An engineering approach to automatic programming

An exploratory study of the automatic generation and optimization of symbolic programs using DECOM - a prototypical requirement specification model implemented in pure LISP was undertaken. It was concluded, on the basis of this study, that symbolic processing languages such as LISP can support a style of programming based upon formal transformation and dependent upon the expression of constraints in an object-oriented environment. Such languages can represent all aspects of the software generation process (including heuristic algorithms for effecting parallel search) as dynamic processes since data and program are represented in a uniform format

A Dynamic Approach to Estimate the Efficiency of U.S. Electric Utilities

The static production efficiency model and the dynamic duality model of intertemporal decision making using a parametric approach have been continuously developed but in separate direction. The parametric approach takes statistical noise into account, which consequently provides accurate measures in a stochastic environment. In this study the static shadow cost approach and the dynamic duality model of intertemporal decision making are integrated to formulate theoretical and econometric models of dynamic efficiency with intertemporal cost minimizing firm behavior. The dynamic efficiency model is a dynamic measure of firms’ inefficiency and it accounts for allocative and technical inefficiencies of net investment and of variable inputs. The dynamic efficiency model is implemented by using the Generalized Method of Moment (GMM) estimation and empirically applied into a panel data set of 72 U.S. major investor-owned electric utilities using fossil-fuel fired steam electric power generation during the time period of 1986 to 1999. The major results of this study are that most electric utilities in this study underutilized fuel relative to the aggregated labor and maintenance input and they overutilized capital in production. The estimates of the input price elasticities present the substitution possibilities among the inputs. Finally, the results suggest evidence of increasing returns to scale in the production of the electricity industryEfficiency, GMM estimation, shadow cost approach, dynamic duality, deregulation, electricity

Modelling of priority pollutants releases from urban areas

In the framework of the EU project ScorePP (Source Control Options for Reducing Emissions of Priority Pollutants), dynamic PPs (priority pollutants) fate models are being developed to assess appropriate strategies for limiting the release of PPs from urban sources and for treating PPs on a variety of spatial scales. Different possible sources of PP releases were mapped and both their release pattern and their loads were quantified as detailed as possible. This paper focuses on the link between the gathered PP sources data and the dynamic models of the urban environment. This link consists of: (1) a method for the quantitative and structured storage of temporal emission pattern information, (2) the coupling of GIS-based spatial emission source data with temporal emission pattern information and (3) the generation of PP release time series to feed the dynamic sewer catchment model. Steps 2 and 3 were included as the main features of a dedicated software tool. Finally, this paper also illustrates the method’s applicability to generate model input timeseries for generic pollutants (N, P and COD/BOD) in addition to priority pollutants

Optimal trajectory generation in ocean flows

In this paper it is shown that Lagrangian Coherent Structures (LCS) are useful in determining near optimal trajectories for autonomous underwater gliders in a dynamic ocean environment. This opens the opportunity for optimal path planning of autonomous underwater vehicles by studying the global flow geometry via dynamical systems methods. Optimal glider paths were computed for a 2-dimensional kinematic model of an end-point glider problem. Numerical solutions to the optimal control problem were obtained using Nonlinear Trajectory Generation (NTG) software. The resulting solution is compared to corresponding results on LCS obtained using the Direct Lyapunov Exponent method. The velocity data used for these computations was obtained from measurements taken in August, 2000, by HF-Radar stations located around Monterey Bay, CA

COFS 1: Beam dynamics and control technology overview

The Control of Flexible Structures (COFS) 1 Project provides the invaluable opportunity to test, validate, and measure the effectiveness of theories, structural concepts, control systems, and flight certification processes for future missions through a research program focusing on multiple issues in large flexible structures, dynamics, and controls. The COFS 1 Project consists of a series of ground and flight activities building progressively from modeling and dynamic characterization of large space systems to the more complex issues of flexible-body control. The program objectives are to: determine the degree to which theory and ground testing can predict flight performance of next-generation low-frequency structures; evaluate structural fidelity of representative next-generation large deployable precision structure; assess math modeling requirements for large lightweight complex systems on which ground test results are questionable; determine degree to which scale model analysis and tests can be correlated to full-scale performance; evaluate system identification and state estimation algorithms on complex lightweight structures in the space environment; evaluate and verify controls/structures modeling capability; evaluate control laws and control systems; and evaluate damping effects in micro-g environment

Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion

Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of urban applications. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation