Search Results (7)

The Hjorth distribution is more flexible in modeling various hazard rate shapes, including increasing, decreasing, and bathtub shapes. This makes it highly useful in reliability analysis and survival studies, where different failure rate behaviors must be captured effectively. In some practical experiments, the observed data may appear to be continuous, but their intrinsic discreteness requires the development of specialized techniques for constructing discrete counterparts to continuous distributions. This study extends this methodology by discretizing the Hjorth distribution using the survival function approach. The proposed discrete Hjorth distribution preserves the essential statistical characteristics of its continuous counterpart, such as percentiles and quantiles, making it a valuable tool for modeling lifetime data. The complexity of the transformation requires numerical techniques to ensure accurate estimations and analysis. A key feature of this study is the incorporation of Type-II censored samples. We also derive key statistical properties, including the quantile function and order statistics, and then employ maximum likelihood and Bayesian inference methods. A comparative analysis of these estimation techniques is conducted through simulation studies. Furthermore, the proposed model is validated using two real-world datasets, including electronic device failure times and ball-bearing failure analysis, by applying goodness-of-fit tests against alternative discrete models. The findings emphasize the versatility and applicability of the discrete Hjorth distribution in reliability studies, engineering, and survival analysis, offering a robust framework for modeling discrete data in practical scenarios. To our knowledge, no prior research has explored the use of censored data in analyzing discrete Hjorth-distributed data. This study fills this gap, providing new insights into discrete reliability modeling and broadening the application of the Hjorth distribution in real-world scenarios. Full article

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences. Full article

Several variations of stochastic processes have been studied in the literature to obtain reliability estimations of products and systems from degradation data. As the degradation trajectories may have different degradation rates, it is necessary to consider alternatives to characterize their individual behavior. Some stochastic processes have a constant drift parameter, which defines the mean rate of the degradation process. However, for some cases, the mean rate must not be considered as constant, which means that the rate varies in the different stages of the degradation process. This poses an opportunity to study alternative strategies that allow to model this variation in the drift. For this, we consider the Hjorth rate, which is a failure rate that can define different shapes depending on the values of its parameters. In this paper, the integration of this hazard rate with the Wiener process is studied to individually identify the degradation rate of multiple degradation trajectories. Random effects are considered in the model to estimate a parameter of the Hjorth rate for every degradation trajectory, which allows us to identify the type of rate. The reliability functions of the proposed model is obtained through numerical integration as the function results in a complex form. The proposed model is illustrated in two case studies based on a crack propagation and infrared LED datasets. It is found that the proposed approach has better performance for the reliability estimation of products based on information criteria. Full article

Machine learning techniques are a widespread approach to monitoring and diagnosing faults in electrical machines. These techniques extract information from collected signals and classify the health conditions of internal components. Among all internal components, bearings present the highest failure rate. Classifiers commonly employ vibration data acquired from electrical machines, which can indicate different levels of bearing failure severity. Given the circumstances, this work proposes a methodology for detecting early bearing failures in wind turbines, applying classifiers that rely on Hjorth parameters. The Hjorth parameters were applied to analyze vibration signals collected from experiments to distinguish states of normal functioning and states of malfunction, hence enabling the classification of distinct conditions. After the labeling stage using Hjorth parameters, classifiers were employed to provide an automatic early fault identification model, with the decision tree, random forest, support vector machine, and k-nearest neighbors methods presenting accuracy levels of over 95%. Notably, the accuracy of the classifiers was maintained even after undergoing a dimensionality reduction process. Therefore, it can be stated that Hjorth parameters provide a feasible alternative for identifying early faults in wind generators through time-series analysis. Full article

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy. Full article

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented. Full article

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points. Full article