| 1. |
- Fu, Jiahong, et al.
(författare)
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Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine
- 2022
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Ingår i: Applied Thermal Engineering. - : Elsevier BV. - 1359-4311. ; 201
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Tidskriftsartikel (refereegranskat)abstract
- Increasing the application of machine learning algorithms in engine development has the potential to reduce the number of experimental runs and the computation cost of computational fluid dynamics simulations. The objective of this study is to assess if such a statistical modelling approach can predict engine efficiency and emissions at any given condition for an already calibrated spark ignition (SI) engine. Engine responses at various engine speeds and load are recorded and used for correlative modelling. The artificial neural network (ANN) algorithm is utilized in this study, with engine speed and load as the model inputs, and fuel consumption and emission as the model outputs. The comparisons between experimentally measured data and model predictions indicate that the well-trained network is capable of forecasting engine efficiency, unburned hydrocarbons, carbon monoxide, and nitrogen oxide emissions with close-to-zero root mean squared error performance metric. In addition, the relatively small errors do not affect the relations between model inputs and outputs, as evidenced by the close-to-unity coefficient of determination. Overall, all these results indicate ANN model is appropriate for the application investigated in this study. Moreover, this study also suggests that the “black-box” modelling approach has the potential to effectively predict engine-related variables. And the predicted engine map can be used as a reference to accelerate the motor development in the hybrid vehicles. Also, the ANN model forecast the fuel consumption and emissions under transient operating conditions, while the literature is scarce to date on the investigation of the prediction of engine responses for transient conditions.
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| 2. |
- Lu, Xin, et al.
(författare)
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A Generic Digital Twin Framework for Collaborative Supply Chain Development
- 2022
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Ingår i: 2022 5th International Conference on Computing and Big Data (ICCBD 2022). - : IEEE. - 9781665457163 - 9781665457156 - 9781665457170 ; , s. 177-181
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Konferensbidrag (refereegranskat)abstract
- Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry.
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| 3. |
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| 4. |
- Li, Xiaoxia, et al.
(författare)
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Review on Learning-based Methods for shop Scheduling problems
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 294-298
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Konferensbidrag (refereegranskat)abstract
- Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized.
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| 5. |
- Lu, Xin, et al.
(författare)
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A generic and modularized Digital twin enabled human-robot collaboration
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 66-73
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Konferensbidrag (refereegranskat)abstract
- Recently, the manufacturing paradigm shifts from mass production to mass customization, which results in urgently demands for the development of intelligent, flexible and automatic manufacturing systems for handling complex manufacturing tasks with high efficiency. The use of collaborative robots, an essential enabling technology for developing human-robot collaboration (HRC), is on the rise for human-centric intelligent automation design. An effective virtual simulation platform, which can continuously simulate and evaluate HRC performance in different working scenarios, is lacking in developing an HRC system in a sophisticated industrial arena. This paper presents a generic and modularized digital twin enabled HRC framework based on the synergy effect of human, robotic and environment-related factors to provide a flexible, compatible, re-configurable solution to ease the implementation of HRC in the real world. The feasibility of the proposed framework is validated through the practical implementation of a food packaging job, which involves a human operator and an ABB robotic arm collaboratively working together, on an industrial shop.
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| 6. |
- Lu, Yiping, et al.
(författare)
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Preparing bulk ultrafine-microstructure high-entropy alloys: Via direct solidification
- 2018
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Ingår i: Nanoscale. - : Royal Society of Chemistry (RSC). - 2040-3372 .- 2040-3364. ; 10:4, s. 1912-1919
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Tidskriftsartikel (refereegranskat)abstract
- In the past three decades, nanostructured (NS) and ultrafine-microstructure (UFM) materials have received extensive attention due to their excellent mechanical properties such as high strength. However, preparing low-cost and bulk NS and UFM materials remains to be a challenge, which limits their industrial applications. Here, we report a new strategy to prepare bulk UFM alloys via the direct solidification of high-entropy alloys (HEAs). As a proof of concept, we designed AlCoCr x FeNi (1.8 ≤ x ≤ 2.0) HEAs and achieved a complete UFM in bulk materials. The compositional requirements for obtaining the formation of the UFM are highly demanding, necessitating the coupling of near eutectic alloy composition and the high temperature decomposition of supersaturated primary and secondary phases. Our strategy provides a low-cost and highly efficient method to prepare bulk UFM alloys, with great potential to accelerate the engineering application of these materials.
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| 7. |
- Ma, Liang, et al.
(författare)
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Room-Temperature Near-Infrared Photodetectors Based on Single Heterojunction Nanowires
- 2014
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Ingår i: Nano Letters. - : American Chemical Society (ACS). - 1530-6992 .- 1530-6984. ; 14:2, s. 694-698
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Tidskriftsartikel (refereegranskat)abstract
- Nanoscale near-infrared photodetectors are attractive for their potential applications in integrated optoelectronic devices. Here we report the synthesis of GaSb/GaInSb p-n heterojunction semiconductor nanowires for the first time through a controllable chemical vapor deposition (CVD) route. Based on these nanowires, room-temperature, high-performance, near-infrared photodetectors were constructed. The fabricated devices show excellent light response in the infrared optical communication region (1.55 mu m), with an external quantum efficiency of 10(4), a responsivity of 10(3) A/W, and a short response time of 2 ms, which shows promising potential applications in integrated photonics and optoelectronics devices or systems.
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| 8. |
- Wu, Keming, et al.
(författare)
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Surface Reconstruction on Uniform Cu Nanodisks Boosted Electrochemical Nitrate Reduction to Ammonia
- 2022
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Ingår i: ACS Materials Letters. - : American Chemical Society (ACS). - 2639-4979. ; 4, s. 650-656
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Tidskriftsartikel (refereegranskat)abstract
- The Haber-Bosch (HB) process has provided most of commercial ammonia at the expense of high energy consumption and high CO2 emission. Nitrate electroreduction is showing great potential as an alternative route for the green and scale-up synthesis of ammonia at ambient conditions. However, the performance has lagged due to lack of efficient electrocatalysts. In this work, we present the facile synthesis of uniform Cu nanodisks with exposed (111) facets as highly active electrocatalyst for electrochemical ammonia synthesis, delivering a high ammonia yield of 2.16 mg mg-1cat h-1 and a maximum Faradaic efficiency of 81.1% at -0.5 V versus a reversible hydrogen electrode (RHE). The remarkable activity is originated from the surface reconstructed triatomic Cu clusters due to the cathodic deoxygenation process. As a result, the reconstructed surface shows enhanced affinity to the adsorption of nitrate ions which undergo successive break of three N-O bonds, followed by subsequent formation of three N-H bonds to finally form NH3. The present study provides the feasible preparation of Cu based advanced catalysts and a unique insight into the mechanism of nitrate electroreduction.
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| 9. |
- Yan, Li, et al.
(författare)
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Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks
- 2019
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Ingår i: IEEE Transactions on Wireless Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1536-1276 .- 1558-2248. ; 18:10, s. 4873-4885
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Tidskriftsartikel (refereegranskat)abstract
- The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.
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