Search

Solving partial differential equations using neural networks is mostly a proof of concept approach. In the case of direct function approximation, a single neural network is constructed to be the solution of a particular boundary value problem. Independent variables are fed into the input layer, and a single output is considered as the solution’s value. The network is substituted into the equation, and the residual is then minimized with respect to the weights of the network using a gradient-based method. Our previous work showed that by minimizing all derivatives of the residual up to the third order one can obtain a machine precise solution for 2D boundary value problem using very sparse grids. The goal of this paper is to use this grid complexity advantage in order to obtain a solution faster than finite differences. However, the number of all possible high-order derivatives (and therefore the training time) increases with the number of dimensions and it was unclear whether this goal can be achieved. Here, we demonstrate that this increase can be compensated by using random directional derivatives instead. In 2D case neural networks are slower than finite differences, but for each additional dimension the complexity increases approximately 4 times for neural networks and 125 times for finite differences. This allows neural networks to catch up in 3D case for memory complexity and in 5D case for time complexity. For the first time a machine precise solution was obtained with neural network faster than with finite differences method.

This paper provides a concise review on the recent development of nanoscale hybrid systems based on carbon nanotubes (CNTs) for biological sensing and control. CNT-based hybrid systems have been intensively studied for versatile applications of biological interfaces such as sensing, cell therapy and tissue regeneration. Recent advances in nanobiotechnology not only enable the fabrication of highly sensitive biosensors at nanoscale but also allow the applications in the controls of cell growth and differentiation. This review describes the fabrication methods of such CNT-based hybrid systems and their applications in biosensing and cell controls.

Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish the number of numerical experiments. The FEM is developed to retrieve the strain energy and the reaction torque of joint. And then, the Kigring metamodel is used as a black-box to find the pseudoobjective functions. Based on pseudoobjective functions, the MOGA is applied to find the optimal solutions. Traditionally, an evolutionary optimization algorithm can only find one Pareto front. However, the proposed approach can generate 6 Pareto-optimal solutions, as near optimal candidates, which provides a good decision-maker. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results found that the optimal strain energy is about 0.0033 mJ and the optimal torque is approximately 588.94 Nm. Analysis of variance is performed to identify the significant contribution of design variables. The sensitivity analysis is then carried out to determine the effect degree of each parameter on the responses. The predictions are in a good agreement with validations. It confirms that the proposed hybrid optimization approach has an effectiveness to solve for complex optimization problems.

Checking the semantic equivalence of operations is an important task in software development. For instance, regression testing is a routine task when software systems are developed and improved, and software package managers require the equivalence of operations in different versions of a package within the same major version. In order to support a good automation of this process, a solid foundation is required. It has been shown that the notion of equivalence is not obvious when non-deterministic features are present. In this paper, we discuss a general notion of equivalence in functional logic programs and develop a practical method to check it. Our method can be integrated in a property-based testing tool which is used in a software package manager to check the semantic versioning of software packages.

The process of selective laser melting (SLM) is complex and instantaneous, which communicates with a variety of physical phenomena such as solid–liquid phase transition. The microstructure and material properties are favorable evidence to explain the SLM forming mechanism. In this paper, the forming properties of the iron-based alloy, titanium-based alloys, and aluminum-based alloys are discussed, which are relatively mature in research. The influence of processing strategy on the density and quality of metal specimens is briefly introduced. The research progress of microstructure and mechanical properties of metal specimens formed by SLM is mainly introduced. Also, the influence of processing parameters and heat treatment on the microstructure and the effect of microstructure on mechanical properties are summarized. The corrosion behavior of metal specimens formed by SLM is analyzed. Furthermore, the research development of copper-based alloys, shape memory alloys, and high-entropy alloys is presented. Finally, the problems encountered in the alloys forming process are described, and the future development direction has prospected.

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

Social robots are finding increasing application in the domain of education, particularly for children, to support and augment learning opportunities. With an implicit assumption that social and adaptive behaviour is desirable, it is therefore of interest to determine precisely how these aspects of behaviour may be exploited in robots to support children in their learning. In this paper, we explore this issue by evaluating the effect of a social robot tutoring strategy with children learning about prime numbers. It is shown that the tutoring strategy itself leads to improvement, but that the presence of a robot employing this strategy amplifies this effect, resulting in significant learning. However, it was also found that children interacting with a robot using social and adaptive behaviours in addition to the teaching strategy did not learn a significant amount. These results indicate that while the presence of a physical robot leads to improved learning, caution is required when applying social behaviour to a robot in a tutoring context. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems

Non-motorized transport is becoming increasingly important in urban development of cities in China. How to evaluate the non-motorized transport popularity of urban roads is an interesting question to study. The great amount of tracking data generated by smart mobile devices give us opportunities to solve this problem. This study aims to provide a data driven method for evaluating the popularity (walkability and bikeability) of urban non-motorized transport system. This paper defines a p-index to evaluate the popular degree of road segments which is based on the cycling, running, and walking GPS track data from outdoor activities logging applications. According to the p-index definition, this paper evaluates the non-motorized transport popularity of urban area in Wuhan city within different temporal periods., 5 pages, 4 figures, CPGIS2018-The 26th International Conference on Geoinformatics

The paper presents a novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites. The proposed method uses the Chebyshev Neural Network and genetic algorithm in conjunction with satellite attitude rate data only. A data-driven model is first developed that fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. Next, the data is trained using Chebyshev Neural Network. An adaptive threshold-based fault detection algorithm is applied to detect the faults in the spin and gimbal motors of the control moment gyroscopes. A fault isolation scheme is developed wherein an objective function is optimized using a genetic algorithm for different cases of system parameters. The proposed scheme has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. Overall, the proposed methodology can be regarded as a promising fault diagnostic tool for satellites using limited historical data and measurements.

BackgroundRecent advances in mobile technologies have created new opportunities to reach broadly into populations that are vulnerable to health disparities. However, mobile health (mHealth) strategies could paradoxically increase health disparities, if low socioeconomic status individuals lack the technical or literacy skills needed to navigate mHealth programs. ObjectiveThe aim of this study was to determine whether patients from vulnerable populations could successfully navigate and complete an mHealth patient decision aid. MethodsWe analyzed usability data from a randomized controlled trial of an iPad program designed to promote colorectal cancer (CRC) screening. The trial was conducted in six primary care practices and enrolled 450 patients, aged 50-74 years, who were due for CRC screening. The iPad program included a self-survey and randomly displayed either a screening decision aid or a video about diet and exercise. We measured participant ability to complete the program without assistance and participant-rated program usability. ResultsTwo-thirds of the participants (305/450) were members of a vulnerable population (limited health literacy, annual income < US $20,000, or black race). Over 92% (417/450) of the participants rated the program highly on all three usability items (90.8% for vulnerable participants vs 96.6% for nonvulnerable participants, P=.006). Only 6.9% (31/450) of the participants needed some assistance to complete the program. In multivariable logistic regression, being a member of a vulnerable population was not associated with needing assistance. Only older age, less use of text messaging (short message service, SMS), and lack of Internet use predicted needing assistance. ConclusionsIndividuals who are vulnerable to health disparities can successfully use well-designed mHealth programs. Future research should investigate whether mHealth interventions can reduce health disparities.

Botnets remain an active security problem on the Internet and various computer networks. They are continuously developing with regard to protocols, structure and quality of attacks. Many botnet detection programs are currently available, but only few can detect bots in real-time. The sooner bots are detected the lesser damage they can cause. In this paper, a novel botnet detection system, is proposed to detect peer-to-peer bots. The system consists of three-phases filtering, P2P detection and P2P botnet detection phases. For the third phase, P2P network behavior analysis is performed to detect P2P bots. Experimental results showed that the system exhibits high average true positive rate and extremely low average false positive rate during botnet detection.

Let B be a set of b blue points and R be a set of r red points in the plane. In this paper we study the problem of finding rectangles that contain the maximum number of blue points without containing any red points, known as the maximum-box problem. First we study this problem for axis-aligned rectangles, and propose an exact worst-case optimal O ( r 2 + r b + b log ⁡ b ) time algorithm using O ( r + b ) space to find all maximum boxes. We also provide a 2-approximation algorithm running in O ( ( r + b ) log ⁡ ( r + b ) ) time and using O ( r + b ) space to find a single maximum box in the axis-aligned case. Then we generalize the exact algorithm for the axis-aligned case to find all arbitrarily oriented maximum boxes leading to a worst-case optimal O ( ( r + b ) 2 ( r + log ⁡ b ) ) time algorithm using O ( ( r + b ) 2 ) space to solve the problem. We conclude the paper by discussing time and space trade-offs. Our results improve the previously best known solutions to the maximum-box problem.

In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

Present-day power conversion and conditioning systems focus on transferring energy from a single type of power source into a single type of load or energy storage system (ESS). While these systems can be optimized within their specific topology (e.g. MPPT for solar applications and BMS for batteries), the topologies are not easily adapted to accept a wide range of power flow operating conditions. With a hybrid approach to energy storage and power flow, a system can be designed to operate at its most advantageous point, given the operating conditions. Based on the load demand, the system can select the optimal power source and ESS. This paper will investigate the feasibility of combining two types of power sources (main utility grid and photovoltaics (PV)) along with two types of ESS (ultra-capacitors and batteries). The simulation results will show the impact of a hybrid ESS on a grid-tied residential microgrid system performance under various operating scenarios.

The Spanish wholesale electricity market remains controversial due to the suspicion of there being collusive behaviour, as the main generation companies still share 55% of the wholesale market. Up to 2018, the Spanish National Competition Authority had to investigate all the complaints received, contributing to discouraging collusive behaviours in the electricity market but also employing a significant amount of resources. Since 2018, the ECN + Directive has given more independence to the National Competition Authorities of EU Member States and has allowed them to reject the complaints that are not considered a priority and choose the most relevant ones. This paper provides a tool that makes the supervision of the wholesale electricity market easier and enables a better use of resources. The tool is tested for a timespan including the same month (December) from 2012 to 2017. The period selected starts once non-conventional renewable technologies began to operate and lasts until the ECN + Directive came into force. In the first two steps, the tool allows a screening of the timespan, looking for possible anomaly periods based on the variance values of the daily electricity cost and the average monthly value. Secondly, the third tool step presents a methodology based on a spatial LMDI-I decomposition analysis. This approach allows analysing the key driver effects that explain the differences between the daily cost of electricity of some technologies and the monthly average of those periods pointed out in the two previous steps. To ensure robustness, the same approach has been tested on control periods prior to and after the anomaly month.

Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

This research was supported by a grant of SFC/RGC Joint Research Scheme (X-PolyU/501/14) from Research Grant Council, University Grants Committee, Hong Kong SAR. Co-electrolysis of H2O and CO2 in a solid oxide electrolysis cell (SOEC) is promising for simultaneous energy storage and CO2 utilization. Fuel-assisted H2O electrolysis by SOEC (SOFEC) has been demonstrated to be effective in reducing power consumption. In this paper, the effects of fuel (i.e. CH4) assisting on CO2/H2O co-electrolysis are numerically studied using a 2D model. The model is validated with the experimental data for CO2/H2O co-electrolysis. One important finding is that the CH4 assisting is effective in lowering the equilibrium potential of SOEC thus greatly reduces the electrical power consumption for H2O/CO2 co-electrolysis. The performance of CH4-assisted SOFEC increases substantially with increasing temperature, due to increased reaction kinetics of electrochemical reactions and CH4 reforming reaction. The CH4-assisted SOFEC can generate electrical power and syngas simultaneously at a low current density of less than 600 Am−2 and at 1123 K. In addition, different from conventional SOEC whose performance weakly depends on the anode gas flow rate, the CH4-assisted SOFEC performance is sensitive to the anode gas flow rate (i.g. peak current density is achieved at an anode flow rate of 70 SCCM at 1073 K). The model can be used for subsequent design optimization of SOFEC to achieve high performance energy storage. Postprint

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people’s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user’s state as well as the user’s profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly’s context information and state.

A novel, on-demand microfluidic droplet merging mechanism is presented in this paper. We demonstrate that a narrow beam surface acoustic wave, targeted at the oil buffer, causes nearby surfactant-stabilized droplets to coalesce. The lack of direct exposure of the droplet to the excitation stimulus makes this method ideal for sensitive samples as harm will not occur. This powerful technique works on a straight channel with no special design, is not affected by surfactant concentration and droplet volume hence promises seamless integration into existing microfluidic systems. It offers high-throughput, biologically safe, on-demand droplet merging for applications ranging from fast reaction kinetics to microfluidic high throughput screening. We thoroughly characterize the physical mechanism triggering droplet-droplet coalescence and observe a cutoff distance from the center of the acoustic beam to the droplet-droplet interface after which the merging mechanism does not work anymore. We establish that the most likely mechanism for merging is acoustic streaming induced droplet deformation.

Among social media websites, Reddit has emerged as a widely used online message board for focused mental health topics including depression, addiction, and suicide watch (SW). In particular, the SW community/subreddit has nearly 40,000 subscribers and 13 human moderators who monitor for abusive comments among other things. Given comments on posts from users expressing suicidal thoughts can be written from any part of the world at any time, moderating in a timely manner can be tedious. Furthermore, Reddit's default comment ranking does not involve aspects that relate to the “helpfulness” of a comment from a suicide prevention (SP) perspective. Being able to automatically identify and score helpful comments from such a perspective can assist moderators, help SW posters to have immediate feedback on the SP relevance of a comment, and also provide insights to SP researchers for dealing with online aspects of SP. In this paper, we report what we believe is the first effort in automatic identification of helpful comments on online posts in SW forums with the SW subreddit as the use-case. We use a dataset of 3000 real SW comments and obtain SP researcher judgments regarding their helpfulness in the contexts of the corresponding original posts. We conduct supervised learning experiments with content based features including n-grams, word psychometric scores, and discourse relation graphs and report encouraging F-scores (≈ 80 – 90%) for the helpful comment classes. Our results indicate that machine learning approaches can offer complementary moderating functionality for SW posts. Furthermore, we realize assessing the helpfulness of comments on mental health related online posts is a nuanced topic and needs further attention from the SP research community.

The spontaneous bubble migration and the active manipulation have widely applications in outer space. In this paper, the bubble migration driven by the thermal capillary effect is numerically investigated under variable buoyancy. The numerical model is built up through the transient two-dimensional axisymmetric model with a level set method. It is found that the magnitude and direction of bubble migration velocity is determined by the competition of upward buoyant effect and downward thermal capillary effect. These two effects can be controlled by changing the ratios of density, viscosity, thermal conductivity of bubble over surrounding liquid, as well as Reynolds number, Froude number, Peclet number and Marangoni number. With increasing Froude number or decreasing density ratio, the thermal capillary effect becomes dominant over the buoyant effect, the bubble will migrate from upward to downward. The Marangoni number has negligible effect on the bubble migration at low Reynolds numbers, while at high Reynolds numbers, increasing Marangoni number can reduce the upward migration velocity due to stronger thermal capillary effect. At low Reynolds numbers, with the increasing ratios of viscosity and thermal conductivity, the downward driving force is increased, while at high Reynolds numbers, the ratios have negligible effect on the bubble migration. These findings may provide the guidance for actively manipulating the bubbles under variable buoyancy.

Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an image classification model from large-scale noisy data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. To alleviate these challenges, we assume that every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags and the labels. In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks the first place in the image classification task.

It is inevitable for data collection that IoT sensors are distributed to interested areas. However, not only the proper placement of sensors, but also the replacement of sensors that have run out of energy is very difficult. As a remedy, wireless charging systems for IoT sensors have been researched recently, but it is apparent that the availability of charging system is limited especially for IoT sensors scattered in rugged terrain. Thus, it is important that the sensor relocation models to recover sensing holes employ energy-efficient scheme. While there are various methods in the mobile model of wireless sensors, well-known wheel-based movements in rough areas are hard to achieve. Thus, research is ongoing in various areas of the hopping mobile model in which wireless sensors jump. Many past studies about hopping sensor relocation assume that all sensor nodes are aware of entire network information throughout the network. These assumptions do not fit well to the actual environment, and they are nothing but classical theoretical research. In addition, the physical environment (sand, mud, etc.) of the area in which the sensor is deployed can change from time to time. In this paper, we overcome the theoretical-based problems of the past researches and propose a new realistic hopping sensor relocation protocol considering terrain conditions. Since the status of obstacles around the sensing hole is unknown, the success rate of the hopping sensor relocation is used to predict the condition of the surrounding environment. Also, we are confident that our team is uniquely implementing OMNeT++ (Objective Modular Network Testbed in C++) simulation in the hopping sensor relocation protocol to reflect the actual communication environment. Simulations have been performed on various obstacles for performance evaluation and analysis, and we are confident that better energy efficiency with later appearance of sensing holes can be achieved compared to well-known relocation protocols.

One of the ways used to reduce the emission of carbon dioxide and other harmful substances is the implementation of biomass co-firing processes with coals. Such processes have been implemented for many years throughout many countries of the world, and have included using existing high-power coal boilers. Despite numerous experiments, there are still no analyses in the literature allowing for their generalization. The purpose of this paper is to determine the generalized energy and ecological characteristics of dust steam boilers co-firing hard coal with biomass. The energy characteristics determined in the paper are the dependence of the gross energy efficiency of boilers on such decision parameters as their efficiency and the share of biomass chemical energy in fuel. However, the ecological characteristics are the dependence of emission streams: CO, NOx, SO2, and dust on the same decision parameters. From a mathematical point of view, the characteristics are approximation functions between the efficiency values obtained from the measurements and the emission streams of the analysed harmful substances and the corresponding values of the decision parameters. Second-degree polynomials are assumed in this paper as approximation functions. Therefore, determining the characteristics came down to determining the constant coefficients occurring in these polynomials, the so-called structural parameters. The fit of the determined characteristics was assessed based on the coefficients of random variation and the test of estimated significance of structural parameters. Boiler characteristics can be used when forecasting the impact of changes in operating conditions on the effects achieved in existing, modernized, and designed boilers. The generalization of the characteristics was obtained from the measurement results presented in 10 independent sources used to determine them.

This paper proposes a broadcast strategy for the single-user slowly fading channel in which the transmission power is supplied by an energy harvesting unit. In this strategy, the channel state information (CSI) is known only to the receiver, and the transmitter is assumed to be oblivious to the CSI. The broadcast approach enables preventing outage events in the face of the lack of CSI at the transmitter. In the proposed broadcast approach, the transmitter splits its message into multiple superimposed information layers. This facilitates sustaining reliably decodable transmission rates adapted to the unknown state of the randomly-varying fading state of the channel. The objective is to characterize the optimal allocation of the randomly varying harvested power over time and across information layers in the contexts of maximizing the average communication rate and minimizing the likelihood of outage. First, the setting in which the transmitter has non-causal and complete information about the state of the energy harvesting process is considered. A closed-form characterization of the optimal allocation of power across layers is established, and an algorithm that analytically determines the exact optimal allocation of power over time is provided. Furthermore, an online algorithm is proposed for a setting in which the energy harvesting process is known only causally to the transmitter.

The Kawasaki CO2 Capture (KCC) system involves a solid adsorbent method that captures CO2 in exhaust gases using low-temperature steam; thus, it can use waste heat easily and save more energy than conventional technology. In this paper, we establish a design method for a large-scale plant using the KCC moving-bed method, and conduct parametric testing using a 5 ton/d bench scale test plant to find the characteristics of the moving-bed method. Using the relationship between the residence time of the adsorbent material and the CO2 adsorption load, we found that design specifications could be applied to a large-scale plant.

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03% accuracy on our G. dataset (12 gestures) and 94.53% on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.

Computational aspects of a recently developed gradient elasticity model are discussed in this paper. This model includes the (Aifantis) strain gradient term along with two higher-order acceleration terms (micro-inertia contributions). It has been demonstrated that the presence of these three gradient terms enables one to capture the dispersive wave propagation with great accuracy. In this paper, the discretisation details of this model are thoroughly investigated, including both discretisation in time and in space. Firstly, the critical time step is derived that is relevant for conditionally stable time integrators. Secondly, recommendations on how to choose the numerical parameters, primarily the element size and time step, are given by comparing the dispersion behaviour of the original higher-order continuum with that of the discretised medium. In so doing, the accuracy of the discretised model can be assessed a priori depending on the selected discretisation parameters for given length-scales. A set of guidelines can therefore be established to select optimal discretisation parameters that balance computational efficiency and numerical accuracy. These guidelines are then verified numerically by examining the wave propagation in a one-dimensional bar as well as in a two-dimensional example.

This paper presents a robust two stage maximum power point tracking (MPPT) system of the photovoltaic (PV) module using an improved restricted control set model predictive control (iRCS-MPC) technique. The suggested work is improved in two aspects; a revision in conventional P&O algorithm is made by employing three distinct step sizes for different conditions, and an improvement in conventional MPC algorithm. The improved MPC algorithm is based on the single step prediction horizon that provides less computational load and swift tracking of maximum power point (MPP) by applying the control pulses directly to the converter switch. The computer aided experimental results for various environmental scenarios revealed that compared with the conventional method (conventional P&O + MPC), for the PV power and inductor current, the undershoot and overshoot is decreased to 68% and 35% respectively under stiff environmental conditions. In addition, the settling time needed to reach a stable state is significantly reduced in the proposed system. The viability of the solution suggested is verified in MATLAB/Simulink and by hardware experimentation.

In this paper, we reported the fabrication, characterization, and application of carbon nanotube (CNT)-platinum nanocomposite as a novel generation of cathode catalyst in microbial fuel cells (MFCs) for sustainable energy production and wastewater treatment. The efficiency of the carbon nanocomposites was compared by platinum (Pt), which is the most effective and common cathode catalyst. This nanocomposite is utilized to benefit from the catalytic properties of CNTs and reduce the amount of required Pt, as it is an expensive catalyst. The CNT/Pt nanocomposites were synthesized via a chemical reduction technique and the electrodes were characterized by field emission scanning electron microscopy, electronic dispersive X-Ray analysis, and transmission electron microscopy. The nanocomposites were applied as cathode catalysts in the MFC to obtain polarization curve and coulombic efficiency (CE) results. The catalytic properties of electrodes were tested by linear sweep voltammetry. The CNT/Pt at the concentration of 0.3 mg/cm2 had the highest performance in terms of CE (47.16%), internal resistance (551 Ω), COD removal (88.9%), and power generation (143 mW/m2). In contrast, for the electrode with 0.5 mg/L of Pt catalyst, CE, internal resistance, COD removal, and power generation were 19%, 810 Ω, 96%, and 84.1 mW/m2, respectively. So, it has been found that carbon nanocomposite cathode electrodes had better performance for sustainable clean energy production and COD removal by MFC.

© 2018 Elsevier B.V. This paper investigates the adsorption of nitrate anions from aqueous solutions on ammonium-functionalized magnetic mesoporous silica. The adsorbent was prepared via two-step coating process of silica on magnetic core (Fe3O4). The resultant structure was modified by 3-aminopropyl triethoxysilane (APTES), and finally acidified in HCl solution to convert the grafted amino groups to ammonium ones. Field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibration sample magnetometer (VSM), Energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR), and N2 adsorption/desorption were used to characterize the obtained samples. Experimental results showed that several factors affected the uptake behavior such as pH, contact time, and initial concentration of nitrate. The amount of sorbent loading were examined and the adsorbent shows great adsorption capacity for NO3¯ (ca.51.28 mg g−1 at 25 °C). The nitrate loaded multifunctional microsphere can be easily regenerated with NaOH solution. The separation of multifunctional magnetic microspheres from solution by novel high gradient magnetic separation (HGMS), using the collection of rods, was also investigated in details. Contrast to other methods based on filter and batch conditions, large volumes of water can be easily handled by the new designed HGMS due to the decreasing pressure drop and retention times. The effect of a set of two different experimental variables, i.e. flowrate and magnetic field strength, were investigated to identify the best working conditions for the separation of adsorbent from treated water. The most efficient backwash system was offered to reuse the magnetic particles, too. The removal efficiency of NO3¯ from solution was around 86.24% by the constructed HGMS under the optimal experimental conditions of 7.5 mL s −1 flowrate and: 3.49 mT magnitude of the magnetic field.

Communication-based train control systems (CBTCs) have been widely used as crucial systems in urban rail transit networks. CBTCs typically utizes different levels of symmetry structure according to different geographic deployments. While, in practice, CBTCs crashes have destroyed the transportation systems of the whole city level for many times. Based on the extended object-oriented Petri net (EOOPN), this paper proposes a vulnerability model and an evaluation procedure, which are capable of considering the vulnerability factors in both inner system level and equipment level. On the system level, it establishes a complex dynamic communication structure model among the distributed subsystems, while on the equipment level, it details the equipment changing state during train operation. The searching algorithm of EOOPN depicts possible failed paths of CBTCs via the token transition among train&not, &ndash, ground communication EOOPN subnets. The vulnerability calculation is applied to the metro company&rsquo, s in situ CBTCs to illustrate the effectiveness of the approach.

The Internet of Things (IoT) will bring together billions of devices, denoted as Smart Objects (SOs), in an Internet-like architecture. Typically, SOs are embedded devices with severe constraints in terms of processing capabilities, available memory (RAM/ROM), and energy consumption. SOs tend to be deployed in environments in which the human intervention is not suitable or needs to be minimized (e.g., smart city maintenance). They must adapt to the surrounding environment by self-configuring: to this end, several mechanisms have been proposed (e.g., UPnP, ZeroConf, etc.). In this paper, we focus on IEEE 802.15.4 networks with IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) adaptation layer, where IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the routing protocol of choice. In this context, we propose a lightweight RPL-based mechanism to Resource Discovery (RD) and Service Discovery (SD), denoted as DiRPL. In particular, DiRPL exploits the RPL handshake to detect new nodes in the network, resources are then simply discovered with a Constrained Application Protocol (CoAP) request and can thus be published in a local resource directory. A very attractive feature of the proposed DiRPL approach is that it builds on well-defined and well-known standard protocols. The performance of the proposed system is investigated with WisMote nodes deployed inside the Cooja simulator, running the Contiki operating system. Practical application scenarios to large-scale smart city monitoring, such as smart lighting and large-scale water consumption monitoring, are investigated.

Credit and debit cards enable financial transactions at unattended "pay-at-the-pump" gas station terminals across North America. Attackers discreetly open these pumps and install skimmers, which copy sensitive card data. While EMV (“chip-and-PIN”) has made substantial inroads in traditional retailers, such systems have virtually no deployment at pay-at-the-pump terminals due to dramatically higher costs and logistical/regulatory constraints, leaving consumers vulnerable in these contexts. In an effort to improve security, station owners have deployed security indicators such as low-cost tamper-evident seals, and technologists have developed skimmer detection apps for mobile phones. Not only do these solutions put the onus on consumers to notice and react to security concerns at the pump, but the efficacy of these solutions has not been measured. In this paper, we evaluate the indicators available to consumers to detect skimmers. We perform a comprehensive teardown of all known skimmer detection apps for iOS and Android devices, and then conduct a forensic analysis of real-world gas pump skimmer hardware recovered by multiple law enforcement agencies. Finally, we analyze anti-skimmer mechanisms deployed by pump owners/operators, and augment this investigation with an analysis of skimmer reports and accompanying security measures collected by the Florida Department of Agriculture and Consumer Services over four years, making this the most comprehensive long-term study of such devices. Our results show that common gas pump security indicators are not only ineffective at empowering consumers to detect tampering, but may be providing a false sense of security. Accordingly, stronger, reliable, inexpensive measures must be developed to protect consumers and merchants from fraud.

Spatial summation in optical orthogonal frequency division multiplexing (O-OFDM) provides opportunity of simultaneously transmitting narrowband visible light communications (VLC) signals using G light emitting diode (LED) groups. However, the number of transmit chains increases as the number of LED groups G increases. The limited dynamic range of these LEDs reinforces the need to reduce peak-to-average-power-ratio (PAPR) of O-OFDM signal in the VLC system. In this paper, we present study of the pilot-assisted (PA) PAPR reduction technique in O-OFDM using multiple LEDs. The PA technique is based on data symbol phase rotation with P iterations of pilot symbol to achieve PAPR reduction. Results show tradeoff between multiple LEDs hardware complexity and the PA technique to achieve desirable PAPR reduction. In comparison with basic O-OFDM with G = 12, simulation results show that PA technique is capable of reducing G by about half to achieve same PAPR of approximately 12 dB using P = 2 at a complementary cumulative distribution function (CCDF) of 10−3. Using the PA technique, further PAPR reduction can be achieved with the combination of increased P and G.

The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership. In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.

Most existing high-power solar array simulators are suitable for simulating solar array energy sources in terrestrial applications. However, they are not sufficiently fast for spacecraft power-system testing using terrestrial testing applications. This paper proposes a UI-RI hybrid lookup table method with high linearity and high-speed convergence performance, which outperforms the traditional single UI lookup table method by employing a field-programmable gate array digital I–V outer loop. The proposed method overcomes the difficulty in guaranteeing stable output characteristics of the current output-type space solar array simulator (SSAS) because of the nonlinear I–V curves and slow convergence speed to the target operating point of the latter. The new lookup method is applied to a 2.4-kW hardware power platform of SSAS, and the SSAS can quickly respond to a load step between the short-circuit and open-circuit (OC) states, which is the harshest working condition, using the UI-RI hybrid lookup table method with a shunt switching frequency of 2 kHz. It can also quickly respond to a load step between the OC and rated-load states using a series-switching frequency of 10 kHz. The experimental data-point distribution trajectory of the switching process shows a greater tendency to converge to the designed I–V curve compared with the single UI lookup table method.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works This paper presents a 1.2 V 36 µW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates 1/f noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.

Post-war property reforms in transitional Cambodia plunged the country into new conflict: a war of land. Under the guise of ‘beautification’, 11% of the capital’s residents have been displaced in under two decades in a wave of violent gentrification, enacted through forced eviction and dispossession. Mounting resistance shows signs of taking effect, however, evincing a turning point in state–society relations. Here, the government has trialled a new approach, moving from techniques of violent expropriation towards a conciliatory method, built on dialogue, consultation and negotiation. Responding to calls for more work on resistance to gentrification and success in the fight to stay put, in this paper, I investigate these claims, bringing the literatures on gentrification and post-politics to bear on the evictions crisis in Cambodia. Drawing on testimony of former residents and media analysis, I examine techniques of removal and resistance in a case study of the eviction and demolition of Cambodia’s White Building (1963–2017). I argue that recent shifts are not an abandonment of the state’s compulsion to expropriation, exclusion and expulsion but a subtle modification of its gentrification strategy: away from the naked coercion associated with its own kleptocratic variant of authoritarian neoliberalism towards the post-political manufacture of hollow consent.

Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks., National Science Foundation Graduate Research Fellowship Program

International audience; This editorial introduces the special issue on energy-aware manufacturing operations in the International Journal of Production Research. The 12 papers in this special issue were selected because of their high quality and also because they deal with topics related to energy-aware manufacturing operations. Three broad challenges are collectively addressed by the papers in this special issue: energy-efficiency vs. manufacturing-system effectiveness in optimisation; the volatility in energy availability, supply and cost; modelling energy consumption in varying scales and across different sub-systems. Previous global discussions about the state of the art in energy-aware manufacturing operations are provided, as well as exploratory guidelines for future research in this area.

In this paper, we present an approach to identify changes in the writing style of 7 writers of novels written in English. We defined 3 stages of writing for each author, each stage contains 3 novels with a maximum of 3 years between each publication. We propose several stylometric features to represent the novels in a vector space model We use supervised learning algorithms to determine if by means of his stylometric-based representation is possible to identify to which stage of writing each novel belongs.

In this paper, a comprehensive investigation of the capacitive active ripple compensation (ARC) techniques is made to conclude which one is optimal to be used in on-board chargers of electric vehicles. Crucial aspects in such an application are: lifetime, volumetric and specific power density (including components’ size and the needed cooling solution), and overall efficiency of the charger. As presented in this paper, all capacitive ARC topologies (buck, boost, and buck–boost) have successfully diverted the low-frequency ripple from the dc side with a maximized power density. The ARC circuit consists of two additional switches, a smoothing auxiliary inductor, and a storage auxiliary capacitor. Finally, the buck capacitive ARC topology proves to be the optimal ARC technique for on-board chargers because of its maximal power density, minimal loss behavior and voltage stress, and long lifetime capability as it requires a downsized capacitance to the extent, where film capacitors or ceramic capacitors can replace the normally used bulky electrolytic capacitors. The performance of the three capacitive ARC techniques is proved by simulation results.

Packaging using thermoplastic molding for hollowed GaN chips were requested for a leak-proof micro-joining between plastic molds and copper-based substrates. The design and engineering of micro-textures is a key technology for putting leak-proof packaging into practice. In the present paper, a micro-meshing punch array was prepared using plasma-nitriding-assisted printing. Two-dimensional original patterns were screen-printed onto an AISI316 die substrate and plasma nitrided at 673 K for 14.4 ks (or 4 h). The unprinted surfaces were selectively nitrogen super-saturated to have more nitrogen content than 5 mass% and a higher hardness than 1200 HV. The printed surfaces were selectively sand blasted to fabricate the micro-meshing punch array for micro-embossing. A computer numerically controlled stamping system was utilized to describe the micro-embossing behavior onto copper substrates and to investigate how the micro-textures on the array was transcribed onto the copper. Reduction of takt time as well as flexibility in the micro-grooving were discussed with reference to the picosecond laser machining and mechanical milling processes.

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier., 5 pages, 4 figures, paper presented at ITW 2018, corrected version and updated reference list

Semantic annotation is the basement of semantic retrieval which has become a hot motivation of research in recent years. Social annotation makes it possible for semantic annotation of large amount of web resources. Because social tags express the semantic information of the resource, resources can be organized and managed by their semantics with the help of social tags. Information classification will help semantic retrieval greatly. We propose a kind of tourism classification which includes concepts related with tourism tags and a Bayesian based algorithm which aggregates concepts from social tags in this paper. Applying the algorithm to the automatic classification of tourism resources, the results show the effectiveness of the proposed algorithm.

The paper deals with simulations of power line channel transfer functions in Network Simulator version 3. Firstly, an empirical model and calculation of the channel transfer function are given to reflect the necessity of channel transfer function for Power Line Communication system design. The framework for Power Line Communication in Network Simulator version 3 and then the necessary extension implementation are introduced. Other simulators are also mentioned. Secondly, various scenarios were implemented for the analysis and simulation of power line channel transfer functions. New scenarios for large topologies and for different approaches to calculate primary parameters were created. In the simulations, various kinds of topologies are considered for an analysis of the power line transfer function. The simulation part also focuses on the simulation of channel transfer function where the time- and frequency-selective impedances are considered. Finally, the last part focuses on measurements and a comparison of the simulation results with real measurements are given.