A5044078463- Advanced Clustering Algorithms Research
- Face and Expression Recognition
- Complex Network Analysis Techniques
- Image Retrieval and Classification Techniques
- Dam Engineering and Safety
- Hydrology and Sediment Transport Processes
- Human Mobility and Location-Based Analysis
- Neural dynamics and brain function
- Medical Image Segmentation Techniques
- Advanced Memory and Neural Computing
- Energetic Materials and Combustion
- Hydraulic flow and structures
- Ferroelectric and Negative Capacitance Devices
- Algorithms and Data Compression
- Data Stream Mining Techniques
- Remote-Sensing Image Classification
- Infrastructure Maintenance and Monitoring
- Video Surveillance and Tracking Methods
- High-Velocity Impact and Material Behavior
- Topological and Geometric Data Analysis
- Research studies in Vietnam
- Data Management and Algorithms
- Advanced Image and Video Retrieval Techniques
- Structural Response to Dynamic Loads
Zhejiang University of Technology
2021-2024
Hangzhou Normal University
2024
Zhengzhou University
2022-2023
Yellow River Institute of Hydraulic Research
2023
Northwest Institute of Nuclear Technology
2023
Fuzzy c-means (FCM) algorithm as a traditional clustering for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor results with inconsistent regions. For the remedy, superpixel technologies are applied, but preservation highly relies on quality superpixels. Density peak (DPC) can reconstruct arbitrary-shaped clusters, its high time complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...
Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and robustness in real-world applications, yet privacy risks remain largely unexamined. In this work, we investigate the susceptibility of SNNs to Membership Inference Attacks (MIAs) -- a major threat where an adversary attempts determine whether given sample was part training dataset. While prior work suggests that may offer inherent due discrete, event-driven nature, find its resilience diminishes as...
As a foundational clustering paradigm, Density Peak Clustering (DPC) partitions samples into clusters based on their density peaks, garnering widespread attention. However, traditional DPC methods usually focus high-density regions, neglecting representative peaks in relatively low-density areas, particularly datasets with varying densities and multiple peaks. Moreover, existing variants struggle to identify correctly high-dimensional spaces due the indistinct distance differences among...
As partial samples are often absent in certain views, incomplete multi-view clustering has become a challenging task. To tackle data with missing current methods either utilize the similarity relations to recover or primarily consider available information of existing samples, typically facing some inherent limitations. Firstly, traditional solutions cannot fully explore potential contained due their omission strategy, leading sub-optimal graphs. Moreover, most mainly focus on recovery from...
Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype have been proposed. Nevertheless, the automatic estimation of number clusters and detection complex shapes still challenging, to solve such problems usually relies on user-specified parameters may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak algorithm (SMMP) is proposed, which can achieve fast, automatic, effective without...
Most existing clustering algorithms require presetting cluster number and often fail to capture complex shapes. Herein, we propose a algorithm by pruning density-boosting tree of density mounts—DEnsity MOuntains Separation (DEMOS). A is assumed be density-connected area with multiple (or single) mounts (i.e., single-peak clusters) relatively large dis-connectivity from areas higher densities. Based on this assumption, DEMOS can easily detect the clusters robustly reconstruct their It first...
The research on the structural response of explosive vessel is an important basis for design vessels. Double-layer cylinder structures are widely used in various This paper studied deformation a steel cylindrical shell under internal explosion and proposes new method measuring by PDV (photonic Doppler velocimetry). We carried out many spherical experiments obtained useful results that show displacement double-layer cylinders time. above process simulated LS-DYNA with finite element numerical...