Cockpit crew pairing Pareto optimisation in a budget airline

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

COVID-19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm-Based Feature Selection from X-Ray Images

The new COVID-19 is rapidly spreading and has already claimed the lives of numerous people. The virus is highly destructive to the human lungs, and early detection is critical. As a result, this paper presents a hybrid approach based on deep convolutional neural networks that are very effective tools for image classification. The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID-19, pneumonia, and a healthy category, included 1092 X-ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID-19 detection using X-ray images.

Design of Precision Box-like Flexible Manufacturing System Based on Symmetric Differential Algorithm

The global manufacturing model is changing, and the manufacture of precision box parts is developing in the direction of automation and flexibility. Through the application of the information integration management module, integrated device information is collected to develop the system operation plan, which is transferred to the logistics management module and converted into control information executable by the processing module. Finally, the flexible machining of precision box parts is automatically implemented. Through the dynamic scheduling optimization strategy based on filtered directed search, the dynamic scheduling of system processing is completed. The machining target segmentation method based on symmetric differential algorithm is used to accurately extract the contour information of the precision machining target of the box. This process is the working principle of the precision boxlike flexible manufacturing system based on the symmetric differential algorithm designed in this paper. The results show that the system can effectively reduce the number of marks and the maximum completion time of the processing of precision box parts, and its balance ability and processing efficiency are fast.

Research on Flexibility Test of Aerobics Staff Based on Discrete Differential Algorithm

Objectives: With the development of economy and the improvement of people’s life quality, people are paying more and more attention to their own health condition.. Methods: For aerobics personnel, they often have higher requirements on flexibility, and put forward the flexibility of aerobics personnel based on discrete differential algorithm testing research. Through literature comparative analysis, the basic concepts of fractional Fourier transform are expounded. The basic principle of discrete sampling algorithm of DFRFT is analyzed. The realization steps of dimension normalization are designed and applied in case analysis.. Results: The study found that this algorithm has a strong versatility and a moderate amount of calculation, which can be applied to most of the flexibility testing, Conclusion: providing a theoretical basis for the future detection methods of people’s health.

Distributed convex optimization via proportional-integral-differential algorithm

This paper studies the distributed convex optimization problem, where the global utility function is the sum of local cost functions associated to the individual agents. Only using the local information, a novel continuous-time distributed algorithm based on proportional-integral-differential (PID) control strategy is proposed. Under the assumption that the global utility function is strictly convex and local utility functions have locally Lipschitz gradients, the exponential convergence of the proposed algorithm is established with undirected and connected graph among these agents. Finally, numerical simulations are presented to illustrate the effectiveness of theoretical results.

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit

Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure loses its purpose. To help eliminate the difficulty, we present a system to detect flocks and to trigger an actuator that will scare the objects only when a flock passes through the monitored space. The actual detection is performed with artificial intelligence utilizing a convolutional neural network. Before teaching the network, we employed videocameras and a differential algorithm to detect all items moving in the vineyard. Such objects revealed in the images were labeled and then used in training, testing, and validating the network. The assessment of the detection algorithm required evaluating the parameters precision, recall, and F1 score. In terms of function, the algorithm is implemented in a module consisting of a microcomputer and a connected videocamera. When a flock is detected, the microcontroller will generate a signal to be wirelessly transmitted to the module, whose task is to trigger the scaring actuator.

Intelligent restoration of ancient murals based on discrete differential algorithm

Many defects now appear in ancient murals due to both natural and man-made factors. To better repair the damaged ancient murals, this paper improves the existing intelligent restoration method. It identifies and marks the crack-producing area, categorizes the mural image into two types – texture area and flat area according to the local gradient features, decides the initial sample block and calculates the weight, analyzes the extracted pixel data and applies discrete differential algorithm to supplement image defects. Through experiments, the method is validated in meeting the needs of image continuity law and human vision. It can shorten the repair time and restore mural cracks in an intelligent way.