This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the ro...
The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular *** reconstructing this process and analyzing the changes in receptor conformation on the cell me...
详细信息
The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular *** reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical *** this paper,a time-coded multiconcentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution,high throughput,and multi-concentration combination *** on innovative microchannel structure,sophisticated external control methods and multiplexing technology,the system not only allowed for temporally sequential permutations of the four concentrations of stimuli(time code),but also generated pulsed and continuous waveforms at different frequencies in a highly controllable ***,the single-cell trap array was set up to efficiently capture cells in suspension,dramatically increasing throughput and reducing experiment preparation *** maximum frequency of the platform was 1 Hz,and one cell could be stimulated at multiple *** show the ability of the system to investigate rapid biochemical events in high throughput,pulse stimulation and continuous stimulation of different frequencies and different time codes,combined with four concentrations of histamine(HA),were generated for probing G protein-coupled receptor(GPCR)signaling in He La ***,statistical analysis was performed for the mean peak height and mean peak area of the cellular *** believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry,biophysics,cell signaling,and individualized medicine applications.
This article addresses distributed adaptive fuzzy 3-D formation tracking control of multiple autonomous underwater vehicles (AUVs) subject to marine environmental disturbances. First, by constructing a coordinate tran...
详细信息
This article addresses distributed adaptive fuzzy 3-D formation tracking control of multiple autonomous underwater vehicles (AUVs) subject to marine environmental disturbances. First, by constructing a coordinate transformation, AUV model is transformed into a simple second-order systems with nonlinear dynamics. Second, fuzzy logic systems are utilized to approximate the complex nonlinear dynamics and a distributed adaptive control scheme is carried out under the assumption that all AUVs have access to the real-time information of themselves and their neighbors. Third, we assume that only sampling states of their neighbors under the event-triggered conditions are available. System states are reconstructed via the fuzzy state observers, and then the other distributed adaptive control scheme is proposed to ensure that all AUVs track the leader with the desired formation configuration under local communication. The coordinate transformation and fuzzy logic approximation method not only reduce computation but also simplify control design. Finally, a numerical simulation is carried out to demonstrate the effectiveness of the proposed control design.
In clinical practice, one patient may suffer from more than one arrhythmia simultaneously, that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there are inherent dependencies between...
详细信息
In clinical practice, one patient may suffer from more than one arrhythmia simultaneously, that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there are inherent dependencies between arrhythmias. However, previous studies have mainly focused on multi-class (single-label) ECG classification, which addresses each type of arrhythmia independently and ignores the multi-label correlation between different ECG abnormalities. To address the lack of ECG multilabel classification methods, we proposed a label correlation embedding guided network (LCEGNet) model to effectively recognize multi-label ECG arrhythmias and explore the correlation between ECG abnormalities. First, label correlation embedding was obtained based on the correlation matrix between different arrhythmias to guide feature extraction. Subsequently, the category-specific attention coefficient was obtained by calculating the cosine similarity coefficient between the label embedding and feature spaces. Experiments on public and self-collected ECG datasets were conducted. The LCEGNet achieved F1 scores of 0.777 and 0.872 and subset accuracy of 0.750 and 0.828 on the two datasets, respectively. A classification speed of 7.796 ms was achieved. The experimental results demonstrate that the proposed LCEGNet achieved approximately a 11% and 9.1% improvement in the F1 score and subset accuracy, respectively, compared with traditional ResNet architecture and a 4.3% and 5.54% improvement in the F1 score and subset accuracy, respectively, compared with the state-of-the-art approaches.
Over 80% of global trade is carried by sea, and 70% of global seaborne trade by value is realized by liner shipping. The global liner shipping network (GLSN) is essential to facilitating international trade. To explor...
详细信息
Over 80% of global trade is carried by sea, and 70% of global seaborne trade by value is realized by liner shipping. The global liner shipping network (GLSN) is essential to facilitating international trade. To explore the transportation mechanism of the GLSN, we study a structure termed "two-hop biconnected component"(TBCC) which is a type of highly robust and efficient connected local structure. We find that TBCC is a salient structural property of the GLSN and is strongly associated with international trade statuses. Ports that appear more frequently in TBCCs tend to gain higher container throughputs in reality and play more important roles in maintaining the overall network robustness. Furthermore, we find that countries belonging to the same TBCCs tend to have closer trading relationships and form meaningful multilateral trade clusters in the future. The discovery of TBCC can help better understand international trade statuses and enable better design of robust and efficient liner shipping service routes.
In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for messag...
详细信息
With the increasing penetration of wind power in power grid, accurate and reliable wind power forecasting is of great significance for the economic operation and safe dispatching of electrical power system. In practic...
详细信息
With the increasing penetration of wind power in power grid, accurate and reliable wind power forecasting is of great significance for the economic operation and safe dispatching of electrical power system. In practice, there exists complex spatial correlation between wind power variables, which brings great challenges to the accurate forecasting of wind power. However, traditional deep learning -based methods mostly focus on temporal feature while ignoring the spatial correlation between wind power variables, leading to low forecasting accuracy. To explore the spatial correlation among wind power variables and extract temporal features simultaneously, we propose a double attention -based spatial-temporal neural network (DA-STNet). First, graph attention network is employed to explore the spatial correlation between wind power variables based on a graph constructed by maximal information coefficient, which can consider the combined influences of multivariate on the wind power output. Then, by incorporating causal reasoning and data -driven elementwise attention measures, a novel temporal attention layer is proposed to extract the temporal feature of wind power sequences. Comprehensive experiments were conducted on one self -collected and one public dataset with three different multi -steps ahead forecasting tasks, and the experimental results demonstrated that the performance of proposed DA-STNet is superior to the existing methods on both real -world datasets. In the 24 h ahead experiment on the NWWPF dataset, the MSE of our model can reach as low as 0.136 and MAE can be decreased to 0.275.
Current linkage-driven prosthetic hands still show limitations in aspects such as the thumb design and fingertip sensor. Moreover, linkage-driven prosthetic hands still lack quantitative precision grasp quality. In th...
详细信息
Current linkage-driven prosthetic hands still show limitations in aspects such as the thumb design and fingertip sensor. Moreover, linkage-driven prosthetic hands still lack quantitative precision grasp quality. In this study, we developed a novel thumb structure with coupled abduction-adduction and pronation-supination movement in the trapeziometacarpal joint. We also developed a fully integrated fingertip tactile sensor with all components embedded in the distal phalanx designed to facilitate in-hand precision manipulation. Furthermore, we devised a new metric to evaluate the precision grasp quality based on the force conditions during grasp. On the basis of this metric, we optimized the geometry parameters of the thumb and index finger using the Monte Carlo method. The results show that, compared with the anthropomorphic trajectory measured from a human index, the proposed method improves the grasping ability by more than 10%. Finally, we developed a prototype prosthetic hand based on the proposed design methods and demonstrated by experiment that it was able to perform human-like thumb opposition and to pass both precision and power grasp tests.
Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and al...
详细信息
Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.
Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i.e., pedestrian detection and person re-identification. The dominant fashion is termed as...
详细信息
Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i.e., pedestrian detection and person re-identification. The dominant fashion is termed as the one-step person search that jointly optimizes detection and identification in a unified network, exhibiting higher efficiency. However, there remain major challenges: (i) conflicting objectives of multiple sub-tasks under the shared feature space, (ii) inconsistent memory bank caused by the limited batch size, (iii) underutilized unlabeled identities during the identification learning. To address these issues, we develop an enhanced decoupled and memory-reinforced network (DMRNet++). First, we simplify the standard tightly coupled pipelines and establish a task-decoupled framework (TDF). Second, we build a memory-reinforced mechanism (MRM), with a slow-moving average of the network to better encode the consistency of the memorized features. Third, considering the potential of unlabeled samples, we model the recognition process as semi-supervised learning. An unlabeled-aided contrastive loss (UCL) is developed to boost the identification feature learning by exploiting the aggregation of unlabeled identities. Experimentally, the proposed DMRNet++ obtains the mAP of 94.5% and 52.1% on CUHK-SYSU and PRW datasets, which exceeds most existing methods.
暂无评论