Replicating sucess: a model for conservation and development projects

"This study investigates how successful development and conservation projects can be replicated without investing inordinate amounts of money and manpower in the planning process. It offers examples and ideas from 12 different development fields, details key requirements, outlines the strengths and weaknesses of these components and shows how best practices can be emulated elsewhere." [author's abstract

Equipment Using a Predictive Health Model

Abstract—In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based on the health state, the failure rate of the equipment can be estimated. We propose to use this predictive health model to predict the effects of different maintenance actions. The effects of maintenance actions over a future time window are evaluated by a cost function. The maintenance actions are optimized using this cost function. The proposed framework is applied in the optimization of the loading of transformers based on the thermal degradation of the paper insulation

Vehicle Detection for RCTA/ANS (Autonomous Navigation System)

Using a stereo camera pair, imagery is acquired and processed through the JPLV stereo processing pipeline. From this stereo data, large 3D blobs are found. These blobs are then described and classified by their shape to determine which are vehicles and which are not. Prior vehicle detection algorithms are either targeted to specific domains, such as following lead cars, or are intensity- based methods that involve learning typical vehicle appearances from a large corpus of training data. In order to detect vehicles, the JPL Vehicle Detection (JVD) algorithm goes through the following steps: 1. Take as input a left disparity image and left rectified image from JPLV stereo. 2. Project the disparity data onto a two-dimensional Cartesian map. 3. Perform some post-processing of the map built in the previous step in order to clean it up. 4. Take the processed map and find peaks. For each peak, grow it out into a map blob. These map blobs represent large, roughly vehicle-sized objects in the scene. 5. Take these map blobs and reject those that do not meet certain criteria. Build descriptors for the ones that remain. Pass these descriptors onto a classifier, which determines if the blob is a vehicle or not. The probability of detection is the probability that if a vehicle is present in the image, is visible, and un-occluded, then it will be detected by the JVD algorithm. In order to estimate this probability, eight sequences were ground-truthed from the RCTA (Robotics Collaborative Technology Alliances) program, totaling over 4,000 frames with 15 unique vehicles. Since these vehicles were observed at varying ranges, one is able to find the probability of detection as a function of range. At the time of this reporting, the JVD algorithm was tuned to perform best at cars seen from the front, rear, or either side, and perform poorly on vehicles seen from oblique angles

Development of a 1 kW Gravitational Water Vortex Hydropower Plant Prototype

A pilot testing of a Gravitational Water Vortex Hydropower Plant (GWVHP) has been done to evaluate the applicability in a real-world scenario and validate the results from the lab-scale model. A scaled-up model of a capacity of 1 kW was constructed for the evaluation purpose. The test provided data in good agreement with a lab-scale model and a proper visualization to install Gravitational Water Vortex in real-world scenarios. The project lasted for nearly four months and thus provided important information on the problems that might arise in scaling up the lab model to a micro-hydro system. The pilot testing shows an overall plant efficiency of 49%, validating the lab-based studies conducted beforehand. The information obtained from this pilot study shall be implemented in a micro-hydro project on a larger scale

Narrowing gaps between research and policy development in climate change adaptation work in the water resources and agriculture sectors of Cambodia

Narrowing research and policy, while challenging, is especially important in climate change adaptation work (CCA) due to the high uncertainties involved in planning for climate change. This article aims to seek stakeholders’ opinions regarding how research and policy development can be bridged within the Cambodian water resources and agriculture sectors. The study used institutional ethnography methods with informants from government organizations, local academia, and development partners (DPs). This article identifies a number of challenges, and barriers for narrowing research–policy development gaps, including: limited effectiveness of governmental policies and planning; lack of relevant information required to promote evidence-based planning and policy development; and communication barriers. Evidence-based planning is valued by government officials most when there is actual and effective implementation of policies and plans. In practice, this often implies that governmental policies and plans need be scoped and scaled down to meet the available budget, and thus be achievable. In the long term, it also means building the capacity for policy-relevant research on climate change adaptation within Cambodia. Engaging policy stakeholders in research process for co-producing adaptation knowledge, and introducing knowledge intermediaries are suggested by informants as means to narrowing gaps between research and policy development. The presence of the Cambodia's Prime Minister in research–policy dialogues is recommended as important for attracting the attention of high-level policy makers