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We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

An immunoassay is a biochemical test based on the interaction between antibodies and their corresponding antigens. The traditional immunoassays, such as enzyme-linked immunosorbent assay and radioimmunoassay, are sensitive and specific in various research fields, including food safety, clinical diagnosis, and environment monitoring. However, these methods often suffer from some drawbacks, such as relying on expensive instruments and skillful technicians, and thus are hard to apply in developing countries. To address these issues, paper-based immunoassays provide an alternative solution by means of an affordable, portable, and accessible strategy in various application fields. This chapter presents the strategies of paper-based immunoassays based on different detection methods (e.g., colorimetric, thermal, electrochemical, and magnetic). Moreover, we also review the advances in paper-based immunoassays, including signal amplification strategies, automatic detections, and semiquantification or quantification. At last, we summarize the future development tendency of paper-based immunoassay in this chapter.

The design of appropriate diagnostic assays for the point of care requires development of suitable biosensors, detection methods, and diagnostic platforms for sensitive, quantitative detection of biological analytes. Protein targets in particular are especially challenging to detect quantitatively and sensitively due to the lack of amplification strategies akin to nucleic acid amplification. However, recent advances in transducer and biosensor design, new detection labels, and paper-based microfluidics may realize the goal of sensitive, fast, portable, and low-cost protein detection. In this review, we discuss the biochemistry, optics, and engineering advances that may be leveraged to design such a sensitive protein diagnostic assay. The binding kinetics, mechanisms of binding in porous networks, and potential transducers are explained in detail. We discuss the relative merits of various optical detection strategies, potential detection labels, optical readout approaches, and image-processing techniques that are amenable to point-of-care use. To conclude, we present a systematic analysis of potential approaches to enhance the sensitivity of paper-based assays. The assay development framework presented here provides bioassay developers a strategy to methodically enhance the sensitivity and point-of-care suitability of protein diagnostics.

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.

This data article contains data related to the research article entitled, “Solving the multi-vehicle multi-covering tour problem” (Pham et al., 2017) [4]. All data of this article was generated from instances kroA100, kroB100, kroC100, kroD100, kroA200, and kroB200 from TSPLIB. It can be downloaded from public repository. This data can be used as benchmarks for the covering tour problem (CTP) variants, such as m-CTP-p, m-CTP, mm-CTP-p, mm-CTP, mm-CTP-o, mm-CTP-wo. We tested our algorithm on these data and results are shown in Pham et al. (2017) [4].

International audience

In a recent paper [H.-E. Hwang, P. Han, Opt. Commun. 282 (2009) 351] a speckle based metrology system is proposed which it is claimed provides significant advantages over existing systems. In this paper, we show that the discussion presented in [H.-E. Hwang, P. Han, Opt. Commun. 282 (2009) 351] is deficient, and that several of the statements made are incorrect and/or misleading.

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus., Graphical Abstract ga1

This short paper presents an HPC enhanced Agent Based Model (ABM) developed with the aim of quantitatively estimating the strategies for accelerating emergency mass evacuations, like tsunami evacuation. In order to facilitate inclusion of various influencing factors, such as localized congestion, multi-modes, pedestrian vehicle interactions, fallen debris from damaged buildings, visibility, etc., which demand detailed models, the developed system includes a 1mx1m resolution model of environment, and agents capable of perceiving and autonomously interact with this high resolution environment and visible agents. In order to meet the computational demand of large scale simulations with complex agents, a scalable high performance extension was implemented. Short introductions to the agent based model and the HPC extension are presented in this paper. In order to demonstrate the scalability of the system, both in problem size and computational capability, a 588 km2 region with 10 million agents is simulated in K computer. It is demonstrated that the system has high strong scalability up to 2048 computing nodes, which is equivalent to 16,384 CPU cores., Transportation Research Procedia, 34, ISSN:2352-1465, International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18) Emerging Transport Technologies for Next Generation Mobility

The application of X-ray microtomography for quantitative structural analysis of pharmaceutical multi-particulate systems was demonstrated for commercial capsules, each containing approximately 300 formulated ibuprofen pellets. The implementation of a marker-supported watershed transformation enabled the reliable segmentation of the pellet population for the 3D analysis of individual pellets. Isolated translation- and rotation-invariant object cross-sections expanded the applicability to additional 2D image analysis techniques. The full structural characterisation gave access to over 200 features quantifying aspects of the pellets' size, shape, porosity, surface and orientation. The extracted features were assessed using a ReliefF feature selection method and a supervised Support Vector Machine learning algorithm to build a model for the detection of broken pellets within each capsule. Data of three features from distinct structure-related categories were used to build classification models with an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets. This approach to extract quantitative information on particle quality attributes combined with advanced data analysis strategies has clear potential to directly inform manufacturing processes, accelerating development and optimisation., Graphical abstract Unlabelled Image, Highlights • Coupling micro-XRT analysis with feature selection and machine learning for advanced pharmaceutical product characterisation. • Information on particle 3D-orientation were utilised to extract translation- and rotation-invariant object cross-sections. • Successful extraction of over 200 quantitative pellet descriptors linked to size, shape, porosity, surface and orientation. • Sensitivity analysis and ReliefF feature selection approach to identify predictive features for pellet classification. • Feature-based binary SVM classification model for the detection of broken pellets within the formulated system.

Graphical Abstract The prediction model of oral disintegrating tablets formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques were established. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20) to build prediction model.Image, graphical abstract, Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.

In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty. This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management. We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality., Graphical abstract Image 1, Highlights • Model based decision support is needed for integrative water quality management. • Multi-criteria decision support methods help developing water quality assessments. • We evaluate alternatives that reduce agricultural and urban sources of pollution. • We illustrate tradeoffs between costs and water quality including uncertainty. • Scenario analysis helps evaluating robustness of outcomes to future developments.

This short paper intends first to emphasize the basic distinction between gradual truth and uncertainty, and its relevance when dealing with classification. Then, the representation capabilities of first-order possibilistic logic are pointed out, before briefly providing some hints, which may be of interest for dealing with uncertainty and handling some fuzzy features in description logic.

Frequency-domain system representations offer important system analysis, control design and simulation tools. While this representation concept is well developed for linear time-invariant systems, there has been no general theory established for the linear parameter-varying (LPV) system class. This paper overviews and compares the existing concepts of frozen and instantaneous frequency-domain LPV representations, pointing out the need for a representation form where the effect of the spectrum of a varying scheduling trajectory can be clearly understood and analyzed on the output spectrum. For this purpose, the harmonic frequency response function (hFRF), known from the linear time-varying literature, in introduced for LPV systems. More specifically the paper demonstrates how the hFRF can be computed directly in the frequency domain for discrete-time affine input-output LPV systems.

In this paper the problem of synchronisation of multiple robotic manipulators using Hoo robust distributed control systems with respect to the parameter uncertainties and disturbance inputs acting on the manipulators is addressed. Robust synchronising controllers only use the information of outputs of the manipulators, and the corresponding parameters of these output-feedback controllers are designed by computing a series of linear matrix inequalities instead of solving the complex differential (e.g. Hamilton-Jacobi) (in)equalities. The proposed controller can guarantee H∞ robust performance with respect to the external disturbance inputs and parameters uncertainties, asymptotic stability and synchronisation in the networked manipulators. Using an illustrative example we compare the results extracted in this paper to other works existing in the literature.

In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms.

A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach.

International audience; This paper is a contribution to fasten integration of battery pack innovation in commercial Electric Vehicles (EV) through massive digitalization: a seamless process detailed for battery design, battery safety, and battery management. Selected results of studies carried out on the EV value chain from design to recycling steps are presented, highlighting the importance of seamless integration and holistic state of mind when designing EV. Association between experimental and numerical approaches for efficient innovative EV production is crucial to achieve easy commercialisation. Successful forecasting of aging and thermal runaway evolution from single cell failure at module level using such methods illustrates their great potential. Hardware key counterparts under development are also introduced and give an idea of future architecture of EV battery packs and overall improvement of EV energy efficiency. Finally, a flexible and easily modifiable solution for battery electric vehicle (BEV) that allows rapid and cost-effective integration of future innovation is presented. This paper globally illustrates key breakthroughs gained in the context of the collaborative research project named ‘DEMOBASE’, for DEsign and MOdelling for improved BAttery Safety and Efficiency successfully submitted for funding by the European Commission in response to a 2017 call dedicated to ‘Green Vehicles’ under the EU Horizon 2020 work programme “Smart, green and integrated transport”.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Color medical images hold noteworthy impact in clinical conclusion and treatment. Medical image segmentation reduces the uncertainty by providing detailed information about the shape, size, or location characteristics. However, traditional methods suffer from low accuracy, high complexity, and are less robust. To overcome these drawbacks, this paper proposes an efficient metaheuristic algorithm, exchange market algorithm (EMA) for multilevel thresholding (MLT) of distinct medical images. Optimal threshold is effectively obtained through the most promising objective functions such as Kapur, Otsu and minimum cross entropy (MCE) aided with EMA. The EMA involves exchange of shares among the investors in stable and unstable market situations to achieve profit. Exploration and exploitation are achieved by second and third groups of stable and unstable modes of EMA. Moreover, the execution time is reduced by the highly competent shareholders retaining their top rank without any changes in their shares. The efficacy of the proposed paper is evaluated on three distinct medical images at 4, 5, 6 and 7th threshold levels and compared with the recent algorithms such as Krill herd (KHA), Teaching-learning based optimization (TLBO) and Cuckoo search algorithm (CSA). Quantitative and qualitative validation by metrics such as computational time, Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Wilcoxon rank sum test affirm that the EMA is superior to other algorithms. On the other hand, Otsu based EMA method is found to be more accurate and robust for improved clinical decision making and diagnosis.

In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the open queries about sensitive issues generally results in the high non-response rates or misleading answers. The aim of this paper is to develop an effective randomized response model to overcome with these types of challenges arising due to sensitive nature of characteristic under study. In this paper we have proposed three-stage randomized response model for estimating mean number of individuals who possessed rare sensitive attribute which makes use of Poisson distribution. The properties of the proposed estimation procedures have been deeply examined when the parameter of a rare unrelated attribute is known as well as unknown. Privacy protection of respondents is also an equally important matter of concern. So measure of privacy protection for the proposed randomized response model has also been examined. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimators over well-known contemporary estimators. From the findings of this study we may conclude that proposed randomized response model is rewarding in terms of percent relative efficiencies and privacy protection and may be recommended to survey practitioners for real life applications.

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well.

To better control the PEMFC air flow, this paper introduces a distributed deep reinforcement learning-based adaptive proportional integral (PI) controller for controlling air flow in a proton exchange membrane fuel cell (PEMFC). In this paper a novel algorithm for tuning the controller is proposed, termed the multi-role exploration strategy distributed deep deterministic policy gradient (MESD-DDPG), which builds upon the deep deterministic policy gradient (DDPG) by employing various trick including a multi-role exploration strategy. In this strategy, explorers and followers with different exploration principles are used for distributed exploration to enhance the exploration efficiency. Moreover, a probability experience replay mechanism is included to foster the training efficiency. In addition, hot start mechanism of experience pool is applied to deal with the reward sparsity problem. The MESD-DDPG also includes a number of innovations for solving the problem of Q-value overestimation. The proposed MESD-DDPG algorithm considers the PI coefficients naturally in the objective and enables the controller better online coefficient adjusting ability via learning, which leads to improved controller adaptability. The simulation results demonstrate the adaptability and the excellent performance of the MESD-DDPG adaptive PI controller.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

Harmonic current detection is a key algorithms for shunt active power filter controller, which generates reference currents to compensate the harmonics of grid current. This paper presents a new harmonics detection method based on the triangle orthogonal principle algorithm. Compared to the conventional harmonic current detection methods, the presented method has the advantages of simple structure, low calculation, and fast execution time. Using the phase signal detected by the phase-locked loop synchronized with the power grid signal, the method based on the triangle orthogonal principle can provide a simple method to detect the reference current. At the same time, because the method proposed in this paper has lower implementation complexity and less microcontroller resource consumption, low-cost microcontrollers can be used to implement the proposed method, which has practical engineering value. Finally, this paper analysis the proposed method in detailly and verifies its great performance through experimental results.

Overheating in built environments during climate extreme heat events is a major concern to human health, particularly for people vulnerable to prolonged exposure to heat and humidity. However, currently available methods for assessing the risk of overheating lack robust procedures to evaluate the effects of overheating on the comfort and health of vulnerable occupants residing in the various different dwellings. This paper developed a general methodology to define and characterise overheating events and evaluate the risk to reduced comfort and undesirable health effects of building occupants exposed to extreme heat events. Criteria to declare overheating were developed based on heat-related health outcomes related to limits for body dehydration and core temperature of healthy average-age and older adults. The methodology was then applied to a case study of a residential building with typical local construction practice for cold climates and compared with existing methods for assessing risk from exposure to extreme heat events. The results showed that all the methods predicted that highly insulated and airtight buildings are more prone to overheating than older buildings that are less well insulated and airtight. However, only the proposed methodology described in this paper predicted that natural ventilation through opening windows significantly reduced the overheating risk to below the threshold value. Furthermore, multi criteria methods might be difficult to apply in practice in that it is not guaranteed to violate all the criteria to declare a building space is not overheated. The proposed methodology sets the groundwork for establishing a benchmark model from which different overheating metrics can be compared.

الملخص: عادة ما تكون المقالات ما قبل الطباعة بمثابة مقدمة أولية لأوراق تمت مراجعتها من قِبل النظراء ويتم وضعها على الفور تقريبا، باستثناء بعض الفحص السطحي، في مستودع وصول مفتوح للجمهور للسماح للمعلومات بالوصول إلى القراء بسرعة، والتغلب على الخمول المرتبط عادة بالمعالجة بطريقة المجلات التي تعتمد على مراجعة الأقران. بالنسبة للباحثين في بداية حياتهم المهنية الذين قد يكونون متحمسين للحصول على بعض التقدير لجهودهم، أو الرغبة في الحصول على مدخلات مفتوحة وعلنية حول عملهم، فإن النشر قبل الطباعة هو بالتأكيد خيار نشر مفيد. ومع ذلك، إذا لم يتم فحص البيانات والمعلومات المتعلقة بالصحة بعناية، فقد تشكل خطرا، وقد تعمل أيضا كمصدر لمعلومات مضللة عن الصحة العامة. تشير زيادة النمو والمنافسة بين خوادم ما قبل الطباعة، إلى جانب حجم هائل من المطبوعات المسبقة المتعلقة بـ كوفيد-١٩، وخاصة على خادم ما قبل الطباعة لعلم الأحياء، وخادم ما قبل الطباعة للعلوم الطبية، بالإضافة إلى تحديد الفهرسة التي يتم اختبارها الآن على موقع بوب ميد، إلى أن المطبوعات المسبقة يتم استخدامها بشكل متزايد في العلوم الطبية الحيوية. وهناك حاجة إلى سياسات أخلاقية أقوى وأكثر قوة لفحص المطبوعات المسبقة قبل نشرها للجمهور، وحتى إذا كان هذا يعني تأخيرا طفيفا في النشر، فقد يؤدي ذلك إلى زيادة ثقة الأكاديميين في هذا الشكل من المعلومات العلمية والتواصل. يجب تقديم سياسات أخلاقية واضحة وصارمة بشكل عاجل من قبل مجموعات الأخلاقيات مثل لجنة أخلاقيات النشر، واللجنة الدولية لمحرري المجلات الطبية للمجلات الأعضاء التي تسمح بنشر مقالات ما قبل الطباعة قبل مراجعة النظراء التقليدية. ستفيد الإرشادات الأخلاقية الصارمة التي تتعامل مع سوء السلوك على قدم المساواة في مقالات ما قبل الطباعة والأبحاث التي راجعها النظراء، ستفيد نزاهة النشر الأكاديمي. Abstract: Preprints are typically crude precursors of peer-reviewed papers that are placed almost immediately, save for some superficial screening, on an open-access repository to allow the information to reach readers quickly, circumventing the long-drawn process typically associated with processing in peer-reviewed journals. For early-career researchers who might be enthusiastic about obtaining some recognition for their efforts, or wanting open and public input about their work, preprints are certainly a useful publication choice. However, if health-related data and information have not been carefully scrutinised, they may pose a risk and may even serve as a source of public health misinformation. Surging growth and competition among preprint servers, coupled with a massive volume of COVID-19-related preprints, mainly on bioRxiv and medRxiv, as well as select indexing now being tested on PubMed, suggests that preprints are being increasingly used in the biomedical sciences. Stronger and more robust ethical policies are needed to screen preprints before they are released to the public, and even if this implies a slight delay in publication, it may increase academics' trust in this form of scientific information and communication. Clear and stringent ethical policies need to be urgently introduced by ethics groups such as COPE and the ICMJE, whose many member journals allow preprints to be posted before traditional peer review. Stringent ethical guidelines that treat misconduct equally in preprints and peer-reviewed papers will boost the integrity of academic publishing.

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

This paper presents the optimal placement and sizing (OPAS) of active power filter (APF) considering photovoltaic distributed generation (PVDG) and nonlinear load to control the harmonics as per IEEE-519 standard limits. For the varying nature of PVDG output, the injection of harmonics into the distribution system is thoroughly investigated by incorporating hourly solar irradiance data. In this paper, a novel extended nonlinear load position based APF current injection (ENLPCI) technique is proposed to find the number of APFs and optimal buses for placement of APFs. The optimal placement for APF is calculated here for three distinct cases: a) only one state for minimum number of APFs, b) more than one state with different total harmonic distortion in voltage (THDv) with the same number of APFs and c) more than one state with the same THDv with the same number of APFs. The size and cost of APF are calculated and compared using grey wolf optimization (GWO) and its adaptive version (AGWO) algorithms for each hour. Further, the ENLPCI is also validated by GWO and AGWO algorithms for the optimal results. The proposed algorithm is tested on the IEEE-69 bus test system. The results show that the variation of solar irradiance remarkably affects the OPAS of APF. The AGWO performed better compared to GWO to find the optimal cost of APF.

Each organization faces threats and risks in daily operations. One of the main risks is how to assess the security level to protect from the increasing risks associated with technology evolution. So, organizations can specify the required approaches and skills. In this paper, we propose a security maturity model that classifies the organizations into five levels. Each level determines the technologies and process capability used by the organizations. There is a set of factors that can help in determining the security maturity level, such as technology, people, and infrastructure. This paper adopts an Information Security Management model to assess organization’s security level. The authors make a correspondence between maturity levels and security levels in an organization. Also, the proposed process capability controls influence both levels. The proposed model helps the organizations bridging the cybersecurity gaps. These gaps relate to talent, technology, organizational units, financial, management and operations gaps. Thus, the model helps the cybersecurity auditors to create a comprehensive plan for measuring the security level of the organization. This plan can manage and develop the organization’s automated countermeasures. Also, it can help in applying the suitable standard and framework based on the organization’s daily operation. Cybersecurity auditors use cybersecurity techniques and tools to assess the organization’s postures. Finally, the authors applied the security maturity controls in two case studies: retirement organization and public telecommunication corporation in the Republic of Yemen.

With the advent of digitalization and industry 4.0, education in chemical and biochemical engineering has undergone significant revamping over the last two decades. However, undergraduate students sometimes do lack industrial exposure and are unable to visualise the complexity of actual process plants. Thereby, students might graduate without adequate professional hands-on experience. Similarly, in the process industry, operator training-simulators are widely used for the training of new and skilled operators. However, conventional training-simulators often fail to simulate reality and do not provide the user with the opportunity to experience unexpected and hazardous scenarios. In these regards, virtual reality appears to be a promising technology that can cater to the needs of both academia and industry. This paper discusses the opportunities and challenges for the incorporation of virtual reality into chemical and biochemical engineering education with an emphasis on the fundamental areas of technology, pedagogy and socio-economics. The paper emphasises the need for augmenting virtual reality interfaces with mathematical models to develop advanced immersive learning applications. Further, the paper stresses upon the need for novel educational impact assessment methodologies for the evaluation of virtual-reality-based learning. Finally, an ongoing case study application is presented to briefly discuss the social and economic implications, and to identify the bottlenecks involved in the adoption of virtual reality tools across chemical and biochemical engineering education.

SARISTU was a big cooperation project granted by the European Commission, 7th Framework Programme, carried out between 2011 and 2015. It dealt with smart aeronautic structures, both morphing and sensored; its main target was to demonstrate the feasibility of designing, manufacturing and operating in representative environment, instrumented structures. Till now, it represents the major effort carried out within the European Union on the development of adaptive architectures for air systems. Inside that big activity, the realization of an Adaptive Trailing Edge Device (ATED) for wing camber adaptations aimed at compensating the weight reduction following the fuel consumption during cruise was addressed. It made the core of investigations target variable geometry aircraft components together with two other analyses concerning the development of shape-changing winglet and droop nose. ATED activities were conducted by the Italian Aerospace Research Centre (CIRA) in tight cooperation with the University of Napoli, “Federico II”, who coordinated a group of 12 different partners from 8 different nations (France, Germany, Greece, the Netherlands, Israel, Spain, Turkey, and Italy). In this paper, an integral synthesis of that work is reported, with a focus on the definition and realization of the components of the presented device. The publication is in fact meant as the first part of a series that is aimed at overviewing the whole adaptive trailing edge development, till wind tunnel tests execution. Such a concise report is a critical and harmonized review of what have been performed by many colleagues spread all over Europe, all of which are duly recalled in the reported bibliography where the reader may access more detailed information and descriptions. In detail, the paper starts with a general introduction of the concept and its aims, to move to the specs definition immediately after. Then, it deals with a short but comprehensive description of the main ATED components: structural skeleton, skin, actuation and sensing systems. It is worth remarking that the paragraph dedicated to the body frame includes some discussion about aeroelastic assessment and manufacture, seen as complementation for a complete assessment of the design constraints.

In this paper, different optimization algorithms are introduced to solve the security-constrained optimal power flow between two connected regions, applying two control modes, namely the centralized and decentralized modes. Traditionally, the decentralized control, i.e., each power system controls its variables regardless of the boundary buses, is the most commonly used approach. However, the centralized control has been recently utilized to enhance the system security, because the active power of the generation units and/or the voltage buses on the boundaries are the two variables, which can mitigate the changes, when compared to the decentralized control solution. The optimization techniques applied propose using the Genetic Algorithm (GA) as a tool to validate the solution reached through the Whale Optimization Algorithm. The proposed methodology is investigated on the IEEE 48-bus electric power system, which consists of two regions connected to each other through three transmission lines. The problem can be solved by applying both the AC and DC Optimal Power Flow respectively, using three approaches, namely: 1- Changing the active power of generation unit; 2- Controlling the voltage magnitude and 3- Combining the changes in the real power of generation units and the voltage variations. All the proposed approaches are implemented taking into consideration the technical constraints. All of these different techniques are constructed and modeled by utilizing the Matlab code. The results of each case study are presented; and a final comparison between all the methods utilized is drawn to demonstrate the advantages of each method. The novelty of this paper is represented in investigating the effectiveness of applying the AC Optimal Power Flow and the DC Power Flow, utilizing meta-heuristic techniques.

In today’s scenario the number of mobile devices is increasing by leaps and bounds and hence the networks are highly congested. To use the existing networks efficiently, the insight into the amount of traffic beforehand, would be highly beneficial. Mobile traffic forecasting would enable Software Defined Mobile Network (SDMN) to use network resources and bandwidth effectively. Therefore, this paper aims to forecast mobile traffic for SDMN using nature-inspired​ optimization techniques. The data taken is captured using Wireshark. In order to accurately predict network traffic, this paper considers Least Square Support Vector Machine (LSSVM) function estimation based training model with Whale Optimization Algorithm (WOA). The proposed technique increases the probability of global search by optimizing kernel parameters namely, regularization and kernel width. The parameters: Mean Square Error (MSE), True Positive Rate (TPR), True Negative Rate (TNR),​ accuracy and precision are evaluated for the proposed optimization algorithm. The results of the proposed algorithm Least Square Support Vector Machine-Enhanced Whale Optimization Algorithm (LSSVM-EWOA) for traffic forecasting are compared with Least Square Support Vector Machine-Whale Optimization Algorithm (LSSVM-WOA). The accuracy of prediction of the proposed model is 98.10%, which confirmed the effectiveness and the actual use of the proposed model.

The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.

Device to Device communication involves communique between two devices without routing the packet through the cellular network. Ideally, the process involves reutilizations of the ideal resources of the cellular network for communication purpose. This ensures better management of radio spectrum and reduces communication delay. However, resource allocation has also been a major challenge because it contributes to various problems like interference, throughput issues and many other. A lot of work has been carried out in the recent past to address the above issue. The current paper reviews the work done in the area of resource allocation in the context of device to device communication. Among various mathematical tools available it was bring into being that game theory has been extensively used to address the problem. The main contribution of the paper, it provides the insight into the evolvement that has been made in the area of resource allocation and highlight various open issues that needs to be addressed.

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.

Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) are two of the essential tools to design and develop new products. CAD systems help to design a new product efficiently, whereas CAE systems are used to predict and assess the future functionalities (durability, structural strength, etc.) of a product. Usually, these two systems work independently; however, their mutual dependency is a topic of interest for the researchers. This paper discusses the contemporary research efforts in the domain of CAD to CAE integration and later find the research gap. Based on the research gap this paper develop a methodology to integrate a CAD to CAE systems where scientific data format HDF (Hierarchical Data Format) acts as a bridge to link the gap between a CAD and CAE system. The virtual prototype of a product is getting accurate with the advancement of CAD systems; hence, the transferability of those models to a CAE system has become more sophisticated using traditional (e.g., .step, .iges files) methodologies. Since HDF is often used to handle massive and complex datasets for various research domains; hence, in this research effort HDF acts as a data repository neutral system to interact with the CAD and CAE systems. Based on the comprehensive literature review, this research effort aims to develop an uninterrupted integrated platform to handle complex design and analysis models using a big open-source data handling platform HDF (Hierarchical Data Format). MATLAB is used to bridge from the CAD system to HDF primarily and later from the HDF to CAE. A simple case study of analyzing a classic fluid mixing elbow demonstrates the preliminary implementation effort towards the proposed HDF-based integrated platform.

Secondary Lithium-ion batteries are widely used in a variety of sizes from single cells in personal electronics, to large packs in Electric Vehicles (EVs), and very large packs in grid-scale storage. With the number of applications increasing, this brings more laboratory testing of such batteries. A number of papers discuss the safety intrinsic to cells, however, excluding EVs there is little discussion of cycling larger, multi-cell packs. This paper considers safe laboratory testing of commercial high capacity single cells and multi-cell battery packs. Using the hazards and failure modes that can occur, appropriate control measures are proposed, including fuses, contactors and good practice in system design. Suitable enclosures and fire suppression are also recommended should these measures fail. For single cells without a BMS, a separate protection circuit is presented utilizing analogue components for reliability.

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been used in research and development community due to their strong potential in high-risk missions. One of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires, the focus of this paper. However, wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the mission. In this paper, wildfire growth is simulated using a coupled Partial Differential Equation (PDE) model, widely used in literature for modeling wildfires, in a grid environment with added process and measurement noise. Using principles of Proper Orthogonal Decomposition (POD), and with an appropriate choice of decomposition modes, a low-dimensional equivalent fire growth model is obtained for the deployment of the space–time Kalman Filtering (KF) paradigm for estimation of wildfires using simulated data. The KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the UAV. This information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on wildfire. Path planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk function. The results from numerical simulation are presented to validate the proposed estimation and path planning methods.

This paper proposes an online data splicing power flow analysis method based on sensitivity analysis. Since the step-down transformer in the whole network data is usually equivalent to the 220 kV side, the equipment below 220 kV is not modeled in detail, and the provincial network data is modeled to 110kV and 35 kV voltage level equipment in detail. Different levels of modeling have a greater impact on the accuracy of stability analysis and calculation. Therefore, the purpose of improving data accuracy is achieved by splicing the entire network data (external network) and provincial network data (internal network). This paper is based on the sensitivity analysis method to stabilize the tie-line power by adjusting the output of the external network units, and form the whole network data that can accurately analyze the stability characteristics of the provincial network. The problem of power flow deviation caused by the voltage and angle deviation of the nodes at both ends of the regional tie line during the data splicing process is solved. Through the actual application of splicing the entire network and Hunan Provincial Network data, the effectiveness and necessity of the proposed method have been verified.

In order to improve the reliability of the shunt active power filter(SAPF) system, the fault-tolerant control strategy of the system is studied in this paper. A fault-tolerant control strategy based on improved switch redundancy is designed inside SAPF system, and fault-tolerant is achieved through circuit reconstruction. Then the operating mode and voltage vector characteristics of the fault-tolerant topology are analyzed, and the corresponding SVPWM control algorithm is derived. Finally, experiments verify the practicability and effectiveness of the SAPF fault-tolerant control method proposed in this paper.

The connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a lot of manpower and material resources and is inefficient. Therefore, an efficient method for topology verification of low-voltage substation areas is required. Given this background, a model for error correction and user access phase identification of low-voltage stations based on multi-dimensional voltage data collected by smart meters is presented in this paper, which can provide a certain reference for topology identification and line troubleshooting of low-voltage substations. First, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and the Principal Component Analysis (PCA) performs dimensionality reduction on the original load data to solve the problem of redundancy caused by the high dimension of the original voltage data set. Second, the Local Outlier Factor (LOF) algorithm is used to identify abnormal samples in the voltage data set. Then, the spectral clustering method is used to cluster the dimensionality-reduced load data to realize the phase identification of single-phase users in the low-voltage station area. Finally, the real data of a certain area in Haining, Zhejiang Province of China are used as simulation cases for demonstrating. The results of the case studies show that the model proposed in this paper is feasible and effective.

As the depth of excavation increases, rockburst becomes one of the most serious geological hazards damaging equipment and facilities and even causing fatalities in mining and civil engineering. This has forced researchers worldwide to identify different methods to investigate rockburst-related problems. However, some problems, such as the mechanisms and the prediction of rockbursts, continue to be studied because rockburst is a very complicated phenomenon influenced by the uncertainty and complexity in geological conditions, in situ stresses, induced stresses, etc. Numerical modeling is a widely used method for investigating rockbursts. To date, great achievements have been made owing to the rapid development of information technology (IT) and computer equipment. Hence, it is necessary and meaningful to conduct a review of the current state of the studies for rockburst numerical modeling. In this paper, the categories and the origin of different numerical approaches employed in modeling rockbursts are reviewed and the current usage of various numerical modeling approaches is investigated by a literature research. Later, a state-of-the-art review is implemented to investigate the application of numerical modeling in the mechanism study, and prediction and prevention of rockbursts. The main achievements and problems are highlighted. Finally, this paper discusses the limitations and the future research of numerical modeling for rockbursts. An approach is proposed to provide researchers with a systematic and reasonable numerical modeling framework.

In three-phase grid synchronization applications, the performance of traditional phase-locked loops will decrease under non-ideal grid voltage conditions. In order to solve the above problem, moving average filters (MAF) has been widely studied and utilized in PLLs in recently published papers with the expense of slower transient speed. This paper proposes a paralleled filtering stage to improve the dynamic performance of PLL. The suggested filter with smaller time delay is based on MAF and delay signal cancellation (DSC) operator, which is integrated in quasi-type-1 structure based PLL (QT1-PLL). Compared with differential MAF-PLL (DMAF-PLL) and QT1-PLL, the proposed filter can obtain a faster transient response without degrading phase estimation accuracy. The experimental results demonstrate that the proposed method can provide satisfactory performance.

An accurate estimation of tool wear morphology can provide the opportunity to investigate the influence of tool wear on cutting performance as well as reduce the overall production cost. However, tool wear prediction is still a very challenging research issue. In this paper, a novel method for simulating the actual chip formation and wear evolution thorough the 3D finite element model has been carried out. In order to improve the accuracy of simulation results, the influence of worn tool, stress and temperature distribution on wear rate are considered. Then cutting experiment has been conducted by turning AISI1045 with uncoated carbide tools to validate the accuracy of the proposed model. The comparison between experimental and simulation results show good agreement which proves the ability of the proposed model in forecasting the tool wear. The validated finite element model has been further utilized studying how the worn tool affects the cutting performance including actual cutting rake, stress distribution, cutting force and temperature. The results of this paper not only provide a clear understanding of wear evolution between tool rake face and chip, but also are meaningful to optimize tool design and cutting parameters.

A novel passive frequency beam-steering linear antenna array is proposed in this paper to be utilized at the transmit side of a long-range wireless power transfer (WPT) system. To assure high power handling for a long-range transfer, the proposed antenna is designed based on stripline technology. Moreover, unlike other reported designs, the antenna array in this paper uses parallel feed while frequency beam-steering. This permits to apply a special synthesis at the radiating elements to minimize the energy wasted at the side lobes. The antenna array uses common-fed dipoles as radiating elements to eliminate the surface current at the ground planes that is considered a critical issue in high power applications. The antenna covers the frequency band from 1.1 to 1.5 GHz with a reflection coefficient better than −10 dB (VSWR