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A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm

APPLIED SCIENCES-BASEL 2024年第17期14卷 7482页

作者： Cai, Yongrong Liu, Haibin Li, Mingfei Ren, Fujie Beijing Univ Technol Coll Mech & Energy Engn Beijing 100124 Peoples R China

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.

关键词： dual-AGV-ganged path planning genetic algorithm safe steering radius leader-follower AGVs

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Study on the Influence of Random Phase Interference on the Positioning Performance of a Four-Quadrant Detector

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第4期16卷

作者： Wu, Xiaoyun Zhao, Xin Wang, Tong Ding, Xiaoying Chen, Shu Liu, Dewang Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China

Random phase interference is the main factor leading to uneven optical power distribution, which will affect the positioning accuracy of the four-quadrant detector. This paper uses the power spectrum inversion method to simulate the light spot power distribution affected by random phase interference, and studies the subdivision capability characteristics of the four-quadrant detector under different random phase interference. The simulation experiment results show that when the radius of the light spot received by the detector is 1/2 the radius of the detector, both detection range and positioning accuracy can be taken into account. When the random phase interference is enhanced, the detector's subdivision capability decreases. When the random phase interference is small, the detector can achieve more than 100 subdivisions. When the random phase interference is large, the light spot can only achieve 3 subdivision at the edge of the detector target surface. An experimental system was built to test the subdivision capability of the four-quadrant detector, the experimental test results were basically consistent with the simulation results. The research results provide a technical reference for the application of four-quadrant detector. in free space environments.

关键词： Interference Detectors Optical surface waves Power distribution Optical distortion Location awareness Power lasers Four-quadrant detector random phase interference power spectrum inversion method subdivision capability

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Simultaneous Inversion of Subsurface Thermal Conductivity Using Surface Heat Flow and Borehole Temperature Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zeng, Zhiwen Han, Jiangtao Niu, Pu Xin, Zhonghua Liu, Lijia Jilin Univ Coll Geoexolorat Sci & Technol Changchun 130026 Peoples R China Jilin Univ Key Lab Appl Geophys Changchun 130026 Peoples R China Jilin Univ Coll Geoexolorat Sci & Technol Changchun 130026 Peoples R China Minist Educ Changbai Mt Volcano Comprehens Geophys Field Sci O Changchun 130026 Peoples R China

With the increasing development of geothermal energy resources, obtaining precise subsurface thermal conductivity structures has become crucial. However, current geophysical inversion methods lack a detailed technique for directly estimating subsurface thermal conductivity, especially when utilizing both borehole temperature field data and surface heat flow data as constraints. To address challenges posed by sparse borehole data and the limited resolution of borehole temperature field data, this study introduces a novel approach. It first utilizes boundary detection techniques to refine the extent of anomalous regions using heat flow data. Subsequently, by incorporating inversion results from borehole temperature data, a reference model is established, enabling a joint inversion technique that leverages both borehole temperature field data and surface heat flow data. Model experiments demonstrate the feasibility and effectiveness of this joint inversion method, significantly improving subsurface thermal conductivity imaging. Finally, the analysis of field data further validates the practicality and efficiency of this approach.

关键词： Geothermal joint inversion nonstructured grid surface heat flow thermal conductivity

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CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 6906-6916页

作者： Wang, Wenxuan He, Xingjian Zhang, Yisi Guo, Longteng Shen, Jiachen Li, Jiangyun Liu, Jing Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression. Due to the essentially distinct data properties between image and text, most of existing methods either introduce complex designs towards fine-grained vision-language alignment or lack required dense alignment, resulting in scalability issues or mis-segmentation problems such as over- or under-segmentation. To achieve effective and efficient fine-grained feature alignment in the RIS task, we explore the potential of masked multimodal modeling coupled with self-distillation and propose a novel cross-modality masked self-distillation framework named CM-MaskSD, in which our method inherits the transferred knowledge of image-text semantic alignment from CLIP model to realize fine-grained patch-word feature alignment for better segmentation accuracy. Moreover, our CM-MaskSD framework can considerably boost model performance in a nearly parameter-free manner, since it shares weights between the main segmentation branch and the introduced masked self-distillation branches, and solely introduces negligible parameters for coordinating the multimodal features. Comprehensive experiments on three benchmark datasets (i.e. RefCOCO, RefCOCO+, G-Ref) for the RIS task convincingly demonstrate the superiority of our proposed framework over previous state-of-the-art methods.

关键词： Referring image segmentation cross-modality guidance masked self-distillation vision and language

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Egg Yolk Selenopeptides: Preparation, Characterization, and Immunomodulatory Activity

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JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024年第10期72卷 5237-5246页

作者： Zhao, Qian McClements, David Julian Li, Junhua Chang, Cuihua Su, Yujie Gu, Luping Yang, Yanjun Jiangnan Univ Sch Food Sci & Technol Collaborat Innovat Ctr Food Safety & Qual Control State Key Lab Food Sci & Resources Wuxi 214122 Jiangsu Peoples R China Univ Massachusetts Amherst MA 01003 USA Hunan Engn & Technol Res Ctr Food Flavors & Flavo Jinshi 415400 Peoples R China

In this study, egg yolk selenium peptides (Se-EYP) were prepared using double-enzyme hydrolysis combined with a shearing pretreatment. The properties of the selenopeptides formed were then characterized, including their yield, composition, molecular weight distribution, antioxidant activity, in vitro digestion, and immunomodulatory activity. The peptide yield obtained after enzymatic hydrolysis using a combination of alkaline protease and neutral protease was 74.5%, of which 82.6% had a molecular weight <1000 Da. The selenium content of the lyophilized solid product was 4.01 mu g/g. Chromatography-mass spectrometry analysis showed that 88.6% of selenium in Se-EYP was in the organic form, of which SeMet accounted for 60.3%, SeCys2 for 21.8%, and MeSeCys for 17.9%. After being exposed to in vitro simulated digestion, Se-EYP still had 65.1% of oligopeptides present, and the in vitro antioxidant activity was enhanced. Moreover, Se-EYP exhibited superior immune detection indices, including immune organ index, level of immune factors in the serum, histopathological changes in the spleen, and selenium content in the liver. Our results suggest that Se-EYP may be used as selenium-enriched ingredients in functional food products.

关键词： egg yolkselenopeptides selenium form antioxidantactivity in vitro digestion immunomodulatory activity

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Minimizing Material Usage for Efficient and Compact Coil in Wireless Charging System of Electric Vehicles

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第2期10卷 2886-2898页

作者： Feng, Hao Wang, Dongsheng Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China

For the wireless charging system (WCS), excessive material usage in coils is a major barrier toward their commercialization. In this article, a sequential design method is proposed to minimize material efforts. First, Pareto-based optimization is executed to determine the size of coil, ferrite usage, and litz wire's gauge, which govern the rough geometry. Then strand number, winding geometry, and ferrite placement are studied, respectively, to further reduce the amount of copper while achieving compromise between losses and shielding effect. The proposed concept provides detailed optimization procedure for material reduction and cost evaluation. It can be principally extended to varied coil geometries and power levels. Two types of coils are compared and verified on a scale-down 6.6-kW WCS prototype, where the dc-dc efficiency reaches nearly 95.6% across the air gap of 150 mm under aligned position, with a coil diameter of 450 mm and a weight of 2.63 kg. The total copper and ferrite usage shows noticeable reduction compared with previous ones. The results demonstrate how the proposed concept can improve material utilization without degrading major metrics.

关键词： Ferrites Magnetic fields Windings Couplings Copper Geometry Optical wavelength conversion Coil design economy high power density material minimization wireless charging system (WCS)

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Towards Gradient-Based Saliency Consensus Training for Adversarial Robustness

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第2期21卷 530-541页

作者： Li, Qian Shen, Chao Hu, Qingyuan Lin, Chenhao Ji, Xiang Qi, Saiyu Jiaotong Univ Sch Cyber Sci & Engn Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

In recent works, robust networks have consistently exhibited more discriminative saliency map that proves to indicate sufficient adversarial robustness. In existed safe training paradigms e.g., adversarial training, however, the progressive saliency information regarding on what input semantic feature model prediction relies, have not yet been fully-explored. Due to this, we consider the incorporation of posterior saliency properties of robust model in training, as an efficient supervision signal on robust learning. It thus provides an alternative direction to enhance robustness, from the saliency interpretability perspective. In this article, to harden model we propose to optimize the discrimination of intermediate gradient-based saliency and maintain its consensus in training, which encourage model to behave according to task-relevant feature from the salient region such as object edges in image. Then, we introduce Adversarially Gradient-based Saliency Consensus Training method, dubbed Adv-GSCT. Within it, we preserve the similarity between the learned model saliency and the target one as label, approximated in the most offending case representing the least but essential information scenario. Meanwhile, a constructed pseudo-input coupled with feature importance, is feed into model to ensure the discrimination of estimated target saliency. Besides providing a novel insight into adversarial defense, Adv-GSCT differs from the current most effective adversarial training and does not need multiple iterative generations of adversarial perturbation whose computational cost and sensitivity direction of prediction concern. Finally, extensive performance evaluations on MNIST, CIFAR-10 and ImageNet datasets demonstrate the superiority of our proposed method.

关键词： Adversarial robustness saliency consensus deep neural networks

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Absorption-Constrained Wavelet Power Spectrum Inversion for Robust Q Extraction From VSP Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhao, Haoqi Gao, Jinghuai Li, Zhen Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

Extracting a reliable model of quality factor Q from the spectral information of seismic signals is a significantly important step for seismic imaging and reservoir characterization. However, the conventional Q estimation approach based on the frequency domain is susceptible to the high oscillative spectrum created by the unavoidable random and coherent noise in the observed signal. To address this issue, we introduce an absorption-constrained wavelet power spectrum inversion (AWPSI) method. The inversion involves absorption constraint (AC) and spectrum constraint (SC), where the AC term is a novel constraint for estimating the wavelet's amplitude spectrum or power spectrum. By treating medium absorption as a physical prior and utilizing information from all waveforms rather than individual ones, AWPSI can yield high-quality wavelet power spectra and ensure stable Q estimation results. In addition, the proposed method has no limitations on the type of wavelet. Since its inversion target is wavelet power spectra, AWPSI is broadly applicable to various frequency-based Q estimation approaches. We validate the effectiveness of the proposed method using both synthetic and actual vertical seismic profile (VSP) data.

关键词： Inverse theory inverse wavelet power spectrum Q value seismic attenuation waveform inversion

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HmmSeNet: A Novel Single Domain Generalization Equipment Fault Diagnosis Under Unknown Working Speed Using Histogram Matching Mixup

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7162-7172页

作者： Tang, Jian Ding, Xiaoxi Wei, Chao Xiao, Jiawei Liu, Rui Wang, Liming Huang, Wenbin Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech Engn Chongqing 400044 Peoples R China

Equipments regularly change working speeds during real-time production owing to process requirements. Applying deep learning models trained in a single speed domain straightforwardly to other unknown speed domains is extremely challenging single-domain generalization problem. Therefore, this article proposes a histogram matching mixup based sequential embedding network (HmmSeNet) for single-domain generalization of intelligent fault diagnosis under unknown speeds. HmmSeNet consists of four components: histogram matching mixup (HMM);sequential embedding (SE);separable convolution;and decision making. First, inspired by histogram matching and Mixup, the HMM data augmentation method is proposed. HMM is capable of synthesizing data with the same semantic information, but different distributions from a single source domain data during the training process, thus augmenting the source speed domain to the unknown speed domains. Then, SE utilizes trainable linear dimensional boosting to approximate the distribution between samples, which reduces the effect of sample amplitude distribution shifts caused by speed changes and allows the model to learn domain-invariant features. Finally, three layers of separable convolution and global average pooling are used to accomplish an accurate and robust recognition task. Experimental results on three datasets show that the proposed approach is only trained on a single speed domain, while it has good diagnostic performance on other unknown speed domains, even varying speed domains. The powerful generality and flexible deployment capability of HmmSeNet for speed changes are also demonstrated by ablation experimental analysis and dimensional analysis.

关键词： Vibrations Computational modeling Histogram matching mixup (HMM) intelligent fault diagnosis sequential embedding (SE) single domain generalization unknown speed

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UGTransformer: A Sheep Extraction Model From Remote Sensing Images for Animal Husbandry Management

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Lei Chen, Cheng Chen, Fang Wang, Ning Li, Congrong Zhang, Haiying Wang, Yu Yu, Bo Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Peking Univ Coll Urban & Environm Sci Beijing 100871 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Minist Ecol & Environm Satellite Applicat Ctr Ecol & Environm Beijing 100094 Peoples R China State Environm Protect Key Lab Satellite Remote Se Beijing 100094 Peoples R China

The extraction of sheep from satellite images plays an extremely important role in the precise automation of animal husbandry management. Current methods of extracting sheep mainly use hardware, such as radio frequency equipment and visual ear tags, which are prone to loss or damage. In this study, a new network, UGTransformer, was developed to extract sheep from high spatial resolution remote sensing (RS) images. In UGTransformer, a merge block was designed to fuse two scales of features in the encoder to improve the multiscale feature fusion capability. It enhanced the integration of global context features and spatial detailed features by combining the features in the decoder. A global connectivity module containing two sliding sub-modules, horizontal and vertical, was developed to correlate the horizontal and vertical features and correlate the arbitrary positions of the feature maps through the integration of the two modules, which realized the extraction of global contextual information. Our experimental results showed that the proposed UGTransformer performed well in comparison with UNet, Deeplab v3+, DCSwin, BANet, and UNetFormer, four recently proposed network structures for semantic segmentation. UGTransformer achieved at least a 1.8% increase in mean intersection over the union. This study not only provided potential solutions for the problems inherent in large-scale sheep extraction but also developed mechanisms for small-object extraction. The implementation code is available at https://***/chenchengStore/GlobalLocalAttention, and the RS images used in this study are available at https://***/chencheng-2023/UGTransformer-remote-sensing-images.

关键词： Animal husbandry extraction of sheep high spatial resolution remote sensing (RS) images semantic segmentation small-object extraction

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