Data-driven Processing Research Articles

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Living in a global world, we cannot limit supply chain to only one, two, or even three countries. Hence, supply chain manager’s key function now is to continuously reduce the time of goods flow and minimize the number of chain links making the chain more efficient and cost effective. The article outlines key components of the concept of supply chain management. We regard the management concept itself as planning, management and supply chain development on every stage of creation of goods and services: from raw materials supply to an end user service maintenance. We should streamline supply chain management to exclude all barriers, which different stakeholders may face during the flow. SCM-concept helps control the work of existing processing lines in the supply chain management system, create optimal planning what and when we should deliver, taking into account possible limits of capacity and access to raw materials. Currently, the motor vehicles do the major domestic transportation. Motor transport development is one of the most important aspects of effective economy development. In cross border transportation the share of freight transportation is growing compared to other means of transport. However, domestic motor transport and fuel quality have considerable mismatch of environment properties that lag far behind global requirements. Transport companies inside supply chain have moved beyond conventional services, at the same time they have higher market consolidation, integration, and collaboration with different agents that give new demands to transportation in logistics. The article identifies basic methods of supply chain management. We analyzed impact of modern technologies on logistics and supply chain management: 3D printing, radio frequency identification, unmanned aerial vehicles, connected and autonomous vehicles, and augmented reality. Modern technologies provide small and medium enterprises opportunities to use innovative tools to obtain data-driven analytics, visualization, and processing of cargo. In order to make supply chain management efficient, we should use new technologies, modern devices, and tangible assets of high quality.

Input parameters estimation from time-varying measurements

A measurement is a dynamical process that aims to estimate the true value of a measurand. The measurand is the input that excites a sensor, and, as a consequence, the sensor output is a transient response. The main approach to estimate the input is applying the sensor transient response to another dynamical system. This dynamical system is designed by deconvolution to invert the sensor dynamics and compensate the sensor response. Digital signal processors enable an alternative approach to estimate the unknown input. There exists a data-driven subspace-based signal processing method that estimates a measurand, assuming it is constant during the measurement. To estimate the parameters of a measurand that varies at a constant rate, we extended the data-driven input estimation method to make it adaptive to the affine input. In this paper, we describe the proposed subspace signal processing method for the measurement of an affine measurand and compare its performance to a maximum-likelihood input estimation method and to an existing time-varying compensation filter. The subspace method is recursive and allows real-time implementations since it directly estimates the input without identifying a sensor model. The maximum-likelihood method is model-based and requires very high computational effort. In this form, the maximum-likelihood method cannot be implemented in real-time, however, we used it as a reference to evaluate the subspace method and the time-varying compensation filter results. The effectiveness of the subspace method is validated in a simulation study with a time-varying sensor. The results show that the subspace method estimation has relative errors that are one order of magnitude smaller and converges two times faster than the compensation filter.

Exploration of Oilfield Data Fusion Mode Based on ToGAF Framework

In the process of digitalized management of oilfield striding towards intelligent construction, how to effectively fuse oilfield data with multisource, heterogeneous, massive and distributed characteristics has become one of the problems to be solved urgently. In this paper, various data fusion models are compared, and the oilfield data fusion model based on ToGAF framework is proposed. This model combines master data management (MDM) with enterprise service bus (ESB) organically, realizes the function of oilfield data-driven business processing, and truly enables enterprises to pay more attention to their core business rather than IT system, so as to enhance the comprehensive management ability of enterprises.

Data-driven Koopman operator approach for computational neuroscience

This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta ( sim 13 Hz) and high Gamma ( sim 50 Hz) frequencies, and explore the spatiotemporal dynamics of high- and low- frequency components.

Ultrafast Plane Wave Imaging With Line-Scan-Quality Using an Ultrasound-Transfer Generative Adversarial Network.

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound image processing methods are trained by line-scan images. Since the gray-level distributions of line-scan images and PW images are very different, these gray-level distribution-sensitive methods cannot be generalized to ultrafast ultrasound imaging, which limits further applications. Hence, we propose an ultrasound-transfer generative adversarial network to improve the quality of PW images and extend the existing image processing methods to ultrafast ultrasound imaging by reconstructing PW images into line-scan images. This network adopts a residual dense generator with a self-attention system that fully uses the hierarchical features and generates details from all the relevant physiological information. A projection discriminator and spectral normalization are introduced to increase the discernibility and to maintain a balance between the generator and the discriminator. Moreover, we reorganize the transmit sequence of the transducer array to eliminate the negative influence of human movements and facilitate the convergence of the proposed model. The experimental results are evaluated with five metrics, which confirm the feasibility of the proposed method to obtain a line-scan-quality image with a very high frame rate. This technology could significantly popularize ultrafast medical ultrasound imaging.

Angiotensin involvement in trauma processing-exploring candidate neurocognitive mechanisms of preventing post-traumatic stress symptoms.

The angiotensin-II antagonist losartan is a promising candidate that has enhanced extinction in a post-traumatic stress disorder (PTSD) animal model and was related to reducing PTSD symptom development in humans. Here, we investigate the neurocognitive mechanisms underlying these results, testing the effect of losartan on data-driven and contextual processing of traumatic material, mechanisms proposed to be relevant for PTSD development. In a double-blind between-subject design, 40 healthy participants were randomised to a single oral dose of losartan (50 mg) or placebo, 1 h before being exposed to distressing films as a trauma analogue while heart rate (HR) was measured. Peritraumatic processing was investigated using blurry picture stimuli from the films, which transformed into clear images. Data-driven processing was measured by the level of blurriness at which contents were recognised. Contextual processing was measured as the amount of context information retrieved when describing the pictures' contents. Negative-matched control images were used to test perceptual processing of peripheral trauma-cues. Post-traumatic stress symptoms were assessed via self-report questionnaires after analogue trauma and an intrusion diary completed over 4 days following the experiment. Compared to placebo, losartan facilitated contextual processing and enhanced detail perception in the negative-match pictures. During the films, the losartan group recorded lower HR and higher HR variability, reflecting lower autonomic stress responses. We discuss potential mechanisms of losartan in preventing PTSD symptomatology, including the role of reduced arousal and increased contextual processing during trauma exposure, as well as increased threat-safety differentiation when encountering peripheral trauma-cues in the aftermaths of traumatic events.

Apprehending Fault Crises for an Autogenous Nanogrid System: Sustainable Buildings

This paper presents a novel approach for in-house operators to profile and allege fault interferences in a nanogrid system either locally or externally. The proposed hybridized methodology is modeled to apprehend, classify, and locate fault interventions using a heuristic data-driven processing model and a sensing directional flow theorem. Employment of fuzzy control and discrete Fourier transform systems are fused to recompose directional fault relay functionalities. The algorithm is composed of three computational stages: stage 1 quantifies conditional correlate coefficients (COC) based on current and phase angle features sampled at interconnecting feeders, stage 2 engages a fuzzy logic controller to express linguistic truth values against calculated COCs, and stage 3 directs circuit breaker operations advocating post phase-shift aberrations to isolate the faulted region. A nanogrid model inspired by Singapore's Green Mark Building Incentive is developed in the MATLAB environment consisting of a combine heat and power microturbine, a rooftop photovoltaic system, and battery storage units tied to the 22-kVAC distribution network. Analytical results exhibit practicability and decisive settlements in diagnosing various types of fault crises despite low data logging signal-to-noise ratio. Conjointly, engagements of circuit breakers have rendered accurate switching operations toward isolating faulted regions.

Working Memory Capacity as a Moderator of Intrusive Memories

Intrusions can appear in different forms. The occurrence of different types of intrusion can be determined by how the traumatic information was processed. This study examined whether working memory capacity (WMC) can moderate the two peritraumatic processes (conceptual processing and data-driven processing) to affect intrusions. Participants first completed a symmetry span task, viewed traumatic film footage, and completed a processing-style questionnaire concerning the film. A week later, participants handed in a tabular diary that recorded their intrusions over the past week, and completed a free recall and recognition task about the film; they were then debriefed. WMC can moderate the effect of processing style, depending on the type of intrusion. In the prediction of intrusive thoughts (ITs), we found that higher conceptual processing scores led to more ITs in participants with a high WMC. The current study has important clinical implications, in that it recognizes the existence of less-studied ITs, and the role of WMC in trauma processing.

Passive structural monitoring based on data-driven matched field processing.

A passive data-driven method to localize a defect in a structure using the ambient noise is derived. The approach requires only acoustic measurements in a spatially random noise field and no knowledge of the structure. Measurements are taken before and after the perturbation has occurred and Green's functions are retrieved by cross-correlating acoustic measurements. The difference between measured data reveals the perturbation. A frequency domain method based on matched field processing is then performed to localize the perturbation. The Bartlett, minimum variance and white noise gain constraint processors are implemented and their performances are illustrated on a numerical experiment.

Mechanisms of the Formation and Involuntary Repetition of Trauma-Related Flashback: A Review of Major Theories of PTSD

Trauma-related flashback is one of the typical symptoms of patients suffered from posttraumatic stress disorder (PTSD), which intrudes into the body and mind of patients uncontrobaly and repeatedly. Psychodynamic theories of mechanisms of the formation and involuntary repetition of trauma-related flashback establish a foundation for most cognitive theories of PTSD. Cognitive behavioral theories of PTSD, from the initial use of behavioristic principles (e.g., conditioning, alternative learning, generalization, etc.) to explain fear conditioning to the emphasis on the roles of cognitive and behavioral factors (e.g., cognitive model or schema, completion tendency, associative network, data-driven processing, analogue representation, etc.) in the representation and retrieval of traumatic information, present a trend of multirepresentational theorizing. Inherited from the theories proposed by Pierre Janet in the early years of the 20th century and based on the integration of existing cognitive behavioral theories of PTSD, theories of the multiple memory system bring new viewpoints on the representation and retrieval of traumatic information in memory. In the last twenty years, several recent theories have received some empirical evidences both in theoretical and applicational. The clinical and nonclinical implications of the psychological explanations of trauma-related flashback, the ideas of further explorations in the theories of PTSD and the directions of future researches of the prevention and treatment of PTSD are presented.

A New Layered Architecture for Future Big Data-Driven Smart Homes

In this paper, a new layered architecture is proposed for big data-driven processing and management of future smart homes. The proposed Representational State Transfer (REST)-based architecture includes seven layers: physical, fog-computing, network, cloud-computing, service, session, and application for efficient data exchange and processing tasks of future smart homes. The smart home physical layer includes all the sensing technologies and smart devices within the smart home, which monitors the home environment and its residents. The data of these sensors will be sent to the smart home fog-computing layer that can do limited data storage and processing. Then, all the required data will be sent to the cloud-computing layer using smart home network layer. The cloud-computing layer provides the scalable solution for data processing and storage. The processed data in the cloud-computing layer will be provided as the data-driven services to different smart home and third-party (e.g., smart city) applications via smart home service layer. Based on the proposed architecture, the applications will utilize the session layer and RESTFUL APIs to use the data-driven services of the smart home. The proposed smart home architecture can provide a ubiquitous and shared data environment as the key aspect of Internet-of-Things systems.

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

Can't get it out of my mind: A systematic review of predictors of intrusive memories of distressing events.

Intrusive memories, when persistent and distressing, are theorized to underlie a range of transdiagnostic psychological symptoms and associated impairment. However, little is known about factors predicting the development and persistence of intrusive memories. The aim of this systematic review is to evaluate the literature on pre-event, event-based, and post-event predictors of intrusive memories. A systematic review was conducted, searching for studies that examined intrusive, event-based memories. One hundred and six articles were identified from PsycInfo, PubMed, and Medline databases. Experimental and prospective studies with clinical (N = 14) and nonclinical (N = 92) samples were critically reviewed, provided the inclusion of an analogue stressor with nonclinical samples, and that intrusive memories frequency and/or distress were assessed as primary dependent variables. Pre-existing psychopathology and pre-event appraisal style appear to predict intrusive memories (small to medium effects), whereas trait dissociation did not predict intrusive memories. Of studies examining event-based predictors, higher data-driven processing appears to predict intrusive memories with generally large effects. Post-event negative appraisals consistently predicted intrusive memories (medium to large effects), and preliminary evidence suggests higher post-event conceptual processing predicting fewer intrusive memories. This review synthesizes findings regarding a broad range of pre-event, event-based, and post-event factors that may influence the development of intrusive memories. Methodological issues of current paradigms and the lack of emphasis on memory retrieval processes limit our understanding of what predicts intrusive memory persistence. These limitations are particularly important given that individuals typically seek treatment for distressing intrusive memories once a memory has been fully consolidated, where retrieval processes are of utmost importance. (PsycINFO Database Record

Frightened by the perpetrator’s voice: Startle responsivity and cognitive processing predict earwitness speaker identification

This study was inspired by the case of a robbery victim who was startled and reminded of the crime upon hearing a stranger’s voice, while not clearly recognizing the speaker. To investigate whether specific voices can modulate startle reactions and thereby predict speaker identification, we presented an audio hijack scenario to 84 participants and afterwards asked them to identify the perpetrator among neutral and negative speech fragments, while measuring flash-evoked eye-blink startle responses. Furthermore, we addressed data-driven cognitive processing during the audio scenario as a potential moderator in voice discrimination. Negative speech and the perpetrator’s voice led to potentiated startle. Enhanced startle was positively associated with voice discrimination, but only in neutral speech fragments. In negative fragments, this association was weakened as a function of self-reported levels of data-driven processing during encoding. Thus, startle responses can generally predict accurate voice recognition, but speech emotionality and cognitive processing moderate this relationship.

Event-driven processing for hardware-efficient neural spike sorting

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Capturing spontaneous trait inference with the modified free association paradigm

Spontaneous Trait Inference (STI) is a psychological phenomenon that allows people to infer personality traits from others' behaviors without any intention or awareness. This phenomenon helps us organize our complex social world and ultimately it shapes our interaction with others. The goal of this manuscript is 1) to conduct a systematic analysis of the different types of measures that are currently used to detect inferences, 2) to identify their main limitations and 3) to propose a new paradigm that overcomes most of these limitations. The new paradigm is based on the modified free association task that Hourihan and MacLeod (2007) proposed as a pure conceptual implicit memory measure. This new paradigm simultaneously overcomes the contamination problem of memory measures and the dependency on data-driven processing of the activation measures. In the presented experiments we use the modified free association paradigm to detect STIs and also to compare its results to the naming task and the modified Stroop task. We show that this measure is able to reliably detect STI and discuss why it is more appropriate to study inferences. Additionally, we show that it can be used to investigate the underlying processes responsible for STI and to distinguish it from similar but different phenomena like Spontaneous Trait Transference. This manuscript focuses on STIs, but the arguments presented here apply to any research field that investigates spontaneous inference making.

Dynamic, data-driven processing of multispectral video streams

Video analytics plays an important role in a wide variety of defense-, monitoring- and surveillance-related systems for air and ground environments. In this context, multispectral video processing is attracting increased interest in recent years, due in part to technological advances in video capture. Compared with monochromatic video, multispectral video offers better spectral resolution, and different bands of multispectral video streams can enhance video analytics capabilities in different ways. For example, the infrared bands can provide better separation of shadows from objects, and improved spatial resolution in scenes that are impaired by fog or haze [16].

Wavefield analysis of crosswell seismic data

Despite the numerous advantages of crosswell seismic data over surface seismic data, crosswell seismic geophysics is still underutilized and underdeveloped. The factors limiting the full utilization of crosswell data include the lack of standardized methods for processing and imaging the data. This is because crosswell data is not completely understood. To improve the understanding of crosswell data, we performed acoustic and elastic modeling of a west Texas carbonate oilfield data using finite difference methods and crosswell geometry. To account for the different wave modes in the field data, we decomposed the full data into its constituent wave modes. Results of the forward modeling show that elastic synthetic data is a better representation of crosswell field data than the popular acoustic synthetic data. Wavefield decomposition gave insight into the time-space kinematics behavior of the different wave modes that constitute the full data. Overall, the study improved our understanding of crosswell field data. The learning from this study has been utilized to perform data-driven reflection enhancement processing where the discerned characteristic of different seismic arrival is utilize to suppress unwanted and enhanced the desired wave modes. The processing reduced the complex data to only up-going P-P reflections that can be imaged to reveal the subtle geological structures of the oilfield.

The relationship between processing style, trauma memory processes, and the development of posttraumatic stress symptoms in children and adolescents

Background and objectivesData-driven processing, peri-event fear, and trauma memory characteristics are hypothesised to play a core role in the development of Posttraumatic Stress Disorder. We assessed the relationships between these characteristics and Posttraumatic Stress (PTS) symptoms in a sample of youth. MethodsStudy 1 (N = 36, 7–16 years), involved a sample of children who had undergone a stressful orthopaedic procedure. One week later they answered a series of probed recall questions about the trauma (assessed for accuracy by comparison to a video) and reported on their PTS symptoms. They also rated confidence in their probed recall answers to assess meta-cognitive monitoring of their memory for the trauma. In Study 2, a sample of injured children (N = 57, 7–16 years) were assessed within 1-month of a visit to an Emergency Department, and then at 3-month follow-up. They answered probed recall questions, made confidence ratings, and completed measures of data-driven processing, peri-event fear, PTS and associated psychopathology. Memories were verified using witness accounts. ResultsStudies 1 and 2 did not find an association between PTS symptoms and trauma memory accuracy or confidence. In Studies 1 and 2 data-driven processing predicted PTS symptoms. LimitationsThe studies had modest samples sizes and there were ceiling effects for some accuracy and confidence items. ConclusionsData-driven processing at the time of a trauma was associated with PTS symptoms after accounting for fear at the time of the trauma. Accuracy of recall for trauma memories was not significantly related to PTS symptoms. No decisive conclusion could be drawn regarding the relation between confidence in trauma memories and PTS symptoms.

P.4.003 - Testing familial transmission of smoking with two different research designs

This study was inspired by the case of a robbery victim who was startled and reminded of the crime upon hearing a stranger’s voice, while not clearly recognizing the speaker. To investigate whether specific voices can modulate startle reactions and thereby predict speaker identification, we presented an audio hijack scenario to 84 participants and afterwards asked them to identify the perpetrator among neutral and negative speech fragments, while measuring flash-evoked eye-blink startle responses. Furthermore, we addressed data-driven cognitive processing during the audio scenario as a potential moderator in voice discrimination. Negative speech and the perpetrator’s voice led to potentiated startle. Enhanced startle was positively associated with voice discrimination, but only in neutral speech fragments. In negative fragments, this association was weakened as a function of self-reported levels of data-driven processing during encoding. Thus, startle responses can generally predict accurate voice recognition, but speech emotionality and cognitive processing moderate this relationship.