This study investigates the effects of the interlayer thickness and pore size of foam metal materials on the microstructure and properties of dissimilar material brazed joints. Using finite element simulation calculat...
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This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3- PRS Parallel Kinematics Machines, which are among the most extensively researched and viably successful manipulators for pr...
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ISBN:
(数字)9798350395969
ISBN:
(纸本)9798350395976
This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3- PRS Parallel Kinematics Machines, which are among the most extensively researched and viably successful manipulators for precision machining applications. Despite the wealth of research on these robotic manipulators, no previous work has demonstrated their stiffness performance within the parasitic motion space. Such an undesired motion influences their stiffness properties, as stiffness is configuration-dependent. Addressing this gap, this paper develops a stiffness model that accounts for both the velocity-level parasitic motion space and the regular workspace. Numerical simulations are provided to illustrate the stiffness characteristics of the manipulator across all considered spaces. The results indicate that the stiffness profile within the parasitic motion space is both shallower and the values are smaller when compared to the stiffness distribution across the orientation workspace. This implies that evaluating a manipulator's performance adequately requires assessing its ability to resist external loads during parasitic motion. Therefore, comprehending this aspect is crucial for redesigning components to enhance overall stiffness.
Non-invasive electroencephalography (EEG) signals find widespread application in brain-computer interfaces (BCI), with paradigms such as motor imagery (MI), mental arithmetic (MA) and emotion recognition being particu...
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ISBN:
(数字)9798350388077
ISBN:
(纸本)9798350388084
Non-invasive electroencephalography (EEG) signals find widespread application in brain-computer interfaces (BCI), with paradigms such as motor imagery (MI), mental arithmetic (MA) and emotion recognition being particularly common. This study aims to explore feature extraction and classification methods for MI and MA tasks. We employed convolutional neural networks (CNN) combined with Transformer networks and Fasternet Block to extract features. Through our reaearch, we obtained features for MI and MA tasks, and used softmax classification for binary classification of these tasks. We conducted on a publicly available dataset consisting of data from 29 subjects. The experimental results demonstrate that our method achieved high classification accuracy in MI and MA tasks. The final accuracy rates for MI and MA tasks were 88.67% and 91.23%, respectively.
With the gradual increase of vehicle penetration rate, driving safety has become a problem that people pay attention to, and traffic accidents caused by fatigue are mostly, therefore, it is meaningful to devote oursel...
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ISBN:
(数字)9798350388077
ISBN:
(纸本)9798350388084
With the gradual increase of vehicle penetration rate, driving safety has become a problem that people pay attention to, and traffic accidents caused by fatigue are mostly, therefore, it is meaningful to devote ourselves to research on the method of detecting driving fatigue so as to achieve safety warning. Based on this, in this paper, we collect the EEG signals of the experimenter through driving simulation, then extract the features through differential entropy, classify the features with GBDT, and finally determine the fatigue level of the subject with the classification results. We use our method to analyse the difference with sample entropy and fuzzy entropy at the feature level, and with KNN and SVM at the classification level. The comparison shows that our method has significant accuracy in EEG-based fatigue *** can be used accurately in extraction methods through its properties, and differential entropy-based GBDT methods may be useful in detecting driver fatigue.
In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural ne...
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Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients' survival rates. However, existing deep learning-based methods often yield unsatisfactory s...
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Metastasis and metabolic disorders contribute to most cancer deaths and are potential drug targets in cancer treatment. However, corresponding drugs inevitably induce myeloid suppression and gastrointestinal toxicity....
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Metastasis and metabolic disorders contribute to most cancer deaths and are potential drug targets in cancer treatment. However, corresponding drugs inevitably induce myeloid suppression and gastrointestinal toxicity. Here, we report a nonpharmaceutical and noninvasive electromagnetic intervention technique that exhibited long-term inhibition of cancer cells. Firstly, we revealed that optical radiation at the specific wavelength of 3.6 μm (i.e., 83 THz) significantly increased binding affinity between DNA and histone via molecular dynamics simulations, providing a theoretical possibility for THz modulation- (THM-) based cancer cell intervention. Subsequent cell functional assays demonstrated that low-power 3.6 μm THz wave could successfully inhibit cancer cell migration by 50% and reduce glycolysis by 60%. Then, mRNA sequencing and assays for transposase-accessible chromatin using sequencing (ATAC-seq) indicated that low-power THM at 3.6 μm suppressed the genes associated with glycolysis and migration by reducing the chromatin accessibility of certain gene loci. Furthermore, THM at 3.6 μm on HCT-116 cancer cells reduced the liver metastasis by 60% in a metastatic xenograft mouse model by splenic injection, successfully validated the inhibition of cancer cell migration by THM in vivo. Together, this work provides a new paradigm for electromagnetic irradiation-induced epigenetic changes and represents a theoretical basis for possible innovative therapeutic applications of THM as the future of cancer treatments.
Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constr...
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ISBN:
(数字)9798350387384
ISBN:
(纸本)9798350387391
Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.
In practical clinical applications, vascular intervention surgical robots have developed sophisticated human-machine interaction systems, enabling assistance to physicians in performing remote surgeries and providing ...
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ISBN:
(数字)9798350388077
ISBN:
(纸本)9798350388084
In practical clinical applications, vascular intervention surgical robots have developed sophisticated human-machine interaction systems, enabling assistance to physicians in performing remote surgeries and providing intelligent visual feedback. However, regarding surgical safety, current research predominantly focuses on force feedback and robot control logic, with clamping mechanisms targeting the locking of catheters and guide wires. Excessive clamping force may lead to surface damage to intervention instruments, while insufficient force delivery may result from slippery surfaces. Therefore, addressing these issues, this study proposed a passive, compliant safety strategy for an adaptive clamping device based on a vascular intervention surgical robot platform. This device can accommodate different diameter catheters and maintain a constant delivery force during the intervention process. Finally, through experimentation, the effectiveness, safety, and stability of the device were demonstrated.
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