This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV ...

This study uses PID (Proportional-Integral-Derivative), fuzzy logic, and deep learning algorithm to experimentally achieve real-time position control of a four-wheel-drive symmetric autonomous mobile robot whose design and prototype are realized. ...

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper ...

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers ...

• The framework of cloud energy storage systems with varying structures is built.
• The real-time economic generation controller is designed for the built system.
• A long-term deep reinforcement learning method is proposed for this ...

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Recently, deep learning based fault diagnosis methods have achieved remarkable advancements with monitored data from a single ...

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning ...

Rotating machinery plays a vital role in the industrial sector, and ensuring its health status is crucial for operational efficiency and safety. The construction of accurate health indicators (HIs) have gained significant attention as it ...

This paper presents an innovative output feedback fault-tolerant Q-learning algorithm that can be implemented online without relying on explicit system models or fault details. In the face of actuator faults, finding optimal Fault-Tolerant ...

In this paper, we propose a proactive method to prevent Work-related MusculoSkeletal Disorders (WMSDs) in manufacturing industries. The integrated method includes a Motion Capture System (MCS) for data collection, a Time Series Forecasting (TSF) ...

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• A proactive method is proposed to prevent Work-related MusculoSkeletal Disorders (WMSDs).
• Machine Learning (ML) was used to forecast Ahead-of-Time (AoT) angular movements.
• Standard ergonomics were adopted to detect upper limb high ...

Wave Energy is one of the renewable sources with greatest potential. Since power coming from waves fluctuates, the grid integration of wave energy involves several power conditioning stages to comply with grid quality requirements. However, to ...

Radiofrequency ablation (RFA) is a minimally invasive technique that is widely used to ablate solid tumors. Achieving precise personalized treatment requires feedback information on in situ thermal effects induced by RFA. Although computer ...

• Real-time prediction of thermal effect during RFA.
• Experimental validation using bovine liver tissue.
• Incorporating multiphysics simulations.

The paper proposed a traversability learning method based on the human demonstration for generating risk cost maps. These maps aid mobile robots in identifying safe areas for reliable autonomous navigation over uneven terrain. Firstly, a maximum ...

This study aims to examine the energy-related aspects of a hybrid convection-radiation drying technique employed in the drying process of garlic slices. Several factors, including infrared radiation intensity (1500, 2000, and 3000 W/m²), air ...

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• A novel analysis is implemented into a typical hybrid drying for energy-saving purposes.
• The ANN modeling has been fitted accurately (R = 0.99).
• Air velocity, temperature, and IR power affected the quality parameters.
• The ...

The increasing use of drones across various sectors demands optimized deployment strategies under diverse constraints. This paper tackles the Multiple Capacitated Mobile Depot Vehicles Routing Problem (mCMoD-VRP), a challenging variant of the ...

The least squares–support vector machine (LS-SVM) method has achieved remarkable success in solving electromagnetic equations. However, the boundaries of the entire computational domain for solving multi-physical transient-state field coupled ...

The field of robotic grasping has seen significant progress with the development of deep learning and the creation of large-scale datasets like the Cornell Grasping Dataset (Jiang et al., 2011) and DexNet (Mahler et al., 2016). However, ...

• Propose a method to generate robotic grasping datasets by simulating gripper actions in a virtual environment.
• Design an end-to-end graph convolution network to predict optimal grasping points and orientations from partial point ...

The safe operation of Industrial robots is a major concern in intelligent manufacturing. Accurate compound fault diagnosis is essential to the safe operation of industrial robots, while it is challenging to achieve since the compound fault ...

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several ...

Although the advancements in marine engines diagnosis technologies and systems, estimating faults combinations at the entire operating envelope is challenging. This study aims at first investigating the path logarithmic signatures (logS) method ...

Most large manufacturing fields still rely on manual operations, requiring operators to enter the Confined Space (CS) to conduct assembly and maintenance tasks. The ergonomics analysis combined with the Digital Human Model (DHM) can provide ...

• A method is proposed to quantify the restricted degree of confined operation space.
• A muti-task deep network was presented to predict the operation situation in confined space.
• The proposed method can effectively predict the ...