| 1. |
- Ma, Chao, et al.
(author)
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Applications of X-ray and Electron Crystallography in Structural Investigations of Zeolites
- 2021
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In: Gaodeng xuéxiào huàxué xuébào. - 0251-0790. ; 42:1, s. 188-200
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Journal article (peer-reviewed)abstract
- Zeolites, a class of crystalline microporous materials with unique channels or cavities, have excellent performance in adsorption, separation, and catalysis. In order to correlate the structure-properly relationship, it is essential to determine the crystallographic structures of zeolite at the atomic level. In this review, a series of conventional and emerging techniques regarding X-ray crystallography and electron crystallography for characterizing the crystallographic structures of zeolites through real space and reciprocal space are introduced. Furthermore, 85 recently discovered novel zeolites are systemically summarized based on the structure determination approaches and their chemical compositions. Nine zeolites with different framework type codes(FTCs) are further highlighted due to their unique synthetic methodologies or structural features.
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| 2. |
- Wang, Lihui, et al.
(author)
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Overview of Human-Robot Collaboration in Manufacturing
- 2020
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In: Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing. - Cham : Springer. ; , s. 15-58
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Conference paper (peer-reviewed)abstract
- Human-robot collaboration (HRC) in the manufacturing context aims to realise a shared workspace where humans can work side by side with robots in close proximity. In human-robot collaborative manufacturing, robots are required to adapt to human behaviours by dynamically changing their pre-planned tasks. However, the robots used today controlled by rigid native codes can no longer support effective human-robot collaboration. To address such challenges, programming-free and multimodal communication and control methods have been actively explored to facilitate the robust human-robot collaborative manufacturing. They can be applied as the solutions to the needs of the increased flexibility and adaptability, as well as higher effort on the conventional (re)programing of robots. These high-level multimodal commands include gesture and posture recognition, voice processing and sensorless haptic interaction for intuitive HRC in local and remote collaboration. Within the context, this paper presents an overview of HRC in manufacturing. Future research directions are also highlighted.
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| 3. |
- Gao, Robert X., et al.
(author)
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Human motion recognition and prediction for robot control
- 2021
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In: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 261-282
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Book chapter (other academic/artistic)abstract
- The ever-increasing demand for higher productivity, lower cost and improved safety continues to drive the advancement of manufacturing technologies. As one of the key elements, human-robot collaboration (HRC) envisions a workspace where humans and robots can dynamically collaborate for improved operational efficiency while maintaining safety. As the effectiveness of HRC is affected by a robot's ability to sense, understand and forecast the state of the collaborating human worker, human action recognition and motion trajectory prediction have become a crucial part in realising HRC. In this chapter, deep-learning-based methods for accomplishing this goal, based on the in-situ sensing data from the workspace are presented. Specifically, to account for the variability and heterogeneity of human workers during assembly, a context-aware deep convolutional neural network (DCNN) has been developed to identify the task-associated context for inferencing human actions. To improve the accuracy and reliability of human motion trajectory prediction, a functional unit-incorporated recurrent neural network (RNN) has been developed to parse worker's motion patterns and forecast worker's future motion trajectories. Collectively, these techniques allow the robot to answer the question: "which tool or part should be delivered to which location next?", and enable online robot action planning and execution for the collaborative assembly operation. The methods developed are experimentally evaluated, with the collaborative assembly of an automotive engine as a case study.
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| 4. |
- Ji, Wei, et al.
(author)
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Interface architecture design for minimum programming in human-robot collaboration
- 2018
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In: 51st CIRP Conference on Manufacturing Systems. - : Elsevier. ; , s. 129-134
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Conference paper (peer-reviewed)abstract
- Many metal components, especially large-sized ones, need to be ground or deburred after turning or milling to improve the surface qualities, which heavily depends on human interventions. Robot arms, combining movable platforms, are applied to reduce the human work. However, robots and human should work together due to the fact that most of the large-sized parts belong to small-batch products, resulting in a large number of programming for operating a robot and movable platform. Targeting the problem, this paper proposes a new interface architecture towards minimum programming in human-robot collaboration. Within the context, a four-layer architecture is designed: user interface, function block (FB), functional modules and hardware. The user interface is associated with use cases. Then, FB, with embedded algorithms and knowledge and driven by events, is to provide a dynamic link to the relevant application interface (APIs) of the functional modules in terms of the case requirements. The functional modules are related to the hardware and software functions; and the hardware and humans are considered in terms of the conditions on shop floors. This method provides three-level applications based on the skills of users: (1) the operators on shop floors, can operate both robots and movable platforms programming-freely; (2) engineers are able to customise the functions and tasks by dragging/dropping and linking the relevant FBs with minimum programming; (3) the new functions can be added by importing the APIs through programming.
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| 5. |
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| 6. |
- Lightowler, Molly, et al.
(author)
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Phase identification and discovery of an elusive polymorph of drug-polymer inclusion complex using automated 3D electron diffraction
- 2024
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In: Angewandte Chemie International Edition. - 1433-7851 .- 1521-3773. ; 63:16
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Journal article (peer-reviewed)abstract
- 3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.
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| 7. |
- Lin, Hongyi, et al.
(author)
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Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction
- 2024
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In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:3, s. 111-124
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Journal article (peer-reviewed)abstract
- In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin–destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models.
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| 8. |
- Lin, Hongyi, et al.
(author)
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Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models
- 2024
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In: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; In Press
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Journal article (peer-reviewed)abstract
- With the growing prevalence of autonomous vehicles and the integration of intelligent and connected technologies, the demand for effective and reliable vehicle speed control algorithms has become increasingly critical. Traditional car-following models, which primarily focus on individual vehicle pairs, exhibit limitations in complex traffic environments. To this end, this paper proposes an enhanced state representation for the application of multi-agent reinforcement learning (MARL) in platoon-following scenarios. Specifically, the proposed representation, influenced by feature engineering techniques in time series prediction tasks, thoroughly accounts for the intricate relative relationships between different vehicles within a platoon and can offer a distinctive perspective on traffic conditions to help improve the performance of MARL models. Experimental results show that the proposed method demonstrates superior performance in platoon-following scenarios across key metrics such as the time gap, distance gap, and speed, even reducing the time gap by 63%, compared with traditional state representation methods. These enhancements represent a significant step forward in ensuring the safety, efficiency, and reliability of platoon-following models within the context of autonomous vehicles.
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| 9. |
- Lin, Hongyi, et al.
(author)
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How generative adversarial networks promote the development of intelligent transportation systems: A survey
- 2023
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In: IEEE/CAA Journal of Automatica Sinica. - 2329-9274 .- 2329-9266. ; 10:9, s. 1781-1796
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Journal article (peer-reviewed)abstract
- In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks.
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| 10. |
- Lin, Hongyi, et al.
(author)
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Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems
- 2024
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In: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 150:2
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Journal article (peer-reviewed)abstract
- With the advent of the Internet of Things, bike-sharing systems have seen widespread adoption globally, whereas they often grapple with an uneven spatiotemporal distribution of vehicles. This issue is particularly acute in the wake of electronic fences, with some areas often faced with the predicament of inadequate supply. To tackle this challenge, accurate prediction of borrowing and returning demands at different parking spots and varying times is necessary. In this study, we used a comprehensive data set from Yancheng, Jiangsu, China, covering shared bicycle usage across 394 parking spots. These data enabled us to delve deep into urban travel patterns and discern the various factors influencing these behaviors. To enhance the prediction accuracy, we propose the time-series weighted regression (TSWR) model, a long-term multistep forecasting method, which adeptly addresses issues associated with sparse statistical data and long-term prediction inaccuracies, outperforming other machine learning models in our experiments. Further recognizing the considerable impact of geographical location and weather conditions on shared bicycle demand, we incorporated the rule-based adjustment optimization (RAO) method into our approach, which refines nonlinear components by accounting for various factors. The implementation of RAO resulted in a 10.34% increase in accuracy compared to TSWR alone and an improvement of over 35% in comparison to other approaches. Overall, this study illuminates the underlying influences on urban travel patterns and offers valuable suggestions for bike dispatching to those enterprises, contributing significantly to the research in this field.
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