Features of driving of the steering wheel driving cars

Existing methods of diagnosing steering can be characterized by low efficiency. For various reasons, both declarative and actual (supported by the equipment) methods, as a rule, have low accuracy and inability to localize faults. The car's built-in diagnostics cannot affect the situation due to the small number of sensors in the steering system. The reasons for the low accuracy of the methods include design features, low availability of components (low maintainability). Difficulties in localization of malfunctions are caused by the structural scheme which is characterized by parallel - consecutive construction. The parameters of diagnostic methods are analyzed, the proposed method is based on the structure of the steering, in the implementation of which test effects are applied to the steered wheels. In total it is necessary to carry out three measurements of backlashes and as a result of mathematical processing of results it becomes possible to localize malfunction in three links of consecutive elements of the steering mechanism or a steering drive. In accordance with this approach, steering is considered as a set of three structures - parallel and two sequential. Rack and pinion steering was used as a model. Here, the parallel structure includes elements of the steering linkage: swing arm, left and right; steering rod, left and right; steering rack - left and right hinges. The sequential structure - left, includes a swing arm, left; steering rod, left; steering rack hinge, left; steering gear, steering shaft, steering wheel. Accordingly, the sequential structure of the right includes similar elements with the attribute "right". The structure of the steering play is considered in a similar way. As a result, it becomes possible to obtain a transformed system of three algebraic equations connecting clearances in three groups of mates and backlashes in parallel and two sequential steering structures. To measure the backlash, the turntables of the BOSCH FWA 4410 stand were used; in another version, the wheels were hung out. As a result of tests carried out on VW GOLF, VW PASSAT and RENAULT 25 vehicles with significant mileage, data was obtained indicating the need for technical interventions on localized groups of interfaces.

Influence of the Manufacturing Accuracy of the Planetary Cycloid Gear on the Positioning Accuracy of the Parallel Robot

The increased accuracy in high-speed positioning of the parallel robot effector in comparison with that of serial robots with a sequential structure is often the main reason for their use in various modern industries, such as the manufacture of printed circuit boards for microelectronics. However, despite the higher theoretical positioning accuracy, due to the kinematic structure of the parallel robot, in practice this characteristic largely depends on the accuracy of manufacturing individual elements of this mechanism, the most important of which are the gearboxes of the drives of its input pairs. A solution to the urgent problem of determining the effect of the manufacturing accuracy of planetary pinion gearboxes included in the drive of a five-link parallel robot on the positioning accuracy of its output link is proposed. A specific relationship has been determined between the grade of accuracy number of the gear part dimensions and the robot positioning accuracy. The unevenness of the positioning accuracy along the coordinate axes of its working area is revealed. It was found that near the area of certain robot positions the accuracy of its positioning drops sharply.

Network analysis as an alternative way to interpret constitutions

This article aims to identify networks of constitutional text structured in an imperceptible way. Constitutions has the power to reveal how its devices “dialogue” with each other, forming their own communicative communities. With the help from the network analysis methodology, an interpretative model of textual mapping is proposed based on internal references between the provisions of one Constitution. We use the Brazilian Constitution, whose text is very detailed, analytical and deals with various issues. As a result, a network was identified that included 174 connections between 95 articles of the Constitution and the existence of normative communities that share codes or communicative standards. The study demonstrates the strong possibility to found structuring axis of this communities and relates to the other issues that have reached constitutional stature, showing that the complexity of some Constitutions can be observed beyond its logical sequential structure.

Transformers in computational visual media: A survey

Transformers, the dominant architecture for natural language processing, have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance. Transformers are sequence-to-sequence models, which use a self-attention mechanism rather than the RNN sequential structure. Thus, such models can be trained in parallel and can represent global information. This study comprehensively surveys recent visual transformer works. We categorize them according to task scenario: backbone design, high-level vision, low-level vision and generation, and multimodal learning. Their key ideas are also analyzed. Differing from previous surveys, we mainly focus on visual transformer methods in low-level vision and generation. The latest works on backbone design are also reviewed in detail. For ease of understanding, we precisely describe the main contributions of the latest works in the form of tables. As well as giving quantitative comparisons, we also present image results for low-level vision and generation tasks. Computational costs and source code links for various important works are also given in this survey to assist further development.

STABILIZATION OF THE SPATIAL POSITION OF THE MANIPULATOR OF A PARALLEL-SEQUENTIAL STRUCTURE

A method for constructing terminal control based on the representation of control functions relative to time is considered. The parameters of the functions are determined from the condition of satisfying the boundary conditions at the ends of the trajectory of the generalized coordinates of the manipulator. On the basis of the obtained program movements, a control law with feedbacks is constructed.

A Topic Detection Method Based on Word-attention Networks

Purpose We proposed a method to represent scientific papers by a complex network, which combines the approaches of neural and complex networks. Design/methodology/approach Its novelty is representing a paper by a word branch, which carries the sequential structure of words in sentences. The branches are generated by the attention mechanism in deep learning models. We connected those branches at the positions of their common words to generate networks, called word-attention networks, and then detect their communities, defined as topics. Findings Those detected topics can carry the sequential structure of words in sentences, represent the intra- and inter-sentential dependencies among words, and reveal the roles of words playing in them by network indexes. Research limitations The parameter setting of our method may depend on practical data. Thus it needs human experience to find proper settings. Practical implications Our method is applied to the papers of the PNAS, where the discipline designations provided by authors are used as the golden labels of papers’ topics. Originality/value This empirical study shows that the proposed method outperforms the Latent Dirichlet Allocation and is more stable.

“How was your day?”

Based on longitudinal conversation data between a father and child collected over the period of eighteen months, this study examines “today narrative” where the father asks the child “how was your day” when they meet after being apart during the day. The routine provides a recurrent sequential structure, which is both located within and itself occasions further talk. Examining the talk between this father and child longitudinally reveals how the initial sequential structure, where the child lists activities in short run-on sentences, goes through transformation and elaboration. Indices for development include the emergence of three-part structure in the child’s list construction, more details incorporated in the list, story prefaces, and the emergence of assessment response (or personal voice). The overall sequential organization of the routine moves from heavy reliance on the father’s questions to more volunteered talk by the child. This paper considers the generalizability of longitudinal conversation analytic data.

Sequential robot imitation learning from observations

This paper presents a framework to learn the sequential structure in the demonstrations for robot imitation learning. We first present a family of task-parameterized hidden semi-Markov models that extracts invariant segments (also called sub-goals or options) from demonstrated trajectories, and optimally follows the sampled sequence of states from the model with a linear quadratic tracking controller. We then extend the concept to learning invariant segments from visual observations that are sequenced together for robot imitation. We present Motion2Vec that learns a deep embedding space by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while being pushed away from randomly sampled images of other segments, and a time contrastive loss is used to preserve the temporal ordering of the images. The trained embeddings are segmented with a recurrent neural network, and subsequently used for decoding the end-effector pose of the robot. We first show its application to a pick-and-place task with the Baxter robot while avoiding a moving obstacle from four kinesthetic demonstrations only, followed by suturing task imitation from publicly available suturing videos of the JIGSAWS dataset with state-of-the-art [Formula: see text]% segmentation accuracy and [Formula: see text] cm error in position per observation on the test set.

Sequential U-Net Architecture for Automatic Femoral Artery Segmentation in Ultrasound Images

This work compares three different approaches to automatically segment the femoral artery from 2D ultrasound images. Two of the architectures follow a sequential structure, where each ultrasound image is considered a slice of the whole vessel volume, and its previous segmentation result will be part of the input, thus leading to a spatial prior. The Dice score on test data show a better performance on the baseline U-Net (0.819) compared to the sequential U-Net approaches (0.633, 0.725) for the femoral artery segmentation. This could be attributed to the misalignment of the slices being used in those networks. A possible improvement could be assumed in the implementation of a spatially calibrated and tracked ultrasound probe. Overall, these results indicate promising approaches for an automatic segmentation of the femoral artery using 2D ultrasound data.