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A novel constrained optimization-based parameter-free model updating strategy for enhancing fruit quality evaluation across multiple biological variability

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Food Chemistry 2025年

作者： Penghui Liu Yingjie Zheng Hao Tian Han Chang Xuan Luo Yibin Ying Lijuan Xie College of Biosystems Engineering and Food Science Zhejiang University 866 Yuhangtang Road Hangzhou 310058 PR China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province Hangzhou 310058 PR China Zhejiang Kaipu Technology Co. Ltd Hangzhou 310058 PR China Anhui Anguang Environmental Technology Co. Ltd. Hefei 230071 PR China

Updating calibrations is critical to mitigate prediction performance degradation resulting from samples or measurement conditions. This study proposed a modified semi-supervised parameter-free calibration enhancement (MSS-PFCE) approach to improve fruit quality assessment across multiple biological variability. Six fruit datasets encompassing diverse seasons, origins, and cultivars were used to examine the effectiveness of MSS-PFCE. The results demonstrated that MSS-PFCE substantially reduced average RMSEP for slave spectra by at least 50.63 %, 92.66 %, and 76.47 %, respectively. MSS-PFCE outperformed four benchmark methods (SS-PFCE, the global model, recalibration, and slope/bias correction (SBC)) while enabling model updating using only 5 % of slave samples. Furthermore, it maintained robust reliability and stable fitting for updated models under varying sample proportions and cost thresholds. This study potentially promotes deploying a novel updating technique featuring high accuracy, low sample dependence, fitting stability, and strong scalability in on-site fruit quality assessment applications.

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Improvement of pose recognition by sparse regularized convolutional neural network

Improvement of pose recognition by sparse regularized convol...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Zhang, Yuan Yao, Xiao Wang, Zhongli Su, Hao Yu, Zihan Huo, Guanying Xu, Ning Liu, Xiaofeng Changzhou Key Laboratory of Robotics and Intelligent Technology College of Internet of Things Engineering Chang Zhou China Beijing Jiaotong University Electronic and Information Engineering Beijing China College of Internet of Things Engineering Chang Zhou China Changzhou Key College of Internet of Things Engineering Changzhou Campus of Hohai University Chang Zhou China

ISBN: (纸本)9781728137261

The paper proposes a method of using deep model (CNN) with sparse regularization term to improve the performance of pose recognition. Convolutional neural network shows its limitations for pose recognition because of its bad convergence. It is believed that the activation function could be simplified by adding the sparseness term. Therefore, we present an algorithm applying the sparse regularization for the deep model with ReLU. Experimental results confirm that the proposed method can accelerate the convergence speed while a high recognition rate maintained. © 2019 IEEE.

关键词： Convolution

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

Robotic Electrospinning Actuated by Non-Circular Joint Conti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zicong Wu Chuqian Lou Zhu Jin Shaoping Huang Ning Liu Yun Zou Mirko Kovac Anzhu Gao Guang-Zhong Yang Shanghai Jiao Tong University Shanghai P. R. China Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology Precision Robotics (Hong Kong) Limited Hong Kong China Aerial Robotics Laboratory Imperial College London London United Kingdom Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai P. R. China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

关键词： Drugs Electric potential Fluids Tissue engineering Medical treatment Bending Manipulators

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

arXiv

引用

arXiv 2021年

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa Swiss Federal Laboratories for Materials Science and Technology Limited Hong Kong999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

关键词： Electrospinning

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Application of Iterative Learning Control in X-Y Precision Planar Motion Platform

Application of Iterative Learning Control in X-Y Precision P...

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Chinese Automation Congress (CAC)

作者： Hongyu Yan Danping Jia Tao Zhang Hualiang Zhang Yingjie Xiong Xu Lu Yang Liu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China College Of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426480

The size of the tracking error is one of the important indicators to measure the control system of the X-Y Precision Planar Motion Platform. Iterative learning control (ILC) can obtain higher tracking accuracy through multiple iterations. However, it is found in experiments that the tracking error of the XY precision motion stage using ordinary PD-type iterative learning will eventually fluctuate in a relatively range, and some high-frequency Noise will seriously affect the stability of the system after many iterations. Aiming at the above problems, a PD-type variable gain open-closed loop ILC with a low-pass filter is proposed. This control scheme can effectively remove high-frequency noise and reduce tracking errors. Finally, a hardware-in-the-loop real-time simulator is used to conduct a comparative experiment. Through comparison with PID controller and ordinary PD-type ILC, the experimental consequence show that this arithmetic has a smaller tracking error than the other two algorithms.

关键词： Tracking loops Tracking Low-pass filters Control systems Size measurement Stability analysis Real-time systems

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UAVO-NeRF: 3D reconstruction of orchards and semantic segmentation of fruit trees based on neural radiance field in UAV images

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Computers and Electronics in Agriculture 2025年 237卷

作者： Hongxing Peng Shangkun Guo Xiangjun Zou Hongjun Wang Juntao Xiong Qijun Liang College of Mathematics and Informatics South China Agricultural University Guangzhou 510642 China Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics Foshan 528251 China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs Guangzhou 510642 China College of Inelligent Manufacturing and Modern Industry Xinjiang University Xinjiang 830046 China College of Engineering South China Agricultural University Guangzhou 510642 China

In precision agriculture, accurate 3D reconstruction of orchard environments is essential for crop health monitoring and automating agricultural tasks. This paper introduces UAVO-NeRF, a novel method using Unmanned Aerial Vehicles (UAVs) for high-fidelity 3D reconstruction and semantic segmentation of orchard scenes. To address inefficiencies in large-scale outdoor environments, we employ a nonlinear scene parameterization that compresses the unbounded scene into a cubic space, enabling denser sampling of distant points. We implement multi-resolution hash encoding to capture both global context and local details, significantly enhancing reconstruction speed and quality. To handle lighting variability, we incorporate appearance embeddings that adaptively encode lighting conditions, increasing the model’s robustness under diverse illumination. Our network’s output layer includes a 3D semantic segmentation module that distinguishes fruit trees from background elements, using a cross-entropy loss function to measure the difference between predicted and actual semantic labels. Depth prediction accuracy is improved using depth maps generated by a pre-trained monocular depth estimation model, refined through a composite loss function that combines reconstruction, depth, semantic, visibility, and interlevel losses to minimize artifacts and enhance geometric representation. Experimental results demonstrate that UAVO-NeRF achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.82, outperforming state-of-the-art models like Instant-NGP and Mip-NeRF 360 across metrics such as PSNR, Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Additionally, UAVO-NeRF achieves a mean Intersection over Union (mIoU) of 0.891 for fruit tree semantic segmentation from novel viewpoints, exceeding traditional 2D models by over 5%. This approach offers a robust technological solution for digital twin applications in agriculture.

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Observer-Based Robust Containment Control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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Chinese Control Conference (CCC)

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Nanjing PR China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry ofChina Huazhong University of Science and Technology Wuhan PR China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Multi-agent systems Robustness Observers Electronic mail State feedback Additives

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Probabilistic learning vector quantization on manifold of symmetric positive definite matrices

arXiv

引用

arXiv 2021年

作者： Sonna, Fengzhen Tang Feng, Haifeng Tino, Peter Si, Bailu Ji, Daxiong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of computer Science University of Birmingham BirminghamB15 2TT United Kingdom School of Systems Science Beijing Normal University Beijing100875 China Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data, and motor imagery EEG data demonstrate the superior performance of the proposed method. © 2021, CC BY-NC-SA.

关键词： Learning algorithms

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Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

arXiv

引用

arXiv 2021年

作者： Kong, Zhe Zhang, Wentian Liu, Feng Luo, Wenhan Liu, Haozhe Shen, Linlin Ramachandra, Raghavendra College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Norwegian University of Science and Technology Gjøvik2818 Norway

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://***/kongzhecn/dfdm. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Security of Cloud intelligent Robot Based on RSA Algorithm and Digital Signature

Security of Cloud Intelligent Robot Based on RSA Algorithm a...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Huili Cai Xiaofeng Liu Angelo Cangelosi Changzhou Key Laboratory of Robotics and Intelligent College of IoT Engineering Hohai University Changzhou P. R. China College of IoT Engineering Hohai University Changzhou P. R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Considering the security of message exchange between service robot and cloud, we propose to authenticate the message integrity based on RSA algorithm and digital signature. In the process of message transmission, RSA algorithm is used to encrypt message for service robot and decrypt message for cloud. The digital signature algorithm is used to authenticate the source of the message. The results of experiment have proved that the proposed scheme can guarantee the security of message transmission.

关键词： Cloud computing Service robots Digital signatures Public key Encryption

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