配煤掺烧最小煤质偏差模型及煤场存放优化

定义了混煤和锅炉目标煤的煤质偏差,给出以煤质偏差作为目标函数的掺配优化模型,通过调整某个成分的期望误差,可单独调节该成分与目标煤的接近程度;通过对模型约束条件设定,可限制低灰熔点煤的掺配份额,控制炉膛结焦,或提高采购量大的单煤的掺配比例。基于最小煤质偏差模型,针对某670 MW超临界压力直流锅炉进行掺配优化模拟,提出煤场存放的优化概念和方法。结果表明:该模型能够从复杂的单煤煤源中找到最接近目标煤质的掺配方案,优化后入炉煤质稳定,且接近目标煤质。     

Does the method for Military Utility Assessment of Future Technologies provide utility?

The Military Utility Assessment of Future Technologies (MUAFT) method was developed as a cost-efficient alternative to methods such as NATO's Disruptive Technology Assessment Games, to be used as a part of the Swedish Armed Forces' long-term capability development process. The question addressed in this study is whether MUAFT can be considered to have validity in its context and thus if it has potential to be useful to other small to medium size states. The analysis was based on an operationalization of Clark's framework for science and technology intelligence analysis, combined with a military capability centric view of military utility. MUAFT reports from 2012 to 2018 were reviewed in terms of how they satisfy five key criteria. The study shows that MUAFT provides utility, if used by a suitably composed group of experts, who are aware of the method's limitations. The limitations mainly originate from a lack of explicit support for assessing the impact of forces for change, other than technological forces, on military capability development. The expert group serves as the synthesizing bridge between technology forecasts and military utility assessments. Therefore, comprehensive expertise is needed in various military technology specialisations, in the sponsor's military capabilities and in subjects necessary to master in order to assess other influential societal forces for change.     

A simplified method for optimal design of solar water heating systems based on life-cycle energy analysis

Significant energy mismatch exists in solar water heating systems as the time and amount of solar energy supply are usually different from that of hot water demand. Using a hot water storage tank can reduce or eliminate such mismatch in short term while it is difficult to avoid this mismatch in long term. In many optimal design and life-cycle analysis methods, the energy mismatch is ignored which causes the system performance to be overestimated and also misleads the optimal design of the system. This paper presents a simplified method for optimizing the key parameters of solar water heating systems based on life-cycle energy analysis. This optimal method considering the energy mismatch phenomenon can be implemented through two steps. In the first step, a simplified energy model based hourly energy matching different components of the system, is developed for determining the operating performance of system with different solar collector areas and water storage volumes. In the second step, the law of diminishing marginal utility is employed to determine the optimum size of the system. The optimum size is identified when the maximal life-cycle net energy saving is achieved. A case study on the application of the proposed method in a building is presented as well.     

Resource allocation for multi-class services in multipath networks

In multipath networks, multiple paths are available for each pair of source and destination and can be used to carry data packets parallelly. It has been recognized that using multipath could promote the transmission reliability and fault tolerance, and improve the performance of increasingly bandwidth-hungry multi-media applications. In this paper we propose the resource allocation model for multi-class services in multipath networks with the objective of utility maximization, which is an intrinsically difficult problem of nonconvex optimization. We firstly analyze the model for only elastic services and obtain the optimal rate allocation for them. Then we also discuss the model for inelastic services with nonconcave (sigmoidal or discontinuous) utilities which share common links with elastic ones, and obtain some sufficient conditions under which the global optimum for both elastic and inelastic services can be obtained. For the nonconvex optimization problem, we present a heuristic algorithm using Particle Swarm Optimization (PSO), which can lead to improved solutions over existing approaches. Finally, some numerical examples are given to verify the results obtained.     

通用小型汽油机工况法燃油消耗率测试方法解析

GB/T 37692-2019《非道路移动机械用小型点燃式发动机工况法燃油消耗率限值与测量方法》标准于2019年6月4日发布,将于2020年1月1日正式实施。本文根据标准内容,从试验条件、仪器设备、试验工况的选择、试验流程和数据计算等要点,对通用小型汽油机工况法燃油消耗率测试方法要求进行了解读,为检测机构和企业进行通用小型汽油机油耗测试提供参考。     

Estimation of fog utility pricing: a bio-inspired optimisation techniques' perspective

ABSTRACT

Due to the limited data storage capacity available to Internet service providers and large-scale enterprises, the concept of resource sharing arises. The services can be given on lease to enterprises through Service Level Agreements. Being the extension of the cloud computing, fog computing architecture brings the resources near end users. In order to get the services on lease, the enterprises are supposed to pay for the resources or services which are being used by them. In this paper, four nature inspired algorithms are analysed in order to determine the efficient management of services or resources so that the cost of resources can be reduced and the billing can be attained through calculation of the utilised resources. Pigeon inspired optimization, enhanced differential evolution, binary bat algorithm and simple human learning optimization are used to evaluate the energy consumed by the edge nodes or cloudlets that in turn can be used for estimating the bill through the Time of Use pricing variable. We evaluate the aforementioned techniques to analyse their performance regarding the bill calculation on the basis of fog servers usage. Simulation results demonstrate that BAT algorithm gives significantly better results than other three algorithms in terms of resource utilisation and bill reduction.     

基于优化上界的高平均效用项集垂直挖掘算法

高平均效用项集挖掘是当前研究的热点之一。针对高平均效用项集挖掘算法产生大量无意义的候选项集,而导致高内存消耗和运行时间长的问题,提出了dMHAUI算法。首先定义了集成矩阵Q,并提出了4种基于垂直数据库表示的紧凑平均效用上界及3种有效的修剪策略;将高平均效用项集挖掘所需的信息存储于IDUL结构树,利用改进的diffset技术快速计算项集的平均效用和上界;最后通过递归调用搜索函数得到高平均效用项集。与EHAUPM算法和MHAI算法进行仿真比较,结果表明,dMHAUI算法在运行时间、连接比较次数和可扩展性等方面都有较优的性能。     

A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.     

Stakeholder engagement by an Italian water utility company: Insight from participant observation of dialogism

This paper focuses on the level of dialogism between organizations and participants. The unit of analysis is a water utility's direct engagement with its household customers. Through participant observation and Bebbington et al.’s (2007) model, we analyzed engagement factors to detect elements occasioning a dialogic experience and its determinants. This contribution to the public utilities and social accounting literature outlines interrelated factors impacting barriers to and opportunities for creating dialogism. It enhances public utility business reputation and drives sustainability innovations. Managerially, we identified enablers and limits of an effective stakeholder engagement process.     

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.