Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Magnetic Fluid-Filled Optical Fiber Fabry–Pérot Sensor for Magnetic Field Measurement

Abstract: Magnetic fluid is a new type of optical functional material, which has interesting optical characteristics under an external magnetic field. In this letter, the magneto-optical characteristic of the magnetic fluid was adopted to form a novel fiber-optic magnetic field sensor. The sensor probe was composed of an extrinsic fiber Fabry-Perot interferometer and magnetic fluid. The refractive index of the magnetic fluid would be changed with the increase of magnetic field. Preliminary experiment was carried out to verify the feasibility of the sensor. The magnetic field measurement sensitivity was 0.0431 nm/Gs in the experiment. The measurement resolution was better than 0.5 Gs at the measurement range from 0 to 400 Gs. The sensor has the advantages of simple structure, compact size, and easy fabrication.

Toward a Resilient Holistic Supply Chain Network System: Concept, Review and Future Direction

Abstract: The recent financial crisis and other major crises have suggested that there are some strong interactions and interdependence between several supply chains and their external environments in various ways. A set of supply chains that are interdependent is called a holistic supply chain network (H-SCN) in this paper. There is a need to focus on building the resilience (in short, the ability of a system to recover from damage or disruption) of an entire H-SCN as it is believed that such a network system is strongly relevant to the recent economic recession that is triggered by financial crises. The objectives of this paper are to provide a classification of different SCNs in literature, leading to the identification of a new type of SCN system, i.e., an H-SCN, and to discuss the state of knowledge on the resilience of SCNs, particularly of an H-SCN. A systematic review approach is applied in this paper. Another contribution of this paper is the provision of a more comprehensive definition and description of resilient systems, including SCN systems. A final contribution of this paper is the proposal of the future directions of research on resilient SCN systems, particularly resilient H-SCN systems.

Data-Driven Flotation Industrial Process Operational Optimal Control Based on Reinforcement Learning

Abstract: This paper studies the operational optimal control problem for the industrial flotation process, a key component in the mineral processing concentrator line. A new model-free data-driven method is developed here for real-time solution of this problem. A novel formulation is given for the optimal selection of the process control inputs that guarantees optimal tracking of the operational indices while maintaining the inputs within specified bounds. Proper tracking of prescribed operational indices, namely concentrate grade and tail grade, is essential in the proper economic operation of the flotation process. The difficulty in establishing an accurate mathematic model is overcome, and optimal controls are learned online in real time, using a novel form of reinforcement learning we call interleaved learning for online computation of the operational optimal control solution. Simulation experiments are provided to verify the effectiveness of the proposed interleaved learning method and to show that it performs significantly better than standard policy iteration and value iteration.