Consideration of the errors inherent in mapping historical glacier positions in Austria from the ground and space (1893-2001)

The historical record of in situ measurements of the terminus positions of the Pasterze and Kleines Fleißkees glaciers in the eastern Alps of Austria is used to assess uncertainties in the measurement of decadal scale changes using satellite data. Topographic maps beginning in 1893, and satellite data from 1976 to 2001, were studied in concert with ground measurements to measure glacier changes. Ground measurements show that the tongue of the Pasterze Glacier receded ∼1150 m from 1893 to 2001, while satellite-derived measurements, using August 2001 Landsat Enhanced Thematic Mapper Plus (ETM+) data registered to an 1893 topographic map, show a recession of 1300-1800 m, with an unknown error. The measurement accuracy depends on the registration technique and the pixel resolution of the sensor when two satellite images are used. When using topographic maps, an additional source of error is the accuracy of the glacier position shown on the map. Between 1976 and 2001, Landsat-derived measurements show a recession of the terminus of the Pasterze Glacier of 479±136 m (at an average rate of 19.1 m a⁻¹) while measurements from the ground showed a recession of 428 m (at an average rate of 17.1 m a⁻¹). Four-meter resolution Ikonos satellite images from 2000 and 2001 reveal a shrinkage of 22,096±46 m² in the Pasterze tongue. The nearby Kleines Fleißkees glacier lost 30% of its area between 1984 and 2001, and the area of exposed ice increased by 0.44±0.0023 km², according to Landsat satellite measurements. As more recent satellite images are utilized, especially data that are geocoded, the uncertainty associated with measuring glacier changes has decreased. It is not possible to assess the uncertainty when an old topographic map and a satellite image are coregistered.     

An Autonomous Spacecraft Agent Prototype

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. The architecture supports challenging requirements of the autonomous spacecraft domain not usually addressed in mobile robot architectures, including highly reliable autonomous operations over extended time periods in the presence of tight resource constraints, hard deadlines, limited observability, and concurrent activity. A hybrid architecture, NMRA integrates traditional real-time monitoring and control with heterogeneous components for constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconfiguration. Novel features of this integrated architecture include support for robust closed-loop generation and execution of concurrent temporal plans and a hybrid procedural/deductive executive.We implemented a prototype autonomous spacecraft agent within the architecture and successfully demonstrated the prototype in the context of a challenging autonomous mission scenario on a simulated spacecraft. As a result of this success, the integrated architecture has been selected to fly as an autonomy experiment on Deep Space One (DS-1), the first flight of NASA';s New Millennium Program (NMP), which will launch in 1998. It will be the first AI system to autonomously control an actual spacecraft.     

Goal-driven automation of a deep space communications station: a case study in knowledge engineering for plan generation and execution

Abstract: This paper describes the application of Artificial Intelligence techniques for plan generation, plan execution, and plan monitoring to automate a Deep Space Communication Station. This automation allows a communication station to respond to a set of tracking goals by appropriately reconfiguring the communications hardware and software to provide the requested communications services. In particular this paper describes: (1) the overall automation architecture, (2) the plan generation and execution monitoring AI technologies used and implemented software components, and (3) the knowledge engineering process and effort required for automation. This automation was demonstrated in February 1995, at the DSS13 Antenna Station in Goldstone, CA on a series of Voyager tracks and the technologies demonstrated are being transferred to the operational Deep Space Network stations.     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.     

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.     

The high‐speed measurement of a partial area imaging system applied to photoresist development processing

Abstract

Among the parameters that affect photolithography, the most important are exposure and development time which affect the coating photoresist characteristics. This study further researches the relationship between the exposure and development time using a high speed image inspection system, and the relationship between the development time and photoresist depth using a Scanning Probe Microscope (SPM). A partial scan CCD camera and high speed frame capture card were used to obtain the photoresist development processing parameters. The experimental results verified that this imaging system provides an economical and effective method for producing a micro‐photo‐etched product. It is expected that these experiments can also offer some good references useful in the micro electro mechanical industrial field.     

Efficient development of cycle time response surfaces using progressive simulation metamodeling

In semiconductor manufacturing, hot lots are to provide marketing and engineering with extra flexibility regarding delivery lead times, and in turn enhance its competitive advantages against other companies. On the other hand, hot lots are among major sources of disruption of the smoothness of the manufacturing flow. They can lead to a significant increase of cycle time of normal lots, and in turn result in delayed delivery times and serious service deteriorations. Due to the complex nature of semiconductor manufacturing, evaluating the impact of hot lots on the cycle time of normal lots presents major challenges. In this paper, we propose a methodology, called progressive simulation metamodelling (PSM), that allows for an efficient development of the response surface between the cycle time of normal lots and the percentage of hot lots in semiconductor manufacturing. The response surface generated by the proposed PSM is like an easy-to-use analytical model, but with the fidelity of simulation that takes into account all important manufacturing details. The specially-designed mechanisms, including identifying the critical region and sequentially adding design points in the critical region, further grants PSM computational advantages compared to the traditional response surface method. An empirical study conducted in collaboration with a semiconductor company validates the viability of PSM in real settings.     

Temperature and voltage sensings by mode‐mode interference in polarization‐maintaining optical fibers

Abstract

Two optic fiber sensing systems for temperature and voltage have been developed which utilize the mode‐mode interference of the two orthogonally polarized modes, HE^x _II and HE^y _II, in two commencal polarization‐maintaining fibers (bow‐tie and elliptical core fibers). A package of controlled programs in a Macintosh computer, which can record and process all related data automatically, is established for temperature sensing. The signal drifting problem in voltage sensings has been investigated, and the elimination of signal drifting is obtained by the phase tracking with direct current technology The agreement between the sensing results for temperature and dynamic voltage and those predicted by experimental principles is satisfactory, which confirms the validity of the developed sensing systems.