Energy Modeling Approach Research Articles

Poster 50: Control of a Robotic Aid to Manipulation by Myoelectric Signals in Spinal Cord Injury Subjects

Objective: To test the reliability and the accuracy of movement classifier based on electromyographic (EMG) signals of residual muscles of the upper-body spinal cord injury subjects. These movements will serve to control a robotic aid to manipulation. Design: Randomized, prospective study. Setting: Laboratory of imaging and orthopedics at the École de technologie supérieure. Participants: 30 subjects in total, 15 with spinal cord injury (level C4-C6) and 15 able-bodied subjects, aged from 18 to 65 years of age. Interventions: Participants perform a series of elementary and sequential movements of the upper body such as head rotations, shoulder elevation and depression, and elbow flexion. Main Outcome Measures: During each movement series, 8 EMG muscles activities were collected. For each EMG channel 6 features were extracted based on energy and autoregressive modeling approach. These features were inputted into a linear discriminant analysis (LDA) classifier. The main outcome measure is the performance of LDA classifier in order to recognize elementary and complex sequence of movement based only on EMG feature data. Result: Preliminary results obtained from 3 able-bodied subjects show that the performance of LDA was about 85% for 12 classified movements using the best learning sequences. The classifier's performance may be enhanced by diminishing the number of classified movements from 12 to 8. The performance of the LDA increases then to 95% with unilateral movements only. Conclusions: It is possible to achieve good classification performance by adapting the learning sequences and best classified movements to each subject.

Dynamic energy model of a lithium-ion battery

As an electrochemical component, a lithium-ion battery is clearly a multi-disciplinary system. The choice was made to model it via Bond Graph formalism. Although this tool has been developed since the 1970s, the novelty is its application to lithium-ion batteries, which turns the modeling presented here into an original energy approach. The main objective is to develop and validate a lithium-ion battery model that could be implemented in a global system for energy monitoring. However, nearly every phenomenon occurring in the battery is taken into account for a possible ageing or thermal study. In the first part, the energy modeling approach is described. In the second part, the lithium-ion battery operation is explained. In the third part, the Bond Graph model is proposed. At last, experimental validations are presented.

Insight into the Structure of Light-Harvesting Complex II and Its Stabilization in Detergent Solution

The structure of spinach light-harvesting complex II (LHC II), stabilized in a solution of the detergent n-octyl-beta-D-glucoside (BOG), was investigated by small-angle neutron scattering (SANS). Physicochemical characterization of the isolated complex indicated that it was pure (>95%) and also in its native trimeric state. SANS with contrast variation was used to investigate the properties of the protein-detergent complex at three different H(2)O/D(2)O contrast match points, enabling the scattering properties of the protein and detergent to be investigated independently. The topological shape of LHC II, determined using ab initio shape restoration methods from the SANS data at the contrast match point of BOG, was consistent with the X-ray crystallographic structure of LHC II (Liu et al. Nature 2004 428, 287-292). The interactions of the protein and detergent were investigated at the contrast match point for the protein and also in 100% D(2)O. The data suggested that BOG micelle structure was altered by its interaction with LHC II, but large aggregate structures were not formed. Indirect Fourier transform analysis of the LHC II/BOG scattering curves showed that the increase in the maximum dimension of the protein-detergent complex was consistent with the presence of a monolayer of detergent surrounding the protein. A model of the LHC II/BOG complex was generated to interpret the measurements made in 100% D(2)O. This model adequately reproduced the overall size of the LHC II/BOG complex, but demonstrated that the detergent does not have a highly regular shape that surrounds the hydrophobic periphery of LHC II. In addition to demonstrating that natively structured LHC II can be produced for functional characterization and for use in artificial solar energy applications, the analysis and modeling approaches described here can be used for characterizing detergent-associated alpha-helical transmembrane proteins.

Playing the trade-off game

Modern mobile devices need to be extremely energy efficient. Due to the growing complexity of these devices, energy-aware design exploration has become increasingly important. Current exploration tools often do not support energy estimation, or require the design to be very detailed before estimation is possible. It is important to get early feedback on both performance and energy consumption during all phases of the design and at higher abstraction levels. This article presents a unified optimization and exploration framework to explore source-level transformation to processor architecture design space. The proposed retargetable compiler and simulator framework can map applications to a range of processors and memory configurations, simulate, and report detailed performance and energy estimates. An accurate and consistent energy modeling approach is introduced which can estimate the energy consumption of processor and memories at a component level, which can help to guide the design process. Fast energy-aware architecture exploration is illustrated by modeling both state-of-the-art processors as well as other architectures. Various design trade-offs are also illustrated on different academic as well as industrial benchmarks from both the wireless communication and multimedia domain. We also illustrate a design space exploration on different applications and show that there is large trade-off space between application performance, energy consumption, and area. We show that the proposed framework is consistent, accurate, and covers a large design space including various novel low-power extensions in a unified framework.

Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function

There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent ( λE) and sensible heat flux ( H). In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / ( λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies ( R 2 = 0.88; RMSE = 0.18 (22%)). To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties.

Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan

In this paper, we present the near-term analysis of capacity expansion under various uncertainties from the viewpoints of the decision-making process on the optimal allocation of investment and the value of information. An optimization model based on two-stage stochastic programming was developed using real data to describe the Japanese energy system as a case study. Different uncertainty parameters were taken into consideration by a disaggregate analysis of a bottom-up energy modeling approach, including end-use energy demands, plant operating availability and carbon tax rate. Four policy regimes represented as energy planning or policy options were also studied, covering business as usual, renewable energy target, carbon taxation and nuclear phase-out regimes. In addition, we investigated the role of various energy technologies and the behavior of the value of information with respect to the probability function of the worst-case scenario. This value of information provides decision makers with a quantitative analysis for the cost to obtain perfect information about the future. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in energy planning and policy analyses.

Fundamental thermodynamic aspects of the CaO-Al2O3-SiO2system

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