Lunar Soft-landing Trajectory Design Based on Evolutionary Strategy

Optimal explicit guidance for three-dimensional launch trajectory

Global Navigation Satellite Systems (GNSS) are commonly used for geodetic and land surveying applications. The stand alone accuracy provided by these GNSS are insufficient for the majority of these operations (GPS, 1995), therefore some form of differential correction method is required. Accordingly, the state survey offices of Germany have installed a differential correction service for geodetic applications. Code- and phase-corrections are broadcast in the VHF-band using the RTCM V2.1 format (RTCM, 1994). One major problem is that the accuracy depends on the distance to a reference station (length of baseline) because of residual orbit and atmospheric biases. To achieve a more precise solution, a number of reference stations are connected together to form a network. Within this network these influences are computed and a set of “area correction parameters” are also transmitted in RTCM message Type 59. Field trials and measurements have confirmed the high accuracy of this service. This paper describes the system itself, investigations of communication methods as well as site planning. In addition measurements from field trials will be presented to demonstrate the high accuracy in a real-time environment.

Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: A novel method

Parameters and their error intervals numerically estimated for the observed light curve of the binary eclipsing system YZ Cas as well as for one-, two-, three-, and four-parameter functions, and the associated parameters and their error intervals numerically estimated. The error intervals are calculated using differential corrections method, Monte-Carlo simulations, and confidence areas. We study the error intervals and the reliability of the techniques used.

Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

Nonstationary Bayesian kriging: a predictive technique to generate spatial corrections for seismic detection, location and identification

With the recently revived national interest in Lunar and Mars missions, this design study was undertaken by the author in an attempt to satisfy the long‐term space exploration vision of human travel “to the Moon, Mars, and beyond” with a single design or family of vehicles. This paper describes a conceptual design for an interplanetary spaceship of the not‐to‐distant future. It is a design that is outwardly similar to the spaceship Discovery depicted in the novel “2001 — A Space Odyssey” and film of the same name. Like its namesake, this spaceship could one day transport a human expedition to explore the moons of Jupiter. This spaceship Discovery is a real engineering design that is capable of being implemented using technologies that are currently at or near the state‐of‐the‐art. The ship’s main propulsion and electrical power are provided by bi‐modal nuclear thermal rocket engines. Configurations are presented to satisfy four basic Design Reference Missions: (1) a high‐energy mission to Jupiter’s moon Callisto, (2) a high‐energy mission to Mars, (3) a low‐energy mission to Mars, and (4) a high‐energy mission to the Moon. The spaceship design includes dual, strap‐on boosters to enable the high‐energy Mars and Jupiter missions. Three conceptual lander designs are presented: (1) Two types of Mars landers that utilize atmospheric and propulsive braking, and (2) a lander for Callisto or Earth’s Moon that utilizes only propulsive braking. Spaceship Discovery offers many advantages for human exploration of the Solar System: (1) Nuclear propulsion enables propulsive capture and escape maneuvers at Earth and target planets, eliminating risky aero‐capture maneuvers. (2) Strap‐on boosters provide robust propulsive energy, enabling flexibility in mission planning, shorter transit times, expanded launch windows, and free‐return abort trajectories from Mars. (3) A backup abort propulsion system enables crew aborts at multiple points in the mission. (4) Clustered NTR engines provide “engine out” redundancy. (5) The design efficiently implements galactic cosmic ray shielding using main propellant liquid hydrogen. (6) The design provides artificial gravity to mitigate crew physiological problems on long‐duration missions. (7) The design is modular and can be launched using the proposed upgrades to the Evolved Expendable Launch Vehicles or Shuttle‐derived heavy lift launch vehicles. (8) High value modules are reusable for Mars and Lunar missions. (9) The design has inherent growth capability, and can be tailored to satisfy expanding mission requirements to enable an in‐family progression “to the Moon, Mars, and beyond.”

The strategic plan for the development of the unmanned Chinese Lunar Exploration Program is characterized by three distinct stages: “orbiting around”, “landing on” and “returning from” the Moon. The first Chinese lunar probe, Chang’E-1, which was successfully launched on October 24th, 2007 at Xichang Satellite Launch Center, and guided to crash on the Moon on March 1st, 2009, at 52.36°E, 1.50°S, in the north of Mare Fecunditatis, is the first step towards the “orbiting around” stage. The Chang’E-1 mission lasted 495 days, exceeding the expected life-span by about four months. A total of 1.37 TB raw data was received from Chang’E-1. It was then processed into 4 TB scientific data products at various levels. Many scientific results have been obtained by analyzing these data, including especially the “global lunar image from the first Chinese lunar exploration mission”. All scientific goals of Chang’E-1 have been achieved. It provides much useful materials for further advances of lunar sciences and planetary chemistry. Meanwhile, these results will serve as a firm basis for future Chinese lunar missions.

This study explains a highly accurate predictive model for the interfacial tension (IFT) between crude oil and nitrogen, an important parameter for nitrogen-based gas injection in petroleum reservoirs based on a dataset of 148 experimental points, of which 90% (133 points) were used for training the models and the remaining 10% (15 points) for testing their performance. We used a gradient boosting decision tree (GBDT) algorithm, which was optimized by applying four different methods: Gaussian Processes Optimization (GPO), Batch Bayesian Optimization (BBO), Evolutionary Strategies (ES), and Bayesian Probability Improvement (BPI). These models were trained in experimental data and evaluated their performance using statistical and graphical analyses. The data proved suitable for model construction. A sensitivity examination revealed that pressure, crude oil API gravity and temperature, all negatively impact IFT, while pressure is the most significant feature. Among the evaluated models, the GBDTBBO demonstrated superior accuracy. It demonstrated the highest R-squared values and lowest error rates, and it correctly predicted the trends of IFT in relation to changes in pressure, temperature, and crude oil API.

To perform complex manufacturing operations, two or more manipulators are made to work in concert. When robots work independently of other robots, small errors made (e.g., due to inaccuracies in modeling of the manipulator) by individual robots may be acceptable. However, when robots work together, then high precision is required. This calls for the use of adaptive controllers in order to minimize errors. This paper discusses the application of Temporal Difference Learning (TDL) method, wherein stiffness of manipulator is adapted based on the feedback obtained from force/torque sensors. In order to accomplish this, simulation was carried out by adding feedback (force and change of force) to controllers, so that required trajectory could be adhered. Normally this error (deviation from required trajectory) occurs due to non-availability of the correct values of stiffness of the system. Stiffness of system is difficult to calculate due to inherent complexities in formulating an accurate dynamic model of system. Variation in parameters, for example change of friction due to aging, change of moment of inertia due to changes in payload position and orientation, significantly affect the dynamic model of manipulator. One of the ways to achieve compliance is by updating the dynamic model of the system. The other way is to use the external control loop which provide manipulator with set-points such that the desired compliance can be achieved [1]. This paper demonstrates the appropriateness of TDL method in updating the dynamic model of the system. This updated model is then used to calculate the torques of the joints. As the process of learning converges, the function learned represents a nearly perfect model of the stiffness of the system.

Beginning in 1998, U.S. commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial, interval forecast and distribution forecast methods. Given the low power often exhibited by their corresponding hypothesis tests, these methods can often misclassify forecasts from inaccurate models as acceptably accurate. An alternative evaluation method using loss functions based on probability forecasts is proposed. Simulation results indicate that this method is only as capable of differentiating between forecasts from accurate and inaccurate models as the other methods. However, its ability to directly incorporate regulatory loss functions into model evaluations make it a useful complement to the current regulatory evaluation of VaR models.

We propose a vision-based method to achieve Pixel-Level precision saccadic movements in an active binocular vision system (ABVS). Traditional methods for precise saccadic motion rely heavily on extensive training or accurate kinematic models. However, extensive pre-training reduces the flexibility of ABVS applications, and inaccuracies in the kinematic model can lead to significant errors or failures in saccadic movements. Our proposed saccade control method does not rely on accurate kinematic models. Instead, it utilizes virtual stereo rectification (VSR) and online extrinsic parameters correction (OEPC) to accurately estimate the extrinsic parameters of the binocular cameras. This enables rapid target search based on epipolar constraints using monocular target information, significantly reducing interference from numerous similar targets in the scene and achieving accurate saccades. Experimental results with ABVS demonstrate that our method achieves a 0.994 correct saccade rate and an average effective saccadic motion error of only 3.252 pixels without accurate kinematic models. With accurate kinematic models, the average effective saccadic motion error further decreases to 1.999 pixels when using our method.

<title>Abstract</title> Global navigation satellite system (GNSS) positioning relies on accurate stochastic models for measurement errors to provide reliable position estimates and bounds. This paper presents a novel approach for modeling errors in GNSS measurements with Bayesian distributional regression, using variational inference to scale model complexity and data set size beyond typical computational limits. This methodology is applied to an automotive data set for GNSS pseudorange (multipath) errors, targeting a Student’s t-distributed model to realistically characterize heavy-tailed data. The distribution is regressed on signal quality indicators from the GNSS receiver to segment different environmental effects on the measurements. Bayesian penalized tensor product splines are used to model nonlinear relationships based on the signal quality indicators. Detailed analyses of goodness-of-fit diagnostics show that the model is able to fit well to the data, and model uncertainty is quantified such that users may be aware of and compensate for inaccuracies in modeling.

The authors examine the hydrodynamic stability of imploding ICF capsules by explicitly calculating, the evolution of a realistic surface perturbation far into its nonlinear regime, using, a 2D Lagrangian radiation-hydrodynamics code. The perturbation, which consists initially of mesh displacements in the capsule, is represented by the sum of many spherical harmonic modes, having finite amplitudes and realistic spectrum. A 90-degree sector of the capsule is modeled, allowing proper boundary conditions for all modes simultaneously. Because of the large distortion of the mesh that occurs during the calculations, it is necessary to rezone the mesh frequently, by mapping physical variables to a new undistorted mesh. No model-specific parameters are required in this technique. The authors have used the technique to calculate the yield of several designs for a National Ignition Facility capsule as a function of initial root-mean-square surface roughness {sigma} of the outer ablator surface or the inner cryogenic DT surface. Typically for a capsule they find a ``cliff`` at a critical value of {sigma} = {sigma}{sub crit} such that the yield of the capsule decreases abruptly for {sigma} > {sigma}{sub crit}, indicating a failure to ignite. The values of {sigma}{sub crit} they compute are probably upper limits because of the lack of 3D effects, and inaccuracies in Lagrangian modeling of such unstable flows. It is expected that more accurate modeling, perhaps with 3D Eulerian codes, will lead to smaller values for {sigma}{sub crit}. They are beginning to carry out studies of the coupling of low-mode radiation flux asymmetries to higher-mode surface perturbations. They report also on sensitivity studies that examine the response of a capsule to small variations in the driving laser`s power history. They find that realistic surfaces decrease a capsule`s ability to tolerate drive variations.

Power transformer is one of the most important and expensive equipment in a power system. Its reliability directly affects a power system. To ensure the reliable operation of a power transformer, its condition needs to be continuously monitored and evaluated. Over the past two decades, a number of diagnostic techniques have been developed for transformer condition assessment such as dissolved gas analysis (DGA), degree of polymerization (DP) measurement, polarization and depolarization current (PDC) measurement, frequency domain spectroscopy (FDS), frequency response analysis (FRA), and partial discharge (PD) detection. However, the interpretations of measurement results acquired from these diagnostics are usually based upon the empirical models, which are sometimes inaccurate and incomplete especially in abnormal transformer operation scenarios. Therefore, accurate interpreting on the measurement data obtained by the above techniques and subsequently making explicit condition assessment of transformers is still a challenge task. Nowadays, considerable efforts have been made in the field of transformer condition monitoring and assessment. Majority efforts are dedicated in developing accurate transformer models and reliable transformer fault diagnosis systems. After completing a comprehensive literature review on various diagnostic techniques for transformers condition assessment, this thesis focuses on three main aspects of transformer’s health condition, including oil characteristics and dissolved gases in transformers, moisture concentration of oil-cellulose insulation and hot spot temperature of transformer windings. Since there is a lack of common framework for applying pattern recognition algorithms (i.e. data centric approaches) to interpret oil characteristics and DGA data, this thesis firstly provides a critical review on various pattern recognition techniques for power transformer insulation diagnosis using DGA and oil characteristics datasets. A general pattern recognition application framework is then proposed. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis are also discussed. To improve the data quality of training database and enhance the diagnostic accuracy of pattern recognition algorithms, a hybrid algorithm, SMOTEBoost is proposed. It adopts Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem, where samples belonging to different fault types (insulation conditions) are unevenly distributed in the training database. By using the boosting approach for reweighting and grouping data points in the training database, the SMOTEBoost can facilitate pattern recognition algorithms consistently attaining desirable diagnosis accuracies. To solve the intricate difficulties in moisture estimation of transformer oil-cellulose insulation system, this thesis introduces two modelling approaches, i.e., multi-physics finite element modelling (FEM) and particle tracing method, where the temperature dependent moisture dynamics in transformers’ insulation system is taken into account. In multi-physics approach, moisture dynamics is modelled by coupling the electromagnetic, thermal, fluid flow and moisture migration physics simultaneously. In particle tracing method, moisture diffusion is formulated from a microscopic view of water molecules’ motion. Especially, the transmission probability of water molecules (termed as particles in the paper) is employed to correlate the microscopic particles’ motion with the macroscopic moisture distribution. Extensive ageing and moisture diffusion experiments have been conducted on a prototype transformer to verify the proposed modelling approaches for an accurate estimation of moisture in transformers. This thesis also proposes a distributed parameter model to investigate the effect of moisture dynamics on dielectric response of a transformer’s cellulose insulation. The correlation between moisture distribution (under non-equilibrium conditions due to thermal transients) and dielectric response parameters (dielectric losses and permittivity) of transformer cellulose insulation is revealed. The proposed methodology can help the proper interpretation of dielectric response measurement of field transformers under thermal transients. To overcome the inaccuracy in empirical thermal dynamic models, in this thesis a moisture dependent thermal model (MDTM) is developed for estimating transformer’s hot spot temperature. In this model, nonlinear thermal resistance is formulated by considering both oil and cellulose (paper and pressboard) of the transformer. Especially, the effect of moisture concentration and hot spot temperature on the thermal resistance of cellulose is taken into account. The proposed MDTM is verified by using historical data of moisture-in-oil and temperature measurements on an in-service vegetable oil-filled transformer. To integrate every piece of data and information obtained from different transformer diagnostic measurements and subsequently evaluating the overall health condition of a transformer, this thesis proposes a data and information fusion framework based on Bayesian Network (BN). Within the Bayesian Network, Monte Carlo and Bootstrap methods are employed to extract the most informative characteristics regarding transformer condition from different diagnostic measurements. Results of case studies demonstrate that the proposed data and information fusion framework can evaluate the effectiveness of combinations of different diagnostic measurements and subsequently facilitate determining optimal diagnostic strategies involved in transformer condition assessment. It is expected that the data centric diagnostic approaches developed in this thesis can provide an accurate modelling and reliable assessment of transformer’s health condition.