Research
Research That Drives Innovation.
Dive into insightful publications on Human-Machine Teaming (HMT), highlighting our teams significant contributions to this dynamic field. Our works offer valuable insights and articulate the transformative impact of HMT across diverse sectors.
Publications
Studies and Publications
Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent planes, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover…
Focus: #HMT #Industrial_Safety
This paper proposes a new structure that uses actual and estimated data over the application of forecast analysis and optimization system. Some of the sensors applied in the experiments were integrated in a large IoT system encompassing various devices and collections were made over three months…
Focus: #Energy_Management #IOT
This paper focuses on the topic of deploying deep learning techniques, namely CNNs and LSTMs, to deal with certain issues of IoT data processing. A method integrating the edge and cloud computing architecture is presented to make data analysis fast and accurate. The efficiency has been proved…
Focus: #IOT #Deep_Learning
In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects the best algorithm to extract the material parameters…
Focus: #Additive_Manufacturing
In this study, we focus on a long-short-term memory convolutional neural network (LSTM-CNN) to extract time and / or frequency-dependent features of the sound data to estimate the number / gender of simultaneous active speakers at each frame in noisy environments. Considering the maximum…
Focus: #Audio_Analysis
In this work, we propose to use the Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect detection. We use a U-Net architecture as the base and explore two kinds of encoders: ResNet and DenseNet. We compare these nets’ performance…
Focus: #Defect_Detection #Deep_Learning
This chapter delves into the technologies underpinning the creation of digital twins specifically tailored to agile manufacturing scenarios within the realm of robotic automation. It explores the transfer of trained policies and process optimizations from simulated settings to real-world applications through advanced techniques such as domain randomization, domain adaptation, curriculum learning, and model-based system identification.
Focus: #Digital_Twins #Industrial_Automation
In this research, an effort is made to address microgrid systems’ operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutional and Gated Recurrent Unit (GRU) layers. This approach…
Focus: #Energy_Management #Micro_Grid
This literature review diligently compiles, analyzes, and discusses recent endeavours employing DL methodologies. These methodologies encompass a spectrum of approaches, ranging from Autoencoders (AE) to Convolutional Neural Networks (CNN) (in 1D, 2D, and 3D configurations), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Transfer Learning (TL), Semi-Supervised Learning (SSL), Few-Shot Learning (FSL) and Active Learning (AL). These approaches…
Focus: #Deep_Learning #Image_Analysis
In this paper, we propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and the human team. Followed by the identification of processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method. The effectiveness of this method is demonstrated using two controlled HMT scenarios…
Focus: #HMT
This paper explores a variety of machine learning models, from heuristic statistical techniques to advanced deep learning methods, to forecast the COVID-19 dynamic. We have compared methods such as Liner Regression, Elastic net regularization, Random-forest regressor, XGBoost regressor, Simple exponential smoothing, and…
Focus: #Machine_Learning
This paper presents an innovative idea of designing a DC generator that reduces the hierarchy of power conversion levels involved to improve the efficiency. The back and forth motion of the machine means it operates in a two-quadrant generation mode. The machine was constructed as a square box model with windings placed on both the top and bottom stator plates, and the rotor consisted of a field winding placed between these plates with two axes of operation.
Focus: #Renewable_Energy
In this work, we propose an intelligent voice-based assistant for COVID-19 self-assessment (IVACS). This interactive assistant has been built to diagnose the symptoms related to COVID-19 using the guidelines provided by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).
Focus: #Artificial_Intelligence #Assistive_Technology
