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.
// Latest Journals
Automated ECG and EEG signal interpretation using Machine Learning Accuracy, Sensitivity, and Specificity Analysis for Early Diagnosis of Neurological Disorders
The early diagnosis of neurological disorders is often hindered by subtle and overlapping electrophysiological abnormalities present in electrocardiogram (ECG) and electroencephalogram (EEG) signals. The application of machine learning (ML) and deep learning (DL) algorithms offers a promising avenue for automated, accurate, and scalable interpretation of such biosignals. This study aimed to quantitatively evaluate the performance of automated ECG and EEG interpretation using ML and DL models, emphasizing accuracy, sensitivity, and specificity for early neurological disorder detection. A retros
A method of intelligent support for management decisions based on scoring analysis of tender applications
The paper considers an approach to automating the evaluation of tender applications using an intelligent decision support algorithm based on a scoring model. The algorithm is implemented using logistic regression and ROC analysis, which allows for a quantitative assessment of the relevance of tenders. The model has been tested on historical data from a software company that participated in government and commercial procurement. The paper confirms the feasibility of using a scoring approach to improve the efficiency of selecting tenders. The proposed method is expected to be expanded to other p
Intelligent hyperlocal weather forecasting system based on small-sized neural network models and local data
Accurate short-term weather forecasting remains a key challenge in meteorology, particularly at the local scale where global numerical models (e.g., WRF, GFS) often fail to capture fine-grained atmospheric dynamics. This study presents a neural network–based approach designed to improve the accuracy of short-term (up to 24 hours) weather forecasts by integrating local meteorological observations with outputs from large-scale models. The proposed architecture follows an encoder–decoder structure with multi-head attention, enabling the model to learn spatiotemporal dependencies and correct s
Сreating image datasets for training neural networks of сonveyor video analytics
The paper presents a technology for generating synthetic data for training computer vision neural networks used in industrial quality control tasks. A method of image generation using the Unity game engine is proposed, which makes it possible to create photorealistic scenes of the production process, simulate the movement of objects on the conveyor and form various types of laminate defects. Algorithms for changing textures, lighting, camera angles, and defect characteristics are implemented, which ensures high diversity and realism of the training sample. To test the effectiveness of the appr
An integrated approach to object detector compression and evaluation using the YOLO example
The article presents a software module for complex optimization of object detection models using the YOLO11 architecture as an example. The developed system implements a full cycle of working with the model — training, pruning, fine-tuning, quantization and subsequent comparative analysis. During the experiments, two modifications of YOLO11m and YOLO11n were evaluated using the CompDetect and Safety Helmet Detection datasets, which differ in complexity and image structure. The results showed that the use of the proposed module reduces computational costs while maintaining a high level of det