Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.

Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

A Data-Oriented Method to Optimize Hydraulic Fracturing Parameters of Tight Sandstone Reservoirs

Based on the actual data collected from the tight sandstone development zone, correlation analysis using theSpearman method was conducted to determine the main factors influencing the gas production rate of tightsandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracturepropagation and production was completed. Based on data analysis, the hydraulic fracture parameters wereoptimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influenceof geological and engineering factors in the X1 and X2 development zones in the study area differs significantly.Therefore, it is challenging to adopt a uniform development strategy to achieve rapid production increase. Thedata analysis reveals that the variation in gas production rate is primarily affected by the reservoir thickness andpermeability parameters as geological factors. On the other hand, the amount of treatment fluid and proppantaddition significantly impact the gas production rate as engineering factors. Among these factors, the influence ofgeological factors is more pronounced in block X1. Therefore, the main focus should be on further optimizing thefracturing interval and adjusting the geological development well location. Given the existing well location, thereis limited potential for further optimizing fracture parameters to increase production. For block X2, the fracturingparameters should be optimized. Data screening was conducted to identify outliers in the entire dataset, and adata-driven fracturing parameter optimization method was employed to determine the basic adjustment directionfor reservoir stimulation in the target block. This approach provides insights into the influence of geological,stimulation, and completion parameters on gas production rate. Consequently, the subsequent fracturing parameteroptimization design can significantly reduce the modeling and simulation workload and guide field operations toimprove and optimize hydraulic fracturing efficiency.

Making Data-Driven Transportation Decisions for Freight Operations

Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

融合K-means聚类和序列分解的实车锂电池剩余使用寿命预测

电动汽车锂离子动力电池健康状态(SOH)衰退过程受使用工况影响存在较多波动,导致模型预测精度下降,在锂电池剩余使用寿命(RUL)短期预测时,SOH波动情况不可忽略,为了准确预测SOH短期内波动情况,须从实车上传的锂电池运行数据中提取有效的健康因子。本文建立一种联合分布特征输入和序列分解融合的锂电池RUL预测方法,使用K-means聚类方法构建车辆锂电池运行过程的联合分布特征,并通过S-G滤波器对SOH衰退曲线进行序列分解,分别使用长短时记忆神经网络(LSTM)和多层感知机(MLP)对趋势部分和波动部分进行预测,融合得到最终预测结果。理论分析和实车采集数据验证表明,融合模型可以在预测车辆锂电池RUL短期衰退趋势的同时预测SOH的波动情况,有较高的短期预测精度。

基于Koopman算子与卡尔曼滤波的同步发电机动态状态估计

动态状态估计是监测同步发电机动态行为的重要手段,准确的动态状态估计结果对于指导电力系统安全运行与高效控制具有重要意义。从数据驱动的角度出发,提出了基于Koopman算子与卡尔曼滤波的同步发电机动态状态估计方法。该方法首先利用汉克尔动态模态分解算法从发电机动态响应数据中提取Koopman算子,进而以提取的Koopman算子为基础构建同步发电机状态空间模型,并利用卡尔曼滤波对同步发电机状态变量进行动态估计。该方法无须事先构建发电机模型及参数,实现了完全数据驱动的动态状态估计。仿真实验结果表明,在发电机模型及参数失配的情况下该方法估计精度明显高于传统以模型为基础的估计结果,具有较好的自适应性和鲁棒性。

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

基于自编码器的疾病相关miRNAs的预测方法

MicroRNAs(miRNAs)是一类由内源基因编码的长度约为22个核苷酸的非编码单链RNA分子,它们在动植物中参与转录后基因表达调控。大量研究表明,miRNAs在包括肿瘤在内的多种复杂疾病发生、发展过程中扮演着重要的角色。因此,识别疾病相关的miRNAs对研究疾病的机理及治疗具有重要意义。鉴于湿实验验证方法存在耗时长、成本高的缺点,当前许多研究工作聚焦于开发高效计算模型,识别新的miRNA-disease关联关系。该研究提出一种基于自编码器数据驱动的模型,预测miRNA-disease关联关系。结果表明,作者预测的疾病相关miRNAs在HMDD数据库中对应的疾病相关miRNAs列表上显著富集。此外,通过对排名靠前的miRNAs分析,发现这些miRNAs具有重要的生物学功能,同时对于疾病的分类表现出较高的精度。总之,文章提出的模型,对于疾病相关miRNAs的发现具有重要的辅助作用。

Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

Full field reservoir modeling of shale assets using advanced data-driven analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism（sorption process and flow behavior in complex fracture systems- induced or natural） leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called ＂hard data＂ directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The ＂hard data＂ refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of ＂soft data＂（non-measured, interpretive data such as frac length, width,height and conductivity） in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

Vision for energy material design:A roadmap for integrated data-driven modeling

The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

Data-Driven Based Fault Prognosis for Industrial Systems:A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Data-Driven Anomaly Diagnosis for Machining Processes

To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.

Product Data Model for Performance-driven Design

When designing large-sized complex machinery products, the design focus is always on the overall per- formance; however, there exist no design theory and method based on performance driven. In view of the defi- ciency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of ＂Requirement-Function-Structure＂ to construct a new five-domain design theory of ＂Client Requirement-Function-Performance-Structure-Design Parameter＂. To support design practice based on this new theory, a product data model is established by using per- formance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parame- ters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train＇s parameter design.

Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration

The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.