Value of Information in Feedback Control

In this article, we investigate the impact of information on networked control systems, and illustrate how to quantify a fundamental property of stochastic processes that can enrich our understanding about such systems. To that end, we develop a theoretical framework for the joint design of an event trigger and a controller in optimal event-triggered control. We cover two distinct information patterns: perfect information and imperfect information. In both cases, observations are available at the event trigger instantly, but are transmitted to the controller sporadically with one-step delay. For each information pattern, we characterize the optimal triggering policy and optimal control policy such that the corresponding policy profile represents a Nash equilibrium. Accordingly, we quantify the value of information $\operatorname{VoI}_k$ as the variation in the cost-to-go of the system given an observation at time $k$. Finally, we provide an algorithm for approximation of the value of information, and synthesize a closed-form suboptimal triggering policy with a performance guarantee that can readily be implemented

Computing Simple Roots by an Optimal Sixteenth-Order Class

The problem considered in this paper is to approximate the simple zeros of the function by iterative processes. An optimal 16th order class is constructed. The class is built by considering any of the optimal three-step derivative-involved methods in the first three steps of a four-step cycle in which the first derivative of the function at the fourth step is estimated by a combination of already known values. Per iteration, each method of the class reaches the efficiency index , by carrying out four evaluations of the function and one evaluation of the first derivative. The error equation for one technique of the class is furnished analytically. Some methods of the class are tested by challenging the existing high-order methods. The interval Newton's method is given as a tool for extracting enough accurate initial approximations to start such high-order methods. The obtained numerical results show that the derived methods are accurate and efficient

A Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach

This paper illustrates a method to finding a globaloptimal path in a dynamic environment of known obstacles foran Mobile Robot (MR) to following a moving target. Firstly, theenvironment is defined by using a practical and standard graphtheory. Then, a suboptimal path is obtained by using DijkstraAlgorithm (DA) that is a standard graph searching method. Theadvantages of using DA are; elimination the uncertainness ofheuristic algorithms and increasing the speed, precision andperformance of them. Finally, Continuous Clonal SelectionAlgorithm (CCSA) that is combined with Negative SelectionAlgorithm (NSA) is used to improve the suboptimal path andderive global optimal path. To show the effectiveness of themethod it is compared with some other methods in this area

Factors influencing verbal intelligence and spoken language in children with phenylketonuria

Objectives: To determine verbal intelligence and spoken language of children with phenylketonuria and to study the effect of age at diagnosis and phenylalanine plasma level on these abilities. Design: Cross-sectional. Setting: Children with phenylketonuria were recruited from pediatric hospitals in 2012. Normal control subjects were recruited from kindergartens in Tehran. Participants: 30 phenylketonuria and 42 control subjects aged 4- 6.5 years. Skills were compared between 3 phenylketonuria groups categorized by age at diagnosis/treatment, and between the phenylketonuria and control groups. Main outcome measures: Scores on Wechsler Preschool and Primary Scale of Intelligence for verbal and total intelligence, and Test of Language Development-Primary, third edition for spoken language, listening, speaking, semantics, syntax, and organization. Results: The performance of control subjects was significantly better than that of early-treated subjects for all composite quotients from Test of Language Development and verbal intelligence (P >0.001). Early-treated subjects scored significantly higher than the two groups of late-treated subjects for spoken language (P =0.01), speaking (P =0.04), syntax (P =0.02), and verbal intelligence (P =0.019). There was a negative correlation between phenylalanine level and verbal intelligence (r= �0.79) in early-treated subjects and between phenylalanine level and spoken language (r= �0.71), organization (r= �0.82) and semantics (r= �0.82) for late-treated subjects diagnosed before the age one year. Conclusion: The study confirmed that diagnosis of newborns and control of blood phenylalanine concentration improves verbal intelligence and spoken language scores in phenylketonuria subjects. © 2015, Indian Academy of Pediatrics

Quantum random number generation using an on-chip nanowire plasmonic waveguide

Quantum random number generators employ the inherent randomness of quantum mechanics to generate truly unpredictable random numbers, which are essential in cryptographic applications. While a great variety of quantum random number generators have been realised using photonics, few exploit the high-field confinement offered by plasmonics, which enables device footprints an order of magnitude smaller in size. Here we integrate an on-chip nanowire plasmonic waveguide into an optical time-of-arrival based quantum random number generation setup. Despite loss, we achieve a random number generation rate of 14.4 Mbits/s using low light intensity, with the generated bits passing industry standard tests without post-processing. By increasing the light intensity, we were then able to increase the generation rate to 41.4 Mbits/s, with the resulting bits only requiring a shuffle to pass all tests. This is an order of magnitude increase in the generation rate and decrease in the device size compared to previous work. Our experiment demonstrates the successful integration of an on-chip nanoscale plasmonic component into a quantum random number generation setup. This may lead to new opportunities in compact and scalable quantum random number generation.Comment: 10 pages, 3 figures, appendi

A comparative survey of abundance and biomass of Caspian Sea macrobenthos in coastal waters of Mazandaran Province

Caspian Sea macrobenthos was surveyed every two months from December 2007 to October 2008, in the west, east and central parts of Mazandaran province waters. Each area was sampled with 3 replicates at 2 depths of 5 and 10m by Van Veen grab. Five different classes were recognized, including Polychaeta (52.7%), Oligochaeta (27.8%), Bivalvia (12%), Cnistacea (7.5%) and Insects (0.07%). Total mean (LSD) abundance and biomass were 2727± 1303 individual/m2 and 88.9±22.93, respectively. The Polychaeta demonstrated the highest abundance and Bivalvia had the highest biomass. The highest abundance of macrobenthos was found in eastern and the highest biomass in western coasts of Mazandaran. In August 2008, macrobenthos abundance showed higher values. In October, remarkable difference was observed between the abundance of Polychaeta and other macrobenthos organisms. According to Kniskal-Wallis test, abundance and biomass of the entire macrobenthos classes except Insects, showed a significant difference between sampling months (P<0.05). Macrobenthos biomass had no significant difference among the three areas whereas abundance demonstrated a significant difference within these areas (P< 0.05)

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators

Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs