The Research on the Logic and Value of“Two Combinations”

Through the comprehensive analysis of the connotation and logic,inheritance and innovation and the value of social governance,and the dialectical relationship of“two combination”,the inheritance of the idea of Marxism,the inheritance of excellent traditional culture and the governance of contemporary society.

Improving the understanding of the influencing factors on sea level based on wavelet coherence and partial wavelet coherence

Relationship between sea level change and a single climate indicator has been widely discussed.However,few studies focused on the relationship between monthly mean sea level(MMSL)and several key impact factors,including CO_(2) concentration,sea ice area,and sunspots,on various time scales.In addition,research on the independent relationship between climate factors and sea level on various time scales is lacking,especially when the dependence of climate factors on Nino 3.4 is excluded.Based on this,we use wavelet coherence(WC)and partial wavelet coherence(PWC)to establish a relationship between MMSL and its influencing factors.The WC results show that the influence of climate indices on MMSL has strong regional characteristics.The significant correlation between Southern Hemisphere sea ice area and MMSL is opposite to that between Northern Hemisphere sea ice area and MMSL.The PWC results show that after removing the influence of Nino 3.4,the significant coherent regions of the Pacific Decadal Oscillation(PDO),Dipole Mode Index(DMI),Atlantic Multidecadal Oscillation(AMO),and Southern Oscillation Index(SOI)decrease to varying degrees on different time scales in different regions,demonstrating the influence of Nino 3.4.Our work emphasizes the interrelationship and independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship.The study has an important reference significance for selecting the best predictors of sea level change or climate systems.

Gesture Recognition Based on Time-of-Flight Sensor and Residual Neural Network

With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.

MOLECULAR DYNAMICS SIMULATIONS OF FILLED AND EMPTY CAGE-LIKE WATER CLUSTERS IN LIQUID WATER AND THEIR SIGNIFICANCE TO GAS HYDRATE FORMATION MECHANISMS

Molecular dynamics simulations are performed to observe the evolutions of 512 and 51262 cage-like water clusters filled with or without a methane molecule immersed in bulk liquid water at 250 K and 230 K. The lifetimes of these clusters are calculated according to their Lindemann index δ (t) using the criteria of δ≥0.07. For both the filled and empty clusters, we find the dynamics of bulk water determines the lifetimes of cage-like water clusters, and that the lifetime of 512 62 cage-like cluster is the same as that of 512 cage-like cluster. Although the methane molecule indeed makes the filled cage-like cluster more stable than the empty one, the empty cage-like cluster still has chance to be long-lived compared with the filled clusters. These observations support the labile cluster hypothesis on the formation mechanisms of gas hydrates.

Ultrasonic Testing Combined with Pattern Recognition for the Detection of Kissing Bonds

Kissing bonds are defects in the adhesive bonds with intimate contact of touching surface but considerably lowered shear strength. Their detection specifically in the aerospace area is so not satisfactory. Usually, kissing bonds are inconspicuous in ultrasonic C-scans. However, the determination of attributes in the time domain and the frequency domain of an ultrasound signal provides the opportunity to derive a pattern for bonded area. Deviations from the pattern found in inconspicuous bonding areas indicate kissing bonds. The survey described here deals with the manufacturing of adhesively joint samples that purposefully include kissing bonds, as well as potential solutions for detecting them through ultrasonic testing combined with pattern recognition. The properties of the epoxy-based adhesive were varied by changing the mixing ratios between resin and hardener. Samples with a mixing ratio far apart from the manufacturer’s recommendation with an inconspicuous appearance in a C-scan, but low shear strength values were taken for further evaluation. After a definition and learning phase, a 100 percent hit rate to separate good bondings from kissing bonds could be derived in a blind test. The discriminating feature found is due to the frequency shift between good and kissing bonds as well as the relative amplitude of the second peak.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Detecting the Relationship Between Summer Rainfall Anomalies in Eastern China and the SSTA in the Global Domain with a New Significance Test Method

It is suggested that the multiple samples in a correlation map or a set of correlation maps should be examined with significance tests as per the Bernoulli probability model. Therefore, both the contemporaneous and lag correlations of summertime precipitation R in any one of the three regions of Northern China （NC）, the Changjiang-Huaihe River Valley （CHRV）, and Southern China （SC） with the SSTA in the global domain have been tested in the present article, using our significance test method and the method proposed by Livezey and Chen （1983） respectively. Our results demonstrate that the contemporaneous correlations of sum- mer R in CHRV with the SSTA are larger than those in NC. Significant correlations of SSTA with CHRV R are found to be in some warm SST regions in the tropics, whereas those of SSTA with NC R, which are opposite in sign as compared to the SSTA-CHRVR correlations, are found to be in some regions where the mean SSTs are low. In comparison with the patterns of the contemporaneous correlations, the 1 to 12 month lag correlations between NC R and SSTA, and those between CHRV summer R and SSTA show similar patterns, including the magnitudes and signs, and the spatial distributions of the coefficients. However, the summer rainfall in SC is not well correlated with the SSTA, no matter how long the lag interval is. The results derived from the observations have set up a relationship frame connecting the precipitation anomalies in NC, CHRV, and SC with the SSTA in the global domain, which is critically useful for our understanding and predicting the climate variabilities in different parts of China. Both NC and CHRV summer R are connected with E1 Nifio events, showing a ‘- -＇pattern in an E1 Nifio year and a‘＋ ＋＇ pattern in the subsequent year. Key words summer precipitation; eastern China; global sea surface

Performance Analysis of a Chunk-Based Speech Emotion Recognition Model Using RNN

Recently,artificial-intelligence-based automatic customer response sys-tem has been widely used instead of customer service representatives.Therefore,it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly.Therefore,we analyzed the performance of the emotion recognition(ER)accuracy as a function of the simulation time using the proposed chunk-based speech ER(CSER)model.The proposed CSER model divides voice signals into 3-s long chunks to effi-ciently recognize characteristically inherent emotions in the customer’s voice.We evaluated the performance of the ER of voice signal chunks by applying four RNN techniques—long short-term memory(LSTM),bidirectional-LSTM,gated recurrent units(GRU),and bidirectional-GRU—to the proposed CSER model individually to assess its ER accuracy and time efficiency.The results reveal that GRU shows the best time efficiency in recognizing emotions from speech signals in terms of accuracy as a function of simulation time.

Internal Logic and Significance of Times in Xi Jinping s Important Exposition of Poverty Alleviation

Eliminating poverty is the essential requirement of socialism. Since the 18th National Congress of the Communist Party of China, targeted poverty alleviation has become a major strategy for poverty alleviation and development in China. Xi Jinping s important exposition of poverty alleviation is the theoretical basis and practical guide to direct the effective implementation of China s targeted poverty alleviation strategy. It has gradually developed into an innovative theoretical system for poverty alleviation and development in the new era, with meticulous internal logic and a reputation for the significance of the times at home and abroad. Xi Jinping s thought of targeted poverty alleviation is the development and innovation of the theory and practice of poverty alleviation and development with Chinese characteristics. It is an important guarantee for China to win the battle to get rid of poverty and build a well-off society in an all-round way, and has contributed China s wisdom and China s plan to reducing poverty in the world.

The Research of Event Detection and Characterization Technology of Ticket Gate in the Urban Rapid Rail Transit

Making events recognition more reliable under complex environment is one of the most important challenges for the intelligent recognition system to the ticket gate in the urban rapid rail transit. The motion objects passing through the ticket gate could be described as a series of moving sequences got by sensors that located in the walkway side of the ticket gate. This paper presents a robust method to detect some classes of events of ticket gate in the urban rapid rail transit. Diffused reflectance infrared sensors are used to collect signals. In this paper, the motion objects are here referred to passenger(s) or (and) luggage(s), for which are of frequent occurrences in the ticket gate of the urban railway traffic. Specifically, this paper makes two main contributions: 1) The proposed recognition method could be used to identify several events, including the event of one person passing through the ticket gate, the event of two consecutive passengers passing through the ticket gate without a big gap between them, and the event of a passenger walking through the ticket gate pulling a suitcase;2) The moving time sequence matrix is transformed into a one-dimensional vector as the feature descriptor. Deep learning (DL), back propagation neural network (BP), and support vector machine (SVM) are applied to recognize the events respectively. BP has been proved to have a higher recognition rate compared to other methods. In order to implement the three algorithms, a data set is built which includes 150 samples of all kinds of events from the practical tests. Experiments show the effectiveness of the proposed methods.

The Impact of Switching Standard on Accounting Quality

In a developing country, modernization and change of the accounting regime are possible if the standards are in compliance with global ones. The change of accounting standards adopted by Turkey started to be implemented, and this created a number of qualitative and quantitative results. This study examines the impact of change of accounting standards on accounting quality. In order to determine how switching standard reflects accounting quality, first of all, the earnings management, timely loss recognition, and value relevance variables pertaining to accounting quality were listed and the findings were stated after subjecting the obtained data to statistical analyses. Accordingly, by the transition to International Financial Reporting Standards （IFRS）, the earnings management practices were observed to decrease as compared with the pre-IFRS period and the timely loss recognition and value-relevance values were observed to increase, which constitute the dimensions of accounting quality. It was also concluded that by the switch from domestic accounting standards to International Accounting Standards （IAS）, the quality of accounting in the country was improved and the market became more active than it was before.

Depolarization Degree to Determine Dihedral Attribute of Radar Target

This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB.

一种基于神经网络的OFDM信号识别与符号估计改进方法

针对正交频分复用(OFDM)信号调制识别与符号估计问题,提出了一种基于深度学习神经网络(DNN)的新方法。该方法通过深入分析OFDM信号的实际传输模型,同时采用优化的Dropout策略防止过度拟合,可适用于多种调制类型数据集的训练,增强了网络学习的泛化能力;另一方面,基于改进的OFDM导频数据训练结构,提高计算效率。该新思路增强了抗噪性能,无需大量的数据先验需求,具有良好的稳健性和工程实用性。仿真结果表明新方法的识别及估计性能比起过去传统思路更优,且可在低信噪比情况下成功实现识别及估计。

A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition

Many systems of handwritten digit recognition built using the complete set of features in order to enhance the accuracy. However, these systems lagged in terms of time and memory. These two issues are very critical issues especially for real time applications. Therefore, using Feature Selection (FS) with suitable machine learning technique for digit recognition contributes to facilitate solving the issues of time and memory by minimizing the number of features used to train the model. This paper examines various FS methods with several classification techniques using MNIST dataset. In addition, models of different algorithms (i.e. linear, non-linear, ensemble, and deep learning) are implemented and compared in order to study their suitability for digit recognition. The objective of this study is to identify a subset of relevant features that provides at least the same accuracy as the complete set of features in addition to reducing the required time, computational complexity, and required storage for digit recognition. The experimental results proved that 60% of the complete set of features reduces the training time up to third of the required time using the complete set of features. Moreover, the classifiers trained using the proposed subset achieve the same accuracy as the classifiers trained using the complete set of features.

Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops aModifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(MWHODL-SHAR)model.The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented MWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTM)model is applied for activity recognition.The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm.The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of theMWHODL-SHAR technique over other recent approaches.

Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model(TTPM)

Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more precisely.On the other hand,detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer(DETR)model is used for object(vehicle)detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.

基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法

针对低信噪比条件下的扩频与常规调制信号分类精度低的问题,该文提出一种基于生成式对抗网络(GAN)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多模态注意力机制信号调制识别方法。首先生成待识别信号的时频图像(TFIs),并利用GAN实现TFIs降噪处理;然后将信号的同相正交数据(I/Q data)与TFIs作为模型输入,并搭建基于CNN的TFIs识别支路和基于LSTM的I/Q数据识别支路;最后,在模型中添加注意力机制,增强I/Q数据和TFIs中重要特征对分类结果的决定作用。实验结果表明,该文所提方法相较于单模态识别模型以及其它基线模型,整体分类精度有效提升2%~7%,并在低信噪比条件下具备更强的特征表达能力和鲁棒性。

增强现实技术的软硬件研究

增强现实技术是在现实世界里融入虚拟世界和、或现实世界的技术,它是一门新兴技术。它具有广阔的应用空间,会给人们的工作、生活带来极大的便利。本文尝试着把增强现实系统进行改进,并且根据人们不同的需求,实时的实现各式各样的增强现实技术应用。让增强现实技术不但实用,而且功能强大,内容丰富多彩。由此可发现,大规模应用增强现实技术不但可以提高生产效率,也可以提高人们的生活质量。增强现实技术会带给我们巨大的好处。

新安煤矿人员定位系统升级改造的研究与应用

煤矿井下人员定位系统是加强入井人员管理、提升生命保障、促进煤矿安全生产的高端技术。新安煤矿在用的KJ251型人员定位系统安装于2011年,采用RFID定位技术实现区域定位,该技术具有实现简单、系统建设成本低的特点。随着煤矿井下智能化的推进,对人员定位提出了更高的需求,现主要存在浏览器内核版本、系统中心站软件及数据库架构满足不了当前信息化发展需求、系统数据不稳定和系统未配备唯一检卡装置等问题。结合煤矿井下特殊的作业环境,煤矿井下人员定位系统可实现对入井人员的实时监测、精确定位、轨迹回放、考勤管理、报表查询、信息网络发布、双向通信、人机交互、紧急搜救、生产调度等功能,为煤矿安全生产以及紧急救援提供第一手可靠的决策实时信息。

基于时频Grad-CAM的调制识别网络可解释分析

针对时频深度学习调制识别方法存在可解释性差的问题,提出一种基于时频梯度加权类激活映射(GradCAM)的调制识别网络可解释框架.该框架通过时频Grad-CAM可视化深度模型中隐含层的关键特征,从视觉上解释网络隐含层提取的时频深度特征对于正确与错误识别中的作用,揭示低信噪比环境下网络性能下降的内在机理,并通过量化和排序网络中每层不同卷积核的贡献值来判断网络的冗余程度.仿真实验结果验证了基于时频Grad-CAM的调制识别网络可解释性框架的有效性;可解释分析结果表明,在低信噪比环境下,网络特征提取区域有大量噪声存在,且本文所测试的调制识别网络冗余程度较为严重.