Academic literature on the topic 'Micro fusion framework'

Micro-electro-mechanical system (MEMS) gyro is one of the extensively used inertia sensors in the field of optical target tracking (OTT). However, velocity closed-loop bandwidth of the OTT system is limited due to the resonance and measurement range issues of MEMS gyro. In this paper, the generalized sensor fusion framework, named the closed-loop fusion (CLF), is analyzed, and the optimal design principle of filter is proposed in detail in order to improve measurement of the bandwidth of MEMS gyro by integrating information of MEMS accelerometers. The fusion error optimization problem, which is the core issue of fusion design, can be solved better through the feedback compensation law of CLF framework and fusion filter optimal design. Differently from conventional methods, the fusion filter of CLF can be simply and accurately designed, and the determination of superposition of fusion information can also be effectively avoided. To show the validity of the proposed method, both sensor fusion simulations and closed-loop experiments of optical target tracking system have yielded excellent results.

Abstract Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous data sources. We develop the MCDFM framework to adapt various DF application domains requiring macro and micro perspectives of the observed problems. This framework consists of preprocessing, filtering, and decision as key DF processing phases. These three phases integrate specific purpose algorithms or methods such as data cleaning and windowing methods for preprocessing, extended Kalman filter (EKF) for filtering, fuzzy logic for local decision, and software agents for coordinative decision. These methods perform tasks that assist in achieving local and coordinative decisions for each node in the network of the framework application domain. We illustrate and discuss the proposed framework in detail by taking a stretch of road intersections controlled by a traffic light controller (TLC) as a case study. The case study provides a clearer view of the way the proposed framework solves traffic congestion as a domain problem. We identify the traffic features that include the average vehicle count, average vehicle speed (km/h), average density (%), interval (s), and timestamp. The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296. The results of the TLC case study show that the framework provides various capabilities and flexibility features of both micro and macro views of the scenarios being observed and clearly presents viable solutions.

This paper describes a new modeling framework for microscopic to reservoir-scale simulations of hydraulic fracturing and production. The approach builds upon a fusion of two existing high-performance simulators for reservoir-scale behavior: the GEOS code for hydromechanical evolution during stimulation and the TOUGH+ code for multi-phase flow during production. The reservoir-scale simulations are informed by experimental and modeling studies at the laboratory scale to incorporate important micro-scale mechanical processes and chemical reactions occurring within the fractures, the shale matrix, and at the fracture-fluid interfaces. These processes include, among others, changes in stimulated fracture permeability as a result of proppant behavior rearrangement or embedment, or mineral scale precipitation within pores and microfractures, at µm to cm scales. In our new modeling framework, such micro-scale testing and modeling provides upscaled hydromechanical parameters for the reservoir scale models. We are currently testing the new modeling framework using field data and core samples from the Hydraulic Fracturing Field Test (HFTS), a recent field-based joint research experiment with intense monitoring of hydraulic fracturing and shale production in the Wolfcamp Formation in the Permian Basin (USA). Below, we present our approach coupling the reservoir simulators GEOS and TOUGH+ informed by upscaled parameters from micro-scale experiments and modeling. We provide a brief overview of the HFTS and the available field data, and then discuss the ongoing application of our new workflow to the HFTS data set.

The development of China’s sharing economy has slowed down significantly after experiencing the savage growth since the beginning of 2018 and has entered the turning point of structural adjustment. Factors including homogeneous and single profit model, excessive reliance on capital, and the immaturity of win-win industrial ecosystem are major bottlenecks. Therefore, how to overcome the obstacles is a key issue to be solved urgently. In view of the sharing economy’s characteristics of industry integration and cross-boundary symbiosis, the concept of sharing economy industrial ecosystem was put forward. Furthermore, social network analysis (SNA) was used to solve the problem of weak synergy in the development of China’s sharing economy and strive to break through the development bottleneck in order to realize the optimization of China’s sharing industry ecosystem and the sustainable development of industry. Specially, we proposed a fusion framework of industrial ecosystem and SNA including macro, meso, and micro dimensions. Macro analysis is based on the fusion of ecological environment in ecosystem theory and density analysis in SNA. Meso analysis is based on the fusion of ecological communities in ecosystem theory and subgroup analysis in SNA. Micro analysis is based on the fusion of an ecological niche in ecosystem theory and centrality analysis in SNA. It was found that the ecosystem of sharing mobility industry has been basically established, and the ecological diversity is good, including sharing mobility, third-party platform, automobile manufacturing, insurance and venture capital enterprises and universities. In addition, some sharing enterprises, typically represented by Didi, are upgrading their strategies to ecological development through cross-border integration. Mobile payment plays a vital role in developing China’s sharing mobility industry.

Purpose The purpose of this paper is to investigate the antecedents, internal/external drivers and circumstances that diminish the importance of reputation and permit it to be circumvented in the context of a fusion (Monsanto-Bayer) and propose a comprehensive stakeholder perception-molding (“sense-giving”) model to counter reputation-damaging factors. Design/methodology/approach The present study is a longitudinal case study of merging entities using an intra-organizational evolutionary perspective combined with external analysis. Findings Reputational hurdles can be successfully circumvented by a multifaceted strategy leveraging timely and tailored combinations of cognitive, conative and linguistic perception-molding strategies for effective management of diverse stakeholder perception processes spanning across identity orientation, legitimacy, posture, consistency, commitment, justification and transparency. Research limitations/implications The research is susceptible to the general limitations of case studies such as omission bias in terms of focusing on only two purposefully chosen market-leader firms involved in a merger, though every effort was made to track competitor movements, broad trends in markets and the micro- and macro-environments. Practical implications The proposed heptagonal reputation circumvention perception-molding framework (Figure 1) summarizes various actionable strategies to help managers develop a global vision and portfolio of strategies to proactively or reactively manage attacks on reputation. Originality/value An in-depth and multi-decade study of antecedents and internal/external drivers of the successful mega-fusion of two companies with a long history of reputational shocks was leveraged to provide unique insights into the interplay of various strategies targeting diverse stakeholder perceptions. These insights were then generalized to create a comprehensive perception-molding strategic framework to help firm managers circumvent reputational tarnishment hurdles.

AbstractResearchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

The application of the Micro Electro-mechanical System (MEMS) inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of MEMS sensors, the redundant design is an effective method under the restriction of current technology. The redundant data processing is the most important part in the MEMS redundant inertial navigation system, which includes the processing of abnormal data and the fusion estimation of redundant data. A developed quality index of the MEMS gyro measurement data is designed by the parity vector and the covariance matrix of the distributed Kalman filtering. The weight coefficients of gyros are calculated according to this index. The fault-tolerant fusion estimation of the redundant data is realized through the framework of the distributed Kalman filtering. Simulation experiments are conducted to test the performance of the new method with different types of anomalies.

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

Taking Shanghai as an example, this paper uses remote sensing (RS) and geographical information systems (GIS) technology to conduct multisource data fusion and a spatial pattern analysis of urban carrying capacity at the micro scale. The main conclusions are as follows: (1) based on the “production, living and ecology” land functions framework and land use data, Shanghai is divided into seven types of urban spaces to reveal their heterogeneity and compatibility in terms of land use functions. (2) We propose an urban carrying capacity coupling model (UCCCM) based on multisource data. The model incorporates threshold and saturation effects, which improve its power to explain urban carrying capacity. (3) Using the exploratory spatial data analysis (ESDA) technique, this paper studies the spatial pattern of carrying capacity in different urban spaces of Shanghai. (4) We analyse the causes of the cold spots in each urban space and propose strategies to improve the urban carrying capacity according to local conditions.

This thesis explores perceptions and preferences on regional action in EU-related frameworks among regional actors in Western Sweden. Building upon the literature on Europeanisation and the Fusion approach, three dimensions of Europeanisation are clarified and explored– download, upload and crossload – and together with a set of five variables that constitute the Micro Fusion Framework; a comprehensive analytical tool is developed. The thesis analyses the intense debate among the members of West Sweden that took place from 2011 to 2013 that focused on how to functionally organise the regional office in Brussels in order to meet future challenges. Surprisingly, the members eventually decided to terminate their cooperation and close the jointly owned office in Brussels in spite of the fact that it has been widely regarded as successful and effective. Diverging perceptions and preferences is understood in terms of three positions on regional action; a download-, upload- and a coherent oriented position. Finally, the thesis presents the empirical findings and discusses in relation to three fusion scenarios, infusion, defusion and clustered fusion. In terms of Micro Fusion Framework, the dynamics shaping why West Sweden was finally regarded as a dysfunctional arena for regional action are explained by a shift of attention and action among regional actors in Western Sweden that led to pressure for further institutional adaptation in order to meet the demand of how ‘to get the best out of the EU’. Further, this redefinition of how to handle EU-affairs within the upload-oriented position was accompanied by positive attitudes towards the potential to bypass the state and thereby pursue regional priorities directly in Brussels given the compound nature of the EU. In contrast, those regional actors that are found to be more download-oriented often question the benefits of uploading activities in practice and advocate close relations to the state. A coherent oriented position recognises the importance of activities related to both of the vertical dimensions of Europeanisation.
I avhandlingen studeras regionala aktörers uppfattningar och attityder till regionalt handlande i EU-relaterade frågor. För att hantera EU-frågor etableras ofta regionala representationskontor i Bryssel. Ett av de största och framgångsrikaste regionala kontoren i Bryssel var West Sweden som representerade västsvenska kommuner och regioners intressen i EU. År 2011 inleds en intern diskussion bland dess medlemmar om hur kontoret bör utvecklas för att möta nya utmaningar i en föränderlig omvärld. Diskussionerna är intensiva, och något överraskande beslutar dess medlemmar att lägga ned verksamheten ett par år senare. I avhandlingen analyseras diskussionen utifrån ett europeiseringsperspektiv. Med hjälp av en analysram som definierar tre dimensioner av europeiseringsprocesser identifieras tre olika positioner i diskussionen; en download-, upload- respektive sammanhållen position. Det kan vara frestande att förstå West Swedens nedläggning som ett uttryck för att subnationella aktörer ”drar sig tillbaka” och föredrar aktiviteter inom nationalstatens domäner. Avhandlingen argumenterar för att så är inte fallet. Nedläggningen av West Sweden förstås som en konsekvens av ökade spänningar mellan regionala aktörer som anammar ett download- respektive upload-orienterat förhållningssätt.

Résumé: L'imagerie du sein est développée en associant données micro-ondes (MW) et ultrasonores (US) afin de détecter de manière précoce des tumeurs. On souhaite qu'aucune contrainte soit imposée, le sein étant supposé libre. Une 1re approche utilise des informations sur les frontières des tissus provenant de données de réflexion US. La régularisation intègre que deux pixels voisins présentent des propriétés MW similaires s'il ne sont pas sur une frontière. Ceci est appliqué au sein de la méthode itérative de Born distordue. Une 2de approche implique une régularisation déterministe préservant les bords via variables auxiliaires indiquant si un pixel est ou non sur un bord. Ces variables sont partagées par les paramètres MW et US. Ceux-ci sont conjointement optimisés à partir d'ume approche de minimisation alternée. L'algorithme met alternitivement à jour contraste US, marqueurs, et contraste MW. Une 3e approche implique réseaux de neurones convolutifs. Le courant de contraste estimé et le champ diffusé sont les entrées. Une structure multi-flux se nourrit des données MW et US. Le réseau produit les cartes des paramètres MW et US en temps réel. Outre la tâche de régression, une stratégie d'apprentissage multitâche est utilisée avec un classificateur qui associe chaque pixel à un type de tissu pour produire une image de segmentation. La perte pondérée attribue une pénalité plus élevée aux pixels dans les tumeurs si il sont mal classés. Une 4e approche implique un formalisme bayésien où la distribution a posteriori jointe est obtenue via la règle de Bayes ; cette distribution est ensuite approchée par une loi séparable de forme libre pour chaque ensemble d'inconnues pour obtenir l'estimation. Toutes ces méthodes de résolution sont illustrées et comparées à partir d'un grand nombre de données simulées sur des modèles synthétiques simples et sur des coupes transversales de fantômes mammaires numériques anatomiquement réalistes dérivés d'IRM dans lesquels de petites tumeurs artificielles sont insérées
Imaging of the breast for early detec-tion of tumors is studied by associating microwave (MW) and ultrasound (US) data. No registration is enforced since a free pending breast is tackled. A 1st approach uses prior information on tissue boundaries yielded from US reflection data. Regularization incorporates that two neighboring pixels should exhibit similar MW properties when not on a boundary while a jump allowed otherwise. This is enforced in the distorted Born iterative and the contrast source inversion methods. A 2nd approach involves deterministic edge preserving regularization via auxiliary variables indicating if a pixel is on an edge or not, edge markers being shared by MW and US parameters. Those are jointly optimized from the last parameter profiles and guide the next optimization as regularization term coefficients. Alternate minimization is to update US contrast, edge markers and MW contrast. A 3rd approach involves convolutional neural networks. Estimated contrast current and scattered field are the inputs. A multi-stream structure is employed to feed MW and US data. The network outputs the maps of MW and US parameters to perform real-time. Apart from the regression task, a multi-task learning strategy is used with a classifier that associates each pixel to a tissue type to yield a segmentation image. Weighted loss assigns a higher penalty to pixels in tumors when wrongly classified. A 4th approach involves a Bayesian formalism where the joint posterior distribution is obtained via Bayes’ rule; this true distribution is then approximated by a free-form separable law for each set of unknowns to get the estimate sought. All those solution methods are illustrated and compared from a wealth of simulated data on simple synthetic models and on 2D cross-sections of anatomically-realistic MRI-derived numerical breast phantoms in which small artificial tumors are inserted

In a previous paper (Hansen et al., 1995), a conceptual framework for developing a true prognostic or predictive diagnostic capability was described. The current paper expands on this framework by describing micro-mechanical and dynamic models, sensors and data fusion, signal processing, approximate reasoning, distributed architecture, and human factors research and development being conducted to provide such a capability for a broad range of applications. These include both autonomous and man-in-the-loop decision making about maintenance actions and local and geographically distributed monitoring and data analysis architectures.

Abstract Inkjet 3D printing has broad applications in areas such as health and energy due to its capability to precisely deposit micro-droplets of multi-functional materials. However, the droplet of the inkjet printing has different jetting behaviors including drop initiation, thinning, necking, pinching and flying, and they are vulnerable to disturbance from vibration, material inhomogeneity, etc. Such issues make it challenging to yield a consistent printing process and a defect-free final product with desired properties. Therefore, timely recognition of the droplet behavior is critical for inkjet printing quality assessment. In-situ video monitoring of the printing process paves a way for such recognition. In this paper, a novel feature identification framework is presented to recognize the spatiotemporal feature of in-situ monitoring videos for inkjet printing. Specifically, a spatiotemporal fusion network is used for droplet printing behavior classification. The categories are based on inkjet printability, which is related to both the static features (ligament, satellite, and meniscus) and dynamic features (ligament thinning, droplet pinch off, meniscus oscillation). For the recorded droplet jetting video data, two streams of networks, the frames sampled from video in spatial domain (associated with static features) and the optical flow in temporal domain (associated with dynamic features), are fused in different ways to recognize the droplet evolving behavior. Experiments results show that the proposed fusion network can recognize the droplet jetting behavior in the complex printing process and identify its printability with learned knowledge, which can ultimately enable the real-time inkjet printing quality control and further provide guidance to design optimal parameter settings for the inkjet printing process.