Ecological Planting Imperative: Functional Systems,Not Stylized Ecologies

The ability of landscape architectural projects to mitigate the worst effects of climate change will depend upon designed ecological systems. These systems will be built with plants. Despite the recognition of ecology as an essential driver of landscapes, the professionals of landscape architecture too often lack the knowledge and practical skills to create robust vegetative systems. New approaches and tools are required. This article outlines principles and methods for designing biodiverse plant systems for urban sites. Planting methods that increase species richness, functional diversity, and spatial complexity are emphasized as a way of developing more resilient plantings. Selecting species with similar evolutionary adaptions to stress, disturbance, and competition—as well as creating multi-layered compositions of diverse plant morphologies—allows designers to create compatible, long-lived plant mixes. To balance the increased visual complexity of diverse plant mixes, the article explores design techniques to make plantings more appealing to the public. The strategies explored here are based on the projects, experience, and research of Phyto Studio, a Washington, D.C. based studio. The methods build on work described in the author’s book, Planting in a Post-Wild World, an exploration of how to create designed plant communities.     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

Exploring design trade-offs for achieving social inclusion in multi-tiered design problems

ABSTRACT

The digital age of the future is ‘not out there to be discovered’, but it needs to be ‘designed’. The design challenge has to address questions about how we want to live, work, and learn (as individuals and as communities) and what we value and appreciate, e.g.: reflecting on quality of life and creating inclusive societies. An overriding design trade-off for the digital age is whether new developments will increase the digital divide or will create more inclusive societies. Sustaining inclusive societies means allowing people of all ages and all abilities to exploit information technologies for personally meaningful activities. Meta-design fosters the design of socio-technical environments that end-user developers can modify and evolve at use time to improve their quality of life and favour their inclusion in the society. This paper describes three case studies in the domain of assistive technologies in which end users themselves cannot act as end-user developers, but someone else (e.g.: a caregiver or a clinician) must accept this role requiring multi-tiered architectures. The design trade-offs and requirements for meta-design identified in the context of the case studies and other researchers’ projects are described to inform the development of future socio-technical environments focused on social inclusion.     

An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Interactive design exploration for constrained meshes

In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm.We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately.Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces.     

Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration

This paper describes algorithms that use demand-side management to address large-scale integration of wind power. In particular, demand response (DR) is used to manage wind power intermittency by shifting the time that electrical power system loads occur in response to real-time prices and wind availability. An economic dispatch with transmission, DR capacity and operational constraints is used to model the operation of a transmission constrained system with a high penetration of wind power. This optimization model is used to determine the optimal sizing and distribution of DR given a fixed budget for customer incentives and the installation of enabling technology. We demonstrate the effectiveness of the operational model based on a simple PJM 5-bus system and an IEEE 118-bus system. Simulation results show that transmission constraints have a greater effect on sizing of DR capacity than the location of wind power, which means that buses electrically close to congested lines tend to have higher incentives to deploy DR resources than other buses. The second part of the work examines optimal siting of technology that enables DR based on the frequency of DR based load changes, which are generally a function of the network location.     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.