Assessing Flexible Places for Adaptive Urban Design:A New Method and the Case of Cittaducale

Each place has its own peculiarities,determining that the resilience and adaptation to diverse types of risks will be different,requiring different time and methods for mitigation as well.The coexistence of several risks determines further resolution difficulties as commonly,since risks are sudden and unforeseen,there is a tendency to treat the last risk more carefully,neglecting the previous one even if it is just as important.Most of the methodologies used to analyse areas affected by different risks are aimed at focusing in particular on seismic risks without considering those relating to intangible aspects linked to the identity of the places or aspects related to urban health and liveability.And,more in general,in relation to design aspects,urban methodologies do not consider all the results of the analyses,maybe for the difficulty deriving from translating the complexity of collected data in project interventions.Starting from these premises,the objective of the article,elaborated within the SISMI and PRIN 2020 research projects is to propose an original methodology capable of analysing the places affected by multiple risks and planning its resilience attentive to its cultural resources and to its liveability.To this end,the proposed method will be aimed at creating flexible and adaptive places and the case-study of Cittaducale will be illustrated.Criticalities and qualities of this place affected by an earthquake in 2016 will be reported and project interventions to transform its risks into opportunities will be shown.

ADAPTABLE DESIGN OF MACHINE TOOLS STRUCTURES

Adaptable design aims to extend the utilities of design and product. The specific methods are developed for practical applications of adaptable design in the design of mechanical structures, including adaptable platform, interface and module designs. Adaptable redesign problems are formulated as adaptable platform design under adaptability bound constraints. Analysis tools are then suggested for the implementation of the redesign of machine tool structures, including variation techniques based sensitivity analysis, similarity-based commonality analysis, performance improvement, and adaptability measures. The specific measure of adaptability for machine tool structures is measured through the quantification of the structural similarity and performance improvement gained from adaptable design. The method provides designers with an approach that brings adaptability into the design process, with considering the cost and benefits of such adaptability. The redesign of CNC spiral bevel gear cutting machine structures has been included to illustrate these concepts and methods.

Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

The core task of tracking control is to make the controlled plant track a desired trajectory.The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases.In this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control problem.Unlike the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to zero.The discounted iterative scheme under the new cost function for the special case of linear systems is elaborated.Finally,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

Adaptive designs for sequential experiments

Various adaptive designs have been proposed and applied to clinical trials, bio assay, psychophysics, etc.Adaptive designs are also useful in high cost engineering trials.More and more people have been paying attention to these design methods. This paper introduces several broad families of designs, such as the play-the-winner rule, randomized play-the-winner rule and its generalization to the multi-arm case, doubly biased coin adaptive design, Markov chain model.

Adaptive robust dissipative designs on straight path control for underactuated ships

An adaptive robust control algorithm for ship straight path control system in the presence of both modeling uncertainties and the bounded disturbances is proposed. Motivated by the backstepping approach, the algorithm is developed by using the dissipation theory, such that the resulting dosed-loop system is both strictly dissipative and asymptotically adaptively stable for all admissible uncertainties. Also, it is able to steer an underactuated ship along a prescribed straight path with ultimate bounds under external disturbances induced by wave, wind and ocean current. When there are no disturbances, the straight path control can be implemented in a locally asymptotically stable manner. Simulation results on an ocean-going training ship ‘YULONG＇ are presented to validate the effectiveness of the algorithm.

Joint adaptive waveform and baseline range design for bistatic radar

The problems of joint adaptive waveform design and baseline range design for bistatic radar to maximize the practical radar resolution were considered.Distinguishing from the conventional ambiguity function(AF)-based resolution which is only related with the transmitted waveform and bistatic geometry and could be regarded as the potential resolution of a bistatic radar system,the practical resolution involves the effect of waveform,signal-to-noise ratio(SNR)as well as the measurement model.Thus,it is more practical and will have further significant application in target detection and tracking.The constraint optimization procedure of joint adaptive waveform design and baseline range design for maximizing the practical resolution of bistatic radar system under dynamic target scenario was devised.Simulation results show that the range and velocity resolution are enhanced according to the adaptive waveform and bistatic radar configuration.

A new family of covariate-adjusted response adaptive designs and their properties

It is often important to incorporate covariate information in the design of clinical trials. In literature there are many designs of using stratification and covariate-adaptive randomization to balance certain known covariate. Recently, some covariate-adjusted response-adaptive （CARA） designs have been proposed and their asymptotic properties have been studied （Ann. Statist. 2007）. However, these CARA designs usually have high variabilities. In this paper, a new family of covariate-adjusted response-adaptive （CARA） designs is presented. It is shown that the new designs have less variables and therefore are more efficient.

ADAPTABLE DESIGN IN PRODUCT DEVELOPMENT

A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.

DESIGN OF RECONFIGURABLE MANUFACTURING SYSTEMS WITH STRONGLY COUPLED NATURE

Today＇s manufacturing cnvironmem forces manufacturing companies to make as many product variations as possible at affordable costs within a short time. Mass customisation is one of most important technologies for companies to achieve their objectives. Efforts to mass customisation should be made on two aspects： （1） To modularize products and make them as less differences as possible; （2） To design manufacturing resources and make them provide as many processes variations as possible. This paper reports our recent work on aspect （2）, i.e. how to design a reconfignrable manufacturing system （RMS） so that it can be competent to accomplish various processes optimally; Reconfignrable robot system （RRS） is taken as an example. RMS design involves architecture design and configuration design, and configuration design is further divided in design analysis and design synthesis. Axiomatic design theory （ADT） is applied to architecture design, the features and issues of RRS configuration design are discussed, automatic modelling method is developed for design analysis, and concurrent design methodology is presented for design synthesis.

Utility of Adaptive Sample Size Designs and A Review Example

Sample size can be a key design feature that not only affects the probability of a trial＇s success but also determines the duration and feasibility of a trial. If an investigational drug is expected to be effective and address unmet medical needs of an orphan disease, where the accrual period may require many years with a large sample size to detect a minimal clinically relevant treatment effect, a minimum sample size may be set to maintain nominal power. In limited situations such as this, there may be a need for flexibility in the initial and final sample sizes; thus, it is useful to consider the utility of adaptive sample size designs that use sample size re-estimation or group sequential design. In this paper, we propose a new adaptive performance measure to consider the utility of an adaptive sample size design in a trial simulation. Considering that previously proposed sample size re-estimation methods do not take into account errors in estimation based on interim results, we propose Bayesian sample size re-estimation criteria that take into account prior information on treatment effect, and then, we assess its operating characteristics in a simulation study. We also present a review example of sample size re-estimation mainly based on published paper and review report in Pharmaceuticals and Medical Devices Agency （PMDA）.

A KIND OF URN MODEL FOR ADAPTIVE SEQUENTIAL DESIGN

This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems (strong consistency and asymptot- ical normality) have been obtained.

Design and Test of Multibus Adapter System on a Chip for Fault Tolerant Computer Systems

In order to improve the system reliability and performance and to reduce the system cost, volume and weight, we have designed, fabricated and tested the multibus adapter system of a trimodular redundant fault tolerant computer system on a single chip of 5000 gate CMOS gate array. The design, fabrication and test of this single chip system will be discussed..

Enlightenment and Reference of Vaccine Clinical Trial Design Based on Immunological Surrogate Endpoints in the United States

Objectives To explore the optimization method of vaccine clinical trial design based on immunological surrogate endpoint to improve the quality and efficiency of vaccine clinical research and development.Methods As to the problems in the vaccine clinical research in China,the relevant guidelines and literatures of FDA and WHO were used to analyze and summarize the methods of optimizing the design of vaccine clinical trials.Results and Conclusion The adaptive design guidelines are established to guide clinical trial design,encourage the development and application of immunological surrogate endpoints,establish qualification process for drug development tools and information disclosure procedures to improve vaccine development efficiency.

Design of Process Centrifugal Compressor： an Adapted Algorithm

Considering the essential and influential role of centrifugal compressors in a wide range of industries makes most of engineers research and study on design and optimization of centrifugal compressors. Centrifugal compressors are the key to part ofoil, gas and petrochemical industries as well as gas pipeline transports. Since complete 3D design of the compressor consumes a considerable amount of time, most of active companies in the field, are profoundly interested in obtaining a design outline before taking any further steps in designing the entire machine. In this paper, a numerical algorithm, named ACDA （adapted compressor design algorithm） for fast and accurate preliminary design of centrifugal compressor is presented. The design procedure is obtained under real gas behavior, using an appropriate equation of state. Starting from impeller inlet, the procedure is continued on by resulting in numerical calculation for other sections including impeller exit, volute and exit diffuser. Clearly, in any step suitable correction factors are employed in order to conclude in precise numerical results. Finally, the achieved design result is compared with available reference data.

Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is developed.The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance.The admissibility of the iterative control law is analyzed.The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution.To implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each iteration.Finally,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying parameters.The effectiveness of the developed method is illustrated by numerical results and comparisons.

Adaptive Backstepping Slide Mode Control of Pneumatic Position Servo System

With the price decreasing of the pneumatic proportional valve and the high performance micro controller, the simple structure and high tracking performance pneumatic servo system demonstrates more application potential in many fields. However, most existing control methods with high tracking performance need to know the model information and to use pressure sensor. This limits the application of the pneumatic servo system. An adaptive backstepping slide mode control method is proposed for pneumatic position servo system. The proposed method designs adaptive slide mode controller using backstepping design technique. The controller parameter adaptive law is derived from Lyapunov analysis to guarantee the stability of the system. A theorem is testified to show that the state of closed-loop system is uniformly bounded, and the closed-loop system is stable. The advantages of the proposed method include that system dynamic model parameters are not required for the controller design, uncertain parameters bounds are not need, and the bulk and expensive pressure sensor is not needed as well. Experimental performance, as compared with some existing methods. results show that the designed controller can achieve better tracking

Personalization for Massive Product Innovation Using Open Architecture

Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

A policy iteration algorithm of adaptive dynamic programming（ADP） is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

We develop an online adaptive dynamic programming （ADP） based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.