Publications
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Human-centered production scheduling in a flexible and robotic manufacturing workshop
Effective scheduling is critical in machining manufacturing to ensure efficient production flow and meet strict delivery timelines. This paper presents a novel approach to optimizing production scheduling in flexible and robotic manufacturing environments, specifically within a Reentrant Hybrid Flow Shop (RHFS). The proposed method integrates human-system interaction considerations with a hybrid decision-making framework, addressing key […]
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Development of a Web-Based Application for Optimizing Multi-Product Pipeline Scheduling in the Oil Industry
In the context of optimizing logistical operations in the oil industry, effective scheduling is critical for enhancing efficiency and reducing costs. This paper aims to address this challenge by developing a scheduling web application specifically designed for multi-product pipeline transportation systems. The primary objective of this application is to generate a detailed schedule plan that […]
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Task Allocation and Planning for Multi-Robot System Using an Improved Genetic Algorithm
This study focuses on optimizing task allocation and planning within a multi-robot system (MRS) for inspections at multiple sites. The problem is formulated as an optimization challenge aimed at minimizing the overall distance covered. Using an improved genetic algorithm (IGA), our objective is to reduce operating expenses. The IGA is improved with various genetic operators […]
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Fault Diagnosis using Deep Neural Networks for Industrial Alarm Sequence Clustering
Significant progress has been made in the field of industrial alarm management systems (AMS) in terms of diagnostic and prognostic accuracy. However, persistent challenges, such as poorly configured alarm setups and floods, contribute to an increased number of false alarms, consequently reducing the efficiency of the monitoring system. In addition, more sophisticated models and interactive […]
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Synthetic Population Generation for Autonomous Vehicle Demand Forecasting
The growing interest in Automated Mobility on Demand (AMoD) services in passenger transportation necessitates accurate forecasting for successful deployment. However, the paucity of real-world data is a significant challenge. In this study, we present a unique technique for developing a synthetic user population tailored to AMoD car services. We identify possible passengers using selection criteria […]
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OSR: Advancing Multi-Hop Routing for LoRaWAN Mesh Networks in Maritime Scenarios
Reliable data acquisition and transmission from ocean-deployed buoys are crucial for maritime applications. However, wireless data transmission in such contexts faces significant challenges due to limited buoy battery capacity, harsh weather conditions, and potential disruptions from maritime vessels. LoRaWAN technology presents a promising solution due to its low power consumption and long-range communication capabilities. Multi-hop […]
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LoRaCAPS: Congestion-Aware Path Selection Protocol for Offshore LoRaWAN Networking
LoRaWAN technology plays a pivotal role in enabling data transmission from IoT devices across various industries. In the maritime sector, applications such as operational monitoring and environmental surveillance depend critically on reliable data communication. However, wireless data transmission at sea presents significant challenges, including limited device battery life, harsh weather conditions, and interference from vessels. […]
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Optimizing Shared Micro-mobility Services: Edge-Enabled Rebalance for Dock-based System
The user experience is an important aspect of micromobility fleet operations, and placing micro-vehicles in a suitable and optimized manner is a key element to enhancing user service. This paper aims to establish an effective methodology for optimizing shared micro-mobility rebalance operations through spatio-temporal prediction of user demand in dock-based sys tems. It is based […]
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A model-driven approach for prospective ergonomics: Application to ikigai robotics
Prospective Ergonomics requires building a vision of the future, which can be achieved empirically (e.g. analysing unmet needs) and/or creatively (e.g. creating future needs). We develop an alternative way of imagining the future, through a model-driven approach. Based on several developmental models, we provide a global picture of possible future(s) emphasising higher-ordered motivations and values […]
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Multi Objective Optimization of Human-Robot Collaboration: A Case Study in Aerospace Assembly Line
Collaborative robotics is becoming increasingly prevalent in industry 5.0, leading to a growing need to improve interactions and collaborations between humans and robots. However, the current approach to defining the sharing of responsibilities between humans and robots is empirical and uses the robot as an active fixture of parts, which is a sub-optimal method for […]
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An improved 3D skeletons UP-Fall dataset : enhancing data quality for efficient impact fall detection
Detecting impact where an individual makes contact with the ground within a fall event is crucial in fall detection systems, particularly for elderly care where prompt intervention can prevent serious injuries. The UP-Fall dataset, a key resource in fall detection research, has proven valuable but suffers from limitations in data accuracy and comprehensiveness. These limitations […]
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Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions
Pain management and severity detection are crucial for effective treatment, yet traditional self-reporting methods are subjective and may be unsuitable for non-verbal individuals (people with limited speaking skills). To address this limitation, we explore automated pain detection using facial expressions. Our study leverages deep learning techniques to improve pain assessment by analyzing facial images from […]
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