Entrance effects for power-law fluid

Entrance pressure losses for the creeping flow of a power-law fluid are calculated for an abrupt contraction of ratio 2, 4, 8 and ∞ for both the axisymmetric and planar cases using P₂P₁ and PP₁ finite elements. Contrary to some earlier findings in the literature, the entrance pressure loss obtained by using the two different types of finite elements, both of which satisfy the Babus?ka-Brezzi condition, are found to converge to the same results. The present results also confirm that the variational method of Duda and Vrentas gives excellent upper bounds for both the axisymmetric and planar cases with infinite contraction ratio.     

Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.     

Alternating time versus deterministic time: A separation

Paulet al. [12] proved that deterministic Turing machines can be speeded up by a factor of log*t (n) using four alternations; that is, DTIME(t(n) log*t(n)) σ₄(t(n)). Balcázaret al. [1] noted that two alternations are sufficient to achieve this speed-up of deterministic Turing machines; that is, DTIME(t(n) log*t(n)) Σ₂(t (n)). We supply a proof of this speed-up and using that show that for each time-constructible functiont(n), DTIME(t(n)) ⊂ Σ₂(t(n)); that is, two alternations are strictly more powerful than deterministic time. An important corollary is that at least one (or both) of the immediate generalizations of the result DTIME(n) ⊂ NTIME(n) [12] must be true: NTIME(n) ≠ co-NTIME(n) or, for each time-constructible functiont(n), DTIME(t (n)) ⊂ NTIME(t (n)). This work was supported in part by NSF Grant CCR-8909071. The preliminary version of the work was done when the author was a graduate student at The Ohio State University.     

A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality

In this paper, we develop mathematical models for simultaneous consideration of suitability and optimality in asset allocation. We use a hybrid approach that combines behavior survey, cluster analysis, analytical hierarchy process and fuzzy mathematical programming.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

Opportunistic resource utilization networks—A new paradigm for specialized ad hoc networks

We present opportunistic resource utilization networks or oppnets, a novel paradigm of specialized ad hoc networks. We believe that applications can benefit from using specialized ad hoc networks that provide a natural basis for them, the basis more efficient and effective than what general-purpose ad hoc networks can offer. Oppnets constitute the subcategory of ad hoc networks where diverse systems, not employed originally as nodes of an oppnet, join it dynamically in order to perform certain tasks they have been called to participate in. Oppnets have a significant potential to improve a variety of applications, and to create new application niches. We categorize opportunistic networks currently known in the literature as class 1opportunistic networks that use opportunistically only communication resources, and class 2opportunistic networks or oppnets that use opportunistically all kinds of resources, including not only communication but also computation, sensing, actuation, storage, etc. After describing the oppnets and the basics of their operation, we discuss the Oppnet Virtual Machine (OVM)—a proposed standard implementation framework for oppnet applications. It is followed by a discussion of an example application scenario using the OVM primitives. Next, we discuss the design and operations of a small-scale oppnet, named MicroOppnet, originally developed as a proof of concept. MicroOppnet is now being extended to serve as a testbed for experimentation and pilot implementations of oppnet architectures and their components. We conclude with a summary and a list of some open issues for oppnets.     

An ant swarm-inspired energy-aware routing protocol for wireless ad-hoc networks

Primitive routing protocols for ad-hoc networks are “power hungry” and can therefore consume considerable amount of the limited amount of battery power resident in the nodes. Thus, routing in ad-hoc networks is very much energy-constrained. Continuous drainage of energy degrades battery performance as well. If a battery is allowed to intermittently remain in an idle state, it recovers some of its lost charge due to the charge recovery effect, which, in turn, results in prolonged battery life.In this paper, we use the ideas of naturally occurring ants’ foraging behavior (Dorigo and Stuetzle, 2004) [1] and based on those ideas, we design an energy-aware routing protocol, which not only incorporates the effect of power consumption in routing a packet, but also exploits the multi-path transmission properties of ant swarms and, hence, increases the battery life of a node. The efficiency of the protocol with respect to some of the existing ones has been established through simulations. It has been observed that the energy consumed in the network, the energy per packet in the case of EAAR are 60% less compared to MMBCR and the packets lost is only around 12% of what we have in AODV, in mobility scenarios.     

Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input–output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.     

Adaptive broadcast by fault-tolerant spanning tree switching

Adaptation is a desirable requirement in a distributed system as it helps the system to perform efficiently under different environments. For many problems, more than one protocol exists, such that one protocol performs better in one environment while the other performs better in another. In such cases, adaptive distributed systems can be designed by dynamically switching between the protocols as the environment changes. Distributed protocol switching is also important for performance enhancement, or fault-tolerance of a distributed system. In this work, we illustrate distributed protocol switching by providing a distributed algorithm for adaptive broadcast that dynamically switches from a BFS tree to a DFS tree. The proposed switching algorithm can also handle arbitrary crash failures. It ensures that switching eventually terminates in spite of failures and the desired tree (DFS tree) results as the output. We also investigate the properties that can be guaranteed on the delivery of broadcast messages under specific failure conditions. We show that under no failure, each broadcast message is eventually correctly delivered to all the nodes in spite of switching. Under arbitrary crash fault, we ensure that switching eventually terminates with the desired tree as the broadcast topology. We also investigate the specific delivery guarantees that can be provided when a single crash fault happens, both during switching and when no switching is in progress.