Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively. 相似文献
Rapid and real-time mon itoring of food quality plays critical roles in the food industry and for the gen era I public health.Herei n,we specially synthesized a new perylene bisimide(PBI)derivative(PCB-EpE)composed of two PBI units and a rigid o-carborane linker,which was further employed in constructi ng a con ceptual fluoresce nt film sen sor for evaluati ng fish fresh ness.Photophysical studies indicate that PCB-EpE displays an open"Z-type"molecular configuration in chloroform and a closed"△-type"configuration in methyl cyclohexa ne.Comparatively,"Z-type"molecular configurati on possesses higher fluoresce nee qua ntum yield and is more sen sitive toward volatile basic nitrogen(VBN). 相似文献
Summary: The synthesis of magnetic magnetite nanoparticles coated with amphiphilic block copolymers of poly(ethyl methacrylate)‐block‐poly(2‐hydroxyethyl methacrylate) for use as new potential carriers for hydrophobic drug delivery is reported. The results show that a new core‐shell‐corona structural material is obtained with a very narrow molecular weight distribution of the hydrophobic segment (PDI = 1.10). UV‐Vis results show that 37% of progesterone is released from the nanoparticles after 22 h, much slower than free release (99% after 14 h), which demonstrates that the presence of the hydrophobic segment can effectively control the release of hydrophobic drugs.
Synthesis of an amphiphilic block polymer poly(ethyl methacrylate)‐block‐poly(2‐hydroxyethyl methacrylate) on magnetite nanoparticles and their use as potential drug carriers 相似文献
The combined interface boundary condition (CIBC) method has been recently proposed for fluid–structure interaction. The CIBC method employs a Gauss–Seidel-like procedure to transform traditional interface conditions into velocity and traction corrections whose effect is controlled by a dimensional parameter. However, the original CIBC method has to invoke the uncorrected traction when forming the traction correction. This process limits its application to fluid–rigid body interaction. To repair this drawback, a new formulation of the CIBC method has been developed by using a new coupling parameter. The reconstruction is simple and the structural traction is removed completely. Two partitioned subiterative coupling versions of the CIBC method are developed. The first scheme is an implicit strategy while the second one is a semi-implicit strategy. Iterative loops are actualised by the fixed-point algorithm with Aitken accelerator. The obtained results agree with the well-documented data, and some famous flow phenomena have been successfully detected. 相似文献
Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model. 相似文献
