Search Results (99)

Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color–word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals. Full article

The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios. Full article

The global dissemination of SARS-CoV-2 resulted in the emergence of several variants, including Alpha, Alpha + E484K, Beta, and Omicron. Our research integrated the study of eukaryotic translation factors and fundamental components in general protein synthesis with the analysis of SARS-CoV-2 variants and vaccination status. Utilizing statistical methods, we successfully differentiated between variants in infected individuals and, to a lesser extent, between vaccinated and non-vaccinated infected individuals, relying on the expression profiles of translation factors. Additionally, our investigation identified common causal relationships among the translation factors, shedding light on the interplay between SARS-CoV-2 variants and the host’s translation machinery. Full article

With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping. Full article

The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra. Full article

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs. Full article

The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis model to simultaneously excel at high-quality sampling, achieve mode convergence with diverse sample representation, and perform rapid sampling. In this paper, we explore the potential of Quantum Boltzmann Machines (QBMs) for image synthesis, leveraging the D-Wave 2000Q quantum annealer. We undertake a comprehensive performance assessment of QBMs in comparison to established generative models in the field: Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Our evaluation is grounded in widely recognized scoring metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Scores. The results of our study indicate that QBMs do not significantly outperform the conventional models in terms of the three evaluative criteria. Moreover, QBMs have not demonstrated the capability to overcome the challenges outlined in the Trilemma of Generative Learning. Through our investigation, we contribute to the understanding of quantum computing’s role in generative learning and identify critical areas for future research to enhance the capabilities of image synthesis models. Full article

The restricted Boltzmann machine (RBM) is a generative neural network that can learn in an unsupervised way. This machine has been proven to help understand complex systems, using its ability to generate samples of the system with the same observed distribution. In this work, an Ising system is simulated, creating configurations via Monte Carlo sampling and then using them to train RBMs at different temperatures. Then, 1. the ability of the machine to reconstruct system configurations and 2. its ability to be used as a detector of configurations at specific temperatures are evaluated. The results indicate that the RBM reconstructs configurations following a distribution similar to the original one, but only when the system is in a disordered phase. In an ordered phase, the RBM faces levels of irreproducibility of the configurations in the presence of bimodality, even when the physical observables agree with the theoretical ones. On the other hand, independent of the phase of the system, the information embodied in the neural network weights is sufficient to discriminate whether the configurations come from a given temperature well. The learned representations of the RBM can discriminate system configurations at different temperatures, promising interesting applications in real systems that could help recognize crossover phenomena. Full article

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems. Full article

Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data. Full article

This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications. Full article

A new approach is introduced to address the subject dependency problem in P300-based brain-computer interfaces (BCI) by using transfer learning. The occurrence of P300, an event-related potential, is primarily associated with changes in natural neuron activity and elicited in response to infrequent stimuli, which can be monitored non-invasively through an electroencephalogram. However, implementing P300-based BCI in real-time requires many training samples and time-consuming calibration, making it challenging to use in practical applications. To tackle these challenges, the proposed approach harnesses the high-level feature extraction capability of a deep neural network, achieved through fine-tuning. To ensure similar distributions of feature extraction data, the approach of aligning data in Euclidean space is employed, which is then applied to a discriminatively restricted Boltzmann machine with a single layer for P300 detection. The performance of the proposed method on the BCI Competition III dataset II and the BCI competition II dataset II, the state-of-the-art dataset, was evaluated and compared with previous studies. The results showed that robust performance could be achieved using a small number of training samples, demonstrating the effectiveness of the transfer learning approach in P300-based BCI applications. Full article

A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Information (EMI) are used as learning and objective functions. Coincidentally, EMI is essentially the same as Semantic Mutual Information (SeMI) proposed by the author 30 years ago. This paper first reviews the evolutionary histories of semantic information measures and learning functions. Then, it briefly introduces the author’s semantic information G theory with the rate-fidelity function R(G) (G denotes SeMI, and R(G) extends R(D)) and its applications to multi-label learning, the maximum Mutual Information (MI) classification, and mixture models. Then it discusses how we should understand the relationship between SeMI and Shannon’s MI, two generalized entropies (fuzzy entropy and coverage entropy), Autoencoders, Gibbs distributions, and partition functions from the perspective of the R(G) function or the G theory. An important conclusion is that mixture models and Restricted Boltzmann Machines converge because SeMI is maximized, and Shannon’s MI is minimized, making information efficiency G/R close to 1. A potential opportunity is to simplify deep learning by using Gaussian channel mixture models for pre-training deep neural networks’ latent layers without considering gradients. It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep learning but is far from enough. Combining semantic information theory and deep learning will accelerate their development. Full article

Energy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5). Full article

Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms. Full article