Estimating forage crop production: a technique adaptable to remote sensing

The “stress degree day” technique for estimating yields of wheat from canopy and air temperature measurements is extended to handle forage production. It was found to be valid in its extended form for four sequential cuttings of alfalfa grown at Phoenix, Arizona, over the period May–August 1976.     

A High Order Multi-Dimensional Characteristic Tracing Strategy for the Vlasov–Poisson System

In this paper, we consider a finite difference grid-based semi-Lagrangian approach for solving the Vlasov–Poisson (VP) system. Many of existing methods are based on dimensional splitting, which decouples the problem into solving linear advection problems, see Cheng and Knorr (J Comput Phys 22:330–351, 1976). However, such splitting is subject to the splitting error. If we consider multi-dimensional problems without splitting, difficulty arises in tracing characteristics with high order accuracy. Specifically, the evolution of characteristics is subject to the electric field which is determined globally from the distribution of particle density via Poisson’s equation. In this paper, we propose a novel strategy of tracing characteristics high order in time via a two-stage multi-derivative prediction–correction approach and by using moment equations of the VP system. With the foot of characteristics being accurately located, we propose to use weighted essentially non-oscillatory interpolation to recover function values between grid points, therefore to update the solution at the next time level. The proposed method does not have time step restriction as the Eulerian approach and enjoys high order spatial and temporal accuracy. The performance of the proposed schemes are numerically demonstrated via classical test problems such as Landau damping and two stream instabilities.     

The “Stinger gap”: the difference between the “cost of subsistence” and that of a minimum-cost noninstitutional diet with palatability

This study presents two different 1976 diets for a moderately active male, age 23–50, buying foods at retail. The first of these is a “subsistence” diet just adequate for nutrition at minimum cost, with standard serving sizes for all foods, and an upper limit of 90 servings per month. There are upper and lower limits for each of 11 nutrients such as calories, calcium, phosphorus, etc. The solution was obtained by Mixed-Integer Programming (MIP) from a candidate list of 392 foods available in Stillwater, Oklahoma supermarkets in January, 1976. The diet costs $15.55 per month or 51.8¢ a day and includes 20 different foods.The second diet “nutrition plus palatability” has a wider variety of foods obtained by incorporating into the MIP model additional constraints reflecting tastes. This diet costs $34.51 a month or $1.15 a day and includes 68 different foods. The difference between these two costs $1.15–$0.52 = $0.63 is an estimate of the 1976 “Stinger gap,” the cost of palatability in an optimum diet.     

SIMULATING DEMOGRAPHY AND HUMAN DEVELOPMENT DYNAMICS

A deterministic/stochastic model in which the demographic and the well-being subsystems of a country are involved and related is presented as a way to approach human development. The demographic subsystem is a side-by-side, single-gender, age-structured population dynamic model. The well-being subsystem states the dynamics of the United Nations Hybrid Human Development Index. The model has been validated in the case of Spain and Belgium. Some simulations have been performed with the model for the case of Spain in the 2009–2020 period to determine strategies and scenarios that could increase the life expectancy at birth per gender.     

The Changing – and Unchanging – Face of the Digital Divide: an Application of Kohonen Self-Organizing Maps

The goal of this study is to trace the evolution of the digital divide, to begin to understand whether and how the divide is widening or closing, and in achieving this, begin to identify implications and further research moving forward. We are able to do this by using longitudinal data over a period of nine years (1999–2007). We employ the Kohonen self-organizing maps approach. This exploratory approach allows for clear visualization and interpretations of country's positions on the map and illustrates any changes that may have occurred over a given time period. We identify five clusters of countries, based on their digital development; economical; demographic and risk indicators. Our results indicate that a number of Eastern European countries such as Slovakia and Lithuania have made significant progress in the period 1995–2003, while we find that most African countries have made little or no improvement during this period – an issue requiring urgent attention. Overall, this study provides a clear visualization of the progression of digital development indicators alongside economic, demographic, infrastructural and risk factors in 179 countries over a nine-year time frame. These results are relevant as a basis for policy discussions as they highlight the success stories of some countries relative to others.     

3D-freehand-pose initialization based on operator’s cognitive behavioral models

Tracking, recognition and interaction based on 3D freehand are a part of our virtual assembly system, in which monocular camera is used to input online freehand videos and the hand pose tracker requires a reliable initial pose in the first frame. A novel approach to initializing 3D pose and position of freehand is put forward in this paper visualization of 3D hand model and modeling the operators’ cognitive behaviors. Our approach is composed of three phases: hand posture recognition, coarse-tuning and fine-tuning. The operator moves his/her hand onto the to meet the needs of our virtual assembly system. The main contribution of this paper is that the three core techniques are for the first time integrated together, including human–computer interaction (HCI) in the process of initializing, projection of the 3D hand model in the period of coarse-tuning time. Then, the computer repeatedly fine-tunes the 3D hand model until the projection of the 3D hand model is completely superimposed onto the operator’s hand image. We focus on exploring and modeling cognitive behavior of operator’s hand upon which we design our initialization algorithm. Our research shows that cognitive behavioral models are not only beneficial to reducing cognitive loads for operators, because it makes the computers cater for the changes of the operators’ hand poses, but also helpful to address high dimensionality of articulated 3D hand model. Our experimental results also show that the approach presented in this paper is easier, more pleasurable and satisfactory experience for the operators. Our initialization system has successfully been applied to our 3D freehand tracking system and a simulation virtual assembly system.     

Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T–S fuzzy approach

By utilising Takagi–Sugeno (T–S) fuzzy set approach, this paper addresses the robust H_∞ dynamic output feedback control for the non-linear longitudinal model of flexible air-breathing hypersonic vehicles (FAHVs). The flight control of FAHVs is highly challenging due to the unique dynamic characteristics, and the intricate couplings between the engine and fight dynamics and external disturbance. Because of the dynamics’ enormous complexity, currently, only the longitudinal dynamics models of FAHVs have been used for controller design. In this work, T–S fuzzy modelling technique is utilised to approach the non-linear dynamics of FAHVs, then a fuzzy model is developed for the output tracking problem of FAHVs. The fuzzy model contains parameter uncertainties and disturbance, which can approach the non-linear dynamics of FAHVs more exactly. The flexible models of FAHVs are difficult to measure because of the complex dynamics and the strong couplings, thus a full-order dynamic output feedback controller is designed for the fuzzy model. A robust H_∞ controller is designed for the obtained closed-loop system. By utilising the Lyapunov functional approach, sufficient solvability conditions for such controllers are established in terms of linear matrix inequalities. Finally, the effectiveness of the proposed T–S fuzzy dynamic output feedback control method is demonstrated by numerical simulations.     

Early warning prediction system for high inflation: an elitist neuro-genetic network model for the Indian economy

In this paper, we propose elitist genetic algorithms–based artificial neural network (ANN) model for setting up an early warning system for occurrence of high inflation. The proposed warning system uses values of an appropriate set of economic fundamental variables as input and builds an ANN model for quantifying the possibility of high inflation within a fixed period of time window. Elitism-based generational genetic algorithm is used for optimizing the architecture of the ANN model. We empirically evaluate the proposed neuro-genetic approach to identify the class of leading economic indicators and build an early warning signalling system of an occurrence of high inflation (overall and component inflations) using the data from the Indian economy. We further compare the results of the proposed approach with the commonly used data-driven signals approach. In the empirical studies, we observe promising performance of the proposed neuro-genetic warning system, which is capable of generating accurate early warning signals of an impending high inflation.

     

Pulp,paper and allied industry digital process control systems status and trends

Digital process control applications in the paper industry are now some 17 yr old. During this development, the Process Control Committee of the Technical Association of the Pulp and Paper Industry has been active in educational programs, defining standards, determining economics and measuring the extent, character, and impact of these systems. Several surveys have been conducted to support these activities. The results of these surveys are summarized in this paper. To provide a more current data base for presentation to the IFAC PRP₃ Conference in Brussels, Belgium, May 24–26, 1976, one of these surveys was updated in February 1976.     

Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm

In this paper, an integrated machine tool selection and sequencing model is proposed. The model determines machine visiting sequences for all part types, such that the total production time for the production order is minimized and workloads among machine tools are balanced. The model is formulated as a 0–1 integer programming. To solve the model, a genetic algorithm approach based on a topological sort technique is developed. To demonstrate the efficiency of the proposed GA approach on the integrated machine tool selection and sequencing problem, a number of numerical experiments using various size problems are carried out. The numerical experiments show that the proposed GA approach is efficient to this problems.     

Optimal control of a two-class national economic model

In this paper we extend the one-sector national economic system modelled on the basis of the cyclical growth theory developed by Bergstrom (1967) into a two-class model. This is done by combining and extending the model developed by Pasinetti (1962), Hu (1073), Ahmed and Yeo (1970) and Yeo and Teo (1976). The following economic factors—(i) profit margin, (ii) interest rates, (iii) monetary policy, and (iv) rate of increase of wages—arc considered as control variables. Based on this two-class model and its control variables, the objective of the economy is to maximize the sum of total consumption over a given planning period. The Pontryagin Maximum Principle is used to obtain the forms of the optimal controls. For illustrative purposes, numerical examples are solved using the computer programme of Moore and Too (private communication).     

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.     

EXPONENTIAL RECURRENCE DISTRIBUTION IN THE SIMON-YULE MODEL OF TEXT

ABSTRACT

Exponential recurrence phenomenon has been reported in the study of gaps between repetitions of words in a text. The phenomenon has its applications in several computer–based natural language systems. In this article, four leading statistical models of text generation are evaluated and we identify the Simon Yule model of Zipf's law as a promising approach. A realistic refinement of the Simon–Yule model is made to allow for a decreasing entry rate of new words. Simulation methods are used to show that the exponential recurrence phenomenon is preserved with this change in assumptions. Significant implications of the approach are discussed.     

Stabilization for a class of nonlinear systems: A fuzzy logic approach

In this paper, the problem of stabilization for the class of continuous time nonlinear systems which are discretized in closed form is addressed. By using the Takagi–Sugeno model approach, a discrete controller capable of stabilizing the discrete Takagi–Sugeno model and the continuous model as well, is obtained. This scheme allows using a digital controller for stabilizing an analog plant.     

ASM2Bogor: An approach for verification of models specified through Asmeta language

Nowadays, publish–subscribe (pub-sub) and event-based architectures are frequently used for developing loosely coupled distributed systems. Hence, it is desirable to find a proper solution to specify different systems through these architectures. Abstract state machine (ASM) is a useful means to visually and formally model pub–sub and event-based architectures. However, modeling per se is not enough since the designers want to be able to verify the designed models. As the model checking is a proper approach to verify software and hardware systems, in this paper, we present an approach to verify ASM models specified in terms of Asmeta language using Bogor—a well known model checker. In our approach, the AsmetaL specification is automatically encoded to BIR, the input language of the Bogor. Our experimental results show that in the most cases our approach generates more efficient results in comparison with the existing approach.     

A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

In this paper, a novel hybrid approach is proposed for predicting peak particle velocity (PPV) due to bench blasting in open pit mines. The proposed approach is based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO). In this approach, the PSO is used to improve the performance of ANFIS. Furthermore, a model is developed based on support vector regression (SVR) approach. The models are trained and tested based on actual data compiled from 120 blast rounds in Sarcheshmeh copper mine. To determine the accuracy and efficiency of ANFIS–PSO and SVR models, a statistical model (USBM equation) is applied. According to the obtained results, both techniques can be used to predict the PPV, but the comparison of models shows that the ANFIS–PSO model provides better results. Root mean square error (RMSE), variance account for (VAF), and coefficient of determination (R ²) indices were obtained as 1.83, 93.37 and 0.957 for ANFIS–PSO model, respectively.     

Decision Making with Ordinal Payoffs Under Dempster–Shafer Type Uncertainty

Our focus is on decision making in uncertain environments. We first introduce the Dempster–Shafer framework to model the uncertainty associated with possible outcomes. We then describe an approach for decision making when our uncertainty is captured using the Dempster–Shafer model and where the payoffs are numeric values. An important part of this approach is the role of the decision attitude as well as the aggregation of the possible payoffs. We then look at the situation where the payoffs, rather than being numbers, are values drawn from an ordinal scale. This requires us to provide appropriate operations for combining payoffs drawn from an ordinal scale.     

Application of fractional order theory of magneto-thermoelasticity to an infinite perfect conducting body with a cylindrical cavity

A fractional model of the equations of generalized magneto-thermoelasticity for a perfect conducting isotropic thermoelastic media is given. This model is applied to solve a problem of an infinite body with a cylindrical cavity in the presence of an axial uniform magnetic field. The boundary of the cavity is subjected to a combination of thermal and mechanical shock acting for a finite period of time. The solution is obtained by a direct approach by using the thermoelastic potential function. Laplace transform techniques are used to derive the solution in the Laplace transform domain. The inversion process is carried out using a numerical method based on Fourier series expansions. Numerical computations for the temperature, the displacement and the stress distributions as well as for the induced magnetic and electric fields are carried out and represented graphically. Comparisons are made with the results predicted by the generalizations, Lord–Shulman theory, and Green–Lindsay theory as well as to the coupled theory.

     

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

The inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.     

Depth camera based collision avoidance via active robot control

A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.