A dynamic object behavior model based on computational reflection is proposed.This model consists of function level and meta level,the meta objects in meta level manage the base objects and behaviors in function level...A dynamic object behavior model based on computational reflection is proposed.This model consists of function level and meta level,the meta objects in meta level manage the base objects and behaviors in function level,including dynamic binding and unbinding of base object and behavior.We implement this model with RoleJava Language,which is our self linguistic extension of the Java Language.Meta Objects are generated automatically at compile\|time,this makes the reflecton mechanism transparent to programmers.Finally an example applying this model to a banking system is presented.展开更多
This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system w...This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system with high level of variety and supports SPL development with delta modeling. The case study of this thesis is a digital library system called Library Automation and Digital Archive (LONTAR). Originally, LONTAR only uses SOAP-based web service. With ABS, LONTAR will be converted into SPL and implement another web service called REST. The motivation of this conversion of LONTAR from object oriented into SPL is because it is easier to develop system with ABS than using regular object oriented. Product definition in ABS is relatively easier than creating a new subclass and do customization to make it works well.展开更多
OBJECTIVE:To investigate whether Fuzi(Radix Aconiti Praeparata) has fewer "hot" characteristics when administered without Ganjiang(Rhizoma Zingiberis).METHODS:Differences in the thermotropism behaviors of mi...OBJECTIVE:To investigate whether Fuzi(Radix Aconiti Praeparata) has fewer "hot" characteristics when administered without Ganjiang(Rhizoma Zingiberis).METHODS:Differences in the thermotropism behaviors of mice treated either with Fuzi(Radix Aconiti Praeparata),Ganjiang(Rhizoma Zingiberis) or the combination of the two given intragastrically were investigated using the Animal Thermotropism Behavior Surveillance System.The water intake volume,oxygen consumption volume,adenosine triphosphatase(ATPase) activity,total antioxidant capacity(T-AOC) and total superoxide dismutase(T-SOD) activity were determined during the investigation.RESULTS:When Fuzi and Ganjiang were administered together,the rate at which mice remained on a warm plate("remaining rate") and the times and distances of their movement were all significantly reduced(P<0.05).Compared with the Normal group,the reduction was 55.1%,48.3% and 44.8%,while compared with the Fuzi group,the reduction was 57.6%,34.3% and 36.0%,indicating that "cold" tropism was significantly increased.Compared with the Normal and Fuzi groups,the ATPase activity and the respiratory oxygen consumption volume of the Fuzi + Ganjiang group were significantly increased(P<0.05),suggesting an improvement in energy metabolism and showing a "hot" characteristic when Fuzi and Ganjiang are present together.Additionally,the T-AOC and T-SOD activity were significantly enhanced(P<0.05).CONCLUSION:The behavior of mice tending toward "cold" tropism can be regarded as a quantitative reflection of Fuzi having fewer characteristics consistent w ith a "hot" nature when not used with Ganjiang,the functional mechanism of which may be a change in the ATPase activity in liver tissue.展开更多
The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convo...The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.展开更多
基金Supported by the National Natural Science Foundation of China(60373086)
文摘A dynamic object behavior model based on computational reflection is proposed.This model consists of function level and meta level,the meta objects in meta level manage the base objects and behaviors in function level,including dynamic binding and unbinding of base object and behavior.We implement this model with RoleJava Language,which is our self linguistic extension of the Java Language.Meta Objects are generated automatically at compile\|time,this makes the reflecton mechanism transparent to programmers.Finally an example applying this model to a banking system is presented.
文摘This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system with high level of variety and supports SPL development with delta modeling. The case study of this thesis is a digital library system called Library Automation and Digital Archive (LONTAR). Originally, LONTAR only uses SOAP-based web service. With ABS, LONTAR will be converted into SPL and implement another web service called REST. The motivation of this conversion of LONTAR from object oriented into SPL is because it is easier to develop system with ABS than using regular object oriented. Product definition in ABS is relatively easier than creating a new subclass and do customization to make it works well.
文摘在规模化绵羊养殖场中,畜禽的行为特征能够有效反映其健康状况及环境适应能力。针对传统舍饲羊只行为识别方法在羊群密度变化条件下存在的监测效率低、识别精度不足等问题提出了一种基于改进YOLO 11n模型的舍饲羊只行为识别方法。在羊圈斜上方安装2D摄像机,采集羊群的视频数据,并构建包含站立、进食、饮水和休息4种行为的舍饲羊行为数据集。在YOLO 1 1n模型基础上,结合CARAFE(Content-aware reassembly of features)上采样结构,并引入高效多尺度注意力机制EMA(Efficient multi-scale attention)与动态检测头DyHead(Dynamic feature learning for head detection)构成YOLO-CFED模型,提升羊只行为检测的特征提取与识别能力。结果表明,相较原YOLO 11n模型,改进YOLO-CFED模型在自建数据集上的性能提升显著:识别精确率(Precision)为95.6%(提升1.2个百分点)、召回率(Recall)为93%(提升0.4个百分点)、mAP@0.5为94%(提升0.3个百分点)、mAP@0.5:0.95为82.4%(提升1.3个百分点)、F1值为93.4%(提升0.9个百分点)。该方法能够有效识别羊只4种主要行为,为实现羊只行为智能化监测与健康管理提供了有力技术支持。
基金Supported by National Natural Sciences Foundation(No. 81173571)National Basic Research Program of China (No.2007CB512607)
文摘OBJECTIVE:To investigate whether Fuzi(Radix Aconiti Praeparata) has fewer "hot" characteristics when administered without Ganjiang(Rhizoma Zingiberis).METHODS:Differences in the thermotropism behaviors of mice treated either with Fuzi(Radix Aconiti Praeparata),Ganjiang(Rhizoma Zingiberis) or the combination of the two given intragastrically were investigated using the Animal Thermotropism Behavior Surveillance System.The water intake volume,oxygen consumption volume,adenosine triphosphatase(ATPase) activity,total antioxidant capacity(T-AOC) and total superoxide dismutase(T-SOD) activity were determined during the investigation.RESULTS:When Fuzi and Ganjiang were administered together,the rate at which mice remained on a warm plate("remaining rate") and the times and distances of their movement were all significantly reduced(P<0.05).Compared with the Normal group,the reduction was 55.1%,48.3% and 44.8%,while compared with the Fuzi group,the reduction was 57.6%,34.3% and 36.0%,indicating that "cold" tropism was significantly increased.Compared with the Normal and Fuzi groups,the ATPase activity and the respiratory oxygen consumption volume of the Fuzi + Ganjiang group were significantly increased(P<0.05),suggesting an improvement in energy metabolism and showing a "hot" characteristic when Fuzi and Ganjiang are present together.Additionally,the T-AOC and T-SOD activity were significantly enhanced(P<0.05).CONCLUSION:The behavior of mice tending toward "cold" tropism can be regarded as a quantitative reflection of Fuzi having fewer characteristics consistent w ith a "hot" nature when not used with Ganjiang,the functional mechanism of which may be a change in the ATPase activity in liver tissue.
文摘The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.
