Interactive and Visualized Online Experimentation System for Engineering Education and Research

Experimentation is crucial in engineering education. This work explores visualized experiments in online laboratories for teaching and learning and also ...

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education 

Online laboratories play an important role in engineering education. This work discusses a WebVR-based virtual laboratory system. The user enters the ...

wuhan university

This paper is concerned with the security tracking problem under the quadratic cost criterion for a class of discrete-time stochastic linear networked control systems (NCSs) exposed to cyber attacks, covering false data injection attacks as well as a class of DoS attacks. Taking into account factors such as network congestion and the defensive role of intrusion detection systems, successful attack events are modeled as a Bernoulli random sequence. To describe the transient trajectory of an NCS under the impact of a random attack, a probabilistic definition of secure trackability is taken. Therefore, an observer-based dynamic output feedback controller is designed in order to achieve the specified probabilistic secure trackability. Specifically, the probabilistic safety output tracking problem is transformed into an input-to-state stability problem in the probabilistic sense for closed-loop systems with some new sufficient conditions, provided that an augmented incremental model is utilized Then, the controller parameters and the upper bound of the quadratic cost function are determined by solving matrix inequalities, while easy-solution forms of the matrix inequalities to be solved are presented by the Schur complementary lemma. Both simulation studies and practical experiments demonstrate the effectiveness of the proposed control scheme.

Security tracking control for discrete-time stochastic systems subject to cyber attacks.

To overcome the high uncertainty and randomness of wind and enable the grid to optimize advance preparation, a priori-guided and data-driven hybrid method is proposed to provide accurate and reasonable wind power forecasting results. Fuzzy C-Means (FCM) clustering algorithm is used first to recognize the characteristics of the weather in different regions. Then, for the purpose of making full use of both priori information and collected measured data, a three-stage hierarchical framework is designed. First, via fuzzy inference and dimension reduction of Numerical Weather Prediction (NWP), more applicable wind speed information is obtained. Second, the accessible wind power generation patterns are served as a guide for mining the actual power curve. Third, the forecasted power is derived through the recorded data and the predictable wind conditions via data-driven model. This forecasting framework ingeniously introduces a gateway that can import priori knowledge to steer the iterative learning, thus possessing both adaptive learning ability and Volterra polynomial representation, and can present forecasted outcomes with robustness, accuracy and interpretability. Finally, a real-world dataset of a wind farm as well as an open source dataset are used to verify the performance of the proposed forecasting method. Results of the ablation analyses and comparative experiments demonstrate that the introduction of domain knowledge improves the forecasting performance.

Priori-guided and data-driven hybrid model for wind power forecasting.

Universality is crucial for systems to provide services to users with different backgrounds. A good language system can remove language barriers to help to improve system universality. To improve user experience and provide new features to users, the Networked Control System Laboratory (NCSLab) has been redeveloped based on React. To achieve toward an international platform, this paper introduces a web-based multi-language system for remote and virtual laboratories based on React. The language system is implemented based on an open source tool named . The architecture and modular design concept of the language system are investigated and the design and implementation are explored in detail from the perspective of the front-end and back-end separation scheme. The proposed language system has been integrated into the new React-based NCSLab system, which is scalable and can improve universality to serve not only domestic users in China, but also international users from all over the world.

Toward an international platform: A web-based multi-language system for remote and virtual laboratories using react framework.

The monitoring and control of DC-DC converters have become key issues since DC-DC converters are gradually playing increasingly crucial roles in power electronics applications such as electric vehicles and renewable energy systems. As an emerging and transforming technology, the digital twin, which is a dynamic virtual replica of a physical system, can potentially provide solutions for the monitoring and control of DC-DC converters. This work discusses the design and implementation of the digital twin DC-DC converter in detail. The key features of the physical and twin systems are outlined, and the control architecture is provided. To verify the effectiveness of the proposed digital twin method, four possible cases that may occur during the practical control scenarios of DC-DC converter applications are discussed. Simulations and experimental verification are conducted, showing that the digital twin can dynamically track the physical DC-DC converter, detect the failure of the physical controller and replace it in real time.

Digital twin based monitoring and control for DC-DC converters.

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

Wenshan Hu

Department of Artificial Intelligence and Automation

wuhan university

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

Wenshan Hu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Artificial Intelligence and Automation

wuhan university

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

Department of Artificial Intelligence and Automation