图片交换沟通系统(The Picture Exchange Communication System,PECS)是目前广泛应用于孤独症儿童康复训练的一种训练方法。PECS以图片为载体,运用图片之间的内在联系和差异,由浅入深、循序渐进地对孤独症儿童进行干预。其按照六个阶段...图片交换沟通系统(The Picture Exchange Communication System,PECS)是目前广泛应用于孤独症儿童康复训练的一种训练方法。PECS以图片为载体,运用图片之间的内在联系和差异,由浅入深、循序渐进地对孤独症儿童进行干预。其按照六个阶段进行规范训练,通过对孤独症儿童进行单次时间短、频次密的外界刺激,使他们有效感知外部刺激,减弱对固定目标的负强化,改进语言缺失,改善交际障碍。针对低年级生活语文课堂教学,运用图片交换沟通系统理论展开探析。展开更多
目的:近年来的研究数据显示,中国孤独症儿童的患病率持续上升,引起了学术界的关注。如何有效地对孤独症儿童进行干预和治疗,已成为当前亟待解决的问题。社会各界纷纷呼吁重视与关怀孤独症群体,建立更加科学、人性化和本土化的干预辅助...目的:近年来的研究数据显示,中国孤独症儿童的患病率持续上升,引起了学术界的关注。如何有效地对孤独症儿童进行干预和治疗,已成为当前亟待解决的问题。社会各界纷纷呼吁重视与关怀孤独症群体,建立更加科学、人性化和本土化的干预辅助体系。文章探讨符合我国孤独症儿童认知特点的PECS(Picture Exchange Communication System,图片交流沟通系统)行为训练图卡插图的设计表现风格,以增强图卡的有效性和吸引力。方法:通过系统的文献研究、线上调研以及专业意见收集,深入研究儿童插图的发展现状,并结合孤独症儿童的视觉偏好,总结出符合我国孤独症儿童PECS行为训练图卡插图的设计表现风格。结论:研究结果表明,扁平化、简洁具象化的插图风格更符合我国孤独症儿童的审美偏好,清晰的图形及具象化的表达方式能降低认知负担,使图像更易于理解,有助于提供稳定且可预测的视觉信息,符合孤独症儿童对简单直观视觉元素的偏好,能够显著提高孤独症儿童的学习兴趣和参与度,从而增强孤独症儿童的行为能力和沟通表达能力。设计应遵循独特的创意表达、简化和重复、注重细节等原则,从而使孤独症儿童PECS行为训练图卡满足孤独症儿童的学习、表达和发展需求。展开更多
To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to e...To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.展开更多
文摘图片交换沟通系统(The Picture Exchange Communication System,PECS)是目前广泛应用于孤独症儿童康复训练的一种训练方法。PECS以图片为载体,运用图片之间的内在联系和差异,由浅入深、循序渐进地对孤独症儿童进行干预。其按照六个阶段进行规范训练,通过对孤独症儿童进行单次时间短、频次密的外界刺激,使他们有效感知外部刺激,减弱对固定目标的负强化,改进语言缺失,改善交际障碍。针对低年级生活语文课堂教学,运用图片交换沟通系统理论展开探析。
文摘目的:近年来的研究数据显示,中国孤独症儿童的患病率持续上升,引起了学术界的关注。如何有效地对孤独症儿童进行干预和治疗,已成为当前亟待解决的问题。社会各界纷纷呼吁重视与关怀孤独症群体,建立更加科学、人性化和本土化的干预辅助体系。文章探讨符合我国孤独症儿童认知特点的PECS(Picture Exchange Communication System,图片交流沟通系统)行为训练图卡插图的设计表现风格,以增强图卡的有效性和吸引力。方法:通过系统的文献研究、线上调研以及专业意见收集,深入研究儿童插图的发展现状,并结合孤独症儿童的视觉偏好,总结出符合我国孤独症儿童PECS行为训练图卡插图的设计表现风格。结论:研究结果表明,扁平化、简洁具象化的插图风格更符合我国孤独症儿童的审美偏好,清晰的图形及具象化的表达方式能降低认知负担,使图像更易于理解,有助于提供稳定且可预测的视觉信息,符合孤独症儿童对简单直观视觉元素的偏好,能够显著提高孤独症儿童的学习兴趣和参与度,从而增强孤独症儿童的行为能力和沟通表达能力。设计应遵循独特的创意表达、简化和重复、注重细节等原则,从而使孤独症儿童PECS行为训练图卡满足孤独症儿童的学习、表达和发展需求。
文摘To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.
