Conference
Applications Intelligent Systems
About: Applications Intelligent Systems is an academic conference. The conference publishes majorly in the area(s): Computer science & Deep learning. Over the lifetime, 81 publications have been published by the conference receiving 321 citations.
Topics: Computer science, Deep learning, Mobile robot, Artificial neural network, Convolutional neural network
Papers
7 Jan 2020
TL;DR: This paper addresses the problem of PV Panel Detection using a Convolutional Neural Network framework called YOLO and is able to effectively and efficiently segment panels from an image, compared to existing PV panel detection approaches on the biggest publicly available benchmark dataset.
Abstract: Photovoltaic (PV) panels are a clean and widespread way to produce renewable energy from sunlight; at the same time, such plants require maintenance, since solar panels can be affected by many types of damaging factors and have a limited yet variable lifespan. With the impressive growth of such PV installations, it is in the public eye the need of a cheap and effective way to continuously monitor the state of the plants and a standard technique designed to promptly replace broken modules, in order to prevent drops in the energy production. Since the faults mainly appear as Hot Spots on the surface of the PV panels, aerial thermal imaging can be used to diagnose such problems and also locate them in huge plants. To this aim, dedicated automatic Computer Vision methods are able to automatically find hot spots from thermal images, where they appear as white stains. In these methods a fundamental step is the segmentation of the PV panels, which allows to automatically detect each module. In this paper, we address the problem of PV Panel Detection using a Convolutional Neural Network framework called YOLO. We demonstrate that it is able to effectively and efficiently segment panels from an image. The method is quantitatively evaluated and compared to existing PV panel detection approaches on the biggest publicly available benchmark dataset; the experimental results confirm its robustness.
37 citations
7 Jan 2020
TL;DR: A Convolutional Neural Network (CNN) based medication monitoring system and this system is a sub component of an intelligent pill reminder system to minimize medication errors.
Abstract: The ability of a patient to take correct medicine at right time may be reduced when having visual or auditory impairments. Use of inappropriate drug intake can be dangerous and it is important that the patient takes right drug at schedule time. But it is difficult for the elderly persons and the patients with audio and visual impairments to carry out treatment process independently and correctly. This article presents a Convolutional Neural Network (CNN) based medication monitoring system and this system is a sub component of an intelligent pill reminder system.The goal of the intelligent pill reminder system in general, is to assist patient during treatment process at home and role of the monitoring system in particular, is to minimize medication errors. This system is demonstrated on GUI application with a satisfactory accuracy.
22 citations
7 Jan 2020
TL;DR: This article focuses on the development of a novel machine learning based proof of concept for real-time human pose estimation using data collected from sparse inertial measurement units (IMU) system which is cost-effective and least intrusive in the scope of skilled crafts domain.
Abstract: With recent advances in various hardware technologies, human motion capturing (MoCap) has gained importance in the fields such as computer vision, computer animation, gesture recognition in gaming, and most importantly in bio-mechanical analysis. In this direction, human motion is being captured using various kinds of sensors. Correspondingly, many model-based and data-based techniques have been developed in order to decode sensor readings into information understandable by a person. Given that the current technologies still lack applicability in real-world scenarios considering cost and ease of information gathering, leaves substantial room for improvement. This article focuses on the development of a novel machine learning based proof of concept for real-time human pose estimation using data collected from sparse inertial measurement units (IMU) system which is cost-effective and least intrusive in the scope of skilled crafts domain. Training diverse bi-directional recurrent neural networks (bi-RNN) with variable window size and building an ensemble of these models to estimate human pose in terms of human-joints' angles more accurately and robustly is discussed.
22 citations
7 Jan 2019
TL;DR: This work tackles the interactive reinforcement learning (IRL) approach as a way of solution for the training of agents and addresses the problem of continuous representations along with the interactive approach.
Abstract: Research in intelligent systems field has led to different learning methods for machines to acquire knowledge, among them, reinforcement learning (RL). Given the problem of the time required to learn how to develop a problem, using RL this work tackles the interactive reinforcement learning (IRL) approach as a way of solution for the training of agents. Furthermore, this work also addresses the problem of continuous representations along with the interactive approach. In this regards, we have performed experiments with simulated environments using different representations in the state vector in order to show the efficiency of this approach under a certain probability of interaction. The obtained results in the simulated environments show a faster learning convergence when using continuous states and interactive feedback in comparison to discrete and autonomous reinforcement learning respectively.
19 citations
3 Sep 2020
TL;DR: A scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology is proposed that is successfully applied in the context of a research experiment across Scotland and Italy, and is currently adapting it within other initiatives of Europe-wide health data interoperability.
Abstract: As medical research becomes ever finer-grained, experiments require healthcare data in quantities that single countries cannot provide. Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries. Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints. We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology. Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries. We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability.
15 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 37 |
| 2019 | 36 |
| 2018 | 3 |
| 2016 | 1 |
| 2015 | 3 |
| 2011 | 1 |