Puffle-Pod Marine Evacuation System (POMES)

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Evacuation systems have always played a crucial part when designing a transport system. The cornerstone of these systems is to get people to safety in the quickest and safest way possible. When it comes to marine systems, the requirements greatly differ in comparison to those on land and in air. On a day with highly inclement and fierce weather, in the middle of the ocean, getting the crew to safety through a chute or a slide would expose the crew to ferocious danger. Thence, the proposed Puffle-Pod Evacuation System introduces a more protected and secure evacuation without putting the lives of the crew at high risk

Linear and Order Statistics Combiners for Pattern Classification

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the "added" error. If N unbiased classifiers are combined by simple averaging, the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the ith order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.Comment: 31 page

Avoiding Braess' Paradox through Collective Intelligence

In an Ideal Shortest Path Algorithm (ISPA), at each moment each router in a network sends all of its traffic down the path that will incur the lowest cost to that traffic. In the limit of an infinitesimally small amount of traffic for a particular router, its routing that traffic via an ISPA is optimal, as far as cost incurred by that traffic is concerned. We demonstrate though that in many cases, due to the side-effects of one router's actions on another routers performance, having routers use ISPA's is suboptimal as far as global aggregate cost is concerned, even when only used to route infinitesimally small amounts of traffic. As a particular example of this we present an instance of Braess' paradox for ISPA's, in which adding new links to a network decreases overall throughput. We also demonstrate that load-balancing, in which the routing decisions are made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to "side-effects", in this case of current routing decision on future traffic. The theory of COllective INtelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects. We present key concepts from that theory and use them to derive an idealized algorithm whose performance is better than that of the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm avoids Braess' paradox.Comment: 28 page

Using Collective Intelligence to Route Internet Traffic

A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.Comment: 7 page

Vision 2023: Turkey’s National Technology Foresight Program – a contextualist description and analysis

This paper describes and analyses Vision 2023 Turkish National Technology Foresight Program. The paper is not about a mere description of the activities undertaken. It analyses the Program from a contextualist perspective, where the Program is considered in its own national and organizational contexts by discussing how the factors in these contexts led to the particular decisions taken and approaches adopted when the exercise was organized, designed and practiced. With the description and analysis of the Vision 2023 Technology Foresight Program, the paper suggests that each Foresight exercise should be considered in its own context. The exercise should be organized, designed and practiced by considering the effects of the external contexts (national, regional and/or corporate) and organizational factors stemming from these different context levels along with the nature of the issue being worked on, which constitute the content of the exercise.Foresight, contextualism, Vision 2023, Turkey, Science and Technology Policy