Recent studies have shown that joint depth and pose estimation using convolutional neural networks (CNNs) can learn unlabelled monocular frames. However, three problems remain: 1) CNNs can only extract local features due to the limited receptive field, 2) scale ambiguity is inherent in the monocular task, and 3) illness regions violate the photometric consistency assumption and produce large errors. We propose a novel framework, ADPDepth, with corresponding effective strategies to ameliorate the above problems. First, a PCAtt module is designed to capture the correlation between channels and efficiently extract multiscale spatial information using a multibranch parallel strategy. Second, depth-pose consistency loss is proposed based on the geometric consistency in depth and pose to constrain the scale between samples, eliminate scale ambiguity and obtain a globally consistent scale. To further improve performance, a cover mask is derived from depth-pose consistency for filtering dynamic objects and outliers to reduce the adverse effects of these illness regions. Extensive experiments are conducted on the KITTI, NYU-Depth and Make3D datasets. Based on public benchmarks, the experimental results confirm that the proposed ADPDepth framework achieves state-of-the-art performance. The effectiveness of each strategy is also verified in subsequent ablation experiments.

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

For deep learning-based soft sensors, the lack of interpretability and the consequent unreliability has become one of the most important problems. In this article, a neural network scheme called the deep multiple attention soft sensor (DMASS), which consists solely of attention mechanisms, is proposed to develop a self-interpretable soft sensor. DMASS was established to ensure the self-interpretability of data selection and sensor modeling and try to integrate these originally independent phases into the single scheme. First, the existing attention mechanisms’ core implementation steps are summarized as a unified form, and then the variable attention mechanism and time lag attention mechanism are proposed. When DMASS's training is completed, the obtained attention weights provide the self-interpretable data selection results. Then, a self-attention activation structure (SAAS) is proposed to extract the nonlinear spatio-temporal features of data. The mathematical expression for the extracted feature, the SAAS's attention matrix, the information path diagram for DMASS's training, and the uncertainty-aware interval prediction show the self-interpretability of sensor modeling. Finally, DMASS was applied to predict the thermal deformation of the air preheater rotor, and the validity of DMASS's self-interpretability is verified by the known mechanism analysis and information bottleneck theory. Meanwhile, DMASS's great sensing performance was confirmed through comparison with other novel soft sensors.

As a key equipment to switch the direction of a running train, railway turnout works in complex condition which makes its fault diagnosis difficult. Generally, existing methods identify the fault by analyzing the turnout action curve acquired by sensors, which have certain practical value for fault diagnosis, but poor practicability for varied types like double or multiple action turnout. In this paper, fault detection is carried out according to the distance between the normal current curve and the test curve calculated by fast dynamic time warping algorithm. In view of the singular point problem involved, a segmentation method for current curve based on the key nodes in the turnout conversion process is proposed and applied to the fault detection of single action and double action turnouts. Experimental results show that proposed approach can effectively improve the matching accuracy of adaptive diagnosis model which is more than 96%. Furthermore, compared with the traditional dynamic time warping algorithm, the time cost can be reduced by more than 5 times.

The samples representing abnormal situation is usually very few in the dataset, which makes it difficult to learn the features of abnormal samples by machine-learning-based methods. To improve the accuracy of anomaly detection, the number of abnormal samples should be expanded to ensure the balance of the dataset. In this paper, a discrete synthetic minority oversampling technique (D-SMOTE) is proposed to generate new samples. A closed area is constructed using the three nearest abnormal samples in the dataset. The new samples are then uniformly interpolated in a closed area. By this means, the problem of the imbalance for the original dataset is handled, thus improving the data quality. Based on the expanded datasets, a two-dimensional convolutional neural network (2D CNN) is constructed to detect abnormal samples. In experiments, three cases and different machine learning methods are considered for comparison. Several indexes including accuracy, precision, confusion matrix, F1-score, and Recall have been used to evaluate the detection effectiveness. The results show that the abnormal samples can be detected accurately using oversampling data obtained from the proposed D-SMOTE method.

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

PIWI-interacting RNA (PiRNA) was discovered in 2006 and is expected to become a new biomarker for diagnosis and prognosis of various diseases. The purpose of this study is to explore functions of piRNAs and identify cancer subtypes on the basis of the pattern of transcriptome and somatic mutation data. A total of 285 510 SNPs in piRNAs and genes, which might affect piRNA biogenesis or piRNA targets binding were identified. Significant co-expression networks of piRNAs were then constructed separately for 12 major types of cancer. Finally, mutational matrices were mapped to piRNA network, propagated, and clustered for identification of cancer-related piRNAs and cancer subtypes. Findings showed that subtypes of three types of cancer (COAD, STAD and UCEC), which are significantly associated with survival were identified. Analysis of differentially expressed piRNAs in UCEC subtypes showed that piRNA function is closely related to cancer hallmarks “Enabling Replicative Immortality” and contributes to initiation of cancer.

The statistical distribution of radar cross-section (RCS) of complex targets varies for different attitude angles and may even behave as a bimodal distribution. Classical distribution models are unable to fully and accurately characterize the fluctuation properties of RCS in radar targets. In this letter, we present a novel distribution model for describing and analyzing the statistical properties of dynamic RCS. We used RCS statistical data for different attitude angle ranges and performed comparative analysis with Gaussian mixture density models (GMDM) (the second-order GMDM and the third-order GMDM). To evaluate the effectiveness and performance of our proposed method, we calculated various evaluation indicators of the three models. The results suggest that the proposed model has better fitting effect and higher fitting accuracy than the second-order GMDM. Simultaneously, although its performance on the backward RCS data is slightly inferior to that of the third-order GMD, our model requires fewer parameters. So, the model can be more suitable for describing the fluctuation characteristics of dynamic RCS of radar targets. The findings are useful in the detection of radar targets and provide an alternative for improving the total performance of radar.

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Currently, the diagnosis of single fault marked by distinct characteristics has received mass concern, and the related research achievements are remarkable. However, the diagnosis of early multiple intermittent (EMI) faults commonly existing in industrial systems is still an intractable problem, owing to: 1) the scarcity of fault data and the faintness of features and 2) the mutual interaction of components and the coupling of fault characteristics. In order to address the problem, this article proposes an improved sparse autoencoder and multilevel denoising strategy (MDS-ISAE) for diagnosing EMI faults. First, a relational constraint term is constructed to mitigate the effect of data correlation. Second, a multilevel denoising strategy is designed to enhance the robustness of AE feature learning with strong background noise. Third, a resizable resampling strategy is planned to tackle the skew distribution of diagnosis data. Based on the above strategies, an MDS-ISAE-based model is constructed to achieve EMI faults diagnosis. Further, the evaluation criteria of diagnosis performance are proposed and the applicability of the method is tested. Finally, the effectiveness and practicability of the proposed method are verified by artificial damage and real fault experiments, respectively.

Compared to conventional cameras, the new type of vision camera-neuromorphic cameras, which can avoid motion blur and have the advantages of high spatiotemporal resolution, high dynamic range, low latency, etc. In this paper, two motion compensation methods are introduced for operating event flow based on differential neuromorphic camera, one is motion compensation based on event counting image and time image, another is motion compensation based on image of warped events. Comprehensive experimental study is carried on two datasets, the motion compensation method based on event counting image and time image is better at generating clear motion compensation images, highlighting moving objects inconsistent with the background motion, while the motion compensation method based on image of warped events tends to generate motion compensated images of moving objects with more prominent or clearer edges. Simultaneously, two motion compensation methods are further applied to realize the object detection method based on threshold or maximum contrast. The former method can be adapted in many cases, while the latter method can well detect the translational moving target.

Coarse quantization by using low-resolution analog-to-digital converters (ADCs) is an attractive approach to relieve the burden of power consumption and hardware cost of implementing massive multiple-input multiple-output (MIMO) systems. In this article, we analyze the uplink spectral efficiency of a multiuser massive MIMO system with low-resolution ADCs in the context of orthogonal frequency division multiplexing (OFDM) under multipath channels. Firstly, we develop an efficient pilot scheme which results in a constant average power of the quantization noise for different channel delay power spectrums and also minimizes the mean squared error of channel estimation. Then a tight approximation of the uplink achievable rate is derived in a closed form considering both perfect channel state information (CSI) and estimated CSI. Then we analyze the impact of multipath channels on the system performance. Under perfect CSI, we discover that an increment of multipath taps has a positive impact on compensating the performance degradation due to the quantization noise. Under imperfect CSI, the most beneficial channel is uniformly distributed over a specific number of taps. Simulations are conducted to verify our analytical results.

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.

Optimization of island microgrids should configure the module type and size in such a way that multiple objectives can be balanced. This paper presents a bioinspired optimization approach of microgrid sizing, with two salient features. First, the multiple objectives are categorized into four types: reliability, economy, renewable technology, and pollution. We present a triangular aggregation model, which is straightforward and cost effective to compute the fitness. Second, a bioinspired algorithm named Levy-Harmony is developed. We embed the Levy flight into the Harmony vector updating to enhance the global searching ability and, meantime, adopt a bias factor to avoid unnecessary exploration. The searching speed and accuracy are well balanced and improved. The real datasets are used for comparative studies, demonstrating the superiority of the proposed scheme against typical existing approaches.

A wireless monitoring network is an effective way to monitor and transmit information about railway infrastructure conditions. Its lifetime is significantly affected by the energy usage among all sensors. This paper proposes a novel cluster-based valid lifetime maximization protocol (CVLMP) to extend the lifetime of the network. In the CVLMP, the cluster heads (CHs) are selected and rotated with the selection probability and energy information. Then, the clusters are determined around the CHs based on the multi-objective optimization model, which minimizes the total energy consumption and balances the consumption among all CHs. Finally, the multi-objective model is solved by an improved nondominated sorting genetic algorithm II. The simulation results show that, compared with two other strategies in the prior literature, our proposed CVLMP can effectively extend the valid lifetime of the network as well as increase the inspected data packets received at the sink node.

This paper proposes a novel strategy for formally analyzing functional requirements specification (FRS) and applies it to the Automatic Train Protection and Block (ATPB) system, which is proposed to reconstruct conventional rail lines in Japan. Based on the FRS in natural language, firstly, dynamic state transitions are extracted to express the operational mechanisms and determine the system parameters. A complete model of the ATPB system is then established using Unified Modeling Language (UML) to express the system structure graphically and explicitly. After achieving a common understanding, a VDM++ model is established formally to redescribe the original FRS of the ATPB system which is written in natural language (i.e. Japanese). Following that, in order to ensure internal consistency of the specification, proof obligations of the VDM++ model are discharged. Furthermore, a comprehensive testing is implemented to ensure that the FRS meets actual requirements. Finally, the system is simulated strictly in accordance with the formal specification. Without any runtime errors, collisions or derailments, the results of the simulation demonstrate the high quality and safety of the specification.