Continuous querying in database-centric Web applications

Web applications are becoming increasingly database-centric. Unfortunately, the support provided by most Web sites to explore such databases is rather primitive and is based on the traditional database metaphor of submitting an SQL query and packaging the response as an HTML page. Very often, the result set is empty or contains too many records. It is up to the user to refine the query by guessing how the query constraints must be tightened or relaxed and then go through another submit/response cycle. Furthermore, once results are displayed, typically no further exploration capabilities are offered. Web applications requiring interactive exploration of databases (e.g. e-commerce) need that the above submit/response metaphor be replaced with a continuous querying metaphor that seamlessly integrates querying with result browsing. In addition to supporting queries based on predicates on attribute values, queries based on example records should also be supported. We present techniques for supporting this metaphor and discuss their implementation in a Web-based database exploration engine.     

An Empirical Assessment of On-Farm Water Productivity using Groundwater in a Semi-Arid Indian Watershed

Realistic estimation of irrigation volume applied to any crop at farm level generally requires information on event based discharge rates and corresponding periods of irrigation application. Use of mean seasonal discharge rates leads to erroneous estimation of volume due to unaccounted seasonal fluctuations in the water table, upon which the discharge rate of tube well is dependent. In the absence of such information, an alternative approach of estimating farm level water application based upon water table fluctuation data has been adopted in this study. The total actual water extracted during each irrigation event from the watershed was distributed among the farms irrigating crops in proportion to the product of irrigation time and the pump capacity (h_p). Volume of water withdrawal concurrent to an irrigation event was computed based on the water level fluctuations in the wells in conjunction with potential recharge contribution from the surface storage structures to the groundwater aquifer. A production function approach was used to estimate the marginal productivity of water for selected crops at various stages of plant growth. Water, as an input in the production function, encompassed either in-situ soil moisture storage from rainfall or irrigation from groundwater or both. The inter-season as well as intra-season groundwater use, and the consequent groundwater withdrawals were analyzed based on the marginal value and output elasticity of water at different crop growth stages during the season. The cotton crop realized marginal value product of water, ranging from Rs. 1.03/m³ to Rs. 10.43/m³ at different crop growth stages in cotton. Castor crop had the marginal value product ranging from Rs. 2.89/m³ to Rs. 6.81/m³. The availability and use of water, including soil moisture, in the two seasons, coupled with the local harvest prices received, yielded the differential marginal values of water.     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

On the secrecy performance of integrated satellite‐aerial‐terrestrial networks

This paper investigates secure transmission of an integrated satellite‐aerial‐terrestrial network (ISATN), where multiple eavesdroppers (Eves) attempt to overhear the satellite signals cooperatively. The ISATN adopts an unmanned aerial vehicle (UAV) equipped with multiple antennas as a relay with threshold‐based decode‐and‐forward (DF) protocol. By assuming that perfect instantaneous channel state information (CSI) of the satellite‐UAV link and the statistical CSI of the UAV‐user link are available, we first propose a beamforming (BF) scheme for maximizing the achievable secrecy rate (ASR) of the considered network. Then, we derive the analytical expressions of the secrecy outage probability (SOP) and ergodic secrecy rate (ESR) of the considered system with the BF strategy under an assumption that the satellite‐UAV link undergoes the shadowed‐Rician fading, while the UAV‐user link experiences the correlated Rayleigh fading. Finally, numerical results are given to demonstrate the superiority of the proposed BF scheme against zero forcing (ZF) and maximal ratio transmission (MRT) schemes and the validity of the secrecy performance analysis.     

Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Multiple fault diagnosis (MFD) is used as an effective measure to tackle the problems of real-shop floor environment for reducing the total lifetime maintenance cost of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases. Thus, warrants the application of heuristic techniques or AI-based optimization tools to diagnose the exact faults in real time. In this research, rollout strategy-based probabilistic causal model (RSPCM) has been proposed to solve graph-based multiple fault diagnosis problems. Rollout strategy is a single-step iterative process, implemented in this research to improve the efficiency and robustness of probabilistic causal model. In RSPCM instead of finding all possible combinations of faults, collect the faults corresponding to each observed manifestations that can give the best possible result in compared to other methods. Intensive computational experiments on well-known data sets witness the superiority of the proposed heuristic over earlier approaches existing in the literature. From experimental results it can easily inferred that proposed methodology can diagnosed the exact fault in the minimum fault isolation time as compared to other approaches.     

Operation allocation in automated manufacturing system using GA-based approach with multifidelity models

The scheduling problem in manufacturing is considered as among the toughest to solve. The basic drawback of many proposed methods has been the huge amount of computation time for simulation. This paper proposes a framework to solve the operation allocation problem in automated manufacturing systems using the concept of multifidelity. The concept of multifidelity has been proposed by several researchers in order to reduce the computation time for simulation. In this paper, a GA-based heuristic procedure will be developed along with the multifidelity approach to solve a typical manufacturing scheduling problem. Four different fidelity models have been defined on which experimentation is carried out. The proposed method has been tested on a sample dataset and the results have been analysed to choose the fidelity model which best describes the scenario.     

Differential Evolution for learning the classification method PROAFTN

This paper introduces a new learning technique for the multicriteria classification method PROAFTN. This new technique, called DEPRO, utilizes a Differential Evolution (DE) algorithm for learning and optimizing the output of the classification method PROAFTN. The limitation of the PROAFTN method is largely due to the set of parameters (e.g., intervals and weights) required to be obtained to perform the classification procedure. Therefore, a learning method is needed to induce and extract these parameters from data. DE is an efficient metaheuristic optimization algorithm based on a simple mathematical structure to mimic a complex process of evolution. Some of the advantages of DE over other global optimization methods are that it often converges faster and with more certainty than many other methods and it uses fewer control parameters. In this work, the DE algorithm is proposed to inductively obtain PROAFTN’s parameters from data to achieve a high classification accuracy. Based on results generated from 12 public datasets, DEPRO provides excellent results, outperforming the most common classification algorithms.     

Spacecraft Anti‐Unwinding Attitude Control Using Second‐Order Sliding Mode

This paper presents an anti‐unwinding control method for the attitude stabilization of a rigid spacecraft. Quaternion has double stable equilibrium and this may cause unwinding problems in spacecraft attitude control if both the equilibria are not considered in control design. Here, the initial condition of scalar quaternion is included in sliding surface and an anti‐unwinding control method is formulated in second‐order sliding mode. The presented second‐order sliding mode controller can alleviate chattering and ensure a smooth control for actuator. Further, to eliminate the need of advance information about bounds of uncertainty and external disturbance, adaptive laws are applied to estimate the controller gains. The closed‐loop stability is proved using the Lyapunov stability theory. In conclusion, a simulation is conducted in the presence of inertia uncertainty and external disturbances and it is found that the presented control method is efficient to negate the effect of inertia uncertainty and external disturbances, alleviate chattering, eliminate unwinding, and ensure high accuracy and steady state precision.     

DYNAMIC ENVIRONMENT ROBOT PATH PLANNING USING HIERARCHICAL EVOLUTIONARY ALGORITHMS

The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.