Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance

Human activity detection and recognition is a challenging task.Video surveillance can benefit greatly by advances in Internet of Things(IoT)and cloud computing.Artificial intelligence IoT(AIoT)based devices form the basis of a smart city.The research presents Intelligent dynamic gesture recognition(IDGR)using a Convolutional neural network(CNN)empowered by edit distance for video recognition.The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language(PSL).However,the proposed methodology can work efficiently for any type of video.The proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input action.The research proposes recognition of dynamic gestures using image recognition of the keyframes based on CNN extracted from the human activity.Edit distance is used to find out the label of the word to which those sets of frames belong to.The simulation results have shown that at 400 videos per human action,100 epochs,234×234 image size,the accuracy of the system is 90.79%,which is a reasonable accuracy for a relatively small dataset as compared to the previously published techniques.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.

Solid model edit distance: a multi-application and multi-level schema for CAD model retrieval

We present the solid model edit distance(SMED),a powerful and flexible paradigm for exploiting shape similarities amongst CAD models.It is designed to measure the magnitude of distortions between two CAD models in boundary representation(B-rep).We give the formal definition by analogy with graph edit distance,one of the most popular graph matching methods.To avoid the expensive computational cost potentially caused by exact computation,an approximate procedure based on the alignment of local structure sets is provided in addition.In order to verify the flexibility,we make intensive investigations on three typical applications in manufacturing industry,and describe how our method can be adapted to meet the various requirements.Furthermore,a multilevel method is proposed to make further improvements of the presented algorithm on both effectiveness and efficiency,in which the models are hierarchically segmented into the configurations of features.Experiment results show that SMED serves as a reasonable measurement of shape similarity for CAD models,and the proposed approach provides remarkable performance on a real-world CAD model database.

Measuring Musical Rhythm Similarity: Further Experiments with the Many-to-Many Minimum-Weight Matching Distance

Musical rhythms are represented as sequences of symbols. The sequences may be composed of binary symbols denoting either silent or monophonic sounded pulses, or ternary symbols denoting silent pulses and two types of sounded pulses made up of low-pitched (dum) and high-pitched (tak) sounds. Experiments are described that compare the effectiveness of the many-to-many minimum-weight matching between two sequences to serve as a measure of similarity that correlates well with human judgements of rhythm similarity. This measure is also compared to the often used edit distance and to the one-to-one minimum-weight matching. New results are reported from experiments performed with three widely different datasets of real- world and artificially generated musical rhythms (including Afro-Cuban rhythms), and compared with results previously reported with a dataset of Middle Eastern dum-tak rhythms.

基于哈希和编辑距离算法的SCD双层向量化与变更校验技术

针对智能变电站配置描述(substation configuration description,SCD)文件变更、智能变电站二次设备配置不清晰导致运维工作量剧增的问题,提出基于哈希和编辑距离算法的SCD双层向量化与变更校验技术。首先,考虑文件变更的启动与校验,应用节点元素、校验码与元素属性构建面向SCD文件解析的双层向量模型;其次,利用哈希算法将构建的文本向量模型转化为哈希字符串向量模型;然后,引入编辑距离算法分别计算原始文件与变更文件的双层向量模型相似度,基于第一层相似度比较构成第二层校验启动判据,基于第二层相似度结合回溯路径方法实现变更校验;最后,通过算例分析验证了所提算法的有效性。

基于可微池化的层级图相似性学习

目前,大多数关于图相似性学习的工作仅考虑图级匹配或节点级匹配,忽略了多层级的粗图级匹配,为解决该问题提出一种可微池化层级图匹配网络(PHMN)模型。逐层将图的节点表示进行软聚类进而将源图转化为尺寸缩小的粗图;在图对上使用多角度多层级的跨图匹配层,获取匹配矩阵;由注意力机制将图对匹配矩阵转化为匹配向量后,传入LSTM模型和多层感知机进行相似度预测。该模型在图回归任务和图分类任务的对比实验中,分别取得8项最优表现和6项最优表现。

基于Bi-LSTM神经网络的短文本敏感词识别方法

为了准确识别与处理敏感词,针对分词时延较高、识别精度较低的问题,提出基于双向长短期记忆(Bi-LSTM)神经网络的短文本敏感词识别方法。分析敏感词库,将敏感词库划分为两大类、三个等级,预处理短文本干扰信息(特殊字符、繁体字与拆分汉字),引入Bi-LSTM神经网络构建短文本分词模型,二次训练确定最佳参数,反复计算词语的敏感性数值,通过敏感性对比函数,提取短文本敏感词,并匹配敏感词库,确定敏感词的类别与等级,实现短文本敏感词识别。实验结果表明:在不同实验组别下,应用本文方法获得的短文本分词时延均低于给定最大限值,短文本敏感词识别精度高于84.42%,应用性能较佳。

A metric normalization of tree edit distance

Traditional normalized tree edit distances do not satisfy the triangle inequality. We present a metric normalization method for tree edit distance, which results in a new normalized tree edit distance fulfilling the triangle inequality, under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. We prove that the new distance, in the range [0, 1], is a genuine metric as a simple function of the sizes of two ordered labeled trees and the tree edit distance between them, which can be directly computed through tree edit distance with the same complexity. Based on an efficient algorithm to represent digits as ordered labeled trees, we show that the normalized tree edit metric can provide slightly better results than other existing methods in handwritten digit recognition experiments using the approximating and eliminating search algorithm （AESA） algorithm.

Distribution Line Longitudinal ProtectionMethod Based on Virtual Measurement Current Restraint

As an effective approach to achieve the“dual-carbon”goal,the grid-connected capacity of renewable energy increases constantly.Photovoltaics are the most widely used renewable energy sources and have been applied on various occasions.However,the inherent randomness,intermittency,and weak support of grid-connected equipment not only cause changes in the original flow characteristics of the grid but also result in complex fault characteristics.Traditional overcurrent and differential protection methods cannot respond accurately due to the effects of unknown renewable energy sources.Therefore,a longitudinal protection method based on virtual measurement of current restraint is proposed in this paper.The positive sequence current data and the network parameters are used to calculate the virtual measurement current which compensates for the output current of photovoltaic(PV).The waveform difference between the virtual measured current and the terminal current for internal and external faults is used to construct the protection method.An improved edit distance algorithm is proposed to measure the similarity between virtual measurement current and terminal measurement current.Finally,the feasibility of the protection method is verified through PSCAD simulation.

Shape Recognition and Retrieval Based on Edit Distance and Dynamic Programming

An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transform. Then, the features are transformed into a string of symbols with the similarity among those symbols computed based on the edit distance. Finally, the shapes are identified using dynamic programming. Two public datasets are analyzed to demonstrate that the present approach is better than previous approaches.

基于XGBoost算法的医保信息系统入侵安全风险监测方法

目的针对常规的医保信息系统对入侵行为的判定过程较为繁杂、监测反应时间较长的问题,提出基于XGBoost算法的医保信息系统入侵安全风险监测方法。方法按照安全性影响因素的关系,对安全因素关联度修正强度进行计算与修正,并对修正后的安全因素关联进行划分,得到划分的系统入侵安全信息关联度;根据划分关联度参数,基于XGBoost算法对入侵行为进行分级判定;按照结果,使用树编辑距离算法,对入侵行为信息进行匹配,并建立神经网络监测结构;根据匹配结果建立对应报警机制,实现对医保信息系统入侵安全风险的实时监测。结果实验结果表明,本文方法对医保信息系统入侵安全风险的监测平均反应时间小于3 s,反应时间较短,安全性较高。结论本文提出的基于XGBoost算法的医保信息系统入侵安全风险监测方法,能够有效监测医保信息系统的入侵安全风险,具有较高的应用价值。

一种基于编辑距离的中文字符串近似匹配算法

字符串近似匹配是模式匹配领域中的一个重要研究方向。在中文字符串近似匹配中,基于字符操作的编辑距离不能准确衡量由复制、剪贴等操作导致的相似关系。基于此,在传统编辑距离的基础上引入了字符串的平移和复制操作,给出了一种在贪心算法基础上进行动态规划搜索的计算方法,能有效计算改进的编辑距离,在真实数据集上的实验结果和分析显示了对文本检索的有效性。

编辑距离优化的表格识别方法

表格数据在各行业中起着关键作用,但现有技术在处理非标准化和多样化格式的表格时常需人工干预,从而降低了自动化效率。为解决这个问题,本文提出基于编辑距离的表格识别优化方法。通过计算表格元素之间的编辑距离,及引入新的可信度标记系统,来处理数据不一致性、提升数据处理准确性。实验结果表明,该方法提升了识别准确率和处理速度,在数据匹配精度和可信度评估方面有良好的表现。

基于编辑距离算法的技改大修项目工程时间节点数据研究与应用

文章针对电网企业技改大修项目的工程时间节点数据挖掘及其在项目管理中的应用进行了深入研究,旨在解决电网设备老化和规模增加导致设备成新率下降的问题,提高电力系统设备运行可靠性和供电质量。研究方法包括审计模型中的编辑距离算法原理及应用、技改大修工程时间节点数据分析中的审计模型设计、审计规则的梳理和验证、后台数据寻源及数据挖掘分析和建模等。

基于编辑距离的Web数据挖掘

Div+CSS流行于Web页面的布局,在这种布局下,网页中很多数据记录以重复结构的形式聚集在一个层级。为了更好地从网页中挖掘数据,提出了一种新的Web数据挖掘算法,把树编辑距离转化为字符串编辑距离的计算,改进字符串编辑距离算法,利用字符串编辑距离评价树的相似度,进而找到网页中的重复模式,提取数据。通过针对不同重复模式特征的网页的实验说明,基于编辑距离的Web数据挖掘算法不仅能提取具有根节点及上面几层相同的网页的数据,对具有底层节点相同的网页也是有效的。

结合编辑距离和Google距离的语义标注方法

提出了一种在领域本体指导下对网页进行语义标注的方法。该方法利用编辑距离和Google距离从词语的语法和语义两方面综合度量词汇与本体概念之间的语义相关度,从而在网页与本体之间建立映射关系。此外,对网页进行语义标注后,利用标注结果对本体进行有效扩充,使本体更趋于领域化。实验结果表明该方法是行之有效的。

一种基于DTW聚类的水文时间序列相似性挖掘方法

水文时间序列相似性挖掘是水文时间序列挖掘的重要方面,对洪水预报、防洪调度等具有重要意义。针对水文数据的特点,提出了一种基于DTW聚类的水文时间序列相似性挖掘方法。该方法先对数据进行小波去噪、特征点分段以及语义划分,再基于DTW距离对划分后的子序列做层次聚类并符号化;然后根据符号序列间的编辑距离筛选候选集;最后通过序列间的DTW距离进行精确匹配,获取相似水文时间序列。以滁河六合站的日水位数据进行实验,结果表明,所提方法能够有效地缩小候选集,提高查找语义相似的水文时间序列的效率。

基于归一化编辑距离和谱聚类的轨迹模式学习方法

针对欧氏距离和Hausdorff距离等在描述目标运动轨迹差异性时度量不够准确的问题,提出一种基于归一化编辑距离和谱聚类的轨迹分布模式学习方法.首先对目标的运动轨迹进行矢量量化编码;然后采用归一化的编辑距离来度量轨迹编码序列之间的差异,得到归一化编辑距离矩阵;再通过该矩阵进行谱聚类来提取轨迹的分布模式;最后利用所提取的轨迹分布模式确定整条轨迹及其局部是否异常.通过仿真和真实场景的实验验证了该方法的有效性.

基于树编辑距离的工作流距离度量方法

在工作流的发现和聚类等应用中,需要对两个工作流模型的距离进行度量。因此,提出一种计算两个不同结构化工作流的距离定量度量方法。首先介绍了结构化工作流,并将每一个结构化工作流转换为流程结构树;然后基于两个结构树之间的树编辑距离来计算工作流之间的距离及相应相似度。该距离度量方法满足距离度量的3个属性,即同实体不可区分性、对称性和三角不等式性质。这些属性使得该距离度量方法可以在工作流模型管理活动中作为定量分析工具。实验结果表明,基于树编辑距离的工作流度量方法是可行的。同时,与基于邻接矩阵的距离度量方法相比,该方法考虑了不同结构之间的语义距离,有效验证了此方法的合理性。