开发环境OOPascal的设计和实现

ＮＤＯＯＰ是Ｐａｓｃａｌ面向对象扩充语言，我们进一步为ＮＤＯＯＰ设计和实现了支撑环境ＯＯＰａｓｃａｌ系统，该系统由编辑器、浏览器、转换器、编译器组成。该文阐述了ＯＯＰａｓｃａｌ设计和实现的主要思想，并对该系统的功能、特点、实现技术等作了简要介绍。     

适合Windows的网络设计工具

适合Ｗｉｎｄｏｗｓ的网络设计工具利用面向对象的技术．ＮＣｔＳＵｉｔＳ：：ＰｔｏｆｅＳＳＩＯＤ．ａｌＤｅｓｉｇｎ（价格’９５Ｍ元）建立有关一个单位的网络设备和媒体的智能模型．该软件包支撑ＬＡＮＳ、ＷＡＮＳ及ＭＡＮＳ的逻辑和物理描述；允许对从布局技术到收...     

并行计算模型及其算法设计

并行计算模型及其算法设计李晓梅，窦勇（国防科技大学计算机系）ＰＡＲＡＬＬＥＬＣＯＭＰＵＴＡＴＩＯＮＭＯＤＥＬＳＡＮＤＬＧＯＲＩＴＨＭＤＥＳＩＧＮ￥ＬｉＸｉａｏｍｅｉ；ＤｏｎＹｏｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅＣｈａｎｇｓｈ...     

传统程序设计语言的面向对象扩充的若干技术

本文论述用转换方式实现传统程序设计语言的面向对象扩充，给出了一组关键技术以解决由子类型多态和动态定连所引出的问题，其基本思想是将类和对象类型分开处理，从而避免转换过程中的重复工作．这些技术具有适用性强、功效高的特点，并且在ＰＡＳＣＡＬ语言的面向对象扩充ＮＤＯＯＰ和ＭＯＤＵＬＡ－２语言的面向对象扩充ＮＤＯＯＭ的实现中被采用，效果令人满意.     

JOHN A.GREGORY AND HIS PUBLICATIONS

ＪＯＨＮＡ．ＧＲＥＧＯＲＹＡＮＤＨＩＳＰＵＢＬＩＣＡＴＩＯＮＳ￥ＱｕＲｕｉｂｉｎ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｌｉｃｓＮａｔｉｏｎａｌＵｎｉｖｅｒｓｉｔｙｏｆＳｉｎｇａｐｏｒｅ１０ＫｅｎｔＲｉｄｇｅＣｒｅｓｃｅｎｔＳｉｎｇａｐｏｒｅ．１１９...     

一种基于辅助元素的交互式参数化绘图工具

本文介绍了一种新的交互式参数化绘图工具ＯＰＤＴ（Ｏｂｊｅｃｔ－ｏｒｉｅｎｔｅｄ Ｐａｒａｍｅｔｒｉｃａｌ Ｄｒａｆｔｉｎｇ Ｔｏｏｌ）．ＯＰＤＴ通过定义基本几何元素之间的约束关系－－辅助元素来实现交互式参数化绘图。ＯＰＤＴ具有以下特点：使用ＯＰＤＴ建立的参数化模型与设计人员的意图高度一致；绘图过程符合设计人员打样习惯，绘图效率高；尺寸驱动修改图形的算法执行速度快；多个视图保持一致性。此外，ＯＰＤＴ     

THE INFORMATION SUPERHIGHIGHWAY

ＴＨＥＩＮＦＯＲＭＡＴＩＯＮＳＵＰＥＲＨＩＧＨＷＡＹＴａｍｉＺｈｕ（ＢｏｒｌａｎｄｉｎｔｅｒｎａｔｉｏｎａｌＳｃｏｔｔｓＶａｌｌｅｙ，Ｃａｌｉｆｏｒｎｉａ）Ｔｈｅｍｅｄｉａｅｘｃｉｔｅｍｅｎｔｗｉｔｈｉｎｔｈｅｔｅｃｈｎｉｃａｌｃｏｍｍｕｎｉｔｙｗｏ...     

Delphi中数据库通用字段的应用

ＤｅｌＰｈｉ是Ｉｎｐｒｉｓ公司出品的面向对象的编程工具,它采用Ｂｏｒｌａｎｄ公司的ＢＤＥ作为数据库的通用接口,具有功能强大的数据库接口功能,同时由于它是面向对象的Ｐａｓｃａｌ语言,所以它与一般的Ｓｃｒｉｐｔ语言相比具有更灵活更强大的编程功能。下文主要讨论如何利用Ｄｅｌｐｈｉ中的ＴＢｌｏｂＳｔｒｅａｍ对象及ＴＯｌｅＣｏｎｔａｉｎｅｒ控件实现程序中对数据库通用字段的使用。在Ｄｅｌｐｈｉ中提供了２４种字段对象以适应各种数据库应用中的不同字段类型,其中ＴＢｌｏｂＦｉｅｌｄ字段对象是为数据库中的通用字段处…     

AN OBJECT-ORIENTED DISTRIBUTED MULTIMEDIA EDITOR

ＡＮＯＢＪＥＣＴ－ＯＲＩＥＮＴＥＤＤＩＳＴＲＩＢＵＴＥＤＭＵＬＴＩＭＥＤＩＡＥＤＩＴＯＲ￥ＸｕＤａｎ（ＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅＤｅｐａｒｔｍｅｎｔＹｕｎｎａｎＵｎｉｖｅｒｓｉｔｙＫｕｎｍｉｎｇ，ＹｕｎｎａｎＰｒｏｖｉｎｃｅ，６５００９１Ｐ．Ｒ．...     

COMPUTER AIDED DESIGN OF AIRCRAFT CABIN LAYOUT

ＣＯＭＰＵＴＥＲＡＩＤＥＤＤＥＳＩＧＮＯＦＡＩＲＣＲＡＦＴＣＡＢＩＮＬＡＹＯＵＴ￥ＹａｎｇＧｕｏｎｇ；ＨｕａｎｇＹｕｙｕ（ＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅｒｏｎａｕｔｉｃｓａｎｄＡｓｔｒｏｎａｕｔｉｃｓＢｅｉｊｉｎｇ．１０００８３）Ｐ．Ｒ．...     

Operating System Models in a Concurrent Pascal Environment: Complexity and Performance Considerations

Empirical observations of computer operating systems have shown that operating systems are designed with one of two object oriented strategies: a process or a monitor oriented approach. This paper compares the two design approaches in a Concurrent Pascal environment. Resource manager programs that are implemented in conformity with each model are evaluated using software complexity measures and program performance measures. The average complexity of resource manager processes is 94 percent larger than the average complexity of resource manager monitors. The runtime synchronization overhead of the process model program is two-eight times higher than that of its counterpart.     

一种基于SSD改进的目标检测算法

SSD(Single Shot MultiBox Detector)是一种基于深度学习的目标检测算法,它作为当前最为主流的检测算法之一,在极大地提高检测速度的同时,还能保证一定的检测精度,但是仍难以满足实际应用的需求。本文在SSD模型的基础上,引入注意力机制,提出一种基于SSD改进的目标检测算法。注意力机制能够有效地提高卷积神经网络对图片特征的提取能力,从而进一步提高算法的检测精度。改进后的算法在Pascal VOC数据集上进行对比试验。实验结果表明,改进后的模型在Pascal VOC2007测试集上的检测精度达到78.5% mAP（mean Average Precision）,比改进前提高4.2个百分点,在Pascal VOC2012测试集上的检测精度达到77.1% mAP,比改进前提高4.7个百分点。     

Single shot object detection with refined feature

Object classification and localization are two significant aspects of object detector based on the Single Shot MultiBox Detector (SSD). In general, the more feature maps there are, the better the object classification performance will be. However, when the information of excessive feature maps are sparse and unnecessary, the performance of object detection is slightly improved or maybe precisely opposite, which is instead harmful to the production of object localization. The performance of object detectors is not only related to the number of feature maps but also relies partly on the bounding box regression and Non-Maximum Suppression (NMS). In this paper, a detector is constructed based on SSD, called Detection with Refined Feature (DRF), involving center map and scale map, the detection loss is reshaped. Our motivation is to improve the accuracy of classification and localization by searching for central points and predicting the scales of the object points. Center map is used to predict the Intersection over Union (IoU) between the prediction box and ground truth box, while scale map considers the relationships among the different scales. Experimental results on both Pascal VOC and MS COCO 2014 instance datasets demonstrate the effectiveness of DRF. Using Darknet53, we achieve an 86.4% mean Average Precision (mAP) on Pascal VOC2007 and an 87.4% mAP on Pascal VOC2007 and VOC2012. On MS COCO, the DRF with ResNet50 still achieves moderate improvement.

     

Towards reliable object detection in noisy images

Image noise is a common problem frequently caused by insufficient lighting, low-quality cameras, image compression and other factors. While low image quality is expected to degrade results of visual recognition, most of the current methods and benchmarks for object recognition, such as Pascal Visual Object Classes Challenge and Microsoft Common Objects in Context Challenge, focus on relatively high-quality images. Meanwhile, object recognition in noisy images is a common problem in surveillance and other domains. In this work we address object detection in noisy images and propose a novel low-cost method for image denoising. When combined with the standard Deformable Parts Model and Regions with Convolutional Neural Network object detectors, our method shows improvements of object detection under varying levels of image noise. We present a comprehensive experimental evaluation and compare our method to other denoising techniques as well as to standard detectors re-trained on noisy images. Results are presented for the common Pascal Visual Object Classes benchmark for object detection and KAIST Multispectral Pedestrian Detection Benchmark with the real noise presence in night images.     

基于可变权重损失函数和难例挖掘模块的Faster R-CNN改进算法

基于深度卷积神经网络的目标检测算法已成为目标检测领域中的研究热点,它包括基于区域提议的两阶段目标检测算法和基于位置回归的一阶段目标检测算法。Faster R-CNN是两阶段目标检测的典型算法之一,但是,训练数据集中简单样本-〖KG-*8〗难分样本数量不平衡,以及样本数据的类间不平衡,都是影响Faster R-CNN检测精度的重要原因。本文提出一种基于可变权重损失函数Focal Loss和难例挖掘模块的改进Faster R-CNN算法。具体地,在网络的分类部分引入Focal Loss函数,通过权重调节样本数据的类间不平衡,改善简单样本-〖KG-*8〗难分样本的数量不平衡;同时,修改网络结构,引入难例挖掘模块,进一步平衡简单样本-〖KG-*8〗难分样本的数量,提高网络的检测性能。本文采用不同数据集,不同基础网络来测试提出的算法性能。实验结果表明,在VGG-16基础网络下,本文算法在Pascal VOC 2007数据集上平均检测精度较原算法提高了0.9个百分点,在Pascal VOC 07+12数据集上提高了1.7个百分点;在Res-101基础网络上,在Pascal VOC 2007数据集上平均检测精度较原算法提高了1.3个百分点,在Pascal VOC 07+12数据集上提高了1.5个百分点。     

Delphi语言中的面向对象特征探讨

Delphi作为一种有力的面向对象的开发工具,,以Pascal语言为基础,引入面向对象的类机制,从而实现了封装、继承和多态。本文举例论述了这种机制的优越性。     

面向Agent与面向对象的软件技术

面向Agent与面向对象有很多差异,但也有许多相似之处扣联系。吸收扣借鉴面向对象的成功经验扣成熟技术,对面向Agent的研究扣发展十分有利。本文比较了Agent与对象的概念扣模型,以及面向Agent扣面向对象的运行机制扣通信机制,分析了面向对象的不足扣面向Agent的优势,指出了面向Agent是未来软件的发展方向。     

一种面向对象环境的形式化模型

首先提出了作为面向对象环境中最小元素的对象元的概念，突出了面向对象环境的智能化特征，接着给出了一整套关于对象（对象元、复杂对象（结构模型的形式定义，为创建面向对象的信息存储环境和操作运行环境奠定了理论基础。