Stability analysis of PWM feedback control systems with PID regulators

Stability analysis is of great significance in those feedback control systems in which the power amplifier is operated as a pulse-modulator device, since under these circumstances the whole control system is highly non-linear. Stability in PWM feedback control systems with a proportional type regulator has been amply described in the literature. Only recently, however, have such studies been extended to include systems with a proportional-plus-integral regulator. In this paper the problem is considered for the case involving a PWM control system, where the regulator is a proportional-plus-integral-plus-derivative, the PWM modulator is of a very general type and the controlled process is of arbitrary order. The stability of the system is analysed by means of a discrete version of the second Lyapunov method ; this method in turn leads to an investigation of the positivity region of a quadratic form defined in the parameter space of the regulator. To improve the stability region obtained, a procedure is investigated whereby only a few parameters of the quadratic Lyapunov function need to be varied. The method proposed has proved extremely simple and flexible to apply, even in cases where the system is of arbitrary order.     

An efficient algorithm for low-order direct discrete-time implementation of fractional order transfer functions

Fractional order systems become increasingly popular due to their versatility in modelling and control applications across various disciplines. However, the bottleneck in deploying these tools in practice is related to their implementation on real-life systems. Numerical approximations are employed but their complexity no longer match the attractive simplicity of the original fractional order systems. This paper proposes a low-order, computationally stable and efficient method for direct approximation of general order (fractional order) systems in the form of discrete-time rational transfer functions, e.g. processes, controllers. A fair comparison to other direct discretization methods is presented, demonstrating its added value with respect to the state of art.     

How operators make use of wide-choice adaptable automation: observations from a series of experimental studies

The present paper is concerned with the design of adaptable automation. It analyses the aggregated data from five experiments (N = 154) to examine which automation levels operators prefer, how often they switch between them, and whether performance is associated with frequent switching or the automation levels being used. Using wide-choice adaptable automation (i.e. up to six levels were offered), the experiments were conducted using a PC-based simulation of a complex work environment. The results showed that about 95% of operators had a clearly preferred automation level, which they used for more than 50% of the time. They strongly preferred intermediate automation levels over levels at the higher and lower ends of the scale. Most operators switched rarely between levels and when they did, they usually made small adjustment rather than large changes. Several implications for the design of adaptable automation were derived from the empirical data.     

Exponential ergodicity of some Markov dynamical systems with application to a Poisson-driven stochastic differential equation

We are concerned with the asymptotics of the Markov chain given by the post-jump locations of a certain piecewise-deterministic Markov process with a state-dependent jump intensity. We provide sufficient conditions for such a model to possess a unique invariant distribution, which is exponentially attracting in the dual-bounded Lipschitz distance. Having established this, we generalize a result of J. Kazak on the jump process defined by a Poisson-driven stochastic differential equation with a solution-dependent intensity of perturbations.     

A novel inverse data driven modelling approach to performance-based building design during early stages

Energy analysis at the early stage of building design is a critical, yet difficult task in performance-based design. The difficulty arises from the complex, iterative, and uncertain nature of building design and the challenges of integration with well-posed energy assessment tools. The purpose of this article is to first review characteristics of performance-based design and establish requirements for a methodology that includes generating promising design alternatives, assessing the energy performance in tandem with the generation of alternatives, and choosing an alternative design solution with confidence. The study then proposes a novel systematic data-driven method, based on linear inverse modeling that generates plausible ranges for design parameters given a preferred energy target. The energy performance in this method is described as a linear function of the design parameters for a particular scenario of design. The application of the proposed method in a case study shows that it is capable of helping designers make informed decisions regarding energy performance iteratively and confidently at the early stages of building design.     

An interval-valued exceedance method in MCDM with uncertain satisfactions

Multicriteria decision-making problems have been applied to many applications for its practicality. Nevertheless, when the evaluated satisfactions are more complex, such as interval-valued distributions, how to reasonably obtain the aggregation results of alternatives is still an open issue. In this paper, an interval-valued exceedance method is proposed to solve such a question based on the Golden Rule representative value and probabilistic exceedance method. Due to good performance of expressing uncertain information, the Golden Rule representative value method is used to order interval-valued satisfactions after an effective normalization process. In addition, a quantifier-based ordered weighted averaging operator is also introduced to consider the preferences of decision makers. A realistic application of supplier selection is shown to illustrate the practicality of the proposed method.     

改进的区间犹豫算子应用于物流企业选择决策

针对现有区间犹豫模糊Hamacher算子存在的缺陷，构建了一种基于改进的区间犹豫模糊Hamacher加权算子的群决策方法。在分析现有区间犹豫模糊Hamacher算子不能满足幂等性的基础上，定义新的区间犹豫模糊Hamacher四则运算；提出两种改进的区间犹豫模糊Hamacher加权算子，包括改进的区间犹豫模糊Hamacher有序加权平均（I-IVHFHOWA）算子和改进的区间犹豫模糊Hamacher有序加权几何（I-IVHFHOWG）算子，并详细探究它们的常用算子形式以及算子之间的内在联系；建立基于I-IVHFHOWA算子和I-IVHFHOWG算子的物流企业选择决策模型，并通过实例说明模型的有效性。     

Pharmacogenetics Biomarkers and Their Specific Role in Neoadjuvant Chemoradiotherapy Treatments: An Exploratory Study on Rectal Cancer Patients

Background: Pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC) is still ascribed to a minority of patients. A pathway based-approach could highlight the predictive role of germline single nucleotide polymorphisms (SNPs). The primary aim of this study was to define new predictive biomarkers considering treatment specificities. Secondary aim was to determine new potential predictive biomarkers independent from radiotherapy (RT) dosage and cotreatment with oxaliplatin. Methods: Thirty germ-line SNPs in twenty-one genes were selected according to a pathway-based approach. Genetic analyses were performed on 280 LARC patients who underwent fluoropyrimidine-based CRT. The potential predictive role of these SNPs in determining pathological tumor response was tested in Group 1 (94 patients undergoing also oxaliplatin), Group 2 (73 patients treated with high RT dosage), Group 3 (113 patients treated with standard RT dosage), and in the pooled population (280 patients). Results: Nine new predictive biomarkers were identified in the three groups. The most promising one was rs3136228-MSH6 (p = 0.004) arising from Group 3. In the pooled population, rs1801133-MTHFR showed only a trend (p = 0.073). Conclusion: This exploratory study highlighted new potential predictive biomarkers of neoadjuvant CRT and underlined the importance to strictly define treatment peculiarities in pharmacogenetic analyses.     

泛逻辑学理论——机制主义人工智能理论的逻辑基础

当前，世界各主要大国都把人工智能作为它们的国家战略。人工智能的发展正在快速改变着人类的生活方式和思想观念。在中国，有一小批研究者20多年来一直在基于辩证唯物主义潜心研究具有普适性的人工智能基础理论，包括智能的形成机制、逻辑基础、数学基础、协调机理、矛盾转化等。终于，他们各自建立了机制主义人工智能理论、泛逻辑学理论、因素空间理论、协调学、可拓学、集对分析等。其中，机制主义人工智能理论是基于智能形成机制的通用理论，它能把现有的结构主义、功能主义和行为主义三大流派有机地统一起来，使意识、情感、理智成为三位一体的关系；因素空间理论是机制主义人工智能理论的数学基础；泛逻辑学理论是机制主义人工智能理论的逻辑基础。本文介绍了泛逻辑学理论的基本思想、理论基础和应用方法，阐明它的理论意义和应用价值。特别需要指出的是，在广义概率论基础上建立的命题泛逻辑（包括刚性逻辑和柔性逻辑），可看成一个完整的命题级智能信息处理算子库，库中完整地包含了全部18种柔性信息处理模式（包括16种布尔信息处理模式），可用类型编码<a,b,e>来严格区分，用它可寻找到适合自己的信息处理算子完整簇来使用。在每一个信息处理模式中，各种不确定性的组合状态由不确定性程度属性编码<k,h,β,e>来严格区分，用它可在本信息处理模式的算子完整簇中精确选择具体的算子来使用。这表明柔性信息处理本质上是一把密码锁，它需要专门的密码<a,b,e>+<k,h,β,e>才能正常打开，不能乱点鸳鸯谱。通过只有18种模式，每种模式可以从最大算子连续变化到最小算子，已经证明了没有一个命题算子被遗漏。