Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws
ارزیابی قابلیت اطمینان از دقت اندازه گیری سنسورهای FBG مورد استفاده در تست های ساختاری تیغه های توربین بادی بر اساس قوانین انتقال فشار-2020
FBG sensors are often packaged within composites before they are pasted on the blade surface, and many studies have shown that the materials, fatigue properties, geometric parameters, etc. of intermediate layer have influences on the measuring accuracy of the FBG sensors. Thus, this paper established an reliability calculation model based on strain transfer efficiency for the measuring accuracy of FBG sensors packaged by composites, analyzed the influences of material properties and geometric parameters of the adhesive layer on the performance of FBG sensors based on finite element analysis (FEA) method, and then compared the differences of strain transfer efficiency and reliability of the FBG sensors under different load conditions. The results show that the bond length and the bond thickness of the adhesive layer have greater influences on the performance of the FBG sensors compared with other parameters, both the strain transfer efficiency and the reliability of the FBG sensors will reduce over time under suddenly applied load and increase with increasing frequency of the alternating load.
Keywords: FBG sensors | Reliability assessment | Strain transfer law | Static load | Suddenly applied load | Alternating load
الگوریتم تکاملی چند هدفه مبتنی بر شبکه عصبی برای زمانبندی گردش کار پویا در محاسبات ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 45
زمانبندی گردشکار یک موضوع پژوهشی است که به طور گسترده در محاسبات ابری مورد مطالعه قرار گرفته است و از منابع ابری برای کارهای گردش کار استفاده می¬شود و برای این منظور اهداف مشخص شده در QoS را لحاظ می¬کند. در این مقاله، مسئله زمانبندی گردش کار پویا را به عنوان یک مسئله بهینه سازی چند هدفه پویا (DMOP) مدل می¬کنیم که در آن منبع پویایی سازی بر اساس خرابی منابع و تعداد اهداف است که ممکن است با گذر زمان تغییر کنند. خطاهای نرم افزاری و یا نقص سخت افزاری ممکن است باعث ایجاد پویایی نوع اول شوند. از سوی دیگر مواجهه با سناریوهای زندگی واقعی در محاسبات ابری ممکن است تعداد اهداف را در طی اجرای گردش کار تغییر دهد. در این مطالعه یک الگوریتم تکاملی چند هدفه پویا مبتنی بر پیش بینی را به نام الگوریتم NN-DNSGA-II ارائه می¬دهیم و برای این منظور شبکه عصبی مصنوعی را با الگوریتم NGSA-II ترکیب می¬کنیم. علاوه بر این پنج الگوریتم پویای مبتنی بر غیرپیش بینی از ادبیات موضوعی برای مسئله زمانبندی گردش کار پویا ارائه می¬شوند. راه¬حل¬های زمانبندی با در نظر گرفتن شش هدف یافت می¬شوند: حداقل سازی هزینه ساخت، انرژی و درجه عدم تعادل و حداکثر سازی قابلیت اطمینان و کاربرد. مطالعات تجربی مبتنی بر کاربردهای دنیای واقعی از سیستم مدیریت گردش کار Pegasus نشان می¬دهد که الگوریتم NN-DNSGA-II ما به طور قابل توجهی از الگوریتم¬های جایگزین خود در بیشتر موارد بهتر کار می¬کند با توجه به معیارهایی که برای DMOP با مورد واقعی پارتو بهینه در نظر گرفته می¬شود از جمله تعداد راه¬حل¬های غیرغالب، فاصله¬گذاری Schott و شاخص Hypervolume.
|مقاله ترجمه شده|
AI and Reliability Trends in Safety-Critical Autonomous Systems on Ground and Air
روند هوش مصنوعی و قابلیت اطمینان در سیستمهای خودمختار ایمنی در زمین و هوا-2020
Safety-critical autonomous systems are becoming more powerful and more integrated to enable higher-level functionality. Modern multi-core SOCs are often the computing backbone in such systems for which safety and associated certification tasks are one of the key challenges, which can become more costly and difficult to achieve. Hence, modeling and assessment of these systems can be a formidable task. In addition, Artificial Intelligence (AI) is already being deployed in safety critical autonomous systems and Machine Learning (ML) enables the achievement of tasks in a cost-effective way. Compliance to Soft Error Rate (SER) requirements is an important element to be successful in these markets. When considering SER performance for functional safety, we need to focus on accurately modeling vulnerability factors for transient analysis based on AI and Deep Learning workloads. We also need to consider the reliability implications due to long mission times leading to high utilization factors for autonomous transport. The reliability risks due to these new use cases also need to be comprehended for modeling and mitigation and would directly impact the safety analysis for these systems. Finally, the need for telemetry for reliability, including capabilities for anomaly detection and prognostics techniques to minimize field failures is of paramount importance.
Index Terms : SER | safety | AI | ML. reliability
Solder joint reliability risk estimation by AI modeling
برآورد خطر قابلیت اطمینان اتصال لحیم کاری با مدل سازی هوش مصنوعی -2020
This paper studies AI modeling for the solder joint fatigue risk estimation under the thermal cycle loading of redistributed wafer level packaging. The artificial neural network (ANN), recurrent neural network (RNN) and vectorized-gate network long short-term memory (VNLSTM) architectures have been trained by the same dataset to investigate their performance for this task. The learning accuracy criterion, the implementation of all neural network architecture, the learning results and result analysis would be covered. Because the involvement of the time/temperaturedependent nonlinearity material characteristics, it is recommended that more than three hidden layers and a proper neural network architecture, which is capable of the sequential data processing, should be considered in order to guarantee the required accuracy and the satisfied convergence speed.
Keywords: Solder joint fatigue risk estimation | Time/temperature-dependent nonlinearity | ANN | RNN | LSTM | machine learning
AI Powered THz VLSI Testing Technology
فناوری تست THz VLSI با قدرت هوش مصنوعی-2020
Abstract—Increasing complexity of digital and mixed-signal systems makes establishing the authenticity of a chip to be a challenging problem. We present a new terahertz testing technique for non-destructive identification of genuine integrated circuits, in package, in-situ and either with no or under bias, by measuring their response to scanning terahertz and sub-terahertz radiation at the circuit pins. This novel, patent pending non-invasive nondestructive technology when merged with Artificial Intelligence (AI) engine will evolve and self-improve with each test cycle. By establishing and AI processing of the THz scanning signatures of reliable devices and circuits and comparing this signatures with devices under test using AI, this technology could be also used for reliability and lifetime prediction.
Keywords: Terahertz | hardware cybersecurity | reliability | authentication | artificial intelligence
Explainability and Dependability Analysis of Learning Automata based AI Hardware
تحلیل توضیح و قابلیت اطمینان یادگیری سخت افزار هوش مصنوعی مبتنی بر Automata-2020
Explainability remains the holy grail in designing the next-generation pervasive artificial intelligence (AI) systems. Current neural network based AI design methods do not naturally lend themselves to reasoning for a decision making process from the input data. A primary reason for this is the overwhelming arithmetic complexity. Built on the foundations of propositional logic and game theory, the principles of learning automata are increasingly gaining momentum for AI hardware design. The lean logic based processing has been demonstrated with significant advantages of energy efficiency and performance. The hierarchical logic underpinning can also potentially provide opportunities for bydesign explainable and dependable AI hardware. In this paper, we study explainability and dependability using reachability analysis in two simulation environments. Firstly, we use a behavioral SystemC model to analyze the different state transitions. Secondly, we carry out illustrative fault injection campaigns in a low-level SystemC environment to study how reachability is affected in the presence of hardware stuck-at 1 faults. Our analysis provides the first insights into explainable decision models and demonstrates dependability advantages of learning automata driven AI hardware design.
Keywords: Rainfall | Artificial | Computing | Simulation | Architecture
Truth finding by reliability estimation on inconsistent entities for heterogeneous data sets
یافتن حقیقت با برآورد قابلیت اطمینان در واحدهای متناقض برای مجموعه داده های ناهمگن-2020
An important task in big data integration is to derive accurate data records from noisy and conflicting values collected from multiple sources. Most existing truth finding methods assume that the reliability is consistent on the whole data set, ignoring the fact that different attributes, objects and object groups may have different reliabilities even wrt the same source. These reliability differences are caused by the hardness differences in obtaining attribute values, non-uniform updates to objects and the differences in group privileges. This paper addresses the problem how to compute truths by effectively estimating the reliabilities of attributes, objects and object groups in a multi-source heterogeneous data environment. We first propose an optimization framework TFAR, its implementation and Lagrangian duality solution for Truth Finding by Attribute Reliability estimation. We then present a Bayesian probabilistic graphical model TFOR and an inference algorithm applying Collapsed Gibbs Sampling for Truth Finding by Object Reliability estimation. Finally we give an optimization framework TFGR and its implementation for Truth Finding by Group Reliability estimation. All these models lead to a more accurate estimation of the respective attribute, object and object group reliabilities, which in turn can achieve a better accuracy in inferring the truths. Experimental results on both real data and synthetic data show that our methods have better performance than the state-of-art truth discovery methods.
Keywords: Truth finding | Attribute reliability | Object reliability | Group reliability | Entity hardness | Probability graphical mod
Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept
استفاده از روش تجزیه و تحلیل داده های بزرگ برای استخراج داده های مجموعه فعالان حقوق بشر از رویداد گزارش تحقیقات بر اساس مفهوم ایمنی-II-2020
The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (Classification And Regression Tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts.
Keywords: Human reliability analysis | Nuclear power plant | Safety-II | Classification and regression tree | Event investigation report
Rigor and reproducibility for data analysis and design in the behavioral sciences
دقت و تکرارپذیری برای تجزیه و تحلیل داده ها و طراحی در علوم رفتاری-2020
The rigor and reproducibility of science methods depends heavily on the appropriate use of statistical methods to answer research questions and make meaningful and accurate inferences based on data. The increasing analytic complexity and valuation of novel statistical and methodological approaches to data place greater emphasis on statistical review. We will outline the controversies within statistical sciences that threaten rigor and reproducibility of research published in the behavioral sciences and discuss ongoing approaches to generate reliable and valid inferences from data. We outline nine major areas to consider for generally evaluating the rigor and reproducibility of published articles and apply this framework to the 116 Behaviour Research and Therapy (BRAT) articles published in 2018. The results of our analysis highlight a pattern of missing rigor and reproducibility elements, especially pre-registration of study hypotheses, links to statistical code/output, and explicit archiving or sharing data used in analyses. We recommend reviewers consider these elements in their peer review and that journals consider publishing results of these rigor and reproducibility ratings with manuscripts to incentivize authors to publish these elements with their manuscript.
KEYWORDS: statistics | big data | reproducibility | reliability | p-hacking
A new measure of wind power variability with implications for the optimal sizing of standalone wind power systems
اندازه گیری جدیدی از تغییرات انرژی باد با پیامدهای لازم برای اندازه بهینه سیستمهای بادی مستقل-2020
This paper proposes a new measure of wind power variability and investigates the impacts of wind power variability on the optimal sizing of Standalone Wind Power (SWP) systems. The proposed new measure of the wind power variability in the frequency domain, which mainly includes a cumulative energy distribution index and a fluctuation factor, is applied to assess the variability of wind power throughout 6 consecutive years from 6 far apart sites from latitude 0e50 across America. Big data assessment results indicate the intermittent wind power at one site can be treated as Quasi-Time- Invariant (QTI) in the frequency domain. Big data simulations of the six SWP systems with the same residential load demand at the six sites provide QTI responses of the power supply reliability against the sizing of the system components in the mitigation of wind power variability. A case study of optimal sizing of a SWP system at Chicago, was carried out, which aims to minimize the system cost while satisfying the requirement of power supply reliability. It can be found from the study that, the proposed approach provides a new way to significantly reduce the computation in the optimal sizing of SWP systems.
Keywords: Wind power variability measurement | Standalone wind power system | Power fluctuation mitigation | Power supply reliability | Optimal sizing