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A quantum-search-based multi-classifier for image recognition

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 21-31页

作者： Lu Liu Xingyu Wu Lufan Zhang Chuan Wang School of artificial intelligence Beijing Normal University Applied Optics Beijing Area Major Laboratory Beijing Normal University

The multi-class classification of images is a pivotal challenge within the realm of image processing. As the volume of visual data continues to expand, there is a burgeoning interest in harnessing the unique capabilities of quantum computation to augment the efficiency of classification tasks. However, many existing methods for training quantum image multi-classifiers parallel classical machine learning techniques, where the requisite circuit measurements increase linearly with the volume of training data. This work introduces a novel approach for training a quantum image multi-classifier based on the quantum search algorithm. We have meticulously conducted rigorous experiments on a handwritten digit dataset, a classic benchmark in the field. The results have been meticulously compared with previous works, and the comparative analysis not only validates the efficiency of our proposed approach, requiring only O(N/b) measurements during training, but also highlights a significant quadratic speedup of the algorithm.

关键词： quantum computation image recognition quantum search algorithm

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of Computational intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of artificial intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

Progress and Challenge of artificial intelligence

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Journal of Computer Science & Technology 2006年第5期21卷 810-822页

作者：史忠植郑南宁 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 P.R. China The Laboratory of artificial intelligence and Robotics Xi'an Jiaotong University Xi'an 710049 P.R. China

artificial intelligence （AI） is generally considered to be a subfield of computer science, that is concerned to attempt simulation, extension and expansion of human intelligence. artificial intelligence has enjoyed tremendous success over the last fifty years. In this paper we only focus on visual perception, granular computing, agent computing, semantic grid. Human-level intelligence is the long-term goal of artificial intelligence. We should do joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. A new cross discipline intelligence science is undergoing a rapid development. Future challenges are given in final section.

关键词： artificial intelligence visual perception machine learning agent computing semantic web intelligence science

Applying project‐based learning in artificial intelligence and marine discipline:An evaluation study on a robotic sailboat platform

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IET Cyber-Systems and Robotics 2022年第2期4卷 86-96页

作者： Xiongwei Lin Hengli Liu Qinbo Sun Xiuhan Li Huihuan Qian Zhenglong Sun Tin Lun Lam The Shenzhen Institute of artificial intelligence and Robotics for Society The Chinese University of Hong KongShenzhenChina School of Science and Engineering The Chinese University of Hong KongShenzhenChina Faculty of artificial intelligence in Education Central China Normal UniversityWuhanChina

intelligence is penetrating various *** demand for interdisciplinary talent is increasingly important,while interdisciplinary educational activities for high school students are lagging ***‐based learning(PBL)in artificial intelligence(AI)and robotic education activities supported by a robotic sailboat platform,the sailboat test arena(STAr),has been shown to popularise AI and robotic knowledge in young *** the implementation of the programme,PBL was provided for students,and gamification pedagogy was applied to increase participants'learning motivation and *** results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school *** assessment results suggest that most students achieve learning outcomes on *** showed more interest in AI and marine disciplines and were willing to participate in more such educational *** findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

关键词： application platform artificial intelligence educational robots marine robotics robotic game and competition

Multiple Knowledge Representation of artificial intelligence

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Engineering 2020年第3期6卷 216-217页

作者： Yunhe Pan Institute of artificial intelligence Zhejiang UniversityHangzhou 310027China Zhejiang Lab Hangzhou 311121China

In the 1970s,cognitive psychology recognized that the information in the long-term memory is scene and semantic[1]and may be encoded in parallel as verbal and mental imagery[2].In 1991,I pointed out that not all verbal propositions can be derived from the verbal system,and that many can only be transformed from the imagery system[3].I have proposed the concept of visual knowledge,which consists of visual concepts,visual propositions,and visual narratives[4].Visual knowledge can simulate the various spatiotemporal operations that a person can perform on a mental imagery in his/her brain,such as the design process[5].

关键词： visual knowledge Visual

Neural Liénard system: learning periodic manipulation skills through dynamical systems

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Science China(Information Sciences) 2024年第12期67卷 256-271页

作者： Haoyu ZHANG Long CHENG Yu ZHANG Yifan WANG School of artificial intelligence University of Chinese Academy of Sciences State Key Laboratory for Multi-modal artificial intelligence Systems Institute of AutomationChinese Academy of Sciences

Learning from demonstrations provides effective methods for teaching robot manipulation ***, capturing periodic manipulation skills remains challenging with the current techniques. To address this gap, we introduce the neural Liénard system(neural LS), a novel neural network framework that utilizes Liénard-type differential equations to create dynamical systems with stable and distinct limit cycles. We also introduce an innovative technique, which not only manages periodic trajectories across various dimensions but also handles trajectories with intersections, enhancing the capability of the robot for complex tasks. We provide a thorough theoretical analysis of neural LS, focusing on its stability and representational *** evaluations show that neural LS achieves superior performance in modeling complex limit cycles,surpassing the existing methods. We particularly emphasize its effectiveness in handling high-dimensional periodic motions and trajectories with intersections. In addition, we explore the adaptability and robustness of neural LS. A practical application involving the Franka Emika robot in a drawing task further demonstrates the real-world utility of neural LS, confirming its effectiveness and potential to equip robots with advanced periodic manipulation skills.

关键词： learning from demonstrations stable dynamical system periodic motions

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The Neural Mechanism of Knowledge Assembly in the Human Brain Inspires artificial intelligence Algorithm

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Neuroscience Bulletin 2024年第2期40卷 280-282页

作者： Xiang Ji Wentao Jiang Xiaoru Zhang Ming Song Shan Yu Tianzi Jiang University of the Chinese academy of Sciences Beijing 100049China Brainnetome Center Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China Laboratory of Brain Atlas and Brain-inspired intelligence Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and intelligence Technology The Chinese Academy of SciencesBeijing 100190China Research Center for Augmented intelligence Zhejiang LabHangzhou 311100China

When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et *** the neural correlates of knowledge assembly in the human brain using functional ***,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.

关键词： neural knowledge artificial

Current applications of artificial intelligence for intraoperative decision support in surgery Allison

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Frontiers of Medicine 2020年第4期14卷 369-381页

作者： J.Navarrete-Welton Daniel A.Hashimoto Surgical artificial intelligence and Innovation Laboratory Massachusetts General HospitalBostonMA 02114USA Harvard Medical School BostonMA 02114USA

Research into medical artificial intelligence(AI)has made significant advances in recent years,including surgical *** scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical *** the twenty-one(n=21)included papers,three main categories of motivations were identified for developing such technologies:(1)augmenting the information available to surgeons,(2)accelerating intraoperative pathology,and(3)recommending surgical *** many of the proposals hold promise for improving patient outcomes,important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance *** limitations,the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

关键词： artificial intelligence decision support clinical decision support systems intraoperative deep learning computer vision machine learning surgery

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 37-57页

作者： Zhiqiang Pu Yi Pan Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui the School of artificial intelligence University of Chinese Academy of Sciences the Institute of Automation Chinese Academy of Sciences the Nanjing artificial intelligence Research of IA the School of Sports Engineering Beijing Sport University

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) *** addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

关键词： artificial intelligence(AI) decision-making football review soccer sports analytics

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