YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Discussion on the Formation of Anomalously Lower Temperature in Liaoning Province in April,2010

Making use of the temperature data from 53 stations in Liaoning Province in April from 1961 to 2010 and the data of 500 hPa height field and sea surface temperature issued by National Climate Center,the characteristics of temperature,sea surface temperature(SST) and 500 hPa height field in April in 2010 were analyzed.The results showed that the anomalously lower temperature in April in 2010 was mainly caused by the anomalous Arctic Oscillation(AO),so as to provide scientific basis for short-term climate prediction.

Research and Application of In-seam Seismic Survey Technology for Disaster-causing Potential Geology Anomalous Body in Coal Seam

In order to effectively detect potential geology anomalous bodies in coal bearing formation,such as coal seam thickness variation,small faults,goafs and collapse columns,and provide scientific guidance for safe and efficient mining,the SUMMIT-II EX explosion-proof seismic slot wave instrument,produced by German DMT Company,was used to detect the underground channel wave with the help of transmission method,reflection method and transflective method.Region area detection experiment in mining face had been carried out thanks to the advantage of channel wave,such as its great dispersion,abundant geology information,strong anti-interference ability and long-distance detecting.The experimental results showed that:(1)Coal seam thickness variation in extremely unstable coal seam has been quantitatively interpreted with an accuracy of more than 80%generally;(2)The faults,goafs and collapse columns could be detected and predicted accurately;(3)Experimental detection of gas enrichment areas,stress concentration regions and water inrush risk zone has been collated;(4)A research system of disaster-causing geology anomalous body detection by in-seam seismic survey has been built,valuable and innovative achievements have been got.Series of innovation obtained for the first time in this study indicated that it was more effective to detect disaster-causing potential geology anomalies by in-seam seismic survey than by ground seismic survey.It had significant scientific value and application prospect under complex coal seam conditions.

Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)testing.Nevertheless,this testing method is accurate enough for the diagnosis of COVID-19.However,it is time-consuming,expensive,expert-dependent,and violates social distancing.In this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural networks.In multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently.We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.

Deinterleaving of radar pulse based on implicit feature

In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.

夏季欧亚环流的低频振荡对长江流域降水影响的多尺度诊断

长江流域夏季降水与欧亚中高纬大气低频振荡密切相关,定量诊断不同尺度环流异常对降水的贡献,对于提高旱涝灾害预测准确率具有重要意义。利用ERA5逐日再分析资料和CN05.1逐日降水资料,通过定义环流指数(CI),将夏季欧亚环流划分为两脊一槽(DR)型和两槽一脊(DT)型,并探究了其低频特征以及不同尺度环流异常对长江流域降水的影响。结果表明:两类环流型是环流指数低频循环周期中的两个不同位相,均具有10~30 d振荡周期,对长江流域低频降水有着重要影响。其主要表现为:①两类环流型对应的500 hPa低频环流场在欧亚中高纬呈现正、负位势高度异常交替的波列结构,具有明显向东传播的特征,平均传播速度约为每天5°(经度);②当DR型达峰值位相时,850 hPa低频风场中鄂霍次克海反气旋南侧的东北风引导中高纬干冷空气向长江流域入侵,与来自南海反气旋西侧的西南气流交汇,使太平洋和南海的大量水汽向长江流域输送,低频降水正异常滞后于环流2 d达到最强,DT型的特征则与之相反;③欧亚环流对长江流域低频降水的影响以大于30 d的背景水汽条件和10~30 d低频尺度环流的共同作用为主导,小于10 d的天气尺度环流次之,大于30 d的背景环流较前二者作用偏弱,并且水汽辐合项约为水汽平流项贡献的3倍,先由湿平流提供充足的水汽条件,再通过较强的水汽辐合产生降水。

工业加热电炉高耗电异常DCS数据主动采集技术研究

工业加热电炉在运行过程中容易出现高耗电现象,且异常用电数据产生的频率较高、数据量较大,使得对异常数据的采集效率降低。为此,提出工业加热电炉高耗电异常分布式控制系统数据主动采集技术研究。利用局部平均值计算工业加热缺失DCS数据,采用拉格朗日插值法对其插补,对插补后的数据标准化处理。构建用电不平衡特征矩阵,采用局部离群因子检测算法,提取出用电异常数据特征集,构建基于长短期记忆网络(long short-term memory,简称LSTM)的分位数回归模型,实现工业电炉异常DCS数据的主动采集。实验结果表明,所提方法有效地提高了采集效率和采集精度,采集平均用时仅为029ms,其准确率-召回率在98%以上。

基于自适应集成学习的异常流量检测

提出了一种基于自适应集成学习的异常流量检测方法,使用离散傅里叶变换提取流量的频域特征,使得对流量特征提取过程中信息损失较小.用一种基于稳定性和准确性波动的评估指标来动态评估当前流量特征的可靠性,通过评估的特征数据块用于生成新的子分类器.同时,设计了一种集成自适应分类器,其参数和子分类器会根据当前的情况进行实时调整.实验结果表明,该方法对于解决异常流量检测中的概念漂移问题和机器学习对抗攻击问题有良好的效果.

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.

基于全局上下文网络的视频异常行为检测方法

文中针对视频信息中的长距离时间特征关系易被忽略的问题,提出了一种基于全局上下文网络的弱监督视频异常行为检测方法。为了提升对视觉场景的全局理解,提高异常检测的准确性,对时间特征提取模块进行改进,仅计算一个与查询位置无关的全局注意力矩阵,并对所有查询位置共享,有效降低网络计算量和参数量。同时进行网络模块优化,加快运算速度。实验结果表明,基于全局上下文网络的视频异常行为检测算法能够在网络更具轻便性、运算效率更高的情况下有效提高异常检测准确率。

An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTM networks

Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.

基于声振信号的电机故障诊断方法

电机在工作时产生的异常噪音往往表明电机存在潜在故障或处于不良的工作状态.本研究提出了一种基于振动信号分析的电机异音检测方法,该方法通过安装加速度传感器和麦克风等设备采集电机运行时的振动信号数据,并对采集到的振动信号进行信号处理和特征提取.接着构建一个分类模型,利用支持向量机算法对提取的特征进行训练和分类.实验结果表明,该方法在检测电机异常噪音方面展现出良好的性能和准确性.大量实际电机运行数据测试结果表明该方法能够有效地判断电机是否存在异常噪音,并提前预测潜在故障.

Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.

基于改进Hadoop挖掘框架的电力通信异常数据提取研究

电力通信系统异常数据往往隐藏在海量数据当中,导致Hadoop挖掘框架在异常数据提取中的覆盖度较低。因此,提出基于改进Hadoop挖掘框架的电力通信异常数据提取研究。通过预处理策略如标准化、滤波及复数信号归一化提高数据质量。引入本地数据聚合优化组件优化数据传输,采用多NameNode Hadoop架构解决单节点瓶颈问题,并结合K-Means聚类算法进行数据挖掘。通过特征评估与筛选和并行聚类分析,有效识别出关键的异常数据特征。实验结果显示,该方法能显著提高异常数据的提取覆盖度。

Anomalous Features of Extreme Meiyu in 2020 over the Yangtze-Huai River Basin and Attribution to Large-Scale Circulations

Extremely anomalous features of Meiyu in 2020 over the Yangtze-Huai River basin(YHRB)and associated causes in perspective of the large-scale circulation are investigated in this study,based on the Meiyu operational monitoring information and daily data of precipitation,global atmospheric reanalysis,and sea surface temperature(SST).The main results are as follows.(1)The 2020 YHRB Meiyu exhibits extremely anomalous characteristics,which are the most prominent since the 1980 s.The 2020 Meiyu season features the fourth earliest onset,the third latest retreat,the longest duration,the maximum Meiyu rainfall,the strongest mean rainfall intensity,and the maximum number of stations/days with rainstorm.(2)The extremely long duration of the 2020 Meiyu season lies in the farily early onset and late retreat of Meiyu in this particular year.The early onset of Meiyu is due to the earlier-than-normal first northward shift and migration of the key influential systems including the northwestern Pacific subtropical high(NWPSH)and the South Asian high(SAH)along with the East Asian summer monsoon,induced by weak cold air activities from late May to early mid-June.However,the extremely late retreat of Meiyu is because of later-than-normal second northward shift of the associated large-scale circulation systems accompanied with strong cold air activities,and extremely weak and southward located ITCZ over Northwest Pacific in July.(3)The extremely more than normal Meiyu rainfall is represented by its long duration and strong rainfall intensity.The latter is likely attributed to extreme anomalies of water vapor convergence and vertical ascending motion over the YHRB,resulting from the compound effects of the westward extended and enlarged NWPSH,the eastward extended and expanded SAH,and the strong water vapor transport associated with the low-level southerly wind.The extremely warm SST in the tropical Indian Ocean seems to be the key factor to induce the above-mentioned anomalous large-scale circulations.The results from this study serve to improve understanding of formation mechanisms of the extreme Meiyu in China and may help forecasters to extract useful large-scale circulation features from numerical model products to improve medium-extended-range operational forecasts.

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues.Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage;otherwise,it may develop a sight-threatening and even eye-globe-threatening condition.In this paper,we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images.In this approach,we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis.In a comparison,the performance of the proposed sequential-level deep model achieved 80%diagnostic accuracy,far better than the 49.27%±11.5%diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.

Multi-head attention-based long short-term memory model for speech emotion recognition

To fully make use of information from different representation subspaces,a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER).The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM layer.Here,a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context vectors.To provide relative vital information from other dimensions,the output of MHTA,the output of feature-dimension attention,and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected layer.To improve the performance of multiple vectors,feature-dimension attention was employed for the all-time output of the first LSTM layer.The proposed model was evaluated on the eNTERFACE and GEMEP corpora,respectively.The results indicate that the proposed model outperforms LSTM by 14.6%and 10.5%for eNTERFACE and GEMEP,respectively,proving the effectiveness of the proposed model in SER tasks.

成都地震监测中心站大气电场数据特征分析

基于成都地震监测中心站大气电场监测数据,结合气象观测资料,统计分析不同气象条件下大气电场强度的时序变化特征。结果显示:晴天大气电场峰值出现在08∶00,电场峰值变化范围为0.2~0.4 kV/m,日变化特征较为明显,冬季晴天大气电场强度较大,季节变化特征显著。雷暴天气大气电场峰值绝对值大于4.0 kV/m,大气电场强度正负交替、剧烈变化特征明显。降雨天气大气电场峰值绝对值变化范围为0.5~1.7 kV/m,大气电场持续负值变化特征明显。基于区域大气电场异常分析,研究气象因素对大气电场异常影响的共性特征,为有效判识地质构造活动引起的大气电场前兆异常信号提供研究基础。