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Recently, thinning an antenna has resulted in attractive antenna radiation characteristics enhancement. This performance enhancement using a thin antenna array is achieved because the inter-coupling effects of the array elements are reduced. Though the thinning both reduces the weight of the antenna and enhances the radiation characteristics, iterative algorithms can further enhance the performance and hence, in this research, an optimizer algorithm and inter-coupling reducing mechanisms are applied. The excitation weights of the individual array elements are thinned by turning some of the elements off while some of the elements are turned on. The purpose of the thinning is to enhance the antenna performances like reduced SLL, high directivity, reduced power consumption and flexible radiation pattern. The SLL attenuation mechanism is applied to reduce the SLL in addition to the SLL reduction using the thinning and beamforming. Hence, in this paper, the three techniques (thinning, beamforming and SLL attenuation) are proposed to be integrated to enhance the antenna radiation characteristics. In general, the proposed combined method has resulted in much better SLL reduction, directivity improvement, and power wastage reduction.

Flexible poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)/polypyrrole/paper (PEDOT:PSS/PPy/paper) thermoelectric (TE) nanocomposite films were prepared by a two-step method: first, PPy/paper nanocomposite films were prepared by an in situ chemical polymerization process, and second, PEDOT:PSS/PPy/paper TE composite films were fabricated by coating the as-prepared PPy/paper nanocomposite films using a dimethyl sulfoxide-doped PEDOT:PSS solution. Both the electrical conductivity and the Seebeck coefficient of the PEDOT:PSS/PPy/paper TE nanocomposite films were greatly enhanced from 0.06 S/cm to ~0.365 S/cm, and from 5.44 μV/K to ~16.0 μV/K at ~300 K, respectively, when compared to the PPy/paper TE nanocomposite films. The thermal conductivity of the PEDOT:PSS/PPy/paper composite film (0.1522 Wm−1K−1 at ~300 K) was, however, only slightly higher than that of the PPy/paper composite film (0.1142 Wm−1K−1 at ~300 K). As a result, the ZT value of the PEDOT:PSS/PPy/paper composite film (~1.85 × 10−5 at ~300 K) was significantly enhanced when compared to that of the PPy/paper composite film (~4.73 × 10−7 at ~300 K). The as-prepared nanocomposite films have great potential for application in flexible TE devices.

This paper describes a study for waste of electrical and electronic equipment (WEEE) to characterise the plastic composition of different mixed plastic fractions. Most of the samples studied are currently excluded from material recycling and arise as side streams in state-of-the-art plastics recycling plants. These samples contain brominated flame retardants (BFR) or other substances of concern listed as persistent organic pollutants or in the RoHS directive. Seventeen samples, including cathode ray tube (CRT) monitors, CRT televisions, flat screens such as liquid crystal displays, small domestic appliances, and information and communication technology, were investigated using density- and dissolution-based separation processes. The total bromine and chlorine contents of the samples were determined by X-ray fluorescence spectroscopy, indicating a substantial concentration of both elements in density fractions above 1.1 g/cm3, most significantly in specific solubility classes referring to ABS and PS. This was further supported by specific flame retardant analysis. It was shown that BFR levels of both polymers can be reduced to levels below 1000 ppm by dissolution and precipitation processes enabling material recycling in compliance with current legislation. As additional target polymers PC and PC-ABS were also recycled by dissolution but did not require an elimination of BFR. Finally, physicochemical investigations of recycled materials as gel permeation chromatography, melt flow rate, and differential scanning calorimetry suggest a high purity and indicate no degradation of the technical properties of the recycled polymers.

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms., 19 pages, 10 figures, 2 tables

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release of 14 long-term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p, thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse. In this context, an important aspect still little studied, is therefore that of identifying references that allow, first, to have a perception of the space in which the resource allocation algorithms move and, second, to verify the performance of new proposals. In this paper, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

none 12 si In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM’s POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform. open Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone

Concentrated suspensions, composed of solid particles and fluids, are used in many industrial applications. In this paper, we study the effects of temperature on the flow of a concentrated (dense) suspension between two long rotating cylinders. The viscosity of the suspension is assumed to depend on temperature and volume fraction of the solid particles. Based on these concepts, a generalized viscosity model is proposed and the model parameters are fitted with experimental data. The numerical results show good agreement with the available experimental measurements.

This paper presents the results of cement-based mortars characterizations that were taken from rendered layers of military bunkers in the Rupnik Line. The Rupnik Line was conceived as a part of the fortified defence system protecting the Rapallo Border between the Kingdom of Italy and the Kingdom of Yugoslavia, established in 1920. The bunkers were built between 1938 and 1941, and the renders of various compositions were applied as a camouflage layer that merged the bunkers with the environment. Results of petrographic examination and microstructural and chemical analysis of the samples confirmed that locally available crushed dolomite or limestone sands were used as an aggregate in the mortars. As binders, pure Portland cement or a mixture of cement and ground granulated blast furnace slag were used. The selection of the aggregate and binder type in a particular mortar was based on the colour of the artificial stone produced. All samples with the dolomite aggregate show the presence of alkali-aggregate reactions, dedolomitization of the grains and secondary calcite formation in the cement binder, along with the Mg-Al, Mg-Si and Mg-Al-Si phase formations. In mortar compositions with a high Portland cement content, the presence of delayed ettringite formation was also confirmed.

The rolling element skidding may lead to the failure of the rolling bearing. The skidding characteristics can be effectively analyzed by using dynamic response of the rolling bearing. A dynamic model is established to investigate the vibration response of the rolling bearing with local defect on inner/outer race in this paper. In the proposed model, the rolling element skidding, contact stiffness and displacement, the interaction force between ball and race, the interaction force between cage and race, elastohydrodynamic lubrication are taken into consideration. The dynamic responses of the rolling bearing with the rolling element skidding are solved by the proposed model in the time and frequency domains. The effects of defect size, rotational speed, external load, and compound factors on skidding characteristics are investigated. The proposed model is verified by the experiments. The results show that the rolling element skidding leads to the significant difference of dynamic characteristics in the time and frequency domains, which aggravates the failure of the rolling bearings.

Screw and Lie group theory allows for user-friendly modeling of multibody systems (MBS) while at the same they give rise to computationally efficient recursive algorithms. The inherent frame invariance of such formulations allows for use of arbitrary reference frames within the kinematics modeling (rather than obeying modeling conventions such as the Denavit-Hartenberg convention) and to avoid introduction of joint frames. The computational efficiency is owed to a representation of twists, accelerations, and wrenches that minimizes the computational effort. This can be directly carried over to dynamics formulations. In this paper recursive $O\left( n\right) $ Newton-Euler algorithms are derived for the four most frequently used representations of twists, and their specific features are discussed. These formulations are related to the corresponding algorithms that were presented in the literature. The MBS motion equations are derived in closed form using the Lie group formulation. One are the so-called 'Euler-Jourdain' or 'projection' equations, of which Kane's equations are a special case, and the other are the Lagrange equations. The recursive kinematics formulations are readily extended to higher orders in order to compute derivatives of the motions equations. To this end, recursive formulations for the acceleration and jerk are derived. It is briefly discussed how this can be employed for derivation of the linearized motion equations and their time derivatives. The geometric modeling allows for direct application of Lie group integration methods, which is briefly discussed.

The widespread diffusion of connected smart devices has greatly contributed to the rapid expansion and evolution of the Internet at its edge, where personal mobile devices follow the behavior of their human users and interact with other smart objects located in the surroundings. In such a scenario, the user context is represented by a large variety of information that can rapidly change, and the ability of personal mobile devices to locally process this data is fundamental to make the system able to quickly adapt its behavior to the current situation. This ability, in practice, can be represented by a single elaboration process integrated in the final user application, or by a middleware platform aimed at implementing different context processing and reasoning to support third-party applications. However, the lack of public datasets that take into account the complexity of the user context in the mobile environment strongly limits the advance of the research in this field. In this paper, we present MyDigitalFootprint, a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modeling. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users, in their natural environment, without limiting their usual behavior. Existing public datasets generally consist of a limited set of context data, aimed at optimizing specific application domains (human activity recognition is the most common example). On the contrary, our dataset contains a comprehensive set of information describing the user context in the mobile environment. In order to demonstrate the efficacy of the proposed dataset, we present three context-aware applications based on different machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) the recognition of daily-life activities based on smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. To the best of our knowledge, this is the first large-scale dataset containing such heterogeneity of information, representing an invaluable source of data to validate new research in mobile and edge computing.

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users’ interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.

We show that a A-linear map of Hilbert A-modules is induced by a unitary Hilbert module operator if and only if it extends to an ordinary unitary on appropriately defined enveloping Hilbert spaces. Applications to the theory of multiplicative unitaries let us to compute the equivalence classes of Hilbert modules over a class of C*-algebraic quantum groups. We, thus, develop a theory that, for example, could be used to show non-existence of certain co-actions. In particular, we show that the Cuntz semigroup functor takes a co-action to a multiplicative action.

This paper presents an Expert-guided Mixed-Initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster response inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; b) in the context of the simulated robot experiment, Mixed-Initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Results also reinforce previous human-robot interaction evidence regarding the importance of the operator's personality traits and their trust in the autonomous system. Lastly, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: a) the design of context-aware MI control systems; and b) the conflict for control between the robot's MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed., Submitted for journal publication, under review

International audience; The industrial internet of things (IIoT) known as industry 4.0, is the use of internet of things technologies, via the Wireless Sensor Network (WSN), to enhance manufacturing and industrial processes. It incorporates machine learning and big data technologies, to allow machine-to-machine communication that have existed for years in the industrial world. Therefore, it is necessary to propose a robust and functional communication architecture that is based on WSNs, inside factories, in order to show the great interest in the connectivity of things in the industrial environment. In such environment, propagation differs from other conventional indoor mediums, in its large dimensions, and the nature of objects and obstacles inside. Thus, the industrial medium is modeled as a fading channel affected by an impulsive and Gaussian noise. The objective of this paper is to improve robustness and performances of multi-user WSN architecture, based on Discrete Wavelet Transform, under an industrial environment using conventional channel coding and an optimal thresholding receiver.

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings. 1 We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energetique du Bâtiment for providing the data.

Marine litter is one of the main sources of anthropogenic pollution in the marine ecosystem, with plastic representing a global threat. This paper aims to assess the spatial distribution of plastic macro-litter on the seafloor, identifying accumulation hotspots at a northern Mediterranean scale. Density indices (items km–2) from the MEDITS trawl surveys (years 2013-2015) were modelled by generalized additive models using a Delta-type approach and several covariates: latitude, longitude, depth, seafloor slope, surface oceanographic currents and distances from main ports. To set thresholds for the identification of accumulation areas, the percentiles (85th, 90th and 95th) of the plastic spatial density distribution were computed on the raster data. In the northern Mediterranean marine macro-litter was widespread (90.13% of the 1279 surveyed stations), with plastic by far the most recurrent category. The prediction map of the plastic density highlighted accumulation areas (85th, 90th and 95th percentiles of the distribution, respectively, corresponding to 147, 196 and 316 items km–2) in the Gulf of Lions, eastern Corsica, the eastern Adriatic Sea, the Argo-Saronic region and waters around southern Cyprus. Maximum densities were predicted in correspondence to the shallower depths and in proximity to populated areas (distance from the ports). Surface currents and local water circulation with cyclonic and anticyclonic eddies were identified as drivers likely facilitating the sinking to the bottoms of floating debris, Sí

Resilient suppliers can reduce supply chain risk, effectively avoid supply chain disruption, and bring profits to enterprises. However, there is no united measuring index system to evaluate the resilient supplier under supply chain environment, and the assessment language sets are usually crisp values. Therefore, in order to fill the research gap, this paper proposes a hybrid method, which combines triangular fuzzy number, the best-worst method (BWM), and the modular TOPSIS in random environments for group decision-making (GMo-RTOPSIS) to solve the above problem. Firstly, the weight of decision-maker is calculated by using fuzzy BWM which can deal with triangular fuzzy numbers. Secondly, triangular fuzzy number is introduced into GMo-RTOPSIS, and combined with fuzzy BWM, alternatives are sorted to select the best resilient supply chain partner. Finally, the feasibility and universality of this method are proved by illustrative examples.

International audience Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework.

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

Maintenance of miter gates in navigation locks is crucial to facilitate water navigation of cargo ships. Scheduled and unscheduled maintenance are the two types of actions performed to keep miter gates continuously operational. In practice, visual inspections and rating systems are used to monitor the current state of miter gate components in order to prioritize maintenance. This paper shows an example of how to find the optimal time to plan for maintenance using deterioration curves obtained from real visual inspection assessments of components in miter gates. Predictive deterioration curves are derived using a Markov Chain approach. In addition, the advantages of implementing Structural Health Monitoring (SHM) are discussed.

This paper presents a collaboratively adaptive vibration monitoring system that captures high-fidelity structural vibration signals induced by pedestrians. These signals can be used for various human activities’ monitoring by inferring information about the impact sources, such as pedestrian footsteps, door opening and closing, and dragging objects. Such applications often require high-fidelity (high resolution and low distortion) signals. Traditionally, expensive high resolution and high dynamic range sensors are adopted to ensure sufficient resolution. However, for sensing systems that use low-cost sensing devices, the resolution and dynamic range are often limited; hence this type of sensing methods is not well explored ubiquitously. We propose a low-cost sensing system that utilizes (1) a heuristic model of the investigating excitations and (2) shared information through networked devices to adapt hardware configurations and obtain high-fidelity structural vibration signals. To further explain the system, we use indoor pedestrian footstep sensing through ambient structural vibration as an example to demonstrate the system performance. We evaluate the application with three metrics that measure the signal quality from different aspects: the sufficient resolution rate to present signal resolution improvement without clipping, the clipping rate to measure the distortion of the footstep signal, and the signal magnitude to quantify the detailed resolution of the detected footstep signal. In experiments conducted in a school building, our system demonstrated up to 2× increase on the sufficient resolution rate and 2× less error rate when used to locate the pedestrians as they walk along the hallway, compared to a fixed sensing setting.

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.

In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

Culturing cells at the air–liquid interface (ALI) is essential for creating functional in vitro models of lung tissues. We present the use of direct-patterned laser-treated hydrophobic paper as an effective semi-permeable membrane, ideal for ALI cell culture. The surface properties of the paper are modified through a selective CO2 laser-assisted treatment to create a unique porous substrate with hydrophilic regions that regulate fluid diffusion and cell attachment. To select the appropriate model, four promising hydrophobic films were compared with each other in terms of gas permeability and long-term strength in an aqueous environment (wet-strength). Among the investigated substrates, parchment paper showed the fastest rate of oxygen permeability (3 times more than conventional transwell cell culture membranes), with the least variation in its dry and wet tensile strengths (124 MPa and 58 MPa, remaining unchanged after 7 days of submersion in PBS).The final paper-based platform provides an ideal, robust, and inexpensive device for generating monolayers of lung epithelial cells on-chip in a high-throughput fashion for disease modelling and in vitro drug testing.

A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient.

Estimating experimentally the equivalent concrete thickness for perforation of mild steel plate targets subjected to hard projectiles (regardless the steel plate will be used as steel linear or not) was the aim of this study. The paper presents the experimental results on perforation of plain concrete panel targets of different thicknesses (100–600 mm) in addition to experimental results on perforation of mild steel plate targets tested under the same circumstances. Twenty-seven shots of hard projectiles perforation test on plain concrete and mild steel plates were conducted (some of these experiments have been presented before). Two formulae relating the perforation resistance of mild steel plate to an equivalent concrete thickness are proposed; one for converting a thin steel liner to an equivalent concrete thickness, and the other for replacing a steel plate target with an equivalent concrete target (and vice versa). Both formulae are influenced by the concrete strength, mass and diameter of projectile.

In this paper, we have the percussive rotational drilling regime that is determined by the combination of the rotational speed of the tool, the axial force applied on the rock the optimal values of these parameters d epend in particular on the type of tool and on the physico mechanical properties of the rocks to be felled. The aim of the research is to improve the operating regimes of percussive d rilling machines under the conditions of the Algerian limestone quarries (Hadjar soud quarry) and to obtain a better compromise between the technical and economic indices, in order to ensure a minimum cost price for a meter of drilled hole. The experimenta l results carried that this methodology, which consists of assuming that under the concrete conditions can find the combination of the setting parameters that ensure the best techno economic indices, as well as to carpenter the cost price for a meter of dr illed holes.

In this paper, the influence of the variation in transformer oil temperature on the accuracy of the all-acoustic non-iterative method for partial discharge location in a power transformer is researched. The research can improve power transformers’ testing and monitoring, particularly given the large transformer oil temperature variations during real-time monitoring. The research is based on quantifying the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm by using analytical, statistical, and Monte Carlo methods. The contribution can be quantified and controlled. The contribution varied significantly with different mutual placements of partial discharge and acoustic sensors. The correlation between the contribution and the mean distance between partial discharge and acoustic sensors was observed. Based on these findings, the procedure to quantify and control the contribution in practice was proposed. The procedure considers the specificity of the method’s mathematical model (the assumption that the oil temperature is constant), the non-iterative algorithm’s nonlinearity, and the large variations in transformer oil temperature. Existing studies did not consider the significant effect of the oil temperature on the combined measurement uncertainty of partial discharge location influenced by those phenomena. The research is limited to partial discharge located in the transformer oil.

In this paper, we present performance simulations and techno-economic analysis of a modular dispatchable solar power tower. Using a heliostat field and power block three orders of magnitude smaller than conventional solar power towers, our unique configuration locates thermal storage and a power block directly on a tower receiver. To make the system dispatchable, a valved thermosyphon controls heat flow from a latent heat thermal storage tank to a Stirling engine. The modular design results in minimal balance of system costs and enables high deployment rates with a rapid realization of economies of scale. In this new analysis, we combine performance simulations with techno-economic analysis to evaluate levelized cost of electricity, and find that the system has potential for cost-competitiveness with natural gas peaking plants and alternative dispatchable renewables.

This paper presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as an inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low-level image cues and automatic tuning of parameters. The proposed scheme is experimentally shown to compare favorably with contemporary semi-supervised and unsupervised image segmentation schemes, when applied to contemporary state-of-the-art image sets.

We consider a one-dimensional morphoelastic model describing post-burn scar contraction. Contraction can lead to a limited range of motion (contracture). Reported prevalence of burn scar contractures are 58.6% at 3-6 weeks and 20.9% at 12 months post-reconstructive surgery after burns. This model describes the displacement of the dermal layer of the skin and the development of the effective Eulerian strain in the tissue. Besides these components, the model also contains components that play a major role in the skin repair after trauma. These components are signaling molecules, fibroblasts, myofibroblasts, and collagen. We perform a sensitivity analysis for many parameters of the model and use the results for a feasibility study. In this study, we test whether the model is suitable for predicting the extent of contraction in different age groups. To this end, we conduct an extensive literature review to find parameter values. From the sensitivity analysis, we conclude that the most sensitive parameters are the equilibrium collagen concentration in the dermal layer, the apoptosis rate of fibroblasts and myofibroblasts, and the secretion rate of signaling molecules. Further, although we can use the model to simulate significant distinct contraction densities in different age groups, our results differ from what is seen in the clinic. This particularly concerns children and elderly patients. In children we see more intense contractures if the burn injury occurs near a joint, because the growth induces extra forces on the tissue. Elderly patients seem to suffer less from contractures, possibly because of excess skin. The authors are grateful for the fnancial support by the Dutch Burns Foundation under Project 17.105. This project is funded by the Dutch Burns Foundation under Project 17.105.

Purpose Occupants dwelling in hot climatic regions of India for a longer term are tolerable to high temperature levels than predicted by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. The purpose of this study is to evaluate the thermal sensations (TS) and neutral temperature of the occupants in naturally ventilated (NV) and air-conditioned (AC) classrooms of two technical institutions located in the same premises in the suburbs of Madurai. The main focus of this study is to understand the occupants’ behaviour in response to the thermal conditions of the educational buildings particularly in the warm and humid climatic zone of Madurai. Design/methodology/approach This research collected data through field studies. The data included 383 survey questionnaires from NV classrooms and 285 from AC classrooms, as well as on-site measurements of interior and exterior weather conditions. The TS results show that the students preferred well-designed NV classrooms than AC classrooms. A new adaptive comfort equation derived from this study can be applied to NV classrooms in warm and humid climates where mean outdoor temperature exceeds 40°C. Findings The neutral temperature derived for NV classrooms in Madurai ranged from 29°C to 34°C. Thus, the occupants in the NV classrooms of the higher learning educational institutions in the warm and humid climatic region of Madurai can adapt well to higher indoor temperature levels than predicted by ASHRAE comfort levels with minimum adjustments. Research limitations/implications The study was limited to only occupants in two premier higher learning technical educational institutions located in Madurai region within 5–10 km within the city limits to understand the implications of microclimate with respect to the urban context. Thus, further research is required to examine the tendency under local conditions in other regions beyond those applied to this study. Social implications The findings of this study showed that occupants in higher learning educational intuitions in Madurai prefer NV classrooms than AC classrooms. Therefore, with rising demands of energy use for mechanical ventilation and the associated high cost for running AC buildings, architects should prioritize the design of energy efficient buildings through the optimal use of passive design strategies for ventilation and thermal comfort. This study gives a base data for architects to understand the adaptive limitations of occupants and design NV buildings that can promote natural ventilation and provide better thermal environments that can help increase the productivity of students. Originality/value This paper was an attempt to develop the adaptive comfort model for NV classrooms in Madurai regions. There has been no attempt to identify the adaptive comfort levels of occupants in higher learning technical educational institutions located in warm and humid climatic region of India.

Landslide mapping (LM) is essential for hazard prevention, mitigation, and vulnerability assessment. Despite the great efforts over the past few years, there is room for improvement in its accuracy and efficiency. Existing LM is primarily achieved using field surveys or visual interpretation of remote sensing images. However, such methods are highly labor-intensive and time-consuming, particularly over large areas. Thus, in this paper a change detection-based Markov random field (CDMRF) method is proposed for near-automatic LM from aerial orthophotos. The proposed CDMRF is applied to a landslide-prone site with an area of approximately 40 km2 on Lantau Island, Hong Kong. Compared with the existing region-based level set evolution (RLSE), it has three main advantages: 1) it employs a more robust threshold method to generate the training samples; 2) it can identify landslides more accurately as it takes advantages of both the spectral and spatial contextual information of landslides; and 3) it needs little parameter tuning. Quantitative evaluation shows that it outperforms RLSE in the whole study area by almost 5.5% in Correctness and by 4% in Quality. To our knowledge, it is the first time CDMRF is used to LM from bitemporal aerial photographs. It is highly generic and has great potential for operational LM applications in large areas and also can be adapted for other sources of imagery data.

This paper studies the buckling behaviour and design of welded I-section stainless steel columns. Experimental and numerical structural performance data together with the design methods for stainless steel welded I-section columns available in the literature have been collated and reviewed. A numerical modelling programme including validation and parametric studies has been carried out to supplement the literature experimental and numerical data for the assessment of the existing codified and literature proposed flexural buckling design formulations for stainless steel welded I-section columns. Columns of austenitic, duplex and ferritic stainless steel grades undergoing major axis and minor axis flexural buckling have been investigated. From comparisons with the EN 1993–1-4 (EC3) flexural buckling capacity predictions, it was found that (1) for the austenitic welded I-section columns, the EC3 buckling curve (α = 0.76 and λ¯ 0 = 0.2) is suitable for both axes, (2) for the duplex and ferritic grades, the EC3 buckling curve (α = 0.76 and λ¯ 0 = 0.2) is conservative, and a higher buckling curve with (i) α = 0.49 and λ¯ 0 = 0.2 for both axes or (ii) α = 0.49 and λ¯ 0 = 0.2 for minor axis and α = 0.34 and λ¯ 0 = 0.2 for major axis may be adopted. In addition, comparisons with the recently proposed Continuous Strength Method showed marginally improved strength predictions but with slightly higher scatter.

In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road- sidewalk, and building-ground. While prior work has shown promising results by utilizing the boundary between prominent classes such as sky and building using skylines, we generalize this approach to consider semantic edge features that arise from 19 different classes. Our localization algorithm is simple, yet very powerful. We extract semantic edge features using a recently introduced CASENet architecture and utilize VLAD framework to perform image retrieval. Our experiments show that we achieve improvement over some of the state-of-the-art localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use ablation study to study the importance of different semantic classes and show that our unified approach achieves better performance compared to individual prominent features such as skylines.

The paper proposes a technique for Human Action Recognition (HAR) that uses a Convolutional Neural Network (CNN). Depth data sequences from the motion sensing devices are converted into images and fed into a CNN rather than using any conventional or statistical method. The initial data was obtained from 10 actions performed by six subjects captured by the Kinect v2 sensor as well as 20 actions performed by 7 subjects from the MSR 3D Action data set. A custom CNN architecture consisting of three convolutional and three max pooling layers followed by a fully connected layer was used. Training, validation, and testing was carried out on a total of 39715 images. An accuracy of 97.23% was achieved on the Kinect data set. On the MSR data set the accuracy was 87.1%.

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods., Comment: Presented at IEEE BIBM 2017

This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained AlexNet on these images. Then, inertial data is converted into Signal Images (SI) and another convolutional neural network (CNN) is trained on these images. Finally, learned features are extracted from both CNN, fused together to make a shared feature layer, and these features are fed to the classifier. We experiment with two classifiers, namely Support Vector Machines (SVM) and softmax classifier and compare their performances. The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities. Experimental results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves state of the art results when compared to other recently proposed visual-inertial action recognition methods.

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning has been a subject of intense research in recent years. Recent success of deep learning in computer vision has progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images. The network is then fine-tuned with layer-wise tuning, where only a pre-defined group of layers are trained on MRI images. To shrink the training data size, we employ image entropy to select the most informative slices. Through experimentation on the ADNI dataset, we show that with training size of 10 to 20 times smaller than the other contemporary methods, we reach state-of-the-art performance in AD vs. NC, AD vs. MCI, and MCI vs. NC classification problems, with a 4% and a 7% increase in accuracy over the state-of-the-art for AD vs. MCI and MCI vs. NC, respectively. We also provide detailed analysis of the effect of the intelligent training data selection method, changing the training size, and changing the number of layers to be fine-tuned. Finally, we provide Class Activation Maps (CAM) that demonstrate how the proposed model focuses on discriminative image regions that are neuropathologically relevant, and can help the healthcare practitioner in interpreting the model's decision making process.

The intrinsic permeability is a crucial parameter to characterise and quantify fluid flow through porous media. However, this parameter is typically uncertain, even if the geometry of the pore structure is available. In this paper, we perform a comparative study of experimental, semi-analytical and numerical methods to calculate the permeability of a regular porous structure. In particular, we use the Kozeny-Carman relation, different homogenisation approaches (3D, 2D, very thin porous media and pseudo 2D/3D), pore-scale simulations (lattice Boltzmann method, Smoothed Particle Hydrodynamics and finite-element method) and pore-scale experiments (microfluidics). A conceptual design of a periodic porous structure with regularly positioned solid cylinders is set up as a benchmark problem and treated with all considered methods. The results are discussed with regard to the individual strengths and limitations of the used methods. The applicable homogenisation approaches as well as all considered pore-scale models prove their ability to predict the permeability of the benchmark problem. The underestimation obtained by the microfluidic experiments is analysed in detail using the lattice Boltzmann method, which makes it possible to quantify the influence of experimental setup restrictions., Deutsche Forschungsgemeinschaft, Projekt DEAL

In the last decades, several studies have revealed how critical the urban heat island (UHI) effect can be in cities located in cold climates, such as the Canadian one. Meanwhile, many researchers have looked at the impact of the city design over the urban microclimate, and have raised concerns about the development of too dense cities. Under the effect of the “Places to Growth” plan, the city of Toronto is experiencing one of the highest rates of building development in North America. Over 48,000 and 33,000 new home permits were issued in 2012 and 2013 respectively, and at the beginning of 2015, almost 500 high-rise proposals across the Greater Toronto Area were released. In this context, it is important to investigate how new constructions will affect the urban microclimate, and to propose strategies to mitigate possible UHI effects. Using the software ENVI-met, microclimate simulations for the Church-Yonge corridor both in the current situation and with the new constructions are reported in this paper. The outdoor air temperature and the wind speed are the parameters used to assess the outdoor microclimate changes. The results show that the new constructions could increase the wind speed around the buildings. However, high-rise buildings will somewhat reduce the air temperature during day-time, as they will create large shadow areas, with lower average mean radiant temperature.

This paper shows that N-degree-of-freedom generic unstable potential systems, whose potential matrices have an even number of negative eigenvalues and which are gyroscopically stabilized, can alway...

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

For downlink multiple-input multiple-output-sparse code multiple access (MIMO-SCMA) system, the convergence reliability of the detected codeword in each decision process is unsatisfactory at the message passing algorithm receiver. To address this problem, a downlink MIMO-mixed sparse code multiple access (MIMO-MSCMA) system is proposed in this paper. For each user of the downlink MIMO-MSCMA, the transmitted codewords at different antennas come from different codebooks of MSCMA, respectively. On the other side, a novel basic complex multi-dimension constellation design is proposed in the MSCMA codebook design. Simulation results show that the bit error rate performance of the downlink MIMO-MSCMA outperforms than that of downlink MIMO-SCMA.

VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation. Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems and facilitate the VLSI design cycle. In this paper, we focus on a heterogeneous OPC framework that assists mask layout optimization. Preliminary results show the efficiency and effectiveness of proposed frameworks that have the potential to be alternatives to existing EDA solutions., 6 pages; accepted by 25th Asia and South Pacific Design Automation Conference (ASP-DAC 2020)