A novel integrated control framework of AFS,ASS, and DYC based on ideal roll angle to improve vehicle stability

The current research on vehicle stability control mainly focuses on following the ideal yaw rate and sideslip angle, without considering the potential of ideal roll angle in improving the vehicle stability. In addition, the mutation of tire-road friction coefficient promotes a great challenge to the stability control. To improve the vehicle stability, in this study, firstly, the three-dimensional stability region of “lateral speed-yaw rate-roll angle” was studied, and a method to determine the ideal roll angle was proposed. Secondly, a novel integrated control framework of AFS, ASS, and DYC based on ideal roll angle was proposed to actively control the front tire slip angles, suspension forces, and motor torques: In the upper-level controller, model predictive control and tire force distribution algorithm were used to obtain the optimal four-tire longitudinal forces, front tire lateral forces and additional roll moment under constraints; In the lower-level controller, the upper virtual target was realized by the optimal allocation algorithm of actuators and the tire slip controller. Finally, the proposed control framework was verified on the varied-µ road. The results indicated that compared with the two existing control strategies, the proposed framework can significantly improve the vehicle following performance and stability.     

Stacking Deep learning and Machine learning models for short-term energy consumption forecasting

Accurate prediction of electricity consumption is essential for providing actionable insights to decision-makers for managing volume and potential trends in future energy consumption for efficient resource management. A single model might not be sufficient to solve the challenges that result from linear and non-linear problems that occur in electricity consumption prediction. Moreover, these models cannot be applied in practice because they are either not interpretable or poorly generalized. In this paper, a stacking ensemble model for short-term electricity consumption is proposed. We experimented with machine learning and deep models like Random Forests, Long Short Term Memory, Deep Neural Networks, and Evolutionary Trees as our base models. Based on the experimental observations, two different ensemble models are proposed, where the predictions of the base models are combined using Gradient Boosting and Extreme Gradient Boosting (XGB). The proposed ensemble models were tested on a standard dataset that contains around 500,000 electricity consumption values, measured at periodic intervals, over the span of 9 years. Experimental validation revealed that the proposed ensemble model built on XGB reduces the training time of the second layer of the ensemble by a factor of close to 10 compared to the state-of-the-art , and also is more accurate. An average reduction of approximately 39% was observed in the Root mean square error.     

Adaptive similarity search for the retrieval of rare events from large time series databases

Improving the recall of information retrieval systems for similarity search in time series databases is of great practical importance. In the manufacturing domain, these systems are used to query large databases of manufacturing process data that contain terabytes of time series data from millions of parts. This allows domain experts to identify parts that exhibit specific process faults. In practice, the search often amounts to an iterative query–response cycle in which users define new queries (time series patterns) based on results of previous queries. This is a well-documented phenomenon in information retrieval and not unique to the manufacturing domain. Indexing manufacturing databases to speed up the exploratory search is often not feasible as it may result in an unacceptable reduction in recall. In this paper, we present a novel adaptive search algorithm that refines the query based on relevance feedback provided by the user. Additionally, we propose a mechanism that allows the algorithm to self-adapt to new patterns without requiring any user input. As the search progresses, the algorithm constructs a library of time series patterns that are used to accurately find objects of the target class. Experimental validation of the algorithm on real-world manufacturing data shows, that the recall for the retrieval of fault patterns is considerably higher than that of other state-of-the-art adaptive search algorithms. Additionally, its application to publicly available benchmark data sets shows, that these results are transferable to other domains.     

Virtual field strategy for collaborative signal and information processing in wireless heterogeneous sensor networks

A novel collaborative signal and information processing (CSIP) method, which is based on virtual fields excited by sensor nodes, is proposed for wireless heterogeneous sensor networks. These virtual fields influence states and operations in sensor nodes located in their regions of influence (ROIs) and thus collaboration is implemented through interactions between surrounding virtual fields and sensor nodes. Described by a group of radial basis functions (RBFs), virtual fields have different magnitudes and ROIs due to different initial energy, communication ranges, sensing ranges and information processing capabilities in heterogeneous sensor nodes. Dynamic mobile agent itinerary decision and adaptive node active probability updating are studied with virtual field strategies in a heterogeneous sensor network using mobile-agent-based computing paradigm. Simulation results demonstrate that this approach can reduce energy consumption in sensor nodes. Information gain efficiency and network lifetime are also increased.     

Similar extrusion and mapping optimization of die cavity modeling for special-shaped products

1 Introduction For special-shaped products, similar extrusion is defined as the extruding deformation process which can make geometrical profile of metal arbitrary cross-section in deforming region be mutually similar to that of its original billet. Duri…     

Mechanical properties of graphene nanoplatelet/carbon fiber/epoxy hybrid composites: Multiscale modeling and experiments

Because of the relatively high specific mechanical properties of carbon fiber/epoxy composite materials, they are often used as structural components in aerospace applications. Graphene nanoplatelets (GNPs) can be added to the epoxy matrix to improve the overall mechanical properties of the composite. The resulting GNP/carbon fiber/epoxy hybrid composites have been studied using multiscale modeling to determine the influence of GNP volume fraction, epoxy crosslink density, and GNP dispersion on the mechanical performance. The hierarchical multiscale modeling approach developed herein includes Molecular Dynamics (MD) and micromechanical modeling, and it is validated with experimental testing of the same hybrid composite material system. The results indicate that the multiscale modeling approach is accurate and provides physical insight into the composite mechanical behavior. Also, the results quantify the substantial impact of GNP volume fraction and dispersion on the transverse mechanical properties of the hybrid composite while the effect on the axial properties is shown to be insignificant.     

Influence of sliding speed on modes of material transfer as steel slides against PVD tool coatings

An intermittent sliding test was used in order to study the formation and build-up of tribofilms during intermittent sliding of PVD coated HSS against case hardening steel (20NiCrMo2). Two cutting tool coatings were tested, TiN and AlCrN, and the influence of sliding speed was evaluated. With moderate speed, two tribofilms were formed separately, one consisting of Mn, Si, Al and O on an intermediate layer of Fe and one consisting of Fe, Mn, Cr and O on an intermediate layer of Cr and Mn. At low sliding speeds an uneven transfer of steel occured while high sliding speeds resulted in thermal softening of the substrate leading to coating failure. AlCrN provided better substrate protection at high speeds than TiN did.     

Atomistic simulations on multilayer graphene reinforced epoxy composites

We use molecular dynamics simulations to characterize multilayer graphene reinforced epoxy composites. We focus on two configurations, one where the graphene layers are parallel to polymer/graphene interface and a perpendicular case, and characterize the in situ curing process of the resin and the thermo-mechanical response of the composites. The yield stress of the composites under uniaxial loading normal to the interface is in all cases larger than that of the bulk polymer even after the constraint of the reinforcement to transverse relaxation is taken into account. While both the parallel and normal configurations have very similar strengths, the parallel case exhibits cohesive yield with strain localization and nano-void formation within the bulk polymer while the case with graphene sheets oriented normal to the interface exhibit interfacial debonding. These two mechanisms lead to different post yield behavior and provide key insight for the development of predictive models of carbon fiber polymer composites.     

Influence of the modification with MWCNT on the interlaminar fracture properties of long carbon fiber composites

This paper reports the processing of a carbon fiber reinforced epoxy composite with carbon nanotube integration and examines the potential improvement on the interlaminar toughness. Carbon nanotube enhanced composites were fabricated by spreading a solution of reinforcing nanoparticles between prepreg layers with the aid of an airbrush. The influence of MWCNTs incorporation has been studied by the End Notched Flexure (ENF) test by means of a new test methodology named “Beam Theory including Bending Rotation effects” (BTBR) proposed recently. This method allows the determination of the critical energy release rate at each point of the test when stable crack advance occurs, obtaining the R-curve. A maximum increase of 22% in initiation fracture toughness was observed in the samples with functionalized CNTs. Moreover, the propagation fracture toughness increased 14%.     

Experiences with optimizing two stream-based applications for cluster execution

We explore optimization strategies and resulting performance of two stream-based video applications, video texture and color tracker, on a cluster of SMPs. The two applications are representative of a class of emerging applications, which we call “stream-based applications”, that are sensitive to both latency of individual results and overall throughput. Such applications require non-trivial parallelization techniques in order to improve both latency and throughput, given that the stream data emanates from a limited set of sources (exactly one in the two applications studied) and that the distribution of the data cannot be done a priori.We suggest techniques that address in a coordinated fashion the problems of data distribution and work partitioning. We believe the two problems are related and need to be addressed together. We have parallelized two applications using the Stampede cluster programming system that provides abstractions for implementing time- and throughput-sensitive applications elegantly and efficiently. For the Video Textures application we show that we can achieve a speedup of 24.26 on a 112 processor cluster. For the Color Tracker application, where latency is more crucial, we identify the extent of data parallelism that ensures that the slowest member of the pipeline is no longer the bottleneck for achieving a decent frame rate.