Robustness of mobile robot localization using recurrent convolutional neural network

In this paper, alternative direction finding methods have been proposed using deep learning techniques. Firstly, Regeression and Classification models have created by using Convolutional Neural Networks (CNNs). In the second Convolutional Neural Networks and Recurrent Neural Networks (RNNs) have been utilized in the proposed methods. Despite having vast amount of direction finding studies, utilization of neural networks is scarce in literature and past works mostly only includes usage of CNNs. In this study, direction finding is performed by learning signals reaching multiple antenna arrays by networks. Created neural networks have been fed with different data formats and their performances against noisy and no-noise data have been shown. In addition, comparative analysis of the developed models were made in the similar Signal-to-Noise Ratio (SNR) range with the subspace based MUSIC algorithm, which is frequently used in direction finding.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers. Our system was among the top ranked. Our best model submitted during this challenge (based on recurrent neural networks and support vector machines) achieved lenient F1 scores of 0.9287 for concept extraction (ranked third), 0.9459 for relation classification (ranked fourth), and 0.8778 for the end-to-end relation extraction (ranked second). We developed a novel named entity recognition model based on a recurrent convolutional neural network and further investigated gradient boosting for relation classification. The new methods improved the lenient F1 scores of the 3 subtasks to 0.9292, 0.9633, and 0.8880, respectively, which are comparable to the best performance reported in this challenge. This study demonstrated the feasibility of using machine learning methods to extract the relations of medications with adverse drug events from clinical narratives.

The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.

Global climate change has become a critical factor influencing marine and coastal safety. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), even with global mitigation efforts, the trend of global warming remains irreversible. The increasing intensity of future extreme climate events is expected to affect wave dynamics, storm surges, and coastal hazards. This study aims to assess long-term variations in extreme waves and storm surges around Taiwan under climate change scenarios and to establish design parameters that reflect future conditions for coastal hazard mitigation and engineering applications.Atmospheric data from the EC-Earth3 global climate model under the high-emission scenario (SSP5-8.5) were used to drive wave and ocean models simulating wave fields and storm surge residuals over the Northwest Pacific. To address the coarse resolution of global climate models, an artificial intelligence–based statistical downscaling framework was developed. This approach integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) into a Convolutional Recurrent Neural Network (CRNN) architecture, improving spatial and temporal resolution and generating data representative of coastal-scale processes. The downscaled wave and surge results were analyzed using the Generalized Pareto Distribution (GPD) for extreme-value statistics to estimate design wave heights and storm surge residuals corresponding to return periods of 10, 25, 50, 75, 100, and 200 years. Statistical uncertainties were evaluated using a bootstrap resampling method.Results show that under the high-emission scenario, significant wave heights and storm surge residuals exhibit an increasing trend with longer return periods. Wave analysis reveals marked changes in northern coastal waters, where design wave heights increase from 3.05 m to 5.65 m. The eastern coast shows moderate increases (4.08–4.22 m), while the Taiwan Strait and southern waters remain relatively stable at about 3.6 m and 5.2 m, indicating higher sensitivity of the north to extreme forcing. For storm surges, historical maximum residuals ranged from 0.36 m to 1.49 m, while future projections range from 0.31 m to 1.35 m, showing a slight decrease but similar spatial distribution, with larger deviations along western and island coasts. Design storm surge residuals increase with return period, from about 0.20 m for a 10-year event to 0.32 m for a 200-year event. Under future conditions, increases are projected mainly for western and island coasts, with southern and eastern shores also showing gradual rises.Overall, extreme waves and storm surges around Taiwan exhibit long-term variations under climate change. Although short-term fluctuations remain moderate, both wave and surge intensities increase at longer return periods, implying that future coastal design standards should consider higher thresholds. The AI-based downscaling and extreme-value framework established in this study supports quantitative assessment of coastal hazards, engineering design, and adaptation planning in Taiwan.

To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary resuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compressions, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary classification tasks: (1) shockable rhythms (VF and pVT) versus non-shockable rhythms (asystole and PEA) in all ECG segments; (2) the same classification in compression-affected ECG segments; (3) pulse-generating rhythms (ROSC rhythm) versus non-pulse-generating rhythms (asystole, PEA, VF and pVT) in all ECG segments; and (4) the same classification in compression-affected ECG segments. The 1D-CNN model consistently outperformed the RNN model across all classification tasks. For shockable versus non-shockable rhythm classification, the 1D-CNN achieved accuracies of 91.3% and 89.8% for all ECG segments and compression-affected ECG segments, respectively, compared to 50.6% and 54.5% for the RNN. In detecting pulse-generating rhythms, the 1D-CNN demonstrated accuracies of 90.9% and 85.7% for all ECG segments and compression-affected ECG segments, respectively, while the RNN achieved 92.2% and 84.4%. The 1D-CNN model demonstrated superior performance in cardiac arrest rhythm classification, maintaining high accuracy even with compression-affected ECG data.

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.

In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.

In this digital era, there is an exponential growth of text-based content in the electronic world. Data as texts exist in the form of documents, social media posts on Facebook, Twitter, etc., logs, sensor data, and emails. Twitter is a social platform where users express their views on various aspects in a day to day life. Twitter produces over 500 million tweets daily that is 6000 tweets per second. Twitter data is, by definition, very noisy and unstructured in nature. Text classifications based on the machine learning techniques have problems like poor generalization ability and sparsity dimension explosion. Classifiers based on deep learning techniques are implemented to improve accuracy to overcome shortcomings of machine learning techniques and to avoid feature extraction processes and have high prediction accuracy and strong learning ability. In this work, the classification of tweets is performed on Covid-19 dataset by implementing deep learning techniques namely Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Recurrent Convolution Neural Network (RCNN), Recurrent Neural Network with Long Short Term Memory (RNN+LSTM), and Bidirectional Long Short Term Memory with Attention (BI-LSTM + Attention). The algorithms are implemented using two-word embedding techniques namely Global Vectors for Word Representation (GloVe) and Word2Vec. RNN with Bidirectional LSTM model has performed better than all the classifiers considered. It has classified the text with an accuracy of 93% and above when used with GloVe and Word2Vec.

Self-attention based recurrent convolutional neural network for disease prediction using healthcare data

A floating foundation vibration reduction system with an air spring as a vibration isolation element has been widely used in the foundation of large ultra-precision instruments. With the excellent vibration isolation performance of the air spring, its complex dynamic nonlinear behavior is always a research difficulty. In this paper, a dynamic model of a floating foundation vibration reduction system based on the restoring force of air spring is derived. The structure of the recurrent convolution neural network (RCNN) is proposed based on combining the working characteristics of a convolution neural network and a long short-term memory neural network, and the dynamic model of a floating foundation vibration reduction system is established with the restoring force of the air spring calculated by the RCNN as the input. Finally, a test experiment was designed to compare the dynamic characteristics of the traditional numerical model and three deep neural network models in the floating foundation. The results show that convolution neural network, long short-term memory, and RCNN models could predict the vibration response of floating foundation vibration reduction system, and the RCNN model had better performance for a floating foundation.

Aiming at the problem that the convolutional recurrent neural network speech enhancement method cannot extract the speech harmonic information efficiently. This paper proposes a Tibetan speech enhancement method based on the Attention Mechanism Cascaded Convolutional Recurrent Neural (ACRNN) network model. Firstly, the input time domain speech signal is converted into a frequency domain signal using short-time Fourier transform. Secondly, the harmonic characteristics are extracted through the harmonic perception attention mechanism module, and the attention tensor and the original speech spectrum are spliced into the CRNN network for speech enhancement. Finally, the short-time inverse Fourier transform is used to transform the signal from the frequency domain to the time domain to generate the speech waveform after noise reduction. Experimental data shows that compared to directly using CRNN network speech noise reduction, this method has improved speech quality and intelligibility.

Sound-event recognition often utilizes time-frequency analysis to produce an image-like spectrogram that provides a rich visual representation of original signal in time and frequency. Convolutional Neural Networks (CNN) with the ability of learning discriminative spectrogram patterns are suitable for sound-event recognition. However, there is relatively little effort that CNN makes full use of the important temporal information. In this paper, we propose MCRNN, a Convolutional Recurrent Neural Networks (CRNN) architecture for sound-event recognition, the letter “M” in the name “MCRNN” of our model denotes the multi-sized convolution filters. Richer features are extracted by using several different convolution filter sizes at the last convolution layer. In addition, cochleagram images are used as the input layer of the network, instead of the traditional spectrogram image of a sound signal. Experiments on the RWCP dataset shows that the recognition rate of the proposed method achieved 98.4% in clean conditions, and it robustly outperforms the existing methods, the recognition rate increased by 0.9%, 1.9% and 10.3% in 20 dB, 10 dB and 0 dB signal-to-noise ratios (SNR), respectively.

Recent advancements in digital technologies have profoundly transformed healthcare delivery, sports analytics, and physical activity surveillance. The accelerated proliferation of wearable sensing devices and Internet of Things (IoT) infrastructures has facilitated unprecedented large-scale acquisition of multimodal physiological and kinematic data. However, accurate recognition of complex human activities remains a formidable challenge, primarily due to inadequate modeling of the spatiotemporal dependencies inherent in wearable-sensor signals. To address these fundamental limitations, this paper proposed a novel IoT-oriented activity recognition framework predicated on a Convolutional Recurrent Neural Network (CRNN) architecture, engineered to simultaneously model the spatial and temporal characteristics of multimodal wearable-sensor data streams. The proposed framework synergistically integrates Convolutional Neural Networks (CNNs) for hierarchical spatial feature extraction with Recurrent Neural Networks (RNNs) for temporal sequence modeling, thereby enabling more discriminative and robust activity classification. The methodological pipeline comprises several sequential stages: First, multidimensional wearable-sensor datasets are employed, encompassing physiological and inertial measurements including heart rate variability, triaxial accelerometer readings, gyroscopic angular velocity, magnetometer orientation data, and cutaneous temperature signals acquired from multiple anatomical locations. Second, raw sensor signals undergo preprocessing and temporal segmentation procedures to enhance data quality and optimize temporal representation. Third, spatiotemporal feature representations are learned autonomously within the hierarchical CRNN architecture. Finally, the proposed model is systematically evaluated through a comparative analysis with five representative baseline methods, encompassing both conventional machine learning algorithms and contemporary deep learning approaches. Experimental results demonstrate that the proposed CRNN framework achieves superior performance, achieving 98.2% classification accuracy, 97.2% sensitivity, 99.2% specificity, 97.4% recall, and 97.6% precision on the evaluated wearable-sensor datasets. Compared with existing methodologies, the proposed model consistently achieves higher recognition accuracy and greater generalization robustness, underscoring its efficacy for wearable-sensor-based activity recognition and its considerable potential for broader deployment in IoT-enabled monitoring paradigms and adaptable solutions in educational settings.