2025年10月31日至11月2日，第四届先进传感与智能制造学术会议（ASIM 2025）在常州大学召开。本次会议由常州大学、智能制造龙城实验室、常州工学院共同主办，江苏理工学院、南京工业大学、爱迩思出版社（ELSP）协办，并获IEEE南京分会、武汉理工大学、江汉大学、ESBK国际学术中心、AC学术平台等多家单位支持。

会议积极响应国家制造强国战略，聚焦先进传感与智能制造前沿议题，吸引了来自97家高校、科研机构与企业的200余名专家学者参会，深入探讨该领域的最新进展与发展趋势。智能制造龙城实验室执行主任王永青，西安交通大学米兰理工联合学院执行院长杨树明，南京大学博士生导师李根喜，香港理工大学教授徐宾刚，常州大学党委副书记、校长陈海群出席会议。机械学院院长刘麟主持大会开幕式。

大会现场

陈海群在大会开幕式致欢迎词。他强调，在新一轮科技革命推动下，制造业正加速向“高端化、智能化、绿色化”迈进。本次会议围绕先进传感与智能制造的关键科学与工程问题，搭建了跨学科对话平台，具有重要意义。他希望以此次学术会议为契机，进一步促进智能制造领域科技创新与产业融合，深化与国内外高校及企业的合作，为制造业转型升级贡献“常大智慧”。

与会专家学者

大会报告环节，王永青、杨树明、李根喜、徐宾刚分别作《智能制造中的集成测量-加工技术》《半导体芯片缺陷检测新方法探索》《基于生物传感器的外泌体分析方法及其在癌症精准诊断中的应用》《先进的可穿戴传感技术》大会主旨报告，分享了最新研究成果。常州大学沈惠平教授作《一种新型折叠运动平台并联机构的设计与分析》特邀报告，江苏汤姆森智能装备有限公司董事长汤建华作《智能制造赋能包装装备产业新质生产力》行业特邀报告，展现了产学研深度融合的实践成果。

院长论坛现场

会议期间举行了院长论坛，河海大学机电工程学院院长丁坤、常州工学院电气信息工程学院院长毛国勇、江苏理工学院机械学院副院长（主持工作）康绍鹏、江南大学机械学院副院长钱善华、苏州工学院机械学院副院长张斌，以及常州大学机械学院院长刘麟，围绕各学院的学科特色、人才培养亮点等开展了深入交流。

11月1日下午，会议设置四个平行分会场，86场报告围绕柔性电子、生物传感、纳米制造、数字孪生、机器视觉、智能机器人等热点方向展开多维探讨，会议现场学术氛围浓厚。会后，部分参会人员参观了中国(常州)德国中心、同方威视科技江苏有限公司，实地感受常州金坛的智能制造产业生态。

本次会议为专家学者搭建了高水平学术交流平台，有力促进了先进传感与智能制造领域的创新协作与成果转化，为推动智能制造行业高质量发展汇聚了智慧与力量。

The survey “A Survey on Omni-Modal Language Models” offers a systematic overview of the technological evolution, structural design, and performance evaluation of omni-modal language models (OMLMs). The work highlights how OMLMs enable unified perception, reasoning, and generation across modalities, contributing to the ongoing progress toward Artificial General Intelligence (AGI).

image: Omni-modal language models integrate modality alignment, semantic fusion, and joint representation to enable unified perception and reasoning across text, image, and audio modalities.

Credit: Zheyun Qin & Lu Chen / Shandong University & Shandong Jianzhu University

Recently, Lu Chen, a master’s student at the School of Computer and Artificial Intelligence, Shandong Jianzhu University, in collaboration with Dr. Zheyun Qin, a postdoctoral researcher at the School of Computer Science and Technology, Shandong University, published a comprehensive review entitled “A Survey on Omni-Modal Language Models” in AI+ Journal.

The paper provides an in-depth analysis of the core technological evolution, representative architectures, and multi-level evaluation frameworks of omni-modal language models (OMLMs)—a new generation of AI systems that integrate and reason across multiple modalities, including text, image, audio, and video.

Unlike traditional multimodal systems dominated by a single input form, OMLMs achieve modality alignment, semantic fusion, and joint representation learning, enabling dynamic collaboration among modalities within a unified semantic space. This paradigm allows end-to-end task processing—from perception to reasoning and generation—bringing AI one step closer to human-like cognition.

The study also introduces lightweight adaptation strategies, such as modality pruning and adaptive scheduling, to improve deployment efficiency in real-time medical and industrial scenarios. Furthermore, it explores domain-specific applications of OMLMs in healthcare, education, and industrial quality inspection, demonstrating their versatility and scalability.

“Omni-modal models represent a paradigm shift in artificial intelligence,” said Lu Chen, the first author of the paper.

“By integrating perception, understanding, and reasoning within a unified framework, they bring AI closer to the characteristics of human cognition.”

Corresponding author Dr. Zheyun Qin added:

“Our survey not only summarizes the current progress of omni-modal research but also provides forward-looking insights into structural flexibility and efficient deployment.”

This work offers a comprehensive reference for researchers and practitioners in the field of multimodal intelligence and contributes to the convergence of large language models and multimodal perception technologies.

This paper was published in AI Plus (Chen L., Mu J., Wang J., Kang X., Xi X., Qin Z., A Survey on Omni-Modal Language Models, AI Plus, 2026, 1:0001. DOI: 10.55092/aiplus20260001).

Source from [https://www.eurekalert.org/news-releases/1105454].

Qualification of additively manufactured parts can now be supported by Artificial Intelligence. Researchers have developed a new method to process high-frequency welding data collected from a Wire Arc Direct Energy Deposition process, achieving an improvement in anomaly detection performance from 57% to 85.3%. This demonstrates the potential of AI in this field. Published in Advanced Manufacturing, the proposed methodology has the potential to reduce both the time and cost associated with AM production, ultimately lowering overall product costs.

image: AI-driven process monitoring enhances the qualification of additively manufactured stainless-steel parts. By analysing high-frequency welding current and voltage signals in both the time and frequency domains, the proposed Isolation Forest–based method detects anomalies with 85.3% accuracy—outperforming traditional Statistical Process Monitoring, enabling preventive quality assessment, reducing inspection time and production costs while improving reliability in Wire Arc Direct Energy Deposition processes.

Credit: Giulio Mattera/University of Naples “Federico II”, Zengxi Pan/University of Wollongong, Elena Manoli/University of Naples “Federico II”, Luigi Nele/University of Naples “Federico II”

Additive manufacturing (AM) today enables the production of components that are difficult or even impossible to fabricate using traditional technologies. It also helps reduce scrap rates (generally referred to as the buy-to-fly ratio) and the environmental impact associated with each part’s production. However, due to the complex physical phenomena involved, AM parts are often more prone to defects, leading to scrap, financial losses for companies, and increased production costs per part. For this reason, every AM product, particularly those intended for structural or safety-critical applications in metals, must undergo certification through non-destructive testing (NDT) equipment. However, these procedures are typically performed on random samples and are time-consuming. To address this issue, Dr Giulio Mattera from the University of Naples “Federico II” (Italy) and his colleagues, and Professor Zengxi Pan from the University of Wollongong (Australia) have developed a methodology that employs Artificial Intelligence to streamline the certification process, with potential benefits in terms of both cost reduction and product quality improvement.

“Data exhibit complex structures and relationships, especially in welding-based technologies,” explains Dr Mattera. “Therefore, a more sophisticated analysis of the data, enabled by the combination of advanced data analytics tools and Artificial Intelligence, can outperform traditional methods based on simple statistical descriptors such as the mean and variance of the process.”

The newly developed approach processes welding current and voltage data collected at high frequencies, above 5,000 samples per second, to extract information from both the time and frequency domains. This allows for the capture of not only the statistical descriptors of these process variables but also the structural patterns underlying their repetitive nature.

As Dr Mattera emphasises, “A stable welding process, such as the Wire Arc Direct Energy Deposition process, is characterised by the repetition of similar waveforms in both welding current and voltage, which are directly associated with the melting and deposition of the filler wire in the component.” For the research team, by jointly analysing information from both domains, it becomes possible to better assess process stability and identify any anomalous conditions that may be linked to defects, thereby preventing quality degradation.

“Another key innovation of this work lies in the minimal prior knowledge required to replicate the methodology across different industrial systems,” explains Dr Mattera. “Unlike conventional AI models that demand extensive datasets containing both good and defective samples, our approach only needs data from non-defective conditions. This drastically cuts down the time and cost of data collection. The model then learns to recognise normal behaviour and automatically flags any anomalies, effectively detecting defects without ever having seen one.”

The researchers carried out a series of experiments in which they varied several process parameters to collect data from high-quality deposition conditions. They then trained an AI model - specifically, an algorithm known as Isolation Forest - to learn the map of normal behaviour within the data. When tested, the algorithm successfully identified anomalous conditions and assessed the component’s quality. In one case, it detected irregularities that indicated the need to clean the welding torch nozzle during production, preventing the formation of defects such as porosity in the metal part. Such defects, if left unchecked, could compromise the mechanical properties of the final product.

“By comparing the proposed methodology with traditional Statistical Process Monitoring (SPM) techniques, we found that our method was able to detect anomalous conditions that conventional approaches could not,” said Dr Mattera. “This shows that current in-process monitoring techniques are effective at identifying extreme conditions but are not suitable for preventive analysis, where the system is monitored and maintained before a defect occurs.”

In the study, traditional Statistical Process Monitoring methods struggled to detect irregularities. The results showed that the SPM approach correctly identified only 43 anomalies, while 105 were missed or misclassified as normal. By contrast, the proposed AI-based method performed far better, correctly identifying 116 anomalies and missing just 32, demonstrating its much greater accuracy in spotting potential defects.

“The results of this study are very encouraging. We’re putting significant effort into this line of research to provide industry with new tools and to demonstrate their potential impact for both manufacturers and end users,” concluded Dr Mattera. “I would like to thank the entire research team for their work over the years, as well as our industrial partners for their continued support.”

He added, “Despite the promising results, our next step is to refine the methodology and turn it into a practical tool for manufacturing. We are working on adding features such as explainability and quality index estimation, which will make it more intuitive and easier for humans to interact and collaborate with intelligent machines.”

Dr Mattera and his co-authors acknowledge this as an important step toward integrating AI into manufacturing for quality improvement. However, they note that further regulatory and legal developments will be essential for real industrial adoption. “In the meantime,” Dr Mattera concluded, “we’re pushing the research forward to make these tools ready and to help mature this field for industrial use.”

This paper ”Process monitoring of P-GMAW-based wire arc direct energy deposition of stainless steels via time-frequency domain analysis and Isolation Forest” was published in Advanced Manufacturing.

Mattera G, Manoli E, Pan Z, and Nele L. Process monitoring of P-GMAW-based wire arc direct energy deposition of stainless steels via time-frequency domain analysis and Isolation Forest. Adv. Manuf. 2025(2):0010, https://doi.org/10.55092/am20250010.

Source from [https://www.eurekalert.org/news-releases/1105450].

11月5日，据西湖大学消息，世界知名华裔化学家、材料化学家和肿瘤学专家，美国原芝加哥大学化学系James Franck讲席教授林文斌，于11月3日全职加入西湖大学理学院，任化学讲席教授、可持续发展与人类健康分子材料实验室负责人，同时在西湖大学医学院和工学院兼任职务。

林文斌简介

林文斌，国际知名分子材料化学家与化学生物学家，金属有机框架（MOF）领域的奠基者和引领者之一。

林文斌于1988年毕业于中国科学技术大学，获学士学位；在伊利诺伊大学厄巴纳-香槟分校（University of Illinois at Urbana–Champaign）师从 Ralph G. Nuzzo 教授与 Gregory S. Girolami 教授，于1994年获得博士学位。随后，他在西北大学（Northwestern University）师从 Tobin J. Marks 教授，作为美国国家科学基金会博士后（NSF Postdoctoral Fellow）从事研究工作。1997年至2001年在美国布兰迪斯大学（Brandeis University）化学系任助理教授；2001年至2013年在北卡罗来纳大学教堂山分校（University of North Carolina at Chapel Hill）化学系与药学院任教，期间历助理教授（2001–2003）、副教授（2003–2007）、教授（2007–2011）及Kenan 杰出教授（2011–2013）。2013年起，加入芝加哥大学，任化学系与放射与细胞肿瘤学系詹姆斯·弗兰克讲席教授。2025年，加入西湖大学任化学讲席教授，继续开展科学研究与人才培养工作。

林文斌的研究聚焦于分子材料的设计与开发，已发表477余篇经同行评议的学术论文，其成果被引用超过87,000次。自2014年起，他连续入选全球高被引化学家行列，并被评为1999–2009十年间全球单篇论文引用影响力前十位的化学家之一。他当选为美国科学促进会（2011）、欧洲科学院（2023）、美国国家发明家科学院（2024）及美国医学与生物工程院（2025）的院士。

来源：西湖大学，仅用于学术分享，如有侵权请联系删除。