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Target Set (target + set)
Selected AbstractsSoft Decision with Soft Target for Car-like Mobile Vehicle in Dynamic EnvironmentIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2009Yougen Chen Non-member Abstract Online flexible operation of a car-like mobile vehicle with non-holonomic constraints in dynamic environment is still a very challenging problem because the surrounding situations are not qualified in static, knowledge is only partial and the execution is often associated with uncertainty. The difficulty lies in the setting of appropriate moving sub-targets in real-time to obtain a collision-free and low-cost path. In this paper, we present a new approach for the autonomous motion control of mobile vehicle in a narrow area with static and dynamic obstacles. It is based on the selection of sub-target points of vehicle's movement called ,soft target' which is a target set defined as all possible and reachable via-points in a navigation space. The soft target is acquired by online learning based on the final target and environment information. Each element of it has its membership value in [0, 1] denoting its evaluation degree. With the acquired soft target, soft decision is made like human's decision process by predictive fuzzy control (PFC) to achieve final target safely and economically. The simulation results show the effectiveness and flexibility of the proposed vehicle motion control method. © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Demand side management for water heating installations in South African commercial buildingsINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 4 2001P. G. Rousseau Abstract The largest percentage of the sanitary hot water used in South African buildings is heated by means of direct electrical resistance heaters. This is one of the major contributing factors of the undesirable high morning and afternoon peaks imposed on the national electricity supply grid. Water heating therefore continues to be of concern to ESKOM, the country's only electrical utility company. The so-called in-line water heating system design methodology was developed to address this problem. This paper investigates the potential impact of in-line systems on the national peak electrical demand. A computer simulation model was developed that combines a deterministic mathematical model with a statistical approach in order to predict the diversity factors associated with both the existing and in-line design methodologies. A study was also conducted to estimate the total installed water heating capacity in the national commercial building sector. This figure can be combined with the simulated diversity factor to determine the peak electrical demand. The deterministic model includes the detailed simulation of the hot water storage vessel, the electrical heater and the system control algorithm. The mathematical model for the storage vessel is based on an electrical analogue approach that includes the effects of conduction as well as forced and natural convection. This model was verified extensively with the aid of laboratory measurements and compared with existing storage vessel models. It was found that the new storage vessel model could predict the supply temperature within 2 per cent for a system configuration with the heater in parallel outside the reservoir and within 12 per cent for a configuration with the heater situated inside the reservoir. This compares favourably with existing models found in the literature. The complete simulation based on the statistical approach showed that extensive application of the new design methodology could result in a reduction of approximately 75 MW in the total maximum peak demand imposed on the electricity supply grid in wintertime. This is 58 per cent of the current peak demand due to commercial water heating and 12.5 per cent of the peak load reduction target set by ESKOM until the year 2015. Copyright © 2001 John Wiley & Sons, Ltd. [source] Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic AlgorithmJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 3 2009Matthew Finkelman Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to minimize the average number of classification errors, minimize the maximum error rate across all attributes being measured, hit a target set of error rates, or optimize any other prescribed objective function. Under multiple simulation conditions, the algorithm compared favorably with a standard method of automated test assembly, successfully finding solutions that were appropriate for each stated goal. [source] Target setting in intensive insulin management is associated with metabolic control: the Hvidoere Childhood Diabetes Study Group Centre Differences Study 2005PEDIATRIC DIABETES, Issue 4 2010PGF Swift Swift PGF, Skinner TC, de Beaufort CE, Cameron FJ, Åman J, Aanstoot H-J, Castaño L, Chiarelli F, Daneman D, Danne T, Dorchy H, Hoey H, Kaprio EA, Kaufman F, Kocova M, Mortensen HB, Njølstad PR, Phillip M, Robertson KJ, Schoenle EJ, Urakami T, Vanelli M, Ackermann RW, Skovlund SE for the Hvidoere Study Group on Childhood Diabetes. Target setting in intensive insulin management is associated with metabolic control: the Hvidoere Childhood Diabetes Study Group Centre Differences Study 2005. Objective: To evaluate glycaemic targets set by diabetes teams, their perception by adolescents and parents, and their influence on metabolic control. Methods: Clinical data and questionnaires were completed by adolescents, parents/carers and diabetes teams in 21 international centres. HbA1c was measured centrally. Results: A total of 2062 adolescents completed questionnaires (age 14.4 ± 2.3 yr; diabetes duration 6.1 ± 3.5 yr). Mean HbA 1c = 8.2 ± 1.4% with significant differences between centres (F = 12.3; p < 0.001) range from 7.4 to 9.1%. There was a significant correlation between parent (r = 0.20) and adolescent (r = 0.21) reports of their perceived ideal HbA1c and their actual HbA1c result (p < 0.001), and a stronger association between parents' (r = 0.39) and adolescents' (r = 0.4) reports of the HbA1c they would be happy with and their actual HbA1c result. There were significant differences between centres on parent and adolescent reports of ideal and happy with HbA1c (8.1 < F > 17.4;p < 0.001). A lower target HbA1c and greater consistency between members of teams within centres were associated with lower centre HbA1c (F = 16.0; df = 15; p < 0.001). Conclusions: Clear and consistent setting of glycaemic targets by diabetes teams is strongly associated with HbA1c outcome in adolescents. Target setting appears to play a significant role in explaining the differences in metabolic outcomes between centres. [source] | ||||||||||