On the Energy Efficiency of Electric Vehicles with Multiple Motors

Electric Vehicles (EVs) with multiple motors permit to design the steady-state cornering response by imposing reference understeer characteristics according to expected vehicle handling quality targets. To this aim a direct yaw moment is generated by assigning different torque demands to the left and right vehicle sides. The reference understeer characteristic has an impact on the drivetrain input power as well. In parallel, a Control Allocation (CA) strategy can be employed to achieve an energy-efficient wheel torque distribution generating the reference yaw moment and wheel torque. To the knowledge of the authors, for the first time this paper experimentally compares and critically analyses the potential energy efficiency benefits achievable through the appropriate set-up of the reference understeer characteristics and wheel torque CA. Interestingly, the experiments on a four wheel-drive EV demonstrator show that higher energy savings can be obtained through the appropriate tuning of the reference cornering response rather than with an energy efficient CA

An on-line interactive self-adaptive image classification framework

In this paper we present a novel image classification framework, which is able to automatically re-configure and adapt its feature-driven classifiers and improve its performance based on user interaction during on-line processing mode. Special emphasis is placed on the generic applicability of the framework to arbitrary surface inspection systems. The basic components of the framework include: recognition of regions of interest (objects), adaptive feature extraction, dealing with hierarchical information in classification, initial batch training with redundancy deletion and feature selection components, on-line adaptation and refinement of the classifiers based on operators' feedback, and resolving contradictory inputs from several operators by ensembling outputs from different individual classifiers. The paper presents an outline on each of these components and concludes with a thorough discussion of basic and improved off-line and on-line classification results for artificial data sets and real-world images recorded during a CD imprint production process.status: publishe

On Human-Machine Interaction During Online Image Classifier Training

This paper considers a number of issues that arise when a trainable machine vision system learns directly from humans, rather than from a "cleaned" data set, i.e.\ data items which are perfectly labelled with complete accuracy. This is done within the context of a generic system for the visual surface inspection of manufactured parts. The issues treated are relevant not only to wider computer vision applications, but also to classification more generally. Some of these issues arise from the nature of humans themselves: they will be not only internally inconsistent, but will often not be completely confident about their decisions, especially if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the machine learning to have the capacity for real-time, online adaptation in response to users' input. It may be that the users cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. Using a range of classifiers trained on real data sets from a CD imprint production process, we will present results which show that properly addressing most of these issues may actually lead to improved performance.status: publishe

Preclinical evaluation of 18F-JNJ41510417 as a radioligand for PET imaging of phosphodiesterase-10A in the brain

UNLABELLED: Phosphodiesterases are enzymes that inactivate the intracellular second messengers 3',5'-cyclic adenosine-monophosphate and/or cyclic guanosine-monophosphate. Of all 11 known phosphodiesterase families, phosphodiesterase-10A (PDE10A) has the most restricted distribution, with high expression in the striatum. PDE10A inhibitors are pursued as drugs for treatment of neuropsychiatric disorders. We have synthesized and evaluated (18)F-JNJ41510417 as a selective and high-affinity radioligand for in vivo brain imaging of PDE10A using PET. METHODS: The biodistribution of (18)F-JNJ41510417 was evaluated in rats. Rat plasma and perfused brain homogenates were analyzed by high-performance liquid chromatography to quantify radiometabolites. Dynamic small-animal PET was performed in rats and in wild-type and PDE10A knock-out mice and compared with ex vivo autoradiography. Blocking and displacement experiments were performed using the nonradioactive analog and other selective PDE10A inhibitors. RESULTS: Tissue distribution studies showed predominant hepatobiliary excretion, sufficient brain uptake (0.56 ± 0.00 percentage injected dose at 2 min after tracer injection), and continuous accumulation of the tracer in the striatum over time; rapid washout of nonspecific binding from other brain regions was observed. Polar radiometabolites were detected in plasma and brain tissue. Dynamic small-animal PET showed continuous tracer accumulation in the striatum, with rapid decline in the cortex and cerebellum. Pretreatment and chase experiments with PDE10A inhibitors showed that the tracer binding to PDE10A was specific and reversible. Imaging in PDE10A knock-out and wild-type mice further confirmed that binding in the striatum was specific for PDE10A. CONCLUSION: Experiments in rats and PDE10A knock-out mice indicate that (18)F-JNJ41510417 binds specifically and reversibly to PDE10A in the striatum, suggesting that this new fluorinated quinoline derivative is a promising candidate for in vivo imaging of PDE10A using PET.status: publishe

Preclinical evaluation of [(18)F]JNJ42259152 as a PET tracer for PDE10A

Phosphodiesterase-10A (PDE10A) is implicated in several neuropsychiatric disorders involving basal ganglia neurotransmission, such as schizophrenia, obsessive-compulsive disorder and Huntington's disease. To confirm target engagement and exposure-occupancy relationships of clinical candidates for treatment, and to further explore the in vivo biology of PDE10A, non-invasive imaging using a specific PET ligand is warranted. Recently we have reported the in vivo evaluation of [(18)F]JNJ41510417 which showed specific binding to PDE10A in rat striatum, but with relatively slow kinetics. A chemically related derivative JNJ42259152 was found to have a similar in vivo occupancy, but lower lipophilicity and lower PDE10A in vitro inhibitory activity compared to JNJ41510417. (18)F-labeled JNJ42259152 was therefore evaluated as a potential PDE10A PET radiotracer. Baseline PET in rats and monkey showed specific retention in the PDE10A-rich striatum, and fast wash-out, with a good contrast to non-specific binding, in other brain regions. Pretreatment and chase experiments in rats with the selective PDE10A inhibitor MP-10 showed that tracer binding was specific and reversible. Absence of specific binding in PDE10A knock-out (KO) mice further confirmed PDE10A specificity. In vivo radiometabolite analysis using high performance liquid chromatography (HPLC) showed presence of polar radiometabolites in rat plasma and brain. In vivo imaging in rat and monkey further showed faster brain kinetics, and higher striatum-to-cerebellum ratios for [(18)F]JNJ42259152 compared to [(18)F]JNJ41510417. The arterial input function corrected for radiometabolites was determined in rats and basic kinetic modeling was established. For a 60-min acquisition time interval, striatal binding potential of the intact tracer referenced to the cerebellum showed good correlation with corresponding binding potential values of a Simplified Reference Tissue Model and referenced Logan Plot, the latter using a population averaged reference tissue-to-plasma clearance rate and offering the possibility to generate representative parametric binding potential images. In conclusion we can state that in vivo imaging in PDE10A KO mice, rats and monkey demonstrates that [(18)F]JNJ42259152 provides a PDE10A-specific signal in the striatum with good pharmacokinetic properties. Although presence of a polar radiometabolite in rat brain yielded a systematic but reproducible underestimation of the striatal BPND, a Logan reference tissue model approach using 60 min acquisition data is appropriate for quantification.publisher: Elsevier articletitle: Preclinical evaluation of [18F]JNJ42259152 as a PET tracer for PDE10A journaltitle: NeuroImage articlelink: http://dx.doi.org/10.1016/j.neuroimage.2013.04.123 content_type: article copyright: Copyright © 2013 Published by Elsevier Inc.status: publishe