摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据来自纽约波茨坦的新闻，NewsRx记者报道，“在连续生产(CM)的过程质量监控和实时释放(RTR)测试的关键质量属性(CQA)评估中，超声表征是其直接、非侵入性、快速和成本效益的关键技术。在超声质量评估中，将片剂的超声响应与其缺陷状态和质量参数联系起来是至关重要的。”我们的新闻记者从克拉克森大学的研究中获得了一句话，“然而，超声CQA表征需要一个稳健的数学模型，而传统的基于第一原理的建模APPR Oaches无法获得这一模型。使用实验数据的机器学习(ML)正在成为克服这种建模挑战的关键分析工具。在这项工作中，提出了一种基于D-EEP神经网络的MLdriven无损评价模型(ML-NDE),并对缺陷状态、压缩力水平和崩解量三个CQA进行了提取和预测,利用机器人平板搬运实验台采集了每个ATTR IBUTE的不同波形数据并进行了训练、验证和分析。本研究详细介绍了一个先进的药品CM质量评估框架，其中自动化RTR测试有望成为开发成本效益高的过程中实时监测系统的关键。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Potsdam, New York, by NewsRx correspondents, research stated, “In in-process quality monitoring for Continuou s Manufacturing (CM) and Critical Quality Attributes (CQA) assessment for Real-t ime Release (RTR) testing, ultrasonic characterization is a critical technology for its direct, non-invasive, rapid, and cost-effective nature. In quality evalu ation with ultrasound, relating a pharmaceutical tablet’s ultrasonic response to its defect state and quality parameters is essential.” Our news journalists obtained a quote from the research from Clarkson University , “However, ultrasonic CQA characterization requires a robust mathematical model , which cannot be obtained with traditional first principles-based modeling appr oaches. Machine Learning (ML) using experimental data is emerging as a critical analytical tool for overcoming such modeling challenges. In this work, a novel D eep Neural Network-based MLdriven Non-Destructive Evaluation (ML-NDE) modeling f ramework is developed, and its effectiveness for extracting and predicting three CQAs, namely defect states, compression force levels, and amounts of disintegra nt, is demonstrated. Using a robotic tablet handling experimental rig, each attr ibute’s distinct waveform dataset was acquired and utilized for training, valida ting, and testing the respective ML models. This study details an advanced algor ithmic quality assessment framework for pharmaceutical CM in which automated RTR testing is expected to be critical in developing cost-effective in-process real -time monitoring systems.”
NETL
NSTL