为解决挖拔式木薯智能收获机械在作业过程需要快速准确地确定茎秆位置的问题,基于YOLO(You only look once)卷积神经网络提出一种检测速率更快且满足准确率的网络设计(CS-YOLO)。首先,采集并扩增木薯茎秆图像数据集,对样本集进行标注与...为解决挖拔式木薯智能收获机械在作业过程需要快速准确地确定茎秆位置的问题,基于YOLO(You only look once)卷积神经网络提出一种检测速率更快且满足准确率的网络设计(CS-YOLO)。首先,采集并扩增木薯茎秆图像数据集,对样本集进行标注与划分;然后,改进YOLOv1网络结构,利用全局平均池化替代全连接层,并适当调整网络深度和宽度,设计了一种新的网络;最后,对网络进行检测性能试验和对比分析。结果表明:新网络模型尺寸较原网络大小减少约一半,平均每张图像的检测耗时约0.015s,检测速度显著提升;当测试阶段IOU(Intersection Over Union)阈值为0.1时,模型准确率达到了99%,提出的检测方法可满足木薯收获机精准作业要求。研究可为实时、准确地检测田间木薯茎秆位置提供了一种新的思路和方法,也为仿生挖拔式木薯收获机提供了技术支撑。展开更多
Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inv...Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.展开更多
ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, th...ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.展开更多
Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many paramete...Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.展开更多
In the paper, an artificial neural network (ANN) method is put forward to optimize melting temperature control, which reveals the nonlinear relationships of tank melting temperature disturbances with secondary wind fl...In the paper, an artificial neural network (ANN) method is put forward to optimize melting temperature control, which reveals the nonlinear relationships of tank melting temperature disturbances with secondary wind flow and fuel pressure, implements dynamic feed-forward complementation and dynamic correctional ratio between air and fuel in the main control system. The application to Anhui Fuyang Glass Factory improved the control character of the melting temperature greatly.展开更多
文摘为解决挖拔式木薯智能收获机械在作业过程需要快速准确地确定茎秆位置的问题,基于YOLO(You only look once)卷积神经网络提出一种检测速率更快且满足准确率的网络设计(CS-YOLO)。首先,采集并扩增木薯茎秆图像数据集,对样本集进行标注与划分;然后,改进YOLOv1网络结构,利用全局平均池化替代全连接层,并适当调整网络深度和宽度,设计了一种新的网络;最后,对网络进行检测性能试验和对比分析。结果表明:新网络模型尺寸较原网络大小减少约一半,平均每张图像的检测耗时约0.015s,检测速度显著提升;当测试阶段IOU(Intersection Over Union)阈值为0.1时,模型准确率达到了99%,提出的检测方法可满足木薯收获机精准作业要求。研究可为实时、准确地检测田间木薯茎秆位置提供了一种新的思路和方法,也为仿生挖拔式木薯收获机提供了技术支撑。
文摘Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.
文摘ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.
文摘Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.
文摘In the paper, an artificial neural network (ANN) method is put forward to optimize melting temperature control, which reveals the nonlinear relationships of tank melting temperature disturbances with secondary wind flow and fuel pressure, implements dynamic feed-forward complementation and dynamic correctional ratio between air and fuel in the main control system. The application to Anhui Fuyang Glass Factory improved the control character of the melting temperature greatly.
