Neural Network Pattern Recognition Research Articles

Analysis of the Monitoring and Identification Effect of Cognitive Service Technology on Dc System in Power Grid

This study investigates the impact of cognitive service technology on the monitoring and identification capabilities within a direct current (DC) system in a power grid. This paper introduces a novel Pattern Recognition Neural Network (PRNN) by integrating a Pattern Recognition Algorithm (PRA) and Convolutional Neural Networks (CNN) to analyse patterns in the DC system's data. With the increasing complexity and interconnectivity of modern power systems, the need for advanced monitoring and identification solutions becomes crucial. Cognitive service technology, known for its adaptability and learning capabilities, offers a promising avenue for enhancing the performance and reliability of DC systems. The research employs a comprehensive approach, incorporating data analysis, modelling, and simulation to assess the effectiveness of cognitive service technology in monitoring and identifying critical parameters within the DC system. The study aims to contribute valuable insights into the application of cognitive services for improving the overall efficiency and resilience of power grids operating on direct current, thereby fostering advancements in smart grid technologies

Using machine learning to predict artistic styles: an analysis of trends and the research agenda

AbstractIn the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles.

Efficient water-related failure detection in PEM fuel cells: Combining a PEMFCs fractional order impedance model with FFT-PWM techniques and artificial neural network classification

Water management and early detection of faults in proton exchange membrane fuel cells (PEMFCs) are among the most critical constraints that limit the optimal spread of this type of energy. Consequently, it is necessary to enhance the reliability and durability of PEMFCs by developing an approach to diagnose and identify water failure modes. This paper proposes an effective and simple method to detect, diagnose, and classify various water failure modes in PEMFCs using a hybrid diagnostic approach. This approach combines the PEMFC fractional order impedance model (FOIM) with fast Fourier transform pulse width modulation (FFT-PWM) techniques and artificial neural network pattern recognition (ANN-PR) classification. The results show an accurate match between the electrochemical impedance spectroscopy (EIS) experimental data, the Nyquist impedance spectra of FOIM, and the FFT-PWM algorithm as a proposed alternative technique to EIS measurements. Learning of ANN-PR was performed using the frequency spectrum amplitude (FSA) database of the voltage and current signals produced by the PEMFCs FOIM DC/DC boost converter, which was generated using the FFT-PWM algorithm. The ANN-PR achieved low values for error accuracy, with the Low Square Error and Learning Error reaching 6.676 × 10−19 and 1.888 × 10−16, respectively. The elements inside the confusion matrix and the rest of the matrices confirm that the proposed model's accuracy, precision, recall, and high F1 score reached 100%. Furthermore, all predictions made by the ANN-PR model were consistently accurate across all areas of failure detection. Overall, the proposed method helps in analyzing, diagnosing, and classifying fuel cell failure modes such as flooding and drying, which may simplify the health assessment of PEMFC.

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature.

Метод синтеза данных для повышения эффективности обучения нейронных сетей

The article is devoted to the formation of the initial data set for training neural networks. A proven method for creating synthetic data based on a graphics processor using a graphics pipeline is presented. A distinctive feature of this method is its modular architecture, which makes it easy to modify, delete or add individual stages to the synthetic image generation pipeline. A one-stage automatic detector based on a convolutional neural network of the Yolo type has been trained, the quality of the trained model and the operation of the recognition algorithm were also evaluated. Conclusions are drawn regarding the possibility of using this approach when creating representative samples of a large volume and their further use for training neural networks in pattern recognition.

Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis

The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.

Coexistence of Cyclic Sequential Pattern Recognition and Associative Memory in Neural Networks by Attractor Mechanisms.

Neural networks are developed to model the behavior of the brain. One crucial question in this field pertains to when and how a neural network can memorize a given set of patterns. There are two mechanisms to store information: associative memory and sequential pattern recognition. In the case of associative memory, the neural network operates with dynamical attractors that are point attractors, each corresponding to one of the patterns to be stored within the network. In contrast, sequential pattern recognition involves the network memorizing a set of patterns and subsequently retrieving them in a specific order over time. From a dynamical perspective, this corresponds to the presence of a continuous attractor or a cyclic attractor composed of the sequence of patterns stored within the network in a given order. Evidence suggests that the brain is capable of simultaneously performing both associative memory and sequential pattern recognition. Therefore, these types of attractors coexist within the neural network, signifying that some patterns are stored as point attractors, while others are stored as continuous or cyclic attractors. This article investigates the coexistence of cyclic attractors and continuous or point attractors in certain nonlinear neural networks, enabling the simultaneous emergence of various memory mechanisms. By selectively grouping neurons, conditions are established for the existence of cyclic attractors, continuous attractors, and point attractors, respectively. Furthermore, each attractor is explicitly represented, and a competitive dynamic emerges among these coexisting attractors, primarily regulated by adjustments to external inputs.

Application of Neural Networks for Pattern Recognition

This article details the process of creating and training a neural network for the task of classifying dog and cat images using TensorFlow and the MobileNetV2 architecture. Data preparation and preprocessing, including image resizing and normalization, are described. Details of the integration of the pre-trained MobileNetV2 model are given, the process of pre-training the model on specific data is demonstrated, as well as methods of model construction and optimization, including the addition and tuning of additional layers. Special attention is paid to practical application of the trained model for classification of new images, including loading, processing, prediction and visualization of results.

Investigation of the acoustic properties of underwater rainfall noise measured by a bottom-mounted hydrophone and its application to deep learning to classify and estimate rainfall

Although the ocean covers more than 70 % of the Earth's surface, it is difficult to measure precipitation over the ocean due to the technical limitations of platforms such as surface buoys and satellites. Recently, the estimation of precipitation has been tried using the underwater sound generated by rainfall. In this talk, we present the results of comparing underwater noise and rainfall with and without precipitation using the underwater noise and rainfall data provided by OOI (Ocean Observatories Initiative, USA). The acoustic data was acquired by a bottom-moored hydrophone at a depth of 80 m off the coast of Oregon, and the rainfall data was measured from a rain gauge installed on a surface buoy. The 1-minute averaged power spectral densities for ambient noise were computed for 26 days from 5 - 30 April 2018, which were compared with rain rate and wind speed. Based on the comparison results, the presence or absence of rainfall was determined through a pattern recognition neural network, and finally, the amount of precipitation was estimated through multiple linear regression analysis.

Artificial visual‐tactile perception array for enhanced memory and neuromorphic computations

AbstractThe emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross‐modal human–machine interfaces. Inspired by human multisensory signal generation and neuroplasticity‐based signal processing, here, an artificial perceptual neuro array with visual‐tactile sensing, processing, learning, and memory is demonstrated. The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer, endowing individual and synergistic plastic modulation of optical and mechanical information, including short‐term memory, long‐term memory, paired pulse facilitation, and “learning‐experience” behavior. Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of “Pavlov's dog”. The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process. A machine‐learning algorithm is coupled with an artificial neural network for pattern recognition, achieving a recognition accuracy of 70% for bimodal training, which is higher than that obtained by unimodal training. In addition, the artificial perceptual neuron has a low energy consumption of ∼20 pJ. With its mechanical compliance and simple architecture, the neuromorphic bimodal perception array has promising applications in large‐scale cross‐modal interactions and high‐throughput intelligent perceptions.image

Monitoring of polycyclic aromatic hydrocarbons and toxic metals in blue mussels and oysters for human consumption from Danish waters (2005–2019)

Bivalve molluscs have for many years been used as indicator organisms for environmental pollution with various chemical substances. However, limited information is available on the levels of chemical contaminants in mussels and oysters intended for human consumption. Polycyclic aromatic hydrocarbons (PAHs) and toxic metals, such as cadmium (Cd), mercury (Hg) and lead (Pb) have been analysed in edible bivalve samples and the present study provides the results from samples collected in Danish waters from 2005 to 2019. Principal Component Analysis (PCA) and Pattern Recognition Neural Network (PRNN) analysis were applied to data to identify any differences according to organism, locality, and contaminant levels. Differences were confirmed for Pb concentrations being generally higher in mussels than oysters. Based on toxic metals results, the PCA and PRNN analyses made it possible to categorise samples as a mussel or an oyster, as well as make decisions on the origin of a specific sample. All concentrations in mussels and oysters were below the current European Union maximum levels for the contaminants.

The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfOx/W Bilayer-Structured Memory Device.

This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments at the interface of ZnO and HfOx contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfOx layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfOx/W structure holds the potential to be a viable memory component for integration into neuromorphic systems.

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

Transfer of processed data and parameters to ungauged catchments from the most similar gauged counterpart is a common technique in water quality modelling. But catchment similarities for Dissolved Inorganic Nitrogen (DIN) are ill posed, which affects the predictive capability of models reliant on such methods for simulating DIN. Spatial data proxies to classify catchments for most similar DIN responses are a demonstrated solution, yet their applicability to ungauged catchments is unexplored. We adopted a neural network pattern recognition model (ANN-PR) and explainable artificial intelligence approach (SHAP-XAI) to match all ungauged catchments that flow to the Great Barrier Reef to gauged ones based on proxy spatial data. Catchment match suitability was verified using a neural network water quality (ANN-WQ) simulator trained on gauged catchment datasets, tested by simulating DIN for matched catchments in unsupervised learning scenarios. We show that discriminating training data to DIN regime benefits ANN-WQ simulation performance in unsupervised scenarios ( p< 0.05). This phenomenon demonstrates that proxy spatial data is a useful tool to classify catchments with similar DIN regimes. Catchments lacking similarity with gauged ones are identified as priority monitoring areas to gain observed data for all DIN regimes in catchments that flow to the Great Barrier Reef, Australia.

The KI-ASIC Dataset

We present a novel dataset captured from a BMW X5 test carrier within the German research project KI-ASIC for use in radar sensor development and autonomous driving research. Our work aims at providing a blueprint for the process of creating labeled datasets for the development of neural networks for pattern recognition in radar data in the automotive environment. With a variety of different sensor types such as wide angle color cameras, a high-resolution color stereo camera, an Ouster OS1-64 laser scanner and three novel Infineon radar sensors, we recorded over 100,000 scenes of real traffic scenarios as well as defined test scenarios with a frequency of 10 Hz. The scenarios in real traffic contain inner-city situations, but also scenes from rural areas with static and dynamic objects. Besides, the defined test scenarios are based on the NCAP scenarios and focus mostly on turning, overtaking and follow-up maneuvers. The data from the different sensors is calibrated, synchronized and timestamped including raw and rectified information. Our dataset also contains labels for all detected objects from a defined class list with distance and angle properties. The content of the paper aims at the description of the recording test carrier, the format of the provided sensor data and the structure of the overall dataset

Supervised learning and pattern recognition in photonic spiking neural networks based on MRR and phase-change materials

Traditional computers are limited by the separation of memory and processor units, is difficult to achieve fast, efficient, and low-power computing. While photonic spiking neural networks (SNNs) can overcome these shortcomings, they encounter limitations in large-scale integration. Silicon photonics platform, compatible with mature Complementary Metal Oxide Semiconductor (CMOS) platforms, is a promising candidate for realizing large-scale photonic SNNs. In this work, we proposed an integrated photonic SNN by exploiting the photonic properties of phase-change material (PCM) Ge2Sb2Te5 (GST) and micro-ring resonators (MRR), and demonstrated its integrate-and-fire (IF) behavior. Based on a system-level behavioral model, we adopt an improved Tempotron-like ReSuMe supervised learning algorithm to train the proposed photonic SNNs and complete a pattern recognition task for the clock's 12 clockwise directions. Then the influence of different noise levels is considered, and the accuracy is close to 1 when the noise level is less than 0.2. We propose a photonic implementation of such an SNN system, and use wavelength division multiplexing to achieve a scalable architecture for the pattern recognition task. The collaborative design and optimization of hardware architecture and algorithm are realized, providing a theoretical basis for the realization of photonic SNN based on MRRs and PCM.

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the joints positions of a 2-DOF robot were used to estimate the external force sensor signal, which was attached at the robot end-effector, and the external joint torques of this robot based on a multilayer feedforward NN (MLFFNN). In the current work, the estimated force sensor signal and the external joints’ torques from the previous work are used as the inputs to the proposed designed PRNN, and its output is whether a collision is found or not. The designed PRNN is trained using a scaled conjugate gradient backpropagation algorithm and tested and validated using different data from the training one. The results prove that the PRNN is effective in classifying the force signals. Its effectiveness for classifying the collision cases is 92.8%, and for the non-collisions cases is 99.4%. Therefore, the overall efficiency is 99.2%. The same methodology and work are repeated using a PRNN trained using another algorithm, which is the Levenberg–Marquardt (PRNN-LM). The results using this structure prove that the PRNN-LM is also effective in classifying the force signals, and its overall effectiveness is 99.3%, which is slightly higher than the first PRNN. Finally, a comparison of the effectiveness of the proposed PRNN and PRNN-LM with other previous different classifiers is included. This comparison shows the effectiveness of the proposed PRNN and PRNN-LM.

An intelligent pattern recognition model for assessment of terrorists’ activities in Nigeria

Terrorism and its brutal tendencies constitute a major setback to the development process of the Nigerian economy leading to severe loss of lives, destruction of properties, and a decline of interest in investment by both local and foreign investors. Many models for assessment of terrorist’s activities lack the ability of learning from previous patterns in order to guide pre-emptive actions against future occurrences, and there are no established regional pattern of weaponry, types of attack, as well as types of victims of terrorists’ operations. This study seeks to build a robust intelligent model for recognizing several terrorists’ patterns in each of the six geo-political zones of Nigeria. A data set of 5,503 instances of terrorists’ activities in Nigeria was obtained and a pattern recognition model was built using Artificial Neural Networks (ANN) with 70%, 15%, and 15% data splits for training, validation, and testing respectively. A 10-10-6 ANN architecture was designed and trained using the scaled conjugate gradient backpropagation algorithm. The training was carried out using Matlab’s neural network pattern recognition toolkit. In order to numerically represent categorical data, a sort-order scheme was developed by the authors and utilized. The results showed average percentage scores for accuracy, precision, recall and F1-score as 99.89, 99.96, 100 and 99.98 respectively. This showed acceptable performance. The developed model is therefore considered a robust one for recognition of terrorists’ patterns in Nigeria. This would assist security agencies to deal with terrorists’ incidences with high intelligent information and advanced preparation for prevention and control. The developed model is highly recommended for use by the security agencies in the country.

Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME

In this work, as an extension of previous machine learning studies, three novel techniques, namely local interpretable model-agnostic explanations (LIME), neural network pattern recognition and association rule mining (ARM) were utilized for proton exchange membrane (PEM) and anion exchange membrane (AEM) electrolyzer database for hydrogen production. The main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density, power density and polarization. Using this technique, it was possible to uncover rules or paths that lead to high current density, low power density and low polarization. ARM provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer, using pure Pt on the cathode surface and using pure carbon as the cathode support. In addition, LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cathode/anode support/surface elements, operational variables like K2CO3 or KOH concentration in the electrolyte, certain membrane types, gas diffusion layers, and applied potential on current density. It was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation, which can also guide future research.

Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown.

Using artificial neural networks for pattern recognition of post-surgical infections

The objective is to use Artificial Intelligence (AI) for identifying which surgical patients have a likelihood ratio of developing an infection. We included in the study all the patients who underwent surgeries with wound class considered clean at a regional public hospital in Brazil. The first step was a retrospective analysis of risk factors and a correlation test for identifying which clinical variables are best related to post-discharge infections. Then, we developed an Artificial Neural Network (ANN) for pattern recognition to detect incidence of infections.  The ANN can make accurate predictions in 77.3% of the cases in which an infection will occur, and the AUROC of the model is 0.9050. Thus, it is possible to take actions before the patients develop it, improving the quality of life and mental health as well as avoid increasing costs.