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.     

A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

破损水文观测孔涌水封堵技术在高河矿的应用

针对高河矿奥灰水文观测孔空水管锈蚀断裂漏水严重的问题,分析了存在的问题,制定了钻孔涌水封堵技术方案及相应的注浆施工工艺,并进行了为期7天的井下试验。封堵注浆压力达到6 MPa,使用注浆水泥50 t,水泥浆液凝固良好,注浆阀门打开后无出水现象。     

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。     

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.     

Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility

Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under various linear uncertainty distribution-based preferences. First, the uncertain distance is used to measure the deviation between individual opinions and the consensus through uncertainty distributions. A nonlinear analytical formula is derived to avoid the computational complexity of integral and piecewise function operations, thus reducing the calculation cost of the uncertain distance measure. The consensus utility function defined in this article characterizes the adjustment value and degree of aggregation of individual opinions. Three new consensus models are constructed based on the consensus utility and linear uncertainty distribution. The results show that, in complex group decision-making contexts, the uncertain consensus models are more flexible than traditional minimum-cost consensus models: compared with the high volatility of the adjusted opinions in traditional deterministic consensus models with crisp number-based preferences, the variation trends of both individual adjusted opinions and the collective opinion with a linear uncertainty distribution are much smoother and the fitting range is closer to reality. The introduction of the consensus utility not only reflects the relative changes of individual opinions, but also accounts for individual psychological changes during the opinion-adjustment process. Most importantly, it reduces the cost per unit of consensus utility, facilitates the determination of the optimal threshold for the consensus utility, and improves the efficiency of resource allocation.     

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.     

Mathematical foundations of moral preferences

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary pay-offs of others. However, during the last 10 years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment and trustworthiness are in fact better explained by preferences for following one''s own personal norms—internal standards about what is right or wrong in a given situation. Beyond better organizing various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.     

惠州水文监测综合管理平台设计与实现

针对目前部分水文要素监测工作中还存在大量人工记载、计算、录入，成果出错率较高，时效性低， 监测业务融合度不够和内部管理难度大等方面的问题，深度融合互联网技术和水文监测业务，开展“互联网 + 水文监测”研究，设计并开发分局水文监测综合管理平台是十分必要的。深入分析水文监测各项业务流程，打造水文监测在线业务流、数据流和管理流，结合程序开发语言和互联网技术，完成惠州水文监测综合管理平台设计。平台在 2020 年 6 月东江流域洪水中进行实际应用，结果表明：平台实现水文监测要素人工作业无纸化、 自动化、智能化，进一步提高水文监测工作效率和信息化水平，具有很好的推广应用前景。