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.
The main content of virgin coconut oil is fatty acid ethyl ester and other ingredients that have the potential as a source of antioxidants which are added during the manufacture of oil from natural ingredients on the market. The aim of this study was to analyze FAEE of homemade virgin coconut oil (VCO) as well as cooking oil added with VCO by using GC-MS. Fresh coconut fruit as a source of natural VCO and cooking oil containing VCO were used in this study. Both materials were prepared using pro-analytical extract solvents. Phytoconstituent profiles were analyzed using GC-MS qualitatively. The
In terms of the problems that the gray wolf optimization algorithm has low convergence accuracy and is easy to fall into local solutions, this paper proposes a hybrid gray wolf optimization algorithm based on the teaching-learning optimization. Firstly, the good-point set theory is used to generate the initial population to improve its ergodicity. Then, a nonlinear control parameter strategy is proposed to increase the global search capability in the early stage of the iteration to avoid the algorithm from falling into the local optimum, and increase the local development capability in the
This paper studies the alarm response of false data injection attack in cyber-physical system under limited bandwidth constraints. Firstly, the false data injected in the actuator is modeled as unknown input, and the local residual generators are designed by the given H_infinity performance index to generate the residual signals approaching the attack signal. Subsequently, in order to improve the alarm response, all the residual signals are quantized and then send to the detection center under the distributed fusion framework, the optimization objective is designed where the H_infinity perf
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
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