A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslide-susceptibility mapping.     

一种用作超声波信号源的AWG的研制

研制了一种采用单片机与复杂可编程逻辑器件(CPLD)相结合的方法设计的任意波形发生器(AWG),并详细介绍了利用 MATLAB 获取波形数据的两种方法。实验证明,该系统能输出频率和幅度可调的多种标准函数波以及任意波形。在满足所需频带宽度的同时,提高了频率的分辨率和准确度。     

Development and application of a neural network based ocean colour algorithm in coastal waters

An algorithm for determining chlorophyll‐a concentrations in shallow, case II waters has been developed and applied to nearly six years of Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) data in order to observe the general chlorophyll‐a patterns in a coastal estuarine environment. Due to the fact that the current empirical chlorophyll‐a algorithm (OC4) used to process SeaWiFS data breaks down in coastal waters, a neural network based algorithm was developed. The neural network in the study uses SeaWiFS remote sensing reflectance data paired with in situ chlorophyll‐a data in the Delaware Bay and its adjacent coastal zone (DBAC) from a number of different days and seasons in an effort to overcome the limitations of single day algorithms and simulated dataset algorithms. Although the neural network model (NN) in this study displayed some difficulty representing high chlorophyll‐a values, it showed significant improvement over the OC4 algorithm. The performance parameters of the NN were an r ² of 0.79, a root mean square (RMS) error of 3.69?mg m^?3 and a relative RMS error of 0.77. The NN was used to reprocess approximately six years of cloud free imagery of the DBAC from which the spatial and temporal variability of the chlorophyll‐a distributions in the DBAC were analysed. Time series of absolute chlorophyll‐a values for five stations along the central axis of the Delaware Bay were analysed using Fourier analysis techniques, from which chlorophyll‐a patterns were found to have a quasi‐annual period. Furthermore, the spatial distributions of the chlorophyll‐a patterns were analysed using a general climatology and monthly climatologies of normalized chlorophyll‐a values. The climatologies generally agreed with spatial distributions determined from historic ship‐based data. The study found that summer blooms in the mid‐estuary of the Delaware Bay may be more important than previously observed. This suggests that more frequent and synoptic measurements via satellite can reveal important new information about even well studied regions.     

Development and application of an integrated neural system for anHDCL

This study presents the development and industrial application of an integrated neural system in coating weight control for a modern hot dip coating line (HDCL) in a steel mill. The neural system consists of two multilayered feedforward neural networks and a neural adaptive controller. They perform coating weight real-time prediction, feedforward control (FFC), and adaptive feedback control (FBC), respectively. The production line analysis, neural system architecture, learning, associative memories, generalization and real-time applications are addressed in this paper. This integrated neural system has been successfully implemented and applied to an HDCL at Burns Harbor Division, Bethlehem Steel Co., Chesterton, IN. The industrial application results have shown significant improvements in reduction of coating weight transitional footage, variation of the error between the target and actual coating weight, and the coating material used. Some practical aspects for applying a neural system to industrial control are discussed as concluding remarks.     

Development of a credit limit allocation model for banks using an integrated Fuzzy TOPSIS and linear programming

In this study, a credit risk concentration allocation model is developed for the banks to determine the credit risk concentration limits of their regional head’s. The proposed model is based on the Fuzzy TOPSIS (FTOPSIS) and Linear Programming (LP) approaches. FTOPSIS is easy to use and capable to keep tract of decision made in reaching the overall score by combining different types of criteria. LP combines the results of FTOPSIS and other constraints and objectives determined by the bank. Using FTOPSIS and LP together in the same model brings uniformity and a structure in credit risk concentration decisions of the banks. The developed model is tested with a real case banking application and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.     

Development of an integrated expert system for fluvial hydrodynamics

During recent decades, rapid developments in computer science and industry have been influencing every aspect of our social life. Computer Aided Design (CAD) techniques have a significant impact in engineering fields. For fluvial engineering fields, a large number of microcomputer-based programs are applied for hydrodynamics analyses during the project design stage. Recently, achievements in Artificial Intelligence (AI) have been applied in many fields. The application of expert system techniques is an attractive trend in modern design practice. It is believed that the synthetic utilization of CAD and AI techniques would become more attractive for engineers. Yet the studies on this topic are scarce. This paper describes the architecture and implementation of an integrated expert system that assists hydraulic engineers to solve the unsteady open channel flow in river networks. A methodology of combining numerical analysis software into an expert system is presented. The verification, validation, application, and future development of this system are also presented.     

Sedtrans05: An improved sediment-transport model for continental shelves and coastal waters with a new algorithm for cohesive sediments

The one-dimensional (vertical) sediment-transport model SEDTRANS96 has been upgraded to predict more accurately both cohesive and non-cohesive sediment transport. Sedtrans05 computes the bed shear stress for a given set of flow and seabed conditions using combined wave-current bottom boundary layer theory. Sediment transport (bedload and total load) is evaluated using one of five methods. The main modifications to the original version of the model are: (1) a reorganization of the code so that the computation routines can be easily accessed from different user interfaces, or may be called from other programs; (2) the addition of the Van Rijn method to the options for non-cohesive sediment transport; (3) the computation of density and viscosity of water from temperature and salinity inputs; and (4) the addition of a new cohesive sediment algorithm. This latter algorithm introduces variations of sediment properties with depth, represents the suspended sediment as a spectrum of settling velocities (i.e. size classes), includes the flocculation process, and models multiple erosion–deposition cycles. The new model matches slightly better the field measurements of non-cohesive sediment transport, than does the predictions by SEDTRANS96. The sand-transport calibration has been extended to high transport rates. The cohesive sediment algorithm reproduced well experimental data from annular flume experiments.     

On Bayesian estimation and model comparison of an integrated structural equation model

In this paper, we introduce a Bayesian approach to the estimation and model comparison of an integrated two-level nonlinear structural equation model with mixed continuous, dichotomous, and ordered categorical data that may be missing at random. This general model can accommodate nonlinearities of latent variables and the effects of fixed covariates on measurement and structural equations in within-groups and between-groups models. A sampling-based algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm is proposed for posterior simulation. A procedure that utilizes path sampling is implemented to compute the Bayes factor for model comparison under the framework of the proposed integrated model. Empirical performances of Bayesian methodologies are illustrated via analysis of a real example.     

Remote sensing of coastal waters by airborne lidar and satellite radiometer Part 1: A model study

Abstract

Radiative transfer calculations for remote sensing of coastal waters by airborne lidar and satellite radiometer have been compared in order to answer the question, whether an airborne lidar may be used instead of in situ measurements from ships to calibrate a satellite radiometer. The radiative transfer of laserlight measuring the Raman-scattering of water molecules, the fluorescence of chlorophyll-a and the fluorescence of yellow substance or Gelbstoff is simulated by the lidar equations while the radiance to a satellite radiometer is calculated with an ocean-atmosphere model based on the matrix-operator method. Including multiple scattering in the lidar equations, an eigenvalue analysis shows that three oceanic constituents (chlorophyll-a, nonchlorophyllous particles and Gelbstoff) can be separated measuring the backscattered laserlight at three wavelengths from a height of 100 to 200m. Changes in the concentration of all three substances are detected with higher accuracy with an airborne lidar than with a radiometer even at the same height. A comparison of different algorithms indicates that the common blue-green algorithms fail in coastal waters due to the variability of several oceanic constituents, which influence the colour of sea water. In this case, algorithms using the sun-induced chlorophyll-a fluorescence at 685 nm, are superior to blue-green algorithms. Airborne lidar measurements of the chlorophyll-a fluorescence at 685 nm, normalized by the Raman-signal at 650 nm, are as good as in situ data and can be used to calibrate satellite measurements of chlorophyll-a.     

Method for retrieving the power of the point source of an admixture being diffused in a turbulent atmosphere

The problem of retrieving the power of a point source of an admixture being diffused in a turbulent atmosphere is thoroughly studied. This problem is reduced to the solution of a few integral equations of the first kind. We suggest a method for constructing a stable algorithm of an approximate solution of these equations on the basis of the Kalman-Bucy filter.     

Laying the foundation for smart contract development: an integrated engineering process model

Information Systems and e-Business Management - Smart contracts are seen as the major building blocks for future autonomous blockchain- and Distributed Ledger Technology (DLT)-based applications....     

Business model innovation: an integrated approach based on elements and functions

Business model innovation can be used to acquire competitive advantages. In this paper, we applied an integrative approach to study business model in terms of elements and functions. In the paper, three types of business model innovations were discussed: original innovation, induced innovation, and imitative innovation. The major contributions of this paper are: (1) improving the understanding of the business models; (2) providing a new perspective on business model innovation; and (3) proposing the paths for business model innovation. The paper also discussed how new information technology may promote business model innovation.     

New results using an integrated model and recursive algorithm for image motion estimation

A linear equation in the affine parameters used to model image motion may be derived by Taylor series expansion and truncation, and windowed spatial integration. Two methods for reducing errors in the Taylor approximation are discussed and results are presented.     

Making the cut: the impact of an integrated learning system on low achieving middle school students

Abstract Research in integrated learning systems has demonstrated a need for rigorous studies that identify how such systems influence learning, and in particular that of low achieving students. No Child Left Behind legislation mandated evidence-based interventions as the standard for instructional approaches in American public schools. This quasi-experimental study investigated the impact of Merit literacy software on students in West Virginia. The study confirmed that the software supported the reading and language arts curriculum and significantly improved the scores of low achieving middle school students on three variables of the WESTEST, a criterion-referenced state test: reading and language arts, science, and social science. This integrated learning system was effective with rural and urban school populations.     

Developing an integrated model for the evaluation and selection of six sigma projects based on ANFIS and fuzzy goal programming

Six sigma is one of the most popular tools to eliminate waste in organizations, reduce the cost and improve quality. The process of creating and evaluating projects is an initial activity in implementing six sigma. This paper aims at proposing a comprehensive methodology for the evaluation and selection of the six sigma projects. For the evaluation of projects, reviewing the literature and decision team’s opinion, we identified three main categories of criteria including business criteria, technological & process criteria and financial criteria which contain eight sub-criteria. For deriving the overall utility of projects, we designed an adaptive neuro fuzzy inference system which is capable to consider interrelations among criteria. Then, applying a fuzzy weighted additive goal programming model, we obtained the optimal portfolio of projects which should be implemented. Finally, we applied the proposed model in a leading company in Iran to illustrate the applicability of the model.     

The impact of programming paradigms on the efficiency of an individual-based simulation model

We look in detail at an individual-based simulation of the spread of barley yellow dwarf virus. The need for a very large number of individual plants and aphids along with multiple runs using different model parameters mean that it is important to keep memory and processor requirements within reasonable bounds. We present implementations of the model in both imperative and object-oriented programming languages, particularly noting aspects relating to ease of implementation and runtime performance. Finally, we attempt to quantify the cost of some of the decisions made in terms of their memory and processor time requirements.     

Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model

Much interest in privacy and trust studies is about shopping, but privacy research in other forms of online activities is beginning to emerge. This study examined the antecedents of privacy, trust and risk as well as their joint effect on two similar but fundamentally different activities: online transactions and retrieval of privileged information. Both activities involve the delivery of private user information, but the latter gives some leeway for users to control (or even falsify) their true identity. User shopping experience in the present study moderated the relationships and strengths of constructs. The effect of Internet literacy, social awareness and disposition to trust on privacy concern and trust was weaker for experienced shoppers. Privacy concern, trust and risk assessment played a lesser role on the two activity variables for those who were more experienced. Perceived privacy risk stood out as a strong antecedent for respondents in both experience groups, but the effect of Internet literacy, social awareness and disposition on trust was statistically insignificant for the same group. Further practical and managerial implications are provided.