Evacuation characteristics of preschool children through bottlenecks

Pedestrian movement through bottlenecks have been widely studied from various aspects to understand the effects of bottlenecks on the pedestrian flow. However, few attentions have been paid to the movement characteristics of preschool children, who show obvious differences behaviour compared to adults due to the poor balance and understanding of danger especial under emergencies. In this study, we focus on the evacuation characteristics of preschool children through bottlenecks with laboratory experiments. From all the experiment, we do not observe clear lane formation process from the trajectories diagrams. It is found that the first arrive first out principle does not work in the situation with competition. Compared to adults, children are more likely to fall and hard to be controlled during movement, which is very dangerous in emergencies. The highest speed for the preschool children can beyond 3 m/s and is depend on the location in the crowd for each individual. For a given number of evacuees, the total evacuation time firstly decreases a linear with the increasing the bottleneck width and then keeps a constant if nobody falls down during the movement. Falling down of children will increase the evacuation time incredibly. The findings will be beneficial for the evacuation drill design in kindergarten as well as the facility design for young children

No One Left Behind: Real-World Federated Class-Incremental Learning

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known and fixed in advance. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to FL training irregularly. These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model. It ensures no local clients are left behind as they learn new classes continually, by addressing local and global catastrophic forgetting. Specifically, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure consistent class-relations within different tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation, thus improving robustness to choose the best old global model for local-side semantic distillation loss. Experiments on representative datasets verify superior performance of our model against comparison methods. The code is available at https://github.com/JiahuaDong/LGA.Comment: 17 pages, 8 figure

Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity types without accessing the training data of previously learned types. However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i.e., old and future entity types are labeled as the non-entity type in the current task). To address these challenges, we propose a method called task Relation Distillation and Prototypical pseudo label (RDP) for INER. Specifically, to tackle catastrophic forgetting, we introduce a task relation distillation scheme that serves two purposes: 1) ensuring inter-task semantic consistency across different incremental learning tasks by minimizing inter-task relation distillation loss, and 2) enhancing the model's prediction confidence by minimizing intra-task self-entropy loss. Simultaneously, to mitigate background shift, we develop a prototypical pseudo label strategy that distinguishes old entity types from the current non-entity type using the old model. This strategy generates high-quality pseudo labels by measuring the distances between token embeddings and type-wise prototypes. We conducted extensive experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003, I2B2, and OntoNotes5). The results demonstrate that our method achieves significant improvements over the previous state-of-the-art methods, with an average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.Comment: Accepted by CIKM2023 as a long paper with an oral presentatio

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Introduction: With the aggravation of aging and the growing number of stroke patients suffering from hemiplegia in China, rehabilitation robots have become an integral part of rehabilitation training. However, traditional rehabilitation robots cannot modify the training parameters adaptively to match the upper limbs’ rehabilitation status automatically and apply them in rehabilitation training effectively, which will improve the efficacy of rehabilitation training.Methods: In this study, a two-degree-of-freedom flexible drive joint rehabilitation robot platform was built. The forgetting factor recursive least squares method (FFRLS) was utilized to estimate the impedance parameters of human upper limb end. A reward function was established to select the optimal stiffness parameters of the rehabilitation robot.Results: The results confirmed the effectiveness of the adaptive impedance control strategy. The findings of the adaptive impedance control studies showed that the adaptive impedance control had a significantly greater reward than the constant impedance control, which was in line with the simulation results of the variable impedance control. Moreover, it was observed that the levels of robot assistance could be suitably modified based on the subject’s different participation.Discussion: The results facilitated stroke patients’ upper limb rehabilitation by enabling the rehabilitation robot to adaptively change the impedance parameters according to the functional status of the affected limb. In clinic therapy, the proposed control strategy may help to adjust the reward function for different patients to improve the rehabilitation efficacy eventually

Doping inorganic ions to regulate bioactivity of Ca–P coating on bioabsorbable high purity magnesium

AbstractPerformance of biomaterials was strongly affected by their surface properties and could be designed artificially to meet specific biomedical requirements. In this study, F−(F), SiO42−(Si), or HCO3−(C)-doped Ca–P coatings were fabricated by biomimetic deposition on the surface of biodegradable high-purity magnesium (HP Mg). The crystalline phases, morphologies and compositions of Ca–P coatings had been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The biomineralization and corrosion resistance of doped Ca–P coatings had also been investigated. The results showed that the Ca–P coating with or without doped elements mainly contained the plate-like dicalcium phosphate dehydrate (DCPD) phase. The doped F, Si, or C changed the surface morphology of Ca–P coatings after mineralization. Doped F enhanced the mineralization of Ca–P coating, and doped Si retarded the mineralization of Ca–P coating. However, H2 evolution of HP Mg discs with different Ca–P coatings was close to 0.4–0.7ml/cm2 after two-week immersion. That meant that the corrosion resistance of the Ca–P coatings with different or without doped elements did not change significantly

Comparative Study of Human Skin Detection Using Object Detection Based on Transfer Learning

With the increasing aging of the population, the design of automatic bath robot has the forward-looking significance. The robot needs to detect the skin position, so as to perform the bathing task. The perception of skin is the key technology to achieve the bathing task. In this paper, object detection is used to identify the skin, which provides reference information for the pose of the robot. According to the classification of the object detection algorithms, this paper selects four typical object detection algorithms, namely, Faster R-CNN, YOLOv3, YOLOv4 and CenterNet. Due to the limitation of the self-built data set, this paper adopts the transfer learning to promote the completion of new tasks, which takes the pre-trained model as the starting point. The experimental results show that the detection results of YOLOv4 is the best, with mAP of 78%. This paper proves the feasibility and effectiveness of object detection completing the human skin detection in the bathing task

Physiological parameters analysis of transfemoral amputees with different prosthetic knees

Physiological parameters analysis allows for a precise quantification of energy expenditure of transfemoral amputees with different prosthetic knees. Comparative physiological parameters analysis that indicate the functional characteristics of knee joints is essential to the choice of transfemoral amputee. The aim of this study was to propose a microprocessor-controlled prosthetic knee (i-KNEE) and conducted physiological parameters (energy cost, gait efficiency and relative exercise intensity) comparison of transfemoral amputees with C-leg, Rheo Knee and Mauch under different walking speeds. Methodsː A microprocessor-controlled prosthetic knee with hydraulic damper (i-KNEE) was developed. A two-factor repeated measurement experiment design was used. Each subject was instructed to accept the same treatments. The two factors were type of prosthetic knees (the i-KNEE, the C-Leg, the Rheo Knee and the Mauch) and speed (0.5, 0.7, 0.9, 1.1, 1.3 m/s). The energy cost, gait efficiency and relative exercise intensity of ten transfemoral amputees were measured. Resultsː For all the prosthetic knees, the energy cost increased along with walking speed. There was no significant difference between three microprocessor-controlled prosthetic knees in energy cost. The gait efficiency of Mauch was always less than or equal to other three microprocessor-controlled prosthetic knees in specific walking speed. The relative exercise intensity increased with speed for all the prosthetic knees. More effort was needed for the transfemoral amputees with Mauch than other three microprocessorcontrolled prosthetic knees in the same walking speed. Conclusionsː The use of the microprocessor-controlled knee joints resulted in reduced energy cost, improved gait efficiency and smaller relative exercise intensity