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Optimization Algorithms for Predictive Control Approach to Networked Bilinear systems

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自动化学报 2017年第7期43卷 1234-1240页

作者： Binglin Wang Yu Kang Jiahu Qin Yanmei Li Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory of Fire Science Department of Automation Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China and also with the Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chi- nese Academy of Sciences Beijing 100190 China Physics and Electronic Engineering Anqing Normal University Anqing 246011 China

This paper is concerned with the networked predictive control of discrete-time bilinear *** deal with the network-induced communication delay that exists in both forward channel（controller to actuator）and feedback channel（sensor to controller）,a bilinear networked predictive control scheme is *** a non-convex optimization problem of solving the predictive control sequence is presented,for which two gradually-optimized algorithms are proposed based on the special structure of bilinear system dynamics *** numerical simulation indicates that the resulting predictive control sequence can compensate for the network-induced issues actively,which proves the effectiveness of the proposed predictive control strategy.

关键词：离散双线性系统预测控制策略逐步优化算法网络化系统动力学模型控制序列反馈通道通信延迟

From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection

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arXiv 2021年

作者： Deng, Jiajun Zhou, Wengang Zhang, Yanyong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China The Department of Computer Science University of Science and Technology of China Hefei230026 China

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H23D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H23D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at https://***/djiajunustc/H-23D_R-CNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

Recurrent Generic Contour-based Instance Segmentation with Progressive Learning

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arXiv 2023年

作者： Feng, Hao Zhou, keyi Zhou, Wengang Yin, Yufei Deng, Jiajun Sun, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China University of Adelaide Australian Institute for Machine Learning Australia

Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://***/fh2019ustc/PolySnake Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

An Energy Efficient Carry-Free Inner Product Unit

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An Energy Efficient Carry-Free Inner Product Unit

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wen Yan Miloš D. Ercegovac Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Los Angeles CA USA

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy.

关键词：

Deep Unrestricted Document Image Rectification

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arXiv 2023年

作者： Feng, Hao Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China The University of Adelaide Australian Institute for Machine Learning Australia

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://***/fh2019ustc/DocTr-Plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

The application of the alternate descent conditional gradient method in tomographic SAR off-grid imaging

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The application of the alternate descent conditional gradien...

IET International Radar Conference (IRC 2023)

作者： Mingxiao Shao Cikang Su Zhe Zhang Bingchen Zhang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing People's Republic of China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology and Suzhou Aerospace Information Research Institute Suzhou Jiangsu People's Republic of China

ISBN: (数字)9781837240982

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments.

关键词：

AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding

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arXiv 2024年

作者： Wang, Yonghui Zhou, Wengang Feng, Hao Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Over the past few years, the advancement of Multimodal Large Language Models (MLLMs) has captured the wide interest of researchers, leading to numerous innovations to enhance MLLMs’ comprehension. In this paper, we present AdaptVision, a multimodal large language model specifically designed to dynamically process input images at varying resolutions. We hypothesize that the requisite number of visual tokens for the model is contingent upon both the resolution and content of the input image. Generally, natural images with a lower information density can be effectively interpreted by the model using fewer visual tokens at reduced resolutions. In contrast, images containing textual content, such as documents with rich text, necessitate a higher number of visual tokens for accurate text interpretation due to their higher information density. Building on this insight, we devise a dynamic image partitioning module that adjusts the number of visual tokens according to the size and aspect ratio of images. This method mitigates distortion effects that arise from resizing images to a uniform resolution and dynamically optimizing the visual tokens input to the LLMs. Our model is capable of processing images with resolutions up to 1008 × 1008. Extensive experiments across various datasets demonstrate that our method achieves impressive performance in handling vision-language tasks in both natural and text-related scenes. The source code and dataset are now publicly available at https://***/harrytea/AdaptVision. Copyright © 2024, The Authors. All rights reserved.

关键词： Modeling languages

Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels

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arXiv 2021年

作者： Hu, Zexi Chen, Xiaoming Wing, Henry Yeung, Fung Chung, Yuk Ying Chen, Zhibo School of Computer Science University of Sydney Australia School of Computer Science and Engineering Beijing Technology and Business University China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China

Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data. To cope with such high-dimensional LF data, most of the existing LFSR methods resorted to decomposing it into lower dimensions and subsequently performing optimization on the decomposed sub-spaces. However, these methods are inherently limited as they neglected the characteristics of the decomposition operations and only utilized a limited set of LF sub-spaces ending up failing to sufficiently extract spatio-angular features and leading to a performance bottleneck. To overcome these limitations, in this paper, we comprehensively discover the potentials of LF decomposition and propose a novel concept of decomposition kernels. In particular, we systematically unify the decomposition operations of various sub-spaces into a series of such decomposition kernels, which are incorporated into our proposed Decomposition Kernel Network (DKNet) for comprehensive spatio-angular feature extraction. The proposed DKNet is experimentally verified to achieve considerable improvements compared with the state-of-the-art methods. To further improve DKNet in producing more visually pleasing LFSR results, based on the VGG network, we propose a LFVGG loss to guide the Texture-Enhanced DKNet (TE-DKNet) to generate rich authentic textures and enhance LF images’ visual quality significantly. We also propose an indirect evaluation metric by taking advantage of LF material recognition to objectively assess the perceptual enhancement brought by the LFVGG loss. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

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Stereo Matching of VHR Remote Sensing Images via Bidirection...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Rongshu Tao Yuming Xiang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.

关键词： Estimation Remote sensing Optical sensors Optical imaging Three-dimensional displays Buildings Satellites