Automatic Makers as a Source for Olympiad Tasks

Automatic maker is meant as a device to produce (or compose) things by itself, by a model, customization or a program. Nowadays the most advanced automatic makers in mass use are 3D-printers.The purpose of the paper is to present methods of generating various Olympiad tasks by using evident images of virtual automatic makers. As a perspective, production (performing) of processes is also considered (as automatic 4D-makers). Besides of the main operation: putting a pixel (voxel), the following primitives with virtual things can be involved: cutting; gluing; putting a building block; erasing a building block; copying a fragment, (for 4D-) shifting a building block. Tasks are generated naturally: to make a given thing (perform a given simple process) optimally in any sense (with respect to time; to number of primitives; to number of building blocks etc.). Such tasks are well-understood, have short formulations and are difficult to be solved even with initial data of small volume; a “brute force” method is either inapplicable or gives too overestimate of complexity.

Offline Configuration Resources about Smart Substation Multi-Dimensional and Multi-Objective Customization and Information Merger

Offline Configuration Resources about Smart substation Multi-dimensional and multi-objective customization and information merger is an important means of the development for the large-scale grid operation. In this paper, the method of smart substation model customization and information merger for multi-level control center is proposed. This paper puts forward the scheme、data flow and key technologies of model customization and information merger. The software of model customization and information merger, which can flexibly configure the rule of model reduction and information merger according to different scenarios, is designed and developed. The software is validated in the laboratory, which illustrates that idea of this paper has good potential value.

Evaluation of a Novel Statistical Method for EMG-to-Moment Estimation During Gait

Neuromusculoskeletal models have the potential to improve the design of clinical interventions for disorders such as stroke and osteoarthritis that affect walking function. Application of such models to clinical problems will likely require model customization to the unique anatomical and neurological conditions of each patient. Unfortunately, current modeling methods make model customization to patient data difficult, especially for model parameter values related to muscle-tendon models and musculoskeletal geometry [1]. Improved methods are needed so that patient-specific neural control capabilities and limitations can be easily incorporated into predictive gait optimizations to be used for intervention planning purposes.