3,261 research outputs found

    Application of Saliency Maps for Optimizing Camera Positioning in Deep Learning Applications

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    In the fields of process control engineering and robotics, especially in automatic control, optimization challenges frequently manifest as complex problems with expensive evaluations. This thesis zeroes in on one such problem: the optimization of camera positions for Convolutional Neural Networks (CNNs). CNNs have specific attention points in images that are often not intuitive to human perception, making camera placement critical for performance. The research is guided by two primary questions. The first investigates the role of Explainable Artificial Intelligence (XAI), specifically GradCAM++ visual explanations, in Computer Vision for aiding in the evaluation of different camera positions. Building on this, the second question assesses a novel algorithm that leverages these XAI features against traditional black-box optimization methods. To answer these questions, the study employs a robotic auto-positioning system for data collection, CNN model training, and performance evaluation. A case study focused on classifying flow regimes in industrial-grade bioreactors validates the method. The proposed approach shows improvements over established techniques like Grid Search, Random Search, Bayesian optimization, and Simulated Annealing. Future work will focus on gathering more data and including noise for generalized conclusions.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and OutlookIm Bereich der Prozessleittechnik und Robotik, speziell bei der automatischen Steuerung, treten oft komplexe Optimierungsprobleme auf. Diese Arbeit konzentriert sich auf die Optimierung der Kameraplatzierung in Anwendungen, die Convolutional Neural Networks (CNNs) verwenden. Da CNNs spezifische, fĂŒr den Menschen nicht immer ersichtliche, Merkmale in Bildern hervorheben, ist die intuitive Platzierung der Kamera oft nicht optimal. Zwei Forschungsfragen leiten diese Arbeit: Die erste Frage untersucht die Rolle von ErklĂ€rbarer KĂŒnstlicher Intelligenz (XAI) in der Computer Vision zur Bereitstellung von Merkmalen fĂŒr die Bewertung von Kamerapositionen. Die zweite Frage vergleicht einen darauf basierenden Algorithmus mit anderen Blackbox-Optimierungstechniken. Ein robotisches Auto-Positionierungssystem wird zur Datenerfassung und fĂŒr Experimente eingesetzt. Als Lösungsansatz wird eine Methode vorgestellt, die XAI-Merkmale, insbesondere solche aus GradCAM++ Erkenntnissen, mit einem Bayesschen Optimierungsalgorithmus kombiniert. Diese Methode wird in einer Fallstudie zur Klassifizierung von Strömungsregimen in industriellen Bioreaktoren angewendet und zeigt eine gesteigerte performance im Vergleich zu etablierten Methoden. ZukĂŒnftige Forschung wird sich auf die Sammlung weiterer Daten, die Inklusion von verrauschten Daten und die Konsultation von Experten fĂŒr eine kostengĂŒnstigere Implementierung konzentrieren.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and Outloo

    A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

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    Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of −1.658 × 10−3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: −0.4156 × 10−3) and DispEn (slope: −0.1675 × 10−3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis

    Separately, Connectedly: Exploring Trauma Through Ekphrasis in Contemporary Novels

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    This thesis examines ekphrasis as a rhetorical tool to explore, represent, and contemplate trauma affect in contemporary novels. From the Greek phrase for ‘description,’ ekphrasis is part of a long and ancient literary tradition, dating as far back as the ancient depictions of art on urns, weaponry, as well as more disambiguated descriptions of scenes and people. The uses of ekphrasis as a literary device are broad and complex, but its use is under-researched in contemporary novels, and there is a near total absence of investigation into ekphrasis within the novel as a means of contemplating and understanding the affect of a condition that is inherently abstract and disorienting.Literary trauma theory has evolved considerably in recent years. In keeping with important findings in psychology and psychiatric research, there is a broad recognition that rethinking trauma representation beyond the recitation and reliving of events and into textured descriptions of trauma affect is essential for thoughtful, nuanced explorations of an experience that resists narrative convenience. As a result, there are increased calls to accept and represent its inherent fractured nature and resist the authorial temptation to forge a story around it that fits neatly into a cohesive whole. This thesis proposes a framework for considering how various aspects of ekphrastic descriptions of real and imagined art as well as their connotative and denotative significance in the novel reveals nuance in the representation of trauma affect through the activation of language and image. The contemporary novels explored herein are: The Goldfinch by Donna Tartt, What I Loved by Siri Hustvedt, and How to Be Both by Ali Smith. Each of these novels present ekphrasis and affect differently, which enables broader testing of the flexibility of the proposed framework

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    TRANSPARENCY AS A TOOL: SECURING COLLABORATIVE APPROACHES TO FEDERAL HOMELAND SECURITY FUNDING IN THE LOS ANGELES AREA

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    The Los Angeles Area Urban Area Security Initiative (LAA UASI) is a federal grant program that takes a regional approach to grant projects that improve safety and security in Los Angeles. The research conducted for this thesis aimed to contribute to ongoing efforts to enhance the LAA UASI’s safety, provide a framework for collaborative networks, and create a positive environment with real-time information for managing large-scale incidents, including natural and human-made disasters. The research found that the LAA UASI has significantly enhanced the Los Angeles community’s preparedness and response capabilities and identified some areas for improvement. After analyzing best practices from many sectors, including private industry, public safety, the energy sector, not-for-profit governance, and corporate business, this thesis offers several recommendations for future implementation of the program to enhance overall collaboration and cooperation—the bridge to building strong networks of partners and keeping American cities safe. Overall, this thesis lends valuable insights and recommendations for decision-makers working to improve the safety and security of the Los Angeles community.Civilian, Los Angeles City Fire DepartmentApproved for public release. Distribution is unlimited

    Exploring a community of musical practice: a case study of music generation Limerick City

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    This research provides a case study of Music Generation Limerick City. Music Generation (MG), established in 2011, is a national music education programme in the Republic of Ireland. As of September 2022, MG provides music education programmes in 25 areas of the country with plans to expand nationwide. This study examines the work of one MG area – Music Generation Limerick City (MGLC). The research investigates to what extent does the performance music education (PME) approach of Music Generation Limerick City (MGLC) create communities of musical practice and to what extent do these foster social action? Research findings have been presented using Wenger’s domains of his social theory of learning (1998) – these include community, identity, meaning and practice. Two phases of data collection took place. Phase one, using focus group interviews and semi-structured interviews explored the experience of MGLC musician educators, classroom teachers, school principals and the MGLC development officer. Phase two of this study using semi-structured interviews explored the experience of MGLC programmes from the viewpoint of past and current MGLC participants. The findings of this study showed that Music Generation Limerick City did indeed create multiple CoMP. This study has demonstrated that the creation of CoMP has the potential to provide the components needed for social action to flourish however, in the case of MGLC, this social action was limited and secondary to the educational remit of MGLC. Furthermore, it was evident in this research that CoMP also provide a structure to which PME programmes can be implemented and delivered. This research provides important insights into the role of partnership in the Irish music education system and demonstrates that while partnerships can be effective in the provision of music education, certain conditions of collaboration and communication are important factors in determining the success of such partnerships. The findings of this research will inform the future development of policy, practice and research of Music Generation Limerick City, Music Generation nationally and similar music programmes nationally and internationally.N

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Microwave-shielded ultracold polar molecules

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    Since the realization of Bose--Einstein condensates and degenerate Fermi gases, ultracold atoms with tunable interactions have become an essential platform for studying quantum many-body phenomena. Notable examples include the realization of BCS--BEC crossover and the simulation of the Bose/Fermi Hubbard model. Ultracold polar molecules could enrich the quantum gas toolbox with their long-range dipole-dipole interaction, which offers not only new opportunities in many-body physics, such as realizing the topological superfluid and the extended Hubbard model, but also applications in quantum chemistry, quantum computation, and precision measurements. However, the large number of internal degrees of freedom of molecules present a significant challenge in both cooling them to quantum degeneracy and controlling their interactions. Unlike atomic gases, a dense molecular sample suffers from fast collisional losses, preventing the implementation of evaporative cooling and the observation of scattering resonances. In this thesis, we describe how we solved the long-standing issue of collisional losses by microwave shielding, created a degenerate Fermi gas of NaK molecules, and discovered a new type of scattering resonances via which we created the first ultracold tetratomic molecules in the 100-nK regime. By synchronizing the rotation of polar molecules with a circularly polarized microwave electric field, we equip the molecular sample with a highly tunable intermolecular potential. This not only stabilizes the gas against inelastic collisions but also enables field-linked scattering resonances for precise control over scattering lengths. At long range, the molecules interact via their induced rotating dipole moments. As they approach each other, their orientations realign to produce a repulsive force, thereby mitigating inelastic collisions at close distances. With an elastic-to-inelastic collision ratio of 500, we have achieved evaporative cooling of the molecular gas down to 21 nK and 0.36 times the Fermi temperature, setting a new record for the coldest polar molecular gas to date. Thanks to the collisional stability of microwave-shielded molecules, we can directly load them into predominantly a single layer of a magic 3D optical lattice, achieving a peak filling fraction of 24%. These ultracold molecules, owing to their long lifetimes in their ground state and their long-range dipolar coupling, provide a unique platform to study quantum magnetism. With the achieved high filling fraction, we are prepared to study non-equilibrium spin dynamics such as rotational synchronization and spin squeezing. We demonstrated that the interaction between microwave-shielded polar molecules is highly tunable via the microwave power, detuning, and polarization. When the interaction potential is deep enough to host field-linked bound states at the collisional threshold, a shape resonance is induced, allowing us to tune the scattering rate by three orders of magnitude. The field-linked resonances enables controls over the scattering length in a similar fashion as Feshbach resonance for ultracold atoms, promising the realization of strongly correlated phases, such as dipolar pp-wave superfluid. It also paves the way to investigate the interplay between short-range and long-range interactions in novel quantum matters, such as exotic supersolid. Moreover, through a field-linked resonance, we associated for the first time weakly bound tetratomic molecules in the 100-nK regime, with a phase space density of 0.04. The transition from a Fermi gas of diatomic molecules to a Bose gas of tetratomic molecules paves the way for dipolar BCS--BEC crossover. With microwave-shielded polar molecules, we have realized a quantum gas featuring highly tunable long-range interactions. The technique is universal to polar molecules with a sufficiently large dipole moment, and thus offers a general strategy for cooling and manipulating polar molecules, and for associating weakly bound ultracold polyatomic molecules. Utilizing the toolbox developed in ultracold atoms, this platform possesses the potential to unlock an entirely new realm of quantum simulation of many-body physics
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