Theocharis Tsenis

Mr. Theocharis Tsenis

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Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 February 2025 in Infrastructures
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Journal Article
Open access
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TTTheocharis Tsenis
Theocharis Tsenis
Vassilios KappatosVassilios Kappatos
Vassilios Kappatos
The detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams aiming to increase the reliability of river transport compared to the current situation, thereby, increasing the resilience of transport corridors. During our study, we used lifting dams in both France and Italy where river transport is thriving. A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%.
10 March 2025
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Book Chapter
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Charalampos Rafail MedentzidisCharalampos Rafail Medentzidis
Charalampos Rafail Medentzidis
TTTheocharis Tsenis
Theocharis Tsenis
Vassilios KappatosVassilios Kappatos
Vassilios Kappatos
19 May 2026 in Computers
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Journal Article
Open access
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TTTheocharis Tsenis
Theocharis Tsenis
Vasileios BarmpagiannosVasileios Barmpagiannos
Vasileios Barmpagiannos
Evangelos D SpyrouEvangelos D Spyrou
Evangelos D Spyrou
Vassilios KappatosVassilios Kappatos
Vassilios Kappatos
The integration of quadruped robotics with advanced sensing technologies offers a transformative approach to forest management, particularly for real-time measurement of tree Diameter at Breast Height (DBH). This paper introduces a novel methodology by deploying a quadruped robot equipped with GPS, LiDAR, and an aligned high-definition camera to patrol forest paths via a developed dynamic autonomous mission. Utilizing a YOLO-based model for trunk detection, the methodology retrieves precise DBH measurements and corresponding geotags, constructing a spatial database of DBH ground-truth data. This database serves as a real-time ground-truth lookup table to calibrate allometric equations used in drone-based crown detection missions, enhancing the accuracy of forest biophysical attribute estimations such as tree height, volume, and biomass. Experimental validation demonstrates high precision in DBH estimation (error < 5% in controlled tests), supporting automated, around-the-clock data collection for sustainable forest management in Mediterranean ecosystems.
3 September 2026 in Buildings
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Journal Article
Open access
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Marios GiouvanakisMarios Giouvanakis
Marios Giouvanakis
TTTheocharis Tsenis
Theocharis Tsenis
Vassilios KappatosVassilios Kappatos
Vassilios Kappatos
This paper introduces a non-invasive ultrasonic sensing system for two-phase refrigerant flow characterization in heat pump circuits used in building energy systems, validated through machine learning (ML)-based regression of the acquired signals. Heat pumps play a crucial role in energy-efficient buildings. However, the absence of a low-cost, non-invasive instrument capable of measuring mass flow rate, mixture density, and vapor quality without disrupting the thermodynamics of a refrigerant circuit remains a gap for smart HVAC systems. A carbon dioxide (CO2) refrigerant circuit was designed to calibrate such a sensing system under representative operating heat pump conditions. Ultrasonic measurements were conducted using piezoelectric transducers clamped onto the refrigerant pipeline. A calibration framework was structured with ground-truth flowmeter labels, establishing a thermodynamic envelope across 10–20 bar and down to −25 °C, and achieving R2 = 0.901 for flow rate, 0.997 for density, and 0.971 for quality, with an overall R2 = 0.956. The proposed measurement system is a plug-and-play kit enabling more efficient next-generation heat pumps, supporting building energy management and performance monitoring. The labeled dataset and calibration methodology provide a basis for training and validating ML regression models for real-time flow property inference in operational HVAC systems.