| 1. |
- Chahed, Hamza, et al.
(author)
-
AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications
- 2023
-
In: Internet of Things. - : Elsevier. - 2542-6605. ; 22
-
Journal article (peer-reviewed)abstract
- Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases.
|
|
| 2. |
- Chatterjee, Ayan, et al.
(author)
-
IoT anomaly detection methods and applications : A survey
- 2022
-
In: Internet of Things. - : Elsevier. - 2542-6605. ; 19
-
Research review (peer-reviewed)abstract
- Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.
|
|
| 3. |
- Chatterjee, Ayan, et al.
(author)
-
Testing of machine learning models with limited samples : an industrial vacuum pumping application
- 2022
-
In: ESEC/FSE ’22-Proceedings of the 30<sup>th</sup> ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450394130 ; , s. 1280-1290
-
Conference paper (peer-reviewed)abstract
- There is often a scarcity of training data for machine learning (ML) classification and regression models in industrial production, especially for time-consuming or sparsely run manufacturing processes. Traditionally, a majority of the limited ground-truth data is used for training, while a handful of samples are left for testing. In that case, the number of test samples is inadequate to properly evaluate the robustness of the ML models under test (i.e., the system under test) for classification and regression. Furthermore, the output of these ML models may be inaccurate or even fail if the input data differ from the expected. This is the case for ML models used in the Electroslag Remelting (ESR) process in the refined steel industry to predict the pressure in a vacuum chamber. A vacuum pumping event that occurs once a workday generates a few hundred samples in a year of pumping for training and testing. In the absence of adequate training and test samples, this paper first presents a method to generate a fresh set of augmented samples based on vacuum pumping principles. Based on the generated augmented samples, three test scenarios and one test oracle are presented to assess the robustness of an ML model used for production on an industrial scale. Experiments are conducted with real industrial production data obtained from Uddeholms AB steel company. The evaluations indicate that Ensemble and Neural Network are the most robust when trained on augmented data using the proposed testing strategy. The evaluation also demonstrates the proposed method's effectiveness in checking and improving ML algorithms' robustness in such situations. The work improves software testing's state-of-the-art robustness testing in similar settings. Finally, the paper presents an MLOps implementation of the proposed approach for real-time ML model prediction and action on the edge node and automated continuous delivery of ML software from the cloud.
|
|
| 4. |
- Mandal, Bappaditya, et al.
(author)
-
A Low Profile Button Antenna with Back Radiation Reduced By FSS
- 2020
-
In: 2020 14th European Conference on Antennas and Propagation (EuCAP). - 9788831299008 - 9781728137124
-
Conference paper (peer-reviewed)abstract
- In this article, a button antenna with a reflective frequency selective surface(FSS) is proposed to reduce its back radiation. The proposed antenna is low in profile, circularly polarized and designed for Wi-Fi and WLAN applications. The radiating element is made of copper sheet, while a transparent acrylic fibre sheet is used as a substrate. The antenna is fed by a coaxial line, and the FSS layer is designed on jeans material. The patch type FSS with split ring shape has also been designed to operate in the Wi-Fi and WLAN frequency hand (5.250-5.850 GHz) with the centre frequency of 5.51 GHz. The FSS reduces hack radiation of the antenna by 4 dB. The antenna with FSS is fabricated, and a measured gain of 2.9dBi is obtained that matches well with the theoretical value. The antenna is miniaturized by around 61.15% by the slits. To achieve circular polarization characteristic Defected Ground Structure (DGS) slots etched at the ground plane of the triangular patch. The measured impedance bandwidth is 190MHz, and the 3dB axial-ratio (AR) bandwidth is 160MHz, respectively.
|
|
| 5. |
- Mondal, Ayan, et al.
(author)
-
Cytotoxic and Inflammatory Responses Induced by Outer Membrane Vesicle-Associated Biologically Active Proteases from Vibrio cholerae
- 2016
-
In: Infection and Immunity. - 0019-9567 .- 1098-5522. ; 84:5, s. 1478-1490
-
Journal article (peer-reviewed)abstract
- Proteases in Vibrio cholerae have been shown to play a role in its pathogenesis. V. cholerae secretes Zn-dependent hemagglutinin protease (HAP) and calcium-dependent trypsin-like serine protease (VesC) by using the type II secretion system (TIISS). Our present studies demonstrated that these proteases are also secreted in association with outer membrane vesicles (OMVs) and transported to human intestinal epithelial cells in an active form. OMV-associated HAP induces dose-dependent apoptosis in Int407 cells and an enterotoxic response in the mouse ileal loop (MIL) assay, whereas OMV-associated VesC showed a hemorrhagic fluid response in the MIL assay, necrosis in Int407 cells, and an increased interleukin-8 (IL-8) response in T84 cells, which were significantly reduced in OMVs from VesC mutant strain. Our results also showed that serine protease VesC plays a role in intestinal colonization of V. cholerae strains in adult mice. In conclusion, our study shows that V. cholerae OMVs secrete biologically active proteases which may play a role in cytotoxic and inflammatory responses.
|
|
