Liang Wang

In train-mounted railway clearance intrusion detection, current bi-modality combination strategy cannot satisfy the demand of all time and temperature difference conditions. To alleviate this issue, the present study develops a scheme with integrated tri-modality on-train sensor and an improved evidence-theoretic fusion. The integrated sensor can simultaneously leverage data from camera, Infrared imaging sensoR (IR) and Light Detection And Ranging (LiDAR). The evidence-theoretic fusion is improved to avoid evidence conflict by proposing a Kernelled Belief Cosine Distance (KBCD) as a Reproducing Kernel Hilbert Space (RKHS)-based modality credibility measure. As far as the authors learn, the evidence theory is also first bridged to train-mounted rail clearance intrusion detection. Experiments verify the effectiveness of leveraging tri-modality sensor and evidence-theoretic fusion in on-train rail clearance intrusion inspection.

Adolescent idiopathic scoliosis (AIS), which typically occurs in patients between the ages of 10 and 18, can be caused by a variety of reasons, and no definitive cause has been found. Early diagnosis of AIS or timely recognition of progression is crucial for the prevention of spinal deformity and the reduction of the risk of surgery or postponement. However, it remains a significant challenge. The purpose of this study is to develop an easy-to-use, non-invasive, and portable method for early diagnosis of AIS. A new framework of moving entropy-based computer vision method is presented, which can determine the severity of AIS by analyzing patients’ walking videos. First, Alphapose system and direct linear transformation method are employed to estimate 3D keypoint coordinates. Then, the joint angle-based and joint distance-based dynamic network are constructed. Based on these works, the new measures called moving angle entropy and moving edge-weighted graph entropy are proposed and fused using canonical correlation analysis. Finally, the power spectral exponents of entropy sequences are calculated and used in recognizing the severity of AIS. A comparison with healthy subjects and statistical analysis for entropy values can provide effective information for quantifying AIS. The recognized results of our proposed method were also comparable with the clinical diagnosis of Cobb angle from imaging by a certified clinician.

In current smart classroom research, numerous studies focus on recognizing hand-raising, but few analyze the movements to interpret students’ intentions. This limitation hinders teachers from utilizing this information to enhance the effectiveness of smart classroom teaching. Assistive teaching methods, including robotic and artificial intelligence teaching, require smart classroom systems to both recognize and thoroughly analyze hand-raising movements. This detailed analysis enables systems to provide targeted guidance based on students’ hand-raising behavior. This study proposes a morphology-based analysis method to innovatively convert students’ skeleton key point data into several one-dimensional time series. By analyzing these time series, this method offers a more detailed analysis of student hand-raising behavior, addressing the limitations of deep learning methods that cannot compare classroom hand-raising enthusiasm or establish a detailed database of such behavior. This method primarily utilizes a neural network to obtain students’ skeleton estimation results, which are then converted into time series of several variables using the morphology-based analysis method. The YOLOX and HrNet models were employed to obtain the skeleton estimation results; YOLOX is an object detection model, while HrNet is a skeleton estimation model. This method successfully recognizes hand-raising actions and provides a detailed analysis of their speed and amplitude, effectively supplementing the coarse recognition capabilities of neural networks. The effectiveness of this method has been validated through experiments.

Current single structured light sensor-based rail-track section identification lacks robustness against unstable signal transmission. The decision-level sensor fusion based on evidence theory is fragile to sensor conflict. Aiming at the listed challenges, the present study proposes a robust scheme that combines multi-structured light sensors and a new evidence-theoretic sensor fusion on the strength of kernel method. In this scheme, multiple sensors are involved to tackle the lack of robustness, and a Kernel Induced Belief Metric (KIBM), which firstly connects the Reproducing Kernel Hilbert Space (RKHS) representation to belief distance-dominated sensor credibility evaluation, is newly constructed to address sensor conflict in evidence fusion. Besides, the gap between rail-track section identification and evidence theory is filled. Experiments reveal the efficiency and robustness of involving multi-sensors and the new evidence-theoretic sensor fusion in the identification on rail-track sections.

We propose a monocular visual odometry method for railway localization, FRVO-Mono, which exploits different features in railway environments, such as vanishing points, track lines, and pole objects to provide multi-dimensional geometric constraints for train location estimation. A multi-view measurement function between the camera, points, lines, and objects is constructed to correct rotation and scale drift. A 3-step object association strategy is also suggested to improve objects matching accuracy, and the prior information of the camera’s height is used to help restore the absolute scale. Data from four real railway scenarios with different scales and speeds are collected in collaboration with China Academy of Railway Sciences (CARS). The experimental results demonstrate the great accuracy and robustness of our approach, as well as its ability to effectively rectify the scale drift to meter level within a given distance, which makes it possible to generate more accurate positioning when combined with some other semantic information.

Current state-of-the-art intensity and depth modal-based railway surface defect inspection system faces the dilemma between false alarming and miss detection. To overcome this challenge, a bimodal detection scheme using both feature-level fusion and (evidence theory-based) decision-level fusion is designed. Moreover, an improved evidential fusion algorithm is proposed, which adopts three-branched evidential weight structure and introduces the Transferable Belief Model to the improved decision functions, achieving outstanding performance on effectiveness and robustness analysis. Both theoretical justifications and experimental results validate the efficiency of the hybrid fusion-based detection scheme in solving rail surface defect problems.