Kongzhi yu Juece/Control and Decision

Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.

Aim and Scope

Kongzhi yu Juece/Control and Decision

Computer Science and Engineering: Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology Research Journal of Chemistry and Environment

Software Engineering, Data Security, Computer Vision, Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics, Parallel and distributed processing, Artificial Intelligence, Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology, Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks,

Electrical Engineering and Telecommunication Section:

Electrical Engineering, FACTS devices , Insulation systems , Power quality , Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels,

Chemical Engineering :

Chemical engineering fundamentals, Particulate systems, Rheology, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Multifase flows, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Mathematics :

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Information theory, Industrial mathematics, Integral transforms and integral equations, Lie algebras, Magnetohydrodynamics, Mathematical analysis, Logic,

Physics Section :

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering physics, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics. High energy particle physics, Laser, Mechanical engineering, Medical physic, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Magnetohydrodynamics, Robotics, Soft matter and polymers,

// Latest Journals

Repair crew scheduling for damaged road network with enormous demand points using deep Q-learning

Repairing the damaged road network, which mainly focuses on how to reasonably schedule the repair crew to quickly unblock the road network and ensure that rescue teams and emergency resources in the source node can be delivered to different demand nodes in time, is a basic premise for emergency disposal and rescue after the occurrence of extraordinary serious natural disasters. However, it is difficult for the existing methods to find a feasible scheduling strategy under enormous demand nodes. network model and the Markov decision-making process, based on which a double-feedback reward func


Robust multiview graph learning with applications to clustering for incomplete data

The existing multi-view graph learning methods are mainly based on the premise that the data has good completeness, and do not fully consider the learning problem on incomplete data caused by element missing. To address this issue, this paper proposes a multi-view graph learning method with incomplete data. On the one hand, the method puts the data reconstruction and graph learning into the unified framework within view, which learns the view specific neighbor relationship among samples from the reconstructed data to compensate for the influence of data missing on data distribution. On the


A survey on scalability and transferability of multi-agent deep reinforcement learning

Due to the powerful feature representation capability of deep learning and the effective policy learning capability of reinforcement learning (RL), deep reinforcement learning (DRL) has made remarkable achievements in a series of complex sequential decision-making problems. With the popularity of DRL in many single-agent tasks, its application in multi-agent systems is flourishing. Recently, multi-agent deep reinforcement learning (MADRL) has attracted increasing attention in the field of artificial intelligence, and the scalability and transferability have become one of the important issue


Energy saving control for subway station air conditioning systems based on reinforcement learning

The subway station air conditioning system consumes a lot of energy, and traditional control methods cannot take into account the comfort and energy saving issues together, resulting in poor control effect. Moreover, the current subway station air conditioning system and the control air systems the control water system separately, which cannot guarantee the energy saving effect of the whole system. Therefore, this paper proposes an energy-saving control strategy for the system based on reinforcement learning. Firstly, this paper uses a neural network to establish an air system modification


Uncertainty estimation approach in orbital prediction error of space objects based on natural gradient boosting

In view of the insufficient accuracy of the orbital state prediction method based on the physical model in the space surveillance environment, and the insufficient reliability of the error compensation model based on machine learning, as well as the demand for uncertainty modeling in the SSA application, we reformulate the orbital state prediction error estimation problem as a probability prediction problem, and propose a method of using a gradient boosting machine to model the orbital state prediction error distribution. In order to quantify the uncertainty in the state error estimation, t