Nikolaos S . Tachos | University of Patras (original) (raw)

Papers by Nikolaos S . Tachos

Thermal science and engineering progress, Sep 1, 2018

Abstract This work reports the operation of a Latent Heat Thermal Energy Storage system (LHTES) u... more Abstract This work reports the operation of a Latent Heat Thermal Energy Storage system (LHTES) utilizing a staggered heat exchanger (HE) and using various organic Phase Change Materials (PCMs). In a LHTES test rig set measurements regarding energy storage and release were performed in the working temperature range of each Phase Change Material. Nominal melting temperatures of the PCMs used were 40–53 °C. Computational Fluid Dynamics (CFD) simulation was applied to follow the operation of the test rig. The test rig consisted of a compact insulated tank, filled with PCM, a staggered heat exchanger to supply or extract thermal energy by the PCM and a water pump to circulate water as a Heat Transfer Fluid (HTF). Different HTF flow rates affect charging (melting) and discharging (solidification) processes but more significant was the effect of heat transfer mechanisms occurring. The latter was confirmed by inserting buoyancy currents created due to convection in a CFD simulation program where melting time was reduced compared to the same conditions with only conduction occurring. The suggested LHTES configuration is a promising compact unit despite the PCMs thermal resistance and solidification hysteresis phenomena, as well as the heat transfer mechanism strongly affecting the energy storage process.

International Journal of Sustainable Energy, Oct 3, 2017

ABSTRACT This paper presents results of the experimental study for a 10-kWe Organic Rankine Cycle... more ABSTRACT This paper presents results of the experimental study for a 10-kWe Organic Rankine Cycle (ORC) unit that has been designed and developed in order to study its performance when solar or geothermal sources are used. A boiler was used to simulate those conditions and adjustments were made to generate the final heat by a solar or geothermal source in the same pace, size and way, which accurately reflects the operation of each system. R134a was selected as the working fluid. Measurements for key parameters at various points of the cycle were conducted and performance assessment was attempted. Based on the system operation under the conditions that were applied, and considering the various seasons of the year, it was found that the ORC unit working with low-temperature sources is likely to act as a complementary recovery system of heat quantities as the efficiency is rather small, but able to support other systems.

Brain Sciences

Affective state estimation is a research field that has gained increased attention from the resea... more Affective state estimation is a research field that has gained increased attention from the research community in the last decade. Two of the main catalysts for this are the advancement in the data analysis using artificial intelligence and the availability of high-quality video. Unfortunately, benchmarks and public datasets are limited, thus making the development of new methodologies and the implementation of comparative studies essential. The current work presents the eSEE-d database, which is a resource to be used for emotional State Estimation based on Eye-tracking data. Eye movements of 48 participants were recorded as they watched 10 emotion-evoking videos, each of them followed by a neutral video. Participants rated four emotions (tenderness, anger, disgust, sadness) on a scale from 0 to 10, which was later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled three self-assessment questionnaires. An extensive analysis of the parti...

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

2023 IEEE Conference on Artificial Intelligence (CAI)

Sensors

Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of t... more Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait...

Magnetic Resonance Imaging

Scientific Reports

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most ... more Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is...

arXiv (Cornell University), Nov 23, 2022

During the preceding decades, human gait analysis has been the center of attention for the scient... more During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson's disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication

IEEE Open Journal of Engineering in Medicine and Biology

To develop a computationally efficient and unbiased synthetic data generator for large-scale in s... more To develop a computationally efficient and unbiased synthetic data generator for large-scale in silico clinical trials (CTs). Methods: We propose the BGMM-OCE, an extension of the conventional BGMM (Bayesian Gaussian Mixture Models) algorithm to provide unbiased estimations regarding the optimal number of Gaussian components and yield high-quality, large-scale synthetic data at reduced computational complexity. Spectral clustering with efficient eigenvalue decomposition is applied to estimate the hyperparameters of the generator. A case study is conducted to compare the performance of BGMM-OCE against four straightforward synthetic data generators for in silico CTs in hypertrophic cardiomyopathy (HCM). Results: The BGMM-OCE generated 30000 virtual patient profiles having the lowest coefficient-of-variation (0.046), inter-and intra-correlation differences (0.017, and 0.016, respectively) with the real ones in reduced execution time. Conclusions: BGMM-OCE overcomes the lack of population size in HCM which obscures the development of targeted therapies and robust risk stratification models.

EAI Endorsed Transactions on Bioengineering and Bioinformatics, 2022

The aim of this work is to present the architecture of the See Far solution. See Far is a digital... more The aim of this work is to present the architecture of the See Far solution. See Far is a digitally enabled adaptive<br> solution supporting the ageing workforce with vision deficiencies to remain actively involved in their professional life, helping<br> them to sustain and renew their work and personal life related skills and support independent, active and healthy lifestyles. The<br> See Far solution consists of two components: (i) the See Far smart glasses and the (ii) See Far mobile application.<br>

This study aims at developing a patient-specific model for coronary artery disease (CAD) risk str... more This study aims at developing a patient-specific model for coronary artery disease (CAD) risk stratification based on machine learning modelling of molecular, cellular, inflammatory and omics data.

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2010

The analysis of the near and far flow fields of an experimental National Renewable Energy Laborat... more The analysis of the near and far flow fields of an experimental National Renewable Energy Laboratory (NREL) rotor, which has been used as the reference rotor for the Viscous and Aeroelastic Effects on Wind Turbine Blades (VISCEL) research program of the European Union, is described. The horizontal axis wind turbine (HAWT) flow is obtained by solving the steady-state Reynolds-averaged Navier—Stokes (RANS) equations, which are combined with one of four turbulence models (Spalart—Allmaras, k—∊, k—∊ renormalization group, and k—ω shear stress transport (SST)) aiming at validation of these models through a comparison of the predictions and the free field experimental measurements for the selected rotor. The computational domain is composed of 4.2×106 cells merged in a structured way, taking care of refinement of the grid near the rotor blade in order to enclose the boundary layer approach. The constant wind condition 7.2 m/s, which is the velocity of the selected experimental data, is co...

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)

IEEE Reviews in Biomedical Engineering

Eye behaviour provides valuable information revealing one's higher cognitive functions and state ... more Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The eye-tracking publicly available datasets employed in relevant research efforts were concentrated and described their specifications and details. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eyetracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

IEEE reviews in biomedical engineering, 2021

Eye behaviour provides valuable information revealing one's higher cognitive functions and st... more Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligenc...