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Conference on Synthetic Data for artificial intelligence and machine Learning - Tools, Techniques, and applications II

作者： Soloviov, Andrii Anderson, Derek T. Buck, Andrew R. Alvey, Brendan Univ Missouri Elect Engn & Comp Sci Dept Columbia MO 65211 USA

ISBN: (纸本)9781510673892;9781510673885

Advancements in deep learning have revolutionized the artificial intelligence (AI) landscape. However, despite considerable performance enhancements, their reliance on data and the intrinsic opacity of these models remains a challenge, hindering our ability to understand the reasons behind their failures. This paper introduces a headless open-source framework, coined MizSIM, built on the Unreal Engine (UE) to generate high volume and variety synthetic datasets for AI training and evaluation. Through the manipulation of agent and environment parameters, MizSIM can provide detailed performance analysis and failure diagnosis. Leveraging UE's opensource distribution, cost-effective assets, and high-quality graphics, along with tools like AirSim and the Robotic Operating System (ROS), MizSIM ensures user-friendly design and seamless data extraction. In this article, we demonstrate two MizSIM workflows: one for a single-life computer vision task and the other to evaluate an object detector across hundreds of simulated lives. The overarching aim is to establish a closed-loop environment to enhance AI effectiveness and transparency.

关键词： artificial intelligence Education and training Object detection Sensors Computer simulations Computer vision technology robotics Visualization

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Making electric vehicle batteries safer through better inspection using artificial intelligence and cobots

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第4期62卷 1277-1296页

作者： Sharma, Ajit Wayne State Univ Mike Ilitch Sch Business Detroit MI 48202 USA

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation.

关键词： Electric vehicles batteries artificial intelligence robots cobots digital twin

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artificial intelligence Meets Flexible Sensors:Emerging Smart Flexible Sensing Systems Driven by machine Learning and artificial Synapses

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Nano-Micro Letters 2024年第1期16卷 235-273页

作者： Tianming Sun Bin Feng Jinpeng Huo Yu xiao Wengan Wang Jin Peng Zehua Li Chengjie Du Wenxian Wang Guisheng Zou Lei Liu Department of Mechanical Engineering State Key Laboratory of Tribology in Advanced EquipmentKey Laboratory for Advanced Manufacturing by Materials Processing TechnologyMinistry of Education of PR ChinaTsinghua UniversityBeijing 100084People’s Republic of China College of Materials Science and Engineering Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024People’s Republic of China

The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human *** an essential component that bridges the physical world and digital signals,flexible sensors are evolving from a single sensing element to a smarter system,which is capable of highly efficient acquisition,analysis,and even perception of vast,multifaceted *** challenging from a manual perspective,the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm(machine learning)and the framework(artificial synapses)*** review presents the recent progress of the emerging AI-driven,intelligent flexible sensing *** basic concept of machine learning and artificial synapses are *** new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed,which significantly advances the applications such as flexible sensory systems,soft/humanoid robotics,and human activity *** two of the most profound innovations in the twenty-first century,the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

关键词： Flexible electronics Wearable electronics Neuromorphic Memristor Deep learning

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Imagining machine vision: Four visual registers from the Chinese AI industry

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AI & SOCIETY 2024年第5期39卷 2267-2284页

作者： de Seta, Gabriele Shchetvina, Anya Univ Bergen Dept Linguist Literary & Aesthet Studies Bergen Norway Humboldt Univ GRK Kleine Formen Berlin Germany

machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the national AI market, and technologies like face recognition, object tracking and automated driving play a central role in surveillance systems and social governance projects relying on the large-scale collection and processing of sensor data. Like other novel articulations of technology and society, machine vision is defined, developed and explained by different actors through the work of imagination. In this article, we draw on the concept of sociotechnical imaginaries to understand how Chinese companies represent machine vision. Through a qualitative multimodal analysis of the corporate websites of leading industry players, we identify a cohesive sociotechnical imaginary of machine vision, and explain how four distinct visual registers contribute to its articulation. These four registers, which we call computational abstraction, human-machine coordination, smooth everyday, and dashboard realism, allow Chinese tech companies to articulate their global ambitions and competitiveness through narrow and opaque representations of machine vision technologies.

关键词： artificial intelligence China machine vision Sociotechnical imaginaries Tech industry Visual culture

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Event-enhanced synthetic aperture imaging

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Science China(Information Sciences) 2025年第3期68卷 388-390页

作者： Siqi LI Shaoyi DU Jun-Hai YONG Yue GAO Beijing National Research Center for Information Science and Technology School of SoftwareTsinghua University Institute for Brain and Cognitive Sciences Beijing Laboratory of Brain and Cognitive IntelligenceTsinghua University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and ApplicationsInstitute of Artificial Intelligence and Robotics Xi'an Jiaotong University

Synthetic aperture imaging(SAI) methods aim to see through dense occlusions and reconstruct the target scene behind occlusions. Traditional frame-based SAI methods,e.g., DeOccNet [1], take the occluded light field ima...

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AI-driven grain storage solutions: Exploring current technologies, applications, and future trends

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JOURNAL OF STORED PRODUCTS RESEARCH 2025年 111卷

作者： Anukiruthika, T. Jayas, D. S. Univ Lethbridge Lethbridge AB T1K 3M4 Canada Univ Manitoba Winnipeg Dept Biosyst Engn Winnipeg MB R3T 5V6 Canada

The integration of artificial intelligence (AI) and machine learning (ML) technologies is revolutionizing the food grain industry, particularly in the storage and quality management. This work provides a comprehensive review on the integration of AI and ML in the food grain industry, focusing on current technologies, applications, and future advancements. Various AI technologies including artificial neural networks (ANNs), fuzzy logic systems, and ML methods such as deep learning, supervised learning, and anomaly detection have been discussed. The practical applications of these technologies in addressing critical areas such as pest and insect damage detection, grain classification, crop disease detection, mycotoxin contamination, and supply chain management are highlighted. applications of innovative technological approaches, including edge computing, digital twins, Internet of Things (IoT), and blockchain, have been discussed for their impact on enhancing grain storage quality management. The review also critically examines the challenges and limitations associated with AI and ML, such as data privacy, inaccuracies, and regulatory concerns. In addition, the emerging trends that are set to revolutionize grain quality management such as smart sensors, robotics, remote sensing, and augmented reality are discussed. By synthesizing current knowledge and future prospects, this review aims to provide a holistic understanding of AI's transformative potential in the grain industry.

关键词： Grain storage artificial intelligence machine learning Internet of things Blockchain Quality management

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Synthetic data-augmented explainable vision Transformer for colorectal cancer diagnosis via surface tactile imaging

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ENGINEERING applications OF artificial intelligence 2025年 151卷

作者： Kapuria, Siddhartha Ikoma, Naruhiko Chinchali, Sandeep Alambeigi, Farshid Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA Univ Texas MD Anderson Canc Ctr Dept Surg Oncol Div Surg Houston TX 77030 USA Univ Texas Austin Chandra Family Dept Elect & Comp Engn Austin TX 78712 USA

In this work, we present a synthetic data augmented explainable vision Transformer (ViT) framework designed for the informed and intuitive early diagnosis of colorectal cancer (CRC) polyps. The framework uses textural images - generated by our recently developed vision-based tactile sensor (called HySenSe) and augmented by synthetically generated images from a diffusion model pipeline, to output class-based probabilities of potential CRC polyp types. Additionally, it provides local relevancy-based heatmaps to assist clinicians by highlighting key areas of interest in the tactile images representing CRC polyp textures. We benchmark each aspect of this framework through: (i) Inception Scores for the synthetic images generated by the diffusion pipeline, (ii) Performance evaluation and sensitivity analyses on the effects of synthetic data addition on model generalizability compared with other state-of-the-art architectures, (iii) Dimensionality reduction techniques to confirm the suitability of synthetically generated images, and (iv) Comparison of two independent approaches visualizing explainability.

关键词： Colorectal cancer diagnosis Intuitive machine learning Surface tactile imaging Surgical robotics vision transformer Diffusion model

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artificial intelligence in Urology Current Status and Future Perspectives

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UROLOGIC CLINICS OF NORTH AMERICA 2024年第1期51卷 117-130页

作者： Abid, Rayyan Hussein, Ahmed A. Guru, Khurshid A. Case Western Reserve Univ 10900 Euclid Ave Cleveland OH 44106 USA Roswell Pk Comprehens Canc Ctr Dept Urol Buffalo NY USA Roswell Pk Comprehens Canc Ctr ATLAS Appl Technol Lab Adv Surg Program Elm & Carlton St Buffalo NY 14263 USA

AI has made a significant progress in multiple facets of surgery over the past decade including diagnostics, outcome prediction, and robotics. However, many obstacles still exist before urology can achieve fully autonomous surgery. Autonomous actions will require a complex network of machine learning, natural language processing, and CV. Each of these facets requires large amounts of data in the form of EHRs, images, and videos that all must be annotated. Thus, the future of urologic surgery must focus on data collection, preparation, and annotation. Other questions must also be answered before AI be-comes widespread in surgery. Autonomous sur-gery must be interpretable to its users and patients, and its ethical concerns regarding racial bias, consideration of treatment implications, and consent must be addressed.

关键词： artificial intelligence machine learning Natural language processing Computer vision

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Surface defect detection competition with a bio-inspired vision sensor

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National Science Review 2023年第6期10卷 31-34页

作者： Anjing Guo Jupo Ma Renwei Dian Fuyan Ma Jinjian Wu Shutao Li School of Robotics Hunan University School of Artificial Intelligence Xidian University Pazhou Laboratory (Huangpu) College of Electrical and Information Engineering Hunan University Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province

INTRODUCTION Surface defects are inevitable during the product manufacturing process,such as impurities,scratches and dirt.D efects not only reduce the aesthetics and comfort of products,but may also degrade the functional *** are very concerned about inadequate defect detection because of potential economic and reputation ***,it is significant to study automatic defect detection *** machine vision-based defect detection mostly adopts a traditional CCD or CMOS vision sensor (VS) for ***,in real-world production lines,due to dynamic environments,traditional VS encounters difficulties in meeting the demands of defect detection,especially in modern high-precision product manufacturing.

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artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

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UPDATES IN SURGERY 2024年第3期76卷 783-792页

作者： Bianchi, Valentina Giambusso, Mauro De Iacob, Alessandra Chiarello, Maria Michela Brisinda, Giuseppe Fdn Policlin Univ A Gemelli Emergency Surg & Trauma Ctr Dept Abdominal & Endocrine Metab Med & Surg Sci IRCCS Largo Agostino Gemelli 8 I-00168 Rome Italy Vittorio Emanuele Hosp Gen Surg Operat Unit I-93012 Gela Italy Azienda Sanit Provinciale Cosenza Dept Surg Gen Surg Operat Unit I-87100 Cosenza Italy Univ Dept Translat Med & Surg Catholic Sch Med I-00168 Rome Italy

artificial intelligence is transforming healthcare. artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acut

关键词： Acute appendicitis artificial intelligence Diagnosis Laparoscopy Surgery

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