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

A survey of action recognition methods based on graph convolutional network

Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rel

Selection method for froth image characters based on sparse neural network

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy stateof-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter – scaling factor is first introduced to scale the outputs of specific structures, such as neurons,

Research progress of anomaly detection for complex networks

Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a variety of types have been witnessed. This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work. Specifically, we first pr

Data driven adaptive learning control of nonlinear network system

The existing model-free adaptive control encounters problems, such as too many parameters that need to be determined, some of which with unclear physical significance and whose selection depend entirely on trial and error. Aiming at this problem, a new dynamic linearized model is established by using Taylor series expansion of discrete-time nonlinear systems and the differential mean value theorem. Then, a new data-driven model-free adaptive control is proposed, which reduces the required parameters from six in the existing model-free adaptive control to four in the new model-free adaptive