An Attitude Determination Method for Comprehensive Inspection Vehicle Based on Track Profile Registration

The attitude of the comprehensive inspection vehicle is one of the important factors that affect the accuracy of the inspection of metro line infrastructure, meanwhile the metro environment restricts the employment of common attitude determination methods. A new method of attitude determination is presented in this paper, which takes the track as reference and employs non-contact measurement to acquire the track profile simulta-neously. By registration of measurement track profile and the standard track profile, the relative position between the vehicle and the track reference can be calculated; and the instantaneous attitude of the vehicle can be determined by the matrix inverse calculation. The performance of the method is verified by an experiment using the road-rail comprehensive inspection vehicle

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.Comment: 8 pages, accepted by AAAI201

Anomaly Detection Based on Multiple Streams Clustering for Train Real-Time Ethernet

With the increasing traffic of train communication network (TCN), real-time Ethernet becomes the development trend. However, Train Control and Management System (TCMS) is inevitably faced with more security threats than before because of the openness of Ethernet communication protocol. It is necessary to introduce effective security mechanism into TCN. Therefore, we propose a train real-time Ethernet anomaly detection system (TREADS). TREADS introduces a multiple streams clustering algorithm to realize anomaly detection, which considers the correlation between the data dimensions and adopts the decay window to pay more attention to the recent data. In the experiment, the reliability of TREADS is tested based on the TRDP data set collected from the real network environment, and the models of anomaly detection algorithms are established for evaluation. Experimental results show that TREADS can provide a high reliability guarantee, besides, the algorithm can detect and analyze network anomalies more efficiently and accurately