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PowerShell；抽象语法树；混淆和解混淆；恶意脚本检测 1

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交互式图像分割；多元自适应回归样条；集成学习；薄板样条回归；半监督学习；支持向量回归 1

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Optimal CNN-based semantic segmentation model of cutting slope images

Mansheng LIN; Shuai TENG; Gongfa CHEN; Jianbing LV; Zhongyu HAO

《结构与土木工程前沿（英文）》 2022年 第16卷 第4期   页码 414-433 doi: 10.1007/s11709-021-0797-6

摘要： This paper utilizes three popular semantic segmentation networks, specifically DeepLab v3+, fully convolutional network (FCN), and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection. The elements of cutting slope images are divided into 7 categories. In order to determine the best algorithm for pixel level classification of cutting slope images, the networks are compared from three aspects: a) different neural networks, b) different feature extractors, and c) 2 different optimization algorithms. It is found that DeepLab v3+ with Resnet18 and Sgdm performs best, FCN 32s with Sgdm takes the second, and U-Net with Adam ranks third. This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization. Results show that the contour generated by DeepLab v3+ (combined with Resnet18 and Sgdm) is closest to the ground truth, while the resulting contour of U-Net (combined with Adam) is closest to the input images.

关键词： slope damage     image recognition     semantic segmentation     feature map     visualizations    

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Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

《结构与土木工程前沿（英文）》 2023年 第17卷 第5期   页码 732-744 doi: 10.1007/s11709-023-0965-y

摘要： An algorithm based on deep semantic segmentation called LC-DeepLab is proposed for detecting the trends and geometries of cracks on tunnel linings at the pixel level. The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed of conventional models in complex backgrounds. The novel algorithm is based on the DeepLabv3+ network framework. A lighter backbone network was used for feature extraction. Next, an efficient shallow feature fusion module that extracts crack features across pixels is designed to improve the edges of crack segmentation. Finally, an efficient attention module that significantly improves the anti-interference ability of the model in complex backgrounds is validated. Four classic semantic segmentation algorithms (fully convolutional network, pyramid scene parsing network, U-Net, and DeepLabv3+) are selected for comparative analysis to verify the effectiveness of the proposed algorithm. The experimental results show that LC-DeepLab can accurately segment and highlight cracks from tunnel linings in complex backgrounds, and the accuracy (mean intersection over union) is 78.26%. The LC-DeepLab can achieve a real-time segmentation of 416 × 416 × 3 defect images with 46.98 f/s and 21.85 Mb parameters.

关键词： tunnel engineering     crack segmentation     fast detection     DeepLabv3+     feature fusion     attention mechanism    

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基于分布式表示语义组合的查询子主题挖掘 None

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

《信息与电子工程前沿（英文）》 2018年 第19卷 第11期   页码 1409-1419 doi: 10.1631/FITEE.1601476

摘要： 推断查询意图对于信息检索具有重要意义。查询子主题挖掘旨在找到可能的子主题，用于表示给定查询的潜在意图。由于查询较短，子主题挖掘具有挑战性。学习词或句子分布式表示推动和影响了很多领域的发展。然而，没有清晰的结论表明该分布式表示是否有助于应对查询子主题挖掘面临的挑战。提出并比较利用分布式表示的语义组合进行查询子主题挖掘。采用两种分布式表示策略：能学习任意长度文本分布式表示的段落向量（paragraph vector）以及词向量的语义组合。探索了语义组合策略和数据类型对查询表示的影响。在国家信息学研究所信息获取研究试验平台和社区（National Institute of Informatics Testbeds and Community for Information Access Research，NTCIR）提供的公开数据集上的实验结果表明，与传统语义表示相比，分布式语义表示能获得更优查询子主题挖掘性能。文中做了更多深入探讨。

关键词： 查询子主题挖掘；查询意图；分布式表示；语义组合    

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Digital twin-enabled smart facility management: A bibliometric review

《工程管理前沿（英文）》 doi: 10.1007/s42524-023-0254-4

摘要： In recent years, the architecture, engineering, construction, and facility management (FM) industries have been applying various emerging digital technologies to facilitate the design, construction, and management of infrastructure facilities. Digital twin (DT) has emerged as a solution for enabling real-time data acquisition, transfer, analysis, and utilization for improved decision-making toward smart FM. Substantial research on DT for FM has been undertaken in the past decade. This paper presents a bibliometric analysis of the literature on DT for FM. A total of 248 research articles are obtained from the Scopus and Web of Science databases. VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence, citation, and co-authorship networks; furthermore, the research topics, authors, sources, and countries contributing to the use of DT for FM are identified. The findings show that the current research of DT in FM focuses on building information modeling-based FM, artificial intelligence (AI)-based predictive maintenance, real-time cyber–physical system data integration, and facility lifecycle asset management. Several areas, such as AI-based real-time asset prognostics and health management, virtual-based intelligent infrastructure monitoring, deep learning-aided continuous improvement of the FM systems, semantically rich data interoperability throughout the facility lifecycle, and autonomous control feedback, need to be further studied. This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape, state-of-the-art research trends, and future needs with regard to DT in FM.

关键词： digital twin     building information modeling     facility management     semantic interoperability     artificial intelligence     intelligent monitoring     autonomous control feedback    

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迈向6G智简网络——基于语义通信的网络新范式 Article

张平, 许文俊, 高晖, 牛凯, 许晓东, 秦晓琦, 袁彩霞, 秦志金, 赵海涛, 魏急波, 张钫炜

《工程（英文）》 2022年 第8卷 第1期   页码 60-73 doi: 10.1016/j.eng.2021.11.003

摘要： wisdom-evolutionary and primitive-concise network, WePCN）的新途径——以深入挖掘信息的语义层次内涵为主线，首先提出了全新的语义表征框架模型，即语义基（semanticbase），进而构建了面向“智简”6G的“一面-三层”智能高效语义通信（intelligent and efficient semantic communication

关键词： 第六代（6G）移动通信     语义信息     语义通信     智能通信    

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Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

《机械工程前沿（英文）》 2016年 第11卷 第3期   页码 311-315 doi: 10.1007/s11465-016-0376-z

摘要：

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set Tz according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler’s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample of pre-training set Tz′. The training set Tz increased to Tz+1 by Tz′ if Tz′ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from T0 to T5 by itself.

关键词： multi-color space     k-nearest neighbor algorithm (k-NN)     self-learning     surge test    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

Wenxuan CAO; Junjie LI

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1378-1396 doi: 10.1007/s11709-022-0855-8

摘要： It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater structures. Research to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale cracks. In this paper, a large-scale underwater crack examination method is proposed based on image stitching and segmentation. In addition, a purpose of this paper is to design a new convolution method to segment underwater images. An improved As-Projective-As-Possible (APAP) algorithm was designed to extract and stitch keyframes from videos. The graph convolutional neural network (GCN) was used to segment the stitched image. The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has great potential of application in image segmentation and underwater image processing. The result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

关键词： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutional neural network    

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基于先验形状和局部统计的血管影像图像分割方法 Research Articles

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO

《信息与电子工程前沿（英文）》 2019年 第20卷 第8期   页码 1099-1108 doi: 10.1631/FITEE.1800129

摘要： 快速准确地从医学图像中提取血管结构是许多临床医疗的基础。然而，大多数血管分割方法忽略了分割结果中孤立点和冗余点的存在。本文提出一种基于先验形状和局部统计的血管分割方法，能有效消除异常值并精确分割粗细血管。首先，定义了一种改进的血管滤波器，用于量化每个体素属于管状结构的可能性；其次，执行匹配和连接操作以获得血管掩模；最后，在血管掩模基础上实现基于局部统计的区域生长方法，得到较为完整的无外围值的血管树。与Frangi方法以及Yang方法在实际血管造影图像上的实验和比较，证明该方法在保持血管分支连通的同时，可以有效去除异常值。

关键词： 血管滤波器；邻域；血管分割；外围值    

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融合目标语言端语义角色的串到树翻译模型 Article

Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG

《信息与电子工程前沿（英文）》 2017年 第18卷 第10期   页码 1534-1542 doi: 10.1631/FITEE.1601349

摘要： 串到树模型是统计机器翻译中最为成功的基于句法的模型之一。它通过对目标语言端句法信息进行建模，使得机器输出的译文更符合语法。然而，它并未利用任何语义信息，产生的译文仍然包含语义角色混淆和语块顺序混乱等错误。提出两种方式，利用语义角色提高串到树模型性能：（1）在句法树上添加语义角色标签；（2）先将语义角色转换成树结构，再引入句法信息。将上述两种新的树结构用于串到树机器翻译模型训练，使得系统能够利用语义信息学习或选择更好的翻译规则。实验表明，在口语和新闻两种语料上，我们的方法都超越了传统串到树翻译系统；在大规模新闻语料上，我们的方法超越了基于短语的机器翻译系统。

关键词： 机器翻译；语义角色；句法树；串到树模型    

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基于回归预测集成学习的交互式图像分割 Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

《信息与电子工程前沿（英文）》 2017年 第18卷 第7期   页码 1002-1020 doi: 10.1631/FITEE.1601401

摘要： 对于复杂场景下的自然图像，全自动图像分割方法难以获得与真实情况吻合的结果，人们常常采用交互式分割手段实现精确分割。然而，当前及背景中存在颜色相似的区域时，传统半监督图像分割方法只能通过大量增加手工标记获得精确分割结果。为此，本文提出一种结合半监督学习的基于回归预测的集成学习交互式图像分割方法。通过集成两个互补的样条回归函数，将图像分割视为一个非线性预测问题。首先，基于已标记样本训练出两个在属性上互补的多元自适应回归样条学习器（multivariate adaptive regression splines, MARS）和薄板样条回归学习器（thin plate spline regression, TPSR）；接着，提出一种基于聚类假设和半监督学习的回归器增强算法，该算法从未标记样本中抽选部分样本辅助训练MARS和TPSR；然后，引入支持向量回归方法（support vector regression, SVR）集成MARS和TPSR的预测结果；最后，对SVR集成结果进行GraphCut图像分割。在标准数据库BSDS500和Pascal VOC上进行大量实验，验证了所提算法的有效性。大量对比实验证实，所提算法在交互式自然图像分割上的表现与当前最先进算法相当。

关键词： 交互式图像分割；多元自适应回归样条；集成学习；薄板样条回归；半监督学习；支持向量回归    

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SPSSNet: a real-time network for image semantic segmentation

Saqib Mamoon, Muhammad Arslan Manzoor, Fa-en Zhang, Zakir Ali, Jian-feng Lu,saqibmamoon@njust.edu.cn,arsalaan@njust.edu.cn,zhangfaen@ainnovation.com,alizakir@njust.edu.cn,lujf@njust.edu.cn

《信息与电子工程前沿（英文）》 2020年 第21卷 第12期   页码 1671-1814 doi: 10.1631/FITEE.1900697

摘要： Although deep neural networks (DNNs) have achieved great success in semantic segmentation tasks, it is still challenging for real-time applications. A large number of feature channels, parameters, and floating-point operations make the network sluggish and computationally heavy, which is not desirable for real-time tasks such as robotics and autonomous driving. Most approaches, however, usually sacrifice spatial resolution to achieve inference speed in real time, resulting in poor performance. In this paper, we propose a light-weight semantic segmentation network (SPSSN), which can efficiently reuse the paramount features from early layers at multiple stages, at different spatial resolutions. SPSSN takes input of full resolution 2048×1024 pixels, uses only 1.42×10 parameters, yields 69.4% mIoU accuracy without pre-training, and obtains an inference speed of 59 frames/s on the Cityscapes dataset. SPSSN can run directly on mobile devices in real time, due to its light-weight architecture. To demonstrate the effectiveness of the proposed network, we compare our results with those of state-of-the-art networks.
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基于自适应置信度校准的交互式医疗图像分割框架

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

《信息与电子工程前沿（英文）》 2023年 第24卷 第9期   页码 1332-1348 doi: 10.1631/FITEE.2200299

摘要： 基于人机交互的医疗图像分割方法是一种新的范式，其通过引入专家交互信息来指导算法完成图像分割任务。然而，现有医疗图像分割模型往往容易产生“交互误解”，即无法合理权衡短期和长期交互信息的重要性。为更好地利用不同时间尺度上的交互信息，本文提出一种基于自适应置信度校准的交互式医疗图像分割框架MECCA，其结合了基于分割决策的置信度学习技术和多智能体强化学习技术，并通过预测分割决策与短期交互信息的对齐水平来学习一个新颖的置信度网络。随后，提出一种基于置信度的奖励塑造机制，在策略梯度计算中引入置信度，从而直接纠正模型产生的交互误解。MECCA还通过标签生成和交互指导来降低交互强度和难度，从而实现用户友好交互。实验结果表明，MECCA在不同分割任务中可以显著提高短期和长期交互信息的利用效率，且仅需较少的标注样本。演示视频可通过https://bit.ly/mecca-demo-video访问。

关键词： 医疗图像分割     交互式分割     多智能体强化学习     置信度学习     半监督学习    

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分级移动边缘云中节省开销的资源分配 Special Feature on Future Network-Research Article

Ming-shuang JIN, Shuai GAO, Hong-bin LUO, Hong-ke ZHANG

《信息与电子工程前沿（英文）》 2019年 第20卷 第9期   页码 1209-1220 doi: 10.1631/FITEE.1800203

摘要： 5G网络的云化使第三方服务提供商能够在网络边缘部署服务（例如，边缘缓存与边缘计算）。已有工作都是站在特定服务提供商角度，以最大化其收益为目标来研究服务策略（如，内容缓存策略与虚拟CPU分配策略）。然而，尚未有相关工作从网络运营商角度，在满足第三方服务提供商部署需求基础上进行合理、有效的资源分配。本文针对该问题建立了优化模型，目标是最小化所有服务提供商的部署开销。为描述服务提供商的部署需求，将所有应用分为两类，即计算密集型应用和存储密集型应用，并将这两类应用的需求作为优化问题的输入参数。由于建立的数学模型是非凸优化且是NP难问题，设计了基于精英保留策略的遗传算法来求得最优解。通过仿真验证了所设计算法的可行性和有效性。

关键词： 边缘云；边缘计算；边缘缓存；资源分配；虚拟机分配    

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基于边界分析的森林冠层半球图像中心点定位与分割 Article

Jia-yin SONG,Wen-long SONG,Jian-ping HUANG,Liang-kuan ZHU

《信息与电子工程前沿（英文）》 2016年 第17卷 第8期   页码 741-749 doi: 10.1631/FITEE.1601169

摘要： 分析森林半球图像是测定森林冠层结构参数的重要方法之一。本文主要研究半球图像中圆形区域的分割方法，这是分析半球图像的基础。通过直方图、矩形度和傅里叶描述子进行森林半球图像边界的分析，得到边界特性如下：(1)边缘模型包含三种，分别是台阶、斜坡和屋顶边缘模型；(2)边界点离散；(3)边缘存在线性分布离散点。在此基础上我们提出了森林半球图像圆形区域的分割方法，拟合圆形边界线，同时用最小二乘法计算圆心点坐标及半径。该方法与获取图像的硬件设备参数无关，因此为引入参数自动调整的高性能设备获取森林半球图像奠定了基础。

关键词： 鱼眼镜头；最小二乘法；图像分割；生态学图像处理；半球图像    

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Beyond bag of latent topics: spatial pyramid matching for scene category recognition

Fu-xiang LU,Jun HUANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第10期   页码 817-828 doi: 10.1631/FITEE.1500070

摘要： We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

关键词： Scene category recognition     Probabilistic latent semantic analysis     Bag-of-words     Adaptive boosting    

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标题 作者 时间 类型 操作

Optimal CNN-based semantic segmentation model of cutting slope images

Mansheng LIN; Shuai TENG; Gongfa CHEN; Jianbing LV; Zhongyu HAO

期刊论文

Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

期刊论文

基于分布式表示语义组合的查询子主题挖掘

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

期刊论文

Digital twin-enabled smart facility management: A bibliometric review

期刊论文

迈向6G智简网络——基于语义通信的网络新范式

张平, 许文俊, 高晖, 牛凯, 许晓东, 秦晓琦, 袁彩霞, 秦志金, 赵海涛, 魏急波, 张钫炜

期刊论文

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

期刊论文

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

Wenxuan CAO; Junjie LI

期刊论文

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