Vehicle diagnostic technology

In the article, using the example of a mechatronic control system for the engine and transmission of vehicles (automobiles), the features of the technology of their diagnosis are shown. In an electronic transmission control system, the object of regulation is mainly an automatic transmission. Also, the laws of control (programs) of gear shifting in an automatic transmission ensure the optimal transfer of engine energy to the wheels of the vehicle (TC), taking into account the required traction and speed properties and fuel economy. At the same time, the programs for achieving optimal traction-speed properties and minimum fuel consumption differ from each other, since the simultaneous achievement of these goals is not always possible. Therefore, depending on the driving conditions and the desire of the driver, using a special switch, you can select the "economy" program to reduce fuel consumption, the "power" program - to improve traction and speed properties, or the "manual" program to switch gears by the driver. In turn, self-diagnostic capabilities include: system identification and electronic control units (ECU) (ECU); recognition, storage and reading of information about static and single malfunctions; reading current real data, including environmental conditions and specifications; modeling of system functions; programming of system parameters. The individual programs for the test block are stored in the plug-in modules, while the correction and data transfer in the system is carried out via the data interface. Note also that the diagnostic process begins with the initialization of the systems - their detection in the electrical equipment of the vehicle. Upon successful initialization, it is possible to: read the error memory; erase the error memory; view the data of the next detected system or exit to the main menu; change the readings of the selected category; correct the current time; correct the current date and perform a number of additional functions.

Central Feature Learning for Unsupervised Person Re-identification

The Exemplar Memory (EM) design has shown its effectiveness in facilitating the unsupervised person re-identification (RE-ID). However, there are obvious defects in the update strategies with most existing results, such as the inability to eliminate static errors and ensure convergence stability of learning. To address these issues, in this paper, we propose a novel center feature learning scheme to improve the update strategies of the traditional EM design for unsupervised RE-ID problems. First, the EM module is regarded as a center feature of a cluster of images, then the goal is transformed into pulling the similar images close to while pushing the dissimilar images away from the center feature space. Second, in order to provide effective guidelines on reducing static errors, we propose an error-memory module to improve the central feature learning performances. In addition, an error-prediction module is designed as well to ensure the stability of convergence. Besides, a camera-invariance learning strategy is also introduced to further improve the proposed algorithm. Finally, extensive comparative experiments are conducted on Market-1501 and DukeMTMC-reID datasets to demonstrate the effectiveness and improvements of the proposed method over existing results. The code of this work is available at https://github.com/binquanwang/CFL_master .

Evolution of Motor Memory During the Seconds After Observation of Motor Error

When a movement results in error, the nervous system amends the motor commands that generate the subsequent movement. Here we show that this adaptation depends not just on error, but also on passage of time between the two movements. We observed that subjects learned a reaching task faster, i.e., with fewer trials, when the intertrial time intervals (ITIs) were lengthened. We hypothesized two computational mechanisms that could have accounted for this. First, learning could have been driven by a Bayesian process where the learner assumed that errors are the result of perturbations that have multiple timescales. In theory, longer ITIs can produce faster learning because passage of time might increase uncertainty, which in turn increases sensitivity to error. Second, error in a trial may result in a trace that decays with time. If the learner continued to sample from the trace during the ITI, then adaptation would increase with increased ITIs. The two models made separate predictions: The Bayesian model predicted that when movements are separated by random ITIs, the learner would learn most from a trial that followed a long time interval. In contrast, the trace model predicted that the learner would learn most from a trial that preceded a long time interval. We performed two experiments to test for these predictions and in both experiments found evidence for the trace model. We suggest that motor error produces an error memory trace that decays with a time constant of about 4 s, continuously promoting adaptation until the next movement.

Rapid Technique Correction Using Old Way/new Way: Two Case Studies with Olympic Athletes

Exploratory studies examine the effectiveness of old way/new way, an innovative meta-cognitive learning strategy initially developed in education settings, in the rapid and permanent correction of established technique difficulties experienced by two Olympic athletes in javelin and sprinting. Individualized interventions included video-assisted error analysis, step-wise enhancement of kinesthetic awareness, reactivation of the error memory, discrimination, and generalization of the correct movement pattern. Self-reports, coach’s ratings, and video recordings were used as measures of technique improvement. A single learning trial produced immediate and permanent technique improvement (80% or higher correct action) and full transfer of learning, without the need for the customary adaptation period. Findings are consistent with the performance enhancement effects of old way/new way demonstrated experimentally in nonsport settings.