Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Ab structure prediction has made great strides, but accurately modeling CDR H3 loops remains elusive. Unlike the other five CDR loops, CDR H3 does not adopt canonical conformations and must be modeled de novo. During Antibody Modeling Assessment II, we found that biasing simulations toward kinked conformations enables generating low-root mean square deviation models (Weitzner et al. 2014. Proteins 82: 1611-1623), and since then, we have presented new geometric parameters defining the kink conformation (Weitzner et al. 2015. Structure 23: 302-311). In this study, we use these parameters to develop a new biasing constraint. When applied to a benchmark set of high-quality CDR H3 loops, the average minimum root mean square deviation sampled is 0.93 Å, compared with 1.34 Å without the constraint. We then test the performance of the constrained de novo method for homology modeling and rigid-body docking and present the results for 1) the Antibody Modeling Assessment II targets, 2) the 2009 RosettaAntibody benchmark set, and 3) the high-quality set.

A key challenge in radar micro-Doppler classification is the difficulty in obtaining a large amount of training data due to costs in time and human resources. Small training datasets limit the depth of deep neural networks (DNNs), and, hence, attainable classification accuracy. In this work, a novel method for diversifying Kinect-based motion capture (MOCAP) simulations of human micro-Doppler to span a wider range of potential observations, e.g. speed, body size, and style, is proposed. By applying three transformations, a small set of MOCAP measurements is expanded to generate a large training dataset for network initialization of a 30-layer deep residual neural network. Results show that the proposed training methodology and residual DNN yield improved bottleneck feature performance and the highest overall classification accuracy among other DNN architectures, including transfer learning from the 1.5 million sample ImageNet database.

As an enabling technology for emerging and future generations of wireless networks, sparse code multiple access (SCMA) offers major improvements in terms of spectral efficiency and massive connectivity. Although the message passing algorithm (MPA) for SCMA decoding at the receiver side can achieve near optimum performance, it entails high computational complexity. In this paper, to address this issue, we propose a novel SCMA decoder based on deep residual neural network (ResNet), wherein the decoder is trained to predict the transmit codewords. In our approach, residual blocks are employed to tackle the problems of accuracy saturation and vanishing gradients with deep learning based decoder, while batch normalization is utilized to enhance the stability and robustness of the decoder. The performance of the proposed ResNet decoder for SCMA is validated by means of simulations over AWGN and Rayleigh fading channels. The results show that besides a much reduced complexity, the proposed decoder leads to improvements in term of bit error rate (BER) over competing deep neural network (DNN) based decoders.

This paper proposes a method to evaluate the degradation stages of stability of the screw with data fusion technology and deep residual neural network. Firstly, the data provided by multi-sensors are fused, and then the time domain images of the signals are input into the deep residual neural network for training and testing. The effectiveness of the proposed method is verified using data sets collected from the degradation test bench of ball screw. The data sets contain massive samples involving 3 degradation stages under 9 working conditions obtained by 3 accelerometers. From the results and comparison, it can be found that the deep residual neural network can automatically extract the features from the original data layer by layer and achieve a better evaluation rate when it is used to evaluate the performance stage of the screw, and the multi-sensor data fusion vibration data can better reflect the degradation stages of the screw's performance. It is showed that this method enhances the intelligence of the evaluation compared with the traditional method.

Image acquisition is an essential step in image processing. When the image acquisition is done the image that is generated is subjected to Impulse noise, Gaussian noise etc. We have performed the image denoising on images inflicted with impulse noise. Image denoising is an essential step in all types of image processing. Traditional techniques reduce the noise in the image but it also reduces the quality of the image. Traditional filters like gaussian filter, median filter is analyzed which work in the spatial domain and filters working in the frequency domain are also considered like Butterworth filters, Weiner filter. A Deep residual Neural Network filter is proposed which is compared with the Fuzzy Neural Network denoiser. Their performance is compared on the metrics PSNR and SSIM. The Fuzzy Neural Network system improves the SSIM significantly compared to a deep residual neural network and a comparison is made with traditional image denoising methods. We also compare the performance of the deep residual neural network, Fuzzy Neural Network system and Median denoising algorithm on impulse noise has been compared. The performance of deep neural networks depends on the total number of examples used and the performance can be improved if we have more image pairs.

Automatic modulation classification (AMC) is a signal processing technology used to identify the modulation type of unknown signals without prior information such as modulation parameters for drone communications. In recent years, deep learning (DL) has been widely used in AMC methods due to its powerful feature extraction ability. The significant performance of DL-based AMC methods is highly dependent on large amount of data. However, with the increasingly complex signal environment and the emergence of new signals, several recognition tasks have difficulty obtaining sufficient high-quality signals. To address this problem, we propose an AMC method based on a deep residual neural network with masked modeling (DRMM). Specifically, masked modeling is adopted to improve the performance of a deep neural network with limited signal samples. Both complex-valued and real-valued residual neural networks (ResNet) play an important role in extracting signal features for identification. Several typical experiments are conducted to evaluate our proposed DRMM-based AMC method on the RadioML 2016.10A dataset and a simulated dataset, and comparison experiments with existing AMC methods are also conducted. The simulation results illustrate that our proposed DRMM-based AMC method achieves better performance in the case of limited signal samples with low signal-to-noise ratio (SNR) than other existing methods.

The neuron classification problem is significant for understanding structure-function relationships in computational neuroscience. Advances in recent years have accelerated the speed of data collection, resulting in a large amount of data on the geometric, morphological, physiological, and molecular characteristics of neurons. These data encourage researchers to strive for automated neuron classification through powerful machine learning techniques. This paper extracts a statistical dataset of 43 geometrical features obtained from 116 human neurons, and proposes a neuronal morphology classification approach based on deep residual neural networks with feature scaling. The approach is applied to classify 18 types of human neurons and compares the accuracy of different number of residual block. Then, we also compare the accuracy between the proposed approach and other mainstream machine learning approaches, the classification accuracy of our approach is 100% in the training set and the testing set accuracy is 76.96%. The experimental results show that the deep residual neural network model has better classification accuracy for human neurons.

Current deep learning based image enhancement algorithms attempt to learn the mapping relationship between degraded images and clear images directly. These algorithms often ignore the fidelity constraint of the observational model. In order to improve the image enhancement performance, an improved deep residual neural network based image enhancement algorithm (DRNN-IE) for low dose CT images is proposed in this paper. DRNN-IE embeds the image enhancement task into a deep neural network, and achieves data consistency using multiple enhancement modules and back-projection modules. The enhancement modules in DRNN-IE produce new features through fusing low-level and high-level features. In order to improve the algorithm’s generalization ability, a dual-parameter loss function is adopted to train and optimize the neural network. Experiments on real CT images show that the proposed algorithm has excellent enhancement performance and retains detailed information of low-dose CT images.

We have developed a method for predicting the structure of small RNA loops that can be used to augment already existing RNA modeling techniques. The method requires no input constraints on loop configuration other than end-to-end distance. Initial loop structures are generated by randomizing the torsion angles, beginning at one end of the polynucleotide chain and correlating each successive angle with the previous. The bond lengths of these structures are then scaled to fit within the known end constraints and the equilibrium bond lengths of the potential energy function are scaled accordingly. Through a series of rescaling and minimization steps the structures are allowed to relax to lower energy configurations with standard bond lengths and reduced van der Waals clashes. This algorithm has been tested on the variable loops of yeast tRNA-Asp and yeast tRNA-Phe, as well as the sarcin-ricin tetraloop and the anticodon loop of yeast tRNA-Phe. The results indicate good correlation between potential energy and the loop structure predictions that are closest to the variable loop crystal structures, but poorer correlation for the more isolated stem loops. The number of stacking interactions has proven to be a good objective measure of the best loop predictions. Selecting on the basis of energy and stacking, we obtain two structures with 0.65 and 0.75 A all-atom rms deviations (RMSD) from the crystal structure for the tRNA-Asp variable loop. The best structure prediction for the tRNA-Phe variable loop has an all-atom RMSD of 2.2 A and a backbone RMSD of 1.6 A, with a single base responsible for most of the deviation. For the sarcin-ricin loop from 28S ribosomal RNA, the predicted structure's all-atom RMSD from the nmr structure is 1.0 A. We obtain a 1.8 A RMSD structure for the tRNA-Phe anticodon loop.

This paper makes a research for the speed control of wind turbine system under multiple operating regimes, which also studied the sleep residual recurrent neural network method in this work. We aim at designing deep residual recurrent neural network robust controllers, which guarantee the existence of the multiple regime system poles in some predefined zone and wind speed precise tracking. Moreover, the feedback gains which guarantee desired speed tracking performance are obtained by solving the Lyapunov stability functions. The results are applied to a directly driven wind energy conversion experiment systems and the numerical experiment, comparing with the existing results, shows the satisfactory performance of the proposed method.

The aim of this paper is to provide a deep learning based method that can solve high-dimensional Fredholm integral equations. A deep residual neural network is constructed at a fixed number of collocation points selected randomly in the integration domain. The loss function of the deep residual neural network is defined as a linear least-square problem using the integral equation at the collocation points in the training set. The training iteration is done for the same set of parameters for different training sets. The numerical experiments show that the deep learning method is efficient with a moderate generalization error at all points. And the computational cost does not suffer from “curse of dimensionality” problem.

A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets

Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. Supplementary data are available at Bioinformatics online.

Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

Some industrial processes take place in confined settings only observable by sensors, e.g. infrared (IR) cameras. Drying processes take place while a material is transported by means of a conveyor through a black box equipped with internal IR cameras. While such sensors deliver data at high rates, this is beyond what human operators can analyze and calls for automation. Inspired by numerous implementations monitoring techniques that analyse IR images using deep learning, this paper shows how they can be applied to the confined microwave drying of porous foams, with benchmarking their effectiveness at condition monitoring to conduct fault detection. Convolutional neural networks, derived transfer learning, and deep residual neural network methods are already regarded as cutting-edge and are studied here, using a set of conventional approaches for comparative evaluation. Our comparison shows that state-of-the-art deep learning techniques significantly benefit condition monitoring, providing an increase in fault finding accuracy of up to 48% over conventional methods. Nevertheless, we also found that derived transfer learning and deep residual network techniques do not in our case yield increased performance over normal convolutional neural networks.