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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of the computational complexity and low segmentation precision of the multi-threshold image segmentation method, this paper proposed an ISCA based multi-threshold image segmentation method. Firstly, this method used a chaotic initialization technique to improve the quality of initial population. Secondly, it introduced an adaptive strategy to adjust the parameters according to the fitness values. Finally, it utilized an opposition-based learning strategy, then selected the better particles. The results of the Berkeley image and the plant canopy image segmentation experiments show that this method has a satisfied performance in terms of running time and segmentation accuracy. And it has a strong robustness. [ABSTRACT FROM AUTHOR]

Threshold selection has an important impact on the effect of image segmentation. In traditional image segmentation, there are some problems such as single segmentation result, weak flexibility and easy to fall into local optimum. In order to determine the optimal thresholds for image segmentation, a multilevel-thresholding image segmentation method based on sine cosine algorithm(SCA) is proposed in this paper. The method takes the between-class variance as the fitness function of the sine-cosine algorithm, updates the position of the current solution in each dimension by changing the sine function and cosine function. The candidate solution uses multiple random operators to carry out sine-cosine fluctuation around the optimal solution to complete each optimization process, and updates the optimum through iterative calculation. The optimal thresholds of image segmentation are determined by the location of the solution. Four standard test images are selected for experiment and compared with PSO algorithm in peak signal to noise ratio, structural similarity method, and optimization time. It shows the application of sine-cosine algorithm in image segmentation can obtain more accurate threshold and higher segmentation efficiency, which has a strong practicability. [ABSTRACT FROM AUTHOR]

The plant leaf image is taken as the research target, and the improved particle swarm optimization algorithm is used to optimize the optimal threshold vector of maximum entropy threshold segmentation. Although the traditional maximum entropy segmentation method has many advantages, it also has problems such as slow algorithm operation and insufficient precision. The Improved Particle Swarm Optimization(IPSO) proposed in this paper improves the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, and has the characteristics of fast convergence and high precision. Aiming at the problem of blade overlap in plant leaf image segmentation, the RGB image is converted into HSV(Hue, Saturation, Value) image and the V vector is extracted, the fitness function of the maximum entropy thresholding method is optimized by IPSO algorithm. The experiment compares the maximum entropy value with the running time of the algorithm. The results show that the segmentation accuracy and convergence efficiency of the proposed method are greatly improved. It shows that the proposed method is superior to the traditional maximum entropy method. Therefore, the method can segment the plant leaf image more efficiently and has strong practicability in engineering. [ABSTRACT FROM AUTHOR]

In the process of temperature and humidity changes in micro-plant factories, there are differences in the distribution of temperature and humidity fields. The data collected by the sensors will change with the position of the sensors, which will affect the accuracy of data collection. Aiming at the above problems, this paper proposes a sensor layout scheme, using computational fluid dynamics( CFD) simulation software FLUENT to simulate the temperature and humidity fields under different schemes, and analyze the different sensor layouts and the temperature and humidity changes in the plant growth area. The optimal layout of the sensor is found and verification tests are conducted. The simulation results show that by using computational fluid dynamics simulation, by comparing the parameters such as the goodness of temperature and humidity data fitting of different layout schemes, the data collection differences can be visually analyzed, which is convenient for comparison and screening of the best schemes. The test results show that the relative error between the measured data of temperature and humidity and the simulated data is kept below 5. 6% and 3. 2% respectively. The simulation results are in good agreement with the experimental results. The selected scheme can accurately track the temperature and humidity changes in the plant growth area and verify the effectiveness of the model. This research has certain reference value for improving the control precision of plant factories and reducing the difference of crop growth. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the standard particle swarm algorithm is easy to fall into the local optimum which leads to poor image segmentation effect, a hybrid particle swarm optimization algorithm combined with simulated annealing algorithm is used to optimize the threshold selection process of multi-threshold image segmentation. The variance function between Otsu classes is used as the fitness function of the algorithm and simulated annealing algorithm is used to avoid jumping into local optimum. The experiment results show that the algorithm can effectively deal with the problem of complex plant canopy image segmentation, and can improve the image segmentation accuracy while guaranteeing the operation efficiency. It provides a theoretical basis for improving the reliability of plant growth condition evaluation and the accuracy of leaf information, with a strong engineering practicability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to monitor the real time situation of forest fire, this paper studies the image segmentation algorithm applied to the recognition of forest fire image. The forest fire image with complicated background and interference targets, using the single threshold Otsu method, the segmentation accuracy of fire image is poor. Therefore, the whale optimization algorithm to optimal threshold of multi-threshold image segmentation is studied in this paper. Whale Optimization Algorithm (WOA) is searching for the optimal solution of the high efficiency of the algorithm, which has the features of fast convergence and high accuracy. Aiming at the problem of accurately identifying and segmenting the flame area in forest fire image, WOA is used to optimize fitness degree function of Otsu, and simulation experiments are carried out in the fire image (Red Green Blue, RGB) model. The experimental results show that the proposed algorithm outperforms the traditional single threshold Otsu algorithm, can obtain more accurate segmentation threshold and higher segmentation efficiency, even has strong engineering practicability. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow response speed of underactuated AUV in horizontal plane trajectory, system heavy chattering, big error between trajectory and desired trajectory, the typical AUV movement form, plane trajectory tracking, was researched, the motion law of AUV was analyzed. The level of the error equation was established by converting the ground error variables to hull coordinate variables, underactuated AUV horizontal plane trajectory tracking controller based on backstepping control algorithm was designed, the stability of the closed-loop control system was analyzed by using Lyapunov stability theory. The effectiveness of the designed controller was verified through Matlab numerical simulation, and the stability of tracking algorithm was verified by using backstepping controller designed in trajectory tracking of AUV without interference conditions. The results indicate that the trajectory of the AUV is coincident with the desired curve, the error of horizontal planet horizontal plane trajectory rajectory is controllable, and each index is stable. [ABSTRACT FROM AUTHOR]