The Group II chaperonin from Thermoplasma acidophilum was added to the in vitro amyloid fibrillation reaction of yeast Sup35NM protein to assess its effects. By measuring the formation of Sup35NM fibrils in real time ...The Group II chaperonin from Thermoplasma acidophilum was added to the in vitro amyloid fibrillation reaction of yeast Sup35NM protein to assess its effects. By measuring the formation of Sup35NM fibrils in real time using the fluorescent dye Thioflavin T, we found that the addition of T. acidophilum-cpn α16, α1, and β1 proteins suppressed fibril formation. Addition of a 0.1 molar-equivalent T. acidophilum-cpn α16 relative to Sup35NM prolonged the initial lag-time of fibril formation and decreased the rate of fibril extension. Addition of 1 or 3 molar-equivalents of T. acidophilum-cpn monomers also produced a similar effect. Delayed addition of these chaperonins after the initial lag phase did not suppress fibril formation. Interestingly, these effects were also observed upon adding only the apical domain segments of α and β-subunits, and we also found that deletion of the helical protrusion in the apical domain of these segments led to an abolishment of the suppression effects. A synthetic peptide whose sequence corresponded to the helical protrusion also displayed a suppression effect, which indicated that archaeal group II chaperonin binds to Sup35NM through the helical protrusion of the apical domain. These findings suggest that group II chaperonin might be actively involved in suppressing amyloid fibril formation, in addition to acting as a protein folding assistant.展开更多
任务中全局注意力在长距离视频序列上注意力值分布的方差较大,生成关键帧的重要性分数偏差较大,且时间序列节点边界值缺乏长程依赖导致的片段语义连贯性较差等问题,通过改进注意力模块,采用分段局部自注意力和全局自注意力机制相结合来...任务中全局注意力在长距离视频序列上注意力值分布的方差较大,生成关键帧的重要性分数偏差较大,且时间序列节点边界值缺乏长程依赖导致的片段语义连贯性较差等问题,通过改进注意力模块,采用分段局部自注意力和全局自注意力机制相结合来获取局部和全局视频序列关键特征,降低注意力值的方差。同时通过并行地引入双向门控循环网络(bidirectional recurrent neural network,BiGRU),二者的输出分别输入到改进的分类回归模块后再将结果进行加性融合,最后利用非极大值抑制(non-maximum suppression,NMS)和核时序分割方法(kernel temporal segmentation,KTS)筛选片段并分割为高质量代表性镜头,通过背包组合优化算法生成最终摘要,从而提出一种结合多尺度注意力机制和双向门控循环网络的视频摘要模型(local and global attentions combine with the BiGRU,LG-RU)。该模型在TvSum和SumMe的标准和增强数据集上进行了对比试验,结果表明该模型取得了更高的F-score,证实了该视频摘要模型保持高准确率的同时可鲁棒地对视频完成摘要。展开更多
针对目前电动车头盔小目标检测的精度低、鲁棒性差,相关系统不完善等问题,提出了基于改进YOLOv5s的电动车头盔检测算法。所提算法引入卷积块注意力模块(CBAM)和协调注意力(CA)模块,采用改进的非极大值抑制(NMS),即DIoU-NMS(Distance Int...针对目前电动车头盔小目标检测的精度低、鲁棒性差,相关系统不完善等问题,提出了基于改进YOLOv5s的电动车头盔检测算法。所提算法引入卷积块注意力模块(CBAM)和协调注意力(CA)模块,采用改进的非极大值抑制(NMS),即DIoU-NMS(Distance Intersection over Union-Non Maximum Suppression);同时增加多尺度特征融合检测,并结合密集连接网络改善特征提取效果;最后,建立了电动车驾驶人头盔检测系统。在自建的电动车头盔佩戴数据集上,当交并比(IoU)为0.5时,所提算法的平均精度均值(mAP)比原始YOLOv5s提升了7.1个百分点,召回率(Recall)提升了1.6个百分点。实验结果表明,所提改进的YOLOv5s算法更能满足在实际情况中对电动车及驾驶员头盔的检测精度要求,一定程度上降低了电动车交通事故的发生率。展开更多
Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the...Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallengin...Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.展开更多
文摘The Group II chaperonin from Thermoplasma acidophilum was added to the in vitro amyloid fibrillation reaction of yeast Sup35NM protein to assess its effects. By measuring the formation of Sup35NM fibrils in real time using the fluorescent dye Thioflavin T, we found that the addition of T. acidophilum-cpn α16, α1, and β1 proteins suppressed fibril formation. Addition of a 0.1 molar-equivalent T. acidophilum-cpn α16 relative to Sup35NM prolonged the initial lag-time of fibril formation and decreased the rate of fibril extension. Addition of 1 or 3 molar-equivalents of T. acidophilum-cpn monomers also produced a similar effect. Delayed addition of these chaperonins after the initial lag phase did not suppress fibril formation. Interestingly, these effects were also observed upon adding only the apical domain segments of α and β-subunits, and we also found that deletion of the helical protrusion in the apical domain of these segments led to an abolishment of the suppression effects. A synthetic peptide whose sequence corresponded to the helical protrusion also displayed a suppression effect, which indicated that archaeal group II chaperonin binds to Sup35NM through the helical protrusion of the apical domain. These findings suggest that group II chaperonin might be actively involved in suppressing amyloid fibril formation, in addition to acting as a protein folding assistant.
文摘任务中全局注意力在长距离视频序列上注意力值分布的方差较大,生成关键帧的重要性分数偏差较大,且时间序列节点边界值缺乏长程依赖导致的片段语义连贯性较差等问题,通过改进注意力模块,采用分段局部自注意力和全局自注意力机制相结合来获取局部和全局视频序列关键特征,降低注意力值的方差。同时通过并行地引入双向门控循环网络(bidirectional recurrent neural network,BiGRU),二者的输出分别输入到改进的分类回归模块后再将结果进行加性融合,最后利用非极大值抑制(non-maximum suppression,NMS)和核时序分割方法(kernel temporal segmentation,KTS)筛选片段并分割为高质量代表性镜头,通过背包组合优化算法生成最终摘要,从而提出一种结合多尺度注意力机制和双向门控循环网络的视频摘要模型(local and global attentions combine with the BiGRU,LG-RU)。该模型在TvSum和SumMe的标准和增强数据集上进行了对比试验,结果表明该模型取得了更高的F-score,证实了该视频摘要模型保持高准确率的同时可鲁棒地对视频完成摘要。
文摘针对目前电动车头盔小目标检测的精度低、鲁棒性差,相关系统不完善等问题,提出了基于改进YOLOv5s的电动车头盔检测算法。所提算法引入卷积块注意力模块(CBAM)和协调注意力(CA)模块,采用改进的非极大值抑制(NMS),即DIoU-NMS(Distance Intersection over Union-Non Maximum Suppression);同时增加多尺度特征融合检测,并结合密集连接网络改善特征提取效果;最后,建立了电动车驾驶人头盔检测系统。在自建的电动车头盔佩戴数据集上,当交并比(IoU)为0.5时,所提算法的平均精度均值(mAP)比原始YOLOv5s提升了7.1个百分点,召回率(Recall)提升了1.6个百分点。实验结果表明,所提改进的YOLOv5s算法更能满足在实际情况中对电动车及驾驶员头盔的检测精度要求,一定程度上降低了电动车交通事故的发生率。
基金the National Grid Corporation Headquarters Science and Technology Project:Key Technology Research,Equipment Development and Engineering Demonstration of Artificial Smart Drived Electric Vehicle Smart Travel Service(No.52020118000G).
文摘Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
基金the Directorate General of Higher Education,Research,and Technology,Republic of Indonesia under the grand number 3/E1/KP.PTNBH/2021.
文摘Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.
