Northeastern University
Researchers from Northeastern University have made new findings in the field of information technology, detailing a data-driven constrained optimal tracking control scheme for nonlinear systems subject to input and performance constraints. This research aims to make system states pursue desired trajectories while minimizing cost and strictly limiting tracking errors. The
Northeastern University
A team of researchers from Northeastern University has made a breakthrough in developing a novel approach to fault detection for network systems composed of multiple clusters with unknown system matrices. This approach, which utilizes the subspace instrumental variable method and unknown input decomposition, has been shown to be effective in
Northeastern University
Researchers at Northeastern University in Shenyang, People's Republic of China, have isolated two new sesquiterpenoids, neopinguisane-2b,5b-diol (1) and isoseiricardine B (2), from the culture broth of Streptomyces argenteolus derived from Pantholops hodgsoni feces. The new compounds, along with two known compounds, pentalenic acid (3) and nakienone C
Northeastern University
A team of researchers from Northeastern University has made a breakthrough in developing a novel approach to fault detection for network systems composed of multiple clusters with unknown system matrices. This approach, which utilizes the subspace instrumental variable method and unknown input decomposition, has been shown to be effective in
Robotics industry
Researchers at Northeastern University have made significant breakthroughs in push-grasp policy learning, a crucial aspect of robotics and automation. The study, funded by the JPMorgan Chase PhD Fellowship, National Science Foundation, and National Aeronautics & Space Administration, proposes a novel framework for joint pushing and grasping policy learning. This framework,
Simulation
Researchers from Northeastern University have conducted a comprehensive study on the interaction mechanisms between lithium dendrites and cavitation bubbles in lithium-ion battery recycling. The study utilized a full cycle model of ultrasonic cavitation bubble lithium dendrite interaction in the flow field, which was constructed through finite volume method (FVM) numerical
Batteries
Fresh data from a study in the People's Republic of China shed light on the pressing concerns of high electricity costs and data transmission security risks in energy management systems. The research, supported by the National Natural Science Foundation of China and the National High-Level Talents Special Support
Simulation
Scientists at Northeastern University have made significant findings on the geological conditions of resource mining, particularly in relation to the grouting technique used to prevent hazards in deep floor plates. According to the report, the researchers developed a hybrid model integrating microseismic (MS) sources and finite element simulation to study
Simulation
Researchers from Northeastern University have conducted a comprehensive study on the interaction mechanisms between lithium dendrites and cavitation bubbles in lithium-ion battery recycling. The study utilized a full cycle model of ultrasonic cavitation bubble lithium dendrite interaction in the flow field, which was constructed through finite volume method (FVM) numerical
Artificial neural networks
Researchers from Northeastern University have made a breakthrough in the field of networks by developing a new method for dissipativity analysis and bumpless transfer control for synchronization of switched delayed neural networks (SDNNs). The study, funded by the National Natural Science Foundation of China, China Postdoctoral Science Foundation, and Open
Machine learning
Researchers at Northeastern University have introduced a new algorithm called Q-AD, which aims to mitigate the reduction error in the action space by buffering the Q-value estimation error at each update. The Q-AD algorithm is based on Deep Q-Networks (DQN) and has been shown to significantly reduce value estimation errors
Algorithms
Research from Northeastern University in China has made significant advancements in addressing a critical challenge in engineering, specifically in dynamic multiobjective optimization problems (DMOPs). These problems often involve environmental changes that are difficult to detect, posing a significant obstacle to the existing methods. The researchers propose a dual mutation-based dynamic
