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激光与光电子学进展 2024年

作者：王超孙盛芝钱滨彭思达冯泽祺邱建荣 Laboratory of Infrared Materials and Devices The Research Institute of Advanced Technologies Ningbo University Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer Science Ningbo University State key laboratory of fluid power and mechatronic systems Zhejiang University Hangzhou State Key Laboratory of Modern Optical Instrumentation Zhejiang University

Temperature is a common physical quantity with great significance, especially under harsh conditions, such as high voltage, and strong electric/magnetic fields. Here, we demonstrate the development of an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs). The microstructure was fabricated on a fiber tip by two-photon polymerization technology. The results show that the fluorescence intensity of the QDs microstructure reduced with the increase of temperature while the peak position of photoluminescence exhibits a linear change within the temperature range of 26-70 ℃. The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃. The methods developed in our work could have enormous potential for the development of fiber-end temperature sensors.

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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AGIBench: A Multi-granularity, Multimodal, Human-referenced, Auto-scoring Benchmark for Large Language Models

arXiv

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arXiv 2023年

作者： Tang, Fei Gao, Wanling Peng, Luzhou Zhan, Jianfeng Research Center for Advanced Computer Systems State Key Lab of Processors Institute ofComputing Technology Chinese Academy of Sciences China China University of Chinese Academy of Sciences China Shanghai Lixin University of Accounting and Finance China

Large language models (LLMs) like ChatGPT have revealed amazing intelligence. How to evaluate the question-solving abilities of LLMs and their degrees of intelligence is a hot-spot but challenging issue. First, the question-solving abilities are interlaced with different ability branches like understanding and massive knowledge categories like mathematics. Second, the inputs of questions are multimodal that may involve text and images. In addition, they may have varying levels of difficulty while lacking a unified standard to judge which one is more difficult. Third, the response format of LLMs is diverse and thus poses great challenges for result extraction and evaluation. Even though several benchmarks have been proposed to evaluate LLMs, however, a majority of them only evaluate and report the performance on a collection of blended text questions that may involve multiple ability branches, knowledge categories, or different difficulty levels. Thus, the evaluation may result in indistinguishable performance data and a biased optimization direction. A small fraction of efforts attempt to classify the questions according to the ability branches, however, they still provide coarse-grained benchmarking such as lacking detailed knowledge categories and human-referenced difficulty levels. In addition, existing efforts either adopt prompt engineering to normalize the response format or analyze the result manually, which would introduce unpredictable performance impacts or unacceptable evaluation costs. In this paper, to tackle the above challenges, we propose AGIBench – a multi-granularity, multimodal, human-referenced, and auto-scoring benchmarking methodology for LLMs. Instead of a collection of blended questions, AGIBench focuses on three typical ability branches and adopts a four-tuple to label the attributes of each question. First, it supports multi-granularity benchmarking, e.g., per-question, per-ability branch, per-knowledge, per-modal, per-dataset, and per-diff

关键词： Benchmarking

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A Dual Cavity Pleated Structures Soft Pneumatic Actuator for Soft Robotic Applications

A Dual Cavity Pleated Structures Soft Pneumatic Actuator for...

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AFRICON Conference

作者： Shakiru Olajide Kassim Krisjanis Visnevskis Vahid Vaziri Sumeet S. Aphale Department of Computer Engineering Technology Artificial Intelligence Robotics and Mechatronic Systems Group (ARMS) The Federal Polytechnic Damaturu Yobe State of Nigeria Centre for Advanced Dynamic Research (CADR) School of Engineering University of Aberdeen Aberdeen UK

The overwhelming popularity and growth in evolution of soft robotics is largely credited to the ever-increasing design and capability portfolio of soft pneumatic actuators (SPAs). Despite the numerous challenges that complicate their modeling and control, viz: geometrical and material nonlinearities, intrinsic dynamic behaviour and diverse control requirements, they have found more application in flexible robots design. To this end, this work presents a design of a novel dual cavity pleated structure soft pneumatic actuator (DCPS-SPA) targeted towards providing enhanced dexterous applications. In order to achieve this, the finite-element model (FEM) of the DCPS-SPA is presented to describe its dynamic behaviour using the Ogden hyperelasticity model parameters. The bending angle and contact force are experimentally validated and compared with the FEM results and a single cavity type SPA, respectively. Experimental results obtained show a promising possibility of utilizing the proposed DCPS-SPA for enhanced dexterous manipulation applications.

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： Automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations

arXiv

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arXiv 2023年

作者： Jiang, Zhongliang Bi, Yuan Zhou, Mingchuan Hu, Ying Burke, Michael Navab, Nassir The Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany Shenzhen Institute of Advanced Technology Chinese Academy of Science China The Department of Electrical and Computer Systems Engineering Monash University Australia The Laboratory for Computer Aided Medical Procedures Johns Hopkins University BaltimoreMD21218 United States

Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers. This work proposes an intelligent robotic sonographer to autonomously "explore"target anatomies and navigate a US probe to a relevant 2D plane by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparisons approach in a self-supervised fashion. This process can be referred to as understanding the "language of sonography". Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms ("line"target), two types of ex-vivo animal organs (chicken heart and lamb kidney) phantoms ("point"target) and in-vivo human carotids, respectively. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney). The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in-vivo human carotid data. Code: https://***/yuan-12138/MI-GPSR. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

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A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement

A Precise Hip Protection System with Multi-scale Fall Warnin...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Qiangqiang Chen Yanan Diao Yaping Wang Yumin Chen Yunkun Ning Guanglin Li Guoru Zhao CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Research Center for Neural Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China College of Computer Science and Technology Xiamen University of Technology China Department of Neurology Shenzhen Second People's Hospital Shenzhen China

Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by *** Relevance— This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.

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Adversarial attack method with adaptive step size for ID card text recognition

Adversarial attack method with adaptive step size for ID car...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Weidong Feng Chunjie Zhang Xiaolong Zheng Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing Jiaotong University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In the field of text recognition models, existing adversarial attack algorithms mainly target white-box scenarios, which are usually limited to single-color backgrounds and short text length. However, with the complex color textures of ID card images and long text length, these methods are not satisfactory when performing adversarial attacks on ID card text data. To overcome these challenges, this paper introduces a novel black-box attack method tailored for ID card text recognition models. The proposed approach accounts for the complexities of intricate image backgrounds, extended text data, and the limitations inherent in black-box conditions. The proposed algorithm introduces an adaptive step-size search method based on line search, aiming to overcome the limitations of traditional methods that rely on fixed step sizes and projection techniques for perturbation generation. This refinement aims to enhance the success rate of black-box attacks while maintaining efficacy in white-box scenarios. Spatial transformations and random noise are incorporated to fortify the robustness of adversarial samples. Experimental results, conducted under various norms, demonstrate that the fusion of the traditional method with the adaptive step-size search method yields superior attack performance.

关键词： Adaptation models Text recognition Perturbation methods Noise Closed box Search problems Robustness Kernel Optimization Glass box

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STA+: Spatiotemporal Adaptation with Adaptive Model Selection for Video Action Recognition

STA+: Spatiotemporal Adaptation with Adaptive Model Selectio...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Minghui Li Chunjie Zhang Xiaolong Zheng Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing Jiaotong University Beijing China State Key Laboratory of Multimodal Artifical Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Recent breakthroughs in video models have achieved remarkable success by integrating vision transformers into the video domain through adaptation. However, prevalent approaches in existing literature often entail a single branch, incorporating additional spatiotemporal learning modules. This approach cannot fully satisfy the unique and targeted learning requirements of temporal and spatial representations. It lacks a well-balanced spatiotemporal understanding of videos, particularly in recognizing human actions, which requires a comprehensive understanding of space and time. In this study, we introduce a novel two-stream architecture named SpatioTemporal Adaptation with Adaptive Model Selection (STA+) to achieve synchronized spatiotemporal understanding of video with only RGB inputs. We propose an adaptive model selection method based on the intrinsic categorization judgment of the samples to enhance overall performance. We validate the effectiveness of our proposed method through rigorous experiments conducted on public benchmarks such as Diving-48 and Something-Something- V2. Our method consistently achieves competitive performance on these datasets.

关键词： Adaptation models Adaptive systems Image recognition Transfer learning Benchmark testing Transformers Stability analysis Spatiotemporal phenomena Digital twins Synchronization

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Dense Video Captioning through Convolutional-Transformer Integration

Dense Video Captioning through Convolutional-Transformer Int...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jiaxi Cao Chunjie Zhang Xiaolong Zheng Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing Jiaotong University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Dense video captioning, a task that has recently garnered attention in the field of computer vision, focuses on generating detailed captions for multiple events along with their temporal locations within untrimmed videos. Existing transformer-based methods have achieved notable success. However, some methods utilizing deformable transformer face challenges in processing long videos, as the encoder may struggle to capture the long-term dependencies of events in the video. This issue is partly due to not integrating Convolutional Neural Networks (CNN) with the deformable transformer architecture. In this work, we introduce Dense Video Captioning through Convolutional-Transformer Integration (CTDVC), which links Layer Normalization, the Scalable-Granularity Perception (SGP), and Group Normalization into a network, replacing the encoder part of the deformable transformer. This model better accounts for the sequential relationship between events, especially those events in the video that span over a longer duration, allowing it to generate video captions that are not only more coherent but also more precise in their narrative. We evaluated our method on ActivityNet Captions and YouCook2 datasets. Experiments demonstrate that our proposed method CTDVC achieves an improvement in performance.

关键词： Semantics Neural networks Focusing computer architecture Transformers Digital twins Convolutional neural networks Proposals Meteors Faces

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