Showing papers by "Eric Chu published in 2014"

Variations in approaches to urban climate adaptation: Experiences and experimentation from the global South

Abstract: In recent years, an increasing number of local governments are recognizing the impact of climate change on different urban sectors. This has led many to pursue climate adaptation planning, seeking to achieve preparedness through reducing vulnerability and enhancing resilience of populations, assets, and municipal operations. Although cities typically share these common goals, many are electing to pursue different planning approaches. In this paper, we examine three climate adaptation planning approaches in the cities of Quito (Ecuador), Surat (India), and Durban (South Africa) and analyze the trade-offs associated with different planning pathways and different forms of stakeholder involvement. We assess the potentials and limitations of these different approaches, including their implications for enhancing government integration and coordination, promoting participation and adaptive capacity of vulnerable groups, and facilitating overall urban resilience. We find that, in order to gain widespread commitment on adaptation, sustained political leadership from the top, departmental engagement, and continued involvement from a variety of stakeholders are integral to effective decision-making and institutionalization of programs in the long run. When climate adaptation is advanced with a focus on learning, awareness, and capacity building, the process will likely lead to more sustained, legitimate, and comprehensive adaptation plans and policies that enhance the resilience of the most affected urban areas and residents.

Security Constrained Optimal Power Flow via proximal message passing

Abstract: In this paper, we propose a distributed algorithm to solve the Security Constrained Optimal Power Flow (SC-OPF) Problem. We consider a network of devices, each with its own dynamic constraints and objective, subject to reliability constraints across multiple scenarios. Each scenario corresponds to the failure or degradation of a set of devices and has an associated probability of occurrence. The network objective is to minimize the cost of operation of all devices, over a given time horizon, across all scenarios subject to the constraints of transmission limit, upper and lower generating limits, generation-load balance etc. This is a large optimization problem, with variables for consumption and generation for each device, in each scenario. In this paper, we extend the proximal message passing framework to handle reliability constraints across scenarios. The resulting algorithm is extremely scalable with respect to both network size and the number of scenarios.

A review of decision-support models for adaptation to climate change in the context of development

Abstract: In order to increase adaptive capacity and empower people to cope with their changing environment, it is imperative to develop decision-support tools that help people understand and respond to challenges and opportunities. Some such tools have emerged in response to social and economic shifts in light of anticipated climatic change. Climate change will play out at the local level, and adaptive behaviours will be influenced by local resources and knowledge. Community-based insights are essential building blocks for effective planning. However, in order to mainstream and scale up adaptation, it is useful to have mechanisms for evaluating the benefits and costs of candidate adaptation strategies. This article reviews relevant literature and presents an argument in favour of using various modelling tools directed at these considerations. The authors also provide evidence for the balancing of qualitative and quantitative elements in assessments of programme proposals considered for financing through mechanisms...

Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments

Abstract: Robotic grasping in unstructured environments requires the ability to adjust and recover when a pre-planned grasp faces imminent failure. Even for a single object, modeling uncertainties due to occluded surfaces, sensor noise and calibration errors can cause grasp failure; cluttered environments exacerbate the problem. In this work, we propose a simple but robust approach to both pre-touch grasp adjustment and grasp planning for unknown objects in clutter, using a small-baseline stereo camera attached to the gripper of the robot. By employing a 3D sensor from the perspective of the gripper we gain information about the object and nearby obstacles immediately prior to grasping that is not available during head-sensor-based grasp planning. We use a feature-based cost function on local 3D data to evaluate the feasibility of a proposed grasp. In cases where only minor adjustments are needed, our algorithm uses gradient descent on a cost function based on local features to find optimal grasps near the original grasp. In cases where no suitable grasp is found, the robot can search for a significantly different grasp pose rather than blindly attempting a doomed grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects in cluttered scenes. Our results show that reactive pre-touch adjustment can correct for a fair amount of uncertainty in the measured position and shape of the objects, or the presence of nearby obstacles.