Specific Time Instances Research Articles

Experimental detection of train wheel defects using wayside vibration signal processing

While many studies have been used onboard monitoring methods to detect train wheel defects, this paper aims to extract damage features from vibrational signals obtained by the wayside monitoring system. To provide an appropriate tool for the decomposition of experimental nonstationary vibration signals containing impacts, an analytical amplitude-based signal decomposition method is provided. A two-axle motor car with a flat wheel defect is studied at different operating conditions. Vibrational signals arising from wheel-rail interactions are gathered by the Wayside Data Collection Setup (WDCS) with five piezoelectric accelerometers. The tests are performed at the constant speeds of 10, 20, 30, and 40 km/h. The acquired signals are decomposed by the Multi-channel Singular Spectrum Analysis (MSSA) into the contributing components. Based on the results, increases in the amplitudes of the second reconstructed components (RCs) of the vibrational signals sensed by all the accelerometers can be observed at a specific time instance as a repeating pattern. Similar patterns are obtained at different motor car positions relative to the measurement area, as well as different motor car speeds. To verify the consistency of the second RCs, the crest factors are obtained for all the angular positions. The results show sudden increases in the crest factor at a fixed angular position that indicate the existence of a defect at that specific angular position of the wheel.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users’ particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

A Proactive Statistical Model Supporting Services and Tasks Management in Pervasive Applications

The combination of the Internet of Things (IoT) and Edge Computing (EC) can support intelligent pervasive applications that meet the needs of end users. A challenge is to provide efficient inference models for supporting collaborative activities. EC nodes can interact with IoT devices and each other to conclude those activities producing knowledge. In this paper, we propose a proactive scheme to decide upon the efficient management of services and tasks present/reported at EC nodes. Services can be processing modules applied upon local data while being required for the execution of tasks. We monitor the demand for the available services and reason upon their management, i.e., for their local presence/invocation as the demand is updated by the requested processing activities. For each incoming task, an inference process is fired to proactively meet the strategic targets of the envisioned model. We propose a statistical inference process upon the demand for services and the contextual performance data of nodes combining it with a utility aware decision making model. Instead of exclusively focusing on services migration or tasks offloading as other relevant efforts do, we elaborate on the decision making for the selection of one of the aforementioned activities (the most appropriate at a specific time instance). We present our model and evaluate it through a high number of simulations to expose its pros and cons placing it in the respective literature as one of the first attempts to proactively decide the presence of services to an ecosystem of processing nodes.

The Relation between Induced Electric Field and TMS-Evoked Potentials: A Deep TMS-EEG Study

Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between E-field and the ensuing cortical activation, validate in a clinically relevant manner the results of E-field modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.

A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF

Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.

Optimization of Ultra-Dense Wireless Powered Networks.

The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

Deep‐Learning‐Based Adjoint State Method: Methodology and Preliminary Application to Inverse Modeling

AbstractWe present an efficient adjoint model based on the deep‐learning surrogate to address high‐dimensional inverse modeling with an application to subsurface transport. The proposed method provides a completely code nonintrusive and computationally feasible way to approximate the model derivatives, which subsequently can be used to derive gradients for inverse modeling. This conceptual deep‐learning framework, that is, an architecture of deep convolutional neural network through combining autoencoder and autoregressive structure, efficiently produces an analogously analytical adjoint with the help of auto‐differentiation module in the popular deep‐learning packages. We intentionally retain training data at the specific time instances where the measurements are taken, the storage of the intermediate states and computation of their adjoint, therefore, are completely avoided. This proposed adjoint state method is tested on a synthetic two‐dimensional model for parameter estimations. The preliminary results reveal the feasibility of the proposed adjoint state method in term of computational efficiency and programming flexibility.

Design of state and output feedback pole placement controller in the presence of slow-rate integrated measurement

Lab measurements in the industrial processes are either through instantaneous measurement at a specific time instance, known as Slow-Rate inStantaneous Measurement (SRSM), or through Slow-Rate inTegrated Measurement (SRTM), in which some amount of material is gradually collected for a period of time; so, the average of the collected material during that period is measured. In this paper, control of a system under SRTM is studied for the first time. Pole placement state feedback with feedforward controller is proposed so that both SRTM and conventional fast-rate output can track their step reference input. This controller comprises a periodically time-varying gain and an observer to estimate the slow-rate instantaneous state variable at the sampling instances. An observability condition is studied, and a modified Kalman filter is established to estimate slow-rate instantaneous state variable using SRTM. In the next step, two modified pole placement output feedback controllers are designed using SRTM. In the first control system, the aim is to track a slow-rate integrated reference input. In the second controller, fast-rate reference input is introduced to the control system. The performance of the proposed methods is evaluated through both simulation study in a pH neutralization process plant and experimental implementation on a laboratory scale Quadruple Tank pilot plant.

Active Target Tracking With Self-Triggered Communications in Multi-Robot Teams

We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario, where a team of robots is allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past, which guarantees that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on decentralized strategies to reduce the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to the desired formation when the target is stationary. For the case of a mobile target, we use a decentralized Kalman filter with covariance intersection to share the beliefs of neighboring robots. We evaluate all the approaches through simulations and a proof-of-concept experiment. Note to Practitioners —We study the problem of tracking a target using a team of coordinating robots. Target tracking problems are prevalent in a number of applications, such as co-robots, surveillance, and wildlife monitoring. Coordination between robots typically requires communication amongst them. Most multi-robot coordination algorithms implicitly assume that the robots can communicate at all time steps. Communication can be a considerable source of energy consumption, especially for small robots. Furthermore, communicating at all time steps may be redundant in many settings. With this as motivation, we propose an algorithm where the robots do not necessarily communicate at all times and instead choose specific triggering time instances to share information with their neighbors. Despite the limitation of limited communication, we show that the algorithm converges to the optimal configuration both in theory as well as in simulations.

A methodology for the reassessment of hull-girder ultimate strength of a VLCC tanker based on corrosion model updating

ABSTRACTThe paper presents a new methodology for reassessing hull-girder ultimate strength, assuming that new data become available on a specific time instance during the service life of the vessel. The calculation of ultimate strength is based on Smith’s incremental-iterative method and is examined in probabilistic terms. Uncertainties are associated with material yield strength, Young’s modulus and time-variant corrosion thickness wastage of structural members. The reassessment of ultimate strength is achieved by updating the time-variant corrosion model, taking into advantage on-site observations from thickness measurements reports, via the framework of Bayesian Updating. The results are discussed, along with recommendations for the validation of the suggested approach in practice. The proposed methodology is a tool to reassess the strength of vessel and can also be used for structural reliability analysis on vessels with similar operational and structural profile.

A knowledge discovery framework for the assessment of tactical behaviour in soccer based on spatiotemporal data

ABSTRACTThis paper addresses the problem of designing an explanatory computational model for the assessment of individual tactic skills in team sports. The modelling approach tackles the complexity and difficulty of this problem by fusing fuzzy human-like knowledge related to tactical behaviour with time-continuous position data from a tracking system. For this purpose, a hierarchical architecture is proposed. The bottom layer is represented by physically meaningful variables derived from time-continuous position data at specific time instances. Based thereupon, we introduce a temporal segmentation layer that relates the physical variables to game-situation-specific temporal phases. We show how the vague and imprecisely defined linguistic description of the task at hand can be transferred to fuzzy rules in order to get a meaningful temporal segmentation of the time-continuous position data. Finally, the resulting clusters are interpreted in terms of performance indicators in the top layer in order to provide a meaningful explanatory model for the assessment. We show the usefulness of our approach for the task of player evaluation. We do not only provide the coach with a single number to describe the players’ performance but also relate this number to the measurement variables, presenting a more holistic and sophisticated view of the players’ performance.

Finding Dynamic Dense Subgraphs

Online social networks are often defined by considering interactions of entities at an aggregate level. For example, a call graph is formed among individuals who have called each other at least once; or at least k times. Similarly, in social-media platforms, we consider implicit social networks among users who have interacted in some way, e.g., have made a conversation, have commented to the content of each other, and so on. Such definitions have been used widely in the literature and they have offered significant insights regarding the structure of social networks. However, it is obvious that they suffer from a severe limitation: They neglect the precise time that interactions among the network entities occur. In this article, we consider interaction networks , where the data description contains not only information about the underlying topology of the social network, but also the exact time instances that network entities interact. In an interaction network, an edge is associated with a timestamp, and multiple edges may occur for the same pair of entities. Consequently, interaction networks offer a more fine-grained representation, which can be leveraged to reveal otherwise hidden dynamic phenomena. In the setting of interaction networks, we study the problem of discovering dynamic dense subgraphs whose edges occur in short time intervals . We view such subgraphs as fingerprints of dynamic activity occurring within network communities. Such communities represent groups of individuals who interact with each other in specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones. We prove that the problem we define is NP -hard, and we provide efficient algorithms by adapting techniques for finding dense subgraphs. We also show how to speed-up the proposed methods by exploiting concavity properties of our objective function and by the means of fractional programming . We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our approach and shows that our algorithms can be used to obtain high-quality results.

Adaptive Hybrid Compression for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often deployed to sample the desired environmental attributes and deliver the acquired samples to a central station, termed as the sink, for processing as needed by the application. Many applications stipulate high granularity and data accuracy that results in high data volumes. However, sensor nodes are battery powered, and sending the requested large amounts of data rapidly depletes their energy. Fortunately, environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific analysis and simulations tolerate high latency for sensor data collection. Hence, we exploit the spatiotemporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to minimize the amount of data to be transported to the sink. Accordingly, we develop a fully distributed adaptive hybrid compression scheme that exploits both spatial and temporal data redundancies and fuses both temporal and spatial compression for maximal data compression with accuracy guarantees. We present two main contributions: (i) an adaptive modeling technique that allows frugal and maximized temporal compression on resource-constraint sensor nodes by exploiting the data collection latency, and (ii) a novel model-based hierarchical clustering technique that allows for maximized spatial compression resulting into a hybrid compression scheme. Compared to the existing spatiotemporal compression approaches, our approach is fully decentralized and the proposed clustering scheme is based on sensor data models rather than instantaneous sensor data values, which allows merging nearby nodes with similar models into large clusters over a longer period of time rather than specific time instances. The analysis for computation and message overheads, the analysis for theoretical compressibility, and simulations using real-world data demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data.

Sequential filtering for dispersion tracking and sediment sound speed inversion.

Dispersion curves in ocean environments are accurately estimated from received signals through the extraction of instantaneous modal frequencies and corresponding arrival times for long-range propagation. The ultimate goal is to estimate sediment sound speed using the extracted dispersion pattern. The approach extends work previously conducted in dispersion tracking with sequential filtering, improving on the latter technique. The sequential state-space method that is developed for the extraction of time-frequency information from specific time instances relies on a representation of those as a sum of elemental pulses, resulting from analysis of the received field. The method is tested on synthetic noisy data with different noise levels. After dispersion probability density functions are estimated via a particle filter, they are subsequently employed for sound speed inversion. Correct mode identification is a challenge impacting inversion; this is demonstrated through two examples and a way to remedy the problem is discussed.

Spatio-Temporal Data Construction

On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evolution in each time interval. Because geomodeling software systems usually model objects at specific time instances, we want to interpolate the geometry and properties from two models of an object with only geometrical constraints (no physical or mechanical constraints). This process is called spatio-temporal data construction or morphological interpolation of intermediate geometries. This paper is strictly related to shape morphing, shape deformation, cross-parameterization and compatible remeshing and is only concerned with geological surfaces. In this study, two main sub-solutions construct compatible meshes and find trajectories in which vertices of the mesh evolve. This research aims to find an algorithm to construct spatio-temporal data with some constraints from the geosciences, such as cutting surfaces by faulting or fracturing phenomena and evolving boundaries attached to other surfaces. Another goal of this research is the implementation of the algorithm in a software product, namely a gOcad plug-in. The four main procedures of the algorithm are cutting the surfaces, setting up constraints, partitioning and calculating the parameterizations and trajectories. The software has been tested to construct data for a salt dome and other surfaces in regard to the geological processes of faulting, deposition and erosion. The result of this research is an algorithm and software for the construction of spatio-temporal data.

Bayesian Supply Chain Tracking Using Serial-Level Information

Supply chain visibility is one of the main levers for achieving operational efficiency. Modern supply chain tracking systems can deliver serial-level information about the location of items progressing through the chain. However, these systems still fail to meet the managers' visibility requirements in full, since they provide discrete information about product location at specific time instances only. This paper proposes a model that uses the data provided by these tracking systems to deliver enhanced tracking information to the final user. Following a Bayesian approach, the model produces realistic continuous estimates about the current and future locations of products across a supply network, taking into account the characteristics of the product behavior as well as the configuration of the data-collection points. These estimates can then be used to optimize operational decisions that depend on product availability at different locations. This paper demonstrates how the proposed model can enhance tracking information delivered by the radio frequency identification (RFID) technology and the electronic product code (EPC) network. The enhancement of tracking information quality is highlighted through an example.

An Automatic Personal Calibration Procedure for Advanced Gaze Estimation Systems

Gaze estimation systems use calibration procedures to estimate subject-specific parameters that are needed for the calculation of the point-of-gaze. In these procedures, subjects are required to fixate on a specific point or points in space at specific time instances. Advanced remote gaze estimation systems can estimate the optical axis of the eye without any personal calibration procedure, but use a single calibration point to estimate the angle between the optical axis and the visual axis (line-of-gaze). This paper presents a novel calibration procedure that does not require active user participation. To estimate the angles between the optical and visual axes of each eye, this procedure minimizes the distance between the intersections of the visual axes of the left and right eyes with one or more observation surfaces (displays) while subjects look naturally at these displays (e.g., watching a video clip). Theoretical analysis and computer simulations show that the performance of the proposed procedure improves when the range of angles between the visual axes and vectors normal to the observation surfaces increases. Experiments with four subjects show that the subject-specific angles between the optical and visual axes can be estimated with an rms error of 0.5 degrees.

SU-FF-T-147: Intra Fractional Motion in Clinical IMRT Prostate Treatments, Warrants the Use of Faster Treatment Techniques

Purpose: To estimate intrafracional motion of the prostate in a routine clinical setting and its impact on margin reduction and treatment time. Method and Materials: External beam treatment for cancer of the prostate using an IMRT technique was evaluated. Fifteen patients underwent a marker match procedure ensuring correct positioning at time of treatment. For all fields intra‐treatment images were obtained, yielding 5 to 7 images per fraction. IMRT was delivered using a dynamic sliding window technique. The obtained images were processed to remove IMRT information. The markers were detected in the image using an automated methodology. Every image was timestamped and chronologically adjacent images were backprojected to yield 3D marker coordinates. Allowing to calculate the position of the prostate during the treatment delivery at specific time instances. Using a Poisson model for the probability of movement we can determine the maximal allowable time frame within which to perform this treament. Results: The maximal treament time measured was 1460s, the shortest lasted 343s. The times were measured starting from the last image in the marker‐match procedure and includes the decision and adjustment process. Depending on the elapsed time we noticed an increase in positional confidence level from 5.8mm to 7.6mm. The delivery of the fields are of the order of 250s. Conclusion: We note a significant increase in probability of prostate movement in our treaments as time elapses. This limits the amount of margin reduction possible. There are two strategies possible to reduce this time. 1) Increase the marker match speed, or 2) increase the delivery speed. A good candidate to do this is the use of a volumetric Arc technique (VMAT) which is implemented in our department with RapidArc™. The latter is able to deliver the same or even better dose distribution in under 2 minutes (8 patients).

Enhanced Supply Chain Tracking Based on the EPC Network: A Bayesian Approach

Abstract Supply chain tracking information is one of the main levers for achieving operational efficiency. RFID technology and the EPC Network can deliver serial-level product information that was never before available. However, these technologies still fail to meet the managers’ visibility requirements in full, since they provide information about product location at specific time instances only. This paper proposes a model that uses the data provided by the EPC Network to deliver enhanced tracking information to the final user. Following a Bayesian approach, the model produces realistic ongoing estimates about the current and future location of products across a supply network, taking into account the characteristics of the product behavior and the configuration of the data collection points. These estimates can then be used to optimize operational decisions that depend on product availability at different locations. The enhancement of tracking information quality is highlighted through an example.

Solving seismic wave propagation in elastic media using the matrix exponential approach

Three numerical algorithms are proposed to solve the time-dependent elastodynamic equations in elastic solids. All algorithms are based on approximating the solution of the equations, which can be written as a matrix exponential. By approximating the matrix exponential with a product formula, an unconditionally stable algorithm is derived that conserves the total elastic energy density. By expanding the matrix exponential in Chebyshev polynomials for a specific time instance, a so-called “one-step” algorithm is constructed that is very accurate with respect to the time integration. By formulating the conventional velocity-stress finite-difference time-domain (VS-FDTD) algorithm in matrix exponential form, the staggered-in-time nature can be removed by a small modification, and higher-order in time algorithms can be easily derived. For two different seismic events the accuracy of the algorithms is studied and compared with the result obtained by using the conventional VS-FDTD algorithm.