| 1. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 2. |
- Liu, Quan, et al.
(författare)
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Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function
- 2021
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Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 49
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Tidskriftsartikel (refereegranskat)abstract
- Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.
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| 3. |
- Deng, Wenjun, et al.
(författare)
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Difference between Supine and Upright Blood Pressure Associates to the Efficacy of Midodrine on Postural Orthostatic Tachycardia Syndrome (POTS) in Children
- 2014
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Ingår i: Pediatric Cardiology. - : Springer Verlag (Germany). - 0172-0643 .- 1432-1971. ; 35:4, s. 719-725
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Tidskriftsartikel (refereegranskat)abstract
- Postural orthostatic tachycardia syndrome (POTS) is common, and has a serious impact on childrens quality of life. Midodrine hydrochloride, an alpha 1-adrenoreceptor agonist, is an effective treatment. The study was designed to examine the therapeutic efficacy of midodrine hydrochloride by quantifying changes in blood pressure during the head-up test (HUT), in children with POTS. Overall, 104 out of 110 children with POTS were treated with midodrine hydrochloride and successfully followed-up. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) changes were analyzed during the HUT. In a retrospective analysis, a receiver operating characteristic (ROC) curve was used to analyze the therapeutic predictive value of pre-treatment changes in SBP, DBP, and a combination of both, from the supine position to standing, in the subjects. The increase of SBP and DBP from the supine position to standing in responders were significantly lower than that of the non-responders. The ROC curve showed that midodrine hydrochloride for children with POTS would be predicted to be effective when the pre-treatment increase of SBP was a parts per thousand currency sign0 mmHg, or when the pre-treatment increase of DBP was a parts per thousand currency sign6.5 mmHg (from the supine position to standing), yielding a sensitivity of 72 % and specificity of 88 %. The area under the curve was 0.744 and 0.809, respectively. Hence, the results suggested that looking at the changes in blood pressure during the HUT was useful in predicting the response to midodrine hydrochloride in children with POTS.
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| 4. |
- Liu, Runnan, et al.
(författare)
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Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication
- 2023
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Ingår i: IEEE Transactions on Wireless Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1536-1276 .- 1558-2248. ; 22:11, s. 7834-7847
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Tidskriftsartikel (refereegranskat)abstract
- The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.
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| 5. |
- Liu, Runnan, et al.
(författare)
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Detection of Abrupt Change in Channel Covariance Matrix for Multi-Antenna Communication
- 2021
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Ingår i: 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). - : IEEE. - 9781728181042
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Konferensbidrag (refereegranskat)abstract
- The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multiantenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.
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| 6. |
- 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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| 7. |
- 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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| 8. |
- Chen, Wenjun, et al.
(författare)
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A typological framework of non-floodplain wetlands for global collaborative research and sustainable use
- 2022
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 17:11
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Forskningsöversikt (refereegranskat)abstract
- Non-floodplain wetlands (NFWs) are important but vulnerable inland freshwater systems that are receiving increased attention and protection worldwide. However, a lack of consistent terminology, incohesive research objectives, and inherent heterogeneity in existing knowledge hinder cross-regional information sharing and global collaboration. To address this challenge and facilitate future management decisions, we synthesized recent work to understand the state of NFW science and explore new opportunities for research and sustainable NFW use globally. Results from our synthesis show that although NFWs have been widely studied across all continents, regional biases exist in the literature. We hypothesize these biases in the literature stem from terminology rather than real geographical bias around existence and functionality. To confirm this observation, we explored a set of geographically representative NFW regions around the world and characteristics of research focal areas. We conclude that there is more that unites NFW research and management efforts than we might otherwise appreciate. Furthermore, opportunities for cross-regional information sharing and global collaboration exist, but a unified terminology will be needed, as will a focus on wetland functionality. Based on these findings, we discuss four pathways that aid in better collaboration, including improved cohesion in classification and terminology, and unified approaches to modeling and simulation. In turn, legislative objectives must be informed by science to drive conservation and management priorities. Finally, an educational pathway serves to integrate the measures and to promote new technologies that aid in our collective understanding of NFWs. Our resulting framework from NFW synthesis serves to encourage interdisciplinary collaboration and sustainable use and conservation of wetland systems globally.
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| 9. |
- Deng, J., et al.
(författare)
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Pose synchronization of rigid body networks with switching topologies
- 2015
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Ingår i: 2015 34th Chinese Control Conference (CCC). - : IEEE Computer Society. - 9789881563897 ; , s. 7639-7644
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Konferensbidrag (refereegranskat)abstract
- Coordination control of multiple rigid bodies attracts much attention of researchers due to its wide applications. This paper presents the pose synchronization problem of the moving rigid bodies whose dynamics is described by the group SE(3). The case of bidirectional neighbor graphs with switching interconnection topologies is considered. We design a distributed control law based on relative rotation matrices and relative positions between the neighboring rigid bodies, and show that the SE(3) reaches pose synchronization if and only if the neighbor graphs are infinitely jointly connected, which relaxes the theoretical results in the existing literature.
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| 10. |
- Fu, Li, et al.
(författare)
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Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence
- 2024
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Ingår i: Environment International. - 0160-4120 .- 1873-6750. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- Background: Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) and its specific components with hypertension and blood pressure is limited. Methods: We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007–2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. Results: Each interquartile range (IQR = 16.80 μg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR μg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42−, NO3−, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. Conclusions: These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.
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