| 1. |
- Berglund, Anders, 1974-, et al.
(författare)
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An Assessment Review of Learning Performance when adopting Augmented Reality in Engineering Education
- 2022
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Ingår i: Bidrag från 8:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar. - 9789178672714 ; , s. 32-35
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Konferensbidrag (refereegranskat)abstract
- Augmented Reality (AR) has developed rapidly inrecent years and it is about to become a mainstream technology.We are witnessing how emerging technologies such as AR has been introduced and today widely applied in engineering education.The turmoil caused by the COVID-19 pandemic has in many ways highlighted the importance of AR technology for collaboration and remote assistance of frontline workers. Enabling experts to be much more productive in helping to debug problems and resolve production issues remotely. This kind of hands-on support and tutoring opportunities play well into the possibilities embedded in a more digitalized approach to engineering education. Still, both industries and universities are exploring ways to enhance the value-added credentials that come along with an integration and investment of AR. This paper set out to understand what type of assessment that are used to drive learning performance amongstudents in engineering education.
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| 2. |
- Martinsen, Madeleine, et al.
(författare)
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Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future
- 2023
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Ingår i: Applied Energy. - : ELSEVIER SCI LTD. - 0306-2619 .- 1872-9118. ; 339
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Tidskriftsartikel (refereegranskat)abstract
- Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.
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| 3. |
- Teng, Siyu, et al.
(författare)
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Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 8:1, s. 673-683
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Tidskriftsartikel (refereegranskat)abstract
- End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.
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| 4. |
- Zhou, Yuanye, et al.
(författare)
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A 2D mesh-free simulation of the particle adhesion in a plastic cyclone
- 2019
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Ingår i: Proceedings of the Institution of mechanical engineers. Part C, journal of mechanical engineering science. - : SAGE Publications. - 0954-4062 .- 2041-2983. ; 233:2, s. 649-663
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Tidskriftsartikel (refereegranskat)abstract
- Although the particle adhesion is a classic problem in cyclones, it is not clearly understood in previous studies. This study was set out to determine factors affecting the micro-particle adhesion in an acrylonitrile butadiene styrene cyclone by developing a mesh-free simulation method to predict the dynamic motion of a single particle in a 2D horizontal section of the cyclone with the presence of the wall boundary layer. Simulation results showed that the centrifugal force played a minor role on the particle adhesion but it was important on the transportation of the particle. In contrast, the electrostatic force was important on the particle adhesion but it was not important on the transportation of the particle. Moreover, simulation results suggested that the particle adhesion can be reduced by increasing the inlet velocity of the cyclone or by increasing the coefficient of restitution of the particle-wall collision. In contrast, the particle adhesion can be increased by increasing the radius of the cyclone, the particle charge and the coefficient of friction. Furthermore, simulation results on the effect of the inlet velocity and particle charge were validated by experimental results.
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| 5. |
- Zhou, Yuanye, et al.
(författare)
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A massive reduction of dust particle adhesion in a cyclone by the introduction of a wedge
- 2018
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Ingår i: Proceedings of the Institution of mechanical engineers. Part C, journal of mechanical engineering science. - : SAGE Publications. - 0954-4062 .- 2041-2983. ; 232:17, s. 3102-3114
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Tidskriftsartikel (refereegranskat)abstract
- Particle adhesion in a cyclone, such as a cyclonic vacuum cleaner, can significantly reduce its efficiency. An investigation is presented here on the particle adhesion in a cyclone from a vacuum cleaner that consists of a primary separation stage (a cylindrical chamber) and a secondary separation stage (14 cyclones). The flow direction in the primary separation stage was modified by the use of a wedge of 40mmx40mmx6mm at the inlet of the primary separation stage, which affected the particle trajectory in the primary separation stage and the particle inlet position in the cyclone while keeping the air flow direction and velocity (without particles being loaded), the Hamaker constant, particle size and the particle charge unaffected. The particle inlet position in the cyclone was varied from the lower portion (without wedge) to the upper portion (with wedge). Without the wedge, a spiral pattern of particle (plaster particles, average size 1.13m) adhesion onto the inner wall of the cyclone was found and a thicker deposited layer of particles at the cyclone tip region was observed. With the introduction of the wedge, the spiral particle adhesion pattern was not observed and a reduction of particle adhered to the inner wall by up to 94% was achieved, although there was an increase in the amount of particles entering the cyclone. This demonstrates almost a complete elimination of particle adhesion onto the cyclone wall, without compromising separation efficiencies.
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| 6. |
- Zhou, Yuanye, et al.
(författare)
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An explainable AI model for power plant NOx emission control
- 2024
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Ingår i: Energy and AI. - 2666-5468. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, developing Artificial Intelligence (AI) models for complex system has become a popular research area. There have been several successful AI models for predicting the Selective Non-Catalytic Reduction (SNCR) system in power plants and large boilers. However, all these models are in essence black box models and lack of explainability, which are not able to give new knowledge. In this study, a novel explainable AI (XAI) model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics (SINDy) model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant. This proposed model identifies the system's governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables. In addition, the explainable AI model achieves a considerable accuracy with less than 21 % deviation from base-line models of partial least squares model and artificial neural network model.
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| 7. |
- Zhou, Yuanye, et al.
(författare)
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Characteristics and mechanisms of particle adhesion patterns in an aerodynamic cyclone
- 2017
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Ingår i: Aerosol Science and Technology. - : Informa UK Limited. - 0278-6826 .- 1521-7388. ; 51:11, s. 1313-1323
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Tidskriftsartikel (refereegranskat)abstract
- Characteristics of particle adhesion (deposition) patterns in an aerodynamic cyclone were studied by both experimental methods and computational fluid dynamic (CFD) simulation methods. The cyclone used in the experiment was made of acrylonitrile butadiene styrene (ABS). The particles were a plaster material, with an average size of 1.13 mu m and a density of 2300 kg/m(3). Four levels of particle load rates were examined, ranging from 0.28 g/m(3) to 0.96 g/m(3) at a fixed mass flow rate of 2.1 g/s. Experimental results showed three key features of particle adhesion patterns. They are large-scale spiral patterns (SPs), small-scale wave patterns (WPs), and thick adhesion layer (TAL) at the cyclone tip region. It was observed that the SPs had five turns and the WPs were periodic discrete patterns that crept slowly against the flow direction. The formation of WPs was explained based on the Barchan sand dune mechanism. Under zero particle load rate, six different mass flow rates ranging from 1.24 g/s to 3.16 g/s were simulated using CFD. It was found that the precessional bent vortex end (PBVE), precessing along the circumference of the cyclone tip, occurred close to the cyclone tip. The PBVE was believed to be the cause of the TAL, because there was a weak wall shear stress region below the PBVE. In addition, particle trajectories were simulated at a mass flow rate of 2.26 g/s. Simulation results showed that particles had spiral trajectories that were supposed to be linked with the SPs.
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| 8. |
- Zhou, Yuanye, et al.
(författare)
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Identification of swirling air flow velocity by non-neutrally buoyant tracer particle based on machine learning
- 2023
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Ingår i: Flow Measurement and Instrumentation. - : Elsevier Ltd. - 0955-5986 .- 1873-6998. ; 91
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Tidskriftsartikel (refereegranskat)abstract
- In the non-intrusive measurement of swirling air flow, helium-filled soap bubbles (HFSBs) are ideal neutrally buoyant tracer particles However, there are some researchers that do not use HFSBs in the non-intrusive measurement of swirling air flow, leading to some kind of measurement inaccuracy. Since the flow velocity data has been implicitly included in the physical equations of any kind of tracer particles, it is possible to extract such hidden flow velocity from particle trajectory. In this study we propose a physics-informed procedure of adopting SINDy algorithm to identify the hidden physical equations of non-neutrally buoyant particle dynamics, so that the implicit flow velocity can be discovered. First of all, the numerical experiment is conducted to generate particle trajectory in a 2D swirling air flow in small cyclone separator. Based on the numerical experiment trajectory data, the input variables for SINDy algorithm are properly constructed. The output of SINDy algorithm, which are the identified physical equations, are evaluated and validated on two different-density particle trajectory data. Our results show that the physical equations of tracer particle dynamics can be identified and the discovered flow velocity data has a maximum deviation of 1.4% from the truth (R2≥0.999). The proposed method may remove the requirement of NB tracer particle in non-intrusive measurement of swirling air flow, and may be applied to recognize the physical equations of complex particle laden flow.
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