Adaptive NN output-feedback control for stochastic time-delay nonlinear systems with unknown control coefficients and perturbations

This paper addresses the problem of adaptive output-feedback control for more general class of stochastic time-varying delay nonlinear systems with unknown control coefficients and perturbations. By using Lyapunov–Krasovskii functional, backstepping and tuning function technique, a novel adaptive neural network (NN) output-feedback controller is constructed with fewer learning parameters. The designed controller guarantees that all the signals in the closed-loop system are 4-moment (or mean square) semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme

Grey Situation Group Decision-Making Method Based on Prospect Theory

This paper puts forward a grey situation group decision-making method on the basis of prospect theory, in view of the grey situation group decision-making problems that decisions are often made by multiple decision experts and those experts have risk preferences. The method takes the positive and negative ideal situation distance as reference points, defines positive and negative prospect value function, and introduces decision experts’ risk preference into grey situation decision-making to make the final decision be more in line with decision experts’ psychological behavior. Based on TOPSIS method, this paper determines the weight of each decision expert, sets up comprehensive prospect value matrix for decision experts’ evaluation, and finally determines the optimal situation. At last, this paper verifies the effectiveness and feasibility of the method by means of a specific example

A Game-Theoretic Framework for AI Governance

As a transformative general-purpose technology, AI has empowered various industries and will continue to shape our lives through ubiquitous applications. Despite the enormous benefits from wide-spread AI deployment, it is crucial to address associated downside risks and therefore ensure AI advances are safe, fair, responsible, and aligned with human values. To do so, we need to establish effective AI governance. In this work, we show that the strategic interaction between the regulatory agencies and AI firms has an intrinsic structure reminiscent of a Stackelberg game, which motivates us to propose a game-theoretic modeling framework for AI governance. In particular, we formulate such interaction as a Stackelberg game composed of a leader and a follower, which captures the underlying game structure compared to its simultaneous play counterparts. Furthermore, the choice of the leader naturally gives rise to two settings. And we demonstrate that our proposed model can serves as a unified AI governance framework from two aspects: firstly we can map one setting to the AI governance of civil domains and the other to the safety-critical and military domains, secondly, the two settings of governance could be chosen contingent on the capability of the intelligent systems. To the best of our knowledge, this work is the first to use game theory for analyzing and structuring AI governance. We also discuss promising directions and hope this can help stimulate research interest in this interdisciplinary area. On a high, we hope this work would contribute to develop a new paradigm for technology policy: the quantitative and AI-driven methods for the technology policy field, which holds significant promise for overcoming many shortcomings of existing qualitative approaches

Discussion on the teaching mode of higher vocational nursing specialty based on CDIO model

基于CDIO模式下高职院校护理专业教学模式的探讨是本文的研究核心。CDIO是国际高等工程教育改革的最新成果，兴起于2000年，国内高校在引入CDIO 模式改革过程中，出现一些问题或疑惑是在所难免的，所以CDIO模式是高等教育院校,尤其是高职院校教师对陈旧教学模式的一次大胆尝试。CDIO模式能够促使教师反思已固有的习惯性教学理念，从而在教学过程中改变教学方法，使教师的教学工作能力得以很大程度的提高，同时鼓励学生的学习行为趋于自主性，培养其自身的专业能力、个人发展能力、人际交往能力和系统创新能力等职业综合能力。Discussion on the teaching mode of higher vocational nursing specialty based on CDIO model is the core of this study. CDIO is the latest achievement in the reform of the international higher education of engineering and has been thrived since 2000. There are some inevitable problems when domestic universities introduced and innovated the CDIO mode. Therefore the CDIO model is a bold attempt for the institutions of higher education, especially higher vocational college teachers. The CDIO mode drives teachers to reflect on the existed teaching philosophy, and therefore enables them to change teaching methods in the teaching process and improve their teaching capacity tremendously. Meanwhile, it also encourages students to learn automatically and cultivate their comprehensive abilities such as professional capability, development capability, interpersonal skills, innovation ability, etc

Determining the Optimal Traffic Opening Time Using Piezoelectric Sensors

The Indiana Department of Transportation (INDOT) requires a reliable method of determining the early age quality of concrete to improve traffic opening time. We propose to develop an in-situ method that enables an accurate, efficient, and non-destructive health monitoring of concrete using the electromechanical impedance (EMI) technique coupled with a piezoelectric sensor named Lead Zirconate Titanate (PZT). The test was conducted by mounting a PZT sensor on mortar samples. The PZT sensor was then excited by a voltage to track the strengthening of samples. The data obtained from the EMI technique was refined using the Root Mean Square Deviation (RMSD) model. Simultaneously, identical mortar samples underwent a compressive test to measure sample strength in a destructive manner. Both tests were repeated by varying the mortar sample’s cement type and water-to-cement ratio. Finally, both tests were compared to one another via regression analysis. The outcome has shown a significant correlation between the compressive strength and the EMI data. This indicates that the PZT based EMI technique can potentially be used to non-destructively measure the early age concrete strength for optimizing traffic opening time

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO) problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results

Self-Healing Cementitious Composites (SHCC) with Ultrahigh Ductility for Pavement and Bridge Construction

Cracks and their formations in concrete structures have been a common and long-lived problem, mainly due to the intrinsic brittleness of the concrete. Concrete structures, such as rigid pavement and bridge decks, are prone to deformations and deteriorations caused by shrinkage, temperature fluctuation, and traffic load, which can affect their service life. Rehabilitation of concrete structures is expensive and challenging—not only from maintenance viewpoints but also because they cannot be used for services during maintenance. It is critical to significantly improve the ductility of concrete to overcome such issues and to enable better infrastructure quality. To this end, the self-healing cementitious composites (SHCC) investigated in this work could be a promising solution to the aforementioned problems. In this project, the team has designed a series of cementitious composites to investigate their mechanical performances and self-healing abilities. Firstly, various types of fibers were investigated for improving ductility of the designed SHCC. To enhance the self-healing of SHCC, we proposed and examined that the combination of the internal curing method with SHCC mixture design can further improve self-healing performance. Three types of internal curing agents were used on the SHCC mixture design, and their self-healing efficiency was evaluated by multiple destructive and non-destructive tests. Results indicated a significant improvement in the self-healing capacity with the incorporation of internal curing agents such as zeolite and lightweight aggregate. To control the fiber distribution and workability of the SHCC, the mix design was further adjusted by controlling rheology using different types of viscosity modifiers. The team also explored the feasibility of the incorporation of colloidal nano-silica into the mix design of SHCC. Results suggest that optimum amounts of nano-silica have positive influence on self-healing efficiency and mechanical properties of the SHCC. Better hydration was also achieved by adding the nano-silica. The bonding strength of the SHCC with conventional concrete was also improved. At last, a standardized mixing procedure for the large scale SHCC was drafted and proposed