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Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation threshold for defects detection. Two visual comparison experiments were carried out to demonstrate the efficiency of the proposed methods. Finally, a quantitative comparison experiment shows the LWLC-GSME model achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects. Therefore, LWLC for image enhancement, in conjunction with GSME for defects segmentation, is efficient and feasible for the detection of rail surface defects based on UAV Images.

Rail surface defects (RSDs) are a major problem that reduces operation safety. Unfortunately, the existing RSD detection systems have very limited accuracy. Current image processing methods are not tailored for the railway track and many fully convolutional networks (FCN)-based methods suffer from the blurry rail edges (RE). This paper proposes a new rail boundary guidance network (RBGNet) for salient RS detection. First, a novel architecture is proposed to fully utilize the complementarity between the RS and the RE to accurately identify the RS with well-defined boundaries. The newly developed RBGNet injects high-level RS object information into shallow RS edge features by a progressive fused way for obtaining fine edge features. Then, the system integrates the refined edge features with RS features at different high-level layers to predict the RS precisely. Second, an innovative hybrid loss consisting of binary cross entropy (BCE), structural similarity index measure (SSIM), and intersection-over-union (IoU) is proposed and equipped into the RBGNet to supervise the network and learn the transformation between the input and ground truth. The input and ground truth then further refine the RS location and edges. Conveniently, an image-based model for RSD detection and quantification is also developed and integrated for an automatic inspection purpose. Finally, experiments conducted on the complex unmanned aerial vehicle (UAV) rail dataset indicate the system can achieve a high detection rate with good adaptation capability in complicated environments. INTRODUCTIONThe rapid growth of the railroad network has put tremendous pressure on track inspection and maintenance. As of 2020, United States has over 250,000 km of railroad track, which is the biggest network in the world (Railway Technology, 2020). China operates about 141,400 km of track, ranking the second in the world, while its 36,000 km of high-speed track is the most comprehensive high-speed © 2021 Computer-Aided Civil and Infrastructure Engineering passenger service network in the world (Xinhuanet, 2020). Russia and India rank third and fourth in terms of the track mileage with over 85,500 km and 65,000 km of track, respectively. Rail breakage, rail defects, and derailment are the leading factors of train accidents (Guo et al., 2021;Sharma et al., 2018). Specifically, it is reported that around 90% of railway derailment accidents can be related to rail defects (AlNaimi, 2020). In general, rail surface defects (RSDs) reference to the loss of materials on the rail head

To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.

In previous diffractive-imaging-based optical encryption schemes, it is impossible to totally retrieve the plaintext from a single diffraction pattern. In this paper, we proposed a new method to achieve this goal. The encryption procedure can be completed by proceeding only one exposure, and the single diffraction pattern is recorded as ciphertext. For recovering the plaintext, a novel median-filtering-based phase retrieval algorithm, including two iterative cycles, has been developed. This proposal not only extremely simplifies the encryption and decryption processes, but also facilitates the storage and transmission of the ciphertext, and its effectiveness and feasibility have been demonstrated by numerical simulations.

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In this paper, we propose a novel method for image encryption by employing the diffraction imaging technique. This method is in principle suitable for most diffractive-imaging-based optical encryption schemes, and a typical diffractive imaging architecture using three random phase masks in the Fresnel domain is taken for an example to illustrate it. The encryption process is rather simple because only a single diffraction intensity pattern is needed to be recorded, and the decryption procedure is also correspondingly simplified. To achieve this goal, redundant data are digitally appended to the primary image before a standard encrypting procedure. The redundant data serve as a partial input plane support constraint in a phase retrieval algorithm, which is employed for completely retrieving the plaintext. Simulation results are presented to verify the validity of the proposed approach.

Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy.