چارچوب هوشمند مبتنی بر یادگیری ماشین و معماری مدل‌رانده برای بهینه‌سازی سیستم‌های IoT صنعتی

چکیده مقاله :

با گسترش روزافزون دستگاه‌های متصل به اینترنت اشیاء، حجم بالای داده‌های بدون‌‌برچسب و نیاز به پردازش سریع و مدیریت بهینه منابع، چالش‌های جدیدی در این حوزه ایجاد شده است. در این میان، اهمیت روش‌های نیمه‌نظارتی برای پیش‌بینی و همچنین استخراج وبژگی‌های کلیدی دامنه برای بهبود دقت، بیش از پیش آشکار شده است. در این پژوهش، رویکردی نوین برای بهبود کارایی سیستم‌های IoT صنعتی ارائه می‌شود که در آن یادگیری ماشین با بهره‌گیری از معماری مدل‌رانده (MDA[1])  تقویت شده است. در این چارچوب، مدل‌های مفهومی دامنه‌محور به‌عنوان پایه‌ای برای استخراج ویژگی‌های کلیدی، ساختاردهی داده‌ها و کاهش نویز به کار می‌روند. این یکپارچه‌سازی باعث بهبود فرآیند یادگیری ماشین به ویژه در شرایط کمبود داده‌های برچسب‌دار می‌گردد. در مرحله اول، مدل‌های PIM و PSM برای جداسازی منطق سیستم از جزئیات پیاده‌سازی ایجاد شده و با تغییرات فناوری سازگار می‌شوند. سپس داده‌های پردازش‌شده و ویژگی‌های استخراج‌شده به الگوریتم‌های یادگیری نیمه‌نظارتی Self-training، Label Propagation  و Semi-Supervised SVM  وارد می‌شوند تا از داده‌های بدون برچسب نیز در فرآیند آموزش استفاده شود. ارزیابی این رویکرد در یک محیط شبیه‌سازی صنعتی مبتنی بر IoT  (مانند سامانه پایش و کنترل انرژی) نشان می‌دهد که استفاده از MDA  به‌عنوان لایه تقویت‌کننده ML، دقت پیش‌بینی را به طور میانگین تا ۹۵٪ افزایش داده، همچنین پایداری مدل در برابر داده‌های نویزی به طور قابل توجهی بهبود یافته است. نتایج نشان می‌دهد که ترکیب MDA  و یادگیری ماشین نیمه‌نظارتی می‌تواند به عنوان یک چارچوب مقیاس‌پذیر و قابل اعتماد برای ارتقاء کارایی صنایع مبتنی بر IoT  به کار گرفته شود.

 

 

چکیده انگلیسی :

By increasing proliferation of IoT devices, the large volume of unlabeled data, and the need for fast processing and optimal resource management, new challenges have emerged in this field. In the meantime, the importance of semi-supervised methods for prediction and also for extracting key domain features to improve accuracy has become more important. In this research, a new approach to improving the efficiency of industrial IoT systems is presented, in which machine learning is enhanced by using Model-Driven Architecture. In this framework, domain-oriented conceptual models are used as the basis for extracting key features, structuring data, and reducing noise. This integration improves the machine learning process, especially in conditions of lack of labeled data. In the first step, PIM and PSM models are created to separate the system logic from the implementation details and adapt to technological changes. Then, the processed data and extracted features are fed into the semi-supervised learning algorithms Self-training, Label Propagation, and Semi-Supervised SVM to also use unlabeled data in the training process. Evaluation of this approach in an IoT-based industrial simulation environment shows that using MDA as the ML reinforcement layer increases the prediction accuracy by an average of 95%, and the robustness of the model against noisy data is also significantly improved. The results indicate that the combination of MDA and semi-supervised machine learning can improve the efficiency of IoT-based industries.

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