Two-dimensional mesh-based motion tracking preserves neighboring relations (through connectivity of the mesh) and also allows warping transformations between pairs of frames;thus, it effectively eliminates blocking ar...Two-dimensional mesh-based motion tracking preserves neighboring relations (through connectivity of the mesh) and also allows warping transformations between pairs of frames;thus, it effectively eliminates blocking artifacts that are common in motion compensation by block matching. However, available uniform 2-D mesh model enforces connec-tivity everywhere within a frame, which is clearly not suitable across occlusion boundaries. To overcome this limitation, BTBC (background to be covered) detection and MF (model failure) detection algorithms are being used. In this algorithm, connectivity of the mesh elements (patches) across covered and uncovered region boundaries are broken. This is achieved by allowing no node points within the background to be covered and refining the mesh structure within the model failure region at each frame. We modify the occlusion-adaptive, content-based mesh design and forward tracking algorithm used by Yucel Altunbasak for selection of points for triangular 2-D mesh design. Then, we propose a new triangulation procedure for mesh structure and also a new algorithm to justify connectivity of mesh structure after motion vector estimation of the mesh points. The modified content-based mesh is adaptive which eliminates the necessity of transmission of all node locations at each frame.展开更多
针对可见光通信信号在传输中易受信道环境和背景噪声干扰等因素影响调制格式识别精度的问题,提出一种用于可见光通信信号调制格式识别的改进YOLOv5s(You Only Look Once)算法。首先,通过YOLOv5s算法网络输入端引入Mixup数据增强方式,将...针对可见光通信信号在传输中易受信道环境和背景噪声干扰等因素影响调制格式识别精度的问题,提出一种用于可见光通信信号调制格式识别的改进YOLOv5s(You Only Look Once)算法。首先,通过YOLOv5s算法网络输入端引入Mixup数据增强方式,将其与原网络中的Mosaic数据增强方式相结合,提升网络的鲁棒性,并增强算法在不同调制格式信号间的泛化能力;其次,将自适应空间特征融合(ASFF)引入到Neck网络中,充分提取不同层次的特征,提高检测精度。实验结果表明,在混合信噪比条件下,所提改进算法的平均精度均值(mAP)达到了0.903,比原始YOLOv5s算法提升了0.7%,且在信噪比为20 dB时mAP高达0.993。展开更多
针对卫星编队重构问题,提出了一种具有随机不确定性、推力约束和避障能力的随机模型预测控制(Stochastic model predictive control,SMPC)方法。由于不确定性的概率是有限的,足以违反约束条件,给随机不确定性的考虑带来了巨大的挑战。S...针对卫星编队重构问题,提出了一种具有随机不确定性、推力约束和避障能力的随机模型预测控制(Stochastic model predictive control,SMPC)方法。由于不确定性的概率是有限的,足以违反约束条件,给随机不确定性的考虑带来了巨大的挑战。SMPC利用概率不确定性描述来定义机会约束,通过引入约束违反概率,为处理约束的随机效应提供了一种有效的方法,且上述方法仅需知道不确定性的均值和方差。将SMPC问题转化为确定性凸问题,提出了一种切比雪夫不等式,将机会约束转化为确定性约束。最后对卫星编队重构问题进行了数值模拟,验证了SMPC算法的可行性和优越性。展开更多
文摘Two-dimensional mesh-based motion tracking preserves neighboring relations (through connectivity of the mesh) and also allows warping transformations between pairs of frames;thus, it effectively eliminates blocking artifacts that are common in motion compensation by block matching. However, available uniform 2-D mesh model enforces connec-tivity everywhere within a frame, which is clearly not suitable across occlusion boundaries. To overcome this limitation, BTBC (background to be covered) detection and MF (model failure) detection algorithms are being used. In this algorithm, connectivity of the mesh elements (patches) across covered and uncovered region boundaries are broken. This is achieved by allowing no node points within the background to be covered and refining the mesh structure within the model failure region at each frame. We modify the occlusion-adaptive, content-based mesh design and forward tracking algorithm used by Yucel Altunbasak for selection of points for triangular 2-D mesh design. Then, we propose a new triangulation procedure for mesh structure and also a new algorithm to justify connectivity of mesh structure after motion vector estimation of the mesh points. The modified content-based mesh is adaptive which eliminates the necessity of transmission of all node locations at each frame.
文摘针对可见光通信信号在传输中易受信道环境和背景噪声干扰等因素影响调制格式识别精度的问题,提出一种用于可见光通信信号调制格式识别的改进YOLOv5s(You Only Look Once)算法。首先,通过YOLOv5s算法网络输入端引入Mixup数据增强方式,将其与原网络中的Mosaic数据增强方式相结合,提升网络的鲁棒性,并增强算法在不同调制格式信号间的泛化能力;其次,将自适应空间特征融合(ASFF)引入到Neck网络中,充分提取不同层次的特征,提高检测精度。实验结果表明,在混合信噪比条件下,所提改进算法的平均精度均值(mAP)达到了0.903,比原始YOLOv5s算法提升了0.7%,且在信噪比为20 dB时mAP高达0.993。
文摘针对卫星编队重构问题,提出了一种具有随机不确定性、推力约束和避障能力的随机模型预测控制(Stochastic model predictive control,SMPC)方法。由于不确定性的概率是有限的,足以违反约束条件,给随机不确定性的考虑带来了巨大的挑战。SMPC利用概率不确定性描述来定义机会约束,通过引入约束违反概率,为处理约束的随机效应提供了一种有效的方法,且上述方法仅需知道不确定性的均值和方差。将SMPC问题转化为确定性凸问题,提出了一种切比雪夫不等式,将机会约束转化为确定性约束。最后对卫星编队重构问题进行了数值模拟,验证了SMPC算法的可行性和优越性。