499 research outputs found
Intelligent Leukaemia Diagnosis with Bare-Bones PSO based Feature Optimization
In this research, we propose an intelligent decision support system for acute lymphoblastic leukaemia (ALL) diagnosis using microscopic images. Two Bare-bones Particle Swarm Optimization (BBPSO) algorithms are proposed to identify the most significant discriminative characteristics of healthy and blast cells to enable efficient ALL classification. The first BBPSO variant incorporates accelerated chaotic search mechanisms of food chasing and enemy avoidance to diversify the search and mitigate the premature convergence of the original BBPSO algorithm. The second BBPSO variant exhibits both of the abovementioned new search mechanisms in a subswarm-based search. Evaluated with the ALL-IDB2 database, both proposed algorithms achieve superior geometric mean performances of 94.94% and 96.25%, respectively, and outperform other metaheuristic search and related methods significantly for ALL classification
Mobile Robots
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations
Resource Optimization in Wireless Sensor Networks for an Improved Field Coverage and Cooperative Target Tracking
There are various challenges that face a wireless sensor network (WSN) that mainly originate from the limited resources a sensor node usually has. A sensor node often relies on a battery as a power supply which, due to its limited capacity, tends to shorten the life-time of the node and the network as a whole. Other challenges arise from the limited capabilities of the sensors/actuators a node is equipped with, leading to complication like a poor coverage of the event, or limited mobility in the environment. This dissertation deals with the coverage problem as well as the limited power and capabilities of a sensor node.
In some environments, a controlled deployment of the WSN may not be attainable. In such case, the only viable option would be a random deployment over the region of interest (ROI), leading to a great deal of uncovered areas as well as many cutoff nodes. Three different scenarios are presented, each addressing the coverage problem for a distinct purpose. First, a multi-objective optimization is considered with the purpose of relocating the sensor nodes after the initial random deployment, through maximizing the field coverage while minimizing the cost of mobility. Simulations reveal the improvements in coverage, while maintaining the mobility cost to a minimum. In the second scenario, tracking a mobile target with a high level of accuracy is of interest. The relocation process was based on learning the spatial mobility trends of the targets. Results show the improvement in tracking accuracy in terms of mean square position error. The last scenario involves the use of inverse reinforcement learning (IRL) to predict the destination of a given target. This lay the ground for future exploration of the relocation problem to achieve improved prediction accuracy. Experiments investigated the interaction between prediction accuracy and terrain severity.
The other WSN limitation is dealt with by introducing the concept of sparse sensing to schedule the measurements of sensor nodes. A hybrid WSN setup of low and high precision nodes is examined. Simulations showed that the greedy algorithm used for scheduling the nodes, realized a network that is more resilient to individual node failure. Moreover, the use of more affordable nodes stroke a better trade-off between deployment feasibility and precision
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Scour detection with monitoring methods and machine learning algorithms - a critical review
Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.This research was funded by FCT (Portuguese national funding agency for science, research,
and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and
trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European
Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)
Fuzzy systems and unsupervised computing: exploration of applications in biology
In this thesis we will explore the use of fuzzy systems
theory for applications in bioinformatics. The theory of fuzzy systems is
concerned with formulating decision problems in data sets that are
ill-defined. It supports the transfer from a subjective human classification
to a numerical scale. In this manner it affords the testing of hypothesis and
separation of the classes in the data. We first formulate problems in terms
of a fuzzy system and then develop and test algorithms in terms of their
performance with data from the domain of the life-sciences. From the results
and the performance, we will learn about the usefulness of fuzzy systems for
the field, as well as the applicability to the kind of problems and
practicality for the computation itself.
Computer Systems, Imagery and Medi
- …