Zhiling Chen
๐Ÿ˜Š smile everyday

Zhiling Chen

I am currently a second-year Mechanical Engineering PhD student at the University of Connecticut and a research assistant at the Intelligent Systems and Control Laboratory (ISCL), where I am advised by Dr. Farhad Imani and Dr. Ruimin Chen.

I am currently looking for research internship next summer (2025). Feel free to drop me an email if you are interested!

Email: zhiling.chen@uconn.edu

>> I conduct research on Computer Vision and Robotics. Currently, my research mainly lies in Vision-Language Models for anomaly detection, safety and privacy in Smart Manufacturing.

๐Ÿ“ Updates


๐Ÿ“š Publications

Distributed Hyperdimensional Computing for Real-Time Data Aggregation and Interpretable Quality Monitoring in Manufacturing
IMECE 2024 (Porland, OR)
Zhiling Chen, Danny Hoang, Ruimin Chen, Farhad Imani.

Abstract: The integration of diverse sensors in manufacturing processes offers enhanced potential for real-time quality assurance through the collection of complementary data. However, the limited interpretability of current machine learning models often hampers the effective discrimination of each sensor's unique contributions, primarily due to the complexity of decoding the interdependencies among various signals. This paper proposes a novel computational framework, Distributed Hyperdimensional Computing (DHDC), which is designed to leverage efficient cognitive operations, such as binding and bundling, for interpretable learning across multi-level sensor data. DHDC operates by encoding and aggregating data systematically across a distributed architecture, thereby enhancing transparency in computational efficiency. Our real-world experimental results on the 5-axis machining for the fabrication of the counterbore hole feature demonstrate that the framework not only effectively characterizes the impact of individual sensors but also achieves a high degree of predictive accuracy, as evidenced by an F1 score of 90.4%. The proposed framework holds the potential for interpretable and scalable quality control in distributed additive and subtractive manufacturing. ... See More
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces
Expert Systems with Applications
Zhiling Chen, Hanning Chen, Mohsen Imani, Ruimin Chen, Farhad Imani.
[CODE]
Abstract: Workplace accidents due to personal protective equipment (PPE) non-compliance raise serious safety concerns and lead to legal liabilities, financial penalties, and reputational damage. While object detection models have shown the capability to address this issue by identifying safety items, most existing models, such as YOLO, Faster R-CNN, and SSD, are limited in verifying the fine-grained attributes of PPE across diverse workplace scenarios. Vision language models (VLMs) are gaining traction for detection tasks by leveraging the synergy between visual and textual information, offering a promising solution to traditional object detection limitations in PPE recognition. Nonetheless, VLMs face challenges in consistently verifying PPE attributes due to the complexity and variability of workplace environments, requiring them to interpret context-specific language and visual cues simultaneously. We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance, which comprises four main modules:scene recognition, the visual prompt, safety items detection, and fine-grained verification. The scene recognition identifies the current scenario to determine the necessary safety gear ... See More
Privacy-preserving Federated Learning with Differentially Private Hyperdimensional Computing
Computers and Electrical Engineering
Fardin Jalil Piran, Zhiling Chen, Mohsen Imani, Farhad Imani.

Abstract: Federated Learning (FL) is essential for efficient data exchange in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally and shares only model updates. However, FL is vulnerable to privacy threats like model inversion and membership inference attacks, which can expose sensitive training data. To address these privacy concerns, Differential Privacy (DP) mechanisms are often applied. Yet, adding DP noise to black-box ML models degrades performance, especially in dynamic IoT systems where continuous, lifelong FL learning accumulates excessive noise over time. To mitigate this issue, we introduce Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework that combines the neuro-symbolic paradigm with DP. FedHDPrivacy carefully manages the balance between privacy and performance by theoretically tracking cumulative noise from previous rounds and adding only the necessary incremental noise to meet privacy requirements. In a real-world case study involving in-process monitoring of manufacturing machining operations, FedHDPrivacy demonstrates robust performance, outperforming standard FL frameworksโ€”including Federated Averaging (FedAvg) ... See More
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
Arxiv
Zhiling Chen, Hanning Chen, Mohsen Imani, Farhad Imani.
[CODE]
Abstract: In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and operational changes frequently arise. Recent advancements in Multimodal Large Language Models (MLLMs) hold promise for overcoming these limitations by combining visual and textual information processing capabilities. ... See More
Federated Hyperdimensional Computing for hierarchical and distributed quality monitoring in smart manufacturing
Internet of Things
Zhiling Chen, Danny Hoang, Fardin Jalil Piran, Ruimin Chen, Farhad Imani.

Abstract: In emerging smart manufacturing, the integration of Internet of Things (IoT) and edge devices is essential for in-situ sensing, communication, and adaptive learning. Federated Learning (FL) leverages edge-cloud collaboration to preserve data privacy and minimize communication over-head compared to centralized models. However, conventional FL approaches face significant challenges in manufacturing๏ผš (1) non-Independent and Identically Distributed (non-IID) data and diverse feature distributions complicate local model training within hierarchical, complex industrial data structures; (2) directly overwriting local models with a global model during updates causes clients to lose critical task-specific information unique to environments; and (3) transmitting model updates causes massive communication overhead, limiting scalability. We propose Federated Distributed Hyperdimensional Computing (๐–ฅ๐–พ๐–ฝ๐–ฃ๐–ง๐–ฃ), an FL framework that employs Hyperdimensional Computing (HDC) to optimize communication for hierarchical manufacturing data. Unlike neural networks, HDC offers robust performance with lower computational demands and inherent resilience to noisy, non-IID data, enabling ๐–ฅ๐–พ๐–ฝ๐–ฃ๐–ง๐–ฃ to naturally handle data heterogeneity and reduce computational burdens on edge devices. ๐–ฅ๐–พ๐–ฝ๐–ฃ๐–ง๐–ฃ integrates a hierarchical graph-based learning model with a node pruning module to alleviate computational load and implements a novel client-cloud update strategy leveraging HDCโ€™s high-dimensional representations to streamline synchronization, thereby minimizing communication costs and im- proving scalability. We validate ๐–ฅ๐–พ๐–ฝ๐–ฃ๐–ง๐–ฃ through a case study on hybrid manufacturing using a Sinumerik edge device, focusing on the geometric quality assessment of two counterbore diameters. ๐–ฅ๐–พ๐–ฝ๐–ฃ๐–ง๐–ฃ achieved an F1-score of 95.3% and demonstrated performance improvements of up to 12.6% over state-of-the-art neural network-based FL methods, highlighting its superior efficiency and scalability in complex industrial settings. ... See More


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