The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
目的在智能驾驶领域,准确预测行人穿越行为对于确保车辆和行人安全至关重要。方法设计了一种结合多种计算机视觉技术的行人穿越行为预测模型,该模型通过分析行人的位置、姿态、动作及环境特征来准确判断行人意图。为了增强模型对不同距...目的在智能驾驶领域,准确预测行人穿越行为对于确保车辆和行人安全至关重要。方法设计了一种结合多种计算机视觉技术的行人穿越行为预测模型,该模型通过分析行人的位置、姿态、动作及环境特征来准确判断行人意图。为了增强模型对不同距离行人的感知能力,采用了不同尺度放大的预处理和数据滤波平滑的后处理技术。提出了先条件后预测(Predict after Condition,PAC)两阶段方法,以实现更为有效的行人穿越预测。结果基于JAAD数据集的测试结果表明:所提模型平均精度达89.31%,相较于传统单阶段方法提升了8.76%。结论特征重要度分析进一步表明:加入路面面积特征后,预测准确率从68.43%显著提升至85.06%,强调了行人位置与路面轮廓关系在行人穿越行为研究中的重要性。对降低人车碰撞事故,提高智能驾驶车辆的安全性具有重要意义。展开更多
为应对医疗数据面临的内外部爬取威胁,构建了一套基于用户和实体行为分析(User and Entity Behavior Analytics,UEBA)的协同防护体系。该体系通过建立用户与实体的动态行为基线,并融合流量异常、异地登录、高频访问等多维度特征,实现了...为应对医疗数据面临的内外部爬取威胁,构建了一套基于用户和实体行为分析(User and Entity Behavior Analytics,UEBA)的协同防护体系。该体系通过建立用户与实体的动态行为基线,并融合流量异常、异地登录、高频访问等多维度特征,实现了对外部恶意数据爬取与内部数据窃取行为的有效识别。在医院OA、互联网挂号及医院信息系统(Hospital Information System, HIS)中的实践证明,该体系成功识别了多起外部渗透与内部违规事件,显著增强了系统对数据泄露风险的主动防御能力。证明基于UEBA的防护体系可系统化地应对医疗场景下的数据爬取威胁,为智慧医院信息安全建设提供可复制、可推广的实践路径。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
文摘目的在智能驾驶领域,准确预测行人穿越行为对于确保车辆和行人安全至关重要。方法设计了一种结合多种计算机视觉技术的行人穿越行为预测模型,该模型通过分析行人的位置、姿态、动作及环境特征来准确判断行人意图。为了增强模型对不同距离行人的感知能力,采用了不同尺度放大的预处理和数据滤波平滑的后处理技术。提出了先条件后预测(Predict after Condition,PAC)两阶段方法,以实现更为有效的行人穿越预测。结果基于JAAD数据集的测试结果表明:所提模型平均精度达89.31%,相较于传统单阶段方法提升了8.76%。结论特征重要度分析进一步表明:加入路面面积特征后,预测准确率从68.43%显著提升至85.06%,强调了行人位置与路面轮廓关系在行人穿越行为研究中的重要性。对降低人车碰撞事故,提高智能驾驶车辆的安全性具有重要意义。
文摘为应对医疗数据面临的内外部爬取威胁,构建了一套基于用户和实体行为分析(User and Entity Behavior Analytics,UEBA)的协同防护体系。该体系通过建立用户与实体的动态行为基线,并融合流量异常、异地登录、高频访问等多维度特征,实现了对外部恶意数据爬取与内部数据窃取行为的有效识别。在医院OA、互联网挂号及医院信息系统(Hospital Information System, HIS)中的实践证明,该体系成功识别了多起外部渗透与内部违规事件,显著增强了系统对数据泄露风险的主动防御能力。证明基于UEBA的防护体系可系统化地应对医疗场景下的数据爬取威胁,为智慧医院信息安全建设提供可复制、可推广的实践路径。
