| 1. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Deep Learning Approach for Classification and Measurement of Hazardous Gases Using Multi-Sensor Data Fusion
- 2023
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings.
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Konferensbidrag (refereegranskat)abstract
- Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.
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| 2. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Study on the Correlation between Change in the Geometrical Dimension of a Free-Falling Molten Glass Gob and Its Viscosity
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:2, s. 661-661
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.
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| 3. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Experiences of using LMS tools for implementing asynchronous interactive media to enhance student interaction in distance education
- 2023
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Ingår i: Bidrag från den 9:e utvecklingskonferensen för Sveriges ingenjörsutbildningar. - Västerås : Mälardalens universitet. - 9789174856200 ; , s. 287-295
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Konferensbidrag (refereegranskat)abstract
- Recorded lectures and online tutorials have become common in higher education, offering students flexibility in their learning. New teaching methods like flipped classrooms aim to prioritise active, student-centred learning. However, these recorded materials often lack interactivity. Incorporating interactive annotations in videos and texts can enhance engagement and learning but presents challenges in terms of tool availability, integration with Learning Management System, and impact measurement. Here an action research approach is used to evaluate three tools (FeedbackFruits, H5P, Kaltura Quiz) for creating asynchronous interactive modules in online engineering education, with future research focused on assessing their impact on student learning and engagement.
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| 4. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Multi-Camera Based Setup for Geometrical Measurement of Free-Falling Molten Glass Gob
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:4
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Tidskriftsartikel (refereegranskat)abstract
- High temperatures complicate the direct measurements needed for continuous characterization of the properties of molten materials such as glass. However, the assumption that geometrical changes when the molten material is in free-fall can be correlated with material characteristics such as viscosity opens the door to a highly accurate contactless method characterizing small dynamic changes. This paper proposes multi-camera setup to achieve accuracy close to the segmentation error associated with the resolution of the images. The experimental setup presented shows that the geometrical parameters can be characterized dynamically through the whole free-fall process at a frame rate of 600 frames per second. The results achieved show the proposed multi-camera setup is suitable for estimating the length of free-falling molten objects.
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| 5. |
- Hussain, Mazhar, 1980-
(författare)
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Multi-sensor dataset for normal air, Methyl Mercaptan and Hydrogen Sulfide gas classification
- 2023
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Annan publikationabstract
- The dataset includes time-series data collected by four different sensors, which measure two target gases, Hydrogen Sulfide and Methyl Mercaptan, in the presence of air. To obtain measurements, each gas was individually exposed to the multi-sensor setup while in the presence of air. The dataset is particularly useful for gas classification tasks, as deep learning and data fusion techniques can be applied to identify the target gases.
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| 6. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Selection of optimal parameters to predict fuel consumption of city buses using data fusion
- 2022
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Ingår i: 2022 IEEE Sensors Applications Symposium (SAS). - : IEEE. - 9781665409810
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Konferensbidrag (refereegranskat)abstract
- The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.
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| 7. |
- Shallari, Irida, et al.
(författare)
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Assignments in the ChatGPT-era : Case Study on Plagiarism in Digital Systems Design Courses
- 2024
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Ingår i: Proceedings of the Annual Hawaii International Conference on System Sciences. - Hawaii USA : IEEE Computer Society. - 9780998133171 ; , s. 7510-7519
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Konferensbidrag (refereegranskat)abstract
- We are experiencing a prolific growth of Artificial Intelligence (AI) that is enabling its ubiquitous diffusion. As part of it, generative AI models have gained particular attention due to their promising capabilities in solving complex tasks previously associated solely to human cognitive capabilities. In this article we focus on a specific AI tool, ChatGPT, which has been developed with the vision of behaving as an educational tool tailored to everyone's learning needs. This case study analyses the capabilities of such a tool in solving a predefined set of tasks in the subject area of Digital Systems Design, with the scope of designing robust assignments for students that cannot be solved and plagiarised with this tool. The results observed across different categories of cognitive depth show that ChatGPT has extensive conceptual knowledge in the area. However, this tool has important limitations when it comes to optimisation tasks, device specific configurations and overlaying of concepts, putting an emphasis on the importance of using such aspects in the design of robust tasks.
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| 8. |
- Shallari, Irida, et al.
(författare)
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Image Scaling Effects on Deep Learning Based Applications
- 2022
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Ingår i: 2022 IEEE International Symposium on Measurements & Networking (M&N). - : IEEE. - 9781665483629
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Konferensbidrag (refereegranskat)abstract
- The sophistication and high accuracy of Deep Neural Networks have gotten significant attention in recent years, with a wide range of applications making use of their capabilities. However, the deployment of such networks still faces limitations due to the high volume of data to be processed and the high computational requirements. In this article we focus on the effects that data volume reduction, due to image compression and scaling down the image resolution, will have on the detection accuracy for the design case of a powered wheelchair guidance system. Throughout our analysis we show that the reduction in image resolution to a factor of 16× in image area alongside with JPEG compression provides a detection accuracy of over 0.93 in mAP, while the additional error in the position estimation of the caregiver is less than 0.5 cm. By reducing the data volume we inherently reduce the communication energy consumption, which is reduced by more than one order of magnitude. These results prove that we can overcome the complexity of high data volume for the deployment of DNNs in resource constrained IoT applications by interlacing the effects of image compression and resolution reduction, maintaining the accuracy and reducing the node energy consumption.
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