NX embedded platform. The results demonstrate the effectiveness and practicability of the proposed cow identification method. This study provides effective technical support for the application of identifying individual lying cows.

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

Comparative Study among Term Frequency-Inverse Document Frequency and Count Vectorizer towards K Nearest Neighbor and Decision Tree Classifiers for Text Dataset

Background: Text classification techniques are increasingly important with the exponential growth of textual data on the internet. Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorizer(CV) are commonly used methods for feature extraction. TF-IDF assigning weights to terms based on their frequency. CV simply counts the occurrences of terms. The performance of CV as well as TF-IDF are evaluated and compared with KNN and DT classifiers across text datasets. Methodology: The investigation begins with preprocessing. The feature vectors are created using both TF-IDF and CV. Feature vectors are passed into the KNN and DT classifiers at in training stage. Experiments are executed the usage of Kaggle's public database Ukraine 10K tweets sentiment_analysis dataset and the Womens ecommerce clothing reviews dataset. Findings: The average of precision, recall, f1 score and accuracy of KNN with TF-IDF were 84.5%, 87%, 83%, 87% respectively and KNN with CV were 83.5%, 87%, 83.5%, 87% respectively. Similarly, average of precision, recall, f1 score and accuracy of DT with TF-IDF were 89%, 89%, 89%, 89% respectively and DT with CV were 89%, 89.5%, 89.5%, 89.5% respectively. The results obtained in this research is consistent with previous similar research result. Conclusions: The performance of TF-IDF is almost similar as CV for a particular dataset and a particular classifier in this study. Novelty: The experiment performed using these classifiers and feature extraction methods on the datasets is a novelty and contribution of this research.

Melanoma classification using generative adversarial network and proximal policy optimization.

In oncology, melanoma is a serious concern, often arising from DNA changes caused mainly by ultraviolet radiation. This cancer is known for its aggressive growth, highlighting the necessity of early detection. Our research introduces a novel deep learning framework for melanoma classification, trained and validated using the extensive SIIM-ISIC Melanoma Classification Challenge-ISIC-2020 dataset. The framework features three dilated convolution layers that extract critical feature vectors for classification. A key aspect of our model is incorporating the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm, which effectively handles data imbalance in the training set by rewarding the accurate classification of underrepresented samples. In this framework, the model is visualized as an agent making a series of decisions, where each sample represents a distinct state. Additionally, a Generative Adversarial Network (GAN) augments training data to improve generalizability, paired with a new regularization technique to stabilize GAN training and prevent mode collapse. The model achieved an F-measure of 91.836% and a geometric mean of 91.920%, surpassing existing models and demonstrating the model's practical utility in clinical environments. These results demonstrate its potential in enhancing early melanoma detection and informing more accurate treatment approaches, significantly advancing in combating this aggressive cancer.

Revisiting Winnow: A modified online feature selection algorithm for efficient binary classification

AbstractWinnow is an efficient binary classification algorithm that effectively learns from data even in the presence of a large number of irrelevant attributes. It is specifically designed for online learning scenarios. Unlike the Perceptron algorithm, Winnow employs a multiplicative weight update function, which leads to fewer mistakes and faster convergence. However, the original Winnow algorithm has several limitations. They include, it only works on binary data, and the weight updates are constant and do not depend on the input features. In this article, we propose a modified version of the Winnow algorithm that addresses these limitations. The proposed algorithm is capable of handling real‐valued data, updates the learning function based on the input feature vector. To evaluate the performance of our proposed algorithm, we compare it with seven existing variants of the Winnow algorithm on datasets of varying sizes. We employ various evaluation metrics and parameters to assess and compare the performance of the algorithms. The experimental results demonstrate that our proposed algorithm outperforms all the other algorithms used for comparison, highlighting its effectiveness in classification tasks.

Deep Learning-Based Hand-Drawn Illustration in Packaging Design of Cultural and Creative Products

Packaging design is a critical component of product marketing and branding, encompassing the visual and structural elements that encase and present goods to consumers. The hand-drawn illustration is a timeless art form that embodies the unique style, skill, and creativity of the artist's hand. This paper presents a novel approach to deep learning techniques for enhancing packaging design through the classification of hand-drawn illustrations. The proposed model is stated as a Weighted Augmented Deep Generative Network (WADGN). The proposed WADGN model uses the augmentation network for the generation of the augmented images for the creative products. With the augmented images features are extracted in the hand-drawn illustration of the products. The extracted features are implemented with the weighted augmented feature vector for the application of the generative deep learning network. The proposed WADGN model uses the feature vector of the deep learning model for the design of creative product design. With the deep learning the creative features of the hand-drawn illustration are classified for the creative package design. Simulation results demonstrated that proposed WADGN model higher performance than the conventional technique such as CNN, LSTM and SVM classifier. The proposed WADGN model achieves the ~21% higher performance than the SVM, ~16% than the LSTM and ~9% improvement than the CNN model.

Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology

Music therapy, enriched by the integration of AI technology, represents a cutting-edge approach to harnessing the therapeutic power of music for mental and emotional well-being. AI algorithms are employed to analyze individual preferences, emotional states, and physiological responses, enabling the creation of personalized music interventions. These interventions can range from mood-enhancing playlists to dynamically generated compositions tailored to the specific needs of the listener. This paper introduces an Optimized Sentimental n-gram Classifier (OSC) model tailored for application in the context of music therapy. Leveraging artificial intelligence (AI) technology and sentiment analysis techniques, the OSC model aims to enhance the understanding and classification of sentiments expressed during music therapy sessions. The OSC model uses the n-gram classifier for the estimation of the feature vector in the music speech signal. The classifier model comprises of the Artificial Intelligence (AI) for the evaluation of the music therapy for the sentimental analysis. Through extensive experimentation and evaluation, the OSC model demonstrates high accuracy, precision, recall, and F1 scores across multiple iterations, indicating its effectiveness in accurately predicting sentiments and classifying sessions. The model's robust performance suggests its potential to assist therapists in better understanding participants' emotional states and tailoring interventions accordingly. By providing a valuable tool for sentiment analysis in music therapy, the OSC model contributes to advancing the integration of AI technology into healthcare practices, with implications for improving patient outcomes and well-being.

Dynamic retrieval of events and associations from memory: An integrated account of item and associative recognition.

Memory theories distinguish between item and associative information, which are engaged by different tasks: item recognition uses item information to decide whether an event occurred in a particular context; associative recognition uses associative information to decide whether two events occurred together. Associative recognition is slower and less accurate than item recognition, suggesting that item and associative information may be represented in different forms and retrieved using different processes. Instead, I show how a dynamic model (Cox & Criss, 2020; Cox & Shiffrin, 2017) accounts for accuracy and response time distributions in both item and associative recognition with the same set of representations and processes. Item and associative information are both represented as vectors of features. Item and associative recognition both depend on comparing traces in memory with probes of memory in which item and associative features gradually accumulate. Associative features are slower to accumulate, but largely because they emerge from conjunctions of already-accumulated item features. I apply the model to data from 453 participants, each of whom performed an item and performed associative recognition following identical study conditions (Cox et al., 2018). Comparisons among restricted versions of the model show that its account of associative feature formation, coupled with limits on the rate at which features accumulate from multiple items, explains how and why the dynamics of associative recognition differ from those of item recognition even while both tasks rely on the same underlying representations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hierarchical multi-level dynamic hyperparameter deformable image registration with convolutional neural network.

Objective. To enable the registration network to be trained only once, achieving fast regularization hyperparameter selection during the inference phase, and to improve registration accuracy and deformation field regularity.Approach. Hyperparameter tuning is an essential process for deep learning deformable image registration (DLDIR). Most DLDIR methods usually perform a large number of independent experiments to select the appropriate regularization hyperparameters, which are time-consuming and resource-consuming. To address this issue, we propose a novel dynamic hyperparameter block, which comprises a distributed mapping network, dynamic convolution, attention feature extraction layer, and instance normalization layer. The dynamic hyperparameter block encodes the input feature vectors and regularization hyperparameters into learnable feature variables and dynamic convolution parameters which changes the feature statistics of the high-dimensional features layer feature variables, respectively. In addition, the proposed method replaced the single-level structure residual blocks in LapIRN with a hierarchical multi-level architecture for the dynamic hyperparameter block in order to improve registration performance.Main results. On the OASIS dataset, the proposed method reduced the percentage of the negative Jacobian determinant by 28.01%, 9.78% and improved Dice similarity coefficient by 1.17%, 1.17%, compared with LapIRN and CIR, respectively. On the DIR-Lab dataset, the proposed method reduced the percentage of the negative Jacobian determinant by 10.00%, 5.70% and reduced target registration error by 10.84%, 10.05%, compared with LapIRN and CIR, respectively.Significance. The proposed method can fast achieve the corresponding registration deformation field for arbitrary hyperparameter value during the inference phase. Extensive experiments demonstrate that the proposed method reduces training time compared to DLDIR with fixed regularization hyperparameters while outperforming the state-of-the-art registration methods concerning registration accuracy and deformation smoothness on brain dataset OASIS and lung dataset DIR-Lab.

A Diffusion Approach to Radiance Field Relighting using Multi‐Illumination Synthesis

AbstractRelighting radiance fields is severely underconstrained for multi‐view data, which is most often captured under a single illumination condition; It is especially hard for full scenes containing multiple objects. We introduce a method to create relightable radiance fields using such single‐illumination data by exploiting priors extracted from 2D image diffusion models. We first fine‐tune a 2D diffusion model on a multi‐illumination dataset conditioned by light direction, allowing us to augment a single‐illumination capture into a realistic – but possibly inconsistent – multi‐illumination dataset from directly defined light directions. We use this augmented data to create a relightable radiance field represented by 3D Gaussian splats. To allow direct control of light direction for low‐frequency lighting, we represent appearance with a multi‐layer perceptron parameterized on light direction. To enforce multi‐view consistency and overcome inaccuracies we optimize a per‐image auxiliary feature vector. We show results on synthetic and real multi‐view data under single illumination, demonstrating that our method successfully exploits 2D diffusion model priors to allow realistic 3D relighting for complete scenes.

Research on transformer based online condition monitoring method for IPM

Abstract The Intelligent Power Module (IPM) has the integrated packaging, leading to the advantages of convenient use, safety and reliability. However, once it fails, it will cause the whole power supply system to be inoperative, and it is necessary to perform online Condition Monitoring (CM) of the IPM. In this paper, we extract the aging characteristics such as dynamic equivalent resistance, peak-to-peak value, switching frequency, and turn-off time only from the voltage and current of IPM drain–source port, and then propose a non-intrusive online CM method for the IPM based on the transformer neural network (TNN). We analyse the internal aging mechanism of the IPM, the changing law of aging features, and construct multi-dimensional aging fusion features, and then the TNN model is used to monitor early parameters drift of multi-dimensional fusion feature vectors and realize the accurate online prediction of IPM health condition. The experimental analysis results show that the fault prediction accuracy reaches 96%, and can realize the health CM under the condition of noise interference, weak aging features and few external observable points.

Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning

In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased challenges when multivariate feature vectors are handled. This paper introduces a novel prediction framework that integrates interpolation and extrapolation techniques. Central to this method are two main innovations: an optimization model that effectively classifies new multivariate data points as either interior or exterior to the known dataset, and a hybrid prediction system that combines k-nearest neighbor (kNN) and linear regression. Tested on the port state control (PSC) inspection dataset at the port of Hong Kong, our framework generally demonstrates superior precision in predictive outcomes than traditional kNN and linear regression models. This research enriches the literature by illustrating the enhanced capability of combining interpolation and extrapolation techniques in supervised learning.

A fault diagnosis method of hot spots for photovoltaic clusters based on model parameters

AbstractUtilizing the direct current‐side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults.

DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.