Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels

This study presents a methodology for the automated analysis of commercial medium-range sonar signals for detecting presence/absence of bluefin tuna (Tunnus thynnus) in the Bay of Biscay. The approach uses image processing techniques to analyze sonar screenshots. For each sonar image we extracted measurable regions and analyzed their characteristics. Scientific data was used to classify each region into a class (“tuna” or “no-tuna”) and build a dataset to train and evaluate classification models by using supervised learning. The methodology performed well when validated with commercial sonar screenshots, and has the potential to automatically analyze high volumes of data at a low cost. This represents a first milestone towards the development of acoustic, fishery-independent indices of abundance for bluefin tuna in the Bay of Biscay. Future research lines and additional alternatives to inform stock assessments are also discussed

Robot Trajectories Comparison: A Statistical Approach

The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners

Structure learning approaches in Causal Probabilistics Networks

Causal Probabilistic Networks (CPN) , a method of reasoning using probabilities, has become popular over the last few years within the AI probability and uncertainty community. This paper begins with an introduction to this paradigm, followed by a presentation of some of the current approaches in the induction of the structure learning in CPN . The paper concludes with a concise presentation of alternative approaches to the problem, and the conclusions of this review. 1 Introduction From a informal perspective CPN , they are Directed Acyclic Graphs (DAGs) , where the nodes are random variables, and the arcs specify the independence assumptions that must be hold between the random variables.To specify the probability distribution of a CPN, one must give the prior probabilities of all root nodes (nodes with no predecessors) and the conditional probabilities of all no root nodes, given all possible combinations of their direct predecessors. These numbers in conjunction with the DAG, spec..

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

We present a new approach to structure learning in the field of Bayesian networks: we tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer

Distributed Markov localisation for probabilistic behaviour activation

Probabilistic methods offer the necessary tools with a sound theoretical basis for handling self localisation but they are generally applied to rigid environment representations and thereby, they are hardly capable of coping with dynamic environments. Our current research effort aims to narrow the gap between behaviour based navigation and probabilistic methods. This paper presents a distributed self-localisation system in semi-structured environments

An Expert System for Cardiopulmonary Diseases in Primary Health Care

This paper shows the steps we have taken in order to define and design a first prototype of an expert system aimed at the general practitioner. The first part is devoted to analyze the systemic approach we gave to the definition problem. Also are given the results we reached from a delphi analysis which we accomplished in order to get the medical aspects and specifications that defined the system. The second part shows how the above conclusions drove to the definition of the first prototype: The knowledge acquisition process was accomplished by means of an especially designed tool, the medical knowledge considered was limited to those common aspects of the interaction of the basic entities in which the knowledge is organized. The knowledge is represented as a net where the vertices are diseases and manifestations and the arcs represent different types of relations. The tool provides the user with functions as the include or delete function of objects or vertices to the net or with the modification function of the values given to the arcs. The reasoning mechanism evaluates each diagnostic hypothesis (disease) by (1), how the disease explains the symptoms found in the patient and (2) by the symptoms that are usually found with the disease but are not prevalent in the patient. The system is in the validation phase