Thai power: Net metering comes to Thailand

Thailand has recently introduced net metering legislation that provides streamlined procedures for small renewable energy generators to connect to the grid, and guarantees both a market and good prices. The new laws create income opportunities for rural communities based on locally produced, clean, renewable energy supplies and offer significant potential to reduce Thailand's dependence on imported oil and coal. Chris Greacen, Chuenchom Sangarasri Greacen and Rich Plevin report.     

Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.     

Distributed Sensing and Control Technology for Intelligent Civil Infrastructure Systems

Abstract: Sensing and control functions are the fundamental technologies on which many civil infrastructure control and monitoring systems are constructed. Early civil infrastructure control and monitoring systems employed a centralized computing model for sensor processing and actuator control. These systems required extensive wiring to route power, sensor, and control signals among the distributed input and output devices. Furthermore, the operation of the entire system depended on one or two computers. Abstractly, these civil infrastructure applications are a realtime data acquisition and control system. Due to the dramatic decrease in computing costs, it is now feasible to construct these real-time systems using dozens or even hundreds of distributed processors linked with a data communication network. This allows the controllers to be located adjacent to sensors and actuators, thereby reducing the need for long lengths of multiconductor wire that is subject to noise and breakage. Also, failure of a single processor does not necessarily cause the entire system to fail. This paper introduces the reader to a technology called LonWorks for constructing distributed sensing and control systems. The LonWorks technology is essentially a data communication network that links together extremely small computers called Neurons. Particular emphasis is placed on describing the communication architecture and emphasizing how the distributed Neuron communication processors are designed to ensure a truly modular and open architecture.     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.     

Intra-seasonal NDVI change projections in semi-arid Africa

Early warning systems (EWS) tend to focus on the identification of slow onset disasters such famine and epidemic disease. Since hazardous environmental conditions often precede disastrous outcomes by many months, effective monitoring via satellite and in situ observations can successfully guide mitigation activities. Accurate short term forecasts of NDVI could increase lead times, making early warning earlier. This paper presents a simple empirical model for making 1 to 4 month NDVI projections. These statistical projections are based on parameterized satellite rainfall estimates (RFE) and relative humidity demand (RHD). A quasi-global, 1 month ahead, 1° study demonstrates reasonable accuracies in many semi-arid regions. In Africa, a 0.1° cross-validated skill assessment quantifies the technique's applicability at 1 to 4 month forecast intervals. These results suggest that useful projections can be made over many semi-arid, food insecure regions of Africa, with plausible extensions to drought prone areas of Asia, Australia and South America.     

A comparison of the dissolution of model compounds and the kerogen of Green River oil shale by oxidation with perchloric acid — A model for the kerogen of Green River oil shale

A stepwise degradation of a kerogen concentrate of Green River oil shale was effected by oxidation with perchloric acid of varying boiling point and concentration. Dissolution of the kerogen concentrate occurred between 160 and 170°C. Several model compounds were also oxidized with perchloric acid. The model compounds were polyethylene, graphite, nylon, and polyethylene oxide. The results indicated that the kerogen was similar to polyethylene oxide. The results of this study and previous work were used to present a model for the structure of the kerogen of Green River oil shale.     

fsh—A functional unix command interpreter

Command interpreters under the UNIX operating system typically employ the syntax and semantics of imperative programming languages. Many of the advantages that functional programming languages hold over imperative languages can be incorporated in a functional command interpreter. Unfortunately, functional command interpreters to date have used the syntax of Backus's FP. The advantages of command expressiveness and composition have not been appreciated. A powerful functional command interpreter, fsh, that overcomes many of the deterrents of previous functional command interpreters is presented. Much of the unnecessary complexity of existing UNIX command interpreters is avoided by following the functional paradigm. fsh has been implemented under the UNIX 4.2BSD and System V operating systems.