An exact approach for maximizing the lifetime of sensor networks with adjustable sensing ranges

This paper addresses the problem of target coverage for wireless sensor networks, where the sensing range of sensors can vary, thereby saving energy when only close targets need to be monitored. Two versions of this problem are addressed. In the first version, sensing ranges are supposed to be continuously adjustable (up to the maximum sensing range). In the second version, sensing ranges have to be chosen among a set of predefined values common to all sensors. An exact approach based on a column generation algorithm is proposed for solving these problems. The use of a genetic algorithm within the column generation scheme significantly decreases computation time, which results in an efficient exact approach.     

Universal instability modes in internal and external flows

Two prototypical external and internal flows have been studied which display linear temporal instability followed by nonlinear saturation taking the flows to a new equilibrium state. Direct simulation results are obtained using a specific formulation and numerical methods with very high accuracy. These results are analyzed via proper orthogonal decomposition (POD), which reveal similar modes for flow past a circular cylinder and flow inside a lid-driven cavity, indicating universality of such modes. Unlike many other efforts on reduced order modeling via POD, here the emphasis has been on understanding the physical aspect of the flow instability which requires very high accuracy of the simulation. Then, the obtained POD modes are related to the instability modes (in the classical sense of defining the latter) and new generic types of instability modes are identified in the studied external and internal flows. These new modes have been reported for flow past a circular cylinder [Sengupta TK, Singh N, Suman VK. Dynamical system approach to instability of flow past a circular cylinder. J Fluid Mech 2010;656:82–115] which help one in understanding the instability sequence and the relative importance of these modes in the flow evolution starting from an impulsive start. Present comparative study, furthermore, reveals universality of such instability modes by showing their presence for the flow inside a lid-driven cavity as well. Despite seeming dissimilarities between these two flows, similarities between the instability portrait of these two flows suggest universality of such modes. From the equilibrium amplitude of vorticity time-series, we establish the presence of multiple modes and multiple bifurcation sequences for these flows in parameter space. Existing theory due to Landau and Stuart that considers only a single dominant mode and its self-interaction does not explain all these features. We invoke a multi-modal interaction model in the cited reference above, termed as Landau–Stuart–Eckhaus (LSE) equation in recognition of Eckhaus’ work in modifying the classical Stuart–Landau equation. We also show that the new instability modes do not follow either the classical Stuart–Landau or the newly proposed LSE model equations and for this reason we call these as anomalous modes. Two specific classes of anomalous modes are identified and classified in the present work. Empirical expressions for the evolution of these anomalous modes are presented and their unambiguous role during instabilities is discussed.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Mobile agents

A lot of agents are executing on the Web, and some of them are starting to move around. While most agents are static (existing as a single process or thread on one host), others can pick up and move their code and data to a new host where they resume executing     

Fuzzy bounded least-squares method for the identification of linearsystems

This paper presents the fuzzy bounded least-squares method which uses both linguistic information and numerical data to identify linear systems. This method introduces a new type of fuzzy system, i.e., a fuzzy interval system. The steps in the method are as follows: 1) to utilize all the available linguistic information to obtain a fuzzy interval system and then to use the fuzzy interval system to give the admissible model set (i.e., the set of all models which are acceptable and reasonable from the point of view of linguistic information); 2) to find a model in the admissible model set which best fits the available numerical data. It is shown that such a model can be obtained by a quadratic programming approach. By comparing this method with the least-squares method, it is proved that the model obtained by this method fits a real system better than the model obtained by the least-squares method. In addition, this method also checks the adequacy of linear models for modeling a given system during the identification process and can help one to decide whether it is necessary to use nonlinear models     

Semianalytical Model for Drawdown due to Pumping a Partially Penetrating Large Diameter Well

A semianalytical model is developed for computing drawdowns in and around a partially penetrating large diameter well. The new model can take into account an unsteady pumping discharge and thus drawdowns during recovery can be computed. This model can also yield the unsteady contributions from well and aquifer storages to the pumped discharge. While developing the model, the flow from the bottom of the well is also accounted.     

In Situ Performance Testing of Deteriorating Water Tanks for Durability Assessment

Reports of failure of existing concrete structures due to a lack of durability, rather than a deficiency in structural strength, has made concrete technologists, engineers, and researchers focus research on the parameters influencing durability performance with respect to time. Systematic performance monitoring, with respect to chosen durability parameters of existing concrete structures, will decide the direction of future research in this area. Inferences based on laboratory simulations and testing need to be confirmed by in situ field measurements and studies. In situ condition rating and performance monitoring surveys have been conducted by many researchers, scientists, and professional associations, and reported in literature. Inferences of few such studies are summarized and discussed. Deterioration of concrete structures constructed in recent times is observed at relatively faster rates, and has been mainly attributed to cracking. Cracking is associated with the use of faster-hydrating portland cements with increased fineness and the tricalcium silicate (C3S) content to support the high speed of modern construction. In the present research, a case study of deteriorated water tank structures situated in the semitropical region of India is presented. Some selected parameters—such as concrete cover, carbonation depth, chloride concentration, compressive strength, etc. which influence long term durability of structures—have been measured.     

Communication-aware heuristics for run-time task mapping on NoC-based MPSoC platforms

Efficient run-time mapping of tasks onto Multiprocessor System-on-Chip (MPSoC) is very challenging especially when new tasks of other applications are also required to be supported at run-time. In this paper, we present a number of communication-aware run-time mapping heuristics for the efficient mapping of multiple applications onto an MPSoC platform in which more than one task can be supported by each processing element (PE). The proposed mapping heuristics examine the available resources prior to recommending the adjacent communicating tasks on to the same PE. In addition, the proposed heuristics give priority to the tasks of an application in close proximity so as to further minimize the communication overhead. Our investigations show that the proposed heuristics are capable of alleviating Network-on-Chip (NoC) congestion bottlenecks when compared to existing alternatives. We map tasks of applications onto an 8 × 8 NoC-based MPSoC to show that our mapping heuristics consistently leads to reduction in the total execution time, energy consumption, average channel load and latency. In particular, we show that energy savings can be up to 44% and average channel load is improved by 10% for some cases.     

Rule-based data mining for yield improvement in semiconductor manufacturing

We describe an automated system for improving yield, power consumption and speed characteristics in the manufacture of semiconductors. Data are continually collected in the form of a history of tool usage, electrical and other real-valued measurements—a dimension of tens of thousands of features. Unique to this approach is the inference of patterns in the form of binary regression rules that demonstrate a significantly higher or lower performance value for tools relative to the overall mean for that manufacturing step. Results are filtered by knowledge-based constraints, increasing the likelihood that empirically validated rules will prove interesting and worth further investigation. This system is currently installed in the IBM 300 mm fab, manufacturing game chips and microprocessors. It has detected numerous opportunities for yield and performance improvement, saving many millions of dollars.     

Modified particle swarm optimization for a multimodal mixed-variable laser peening process

Optimization problems that result in shock, impact, and explosion type disciplines typically have mixed design variables, multiple optimal solutions, and high computational cost of an analysis. In the optimization literature, many researchers have solved problems involving mixed variables or multiple optima, but it is difficult to find multiple optima of a mixed-variable and high computation cost problem using an particle swarm optimization (PSO). To solve such problems, a mixed-variable niching PSO (MNPSO) is developed. The four modifications introduced to the PSO are: Latin Hypercube sampling-based particle generation, a mixed-variable handling technique, a niching technique, and surrogate model-based design space localization. The proposed method is demonstrated on the laser peening (LP) problem. The LP process induces favorable residual stress on the peened surface to improve the fatigue and fretting properties of the material. In many applications of LP, geometric configurations and dimensional integrity requirements of the component can constrain implementation of an optimal solution. In such cases, it is necessary to provide multiple alternatives to the designer so that a suitable one can be selected according to the requirements. It takes 24–72 CPU hours to perform an LP finite element analysis.