Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Exhaust gas. The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Electrically operated braking system having a device for operating electric motor of brake to obtain relationship between motor power and braking torque

Abstract: An electrically operated braking system of a motor vehicle, including a brake having a friction member movable to be forced onto a rotor rotating with a vehicle wheel and thereby braking the wheel. The friction member is forced onto the rotor by an electric motor operated by an electric power supplied from an electric power source. A controller determines an amount of the electric power to be supplied to the motor, depending upon an operating amount of the brake operating member, and a relationship estimated by a relationship estimating and utilizing device. The relationship estimating and utilizing device obtains an actual value of the electric power supplied to the motor during operation of the brake while the vehicle is running, and an actual value of a braking torque applied from the brake to the wheel during the brake operation, and estimate a relationship between the electric power to be supplied to the wheel on the basis of the actual values obtained. The estimated relationship is then utilized to control the braking torque to be generated in response to operation of the brake operating member.

Emotional distress and its correlates among parents of children with pervasive developmental disorders.

Abstract: A number of studies have reported that parents of autistic children face higher levels of stress, but few studies examined the stress associated with the home care of children with pervasive developmental disorders (PDD) other than autistic disorder. The aims of the present study were therefore to (i) evaluate the emotional stress level of parents caring for their children with PDD; and (ii) explore the correlates of their emotional stress. Participants were 147 families (147 mothers and 122 fathers) of 158 children with PDD (42 with autistic disorder, 35 with Asperger's disorder and 81 with PDD not otherwise specified). K6 was used to measure the stress level of the parents. Marital relationships and personality were assessed with the Intimate Bond Measure and the NEO Five-Factor Inventory, respectively. The parents also rated the characteristics of their children with PDD through the Pervasive Developmental Disorder-Autism Society Japan Rating Scale (PARS). The mean K6 score of the mothers was significantly higher than that of the women in the general population in Japan. Stepwise multiple regression indicated that the emotional stress of the mothers was correlated with the personality traits of Neuroticism and Agreeableness, perceived Control by the husband, and the children's PARS score. Clinicians can deliver better service by paying appropriate attention to the emotional distress of mothers of children with not only autistic disorder but also other PDD.

SnS thin film solar cells with Zn1−xMgxO buffer layers

Abstract: The conduction band offset (CBO) of SnS as the light absorbing layer and Zn1−xMgxO as the buffer layer in SnS thin film solar cells has been optimized to improve the solar cell conversion efficiency. We controlled the CBO experimentally by varying the Mg content (x) of the Zn1−xMgxO layer. The optimum CBO value range for improved solar cell performance was determined to be from −0.1 to 0 eV. A SnS thin film solar cell sample with the optimum CBO value exhibited conversion efficiency of approximately 2.1%.

Precursors of Global Slip in a Longitudinal Line Contact Under Non-Uniform Normal Loading

Abstract: This article describes the mechanism of precursor events; the mechanism was determined through an experiment and simulation by considering non-uniform normal loading. In the experiment, real-time observations of a contact zone were performed using a longitudinal line contact of PMMA specimens (i.e., a slider on a stationary base block) under a total normal load of 400 N. Partial propagations of the detachment front were considered as precursor events, and it was found that non-uniform normal loading influences the occurrence frequency of the precursor events and the increasing rate of the propagation length. In the simulation, the time evolution of a multi-degree-of-freedom system with Coulomb friction was studied. The model considered in the simulation comprised multiple masses serially connected by linear springs on a stationary rigid plane. By regarding the precursor in the experiment to correspond to a partial slip (i.e., simultaneous slip of some of the masses) in the simulation, the influence of non-uniform normal loading on the precursor events can be explained to a certain extent. Additionally, it was found that the apparent static friction coefficient (i.e., the ratio of the maximum tangential load to the total normal load) could be lesser than the real static friction coefficient due to the residual strain in the slider.

Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets

Abstract: Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes.