A5088345153- Probabilistic and Robust Engineering Design
- Structural Health Monitoring Techniques
- Advanced Multi-Objective Optimization Algorithms
- Water Systems and Optimization
- Model Reduction and Neural Networks
- Optimal Experimental Design Methods
- Nuclear Engineering Thermal-Hydraulics
- Nonlinear Dynamics and Pattern Formation
- Bayesian Methods and Mixture Models
- Statistical Distribution Estimation and Applications
- Privacy-Preserving Technologies in Data
- Advanced Statistical Methods and Models
- Advanced Decision-Making Techniques
- stochastic dynamics and bifurcation
- Network Security and Intrusion Detection
- Speech and Audio Processing
- Advanced Measurement and Detection Methods
- Complex Network Analysis Techniques
- Target Tracking and Data Fusion in Sensor Networks
- Caching and Content Delivery
- Software System Performance and Reliability
- Educational Technology and Assessment
- Statistical Methods and Inference
- Software Testing and Debugging Techniques
- Urban Stormwater Management Solutions
Dalian University of Technology
2016-2024
Shenzhen University
2023-2024
The Ohio State University
2020-2024
Jiangsu University
2021-2024
University of Science and Technology of China
2019-2024
Verisk Analytics (United States)
2023-2024
Jinan University
2024
Northwest Institute of Mechanical and Electrical Engineering
2023
Affiliated Hospital of Jiangxi College of TCM
2021
Tianjin Research Institute of Electric Science (China)
2020
In data mining applications and spatial multimedia databases, a useful tool is the kNN join, which to produce k nearest neighbors (NN), from dataset S, of every point in R. Since it involves both join NN search, performing joins efficiently challenging task. Meanwhile, continue witness quick (exponential some cases) increase amount be processed. A popular model nowadays for large-scale processing shared-nothing cluster on number commodity machines using MapReduce [6]. Hence, how execute...
Kriging has gained significant attention for reliability analysis primarily because of the analytical form its uncertainty information, which facilitates adaptive training and establishing stopping criteria process. Learning functions play a role in both selection points stoppage training. For these functions, most existing learning evaluate candidate individually. However, lack consideration global effects can lead to suboptimal In addition, subjectivity may result over or undertraining...
Abstract An imprecise probabilistic framework for design flood estimation is proposed on the basis of Dempster‐Shafer theory to handle different epistemic uncertainties from data, probability distribution functions, and parameters. These are incorporated in cost‐benefit analysis generate lower upper bounds total cost control, thus presenting improved information decision making floods. Within bounds, a new robustness criterion select that can tolerate higher levels uncertainty. A variance...
Abstract Fast detection of pipe burst in water distribution systems (WDSs) could improve customer satisfaction, increase the profits supply and more importantly reduce loss resources. Therefore, sensor placement for WDSs has been a crucial issue researchers practitioners. This paper presents an economic evaluation indicator named as net cost based on cost–benefit analysis to solve optimal pressure problem. The is defined sum normalized uncovering rate investment sensors. set locations are...
Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible is among alternatives. Value of (VoI) analysis a mathematical framework that quantifies expected potential benefits new data and assists with optimal allocation resources for collection. However, VoI computational very costly because the underlying Bayesian inference especially equality-type information. This paper proposes first surrogate-based analysis....
Abstract The paper focuses on developing a stochastic resonance (SR) system designed for the detection of weak signals under alpha-stable-distributed noises. Initially, in view strong impulsive characteristics noises, linearly-coupled sigmoid bistable (LSBSR) is proposed, which constructed by potential function and function. Through formula derivation, it theoretically proved that output signal-to-noise ratio (SNR) LSBSR superior to classical SR system. Then, new signal processing strategy...
To embrace the era of big data, there has been growing interest in designing distributed machine learning to exploit collective computing power local nodes. Alternating Direction Method Multipliers (ADMM) is one most popular methods. This method applies iterative computations over datasets at each agent and computation results exchange between neighbors. During this process, data privacy leakage arises when performing sensitive data. Although many differentially private ADMM algorithms have...
A novel photoacoustic thermometric method is presented for simultaneously imaging cells and sensing their temperature. With 3 seconds per frame speed, a temperature resolution of 0.2 °C was achieved in photo-thermal cell heating experiment. Compared to other approaches, the has advantage not requiring custom-developed temperature-sensitive biosensors. This feature should facilitate conversion single-cell thermometry into routine lab tool make it accessible much broader biological research community.
In wireless sensor networks (WSNs), the widely distributed sensors make real-time processing of data face severe challenges, which prompts use edge computing. However, some problems that occur during operation will cause unreliability collected data, can result in inaccurate results computing-based processing; thus, it is necessary to detect potential abnormal (also known as outliers) ensure their quality. Although clustering-based outlier detection approaches outliers from static feature...