Monitoring active volcanism with the Autonomous Sciencecraft Experiment on EO-1

The ability to monitor and rapidly react to remote detection of volcanic activity has been greatly improved through use of the Autonomous Sciencecraft Experiment (ASE), an advanced software application installed on a spacecraft in Earth orbit. ASE is a NASA New Millennium Program experiment demonstrating science-driven autonomous command and control of a spacecraft. Flying on the Earth Observing-1 (EO-1) spacecraft, ASE successfully detected thermal emission from the Mt. Erebus lava lake on 7 May 2004, having analyzed a Hyperion hyperspectral data product on board the spacecraft. EO-1 was re-tasked by ASE to obtain a follow-up observation 7 h later and sent a notification of detection of volcanic activity to the ground. The entire process was carried out autonomously. Initial acquisition to receipt on the ground of the positive detection took less than 3 h, a process that without ASE would have taken weeks. The ASE Thermal Classifier has detected several styles of effusive volcanic activity: active lava lakes, pahoehoe flow fields, open channel flows and lava domes. ASE successfully demonstrated that science-driven spacecraft operation greatly enhances science return per returned byte through the identification of the most valuable data, allowing prioritization of downlink products and the discarding of null data sets. This technology has applications on missions elsewhere in the solar system. Modified thermal classifiers can be used for detecting and monitoring active volcanism on the jovian satellite Io, the neptunian moon Triton, and searching for active volcanism on Mars and icy satellites. The success of ASE is an incentive for future instrument and mission designers to consider on-board data-processing requirements (especially data storage capacity, number of processors and processor speed, and RAM) in order to take advantage of this flight-proven technology.     

Multidimensional Monte Carlo model for two-photon laser-induced fluorescence and amplified spontaneous emission

This paper describes the development of a multidimensional model based on the Monte Carlo (MC) method for the modeling of laser-induced fluorescence (LIF) and amplified spontaneous emission (ASE) signals involved in multi-photon processes. Multi-photon LIF finds applications in a broad range of topics; however, the interpretation of the LIF signal is plagued by the nonlinear effects caused by the ASE. Past work focused on developing one-dimensional (1D) models. Therefore, this work developed an MC method to solve the governing equations of ASE and LIF in multidimension. The results were validated using existing 1D data, both experimental and modeling. The results suggest that past 1D models cause noticeable error in the ASE signal even when the measurement volume has a large aspect ratio. We expect this work to facilitate the ongoing research of multi-photon LIF, and to stimulate new experiments that can provide data to validate the model in 2D.     

Financing strategies for a capital‐constrained manufacturer in a dual‐channel supply chain

This study investigates a co‐opetition‐type dual‐channel supply chain that consists of a competitive supplier (CS) and a capital‐constrained manufacturer (CCM). The CCM procures key components from and simultaneously competes with the CS in the consumer market. To address the CCM's capital constraint, we consider three financing strategies, namely, trade credit, bank loan, and hybrid financing (i.e., combined use of bank loan and equity financing). Game models are established to characterize the interactions between the CS and CCM. The corresponding equilibria are derived under each strategy. Then, comparative analyses are conducted, and the CS's and CCM's preference structures regarding the three strategies are revealed. On this basis, the equilibrium strategy can be concluded as either trade credit or hybrid financing, but never bank loan. Specifically, when the equity financing ratio is small or large, trade credit is an equilibrium strategy. When the equity financing ratio is medium, the equilibrium strategy between trade credit and hybrid financing is determined by consumers’ product preference and loan interest rate.     

Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

In this paper, we present a new way of detecting and monitoring flooding through the Autonomous Sciencecraft Experiment (ASE) [Chien, S. T., Debban, C., Yen, R., Sherwood, R. Castano, B., & Cichy, A. G. et al. (2001). ASC Science Study Report, available from http://ASE.jpl.nasa.gov], which is part of the Space Technology 6 effort under NASA's New Millennium Program. Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the ASE flight software have demonstrated the ability of several science algorithms to successfully classify key features including flood-induced changes, in hyperspectral images captured by the EO-1 Hyperion instrument. Furthermore, onboard science analysis on the classified images has been performed, and then used to modify an operational plan without interaction from the ground (Sherwood, R., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A., et al. (2004). Preliminary results of the autonomous sciencecraft experiment. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT). These algorithms are used to downlink science data only when change occurs, and to detect features of scientific interests such as flooding, volcanic eruptions, and the formation and breakup of sea ice. The purpose of this paper is to demonstrate the success of ASE and its implications on detecting, mapping, and monitoring transient processes such as flooding autonomously from space. Mapping of water inundation and its change through time is part of our focus in studying transient processes from space.In 2004, hyperspectral data were acquired from the Hyperion instrument for target areas around the world that have a high potential for flooding to develop and test floodwater classifiers. In addition, classifier thresholds were determined from both normal flows and possible flood conditions. The paper introduces the development, testing, and success of the ASE software in detecting and reacting to flooding in near real-time. ASE is now operational and flight-tested, and, thus, ready to use for space-borne reconnaissance. Successful demonstration of the floodwater classifiers includes the capture of a rare flooding event of the Australian Diamantina River during ground testing in February 2004, and the detection of flood-related changes along the Brahmaputra River in Bangladesh and the Yukon River in Alaska during onboard testing on EO-1 in 2005. Both of these detections led to triggered responses onboard the spacecraft, which included acquiring additional Hyperion scenes. These results pave the way for future smart reconnaissance missions of transient processes on Earth and beyond. It is hoped that ASE will become a default in future missions to increase the science return by introducing spacecraft autonomy for detection and monitoring of science events, which otherwise would be discovered too late or altogether missed.     

A prediction scheme using perceptually important points and dynamic time warping

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price.     

A Metric-Based Approach to Detect Abstract Data Types and State Encapsulations

This article presents an approach to identify abstract data types (ADT) and abstract state encapsulations (ASE, also called abstract objects) in source code. This approach, named similarity clustering, groups together functions, types, and variables into ADT and ASE candidates according to the proportion of features they share. The set of features considered includes the context of these elements, the relationships to their environment, and informal information. A prototype tool has been implemented to support this approach. It has been applied to three C systems (each between 30–38 Kloc). The ADTs and ASEs identified by the approach are compared to those identified by software engineers who did not know the proposed approach or other automatic approaches. Within this case study, this approach has been shown to have a higher detection quality and to identify, in most of the cases, more ADTs and ASEs than the other techniques. In all other cases its detection quality is second best. N.B. This article reports on work in progress on this approach which has evolved since it was presented in the original ASE97 conference paper.     

Estimating intrinsic dimensionality using the multi-criteria decision weighted model and the average standard estimator

Information retrieval today is much more challenging than traditional small document retrieval. The main difference is the importance of correlations between related concepts in complex data structures. As collections of data grow and contain more entries, they require more complex relationships, links, and groupings between individual entries. This paper introduces two novel methods for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). The average standard estimator (ASE) and the multi-criteria decision weighted model are used to estimate matrix intrinsic dimensionality for large document collections. The multi-criteria weighted model calculates the sum of weighted values of matrix dimensions which demonstrated best performance using all possible dimensions [1]. ASE estimates the level of significance for singular values that resulted from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained [1]. Experimental results indicate that ASE improves precision and relative relevance for MEDLINE document collection by 10.2% and 12.9% respectively compared to the percentage of variance dimensionality estimation. Results based on testing three document collections over all possible dimensions using selected performance measures indicate that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. The multi-criteria weighted model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model.     

Neuro-Resistive Grid approach to trainable controllers: A pole balancing example

A new neural network approach is described for the task of pole-balancing, considered a benchmark learning control problem. This approach combines Barto, Sutton and Anderson's [1] Associative Search Element (ASE) with a Neuro-Resistive Grid (NRG) [2] acting as Adaptive Critic Element (ACE). The novel feature in NRG is that it provides evaluation of a state based on propagation of the failure information to the neighbours in the grid. NRG is updated only on a failure, and provides ASE with a continuous internal reinforcement signal by comparing the value of the present state to the previous state. The resulting system learns more rapidly and with fewer computations than that of Barto et al.[1]. To establish a uniform basis of comparison of algorithms for pole balancing, both the systems are simulated using benchmark parameters and tests specified in Geva and Sitte [3].     

Knowledge sharing behaviors in e-learning community: Exploring the role of academic self-efficacy and sense of community

The purpose of this study was to explore the structural relationships between knowledge sharing behaviors (KSB), academic self-efficacy (ASE) and sense of community (SoC) of university students in e-learning community. The study was carried out with students who joined Facebook learning community that was created for the Computing I course which was taught with blended learning method. Data were collected from 316 university students by utilizing three self-report instruments: KSB scale, ASE scale (sub-scales: ‘social status’, ‘cognitive applications’ and ‘technical skills’) and classroom community (CC) scale (sub-scales: ‘connectedness’ and ‘learning’). The path analyses with structural equation modeling (SEM) further verified that students’ KSB were related to their ASE and SoC in e-learning community. The results of the study revealed that the ASE and SoC of the students positively affect their KSB. And in terms of sub-scales, the connectedness to the community, learning perception in the community, the self-efficacy of the students on the cognitive applications in the courses and their social status in the community positively affect KSB. However, students’ self-efficacy perceptions on their technical skills affect KSB positively but its affect size was smaller compared to other sub-scales. Further research studies and implications are presented and discussed.     

The financial-economic crisis and value of equity capital: A case study of Slovenian public limited companies 2006–2011

The purpose of this paper is to discuss the effect of financial-economic crisis on the equity value of companies, as well as present the importance of fair and honest company valuations. The fundamental value of equity capital of a company is important for both management and external shareholders. The wide disparity between market and fundamental values can lead to high value adjustments, which reduces investors confidence in the capital market. This has had a negative impact on the operations of financial institutions, and individual as well as company investment; especially on developing financial markets during a financial-economic crisis. This research was designed to assess the equity value of Slovenian public limited companies based on the discounted free cash flows to equity and comparing it with market value of equity capital of companies before and during the financial-economic crisis. The fundamental value of equity capital of the selected companies (sample of 25) is calculated using a two-tiered model. The paired-sample t-tests method rejected the hypothesis that the fundamental value of equity capital of Slovenian public limited companies better reflects the market value of equity capital in today’s times of financial-economic crisis (2011) than before the crisis (2006). However, we found that the market value of equity capital in relation to the fundamental value of equity capital of the selected companies was lower in 2011 than in 2006. Various models of the basic calculations are used in the model evaluation. This study shows the problem of company valuation on small and emerging capital markets which have a short history of data.     

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.     

实时事务调度及静态可调度性分析

在类似闭环控制的硬实时数据库应用环境,实时事务具有一定的静态可预报性,其中实时事务的可调度性分析是维护实时数据库时间正确性的基础.通过利用抢占阈值,提出了一种新的实时事务处理模型,它集成了CPU调度和数据调度,实现离线并发控制,具有单阻塞的特征与好的静态可预测性,并有利于降低事务系统的负载和改善可调度性.进一步由此建立了实时事务的静态可调度性分析模型以及求最优可行调度的整数规划模型,该模型有利于达到实时事务调度的整体优化.     

Detection of electrocardiogram beats using a fuzzy similarity index

Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time–frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat).     

A wavelet neural network for the detection of heart valve diseases

An expert system is presented for interpretation of the Doppler signals of heart valve diseases based on pattern recognition. We deal in particular with the combination of feature extraction and classification from measured Doppler signal waveforms at the heart valve using Doppler ultrasound. A wavelet neural network model developed by us is used. The model consists of two layers: a wavelet layer and a multilayer perceptron. The wavelet layer used for adaptive feature extraction in the time–frequency domain is composed of wavelet decomposition and wavelet entropy. The multilayer perceptron used for classification is a feedforward neural network. The performance of the developed system has been evaluated in 215 samples. The test results show that this system is effective to detect Doppler heart sounds. The classification rate averaged 91% correct for 123 test subjects.     

Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats

Abstract: Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time–frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.     

The effect of equity strategies on girls' computer usage in school

This study investigated the effect of school-based equity strategies on girl's voluntary computer usage. In the three experimental middle schools, the staff participated in a workshop on computer equity and then selected equity strategies for implementation; staff in the two control schools were not provided with equity strategies. Results indicate that with intervention girls used the computer significantly more than boys in the experimental schools, while this was not true in the control schools.     

绿色供应链的投贷联动融资决策均衡

以一个零售商和一个资金约束的制造商组成的绿色供应链为研究对象,构建了制造商资金约束情况下银行借贷融资、股权融资和组合融资时的融资模型,探讨制造商的资金水平和消费者的绿色偏好对定价、绿色投入水平、产品的市场需求和融资方式选择策略的影响.最后,通过数值分析进行验证.研究发现:消费者绿色偏好与零售价格、批发价格和绿色投入努力...     

Pattern‐Similarity‐Based Model for Time Series Prediction

This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.     

Graphical analysis of mortgage payments

This paper presents a computer package that performs graphical analysis of mortgage. In the computational procedure, the program calculates the monthly payment, the unpaid balance, interest charge, and equity payment for each of the months of the mortgage term. Plots of unpaid balance and total equity versus time are made for a visual analysis. The program, named GAMPS, is equally applicable to any cash flow situation involving equal periodic installments. Of particular importance is the “equity break-even point” which the program also calculates. This break-even point indicates that point in time when the unpaid balance equals the cumulative equity.