Haoyu Fang

Liang Yu, Haoyu Fang, Goran Strbac et al.

Ensuring resilience in multi-energy systems (MESs) has become increasingly urgent and challenging due to the growing frequency and severity of extreme events, such as natural disasters, extreme weather, and cyber-physical attacks. Among the various approaches to enhancing MES resilience, hydrogen integration offers significant potential in cross-temporal, cross-spatial, and cross-sector flexibility, as well as black-start capability. Although considerable efforts have been devoted to this area, a systematic review of resilience enhancement in hydrogen-enabled MESs is still lacking. To address this gap, this paper presents a comprehensive review of hydrogen-enabled MES resilience enhancement. First, advantages, vulnerabilities, and challenges related to hydrogen-enabled MES resilience enhancement are summarized. Next, a resilience enhancement framework for hydrogen-enabled MESs is proposed, based on which existing resilience metrics and event-oriented contingency models are reviewed and discussed. Planning measures are then classified according to the types of hydrogen-related facilities, together with uncertainty handling methods, scenario generation methods, and planning problem formulation frameworks. In addition, operational enhancement measures are categorized into three response stages: prevention, emergency response, and restoration. Finally, research gaps are identified and future directions are discussed, including comprehensive resilience metric design, advanced extreme-event scenario generation, spatiotemporal cyber-physical contingency modeling under compound extreme events, coordinated planning and operation across multiple networks and timescales, low-carbon resilient planning and operation, and large language model-assisted whole-process resilience enhancement.

Haoyu Fang, Yi Fang, Xiaofeng Yang

CT organ segmentation on computed tomography (CT) images becomes a significant brick for modern medical image analysis, supporting clinic workflows in multiple domains. Previous segmentation methods include 2D convolution neural networks (CNN) based approaches, fed by CT image slices that lack the structural knowledge in axial view, and 3D CNN-based methods with the expensive computation cost in multi-organ segmentation applications. This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework. The classifier and performance validator competition contribute to accurate segmentation results via back-propagation. The proposed network organically converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy. Besides, the structural information of one specific organ is depicted by a statistics-meaningful prior bounding box, which is transformed into a global feature leveraging the learning process in 3D fine segmentation. The experiments on the NIH pancreas segmentation dataset demonstrate the proposed network achieves state-of-the-art accuracy on small organ segmentation and outperforms the previous best. High accuracy is also reported on multi-organ segmentation in a dataset collected by ourselves.

Wei-Ting Chen, Cheng-Che Tsai, Hao-Yu Fang et al.

Images acquired from rainy scenes usually suffer from bad visibility which may damage the performance of computer vision applications. The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes. Moderate rain scene mainly consists of rain streaks while heavy rain scene contains both rain streaks and the veiling effect (similar to haze). Although existing methods have achieved excellent performance on these two cases individually, it still lacks a general architecture to address both heavy rain and moderate rain scenarios effectively. In this paper, we construct a hierarchical multi-direction representation network by using the contourlet transform (CT) to address both moderate rain and heavy rain scenarios. The CT divides the image into the multi-direction subbands (MS) and the semantic subband (SS). First, the rain streak information is retrieved to the MS based on the multi-orientation property of the CT. Second, a hierarchical architecture is proposed to reconstruct the background information including damaged semantic information and the veiling effect in the SS. Last, the multi-level subband discriminator with the feedback error map is proposed. By this module, all subbands can be well optimized. This is the first architecture that can address both of the two scenarios effectively. The code is available in https://github.com/cctakaet/ContourletNet-BMVC2021.

Haoyu Fang, Jing Zhu, Yi Fang

Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects. To empower conventional CNNs in learning geometric clues of lanes, we propose a deep network named ContinuityLearner to better learn geometric prior within lane. Specifically, our proposed CNN-based paradigm involves a novel Context-encoding image feature learning network to generate class-dependent image feature maps and a new encoding layer to exploit the geometric continuity feature representation by fusing both spatial and visual information of lane together. The ContinuityLearner, performing on the geometric continuity feature of lanes, is trained to directly predict the lane in traffic scenarios with integrated and continuous instance semantic. The experimental results on the CULane dataset and the Tusimple benchmark demonstrate that our ContinuityLearner has superior performance over other state-of-the-art techniques in lane segmentation.