Detection of Parkinson’s Disease with Multiple Feature Extraction Models and Darknet CNN Classification

Parkinson’s disease (PD) is a neurodegenerative disease in the central nervous system. Recently, more researches have been conducted in the determination of PD prediction which is really a challenging task. Due to the disorders in the central nervous system, the syndromes like off sleep, speech disorders, olfactory and autonomic dysfunction, sensory disorder symptoms will occur. The earliest diagnosing of PD is very challenging among the doctors community. There are techniques that are available in order to predict PD using symptoms and disorder measurement. It helps to save a million lives of future by early prediction. In this article, the early diagnosing of PD using machine learning techniques with feature selection is carried out. In the first stage, the data preprocessing is used for the preparation of Parkinson’s disease data. In the second stage, MFEA is used for extracting features. In the third stage, the feature selection is performed using multiple feature input with a principal component analysis (PCA) algorithm. Finally, a Darknet Convolutional Neural Network (DNetCNN) is used to classify the PD patients. The main advantage of using PCA- DNetCNN is that, it provides the best classification in the image dataset using YOLO. In addition to that, the results of various existing methods are compared and the proposed DNetCNN proves better accuracy, performance in detecting the PD at the initial stages. DNetCNN achieves 97.5 % of accuracy in detecting PD as early. Besides, the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.     

An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction

Certain degree of deformation is natural while dam operates and evolves. Due to the impact of internal and external environment, dam deformation is highly nonlinear by nature. For dam safety, it is of great significance to analyze timely deformation monitoring data and be able to predict reliably deformation. A comprehensive review of existing deformation prediction models reveals two issues that deserves further attention: (1) each environmental influencing factor contributes differently to deformation, and (2) deformation lags behind environmental factors (e.g., water level and air temperature). In response, this study presents a combination deformation prediction model considering both quantitative evaluation of influencing factors and hysteresis correction in order to further improve estimation accuracy. In this study, the complex relationship in deformation prediction is effectively captured through support vector machine (SVM) modeling. Furthermore, a modified fruit fly optimization algorithm (MFOA) is presented for SVM hyper-parameter optimization. Also, a synthetic evaluation method and a hysteresis quantification algorithm are introduced to further enhance the MFOA-SVM-based model in regards to contribution quantification and phase correction respectively. The accuracy and validity of the proposed model is evaluated in a concrete dam case, where its performance is compared with other existing models. The simulated results indicated that the proposed nonlinear MFOA-SVM model considering both quantitative evaluation and hysteresis correction, abbreviated as SEV-MFOA-SVM, is more accurate and robust than conventional models. This novel model also provides an alternative method for predicting and analyzing dam deformation and evolution behavior of other similar hydraulic structures.     

Flexible latent variable models for multi-task learning

Given multiple prediction problems such as regression or classification, we are interested in a joint inference framework that can effectively share information between tasks to improve the prediction accuracy, especially when the number of training examples per problem is small. In this paper we propose a probabilistic framework which can support a set of latent variable models for different multi-task learning scenarios. We show that the framework is a generalization of standard learning methods for single prediction problems and it can effectively model the shared structure among different prediction tasks. Furthermore, we present efficient algorithms for the empirical Bayes method as well as point estimation. Our experiments on both simulated datasets and real world classification datasets show the effectiveness of the proposed models in two evaluation settings: a standard multi-task learning setting and a transfer learning setting.     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

一种基于元模型的访问控制策略描述语言

为了保护云资源的安全,防止数据泄露和非授权访问,必须对云平台的资源访问实施访问控制.然而,目前主流云平台通常采用自己的安全策略语言和访问控制机制,从而造成两个问题：（1）云用户若要使用多个云平台,则需要学习不同的策略语言,分别编写安全策略;（2）云服务提供商需要自行设计符合自己平台的安全策略语言及访问控制机制,开发成本较高.对此,提出一种基于元模型的访问控制策略描述语言PML及其实施机制PML-EM.PML支持表达BLP、RBAC、ABAC等访问控制模型.PML-EM实现了3个性质：策略语言无关性、访问控制模型无关性和程序设计语言无关性,从而降低了用户编写策略的成本与云服务提供商开发访问控制机制的成本.在OpenStack云平台上实现了PML-EM机制.实验结果表明,PML策略支持从其他策略进行自动转换,在表达云中多租户场景时具有优势.性能方面,与OpenStack原有策略相比,PML策略的评估开销为4.8%.PML-EM机制的侵入性较小,与云平台原有代码相比增加约0.42%.     

基于采样的半监督支持向量机软件缺陷预测方法

软件缺陷预测有助于提高软件开发质量,保证测试资源有效分配。针对软件缺陷预测研究中类标签数据难以获取和类不平衡分布问题,提出基于采样的半监督支持向量机预测模型。该模型采用无监督的采样技术,确保带标签样本数据中缺陷样本数量不会过低,使用半监督支持向量机方法,在少量带标签样本数据基础上利用无标签数据信息构建预测模型;使用公开的NASA软件缺陷预测数据集进行仿真实验。实验结果表明提出的方法与现有半监督方法相比,在综合评价指标[F]值和召回率上均优于现有方法;与有监督方法相比,能在学习样本较少的情况下取得相当的预测性能。     

基于尖峰自组织模糊神经网络的需水量预测

短期需水量预测是城市给水管网安全稳定运行的前提和保证.针对日需水量预测提出一种基于尖峰机制的自组织模糊神经网络(SSOFNN)模型.针对影响变量复杂多变的特点,采用主成分分析对原始数据进行降维处理,获取线性无关的主成分变量作为预测模型输入数据.SSOFNN模型根据尖峰强度和误差指标在训练过程中对隐含层神经元进行增长修剪,结合改进Leveberg-Marquardt算法简化参数更新过程中的计算过程,大大减少了计算量,能够获得紧凑的网络结构,且跟踪精度高,运行时间短,预测效果好.     

基于金枪鱼群算法优化极限学习机的混凝土抗压强度预测

混凝土抗压强度是建筑结构设计与评价一个重要指标,它直接关乎建筑的质量与安全。为解决现有机器学习模型对其预测存在预测耗时长、精度不够高,不能很好地满足施工现场对混凝土抗压强度预测实时性与准确性要求的问题,提出一套基于新式仿生算法金枪鱼群算法优化极限学习机（TSO-ELM）的混凝土抗压强度预测方法。该方法通过对ELM隐藏层初始参数中的连接权值与偏置值使用TSO进行寻优,有效提升了ELM的预测准确度。在仿真实验部分,通过两组混凝土数据集对ELM的预测速度、TSO的寻优能力、TSO-ELM模型的泛化性逐一进行验证。结果表明,该方法可以有效提高预测的速度与精准度,迭代次数更少,同时具有良好的泛化性,为现场施工及时进行混凝土抗压强度的预测提供了一种新方法。     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

Development of Box–Jenkins type time series models by combining conventional and orthonormal basis filter approaches

A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column.     

Model reduction for multidisciplinary optimization - application to a 2D wing

Multidisciplinary optimization (MDO) is a growing field in engineering, with various applications in aerospace, aeronautics, car industry, etc. However, the presence of multiple disciplines leads to specific issues, which prevent MDO to be fully integrated in industrial design methodology. In practice, the key issues in MDO lie in the management of the interconnections between disciplines, along with the high number of simulations required to find a feasible multidisciplinary (optimal) solution. Therefore, in this paper, a novel approach is proposed, combining proper orthogonal decomposition to decrease the amount of data exchanged between disciplines, with surrogate models based on moving least squares to reduce disciplines. This method is applied to an original 2D wing demonstrator involving two disciplines (fluid and structure). The numerical results obtained for an optimization task show its benefits in diminishing both the interfaces between disciplines and the overall computational time.     

Big data application in exchange rate financial prediction platform based on FPGA and human-computer interaction

A prediction is a statement about the financial market. The financial market prediction may lack sufficient reasons or any good stock market analysis. The financial prediction may be correct or inaccurate on any given occasion, or average, Model-based or information. The financial prediction is made by various methods, including hundreds of economic evaluation and test systems, which are Observable in the gate array. The Digital signal processing system and IoT (Internet of thing) for exchange rate finical perdition platform in the previous method. The previous method is difficulty in lower investment to reduce inflation and false value setting. The proposed method is based on Programmable Gate and learning for finical predication. A critical challenge of financial forecasting issues, along with opportunities that arise from the unique characteristics of financial data, signal-to-noise ratios, persistent predictors, predictive instability and environmental predictability resulting from competitive pressure and investors learning. The machine approaches for predicting the mean, variance, and probability distribution of asset returns. Programmable Gate Array covers how to evaluate financial forecasts, which leads to data mining concerns, taking into account the possibility that numerous forecast models are being considered.     

Assessing practical usefulness and performance of the PREDIQT method: An industrial case study

ContextWhen adapting a system to new usage patterns, processes or technologies, it is necessary to foresee the implications of the architectural design changes on system quality. Examination of quality outcomes through implementation of the different architectural design alternatives is often unfeasible. We have developed a method called PREDIQT with the aim to facilitate model-based prediction of impacts of architectural design changes on system quality. A recent case study indicated feasibility of the PREDIQT method when applied on a real-life industrial system. The promising results encouraged further and more structured evaluation of PREDIQT.ObjectiveThis paper reports on the experiences from applying the PREDIQT method in a second and more recent case study – on a real-life industrial system from another domain and with different system characteristics, as compared to the previous case study. The objective was to evaluate the method in a fully realistic setting and with respect to carefully defined criteria.MethodThe case study conducted the first two phases of PREDIQT in their entirety, while the last (third) phase was partially covered. In addition, the method was assessed through a thought experiment-based evaluation of predictions and a postmortem review. All prediction models were developed during the analysis and the entire target system was analyzed in a fully realistic setting.ResultsThe evaluation argues that the prediction models are sufficiently expressive and comprehensible. It is furthermore argued that PREDIQT: facilitates predictions such that informed decisions can be made; is cost-effective; and facilitates knowledge management.ConclusionThe experiences and results obtained indicate that the PREDIQT method can be carried out with limited resources, on a real-life system, and result in useful predictions. Furthermore, the observations indicate that the method, particularly its process, facilitates understanding of the system architecture and its quality characteristics, and contributes to structured knowledge management.     

基于BMS与云平台的动力电池健康管理体系

为解决电动汽车现有BMS系统对锂离子动力电池SOH评估与预测难以满足多种工况条件、各种类动力电池,且难同时兼顾预测精度与反馈速度等应用缺陷,提出了一套全新的EVs电池健康管理系统设计思路,采用了结合云计算与存储平台,融入BMS评估体系等关键方法；通过BMS增加5G通讯模块,利用5G/4G信号实时上传电芯数据,经过云平台搭载的多种SOH评估模型与算法,多线程在线计算得到预测结果,及时反馈至用户端和BMS,实现电池健康管理；该体系的设计案例展示出较好的未来应用价值,为电动汽车电池管理设计提供了新方向。     

Transformer-based hierarchical latent space VAE for interpretable remaining useful life prediction

Data-driven prediction of remaining useful life (RUL) has emerged as one of the most sought-after research in prognostics and health management (PHM). Nevertheless, most RUL prediction methods based on deep learning are black-box models that lack a visual interpretation to understand the RUL degradation process. To remedy the deficiency, we propose an intrinsically interpretable RUL prediction method based on three main modules: a temporal fusion separable convolutional network (TF-SCN), a hierarchical latent space variational auto-encoder (HLS-VAE), and a regressor. TF-SCN is used to extract the local feature information of the temporal signal. HLS-VAE is based on a transformer backbone that mines long-term temporal dependencies and compresses features into a hierarchical latent space. To enhance the streaming representation of the latent space, the temporal degradation information, i.e., health indicators (HI), is incorporated into the latent space in the form of inductive bias by using intermediate latent variables. The latent space can be used as a visual representation with self-interpretation to evaluate RUL degradation patterns visually. Experiments based on turbine engines show that the proposed approach achieves the same high-quality RUL prediction as black-box models while providing a latent space in which degradation rate can be captured to provide the interpretable evaluation.     

RELIABILITY PREDICTION OF IMPERFECT SWITCHING SYSTEMS SUBJECT TO WEIBULL FAILURES

Reliability prediction plays a very important role in system design and evaluation. In order to accurately predict the system reliability, one should consider the system configuration and the failure distribution of its components. This paper discusses the imperfect switching system with one component in an active state and n spares in a standby state. When the operating component breaks down, the switch detects the failure via the sensor and the defective component is replaced with a functional spare, so the system can resume operation. The Weibull distribution is one of the most flexible failure distributions which is widely used because it can adequately describe the reliability behavior during the lifetime of present day components/systems. This paper assumes the operating components follow Weibull failures, but the spares, sensor and switch failures follow an exponential distribution. In addition, three assumptions are made with regard to its switch failures: (i) under the energized condition, (ii) under the failing-open condition, and (iii) under the failing-closed condition. Due to the intractability of the Weibull distribution in imperfect switching models, it is difficult to solve the multiple integration involved analytically. Therefore, a numerical integration method using Simpson's rule was selected as a tool to address the problem of multiple integration for the Weibull distribution. A recursive algorithm is developed for the reliability prediction of a series system with m imperfect switching sub-systems subject to Weibull failures. Finally, a sensitivity analysis is performed on the two parameters of the Weibull distribution, on the effect of spare addition, as well as different failing conditions (switch and sensor) on system reliability. A numerical example is also given to explain and demonstrate the practical application of the developed reliability prediction models.     

Survey on mechatronic engineering: A focus on design methods and product models

According to the principles of concurrent engineering and integrated design, engineers intend to develop a mechatronic system with a high level integration (functional and physical integrations) based on a well-organised design method. As a result, two main categories of issues have been pointed out: the process-based problems and the design data-related problems. Several approaches to overcome these issues have been put forward. To solve process-based problems, a dynamic perspective is generally used to present how collaboration can be improved during the mechatronic design. For design data-related problems, solutions generally come from product models and how to structure and store the data thanks to the functionality of data and documents management of Product Lifecycle Management systems. To be able to assess design methods and product models, some criteria are proposed in the paper and used to evaluate their added value on integrated design of mechatronic system. After this assessment, main outcomes which focus on the combination of design method and product model for improving the design of mechatronic system are finally discussed.     

Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks

Sudden changes in weather, in particular extreme temperatures, can result in increased energy expenditures, depleted agricultural resources, and even loss of life. However, these ill effects can be reduced with accurate air temperature predictions that provide adequate advance warning. Support vector regression (SVR) was applied to meteorological data collected across the state of Georgia in order to produce short-term air temperature predictions. A method was proposed for reducing the number of training patterns of massively large data sets that does not require lengthy pre-processing of the data. This method was demonstrated on two large data sets: one containing 300,000 cold-weather training patterns collected during the winter months and one containing 1.25 million training patterns collected throughout the year. These patterns were used to produce predictions from 1 to 12 h ahead. The mean absolute error (MAE) for the evaluation set of winter-only patterns ranged from 0.514°C for the 1-h prediction horizon to 2.303°C for the 12-h prediction horizon. For the evaluation set of year-round patterns, the MAE ranged from 0.513°C for the 1-h prediction horizon to 1.922°C for the 12-h prediction horizon. These results were competitive with previously developed artificial neural network (ANN) models that were trained on the full data sets. For the winter-only evaluation data, the SVR models were slightly more accurate than the ANN models for all twelve of the prediction horizons. For the year-round evaluation data, the SVR models were slightly more accurate than the ANN models for three of the twelve prediction horizons.     

Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R

Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1.     

Model Based Motion State Estimation and Trajectory Prediction of Spinning Ball for Ping-Pong Robots using Expectation-Maximization Algorithm

Motion state “Motion state of a ping-pong ball consists of the flying state and spin state.” estimation and trajectory prediction of a spinning ball are two important but challenging issues for both the promotion of the next generation of robotic table tennis systems and the research on motion analysis of spinning-flying objects. Due to the Magnus force acting on the ball, the flying state “Flying state denotes the real-time translational velocity.” and spin state “Spin state denotes the real-time rotational velocity.” are coupled, which makes the accurate estimation of them a huge challenge. In this paper, we first derive the Extended Continuous Motion Model (ECMM) by clustering the trajectories into multiple categories with a K-means algorithm and fitting them respectively using Fourier series. The ECMM can easily adapt to all kinds of trajectories. Based on the ECMM, we propose a novel motion state estimation method using Expectation-Maximization (EM) algorithm, which in result contributes to an accurate trajectory prediction. In this method, the category in ECMM is treated as a latent variable, and the likelihood of motion state is formulated as a Gaussian Mixture Model (GMM) of the differences between the trajectory predictions and observations. The effectiveness and accuracy of the proposed method is verified by offline evaluation using a collected dataset, as well as online evaluation that the humanoid robotic table tennis system “Wu & Kong” successfully hits the high-speed spinning ball.