Design and analysis of a Cross Car frame. Proposal of a three-level appraisal methodology

This paper analyses the structural design process of a Cross Car frame. Existing international and national technical regulations were analysed. The analysis of the regulatory guidelines led to the conclusion that the design only includes geometric or material restrictions at the international level. There are other more demanding national regulations (Spanish, for instance) that include structural verifications through static calculations for unidirectional actions (vertical, longitudinal or lateral). The main contribution of this paper is a novel three-level appraisal and a proposed redesign methodology. At the first level, a geometric and material verification is carried out. The second level involves the verification under unidirectional static actions. The third level entails a dynamic verification of three-dimensional combined actions. The load case is obtained from the computer multibody dynamics simulation of the full vehicle assembly in the worst case of driving conditions on circuits. This methodology is a far more detailed tool than traditional design processes. The use of this methodology allows for design optimisation, including all the effects of powertrain, brakes, suspension, steering and driver.

Features of inference for abnormal truth values of premises in VTF-logics

The article discusses the issues of logical inference for abnormal truth-values of premises in logics with vector semantics from the VTF-logic class. Such situations as strict lies, uncertainty, and complete contradiction are considered in this article. It is shown that the truth of the conclusion in this case can take an interval value and the nature of this interval is preserved throughout the inference. This feature can be used to detect knowledge base artefacts during dynamic verification of knowledge bases.

Protocol combinators for modeling, testing, and execution of distributed systems

Distributed systems are hard to get right, model, test, debug, and teach. Their textbook definitions, typically given in a form of replicated state machines, are concise, yet prone to introducing programming errors if naïvely translated into runnable implementations. In this work, we present Distributed Protocol Combinators (DPC), a declarative programming framework that aims to bridge the gap between specifications and runnable implementations of distributed systems, and facilitate their modeling, testing, and execution. DPC builds on the ideas from the state-of-the art logics for compositional systems verification. The contribution of DPC is a novel family of program-level primitives, which facilitates construction of larger distributed systems from smaller components, streamlining the usage of the most common asynchronous message-passing communication patterns, and providing machinery for testing and user-friendly dynamic verification of systems. This paper describes the main ideas behind the design of the framework and presents its implementation in Haskell. We introduce DPC through a series of characteristic examples and showcase it on a number of distributed protocols from the literature. This paper extends our preceeding conference publication (Andersen & Sergey, 2019a) with an exploration of randomized testing for protocols and their implementations, and an additional case study demonstrating bounded model checking of protocols.

A Hybrid Set of Handwriting Features for Handwritten Recognition

Handwriting of each person is unique since each person has their own unique and different style of handwriting. Handwriting verification can be performed in two ways, dynamic and static. The dynamic verification process is the writer dependent whereas the static verification process is the writer independent procedure. The features can be spatial, structural, statistical, geometrical, graphological, and from other feature extraction techniques. In this work, we are considering the combination of multilevel feature set for writer recognition and identification purpose. A dataset of different handwriting samples collected from 100 different writers is used for this experiment. A decision tree classifier with random forest implementation is used for recognition and identification of writer with 98.2% accuracy.