面向关键性任务的机载软件服务QoS体系设计技术

随着机载软件系统向灵活开放的服务化架构转变,如何保障服务质量成为服务治理的重点。创新性地将服务质量(QoS)应用至机载软件服务,以保证服务集群的持续可靠运行。综合考虑实际条件下表征机载软件服务质量的数值,并结合关键性任务OODA作战要素,设计机载软件服务QoS指标体系,同时针对每项指标提出相应的QoS策略。在此基础上,构建机载软件服务QoS体系模型,实现对机载软件服务体系能力多层次、多方面质量保障。通过构建不同服务故障场景验证了所提出的机载软件QoS体系模型可较为客观地衡量机载软件服务实时运行状况,并通过策略实施保障机载软件服务平稳可靠运行,从而有效提高服务质量,确保任务高效完成,实现机载软件服务化架构的高可用性。

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Early prediction cardiac arrest in intensive care units:the value of laboratory indicator trends

The incidence of in-hospital cardiac arrest (IHCA) has increased over the past decade,with more than half occurring in intensive care units (ICUs).^([1])ICU cardiac arrest (ICU-CA)presents unique challenges,with worse outcomes than those in monitored wards,highlighting the need for early detection and intervention.^([2])Up to 80%of patients exhibit signs of deterioration hours before IHCA.^([3])Although early warning scores based on vital signs are useful,their eff ectiveness in ICUs is limited due to abnormal physiological parameters.^([4])Laboratory markers,such as sodium,potassium,and lactate,are predictive of poor outcomes,^([5])but static measurements may not capture the patient’s trajectory.Trends in laboratory indicators,such as variability and extremes,may offer better predictive value.^([6])This study aimed to evaluate ICU-CA predictive factors,with a focus on vital signs and trends of laboratory indicators.

Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Short-Term Photovoltaic Power Prediction Based onMulti-Stage Temporal Feature Learning

Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.

Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces

The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.

Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients

AIM:To investigate the influence of postoperative intraocular lens(IOL)positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II(BUII)IOL formula for calculation.METHODS:The prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022.The collected data included the best-corrected visual acuity(BCVA),corneal curvature,preoperative and postoperative central anterior chamber depths(ACD),axial length(AXL),IOL power,and refractive error.BUII formula was used to calculate the IOL power.The mean absolute error(MAE)was calculated,and all the participants were divided into two groups accordingly.Independent t-tests were applied to compare the variables between groups.Logistic regression analysis was used to analyze the influence of age,AXL,corneal curvature,and preoperative and postoperative ACD on MAE.RESULTS:A total of 261 patients were enrolled.The 243(93.1%)and 18(6.9%)had postoperative MAE<1 and>1 D,respectively.The number of females was higher in patients with MAE>1 D(χ^(2)=3.833,P=0.039).The postoperative BCVA(logMAR)of patients with MAE>1 D was significantly worse(t=-2.448;P=0.025).After adjusting for gender in the logistic model,the risk of postoperative refractive errors was higher in patients with a shallow postoperative anterior chamber[odds ratio=0.346;95% confidence interval(CI):0.164,0.730,P=0.005].CONCLUSION:Risk factors for biometry prediction error after cataract surgery include the patient’s sex and postoperative ACD.Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

面向SDN的QoS优化:一种蚁群算法改进策略的设计与实现

服务质量(QoS)优化作为提升网络性能的关键手段,在SDN中的应用尤为重要。蚁群算法作为一种启发式的计算方法,由于其出色的全局搜索和并行处理特性,在QoS优化领域显示出了巨大的应用潜力。但是蚁群算法面对SDN动态网络环境、大规模网络、多目标优化及实时性需求,还面临着适应性、收敛速度、平衡性及实时性方面的挑战。该文针对以上问题提出蚁群算法改进策略,通过设计和实施措施改善SDN环境中蚁群算法QoS优化性能。

A robust statistical prediction model for late-summer heavy precipitation days in North China

Recently,heavy precipitation(HP)events have occurred frequently in North China(NC),causing devastating economic losses and human fatalities.However,the short-term climate prediction of HP is quite limited.Combining year-to-year increment(DY)method and sliding correlations,we developed a robust seasonal prediction model for late-summer HP days(HPDs)in NC during 1982–2022,utilizing three independent predictors—February sea surface temperature(SST)in the Indian Ocean(SST_IO),February snow depth over North Asia(SDE_NA),and May melted snow depth in NC(MSDE_NC).The SST_IO anomalies affect NC's precipitation through the Pacific-Japan pattern.The SDE_NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia.The MSDE_NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies.This prediction model can well simulate the variations of the HPDs,with a correlation coefficient(CC)of 0.81(0.65)between the observed and predicted HPDs_DY(HPDs_anomaly).The percentage with the same sign for 15 extreme HPDs_anomaly years(PSSE)is 100%.Moreover,in the cross-validation test during 1982–2022,the PSSE for HPDs_anomaly is as high as 100%,along with a low rootmean-square error of 1.14.For independent hindcasts during 2013–2022,the CC between the observed and predicted HPDs_DY(HPDs_anomaly)is 0.93(0.83),together with high Nash-Sutcliffe efficiency(0.82)and agreement index(0.89).Specifically,the predictions are broadly consistent with the observations for 2015,2016,2017,2021,and 2022,reflecting excellent performance of this prediction model of HPDs in NC.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

共享制造环境下基于Z-RIM的QoS多方异质评价方法

为解决共享制造环境下决策信息异质、候选服务数量动态变化的制造服务QoS评价问题,基于平台、服务需求方、服务提供方的三方决策信息,提出一种符合共享制造特点的QoS多方异质制造服务评价方法。通过精确数、语言变量和Z-number处理异质信息;基于共识最大化计算决策方权重向量,利用TrFWAA算子集结多方信息,依据离差最大化模型计算属性权重;在此基础上,采用RIM对各服务进行评估。结合共享制造环境下的制造业服务优选案例对提出的方法进行验证分析,结果表明该方法具有较好的实用性和优越性。

基于SDN的卫星网络端到端QoS类映射研究

针对在多个卫星网络域环境下域间协作困难,以及如何在各个卫星网络域内选择合适的QoS类实现端到端QoS类映射的问题,基于软件定义卫星网络架构,首先,提出一种SDN卫星网络域间通信方案,以及面向多QoS类的端到端服务交付数据包处理方法。其次,提出一种基于多准则决策的控制模块。模块利用ELECTRE方法,在ELECTRE方法中通过综合权重法计算出各个卫星网络域内QoS指标相应的权重系数,并且利用ELECTRE方法对每个QoS类的净支配指数进行排序,从而为端到端路径的每个卫星网络域选择合适的QoS类。最后,与现有端到端QoS类映射方案进行比较,仿真结果表明,上述方法在多卫星网络域环境下保证端到端的时延、时延抖动、丢包率和代价。

基于遗传-蚁群优化算法的QoS组播路由算法设计

为了提高网络路由性能,提出并设计了一种基于遗传-蚁群优化算法的服务质量(quality of service,QoS)组播路由算法。首先,设计了自适应变频采集策略用于采集网络与节点信息,以此获得网络和节点的状态,为后续路由优化提供数据支持;其次,计算路径代价,将路径代价最小作为优化目标,建立QoS组播路由优化模型,并设置相关约束条件;最后,结合遗传算法和蚁群算法提出一种遗传-蚁群优化算法求解上述模型,输出最优路径,完成路由优化。实验结果表明,所提算法可有效降低路径长度与路径代价,提高搜索效率与路由请求成功率,优化后的路由时延抖动较小。

基于QoS约束的通信组网链路故障恢复探究

随着网络技术的不断发展,通信网的规模逐渐扩大,网络结构日渐趋于复杂化,发生故障的概率自然就会增高。当通信网出现故障后,必须尽快恢复,否则可能会造成巨大的经济损失,严重时甚至引发各类社会安全风险。智能通信网是解决上述问题的有效策略之一,对网络带宽、时延、丢包率提出了不同的要求。本文对如何恢复网络故障展开了研究,提出了一种基于QoS(Quality of Service)约束的通信组网链路故障恢复方法,根据用户对业务提出的QoS需求以及空闲网络资源,选择恢复路径,确保传输业务的可靠性。在当通信网发生故障后,该方法能够有针对性地快速解决故障,对通信技术的应用与发展具有实用价值。

云服务推荐中基于多源特征和多任务学习的时序QoS预测

随着云计算技术的普及,云服务数量指数级增长,用户不再满足于功能性需求,服务质量(Quality of Service,QoS)成为比较服务优劣的关键性能指标.如何在动态、复杂的云环境中实时、准确地预测服务质量并为用户推荐高质量服务成为热点问题.考虑到云服务器的负载、网络状态、用户接入云环境的偏好等随着时间变化,本文提出了基于多源特征和多任务学习的时序QoS预测方法(T-MST),它可以实时、准确地同时预测多种QoS属性.首先,TMST对用户、服务进行特征表示,通过Time2Vec刻画时序特征,再结合多种QoS属性的历史记录生成多源特征表示.其次,基于滑动窗口采用LSTM感知窗口内的时序关系,借助注意力机制细化窗口内不同时刻的关键性,从而构造待预测时刻的隐藏状态.最后,T-MST采用多任务预测层实现多种QoS属性的同时预测,它们共享上游模型,仅在预测层采用不同的感知模块以提升模型的鲁棒性和计算效率.本文基于真实世界的数据集进行了全面的实验验证,结果表明T-MST在吞吐量和响应时间的时序预测任务上平均绝对误差(Mean Absolute Error,MAE)分别平均提升了37.53%和20.38%,优于现有的时序QoS预测方法;而且TMST的计算效率更高,能够有效应对实时QoS预测的需求.

QoS Aware Power and Hop Count Constraints Routing Protocol with Mobility Prediction for MANET Using SHORT

A mobile ad hoc network (MANET) is composed of mobile nodes, which do not have any fixed wired communication infrastructure. This paper proposes a protocol called “Delay, Jitter, Bandwidth, Cost, Power and Hop count Constraints Routing Protocol with Mobility Prediction for Mobile Ad hoc Network using Self Healing and Optimizing Routing Technique (QPHMP-SHORT)”. It is a multiple constraints routing protocol with self healing technique for route discovery to select a best routing path among multiple paths between a source and a destination as to increase packet delivery ratio, reliability and efficiency of mobile communication. QPHMP-SHORT considers the cost incurred in channel acquisition and the incremental cost proportional to the size of the packet. It collects the residual battery power of each node for each path;selects multiple paths, which have nodes with good battery power for transmission to satisfy the power constraint. QPHMP-SHORT uses Self-Healing and Optimizing Routing Technique (SHORT) to select a shortest best path among multiple selected paths by applying hops count constraint. It also uses the mobility prediction formula to find the stability of a link between two nodes.

Classifying rockburst with confidence:A novel conformal prediction approach

The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.