Browsing by Author "Yang, Ji-Jiang"

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Context-aware, Composable Anomaly Detection in Large-scale Mobile Networks
(2023) Nhu Trang, Nguyen Ngoc; Truong, Linh
A4 Artikkeli konferenssijulkaisussa
In a large-scale mobile network, due to the diversity of data characteristics, detection purposes of operation teams, and analytics and machine learning algorithm abilities, building big data anomaly detection pipelines without considering different analytics and team situations may not yield expected quality of analytics, including detection relevancy, performance and quality. This is especially for analytics subjects, such as mobile network zones, of which characteristics are dynamic and contextual. Moreover, due to the lack of labeled data and the high cost of creating labeled data, building anomaly detection analytics models based on (supervised) deep learning or advanced models is even more challenging from various aspects of effort, cost and deployment. In this paper, we present a novel framework that enables anomaly detection through context-aware, composable components to provide efficient detection pipelines suitable for lightweight, resource constrained and geographical operation teams. First, we identify and categorize different types of analytics feature contexts and evaluate existing algorithms suitable for these contexts, mapping anomaly detection algorithms, patterns and configurations for data pre-processing and unsupervised detection tasks in individual analytics functionality. These context-specific pipelines detect anomalies and their relevancy for dynamic analytics subjects such as mobile network zones. Then we develop dynamic configuration and combination techniques for such pipelines to produce highly relevant, multi-context detection of anomalies. Our framework provides flexibility and configurations for team contexts to carry out the anomaly detection in the team’s operations. We will demonstrate our work through real data gathered for a large-scale mobile network covering multiple types of sites with different geographical zones and equipment. We especially focus on district zones and user-defined zones as analytics subjects that must be managed by teams in our experiments.
Robotic Gel Dispensing based on Visual Servoing for Fiber Threading
(2023-08-02) Bettahar, Houari; Freitas Vieira, Arthur; Zhou, Quan
A4 Artikkeli konferenssijulkaisussa
Dispensing sessile droplets accurately on the top of the needle tip is important for applications such as fiber threading. In this paper, we propose an accurate online gel dispensing method to accurately dispense sessile droplets on top of the dispenser tip for highly repeatable fiber threading. We design a robotic gel dispenser based on positive-displacement piston dispensing that can accurately dispense sessile gel droplets of desired volume using visual servoing and adaptive model predictive control. An online gel volume estimation algorithm based on image processing is constructed to provide the estimation of the volume of the extruded gel droplet to the controller. To compensate for the nonlinear and time-varying process properties, the Adaptive Model Predictive Controller (MPC) adjusts its prediction model at runtime while satisfying a set of constraints. We employ the fiber threading technique based on impedance control with force tracking we developed recently to carry out the fiber threading experiments. To examine the benefits of the accurately dispensed droplet, multiple fibers threading experiments are conducted, where the repeatability of the fibers fabricated using the proposed methods are compared with two other methods: a) conventionally velocity regulation-based fiber fabrication, where the pulling force profile is not controlled, and b) fiber threading using impedance control with force tracking using a commercial time-pressure dispenser. The experimental results show that fibers fabricated using the proposed method have the highest repeatability based on the coefficient of variation of properties of the fabricated fibers, where the obtained coefficient of variation of the toughness, stiffness, elongation, and strength are 6.7%, 3.7%, 3.4%, 5.6% respectively.
Security Orchestration with Explainability for Digital Twins-based Smart Systems
(2024-08-26) Nguyen, Tri; Ngoc Lam, An; Nguyen, Phu; Truong, Linh
A4 Artikkeli konferenssijulkaisussa
The Digital Twin (DT) paradigm has been largely adopted for many smart systems in various domains. Due to the heterogeneous and distributed nature of the physical twins, these systems increasingly incorporate disparate security tools, especially those based on service-based AI/ML capabilities. That presents numerous challenges in achieving a comprehensive understanding of security analytics and explainability in security operations carried out by ML-based security services, which require continuous monitoring and optimization to remain effective. This paper aims to support security service integration and automated analyses with enhanced explainability in DTs. We introduce a novel framework that unifies runtime contexts to facilitate security services unification and operation interpretation in security orchestration. We define a workflow and provide necessary services for generating security reports across physical and logical layers. Leveraging a centralized knowledge service, we let security analysts incorporate domain knowledge in automating incident reasoning and security enforcement at the logical layer. We demonstrate our explainability framework on a DT of an Industry 4.0 toy factory with two ML-based security services detecting network anomalies. Our experiments show a significant reduction in manual effort for orchestrating security incident analysis and mitigation.