Soft Tissue Feature Tracking Based on Deep Matching Network

Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.

Adaptive Recurrent Iterative Updating Stereo Matching Network

When training a stereo matching network with a single training dataset, the network may overly rely on the learned features of the single training dataset due to differences in the training dataset scenes, resulting in poor performance on all datasets. Therefore, feature consistency between matched pixels is a key factor in solving the network’s generalization ability. To address this issue, this paper proposed a more widely applicable stereo matching network that introduced whitening loss into the feature extraction module of stereo matching, and significantly improved the applicability of the network model by constraining the variation between salient feature pixels. In addition, this paper used a GRU iterative update module in the disparity update calculation stage, which expanded the model’s receptive field at multiple resolutions, allowing for precise disparity estimation not only in rich texture areas but also in low texture areas. The model was trained only on the Scene Flow large-scale dataset, and the disparity estimation was conducted on mainstream datasets such as Middlebury, KITTI 2015, and ETH3D. Compared with earlier stereo matching algorithms, this method not only achieves more accurate disparity estimation but also has wider applicability and stronger robustness.

Automatic Matching of Multi-scale Road Networks under the Constraints of Smaller Scale Road Meshes

In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.

Subgraph Matching Using Graph Neural Network

Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.

IMPROVED MODE-MATCHING AND NETWORK ANALYSIS OF E-PLANE WAVEGUIDE BRANCH DIRECTIONAL COUPLERS

The E-plane waveguide branch directional couplers are analyzed by a method which combines the multimode network theory with rigorous mode-matching approach. The electromagnetic field components are expanded by the superposition of LSEx modes rather than TE and TM modes in the mode-matching procedure. Meanwhile, the electromagnetic problem is transferred into the network problem through the mode-matching treatment. It is shown that the present method has the advantages of simplicity and less computation without affecting the accuracy of the calculation.

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

Profile Matching in Electronic Social Networks Using a Matching Measure for Fuzzy Numerical Attributes and Fields of Interests

The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formulated as an optimization problem. For this purpose the underlying search space and the objective function are defined precisely. In particular, data structures of search and offering profiles are proposed, as well as a function measuring the matching of the attributes of a search profile with the corresponding attributes of an offering profile. This objective function, given in Equation (29), is composed of the partial matching degrees for numerical attributes, discrete non-numerical attributes, and fields of interests, respectively. For the matching degree of numerical profile attributes a fuzzy value approach is presented, see Equation (22), whereas for the matching degree of fields of interest a new measure function is introduced in Equation (26). The resulting algorithm is illustrated by a concrete example. It not only is applicable to electronic social networks but also could be adapted for resource discovery in grid computation or in matchmaking energy demand and supply in electrical power systems and smart grids, especially to efficiently integrate renewable energy resources.

A Trusted and Privacy-Preserving Carpooling Matching Scheme in Vehicular Networks

With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportation efficiency and solving environmental problems. However, attackers usually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and privacy-preserving carpooling matching scheme in Vehicular Networks (TPCM). TPCM scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopted the privacy set intersection technology based on the Bloom filter to match the passengers with the vehicles to achieve the purpose of protecting privacy and meeting the individual needs of passengers simultaneously. TPCM scheme adopted a multi-faceted trust management model, which calculated the trust value of different travel preferences of vehicle based on passengers’ carpooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.

Research on Vector Road Data Matching Method Based on Deep Learning

Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.

宁夏回族自治区碳捕集、利用与封存源汇匹配与集群部署

“双碳”(碳达峰、碳中和)战略背景下,碳捕集、利用与封存技术(CCUS)是实现化石能源大规模低碳化利用的关键技术之一。近年,CCUS技术呈现出规模化和集群化发展趋势,而科学、合理的源汇匹配是CCUS集群部署工程选址的重要依据,能够建立高效CO_(2)输运管网、降低减排成本。宁夏是国家能源安全战略布局的重要保障基地,能源结构偏煤、工业结构偏重特征明显,面临着巨大的碳减排压力。针对宁夏CCUS集群部署的源汇匹配问题,调研评估了宁夏工业碳排放源特征和地质碳汇潜力,构建了CCUS源汇匹配模型,在充分考虑源汇性质、捕集-输运-封存成本、CO_(2)运输距离、区域地理条件、土地利用类型、人口密度等因素基础上,应用改进的节约里程法和基于地理信息系统(GIS)的最低成本路径优化法,结合ArcGIS平台和优化求解软件,获得了宁夏CCUS源汇匹配优化和应用方案,并提出宁夏CCUS集群部署建议。结果表明,截止2021年,宁夏工业碳排放源107个,碳排放总量2.26亿t/a,以化工(含自备电厂)和电力行业碳排放为主。宁夏主要封存地质体包括深部咸水层、深部不可开采煤层和油气藏,CO_(2)理论地质封存容量151.55亿t,以深部咸水层封存潜力最大。宁夏CCUS源汇匹配效果较好,在源汇直接相连的情况下,区内年排放量10万t以上的大型工业排放源CCUS集群部署(30 a规划期)总成本约2.45万亿元,并以捕集成本为主,占比83.65%,单位减排成本402.32元/t,共需建设CO_(2)运输管道2 459 km;改进的节约里程法和基于GIS的最低成本路径优化法可大幅降低CCUS集群部署成本,优化后CCUS单位减排成本降至381.76元/t,节约管道建设里程938 km。宁夏应聚焦电力、化工等“两高”(高耗能、高排放)行业,在以宁东能源化工基地为重点的北部、东部地区超前应用CCUS技术,打造宁东能源化工基地、银川—吴忠、石嘴山、中卫和固原5个CCUS特色集群,构建宁夏特色的工程化CCUS全流程技术模式。

基于匹配置信度的路网几何特征融合方法研究

路网几何融合是维护路网数据现势性的重要手段。目前,路网融合的相关研究侧重于对匹配算法、融合策略的改进与提升,多源路网数据融合的具体方案较少。本文结合全球测图项目的迫切需求,提出了一种基于匹配置信度的路网几何特征融合方法。通过多特征因子匹配方法识别同名路网实体,设计路网匹配置信度提取增量路网数据,利用路网接边修复融合路网。实际生产应用表明,文中提出的方法在较大程度上提升了原始路网的完整性及现势性。

A Four-color Matching Method Combining Neural Networks with Genetic Algorithm

A brief review of color matching technology and its application of printing RGB images by CMY or CMYK ink jet printers is presented, followed by an explanation to the conventional approaches that are commonly used in color matching. Then, a four color matching method combining neural network with genetic algorithm is proposed. The initial weights and thresholds of the BP neural network for RGB to CMY color conversion are optimized by the new genetic algorithm based on evolutionarily stable strategy. The fourth component K is generated by using GCR (Gray Component Replacement) concept. Simulation experiments show that it is well behaved in both accuracy and generalization performance.

Geo-Social Profile Matching Algorithm for Dynamic Interests in Ad-Hoc Social Network

Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.

基于匹配信任度机制的移动传感网簇头更新算法

为解决移动传感网部署过程中存在的簇头节点更新质量不佳和节点生存率较低等不足,提出了一种基于匹配信任度机制的移动传感网簇头更新算法。首先,引入K-means算法,利用误差平方根函数来完成网络初始聚类,以快速定位聚类中心,提升聚类形成速度。随后,综合考虑备选簇头剩余能量、备选簇头与当前簇头的欧氏距离、备选簇头覆盖范围内节点总数3个因素,设计了基于匹配信任度的簇头更新方法,按权值对各因素进行平均分配,进而将信任度权值最高的节点作为备选簇头,从而选举出生存质量较高的节点。仿真实验表明,算法具有更高的网络稳定运行时间和簇头节点生存率,以及更低的节点故障概率。其中,网络稳定运行时间提升了80%以上,簇头节点生存率保持在90%以上,节点故障概率也较低,具有明显的优势。

航空平台地磁矢量匹配导航算法研究进展

航空地磁矢量导航技术因其具有自主、无源、可靠性强的优势,在卫星导航系统受到攻击等情况下可有效发挥替代作用,在军民用领域均具有极高的战略意义和应用价值。航空平台具有飞行速度快、短时间跨越地域广的特性,对地磁矢量测量与导航方法提出高精度和高可靠性等要求。该文梳理近年来航空地磁矢量导航系统的研究与发展现状,介绍地磁矢量导航的关键技术,重点对地磁矢量匹配导航算法的研究进展进行分析。针对现有算法存在的不足,提出进一步提升算法的精度和鲁棒性、发展基于机器学习的地磁矢量匹配导航方法、推动无人机等新型航空平台地磁矢量导航技术发展等后续研究方向,意在促进航空地磁矢量导航技术的进一步发展。

数字化情境中的流通效率实现机制——基于匹配与扩张视角的案例研究

当前,快速发展的数字技术深刻影响了流通效率的实现。在数字化情境中探讨流通效率的微观实现机制,需要从流通的时空双重维度出发,将宏观层面的商品流通效率对应为企业在“匹配”和“扩张”的微观流通活动中的效率。采用追踪访谈的一手数据,基于韩都衣舍、酒仙网的案例,探讨了企业在数字化情境中的流通效率实现问题。研究发现:在“去中间化”路径中,数字化驱动了基于供应链整合的匹配效率实现和基于消费社群拓展的扩张效率实现,交叉网络效应与数据网络效应动态放大了这一作用;在“再中间化”路径中,数字化辅助了基于渠道纵向协作的匹配效率实现和基于店面连锁的扩张效率实现,用户网络效应与数据网络效应动态放大了这一作用。因此,承担流通活动的企业需结合自身对于流通渠道演化的路径选择,采取适配的数字化战略,以促进流通效率实现。

基于深度神经网络的树木伐桩轮廓提取及匹配方法

为了及时准确地找到被盗树木,公安机关需要比对被盗伐树木伐桩的上下截面,寻找共同点,并依此确认两者是否属于同一树木。但是由于存放环境不同,伐桩上下截面的颜色、纹理存在巨大差异,由于锯伐方式和树皮的影响,伐桩上下表面的轮廓也存在很大差异,伐桩下表面还很容易受木屑等影响,背景复杂。针对这些难点,本研究在前面的研究基础上,继续把棋盘格作为特征物放置在伐桩表面,用PPYOLO_MobileNetV3卷积神经网络检测图像中的棋盘格,对棋盘格中的角点进行检测、排序,然后进行透视变换,恢复伐桩的原始面积和轮廓等特征,接着用PP-LiteSeg网络在复杂背景下提取伐桩轮廓,然后用CAE_ViT_base网络对轮廓进行匹配,实现了伐桩轮廓匹配的全流程,从而极大程度节约了人工。理论分析和试验结果都表明,基于局部梯度的匹配法、基于局部点集拓扑特征的匹配法和基于轮廓的全局特征匹配方法等,在伐桩图像的匹配中都是不可行的。CAE_ViT_base网络的解码器将输入图像分割为大小一致的图像块,解码器的训练过程需要关注每一个块的特征,伐桩轮廓的匹配难点在于轮廓有局部缺失,局部梯度误差较大。CAE_ViT_base网络的自监督预训练机制很好地弥补了上述缺点;同时,采用对样本图像随机多角度旋转的方法,使得图像的特征提取能够保持旋转不变性。CAE_ViT_base网络提取出来的特征在尺度上高于基于梯度的特征,也高于基于局部点集拓扑的特征,但低于全局特征。因此,只要少部分图像块高度匹配,则CAE_ViT_base网络给出的最终匹配度就比较高;同时,这种工作方式和人工对比2个伐桩轮廓是否匹配的方法也是一致的。在本研究的344幅伐桩图像上进行试验,结果表明:本研究算法对整个测试集的检测成功率为100%,棋盘格检测成功率100%,轮廓提取精度达到98.8%,轮廓匹配准确率100%,无一错检和误匹配;和基于梯度的轮廓提取方法及基于特征描述子的轮廓匹配方法相比,本研究方法具有全方位的优势。本方法中,伐桩检测匹配全流程计算耗时不足30 s,完全满足实际应用需要,具有较好的推广应用价值。

智能电网中基于二分图匹配的网络切片资源分配算法

为了解决智能电网中多类业务的服务质量需求难以同时得到满足的问题并兼顾电力终端和网络侧经济效用,提出了一种基于二分图匹配的网络切片资源分配算法。针对智能电网场景中的控制类和采集类业务,为电力终端分别制定相应的投标信息,并据此计算支付价格和效用矩阵;将网络切片与电力终端之间的资源分配建模为二分图匹配问题,根据不同业务的时延、传输速率或能耗需求,向终端分配不同的切片资源以最大化系统效用。仿真结果表明,相较于已有的双向拍卖算法和贪心算法,所提算法能够提高10%~20%的系统效用。