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49 Papers

Accurate 6D Object Pose Estimation and Refinement in Cluttered Scenes

Topics: Robot Vision; Scene Analysis and Understanding

Authors: Yixiang Jin , John Rossiter and Sandor Veres

Affiliation: Department of Automatic Control Systems and Engineering, University of Sheffield, U.K.

Digital Object Identifier: 10.5220/0010654500003061

Proceedings of ROBOVIS 2021, ISBN: 978-989-758-537-1

Quick Abstract

Estimating the 6D pose of objects is an essential part of a robot’s ability to perceive their environment. This paper proposes a method for detecting a known object and estimating its 6D pose from a single RGB image. Unlike most of the state-of-the-art methods that deploy PnP algorithms for estimating 6D pose, the method here can output the 6D pose in one step. In order to obtain estimation accuracy that is comparable to RGB-D based methods, an efficient refinement algorithm, called contour alignment (CA), is presented; this can increase the predicted 6D pose accuracy significantly. We evaluate the new method in two widely used benchmarks, LINEMOD for single object pose estimation and Occlusion-LINEMOD for multiple objects pose estimation. The experiments show that the proposed method surpasses other state-of-the-art prediction approaches.

Mental Workload Estimation using Wireless EEG Signals

Topics: Medical Signal Acquisition, Analysis and Processing; Real-Time Systems & Biosignal-Based User Interfaces; Pattern Recognition & Machine Learning for Biosignal Data; Physiological Processes and Biosignal Modeling, Non-Linear Dynamics; Frequency Analysis & Wavelet Transform

Authors: Quadri Adewale ¹ and George Panoutsos ²

Affiliations: ¹ Montreal Neurological Institute, McGill University, Montreal, Canada , ² Automatic Control & Systems Engineering, University of Sheffield, Sheffield, U.K.

Digital Object Identifier: 10.5220/0010251302000207

Proceedings of BIOSIGNALS 2021, ISBN: 978-989-758-490-9

Quick Abstract

Previous studies have demonstrated the applicability of electroencephalogram (EEG) in estimating mental workload. However, developing reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, we used a wireless Emotiv EPOC headset to evaluate workload in eight subjects and two mental tasks, namely n-back, and arithmetic tasks. 0-back and 2-back tasks, and 1-digit and 3-digit additions were employed as low and high workloads in the n-back and arithmetic tasks, respectively. Using power spectral density as features, a signal processing and feature extraction framework was developed to classify workload levels. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks, respectively. To facilitate real-time estimation of workload, a fast domain adaptation technique was applied to achieve a cross-task accuracy of 68.6%. Similarly, we obtained accuracies of 80.5% and 76.6% across sessions, and 74.4% and 64.1% across subjects, in n-back and arithmetic tasks, respectively. Although the number of participants is limited, this framework generalised well across subjects and tasks, and provides a promising approach towards developing subject and task-independent models. It also shows the feasibility of using a consumer-level wireless EEG headset in cognitive monitoring for real-time estimation of workload in practice.

Reconfigurable Scheduling as a Discrete-Event Process: Monte Carlo Tree Search in Industrial Manufacturing

Topics: Production Planning, Scheduling and Control; Systems Modeling and Simulation; Operations Optimization; AI-Enhanced Cyber-Physical Systems; Cyber-Physical Logistic Systems

Authors: T. Helliwell ¹ , B. Morgan ² , A. Vincent ³ , G. Forgeoux ³ and M. Mahfouf ¹

Affiliations: ¹ Automatic Control & Systems Engineering Department, University of Sheffield, Sheffield, U.K. , ² Advanced Manufacturing Research Center (AMRC), University of Sheffield, Sheffield, U.K. , ³ Safran Landing Systems, Gloucester, U.K.

Digital Object Identifier: 10.5220/0010711600003062

Proceedings of IN4PL 2021, ISBN: 978-989-758-535-7

Quick Abstract

In this paper we introduce a theoretical basis for reconfigurable makespan scheduling that is computationally-efficient and general purpose in manufacturing. A full-scale scale case study for batch production in the aerospace industry is shown. A knowledge-based Discrete-Event System, based on a Timed Petri Net, is injected with the initial - current - state and simulated to generate trajectories that represent valid possible schedules or policies analogous to the Monte-Carlo Tree Search (MCTS) planning algorithm. A new, concise, evolutionary metaheuristic is proposed called Elitist Trajectory Mutation (ETM) in order to exploit high performing schedules in localising search and optimisation. The advantage of this approach is reconfigurability, extensibility and ability to be parallelised to enable satisficing performance for real-time applications such as intelligent industrial cyber-physical systems scheduling, autonomous control of distributed systems and active industrial informatics.

Searching & Generating Discrete-Event Systems

Topics: Software Agents for Intelligent Control Systems; Modeling, Analysis and Control of Discrete-Event Systems; Systems Modeling and Simulation; Systems Modeling and Simulation; Performance Evaluation and Optimization

Authors: T. Helliwell ¹ , B. Morgan ² and M. Mahfouf ¹

Affiliations: ¹ Automatic Control & Systems Engineering Department, University of Sheffield, Sheffield, England , ² Advanced Manufacturing Research Center (AMRC), University of Sheffield, Sheffield, England

Digital Object Identifier: 10.5220/0010584302030210

Proceedings of ICINCO 2021, ISBN: 978-989-758-522-7

Quick Abstract

In this paper we introduce a new method for the automatic generation and computer experimentation of Discrete-Event Systems (DES). We introduce the concept that DES descriptions may be used to define a searchable configuration space. Configuration instances in this space a represented as a permutation encoding which shows the number-of and types-of resources in a given configuration. Each instance is checked that the number of resources does not exceed a maximum and whether a fixed set of processes (a decomposed goal of uncertain time intervals drawn from a Gaussian distribution) can be logically completed on the given set of resources. If the permutation instance satisfies these constraints, it is subsequently constructed as a simulation model to quantify completion through global features of makespan and total processing time. We claim this is the basis for a powerful tool in high-level informed design of these types of systems that have hitherto avoided autonomous description or have been previously individually designed using time consuming manually defined programs.

A Neural-Fuzzy System for Predicting the Areal Surface Metrology Parameters

Topic: Industrial, Financial, Medical, and Other Applications Using Fuzzy Methods

Authors: Ronak Sharma , Mahdi Mahfouf and Olusayo Obajemu

Affiliation: Department of Automatic Controls and Systems Engineering, The University of Sheffield, S1 3JD, Sheffield, U.K.

Digital Object Identifier: 10.5220/0010125602860293

Proceedings of FCTA 2020, ISBN: 978-989-758-475-6

Quick Abstract

With the increasing demand for faster manufacturing, Industry 4.0 has now only started to contribute towards streamlining the manufacturing processes. Despite the availability of high dimensional manufacturing data, a significant amount of time is still spent on testing the end products. Therefore, with a drive to substitute these inspection processes with a “digital twin”, this paper presents a framework for predicting the optimal surface metrology parameters such as force and vibration, required to achieve the desired surface roughness of an end product. Firstly, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to predict the surface roughness using vibration, force and temperature. A low RMSE of 0.07 was obtained between the predicted and desired surface roughness. This model was then reverse engineered to predict the optimal surface conditions (force, vibration and temperature) required to achieve the desired surface roughness. For this, optimisation was applied to minimise the error between the target and predicted surface roughness. This framework will help manufacturing industries to discard frequent in-depth product inspection processes in favour of this “digital twin” due to the possibility of achieving right-first-time production.

Multi-model Adaptive Learning for Robots Under Uncertainty

Topics: Multi-Agent Systems; Distributed Problem Solving; Cooperation and Coordination; Autonomous Systems; Mobile Agents

Authors: Michalis Smyrnakis ¹ , Hongyang Qu ² , Dario Bauso ³ and Sandor Veres ²

Affiliations: ¹ Science and Technology Facilities Council, Daresboury, U.K. , ² Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K. , ³ Jan C. Willems Center for Systems and Control ENTEG, Faculty of Science and Engineering University of Groningen, Nijenborgh, Groningen, The Netherlands

Digital Object Identifier: 10.5220/0008927700500061

Proceedings of ICAART 2020, ISBN: 978-989-758-395-7

Quick Abstract

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. A novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. In contrast, to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters of the algorithm for a specific problem a priori. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.

A Wearable Silent Speech Interface based on Magnetic Sensors with Motion-Artefact Removal

Topics: Biomedical Sensors; Rehabilitation Technology; Wearable and Mobile Devices

Authors: Lam A. Cheah ¹ , James M. Gilbert ¹ , Jose A. Gonzalez ² , Phil D. Green ² , Stephen R. Ell ³ , Roger K. Moore ² and Ed Holdsworth ⁴

Affiliations: ¹ The University of Hull, United Kingdom , ² The University of Sheffield, United Kingdom , ³ Hull and East Yorkshire Hospitals Trust, United Kingdom , ⁴ Practical Control Ltd, United Kingdom

Digital Object Identifier: 10.5220/0006573200560062

Proceedings of BIODEVICES 2018, ISBN: 978-989-758-277-6

Quick Abstract

For a silent speech interface (SSI) to be truly practical, it has to be able to tolerate motion artefacts generated by the user while engaging in normal activities of everyday living. This paper presents a wearable speech restoration system based on magnetic sensors with an integrated background cancellation technique to eliminate the effect of motion-induced interference. The background cancellation technique is assessed when the user makes no intentional movement, when they performs a set of defined head movements and when they make more natural, conversational head movements. The performance is measured for the TIDigits corpus in terms of whole word recognition rate using a Hidden Markov Model and through Mel Cepstral Distortion for a Direct Synthesis of speech using Deep Neural Networks. The results indicate the robustness of the sensing system with background cancellation against the undesirable motion-induced artefacts.

Related Ontology Subjects/Areas/Topics (4)

Biomedical Engineering		Biomedical Sensors		Rehabilitation Technology
Biomedical Instruments and Devices

Facial Temperature Markers for Mental Stress Assessment in Human-Machine Interface (HMI) Control System

Topics: Human-Machine Interfaces; Surveillance, Fault Detection and Diagnosis; Human Factors & Human-System Interface

Authors: Changjiang He ¹ , M. Mahfouf ¹ and L. A. Torres-Salomao ²

Affiliations: ¹ Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield and U.K. , ² Digital Systems and Solutions, General Electric Infraestructure Queretaro, Queretaro and Mexico

Digital Object Identifier: 10.5220/0006820700210028

Proceedings of ICINCO 2018, ISBN: 978-989-758-321-6

Quick Abstract

Mental state prediction is of great importance to human-machine interface (HMI) as far as both safety and reliability are concerned. In this paper, the use of facial temperature changes for predicting mental stress has been investigated. A carefully designed experiment of HMI has been performed on seven (7) healthy subjects, and the statistical analysis of the results has been provided, and the effectiveness of using facial temperature with the thermal camera to estimate the human mental stress has been established. The biomarkers developed from the data of facial temperature have exhibited a similar or even better ability to differentiate between the mental stress levels in comparison with the traditional biomarkers (e.g. heart rate variability (HRV), task load index (TLI) and pupil size). The mean nasal temperature has been shown to be sensitive to changes in the mental state, and the maximum facial temperature and the mean forehead temperature have also shown clear correlations with mental stress and task performance.

Related Ontology Subjects/Areas/Topics (6)

Human Factors & Human-System Interface		Industrial Engineering		Robotics and Automation
Human-Machine Interfaces		Informatics in Control, Automation and Robotics		Surveillance

A Prime Number Approach to Matching an XML Twig Pattern including Parent-Child Edges

Topics: XML and Data Management; Web Information Filtering and Retrieval

Authors: Shtwai Alsubai and Siobhán North

Affiliation: The University of Sheffield, United Kingdom

Digital Object Identifier: 10.5220/0006225602040211

Proceedings of WEBIST 2017, ISBN: 978-989-758-246-2

Quick Abstract

Twig pattern matching is a core operation in XML query processing because it is how all the occurrences of a twig pattern in an XML document are found. In the past decade, many algorithms have been proposed to perform twig pattern matching. They rely on labelling schemes to determine relationships between elements corresponding to query nodes in constant time, therefore the processing time is improved. In this paper, a new algorithm TwigStackPrime is proposed, which is an improvement to TwigStack (Bruno et al., 2002). To reduce the memory consumption and computation overhead of twig pattern matching algorithms when Parent-Child (P-C) edges are involved, TwigStackPrime efficiently filters out a tremendous amount of irrelevant elements and avoid unnecessary computations by introducing a new labelling scheme, called Child Prime Labels (CPL). Extensive performance studies on various real-world and artificial datasets were conducted to demonstrate the significant improvement of CPL over the previous indexing and querying techniques. The experimental results show that the new technique has a superior performance to the previous approaches.

Related Ontology Subjects/Areas/Topics (3)

Internet Technology

Web Information Systems and Technologies

XML and Data Management

Evolutionary Type-2 Fuzzy Blood Gas Models for Artificially Ventilated Patients in ICU

Topics: Fuzzy Control; Optimization Algorithms

Authors: S. H. Indera-Putera and M. Mahfouf

Affiliation: The University of Sheffield, United Kingdom

Digital Object Identifier: 10.5220/0006434701120121

Proceedings of ICINCO 2017, ISBN: 978-989-758-263-9

Quick Abstract

This paper proposes a new modelling and optimization architecture for improving the prediction accuracy of arterial blood gases (ABG) in the SOPAVent model (Simulation of Patients under Artificial Ventilation). The three ABG parameters monitored by SOPAVent are the partial arterial pressure of oxygen (PaO2), the partial arterial pressure of carbon-dioxide (PaCO2) and the acid-base measurement (pH). SOPAVent normally produces the initial ABG predictions and also the ABG predictions after any changes in ventilator settings are made. Two of SOPAVent’s sub-models, namely the relative dead-space (Kd) and the carbon-dioxide production (VCO2) were elicited using interval type-2 fuzzy logic system. These models were then tuned using a new particle swarm optimization (nPSO) algorithm, via a single objective optimization approach. The new SOPAVent model was then validated using real patient data from the Sheffield Royal Hallamshire Hospital (UK). The performance of the new SOPAVent model was then compared with its previous version, where Kd and VCO2 were modeled using a neural-fuzzy system (ANFIS). For the initial ABG predictions, significant improvements were observed in the mean absolute error (MAE) and correlation coefficient (R) for PaCO2 and pH. When the ventilator settings were changed, significant improvements were observed for the prediction of pH and other improvements were also observed for the prediction of PaCO2.

Related Ontology Subjects/Areas/Topics (8)

Artificial Intelligence	Fuzzy Systems	Optimization Algorithms
Computational Intelligence	Informatics in Control, Automation and Robotics	Soft Computing
Fuzzy Control	Intelligent Control Systems and Optimization

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