| 1861. |
- Liu, Y., et al.
(författare)
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A framework for industrial robot training in cloud manufacturing with deep reinforcement learning
- 2020
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Ingår i: ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020. - : American Society of Mechanical Engineers.
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Konferensbidrag (refereegranskat)abstract
- Cloud manufacturing is a service-oriented networked manufacturing model that embraces the concept of 'Everything-as-a-Service'. In cloud manufacturing, distributed manufacturing resources encompassed in the product lifecycle are transformed into manufacturing services. Industrial robots are an important category of manufacturing resources in cloud manufacturing. During the past years, robots have been demonstrated to be able to learn various dexterous manipulation skills through training with deep reinforcement learning (DRL). In cloud manufacturing, there are many complex industrial application scenarios that require dexterous robots. Hence, robot training, which enables robots to learn various manipulation skills, becomes an important requirement for cloud manufacturing in the future, leading to the concept of 'Robot Training-as-a-Service'. This paper focuses on industrial robot training in the context of cloud manufacturing. First, related work on cloud manufacturing, DRL, DRL-based robot training, and cloud-edge collaboration is briefly reviewed and analyzed. Then, a framework for industrial robot training in cloud manufacturing with DRL is proposed, and a simplified case study is presented to demonstrate the basic principle of the framework. Finally, possible future research issues are discussed.
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| 1862. |
- Liu, Yangyang, et al.
(författare)
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Insight into the Critical Role of Exchange Current Density on Electrodeposition Behavior of Lithium Metal
- 2021
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Ingår i: Advanced Science. - : Wiley. - 2198-3844 .- 2198-3844. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- Due to an ultrahigh theoretical specific capacity of 3860 mAh g−1, lithium (Li) is regarded as the ultimate anode for high-energy-density batteries. However, the practical application of Li metal anode is hindered by safety concerns and low Coulombic efficiency both of which are resulted fromunavoidable dendrite growth during electrodeposition. This study focuses on a critical parameter for electrodeposition, the exchange current density, which has attracted only little attention in research on Li metal batteries. A phase-field model is presented to show the effect of exchange current density on electrodeposition behavior of Li. The results show that a uniform distribution of cathodic current density, hence uniform electrodeposition, on electrode is obtained with lower exchange current density. Furthermore, it is demonstrated that lower exchange current density contributes to form a larger critical radius of nucleation in the initial electrocrystallization that results in a dense deposition of Li, which is a foundation for improved Coulombic efficiency and dendrite-free morphology. The findings not only pave the way to practical rechargeable Li metal batteries but can also be translated to the design of stable metal anodes, e.g., for sodium (Na), magnesium (Mg), and zinc (Zn) batteries.
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| 1863. |
- Liu, Yang, et al.
(författare)
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Reduction of p120ctn isoforms 1 and 3 is significantly associated with metastatic progression of human lung cancer
- 2007
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Ingår i: Acta Pathologica, Microbiologica et Immunologica Scandinavica (APMIS). - : Wiley. - 0903-4641 .- 1600-0463. ; 115:7, s. 848-856
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Tidskriftsartikel (refereegranskat)abstract
- P120-catenin plays an important role in cell adhesion and signalling transduction though the function of its isoforms is unclear. The aim of this study was to examine the expression of p120-catenin isoforms in lung cancer and investigate their relationship to clinicopathological factors in lung squamous cell carcinomas (SCCs) and adenocarcinomas. The expression patterns of p120-catenin in lung cancer tissues and lung cancer cells were examined by p120-catenin immunofluorescence, Western blot, and reverse transcription- polymerase chain reaction (RT-PCR). Clear and continuous red fluorescence of p120-catenin is displayed at the cell membrane of corresponding normal bronchial epithelial cells, but not in lung cancer tissues that show reduction or absence of membrane expression of p120-catenin or cytoplasmic accumulation of p120-catenin. Compared with corresponding normal lung tissues, lung cancer tissues have significantly lower levels of p120-catenin proteins (P<0.001) and mRNA (P<0.001). The isoforms 1 (120 kD) and 3 (100 kD) proteins were major isoforms of p120-catenin expressed in normal lung tissues, which were significantly reduced in lung cancer samples (P=0.001 and P<0.001, respectively). The mRNA of p120-catenin isoforms 1.2, 1.3, 2.3, 3.1 and 3.3 was detected in corresponding normal lung tissues, but was significantly absent in lung cancer samples (P<0.001 and P=0.001, respectively). Furthermore, p120-catenin isoform 1 is negatively associated - whereas p120-catenin isoform 3 is positively associated - with lymph node metastasis. We conclude that reductions of isoforms 1 and 3 may play different roles in metastatic progression of human lung cancer. © Apmis 2007.
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| 1864. |
- Liu, Yongkui, et al.
(författare)
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Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning
- 2023
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 80, s. 102454-
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distrib-uted robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algo-rithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.
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| 1865. |
- Liu, Zhihao, et al.
(författare)
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Adaptive real-time similar repetitive manual procedure prediction and robotic procedure generation for human-robot collaboration
- 2023
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Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- Manual procedure recognition and prediction are essential for practical human-robot collaboration in industrial tasks, such as collaborative assembly. However, current research mostly focuses on diverse human motions, while the similar repetitive manual procedures that are prevalent in real production tasks are often overlooked. Furthermore, the dynamic uncertainty caused by human-robot interferences and the generalisation of individuals, scenarios, and multiple sensor deployments pose challenges for implementing manual procedure prediction and robotic procedure generation. To address these issues, this paper proposes a real-time, similar repetitive procedure-oriented human skeleton processing system that employs the human skeleton as a robust modality. It utilises an improved deep spatial-temporal graph convolutional network and a FIFO queue-based discriminator for real-time data processing, procedure prediction, and generation. The proposed method is validated on multiple datasets with tens of individuals engaged in a real dynamic and uncertain human-robot collaborative assembly cell and able to run on entry-level hardware. The results demonstrate competitive performance of handcraft feature-free, early prediction and generalisation on individual variance, environment background, camera position, lighting conditions, and stochastic interference in human-robot collaboration.
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| 1866. |
- Liu, Z. G., et al.
(författare)
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Hemizygous variants in protein phosphatase 1 regulatory subunit 3F (PPP1R3F) are associated with a neurodevelopmental disorder characterized by developmental delay, intellectual disability and autistic features
- 2023
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Ingår i: Human Molecular Genetics. - 0964-6906 .- 1460-2083. ; 32:20, s. 2981-2995
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Tidskriftsartikel (refereegranskat)abstract
- Protein phosphatase 1 regulatory subunit 3F (PPP1R3F) is a member of the glycogen targeting subunits (GTSs), which belong to the large group of regulatory subunits of protein phosphatase 1 (PP1), a major eukaryotic serine/threonine protein phosphatase that regulates diverse cellular processes. Here, we describe the identification of hemizygous variants in PPP1R3F associated with a novel X-linked recessive neurodevelopmental disorder in 13 unrelated individuals. This disorder is characterized by developmental delay, mild intellectual disability, neurobehavioral issues such as autism spectrum disorder, seizures and other neurological findings including tone, gait and cerebellar abnormalities. PPP1R3F variants segregated with disease in affected hemizygous males that inherited the variants from their heterozygous carrier mothers. We show that PPP1R3F is predominantly expressed in brain astrocytes and localizes to the endoplasmic reticulum in cells. Glycogen content in PPP1R3F knockout astrocytoma cells appears to be more sensitive to fluxes in extracellular glucose levels than in wild-type cells, suggesting that PPP1R3F functions in maintaining steady brain glycogen levels under changing glucose conditions. We performed functional studies on nine of the identified variants and observed defects in PP1 binding, protein stability, subcellular localization and regulation of glycogen metabolism in most of them. Collectively, the genetic and molecular data indicate that deleterious variants in PPP1R3F are associated with a new X-linked disorder of glycogen metabolism, highlighting the critical role of GTSs in neurological development. This research expands our understanding of neurodevelopmental disorders and the role of PP1 in brain development and proper function.
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| 1867. |
- Liu, Zhaofang, et al.
(författare)
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High-pressure homogenization influences the functional properties of protein from oyster (Crassostrea gigas)
- 2021
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Ingår i: Lebensmittel-Wissenschaft + Technologie. - : Elsevier. - 0023-6438 .- 1096-1127. ; 151
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the effects of high-pressure homogenization (HPH) (20, 60, and 100 MPa) on the structure, microstructure, and functional properties of oyster (Crassostrea gigas) protein (CGP) were investigated. Circular dichroism examination showed that the secondary structure of oyster proteins became loose and that the alpha-helix transformed into a random coil structure. The fluorescence spectra had a redshift from 350 nm to 360 nm, exposing the chromophore and changing the tertiary structure of CGP. With the increase in homogeneous pressure, HPH significantly reduced particle sizes of CGP. Compared to other treatments, HPH at 100 MPa showed a higher surface hydrophobic index (86.84), solubility (84.7%), and water holding capacity (15.2 g/g). However, the emulsion activity index (137.5 m(2)/g) and foaming ability (113.3%) were best at a lower pressure (20 MPa). The scanning electron microscope showed that HPH treatment could improve emulsion stability. This work demonstrates that HPH treatment has a positive impact on the physicochemical and functional properties of CGP for potential industrial applications.
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| 1868. |
- Liu, Zhengtao, et al.
(författare)
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Multi-omics network analysis on samples from sequential biopsies reveals vital role of proliferation arrest for Macrosteatosis related graft failure in rats after liver transplantation
- 2023
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Ingår i: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 115:6
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Tidskriftsartikel (refereegranskat)abstract
- To investigate the molecular impact of graft MaS on post-transplant prognosis, based on multi-omics integrative analysis. Rats were fed by methionine-choline deficient diet (MCD) for MaS grafts. Samples were collected from grafts by sequential biopsies. Transcriptomic and metabolomic profilings were assayed. Post-transplant MaS status showed a close association with graft failure. Differentially expressed genes (DEGs) for in-vivo MaS were mainly enriched on pathways of cell cycle and DNA replication. Post-transplant MaS caused arrests of graft regeneration via inhibiting the E2F1 centered network, which was confirmed by an in vitro experiment. Data from metabolomics assays found insufficient serine/creatine which is located on one‑carbon metabolism was responsible for MaS-related GF. Pre-transplant MaS caused severe fibrosis in long-term survivors. DEGs for grafts from long-term survivors with pre-transplant MaS were mainly enriched in pathways of ECM-receptor interaction and focal adhesion. Transcriptional regulatory network analysis confirmed SOX9 as a key transcription factor (TF) for MaS-related fibrosis. Metabolomic assays found elevation of aromatic amino acid (AAA) was a major feature of fibrosis in long-term survivors. Graft MaS in vivo increased post-transplant GF via negative regulations on graft regeneration. Pre-transplant MaS induced severe fibrosis in long-term survivors via activations on ECM-receptor interaction and AAA metabolism.
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| 1869. |
- Liu, Zhihao, et al.
(författare)
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Robot learning towards smart robotic manufacturing : A review
- 2022
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 77, s. 102360-
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Forskningsöversikt (refereegranskat)abstract
- Robotic equipment has been playing a central role since the proposal of smart manufacturing. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy workloads significantly. Towards the next generation of manufacturing, this review first introduces the comprehensive background of smart robotic manufacturing within robotics, machine learning, and robot learning. Definitions and categories of robot learning are summarised. Concretely, imitation learning, policy gradient learning, value function learning, actor-critic learning, and model-based learning as the leading technologies in robot learning are reviewed. Training tools, benchmarks, and comparisons amongst different robot learning methods are delivered. Typical industrial applications in robotic grasping, assembly, process control, and industrial human-robot collaboration are listed and discussed. Finally, open problems and future research directions are summarised.
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| 1870. |
- Liu, Zhihao, et al.
(författare)
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Task-level decision-making for dynamic and stochastic human-robot collaboration based on dual agents deep reinforcement learning
- 2021
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Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Nature. - 0268-3768 .- 1433-3015. ; 115:11-12, s. 3533-3552
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Tidskriftsartikel (refereegranskat)abstract
- Human-robot collaboration as a multidisciplinary research topic is still pursuing the robots’ enhanced intelligence to be more human-compatible and fit the dynamic and stochastic characteristics of human. However, the uncertainties brought by the human partner challenge the task-planning and decision-making of the robot. When aiming at industrial tasks like collaborative assembly, dynamics on temporal dimension and stochasticities on the order of procedures need to be further considered. In this work, we bring a new perspective and solution based on reinforcement learning, where the problem is regarded as training an agent towards tasks in dynamic and stochastic environments. Concretely, an adapted training approach based on the deep Q learning method is proposed. This method regards both the robot and the human as the agents in the interactive training environment for deep reinforcement learning. With the consideration of task-level industrial human-robot collaboration, the training logic and the agent-environment interaction have been proposed. For the human-robot collaborative assembly tasks in the case study, it is illustrated that our method could drive the robot represented by one agent to collaborate with the human partner even the human performs randomly on the task procedures.
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