Silhouettes Based Human Action Recognition in Video via Procrustes Analysis and Fisher Vector Coding

This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC).Firstly,we applied a pose feature extracted from silhouette image by employing Procrustes analysis and local preserving projection(LPP).Secondly,the extracted feature can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation,rotation and scaling.Finally,after the pose feature was extracted,a recognition framework based on FVC and multi-class supporting vector machine was employed to classify the human action.Experimental results on benchmarks demonstrate the effectiveness of the proposed method.

Impact of Earthquake Action on the Design and Sizing of Jointed Masonry Structures in South Kivu, DRC

This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in the province, namely the non-consideration of seismic action in the study of structures by both professionals and researchers. The main objective of the study is to show the importance of dynamic analysis of structures in South Kivu. It adopts a meta-analytical approach referring to previous researches on South Kivu and proposes an efficient and optimal method. To arrive at the results, we use Eurocode 7 and 8. In addition, we conducted static analysis using the Coulomb method and dynamic analysis using the Mononobe-Okabe method and compared the results. At Nyabibwe, the results showed that we have a deviation of 24.47% for slip stability, 12.038% for overturning stability and 9.677% for stability against punching through a weight wall.

Static and Dynamic Analysis of Lasing Action from Single and Coupled Photonic Crystal Nanocavity Lasers

The single and coupled photonic crystal nanocavity lasers are fabricated in the InGaAsP material system and their static and dynamic features are compared. The coupled-cavity lasers show a larger lasing e^ciency and generate an output power higher than the single-cavity lasers, results that are consistent with the theoretical results obtained by rate equations. In dynamic regime, the single-cavity lasers produce pulses as short as 113 ps, while the coupled-cavity lasers show a significantly longer lasing duration. These results indicate that the photonic crystal laser is a promising candidate for the light source in high-speed photonic integrated circuit.

Seismic elastic-plastic time history analysis and reliability study of quayside container crane

Quayside container crane is a kind of huge dimension steel structure,which is the major equipment used for handling container at modern ports.With the aim to validate the safety and reliability of the crane under seismic loads,besides conventional analysis,elastic-plastic time history analysis under rare seismic intensity is carried out.An ideal finite element（FEM） elastic-plastic mechanical model of the quayside container crane is presented by using ANSYS codes.Furthermore,according to elastic-plastic time history analysis theory,deformation,stress and damage pattern of the structure under rare seismic intensity are investigated.Based on the above analysis,the established reliability model according to the reliability theory,together with seismic reliability analysis based on Monte-Carlo simulation is applied to practical analysis.The results show that the overall structure of the quayside container crane is generally unstable under rare seismic intensity,and the structure needs to be reinforced.

An overview of proven Climate Change Vulnerability Assessment tools for forests and forest-dependent communities across the globe：a literature analysis

Climate Change Vulnerability Assessment（VA） tools for forest ecosystems and forest-dependent communities are important for making decisions and understanding the impact of climate change on both social and natural systems.However,the tools are poorly coordinated,making it difficult for policymakers to carry out VAs properly.The aim of this study was to analyze VA literature worldwide to find representative case studies in terms of methods and tools applied and which have been successful in performing VAs on forests and forest-dependent communities.All successful VA studies analyzed had common characteristics such as significant funding,data availability and technical capacity.An additional characteristic was the development of an integrated approach that considered the vulnerability of both ecosystems and communities by combining qualitative and quantitative methods.Community members and relevant stakeholders were significantly involved in a participatory process that concluded with the identification of adaptation measures.The case studies also revealed how policymakers need to choose suitable methods and tools to undertake efficient assessment of vulnerabilities.They need to consider several aspects of the VA process such as subject matter,availability of resources,time and scale.

Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

Reliability Analysis of Unmanned Aerial Vehicles Flight Control System Based on Reliability Analysis Technologies

The unmanned aerial vehicles( UAV) has been becoming more and more important in the aviation industry.Despite the superior performance and advanced technology,major accident of UAV happens frequently due to the impact of their systems,long distance of remote control and skill of manipulator technology.According to the application of engineering application,failure mode effects and criticality analysis( FMECA),failure reporting analysis and corrective action comprehensive analysis systems( FRACAS)and fault tree analysis( FTA)( 3 F) were combined.And also a set of user-friendly,more time,more efficient and accurate reliability analysis system were explored.

Analysis of herbs use law and mechanism of traditional Chinese medicine in preventing and treating postoperative complications of breast cancer

Objective:Excavate the compatibility rules of traditional Chinese medicine for preventing and treating postoperative complications of breast cancer,study the potential mechanism of prevention and treatment with traditional Chinese medicine.Methods:Collate and analyze the relevant prescriptions collected from CNKI and the core drug combination and the new formula are obtained by adopting the traditional Chinese medicine inheritance auxiliary system(TCMISSV2.5).Use BATMAN-TCM online analysis tool to analyze KEGG pathways of high frequency drugs and predict the potential signal pathways.Results:Data mining has selected 10 pairs of core herb combinations and 5 new recipes of 133 prescriptions.In the network pharmacology analysis of bai zhu,huang qi,fu ling,the potential signaling pathways of its action are the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.Conclusion:Herbs for invigorating spleen and qi and herbs for nourishing blood and activating blood are commonly used to prevent and treat postoperative complications of breast cancer.Core herbs may play their role by interfering with the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.

Research Progress on the Pharmacological Effects and Quality Analysis Methods of Bupleurum

Bupleurum has been used as traditional medicine in China,Japan,South Korea,and other Asian countries for more than 2,000 years.The crude extracts and pure compounds isolated from Bupleurum have a variety of biological activities,such as anti-inflammatory,anti-cancer,antipyretic,antibacterial,anti-viral,liver protection,neuroprotection,and immune regxilation.The pharmacological effects of Bupleurum have been classified and summarized in this paper by consulting literatures at home and abroad.At the same time,the quality analysis methods of bupleurum,including high-performance liquid chromatography,thin layer chromatography,ultra-high-performance liquid chromatography mass spectrometry,UPLC-QTOF-MS,and so on,are discussed.This paper provides a reference for further research,development,and application of Bupleurum.

SlowFast Based Real-Time Human Motion Recognition with Action Localization

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is required.However,various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging.Video analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic data.Motion recognition is key in this analysis.Motion recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various objects.The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm.Recently,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent performance.However,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution images.The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods.The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure.Based on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in accuracy.Comparing detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,119,323 cases,which is an increase of 33.23%.

Combining ability and gene action studies for yield and fibre traits in Gossypium arboreum using Griffings numerical and Haymans graphical approach

Background For the purpose of utilising hybrid vigour to produce possible hybrids with a suitable level of stability,the knowledge of gene activity and combining ability is a crucial prerequisite before choosing desirable parents.The present study was carried out with six parents crossed in full diallel fashion and generated 30 F1 hybrids.These hybrids were evaluated in two replications in Randomized Block Design at Department of Cotton,TNAU for combining ability and gene action.Diallel analysis was carried out according to Griffing’s method-I(parents + F_(1) + reciprocals) and model-I and Hayman’s graphical approach by using INDOSTAT software.Results Analysis of variance for combining ability indicated that mean square values of GCA,SCA and reciprocals were highly significant for all the traits except for the uniformity index.RG763 and K12 showed highly positively significant GCA effects for most of the yield traits while PA838 and K12 for fibre quality traits,so they were found as best general combiners.PAIG379 × K12 and PDB29 × K12 for yield traits,and PDB29 × PA838,RG763 × PA838,and CNA1007 × RG763 cross combinations for fibre quality traits could be recommended for future breeding programms.Conclusion The results of both Griffing’s and Hayman’s approaches showed that non-additive gene action predominates as SCA variance was bigger than GCA variance,so heterosis breeding is thought to be a more fruitful option for enhancing GCA of many traits.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

ROC and SAT Analysis of Different Grayscale Test Images (Distractors L and Target T) to Customize a Visual-Search Attention Task

Nowadays, there is a great need to investigate the effects of fatigue on physical as well as mental performance. The issues that are generally associated with extreme fatigue are that one can easily lose one’s focus while performing any particular activity whether it is physical or mental and this decreases one’s motivation to complete the task at hand efficiently and successfully. In the same line of thought, myriads of research studies posited the negative effects of fatigue on mental performance, and most techniques to induce fatigue to require normally long-time and repetitive visual search tasks. In this study, a visual search algorithm task was devised and customized using performance measures such as d’ (d-prime) and Speed Accuracy Trade-Off (SATF) as well as ROC analysis for classifier performance. The visual search algorithm consisted of distractors (L) and a target (T) whereby human participants had to press the appropriate keyboard button as fast as possible if they notice a target or not upon presentation of a visual stimulus. It was administered to human participants under laboratory conditions, and the reaction times, as well as accuracy of the participants, were monitored. It was found that the test image Size35Int255 was the best image to be used in terms of sensitivity and AUC (Area under Curve). Therefore, ongoing researches can use these findings to create their visual stimuli in such a way that the target and distractor images follow the size and intensity characteristics as found in this research.

中国大气污染防治政策与空气污染治理——基于城市级面板数据的实证研究

2013年颁布实施的《大气污染防治行动计划》是我国首个针对环境突出问题开展综合治理的行动计划;为巩固治理效果,应对动态变化的环境问题,2018年国务院进一步发布了《打赢蓝天保卫战三年行动计划》。研究利用中国258个城市2013年1月至2020年9月日度空气污染数据,实证检验了上述两阶段政策对空气质量的影响,并从不同污染物治理效果、时间和区域异质性等角度分析了政策效应的差异。结果指出:第一,“大气十条”和“蓝天保卫战”均显著降低了AQI、PM_(2.5)、PM_(10)、SO_(2)、NO_(2)、CO;第二,相比“大气十条”,“蓝天保卫战”对NO_(2)的减排效应更高,且实现对O_(3)的协同治理;第三,“大气十条”和“蓝天保卫战”对各污染物的减排效应因供暖时期、地区能源资源禀赋呈现出较强的异质性特征。

analysisB循环水泵故障引起线路保护启动及380 V电动机跳闸分析

文章对#6机“事故信号电源故障”、“循环水泵跳闸”、“A2给粉电源跳闸”(实际没跳闸)、“空预器跳闸或火灾报警”光字牌报警发出,“黄碧乙线A套线路保护保护启动”、“黄碧乙线B套线路保护保护启动”报警发出后自动复归;B循环水泵出口碟阀电源消失(黄色)不能操作、B空预器主电机跳闸辅助电机联动正常,A引风机自动调整跳手动,B轴加风机跳闸A轴加风机联动,A转冷泵联动,EH油#1循环泵跳闸,C给煤机跳闸,A引风机油站#2油泵跳闸#1油泵联动,B送风机风机油站#1油泵跳闸#2油泵联动,B送风机电机油站#1油泵跳闸#2油泵联动,A磨煤机油站#1油泵跳闸#2油泵联动,B磨煤机油站#1油泵跳闸#2油泵联动事件进行了分析。

动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法

高压断路器分合闸操作过程包括掣子脱扣、弹簧释能、动静触头碰撞等动作,机械振动信号作为能量传递的表现,蕴含着丰富的运行状态信息。该文基于断路器关键动作节点的时序,提出一种动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法。首先,由高速相机捕捉断路器操动过程主轴拐臂运动图像,利用目标跟踪算法获取动触头关键运动节点时序指标;然后,依据时序指标划分操动过程,通过对不同阶段的振动信号相空间重构,提取相空间吸引子几何和属性特征;最后,利用智能分类算法进行故障辨识,实验结果验证该方法在大幅节省时间开销的同时,有效提高了分类辨识的准确率达96.9%。该文方法在断路器状态监测和带电测试中具有广阔应用前景。

溯源弹簧形变过程的断路器振动信号递归量化分析辨识方法

从锁止机构脱扣引起储能弹簧释能,经部件带动动触头运动再到静止的每个动作具有严格阶段特征,伴随断路器动作的机械振动展现了能量传递及设备健康状态。该文提出一种溯源弹簧形变过程的断路器振动信号递归量化分析方法,首先由高速相机捕捉断路器操动时储能弹簧的动作图像,通过计算机视觉跟踪动态提取反映弹簧形变特征帧,再依据特征帧时序划分操动过程;然后将不同阶段振动信号映射至高维相空间,经递归分析得到体现动力系统变化特征的递归图,并递归量化分析其纹理结构特征;最后利用支持向量机模型对正常及故障状态下的断路器振动特征样本进行分析辨识,对比结果证明,由弹簧释能时序细化振动信号特征分析有效提高了分类识别准确率。该文方法在断路器操动机构状态辨识中具有广阔的应用前景。