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A novel nanopositioning platform (referred as NanoPla) in development has been designed to achieve nanometre resolution in a large working range of 50 mm &times, 50 mm. Two-dimensional (2D) movement is performed by four custom-made Halbach linear motors, and a 2D laser system provides positioning feedback, while the moving part of the platform is levitating and unguided. For control hardware, this work proposes the use of a commercial generic solution, in contrast to other systems where the control hardware and software are specifically designed for that purpose. In a previous paper based on this research, the control system of one linear motor implemented in selected commercial hardware was presented. In this study, the developed control system is extended to the four motors of the nanopositioning platform to generate 2D planar movement in the whole working range of the nanopositioning platform. In addition, the positioning uncertainty of the control system is assessed. The obtained results satisfy the working requirements of the NanoPla, achieving a positioning uncertainty of &plusmn, 0.5 &micro, m along the whole working range.

The smart, rapid, and customizable detection of chemical warfare agents is a huge issue for taking the proper countermeasures in a timely fashion. The printing techniques have established the main pillar to develop miniaturized electrochemical biosensors for onsite and fast detection of nerve and mustard agents, allowing for a lab on a chip in the chemical warfare agent sector. In the fast growth of novel technologies, the combination of miniaturized electrochemical biosensors with flexible electronics allowed for the delivery of useful wearable sensors capable of fast detection of chemical warfare agents. The wearable microneedle sensor array for minimally invasive continuous electrochemical detection of organophosphorus nerve agents, as well as the wearable paper-based origami functionalized with nanomaterials for mustard agents in the gas phase, represent two examples of the forefront devices developed in the chemical warfare agent detection field. This review will highlight the most promising electrochemical biosensors developed by exploiting nanomaterials and cross-cutting technologies for the fabrication of smart and sensitive electrochemical biosensors for the detection of chemical warfare agents.

Reinforced concrete is the most commonly used material in urban, road, and industrial structures. Quantifying the condition of the reinforcing steel can help manage the human and financial risks that arise from unexpected reinforced concrete structure functional failure. Also, a quantitative time history of reinforcing steel condition can be used to make decisions on rehabilitation, decommissioning, or replacement. The self-magnetic behavior of ferromagnetic materials is useful for quantitative condition assessment. In this study, a ferromagnetic rebar with artificial defects was scanned by a three-dimensional (3D) laser scanner. The obtained point cloud was imported as a real geometry to a finite element software platform, its self-magnetic behavior was then simulated under the influence of Earth&rsquo, s magnetic field. The various passive magnetic parameters that can be measured were reviewed for different conditions. Statistical studies showed that 0.76% of the simulation-obtained data of the rebar surface was related to the defect locations. Additionally, acceptable coincidences were confirmed between the magnetic properties from numerical simulation and from experimental outputs, most noticeably at hole locations.

The study focuses on the application of a nonparametric methodology for evaluating the sustainability of retrofitting interventions to be applied on different typologies of buildings and different climate zones of the Mediterranean area. The paper starts from the analysis of data collected through the HAPPEN project, that is a H2020 European project which proposes a holistic approach for a deep and sustainable renovation of the Mediterranean residential Building stock. Even if the European Commission allocated considerable funds for retrofitting interventions, the choice of the optimal solution is not always that easy because several variables have to be considered. The present manuscript proposes a methodology to compare different retrofitting solutions combining Life-Cycle Cost (i.e., LCC) estimations with the nonparametric Directional Distance Function approach (i.e., DDF). In detail, the literature suggests that the DDF can be effectively used for comparing different observations through efficiency scores. The main result of the paper is the definition of a hybrid methodology that, starting from estimates of LCC and applying a DDF technique, represents a simple method for evaluating the best retrofitting intervention. Results are represented by two scores where the former represents a holistic efficiency measure, while the latter shows an environmental efficiency score.

International audience; Over the past decade, interest in autonomous vessels significantly increased as the technology improved, especially in the automotive industry. Unlike cars, ships travel in a wild environment and maritime lanes are not limited by white lines. This makes the design of fully autonomous vessels even more challenging. Additionally, the need to reduce greenhouse gas emissions led to a renewed interest in wind propulsion. Sailboats have several advantages, such as full energy autonomy and a limited environmental impact. The Microtransat Challenge, which consists of crossing the Atlantic Ocean, is a tremendous test field. This paper describes, within that frame, a design procedure for the development of a robust fully autonomous sailboat to be deployed for long-term missions. In this paper, the mechanical and electronic design strategies are presented. A focus is on reliability and power management. Moreover, a test procedure for validating each design increment is described as well as a path plan that considers the risk of collision and weather routing with wind and currents. The Microtransat remains a challenge that no autonomous ship has ever succeeded (and has been completed by a single unmanned vessel, SB Met in 2018). However, the results by Breizh Tigresse and Sealeon in 2015 and 2018 made a step forward in terms of time and distance. They are presented and analyzed in this work

This paper proposes a lane detection algorithm using a laser range finder (LRF) for the autonomous navigation of a mobile robot. There are many technologies for ensuring the safety of vehicles, such as airbags, ABS, and EPS. Further, lane detection is a fundamental requirement for an automobile system that utilizes the external environment information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. In the case of a vision-based system, the recognition of the environment of a three-dimensional space becomes excellent only in good conditions for capturing images. However, there are so many unexpected barriers, such as bad illumination, occlusions, vibrations, and thick fog, that the vision-based method cannot be used for satisfying the abovementioned fundamental requirement. In this paper, a three-dimensional lane detection algorithm using LRF that is very robust against illumination is proposed. For the three-dimensional lane detection, the laser reflection difference between the asphalt and the lane according to color and distance has been utilized with the extraction of feature points. Further, a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been experimentally verified.

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

The paper proposes a new method for adjusting classical terrestrial observations (total station) together with satellite (GNSS-Global Navigation Satellite Systems) vectors. All the observations are adjusted in a single common three-dimensional system of reference. The proposed method does not require the observations to be projected onto an ellipsoid or converted between reference systems. The adjustment process follows the transformation of a classical geodetic network (distances and horizontal and vertical angles) into a spatial linear (distance) network. This step facilitates easy integration with GNSS vectors when results are numerically processed. The paper offers detailed formulas for calculating pseudo-observations (spatial distances) from input terrestrial observations (horizontal and vertical angles, horizontal distances, height of instrument and height of target). The next stage was to set observation equations and transform them into a linear form (functional adjustment model of geodetic observations). A method was provided as well for determining the mean errors of the pseudo-observations, necessary to assess the accuracy of the values following the adjustment (point coordinates). The proposed algorithm was verified in practice whereby an integrated network made up of a GNSS vector network and a classical linear-angular network was adjusted.

This paper addresses a wildlife monitoring problem using a team of unmanned aerial vehicles (UAVs) with the optimal transport theory. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work. In all different simulations, it is shown that the proposed scheme significantly outperforms other UAV-based wildlife monitoring strategies in terms of the target detection rate up to 3.6 times.

Mobile robots that can survive in unstructured wildernesses is essential in many applications such as environment detecting and security surveillance. In many of these applications, it is highly desirable that the robot can adapt robustly to both terrestrial environment and aquatic environment, and translocate swiftly between various environments. A novel concept of amphibious spherical robot with fins is proposed in this paper, capable of both terrestrial locomotion and aquatic locomotion. Unlike the traditional amphibious robots, whose motions are commonly induced by propellers, legs or snake-like tandem joints, the proposed amphibious spherical robot utilizes the rolling motion of a spherical shell as the principal locomotion mode in the aquatic environment. Moreover, spinning motion of the spherical shell is used to steer the spherical robot efficiently and agilely, several fins are attached to the outer spherical shell as an assistance to the rolling motion. These two motion modes, rolling and spinning, can be used unchangeably in the terrestrial environment, leading to a compact and highly adaptive design of the robot. The work introduced in this paper brings in an innovative solution for the design of an amphibious robot.

The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract materials from such structures to accurately ascertain their behavior or their respective parameters. The general approach involves laboratory tests using small material samples or empirical data from the literature. However, such measures lack an accurate representation of the actual scenario, resulting in uncertainties. This limits the suitability of the model in the design process. Inverse analysis provides an option to better understand dam behavior. This procedure involves the use of real monitored data, such as deformations and stresses, from the dam structure via installed instruments. Fundamentally, it is a non-destructive approach that considers optimization methods and actual performance data to determine the values of the parameters by minimizing the differences between simulated and observed results. This paper considers data from an actual rockfill dam and proposes a surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function that measures the differences between the actual and simulated displacement values is defined first. Non-deterministic algorithms are used as the optimization technique, as they can avoid local optima and are more robust when compared to the conventional deterministic methods. Three such approaches, the genetic algorithm, differential evolution, and particle swarm optimization are evaluated to identify the best strategy in solving problems of this nature. A surrogate model in the form of a polynomial regression is studied and recommended in place of the actual numerical model of the dam to reduce computation cost. Finally, this paper presents the relevant dam parameters estimated by the analysis and provides insights into the performance of the three procedures to solve the inverse problem.

Sound environments in large public buildings are likely to be different from those of performance spaces, as well as those not specifically designed for acoustic “performance”, but where sounds still play an important role because of the function they can promote (or disrupt). The aim of this study was identifying common strategies and empirical approaches researchers have been implementing for these acoustically complex enclosures and to provide some methodological indications for future studies on the topic. Studies conducted in three building types for crowd transit, such as museums/exhibition spaces, shopping malls, and transportation hubs/stations, which were collecting data about either physical outcomes or individual responses for such sound environments, were selected. The Scopus databases were searched for peer-reviewed journal papers published in English without time limitations. An additional manual search was performed on the reference lists of the retrieved items. The general consideration on inclusion was to meet the requirement that the case belonged to the three building types, and then the specific inclusion criteria were: (1) including at least an objective acoustic measure of the space; or (2) including at least a subjective measure of the space. The search returned 1060 results; after removing duplicates, two authors screened titles and abstracts and selected 117 papers for further analysis. Twenty-six studies were eventually included. Due to the limited number of items and differences in measures across studies, a quantitative meta-analysis could not be performed, and a qualitative approach was adopted instead. The most commonly used objective measures were SPL, and more specifically often considered as LAeq, and T. The intervals across studies were currently of inconsistency, and the selection is recommended to take space scale factor into account. The used subjective measures can be classified into four categories as annoyance, affective quality, room-acoustic quality, and acoustic spatiality. Four basic perceptual assessments concerning dynamic contents are accordingly suggested as “annoying-not annoying”, “crowded-uncrowded”, “long-short (reverberation)”, and “far away-nearby”. The other descriptors can be project-specific. The methodologies involve measurement, questionnaire/interview, listening test, and software simulation. It is necessary for the former two to consider temporal and spatial features of such spaces, and the adoption of the latter two will lead to better understanding of users’ exposure in such spaces, e.g., acoustic sequences and user amount. The outputs of investigations inform that background noise level, e.g., 90 dB in museum/exhibition spaces, and sound reverberation, e.g., 4.0 to 5.0 s in shopping malls and transportation hubs/station, are of fundamental importance to the design of such spaces. Sufficient acoustic comfort can be achieved with integrated design of indoor soundscape.

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

With the drastically increasing traffic in the last decades, crucial environmental problems have been caused, such as greenhouse gas emission and traffic noise pollution. These problems have adversely affected our life quality and health conditions. In this paper, modelling of traffic noise employing deep learning is investigated. The goal is to identify the best machine-learning model for predicting traffic noise from real-life traffic data with multivariate traffic features as input. An extensive study on recurrent neural network (RNN) is performed in this work for modelling time series traffic data, which was collected through an experimental campaign at an inner city roundabout, including both video traffic data and audio data. The preprocessing of the data, namely how to generate the appropriate input and output for deep learning model, is detailed in this paper. A selection of different architectures of RNN, such as many-to-one, many-to-many, encoder–decoder architectures, was investigated. Moreover, gated recurrent unit (GRU) and long short-term memory (LSTM) were further discussed. The results revealed that a multivariate bi-directional GRU model with many-to-many architecture achieved the best performance with both high accuracy and computation efficiency. The trained model could be promising for a future smart city concept, with the proposed model, real-time traffic noise predictions can be potentially feasible using only traffic data collected by different sensors in the city, thanks to the generated big data by smart cities. The forecast of excessive noise exposure can help the regulation and policy makers to make early decisions, in order to mitigate the noise level.

International audience; The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. The first one adopts a block-sparse model that jointly estimates the sparse coefficients at identical or symmetrical positions around the carrier frequencies. The second is a spike-and-slab model where the spike component takes into account the symmetrical properties of the support of non-zero-coefficients in the spectrum. The resulting posterior distribution is approximated using a Gibbs sampler. Simulations show that considering the structure in the prior model yields better noise shrinkage and better reconstruction of small side-bands. Application to condition monitoring of a gearbox through CS of vibration signals highlights the good performance of the proposed models in reconstructing the signal, offering an accurate fault detection with relatively high compression rate.

This paper deals with a complete finite-element analysis procedure for squirrel cage induction motors, including the presence of skewing and the iron losses evaluation. The machine is analyzed performing only magneto-static finite element analyses. Saturation phenomena are carefully considered in any operating condition, avoiding long time-stepping analyses. The synergy between analytical and finite element model leads to a rapid and precise estimation of the rotor induced current, saving computational time. Furthermore, the procedure proposed in this paper allows the motor performance to be directly derived, without the preliminary knowledge of the machine equivalent circuit. In order to complete the analysis, skewing effect is included, using the 2-D multi-slice technique, based on static simulations. Experimental tests are carried out and reported in order to verify analysis results.

The research presented in this paper aims at creating a bilingual (sr-en), easily searchable, hypertext, born-digital, corpus-based terminological database of raw material terminology for dictionary production. The approach is based on linking dictionaries related to the raw material domain, both digitally born and printed, into a lexicon structure, aligning terminology from different dictionaries as much as possible. This paper presents the main features of this approach, data used for compilation of the terminological database, the procedure by which it has been generated and a mobile application for its use. Available (terminological) resources will be presented—paper dictionaries and digital resources related to the raw material domain, as well as general lexica morphological dictionaries. Resource preparation started with dictionary (retro)digitisation and corpora enlargement, followed by adding new Serbian terms to general lexica dictionaries, as well as adding bilingual terms. Dictionary development is relying on corpus analysis, details of which are also presented. Usage examples, collocations and concordances play an important role in raw material terminology, and have also been included in this research. Some important related issues discussed are collocation extraction methods, the use of domain labels, lexical and semantic relations, definitions and subentries.

The SCOPUS and Wed of Science bibliometric databases were searched for papers related to the use of multi-criteria methods in civil engineering related disciplines. The results were analyzed for information on the reported geographical distribution of usage, the methods used, the application areas with most usage and the software tools used. There was a wide geographical distribution of usage with all northern hemisphere continents well represented. However, of the very many methods available, a small number seemed to dominate usage, with the Analytic Hierarchy Process being the most frequently used. The application areas represented in the documents found was not widely spread and mainly seemed to be focused on issues such as sustainability, environment, risk, safety and to some extent project management, with less usage on other areas. This may be due to individual engineer’s choices in relation to if and how to disseminate the results of their work and to their choice of keywords and titles that determine if their publications are selected in bibliographic searches and thus more visible to a wider readership. A comparison with more topic focused searches, relating to Bridge Design, Earthquake Engineering, Cladding, Sewage Treatment, Foundation design, Truss design, Water Supply, Building Energy, Route selection and Transport mode showed very different results. Analysis of the papers in this area indicated that the full range of supporting software available for multi-criteria decision analysis (many listed in this paper) may not be fully appreciated by potential users.

The research paper presents a novel approach toward constructing motion equations for structures with attached MTMDs (multiple tuned mass dampers). A primary system with MDOF (multiple dynamic degrees of freedom) was reduced to an equivalent system with a SDOF (single degree of freedom) through the modal approach, and equations from additional MTMDs were added to a thus-created system. Optimization based on ℌ2 and ℌ∞ for the transfer function associated with the generalized displacement of an SDOF system was applied. The research work utilized GA (genetic algorithms) and SA (simulated annealing method) optimization algorithms to determine the stiffness and damping parameters for individual TMDs. The effect of damping and stiffness (MTMD tuning) distribution depending on the number of TMDs was also analyzed. The paper also reviews the impact of primary system mass change on the efficiency of optimized MTMDs, as well as confirms the results of other authors involving greater MTMD effectiveness relative to a single TMD.

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.

This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMFs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMFs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMFs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.

Due to the micro-vibration of flywheels, the imaging quality of a high-resolution optical remote sensing satellite will be deteriorated, and the micro-vibration effect on the payload is complicated, so it is essential to establish a reasonable and accurate theoretical simulation model for it. This paper presents a method of full-closed-loop time-domain integrated modeling to estimate the impacts of micro-vibration generated by flywheels on optical satellites. The method consists of three parts. First, according to the satellites’ micro-vibration influence mechanism in orbit, this paper establishes a full-closed-loop model framework. The overall model input is the instructions received and the output is the image shift. Second, in order to meet the requirements of time-domain simulation, this paper proposes a time-domain vibration source subsystem model in the form of cosine harmonic superposition, and it integrates vibration source, structural, control, and optical subsystem models to create a full-closed-loop time-domain analysis model that can obtain the responses of micro-vibration in time and frequency domains. Lastly, the author designs a ground experiment and compares simulation results with experiment results. Compared with the ground experiment, frequency error is less than 0.4% at typical responses. Although the amplitude error is large at some typical responses, the mean root square error is less than 35%. Based on the data, the proposed integrated modeling method can be considered as an accurate methodology to predict the impacts of micro-vibration.

In this paper, we introduce combination constraints for modeling ore bodies based on multiple implicit fields interpolation. The basic idea of the method is to define a multi-labeled implicit function that combines different sub-implicit fields by the combination operations, including intersection, union and difference operators. The contribution of this paper resides in the application of combination of more general implicit fields with combination rules for the implicit modeling of ore bodies, such that the geologist can construct constraints honoring geological relationships more flexibly. To improve the efficiency of implicit surface reconstruction, a pruning strategy is used to avoid unnecessary calculations based on the hierarchical bounding box of the operation tree. Different RBF-based methods are utilized to study the implicit modeling cases of ore bodies. The experimental results of several datasets show that the combination constraints are useful to reconstruct implicit surfaces for ore bodies with mineralization rules involving multiple fields.

This paper present recent advances in the development of local correlation based laminar–to–turbulent transition modeling relying on the Spalart–Allmaras equation. Such models are extremely important for the flow regimes involved in wind energy applications. Indeed, fully turbulent flow models are not completely reliable to predict the aerodynamic force coefficients. This is particularly significant for the wind turbine blade sections. In this paper, we focus our attention on two different transitional flow models for Reynolds–Averaged Navier–Stokes (RANS) equations. It is worth noting that this is a crucial aspect because standard RANS models assume a fully turbulent regime. Thus, our approaches couple the well–known γ– technique and logγ equation with the Spalart–Allmaras turbulence model in order to overcome the common drawbacks of standard techniques. The effectiveness, efficiency, and robustness of the above-mentioned methods are tested and discussed by computing several flow fields developing around airfoils operating at Reynolds numbers typical of wind turbine blade sections.

Vibration absorption is a core research topic in structural dynamics and the mechanics of machines, and antiresonance assignment is an effective solution to such a problem in the presence of harmonic excitation forces. Due to recent developments in the theory of feedback control systems, the use of active control approaches to antiresonance assignment has been recently gaining more attention in the literature. Therefore, several methods exploiting state feedback or output feedback have been proposed in recent years. These techniques that just rely on servo-controlled actuators are becoming an interesting alternative to active approaches that emulate the well-known Tuned Mass Damper in an active (or semi-active) framework. This paper reviews and compares the most important approaches, with a greater focus on the methods exploiting the concept of control theory without adding new degrees of freedom in the system. The theoretical results, with the underlying theory, are discussed to highlight the key features of each assignment techniques. Several numerical examples where different techniques are applied and compared, also providing some analysis usually neglected in the literature, enrich the paper and demonstrate the key concepts.

This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot&rsquo, s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.

The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared, eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.

Various high-performance force/torque sensors have been developed for the purpose of advancing automation systems. However, the demand for simple torque measurement of rotating shafts continues to exist, and expensive multi-axis sensors need not be wasted here. In this paper we propose a simple motor-equipped single-axis reaction torque sensor to measure the applied torque continuously using a load cell. The proposed sensor has long lever and base linkages, and the adjustable moment arm consequently enables adjusting measurement range and sensitivity by repositioning the assembled load cell on the two linkages. This paper shows the design of the proposed torque sensor, and it is evaluated by experiments for various applied torque and lever length. Moreover, the sensor is applied to an existing example: a commercial balanced-arm lamp with and without its balancing spring. The proposed torque sensor can continuously and successfully measure the applied torque, and it will be utilized in various industries and laboratories without much money.

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

The paper presents the application of a coupling methodology between Computational Fluid Dynamics (CFD) and System Thermal Hydraulic (STH) codes developed at the University of Pisa. The methodology was applied to the CIRCE-HERO facility in order to reproduce the recently performed experimental conditions simulating a Protected Loss Of Flow Accident (PLOFA). The facility consists of an internal loop, equipped with a fuel pin simulator and a steam generator, and an external pool. In this coupling application, the System code RELAP5 is adopted for the simulation of the internal loop while the CFD code ANSYS Fluent is used for the sake of simulating the pool. The connection between the two addressed domains is provided at the inlet and outlet section of the internal loop, a thermal coupling is also performed in order to reproduce the observed thermal stratification phenomenon. The obtained results are promising and a good agreement was obtained for both the mass flow rates and temperature measurements. Capabilities and limitations of the adopted coupling technique are discussed in the present paper also providing suggestions for improvements and developments to be achieved in the frame of future applications.

One of the standard diagnostic methods for power transformers is frequency response analysis (FRA). This paper deals with progress in the frequency response interpretation of these very important appliances in the power network. Simulations of the frequency response of the transformer windings are a useful way to improve the correct assessment of FRA results. One of the disadvantages of FRA modelling is time-consuming analysis, especially of large multilayer windings. The aim of this article is to develop a numerical model, which would reduce the duration of the electromagnetic field analysis of the winding with hundreds of turns. The approach introduced in the paper describes the simple two-dimensional (2D) finite elements method (FEM) model, which provides the results comparable to the three-dimensional (3D) model. Moreover, the paper presents the numerical models, which demonstrate the influence of the remaining windings on the inductance of tested winding. As a result of the conducted research, the comparison of inductance curve obtained from FRA measurement on the physical model with modelled resonance slope is presented, both for 2D and 3D cases.

This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.

Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.

A balance control method, the proportional-integral sliding mode control (PISMC), is proposed to control the tilt attitude of an experimental two-wheel vehicle system (TWVS). Based on our previous work of implementing a generalized PISMC to control a linearized dynamical system, this paper extends the algorithm to a wider range: First, the control design of a weighted-control system is proposed. Secondly, our algorithm was realized and verified in a TWVS using its original nonlinear model. Thirdly, a systematical way to tune parameters are presented. The robustness of the proposed algorithm is also discussed in this paper. The simulation results of this work validate that the PISMC has better robustness to counteract the external disturbances than the conventional sliding mode control (SMC) does. Additionally, the experimental results show that the PISMC is capable of autonomously balancing the TWVS more effectively than the conventional SMC. The successful implementation of our algorithm potentially extends the implementation of the PISMC to various nonlinear and emerging systems.

Contribution: This paper examines the literature dealing with the non-invasive estimate of anemia (NEA), and analyzes if the research is developing scientifically with adequate empirical validation. This paper reveals a trend in NEA studies towards an increasing interest in estimating anemia using conjunctiva pallor. Background: Supporting clinical developments and processes to reduce personal discomfort and allow extensive screening needs substantial efforts in researching non-invasive techniques to evaluate anemia. Research Questions: The main aims of this study are the analysis of the area of interest explored in the NEA literature, the evaluation of the peculiarities of papers, giving special consideration to empirical ones, examining them from the point of view of the daily improvement of doctors and healthcare personnel activities and the daily life of patients; and the identification of any considerable research gap to encourage further investigations on new topics. Methodology: The systematic mapping study has been elected as the optimal approach to probe the NEA literature since it defines a rigorous process for data retrieving and interpretation. Findings: Research in this sector is very active, especially in the most populated countries in the world and focuses on improving the technologies currently on the market and on proposing new solutions, especially portable and usable by everyone. A new trend in camera and smartphone-based devices is identified.

Topology and Parametric Optimization are two of the most implemented material optimization approaches. However, it is not clear in the literature which optimization procedure, or possible combination of them, can lead to the best results based on material reduction and optimization time. In this paper, a quantitative comparison of different topology and parametric optimization design processes is conducted using three benchmark examples: A Hollow Plate, an L-Bracket, and a Messerschmitt&ndash, B&ouml, lkow&ndash, Blohm Beam (MBB-Beam). Ten different design processes that were developed in each case study resulted in 30 simulations in total. The design processes were clustered in three main design workflows: The Topology Optimization, the Parametric Optimization, and the Simultaneous Parametric and Topology Optimization. Their results were compared with respect to mass, stress, and time. The Simultaneous Parametric and Topology Optimization approach gave the lightest design solutions without compromising their initial strength but also increased the optimization time. The findings of this paper will help the designers in the pursuit of lightweight structures and will create the basis for the identification of the ideal material optimization procedure.

Freeform surface is one of the research focuses in the measurement field. A composite module is composed of a plane and rotating paraboloid. The composite module array can identify 21 geometric errors of the machine tool in a wide range, which is composed of several composite modules. Eliminating the error of the array itself is of great significance for improving measurement accuracy. For this reason, this paper proposed a noncontact method for calibrating the angle and position of the composite module array. This paper used a self-developed angle sensor to access corresponding information and established the mathematical model according to the freeform surface&rsquo, s geometric characteristics to achieve calibration. In addition, the influence of array placement error on calibration was analyzed. The experimental results showed that the angle repeatability was within 0.4 &rdquo, around the X-axis and within 0.3&rdquo, around the Y-axis, and the position repeatability was within 0.4 &micro, m in the X direction and within 0.7 &micro, m in the Y direction. The measurement comparison experiments with high-precision laser interferometer and uncalibrated array verified the correctness of the experimental results. This method provides an important reference for practical application and freeform surface array calibration, and creates conditions for the implementation of machine tool error detection.

With the continuous development of wireless communication technology, the Internet ofThings (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts ofdata with objects, either tangible or intangible, such as sensors or physical devices, connected to theInternet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in variousIoT systems, such as monitoring, tracking, and detection systems, to extract relevant information anddeliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions.Because sensor nodes have limited resources, such as power and memory, a reduction in the energyefficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance andan increase in packet loss, which affects IoT system performance. Therefore, this study aimed to findan energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery useof sensor nodes to improve the network performance of IoT systems. Conserving energy from sensornodes and increasing network throughput in WSNs involves having protocols. The low-energyadaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol inWSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmittingdata from sensor nodes to the base station (BS) through the cluster head. It is widely adopted inthe WSN network field, and many protocols are being studied to improve cluster header selectionand data transmission to increase the energy efficiency of sensor nodes. In this study, we attemptedto improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when thesensor node is located between the BS and the cluster head, the sensor node transmits data to thecluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in thedirection of the BS. Thus, transmission in the opposite direction consumes unnecessary energy andaffects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-basedLEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiencyof transmission energy considering the data transmission direction of sensor nodes. D-LEACH aimsto balance the energy of the sensor nodes and improve the performance of WSNs in the IoT systemby reducing unnecessary energy consumption caused by reverse transmission considering the datatransmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocolof WSNs to improve network performance and describe LEACH. We also explain the D-LEACHprotocol proposed in this paper and confirm the performance improvement of WSNs in an IoT systemthrough simulation.

The measurement of the phase center of the airborne array antenna can directly affect the accuracy of the Earth observation system. However, the relationship between the relative motion of each sub-antenna cannot be accurately measured because of the adverse environment of the airborne platform. Therefore, it is necessary to find a suitable method to measure the motion parameters of distributed antennas and the phase center of each element antenna accurately in order to improve the imaging resolution of the Earth observation system. Distributed position and orientation system (POS) technology has high precision, but its measurement error will accumulate with time. So it needs to transfer and align continuously to achieve high-precision measurement. The paper introduces the distributed measurement method of measuring the array antenna position based on the combination of fiber Bragg grating (FBG) sensing technology and POS technology on the aircraft wing. The paper first introduces the technical scheme and principle, then carries out the structural design and method analysis. Next, the structural strength of the experimental model is checked and summarized.

In precision machining, expanding mandrels are used for jobs with close tolerances. An expanding mandrel consists of a tapered arbor or shaft, with a thin-slotted clamping sleeve or collet made of hardened steel. The internal tapered and external cylindrical surfaces are ground to a high degree of accuracy, and the mandrel expands to fit the internal bore of the workpiece. Expanding mandrels are, essentially, wedge mechanisms. This paper proposes an automatic expanding mandrel with a novel force transmission system for high stiffness within a novel air sensing system, which allows detection of the correct part position before starting machining. A computational model for determining the dynamic clamping force of the proposed mechanism is developed and implemented using MATLAB. This model considers the influence of the sti ness behaviors of the collet, force transmission structure and workpiece. Additionally, this paper presents the finite element method analyses which were conducted to check the proposed computational model. The amount of clamping force transmitted by a collet chuck holder depends strongly on: clearances, wedge angle, stiffness of the collet chuck holder and workpiece stiffness.

The output torque is an important performance indicator of a motor. Due to the special structure of a permanent magnet spherical motor (PMSM), it is difficult to measure its torque. This paper proposes a novel method to measure the output torque of the PMSM. The proposed method uses the microelectromechanical system (MEMS) gyroscope to measure the rotor motion acceleration, which is then used to calculate the output torque based on the rotor dynamics equation. In this paper, we firstly simulate and analyze the output torque of the PMSM. Secondly, we design a torque-measuring device to measure the output torque. Thirdly, we compare and discuss the experimental and simulation results. The comparison results show that the proposed method is feasible.

The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

A step gauge is a commonly used length standard for international comparison, and its calibration accuracy is often used as a sign to measure a country’s length Calibration and Measurement Capability (CMC). Based on this, some developed countries and developing countries all over the world have been carrying out the research of precision calibration technology for step gauge. On the basis of summarizing the current situation of step gauge calibration technology in other countries, this paper presents a new computational model of step gauge calibration based on the Synthesis Technology of Multi-Path Laser Interferometers (SMLI) and an auto-collimator, which can synthesize the three laser light paths into the measured centerline of step gauge. It is very important to obtain a good measurement accuracy for the step gauge, conformed to the Abbe principle, no matter where it is installed on the CMM measurement platform. In this paper, the development of the mathematical model, the data collection algorithms, data analysis techniques, and measurement uncertainty budgets are discussed. Finally, the experimental measurement is carried out and the measurement accuracy is verified to be effective. The results show that this method can effectively avoid the influence of Abbe error in length measurement, and significantly enhance the calibration accuracy of the step gauge.

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.

In this paper, a novel design of a surface topography measurement system is proposed, to address the challenge of accurate measurement in a relatively large area. This system was able to achieve nanometer-scale accuracy in a measurement range of 100 mm × 100 mm. The high accuracy in a relatively large area was achieved by implementing two concepts: (1) A static coordinate system was configured to minimize the Abbe errors. (2) A differential measurement configuration was developed by setting up a confocal sensor and a film interferometry module to separate the motion error. In order to accommodate the differential measurement probes from both sides of the central stage and ensure the system rigidity with balanced supports, separate linear guides were introduced in this system. Therefore, the motion Degree of Freedom (DoF) was analyzed in order to address the challenge of an over-constrained mechanism due to multiple kinematic pairs. An optimal configuration and a quick assembly process were proposed accordingly. The experimental results presented in this paper showed that the proposed modular measurement system was able to achieve 10 nm accuracy in measuring the surface roughness and 100 nm accuracy in measuring the step height in the range of 100 mm × 100 mm. In summary, the novel concept of this study is the build of a high-accuracy system with conventional mechanical components.

Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures.

Flow-induced noise is a significant concern for the design and operation of centrifugal pumps. The negative impacts of flow-induced noise on operating stability, human health and the environment have been shown in many cases. This paper presents a comprehensive review of the flow-induced noise study for centrifugal pumps to synthesize the current study status. First, the generation mechanism and propagation route of flow-induced noise are discussed. Then, three kinds of study methodologies, including the theoretical study of hydrodynamic noise, numerical simulation and experimental measurement study, are summarized. Subsequently, the application of the three study methodologies to the analysis of the distribution characteristics of flow-induced noise is analyzed from aspects of the noise source identification and comparison, the frequency response analysis, the directivity characteristics of sound field and the noise changing characteristics under various operating conditions. After that, the analysis of the noise optimization design of centrifugal pumps is summarized. Finally, based on previous study results, this paper puts forward the unsolved problems and implications for future study. In conclusion, the information collected in this review paper could guide further study of the flow-induced noise of centrifugal pumps.

The performances of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Designing the physical layer of a reliable data transmission system requires a knowledge of channel characteristics in terms of the specific parameters of the stochastic model. The Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption simplifies the stochastic description of the channel, and thus the estimation of its transmission parameters. However, shallow underwater channels may not meet the WSSUS assumption. This paper proposes a method for testing the Wide-Sense Stationary (WSS) part of the WSSUS feature of a UAC channel on the basis of the complex envelope of a received probe Pseudo-Random Binary Sequence (PRBS) signal. Two correlation coefficients are calculated that can be interpreted, together, as a measure that determines whether the channel is WSS or not. A similar wide-sense stationarity assessment can be performed on the basis of the Time-Varying Impulse Response (TVIR) of a UAC channel. However, the method proposed in this paper requires fewer computational operations in the receiver of a UAC system. PRBS signal transmission tests were conducted in the UAC channel simulator and in real conditions during an inland water experiment. The correlation coefficient values obtained using the method based on the envelope of a probe signal and the method of analysing the TVIR estimates are compared. The results are similar, and thus, it is possible to assess if the UAC channel can be modelled as a WSS stochastic process without the need for TVIR estimation.

This paper presents a design of a cruise control based on a fractional-order proportional and integral (PI) direct torque control applied to the traction system of an electric vehicle (EV). The paper also discusses the modeling, control design and simulation, resulting in a numerical simulator composed of detailed models of the main components: transmission system, induction motor, power electronics, control system, and vehicle dynamics. The simulation was developed in MATLAB/Simulink and will allow the estimation of the energy consumption of an EV under specific configurations. Simulation results show the efficiency of the designed control. These simulations were carried out using the velocity profiles given by the New Europe Drive Cycle (NEDC).

This paper extends multithreaded application source code model and shows how to using it to detect deadlocks in C language applications. Four known deadlock scenarios from literature can be detected using our model. For every scenario we created theorems and proofs whose fulfillment guarantees the occurrence of deadlocks in multithreaded applications. Paper also contains comparison of multithreaded application source code model and Petri nets and describe advantages and disadvantages both of them.