Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models

The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets. While few explorations have been conducted on ensembling such models to combine their strengths. In this work, we propose a simple yet effective method called Saliency-aware Noise Blending (SNB) that can empower the fused text-guided diffusion models to achieve more controllable generation. Specifically, we experimentally find that the responses of classifier-free guidance are highly related to the saliency of generated images. Thus we propose to trust different models in their areas of expertise by blending the predicted noises of two diffusion models in a saliency-aware manner. SNB is training-free and can be completed within a DDIM sampling process. Additionally, it can automatically align the semantics of two noise spaces without requiring additional annotations such as masks. Extensive experiments show the impressive effectiveness of SNB in various applications. Project page is available at https://magicfusion.github.io/

FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs

Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot. Recently, contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs, yet related principles are not well explored. In this paper, we revisit and compare different contrastive learning strategies in DE-GANs, and identify (i) the current bottleneck of generative performance is the discontinuity of latent space; (ii) compared to other contrastive learning strategies, Instance-perturbation works towards latent space continuity, which brings the major improvement to DE-GANs. Based on these observations, we propose FakeCLR, which only applies contrastive learning on perturbed fake samples, and devises three related training techniques: Noise-related Latent Augmentation, Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results manifest the new state of the arts on both few-shot generation and limited-data generation. On multiple datasets, FakeCLR acquires more than 15% FID improvement compared to existing DE-GANs. Code is available at https://github.com/iceli1007/FakeCLR.Comment: Accepted by ECCV202

Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal$\_$Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance

Domain Re-Modulation for Few-Shot Generative Domain Adaptation

In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM). DoRM not only meets the criteria of high quality, large synthesis diversity, and cross-domain consistency, which were achieved by previous research in GDA, but also incorporates memory and domain association, akin to how human brains operate. Specifically, DoRM freezes the source generator and introduces new mapping and affine modules (M&A modules) to capture the attributes of the target domain during GDA. This process resembles the formation of new synapses in human brains. Consequently, a linearly combinable domain shift occurs in the style space. By incorporating multiple new M&A modules, the generator gains the capability to perform high-fidelity multi-domain and hybrid-domain generation. Moreover, to maintain cross-domain consistency more effectively, we introduce a similarity-based structure loss. This loss aligns the auto-correlation map of the target image with its corresponding auto-correlation map of the source image during training. Through extensive experiments, we demonstrate the superior performance of our DoRM and similarity-based structure loss in few-shot GDA, both quantitatively and qualitatively. The code will be available at https://github.com/wuyi2020/DoRM.Comment: Under Revie

Unified Discrete Diffusion for Simultaneous Vision-Language Generation

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks using a single model, performing text-based, image-based, and even vision-language simultaneous generation. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Moreover, we design a mutual attention module with fused embedding layer and a unified objective function to emphasise the inter-modal linkages, which are vital for multi-modality generation. Extensive experiments indicate that our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Expression of CIAPIN1 in human colorectal cancer and its correlation with prognosis

<p>Abstract</p> <p>Background</p> <p>The cytokine-induced anti-apoptotic molecule (CIAPIN1) had been found to be a differentially-expressed gene involved in a variety of cancers, and it was also considered as a candidate tumour suppressor gene in gastric cancer, renal cancer and liver cancer. However, studies on the role of CIAPIN1 in colorectal cancer were still unavailable. The aim of this study was to determine the prognostic impact of CIAPIN1 in 273 colorectal cancer (CRC) samples and to investigate the CIAPIN1 expression in CRC cell lines after inducing differentiation.</p> <p>Methods</p> <p>Immunohistochemical analysis was performed to detect the expression of CIAPIN1 in CRC samples from 273 patients. The relationship between CIAPIN1 expression and patients' characteristics (gender, age, location of cancer, UICC stage, local recurrence and tumour grade factors) was evaluated. In addition, these patients were followed up for five consecutive years to investigate the relationship between CIAPIN1 expression and the prognosis of CRC. We induced the differentiation of the CRC cell lines HT29 and SW480, in order to detect the expression of CIAPIN1 in the process of CRC cells differentiation.</p> <p>Results</p> <p>Results indicated that CIAPIN1 was mainly expressed in the cytoplasm and nucleus, and that its expression level in cancer samples was significantly lower than in normal tissues. The Wilcoxon-Mann-Whitney test showed a significant difference in the differential expression of CIAPIN1 in patients with different T and UICC stages, and tumour grade (<it>P </it>= 0.0393, 0.0297 and 0.0397, respectively). The Kaplan-Meier survival analysis demonstrated that the survival time of CRC patients with high expression of CIAPIN1 was longer than those with low expression during the 5-year follow up period (<it>P </it>= 0.0002). COX regression analysis indicated that low expression of CIAPIN1, cancer stage of > pT1, distant organ metastasis (pM₁), regional lymph node metastasis (> pN₁) and local recurrence (yes) were independent, poor prognostic factors of CRC (<it>P </it>= 0.012, <it>P </it>= 0.032, <it>P <</it>0.001, <it>P <</it>0.001, <it>P <</it>0.001 respectively). Both Western blotting and RT-PCR showed that CIAPIN1 expression was increased with the degree of differentiation of HT29 and SW480 cells.</p> <p>Conclusions</p> <p>CIAPIN1 played an important role in the differentiation of CRC cells, and the differential expression of CIAPIN1 in CRC was closely related to prognosis.</p