Explanipedia

An <span>RL</span> ‐Driven Adaptive Game Approach to Support Cultural Heritage Learning Open

Christina Tsita, Nikolaos Dimitriou, Maya Satratzemi · 2025

Background Study Serious games for cultural heritage offer opportunities for enhancement, particularly in user experience and educational impact. This paper presents a Reinforcement Learning (RL)‐driven adaptive approach to support CH lear…

Explainable AI Methods for Identification of Glue Volume Deficiencies in Printed Circuit Boards Open

Theodoros Tziolas, K. Papageorgiou, T.C. Theodosiou, Dimosthenis Ioannidis, Nikolaos Dimitriou , et al. · 2025

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inhe…

DUAL STREAM NETWORKS FOR 3D HUMAN POSE AND SHAPE ESTIMATION Open

Ioannis Pastaltzidis, Nikolaos Dimitriou, Stelios Krinidis, Dimitrios Tzovaras · 2025

Time-Series Forecasting in Industrial Environments: A Performance Study and a Novel Late Fusion Framework Open

Dimitrios Oikonomou, Lampros Leontaris, Nikolaos Dimitriou, Dimitrios Tzovaras · 2025

Optimized Next-Best-View Planning for 3dreconstruction Via Reinforcement Learning Andsuper-Resolution Open

Anastasios Tzelepakis, Nikolaos Kilis, Michalis Chatzakis, Nikolaos Dimitriou, Dimitrios Tzovaras · 2025

Efficient Deep Q-Learning for Industrial Equipment Calibration in Elevator Manufacturing Open

Apostolos Evangelidis, Nikolaos Dimitriou, Paschalis Charalampous, Theofilos Mastos, Dimitrios Tzovaras · 2024

Industrial equipment calibration is an essential element for the proper functioning of any production plant. Without frequent and proper calibration, the quality and efficiency of the overall production process are threatened. Despite its …

A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events Open

Alexandros Vrochidis, Nikolaos Dimitriou, Stelios Krinidis, Savvas Panagiotidis, Stathis Parcharidis , et al. · 2024

This paper introduces a deep learning methodology for analyzing audience engagement in online video events. The proposed deep learning framework consists of six layers and starts with keyframe extraction from the video stream and the parti…

A real-time wearable AR system for egocentric vision on the edge Open

Iason Karakostas, Aikaterini Valakou, Despoina Gavgiotaki, Zinovia Stefanidi, Ioannis Pastaltzidis , et al. · 2024

Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR…

Real-Time Activity Recognition for Surveillance Applications on Edge Devices Open

Vasileios Tsinikos, Ioannis Pastaltzidis, Iason Karakostas, Nikolaos Dimitriou, Aikaterini Valakou , et al. · 2023

Human Activity Recognition is a crucial task for surveillance systems that has seen great advancements with the emergence of Artificial Intelligence. At the same time, hardware advances have allowed for development of systems that operate …

The DARLENE XR platform for intelligent surveillance applications Open

Christos Ntoumanopoulos, Efstathia Martinopoulou, Iason Karakostas, Nikolaos Dimitriou, Dimitrios Tzovaras · 2023

Closed-circuit television (CCTV) systems play a significant role in the prevention and handling of criminal events. Although multiple cameras can reduce blind spots in surveillance areas, they tend to lack tools for automatic scene compreh…

A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency Open

Lampros Leontaris, Andreana Mitsiaki, Paschalis Charalampous, Nikolaos Dimitriou, Eleni Leivaditou , et al. · 2023

Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite conside…

A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps Open

T.C. Theodosiou, Andriana Rapti, K. Papageorgiou, Theodoros Tziolas, Elpiniki I. Papageorgiou , et al. · 2023

The identification of defects plays a key role in the semiconductor industry as it can reduce production risks, minimize the effects of unexpected downtimes and optimize the production process. A literature review protocol is implemented a…

CENTERNET-BASED MODELS FOR THE DETECTION OF DEFECTS IN AN INDUSTRIAL ANTENNA ASSEMBLY PROCESS Open

Θεοδόσιος Θεοδοσίου, Theodoros Tziolas, K. Papageorgiou, Aikaterini Rapti, Elpiniki I. Papageorgiou , et al. · 2023

This work presents a study for the identification of incorrect antenna assemblies using Artificial Intelligence.The anchor-free and lightweight object detection CenterNets are combined with different feature extractors and their performanc…

INSPECTION OF SURFACE DEFECTS IN METAL PROCESSING INDUSTRY USING UNET-BASED ARCHITECTURES Open

Lampros Leontaris, Nikolaos Dimitriou, Apostolos Nikolousis, Dimitrios Tzovaras, Elpiniki I. Papageorgiou · 2023

Surface inspection is a critical procedure of quality control during metal processing.The raw material is processed in various production stages including cutting, punching, trimming, scrubbing and polishing.Each of these stages introduce …

AN ELEVATOR CALIBRATION RECOMMENDER SYSTEM FOR EFFECTIVE DEFECT DETECTION AND PREVENTION Open

George Margetis, Nikolaos Dimitriou, Elpiniki I. Papageorgiou, T.C. Theodosiou, Konstantinos C. Apostolakis , et al. · 2023

Machine Learning and Recommendation Systems (RSs) have had a significant impact on the manufacturing industry, heralding in the smart manufacturing era of Industry 4.0.An RS is a class of machine learning that recommends items from a knowl…

EFFICIENTDET APPLICATION FOR DETECTION OF INCORRECT ASSEMBLIES IN THE ANTENNA MANUFACTURING PROCESS Open

Anna Feleki, K. Papageorgiou, Theodoros Tziolas, Aikaterini Rapti, Θεοδόσιος Θεοδοσίου , et al. · 2023

This work aims to investigate and effectively apply the new deep learning methods for object detection from the family of EfficientDet-Lite algorithms for the detection of incorrect assemblies in the antenna manufacturing process.In the pr…

AUTOMATED DEFECT DETECTION IN BATTERY LINE ASSEMBLY VIA DEEP LEARNING ANALYSIS Open

Anastasios Tzelepakis, Lampros Leontaris, Nikolaos Dimitriou, Evangelia Koukidou, Dimitrios Bollas , et al. · 2023

Recent technological achievements in computer vision and machine learning have provided promising solutions in industrial quality control.As automated solutions are hard to integrate in the manufacturing process, a common practice during b…

ENHANCING DEFECT TRACEABILITY AND DATA INTEGRITY IN INDUSTRY 4.0 USING BLOCKCHAIN TECHNOLOGY Open

Andreana Mitsiaki, Nikolaos Dimitriou, George Margetis, Konstantinos Votis, Dimitrios Tzovaras · 2023

With the transition to Industry 4.0 factories have achieved significant gains in production with respect to quality, reliability, flexibility, and utilization of resources.Nonetheless, there are open challenges that shall be addressed when…

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing Open

K. Papageorgiou, Θεοδόσιος Θεοδοσίου, Aikaterini Rapti, Elpiniki I. Papageorgiou, Nikolaos Dimitriou , et al. · 2022

The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review pro…

Data augmentation for fairness-aware machine learning Open

Ioannis Pastaltzidis, Nikolaos Dimitriou, Katherine Quezada Tavárez, Stergios Aidinlis, Thomas Marquenie , et al. · 2022

Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of lit…

A Deep Regression Framework Toward Laboratory Accuracy in the Shop Floor of Microelectronics Open

Apostolos Evangelidis, Nikolaos Dimitriou, Lampros Leontaris, Dimosthenis Ioannidis, Gregory Tinker , et al. · 2022

Abstract: Deep learning (DL) has certainly improved industrial inspection, while significant progress has also been achieved in metrology with impressive results reached through their combination. However, it is not easy t…

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions Open

Konstantinos C. Apostolakis, Nikolaos Dimitriou, George Margetis, Stavroula Ntoa, Dimitrios Tzovaras , et al. · 2022

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) em…

A Real-Time Wearable Ar System for Egocentric Vision on the Edge Open

Iason Karakostas, Aikaterini Valakou, Despoina Gavgiotaki, Zinovia Stefanidi, Ioannis Pastaltzidis , et al. · 2022

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions Open

Konstantinos C. Apostolakis, Nikolaos Dimitriou, George Margetis, Stavroula Ntoa, Dimitrios Tzovaras , et al. · 2021

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with severa…

Short Survey of Artificial Intelligent Technologies for Defect Detection in Manufacturing Open

Elpiniki I. Papageorgiou, Theodosis Theodosiou, George Margetis, Nikolaos Dimitriou, Paschalis Charalampous , et al. · 2021

Zero Defect Manufacturing (ZDM) can be described as the set of methodologies and strategies for the elimination of defective components during production, and is one of the main goals of Industry 4.0. ZDM is very appealing to industries gr…

An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning Open

Lampros Leontaris, Nikolaos Dimitriou, Dimosthenis Ioannidis, Konstantinos Votis, Dimitrios Tzovaras , et al. · 2021

A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial application, requ…

Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures Open

Dimitris Tsiktsiris, Nikolaos Dimitriou, Antonios Lalas, Minas Dasygenis, Konstantinos Votis , et al. · 2020

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather …

A Deep Learning framework for simulation and defect prediction applied in microelectronics Open

Nikolaos Dimitriou, Lampros Leontaris, Thanasis Vafeiadis, Dimosthenis Ioannidis, Tracy Wotherspoon , et al. · 2019

A Configurable Design Approach for Virtual Museums Open

Christina Tsita, Anastasis Sinanis, Nikolaos Dimitriou, Kostas Papachristou, Anastasia Karageorgopoulou , et al. · 2018

Virtual Museums (VM) are widely used to preserve and to disseminate cultural heritage to the audience, due to the existing evidence that they can enhance the interest on cultural heritage content, while reinforcing motivation for a real mu…

On the potential of Simulation enhancec conservation of CH artifacts Open

Evdoxia Taka, Konstantinos Papachristou, Nikolaos Dimitriou, Anastasios Drosou, Dimitrios Tzovaras · 2017

The documentation of cultural heritage has long been a useful guide for conservators and restorers. Following the latest advances in HW & SW technologies, the conservation science is keeping up the pace via the incorporation of state-o…

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