A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control

The reliability of the internal micro-motors is crucial for the performance and lifespan of electric toothbrushes. In this paper, a vibration-based fault detection method is proposed to identify micro-motor defects in electric toothbrushes. A dedicated signal acquisition device was designed and developed to capture the vibration signals of micro-motors using a high-precision accelerometer. To effectively characterize the micro-motor conditions, comprehensive features were extracted from the raw vibration data in both the time and frequency domains. A random forest (RF) algorithm was then employed to evaluate the importance of all extracted features. To better interpret the extracted features based on fault mechanisms, and to reduce dimensionality and computational overhead while avoiding overfitting, the top three features with the highest importance scores were selected to form the optimal feature subset. Finally, a support vector machine (SVM) model was utilized to classify the motor states based on the selected features. Experimental results demonstrate that the proposed method, combining RF-based feature selection and SVM classification, achieves outstanding diagnostic performance. Specifically, the model yields a balanced accuracy of 94.44%, a defect recall of 88.89%, a defect F1-score of 94.12%, a Matthews correlation coefficient of 93.74%, a geometric mean of 94.28%, and an area under the receiver operating characteristic curve of 100.00%. These robust metrics confirm that the proposed approach can accurately and efficiently detect micro-motor faults in electric toothbrushes, providing a practical and reliable solution for quality control and condition monitoring in manufacturing.

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation

Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks requiring precise object-level grounding, fine-grained spatial reasoning, and controllable visual manipulation. In particular, existing systems often struggle to identify the correct instance, preserve object identity across interactions, and localize or modify designated regions with high precision. Object-centric vision provides a principled framework for addressing these challenges by promoting explicit representations and operations over visual entities, thereby extending multimodal systems from global scene understanding to object-level understanding, segmentation, editing, and generation. This paper presents a comprehensive review of recent advances at the convergence of LMMs and object-centric vision. We organize the literature into four major themes: object-centric visual understanding, object-centric referring segmentation, object-centric visual editing, and object-centric visual generation. We further summarize the key modeling paradigms, learning strategies, and evaluation protocols that support these capabilities. Finally, we discuss open challenges and future directions, including robust instance permanence, fine-grained spatial control, consistent multi-step interaction, unified cross-task modeling, and reliable benchmarking under distribution shift. We hope this paper provides a structured perspective on the development of scalable, precise, and trustworthy object-centric multimodal systems.

MuseAgent-1: Interactive Grounded Multimodal Understanding of Music Scores and Performance Audio

Despite recent advances in multimodal large language models (MLLMs), their ability to understand and interact with music remains limited. Music understanding requires grounded reasoning over symbolic scores and expressive performance audio, which general-purpose MLLMs often fail to handle due to insufficient perceptual grounding. We introduce MuseAgent, a music-centric multimodal agent that augments language models with structured symbolic representations derived from sheet music images and performance audio. By integrating optical music recognition and automatic music transcription modules, MuseAgent enables multi-step reasoning and interaction over fine-grained musical content. To systematically evaluate music understanding capabilities, we further propose MuseBench, a benchmark covering music theory reasoning, score interpretation, and performance-level analysis across text, image, and audio modalities. Experiments show that existing MLLMs perform poorly on these tasks, while MuseAgent achieves substantial improvements, highlighting the importance of structured multimodal grounding for interactive music understanding.