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2018 5th International Conference on Systems and Informatics (ICSAI)最新文献

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Pedestrian Detection Under Dense Crowd 密集人群下的行人检测
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599382
Ge Yang, Siping Chen
In dense scenes, a large number of individuals can cause more serious problems such as blurred vision, chaotic scenes, complex behaviors and so on. For low density pedestrian detection algorithm, the accuracy of detection will be greatly reduced, even detection failure when facing these problems in high density scenes. In view of the above problems, the detection algorithm based on human head shoulder model is proposed. Support vector machine is used to train the classifier by machine learning. The detection algorithm proposed in this paper achieves 94% detection by using MIT and INRIA data sets. (Abstract)
在密集的场景中,大量的个体会造成视觉模糊、场景混乱、行为复杂等更严重的问题。对于低密度的行人检测算法,在高密度场景中面对这些问题时,检测的准确率会大大降低,甚至检测失败。针对上述问题,提出了基于人头肩模型的检测算法。支持向量机通过机器学习训练分类器。本文提出的检测算法通过使用MIT和INRIA数据集实现了94%的检测。(抽象)
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引用次数: 1
Estimating Path in camera network with non-overlapping FOVs 无重叠视场的摄像机网络路径估计
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599452
Zhanli Li, Junchao Wang, Jiaying Chen
In recent years, wide range of scene surveillance technology, widely used in the field of security monitoring, has become one of the important measure of security monitoring. The paper gives a method, in a single region, first use a low computational complexity of Kalman filter to replace the large computing TLD tracking module to obtain targets trajectories. Thus, in the non-overlapping regions, a Gaussian and mean cross-correlation function method is proposed to estimate the topological nodes between the cameras, which provides a stable camera correlation relationship for the continuous tracking in large scale scenes. The results show that the target tracking under the single region has good effect, and the topological relationship estimation between the cameras also has better anti-interference in the multi-region views, and feasible.
近年来,场景监控技术应用广泛,广泛应用于安防监控领域,已成为安防监控的重要手段之一。本文给出了一种方法,在单个区域内,首先用计算复杂度较低的卡尔曼滤波代替计算量较大的TLD跟踪模块来获取目标轨迹。因此,在非重叠区域,提出了一种高斯均值互相关函数方法来估计摄像机之间的拓扑节点,为大规模场景下的连续跟踪提供了稳定的摄像机相关关系。结果表明,单区域视图下的目标跟踪效果良好,多区域视图下摄像机之间的拓扑关系估计也具有较好的抗干扰性,可行。
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引用次数: 1
A Guided Filtering Method Based on Shen-Castan Operator 一种基于Shen-Castan算子的引导滤波方法
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599414
Yinan Xing, Jianbin Liu
A guided filtering method based on Shen-Castan operator is proposed to solve the problem of a large amount of noise attached to the ultrasound image. In this method, the edge detection result of the Shen-Castan operator was used to reduce the edge halo phenomenon of the guided filter. Apply the Shen-Castan operator to perform edge detection on the noisy image; the edge detection result will be returned to the original image to obtain an edge enhanced guide image; this image will be used as a guided image of the noisy image for guided filtering. Through the simulation experiment, the guided filtering method based on the Shen-Castan operator can not only maintain the original function of smoothing noise, but also improve the signal-to-noise ratio and structural similarity of the passenger peak.
针对超声图像中存在大量噪声的问题,提出了一种基于Shen-Castan算子的引导滤波方法。该方法利用Shen-Castan算子的边缘检测结果来减小导频滤波器的边缘晕现象。应用Shen-Castan算子对噪声图像进行边缘检测;将边缘检测结果返回到原始图像,得到边缘增强的导图;该图像将用作噪声图像的引导图像进行引导滤波。通过仿真实验,基于Shen-Castan算子的引导滤波方法既能保持原有的平滑噪声功能,又能提高客峰的信噪比和结构相似度。
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引用次数: 0
Design of Radar Electromagnetic Environment Simulation System Based on Altera Stratix® III Series FPGA 基于Altera Stratix®III系列FPGA的雷达电磁环境仿真系统设计
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599425
Pan Li, Rui Zhang, Jing Zhang, Jie Li, G. Zhao, Hua Li
Radar signal sorting in complex electromagnetic environment is the key technology of radar reconnaissance interference, which plays an important role in the measurement, analysis and identification of subsequent radar characteristic parameters. Aiming at the fact that the field experiment can not simulate the complex electromagnetic environment and the huge cost of the real battlefield, a radar electromagnetic environment simulation system based on ADC/DAC+FPGA+ARM architecture is proposed, which realizes the embedded electromagnetic environment The simulation and design of the simulation system are carried out, and the RF noise module, the random pulse module, the linear sweep module, the pulse delay superposition interference module and the single batch false target module of the electromagnetic environment simulation system are simulated.
复杂电磁环境下的雷达信号分选是雷达侦察干扰的关键技术,对后续雷达特征参数的测量、分析和识别具有重要作用。针对现场实验无法模拟真实战场复杂电磁环境和成本巨大的问题,提出了一种基于ADC/DAC+FPGA+ARM架构的雷达电磁环境仿真系统,实现了嵌入式电磁环境,并对仿真系统进行了仿真与设计,其中射频噪声模块、随机脉冲模块、线性扫描模块、对电磁环境仿真系统的脉冲延时叠加干扰模块和单批假目标模块进行了仿真。
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引用次数: 0
A Fusion Method for Node Importance Measurement in Complex Networks 复杂网络中节点重要性度量的融合方法
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599440
Feng-Zeng Liu, B. Xiao, Hongbin Jin, Qizeng Zhang
For the problem that closeness is difficult to effectively distinguish the importance of some nodes in complex networks, a new method of node importance measurement is proposed, which fuse the degree and closeness based on node re-ranking in segmentation. According to the network propagation dynamics model and Kendalls Tau coefficient, accuracy indicator and ranking stability indicator for evaluating measurement methods are given. Using the proposed method, simulations are carried out on Barabasi-Albert(BA) scale-free networks and ER random networks with different structures. The results show that compared with degree and closeness, fusion method not only has better measurement accuracy, but also has higher ranking stability.
针对复杂网络中某些节点的重要度难以有效区分的问题,提出了一种新的节点重要度度量方法,该方法在分割中基于节点重排序融合程度和亲密度。根据网络传播动力学模型和肯德尔Tau系数,给出了评价测量方法的精度指标和排序稳定性指标。利用该方法对不同结构的Barabasi-Albert(BA)无标度网络和ER随机网络进行了仿真。结果表明,与程度和紧密度相比,融合方法不仅具有更好的测量精度,而且具有更高的排序稳定性。
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引用次数: 2
Power Load Curve Clustering Algorithm Using Fast Dynamic Time Warping and Affinity Propagation 基于快速动态时间翘曲和亲和性传播的电力负荷曲线聚类算法
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599336
Yu Jin, Zhongqin Bi
Load curve clustering is a basic task for big data mining in electricity consumption. This paper proposed a clustering algorithm to improve the correct and accurate clustering of the load curve data. Firstly, we introduced the FastDTW as the similarity metric to measure the distance between two time series. Secondly, we used the Affinity Propagation (AP) to cluster. At last, we proposed a novel FastDTW-AP clustering algorithm for load curve clustering. As the similarity measures for clustering, we consider the Euclidean distance, Dynamic Time Warping (DTW), and Fast Dynamic Time Warping (FastDTW), and compare the efficiency of three similarity measures using the labelled dataset SCCTS from UCI. To evaluate the clustering algorithm, the real power load data is analyzed. The results show obvious improvement in evaluation index Adjust Rand Index (ARI) and Adjust Mutual Information (AMI).
负荷曲线聚类是电力消费大数据挖掘的基础任务。为了提高负荷曲线数据聚类的正确性和准确性,本文提出了一种聚类算法。首先,引入FastDTW作为度量两个时间序列之间距离的相似度度量。其次,我们使用亲和性传播(Affinity Propagation, AP)进行聚类。最后,我们提出了一种新的用于负载曲线聚类的FastDTW-AP算法。作为聚类的相似度量,我们考虑了欧几里得距离、动态时间翘曲(DTW)和快速动态时间翘曲(FastDTW),并使用来自UCI的标记数据SCCTS比较了三种相似度量的效率。为了评价聚类算法,对实际电力负荷数据进行了分析。结果表明,评价指标调整兰德指数(ARI)和调整互信息(AMI)均有明显改善。
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引用次数: 4
Detection and Recognition of Security Detection Object Based on Yolo9000 基于Yolo9000的安全检测对象的检测与识别
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599420
Zhongqiu Liu, Jianchao Li, Y. Shu, Dongping Zhang
In this paper, a convolutional neural network model based on YOLO9000 is introduced to meet the need of real-time engineering computing. This network model can study and classify the targets in depth, aiming at the characteristics of scissors and aerosols. The characteristics have various kinds such as overlap, cover and multiscale. At the present stage, the average speed is 68 FPS on the windows platform with GPU (Geforce GTX Titan X) acceleration. In addition, the average precision and recall rate are 94. 5%, 92. 6%, respectively.
本文介绍了一种基于YOLO9000的卷积神经网络模型,以满足实时工程计算的需要。该网络模型可以针对剪刀和气溶胶的特点,对目标进行深入的研究和分类。具有重叠、覆盖、多尺度等多种特征。目前,在GPU (Geforce GTX Titan X)加速的windows平台上,平均速度为68 FPS。平均查准率和查全率为94。5%, 92。6%,分别。
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引用次数: 23
Weighted Gene Co-expression Network Analysis of Gene Modules for Lung Adenocarcinoma 肺腺癌基因模块的加权基因共表达网络分析
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599411
Yuanyuan Zhai, Ying-Li Chen, Yue Jiang, Qianzhong Li
Lung cancer is the most common form of malignancies tumor, influenced by complex molecular network. Further understanding of the molecular mechanisms that lead to Lung cancer would be conducive to the detection and supervisory control of cancer, thereby improving clinical cancer treatment and personalized treatment. In this study, 47 co-expression modules were identified by constructing a weighted gene co-expression network. Subsequently, we investigated the biological significance of these modules by studying the GO biological process and KEGG pathways. The results show that two significant modules (green module and green-yellow module) enrich in the progress of blood vessels development, immune response and regulation, respectively. The top 50 genes with the two modules contain 3 LncRNAs and some hub genes, respectively. Therefore, these LncRNAs and the hub genes of SPTBN1, SFTPC, FHL1, and RP5-826L7 in the green module and FCER1G, NLRC4 and SAMHD1 in the green-yellow module may be associated with lung adenocarcinoma. It has been experimentally proved that they may play a crucial part in the pathogenesis of lung adenocarcinoma. In addition, the analysis of these hub genes may provide a reference to further learn about the pathogenesis of lung cancer.
肺癌是最常见的恶性肿瘤,受复杂的分子网络影响。进一步了解导致肺癌的分子机制,有助于癌症的检测和监控,从而改善临床癌症治疗和个性化治疗。本研究通过构建加权基因共表达网络,鉴定出47个共表达模块。随后,我们通过研究氧化石墨烯的生物过程和KEGG途径来研究这些模块的生物学意义。结果表明,在血管发育、免疫应答和调节过程中,绿色模块和绿黄模块分别富集了两个重要模块。含有这两个模块的前50个基因分别包含3个lncrna和一些枢纽基因。因此,这些LncRNAs和绿色模块中的SPTBN1、SFTPC、FHL1、RP5-826L7枢纽基因以及黄绿色模块中的FCER1G、NLRC4、SAMHD1枢纽基因可能与肺腺癌相关。实验证明它们可能在肺腺癌的发病机制中起着至关重要的作用。此外,这些枢纽基因的分析可能为进一步了解肺癌的发病机制提供参考。
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引用次数: 1
Pet Hair Color Transfer Based On CycleGAN 基于CycleGAN的宠物发色转移
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599368
Shimian Zhang, Dexin Yang
Generative adversarial networks (GANs) have shown great performance on image-to-image translation tasks. Many approaches have been proposed for translation of human face images, scene pictures and artful paintings, but few works considered about translating a pet image. In this paper, we propose a method based on cycle-consistent adversarial network (CycleGAN) to solve pet hair color transfer problem. Given a pet image, our model can translate its hair color into a desired one while keeping its other features unchanged, which makes our generated images seem quite realistic. We do several improvements on CycleGAN including doing segmentation to avoid the influence of background, and using spectral normalization to improve the quality of generated images. We build a large pet image dataset consisting of a total number of 7. 5K images, categorized by different hair colors. Our proposed method is trained and tested on this data set and the results show the promising performance on translating between white and orange hair color of dog images.
生成对抗网络(GANs)在图像到图像的翻译任务中表现出了良好的性能。对于人脸图像、场景图片和艺术绘画的翻译,已经提出了许多方法,但很少有人考虑到宠物图像的翻译。本文提出了一种基于周期一致对抗网络(CycleGAN)的方法来解决宠物毛发颜色转移问题。给定宠物图像,我们的模型可以将其头发颜色转换为所需的颜色,同时保持其其他特征不变,这使得我们生成的图像看起来相当逼真。我们对CycleGAN进行了一些改进,包括进行分割以避免背景的影响,以及使用光谱归一化来提高生成图像的质量。我们建立了一个由7个宠物图像组成的大型数据集。5K张图片,根据不同的发色分类。我们提出的方法在该数据集上进行了训练和测试,结果表明该方法在狗的白色和橙色毛发颜色之间的转换上有很好的表现。
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引用次数: 1
A Time-based Leakage-aware Algorithm for Task Placement and Scheduling Problem on Dynamic Reconfigurable FPGA 动态可重构FPGA上基于时间的泄漏感知算法
Pub Date : 2018-11-01 DOI: 10.1109/ICSAI.2018.8599330
Tingyu Zhou, Tieyuan Pan, Zhiguo Bao, Takahiro Watanabe
Field-programmable gate array (FPGA) has enormous potential in the field of Integrated Circuit (IC) due to its programmability, short design cycle, and high flexibility in parallel computing. Nevertheless, increasing chip integration and shrinking transistor size lead to non-negligible power dissipation in FPGA. Specifically, leakage power dissipation issue as a crucial part of power consumption in FPGA requires being concerned urgently. In this paper, a time-based leakage-power aware algorithm (TBLA) is proposed to address the aforementioned issue on 2D dynamic partial reconfigurable FPGA. Experimental results show that the proposed TBLA algorithm reduces the leakage-power and scheduling overhead without increasing the overall execution time of an application compared to traditional algorithms.
现场可编程门阵列(FPGA)具有可编程性、设计周期短、并行计算灵活性高等特点,在集成电路领域具有巨大的应用潜力。然而,芯片集成度的提高和晶体管尺寸的缩小导致FPGA的功耗不可忽略。其中,泄漏功耗问题作为FPGA功耗的重要组成部分,亟待关注。本文在二维动态部分可重构FPGA上提出了一种基于时间的漏功率感知算法(TBLA)来解决上述问题。实验结果表明,与传统算法相比,所提出的TBLA算法在不增加应用程序整体执行时间的前提下降低了泄漏功率和调度开销。
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引用次数: 2
期刊
2018 5th International Conference on Systems and Informatics (ICSAI)
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