Deep generative tread pattern design framework for efficient conceptual design

Tire tread patterns have played an important role in the automotive industry because they directly affect automobile performances. The conventional tread pattern development process has successfully produced and manufactured many tire tread patterns. However, a conceptual design process, which is a major part of the whole process, is still time-consuming due to repetitive manual interaction works between designers and engineers. In the worst case, the whole design process must be performed again from the beginning to obtain the required results. In this study, a deep generative tread pattern design framework is proposed to automatically generate various tread patterns satisfying the target tire performances in the conceptual design process. The main concept of the proposed method is that desired tread patterns are obtained through optimization based on integrated functions, which combine generative models and tire performance evaluation functions. To strengthen the effectiveness of the proposed framework, suitable image pre-processing, generative adversarial networks (GANs), 2D image-based tire performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying the target tire performances such as summer, winter, or all-season patterns.

Flipped classroom – what feedback in the FFL classroom and how to assess the work of learners?

Assessment is not an additional act in the ecology of the classroom. Evaluative gestures, inseparable from those of education or training, encourage the practitioner to reflect on the scope of their actions and to consider the type of interaction that they promote in the classroom. If the flipped classroom uses summative assessment, the fact remains that the sinew of war is a formative assessment in the pedagogy of support, encouragement, and benevolence. We will see in this article what is the act of evaluating, the difference between evaluation of learning and evaluation of teaching by learners, and the different evaluation functions in a flipped classroom.

Evaluating the Capacity Coordination in the Urban Multimodal Transport Network

The urban multimodal transport network is composed of multiple layers of networks; thus, coordinating the capacity equilibrium among different sub-transport networks plays a crucial role to keep the entire network running efficiently. To quantify and evaluate the passenger flow distribution in an urban multimodal transport network, this research proposes a method to evaluate the capacity coordination in an urban multimodal transport network on the basis of assignment results calculated by the Stochastic User Equilibrium (SUE) model considering the link and path impedance of different sub-transport networks. It suggests evaluation functions for the indicator level of service (LOS) of the multimodal transport network, Gini coefficient of transport network, and mode share of transport modes, and it shows how the functions were estimated. Then, it reports on results with the evaluation scheme collected in a multimodal example application for roadway network, transit networks (bus transit network and urban rail transit network), and connection network. The evaluation results under different assumed origin–destination (OD) demand show the coordination degree and can be used to recognize shortcomings of the network. Moreover, the OD demand interval of real network with good coordination can be deduced, which can also help transport planners to find the optimal strategy.

Image Definition Evaluations on Denoised and Sharpened Wood Grain Images

Decorative paper and wood veneer have been widely used in the surface decoration of wood-based panels. These surface decoration methods require two-dimensional image acquisition of natural wood grain to obtain the digital grain. However, optically scanned images sometimes produce noise during the process of image acquisition and transmission. In this situation, scanned images cannot be used directly in wood grain reproductions. To reduce noise and retain or strengthen the image sharpness, studies are mostly aimed at the improvement of classic denoising algorithms and edge width-based sharpness evaluation algorithms. To enhance accessibility for common users, four kinds of wood grain images with distinct colors were chosen, and the noise filter (Dust & Scratches) and sharpen filter (Unsharp Mask, USM) were used to denoise and sharpen the images. According to the properties of the two filters, image definition in this study was considered from two aspects: detail retention and sharpness retention. To have an objective evaluation on the definition of denoised and sharpened images, two types of evaluation functions Roberts gradient function (RGF) and modulation transfer function (MTF) were introduced. The purpose of this study was to estimate the image definition by exploring the relationships between the evaluation functions and the commonly used filters in order to allow the required wood grain images to be quickly and accurately processed by common users. The results showed that RGF was only applicable to the case where the two parameters in Dust & Scratches were changed individually, while MTF was not suitable in any case. When both parameters were changed, the required denoising scheme could be obtained through PSNR and SSIM. For the images with distinct colors, even if they were acquired in the same way, denoising them with the same parameter setting was not recommended. For sharpness retention, the values of Radius and Amount in USM were given, and when the Threshold value was set to 20 (levels), the sharpness of the wood grain images barely changed. In this case, both RGF and MTF were suitable to evaluate the sharpness of the wood grain images sharpened by USM.

ME-MCTS: Online Generalization by Combining Multiple Value Estimators

This paper addresses the challenge of online generalization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can represent existing techniques such as "history heuristics", "RAVE", or "OMA" -- contextual action value estimators or abstractors that generalize across specific contexts. Second, we incorporate recent advances in estimator averaging that enable guiding search by combining the online action value estimates of any number of such abstractors or similar types of action value estimators. Unlike previous work, which usually proposed a single abstractor for either the selection or the rollout phase of MCTS simulations, our approach focuses on the combination of multiple estimators and applies them to all move choices in MCTS simulations. As the MCTS tree itself is just another value estimator -- unbiased, but without abstraction -- this blurs the traditional distinction between action choices inside and outside of the MCTS tree. Experiments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions. While we used deterministic, fully observable games, ME-MCTS naturally extends to more challenging settings.

On coalitional manipulation for multiwinner elections: shortlisting

Shortlisting of candidates—selecting a group of “best” candidates—is a special case of multiwinner elections. We provide the first in-depth study of the computational complexity of strategic voting for shortlisting based on the perhaps most basic voting rule in this scenario, $$\ell $$ ℓ -Bloc (every voter approves $$\ell $$ ℓ candidates). In particular, we investigate the influence of several different group evaluation functions (e.g., egalitarian versus utilitarian) and tie-breaking mechanisms modeling pessimistic and optimistic manipulators. Among other things, we conclude that in an egalitarian setting strategic voting may indeed be computationally intractable regardless of the tie-breaking rule. Altogether, we provide a fairly comprehensive picture of the computational complexity landscape of this scenario.

Integration of “Offline + Online” Teaching Method of College English Based on Web Search Technology

The change of social demand for English application-oriented talents has affected the transformation of the way of cultivating talents in College English. The traditional training mode of indoctrination has exposed more and more disadvantages, and the demand of new teaching mode is imminent. This paper first analyzes the current situation of College English learning platform in China. Then, it discusses the design and implementation of “Online + offline” College English learning platform based on Web. The overall structure and function of the database are designed in detail. The teaching platform provides students with learning tools, learning resources, communication platform, testing and evaluation functions, and can evaluate students’ learning behavior, learning process and learning effect. This paper traces and collects a large amount of data left by learners in learning college English courses, and analyzes learners’ learning habits, learning progress and learning effect. Finally, according to the learning big data, this paper customized a reasonable personalized learning platform and improved the online teaching personalized service system.

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on Intelligent Ions Motion

Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform design algorithm based on intelligent ions motion optimization (IMO) to directly obtain a set of polyphase codes with good orthogonality. The autocorrelation sidelobe and cross-correlation sidelobe are first derived and subsequently integrated into evaluation functions for evaluating the orthogonality of polyphase codes. In order to effectively cope with the aforementioned problem, we present a strengthened IMO that is highly robust and converges rapidly. In the liquid state, an optimal guiding principle of same-charge ions is suggested to enhance global search ability and avoid falling into local optima. An ion updating strategy based on fitness ranking is presented to improve the search efficiency in the crystal state. Finally, the improved algorithm is employed to optimize the polyphase codes. The experimental results, compared with other state-of-the-art algorithms, show that the polyphase codes obtained by the proposed algorithm have better orthogonality.

Funciones que caracterizan la gestión de seguridad industrial, ambiente e higiene ocupacional

Son innumerables las ventajas que representa para cualquier organización contarcon la gestión en seguridad industrial, ambiente e higiene ocupacional, es por ello queen este artículo se buscó describir las funciones que la caracterizan en el contexto delas empresas prestadoras de servicio de inspección de equipos estáticos a la industriapetrolera. Metodológicamente se tipificó como descriptiva, con diseño de campo, noexperimental y transeccional. Para la recolección de datos se utilizó un cuestionario,conformado por 12 ítems con escala de frecuencia. La validez se realizó a través deljuicio de expertos, y para calcular su confiabilidad se empleó el método de Alfa deCronbach, obteniéndose 0,96. La media aritmética se aplicó para el análisis de losdatos. Se caracterizó un conjunto de etapas que instrumentan los cursos de acciónrequeridos por la gestión, en tal sentido, se evidencio moderada presencia en laspolíticas; organización; planificación y evaluación.Palabras clave: Evaluación, funciones, gestión en seguridad industrial, ambiente ehigiene ocupacional, organización, planificación, políticasThere are countless advantages for any organization to have management inindustrial safety, environment and occupational hygiene, which is why this articlesought to describe the functions that characterize it in the context of companiesproviding equipment inspection service static to the oil industry. Methodologically itwas typified as descriptive, with a field design, not experimental and transectional. Fordata collection, a questionnaire was used, consisting of 12 items with a frequency scale.Validity was carried out through expert judgment, and the Cronbach's Alpha methodwas used to calculate its reliability, obtaining 0.96. The arithmetic mean was applied toanalyze the data. A set of stages that implement the courses of action required bymanagement was characterized, in this sense, a moderate presence in policies wasevident; organization; planning and evaluation.Key words: Evaluation, functions, industrial safety management, environment andoccupational hygiene, organization, planning, policies

A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark

A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this article, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies–Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes.