11,101 research outputs found

    Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR

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    Automatic speech recognition (ASR) has gained a remarkable success thanks to recent advances of deep learning, but it usually degrades significantly under real-world noisy conditions. Recent works introduce speech enhancement (SE) as front-end to improve speech quality, which is proved effective but may not be optimal for downstream ASR due to speech distortion problem. Based on that, latest works combine SE and currently popular self-supervised learning (SSL) to alleviate distortion and improve noise robustness. Despite the effectiveness, the speech distortion caused by conventional SE still cannot be completely eliminated. In this paper, we propose a self-supervised framework named Wav2code to implement a generalized SE without distortions for noise-robust ASR. First, in pre-training stage the clean speech representations from SSL model are sent to lookup a discrete codebook via nearest-neighbor feature matching, the resulted code sequence are then exploited to reconstruct the original clean representations, in order to store them in codebook as prior. Second, during finetuning we propose a Transformer-based code predictor to accurately predict clean codes by modeling the global dependency of input noisy representations, which enables discovery and restoration of high-quality clean representations without distortions. Furthermore, we propose an interactive feature fusion network to combine original noisy and the restored clean representations to consider both fidelity and quality, resulting in even more informative features for downstream ASR. Finally, experiments on both synthetic and real noisy datasets demonstrate that Wav2code can solve the speech distortion and improve ASR performance under various noisy conditions, resulting in stronger robustness.Comment: 12 pages, 7 figures, Submitted to IEEE/ACM TASL

    In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

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    Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

    Identifizierung prädiktiver und prognostischer Biomarker in unterschiedlichen Tumorkompartimenten des ösophagealen Adenokarzinoms

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    Das ösophageale Adenokarzinom zeigt eine global steigende Inzidenz und hat mit einer 5-Jahres-Überlebensrate von weniger als 25% eine schlechte Prognose. Personalisierte Therapieansätze sind selten und prognostische/prädiktive Biomarker des Tumormikromilieus sind unzureichend charakterisiert. Die kumulative Promotion nähert sich dieser Problematik in drei unterschiedlichen Schwerpunkten. 1. Zur Identifizierung Kompartiment-spezifischer Biomarker wurde eine Methode entwickelt, welche als kostengünstige Alternative zum sc-Seq Expressionsprofile individueller Zelltypen generiert. Dabei erfolgt die Extraktion der RNA nicht aus Einzelzellen, sondern aus flowzytometrisch-getrennten Zellkompartimenten. Die Separation der Proben in Epithelzellen, Immunzellen und Fibroblasten wurde durch verschiedene Verfahren validiert und eine suffiziente Ausbeute an RNA auch für kleine Gewebemengen gezeigt. 2. Biomarker des Immunzellkompartiments als therapeutische Angriffspunkte wurden in einem Patientenkollektiv von bis zu 551 Patienten auf ihre Bedeutung beim EAC überprüft. Es zeigte sich eine Expression der Immuncheckpoints LAG3, VISTA und IDO auf TILs durch IHC und RNA-Sonden basierte Verfahren in einem relevanten Anteil (LAG3: 11,4%, VISTA: 29%, IDO: 52,6%). Es konnte eine prognostisch günstige Bedeutung der VISTA, LAG3 und IDO Expression gezeigt werden. Durch den Vergleich von Genexpressionsprofilen aus therapienaiven und vorbehandelten Tumoren konnte zudem ein immunsuppressiver Effekt von neoadjuvanten Therapiekonzepten auf das Tumormikromilieu des EACs gezeigt werden. Dabei kam es zur verminderten Expression von Checkpoints und Anzahl TILs nach (Radio-) Chemotherapie. 3. Im Tumorzellkompartiment wurde die Rolle von Amplifikationen in ErbB-Rezeptor abhängigen Signalwegen durch FISH-Technik und Immunhistochemie evaluiert. Es fanden sich KRAS Amplifikationen in 17,1%, PIK3CA Amplifikationen in 5% sowie eine HER2/neu-Überexpression in 14,9% der untersuchten Tumore

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Lift EVERY Voice and Sing: An Intersectional Qualitative Study Examining the Experiences of Lesbian, Gay, Bisexual, and Queer Faculty and Administrators at Historically Black Colleges and Universities

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    While there is minimal literature that address the experiences of lesbian, gay, bisexual, and trans* identified students at Historically Black Colleges and Universities (HBCUs), the experiences of Black, queer faculty and administrators at HBCUs has not been studied. This intersectional qualitative research study focused on the experiences of lesbian, gay, bisexual, and queer identified faculty and administrators who work at HBCUs. By investigating the intersections of religion, race, gender, and sexuality within a predominantly Black institution, this study aims to enhance diversity, equity, and inclusion efforts at HBCUs by sharing the experiences of the LGBQ faculty and administrators that previously or currently work at an HBCU as a full-time employee. The research questions that guided this study were 1) How have LGBQ faculty and staff negotiated/navigated their careers at HBCUs? and 2) How do LGBQ faculty and staff at HBCUs influence cultural (relating to LGBQ inclusion) change at the organizational level? The main theoretical framework used was intersectionality and it shaped the chosen methodology and methods. The Politics of Respectability was the second theoretical framework used to describe the intra-racial tensions within the Black/African American community. The study included 60-120 minute interviews with 12 participants. Using intersectionality as a guide, the data were coded and utilized for thematic analysis. Then, an ethnodramatic performance engages readers. The goals of this study were to encourage policy changes, promote inclusivity for LGBQ employees at HBCUs, and provide an expansion to the body of literature in the field pertaining to the experiences of LGBQ faculty and administrators in higher education

    Coloniality and the Courtroom: Understanding Pre-trial Judicial Decision Making in Brazil

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    This thesis focuses on judicial decision making during custody hearings in Rio de Janeiro, Brazil. The impetus for the study is that while national and international protocols mandate the use of pre-trial detention only as a last resort, judges continue to detain people pre-trial in large numbers. Custody hearings were introduced in 2015, but the initiative has not produced the reduction in pre-trial detention that was hoped. This study aims to understand what informs judicial decision making at this stage. The research is approached through a decolonial lens to foreground legacies of colonialism, overlooked in mainstream criminological scholarship. This is an interview-based study, where key court actors (judges, prosecutors, and public defenders) and subject matter specialists were asked about influences on judicial decision making. Interview data is complemented by non-participatory observation of custody hearings. The research responds directly to Aliverti et al.'s (2021) call to ‘decolonize the criminal question’ by exposing and explaining how colonialism informs criminal justice practices. Answering the call in relation to judicial decision making, findings provide evidence that colonial-era assumptions, dynamics, and hierarchies were evident in the practice of custody hearings and continue to inform judges’ decisions, thus demonstrating the coloniality of justice. This study is significant for the new empirical data presented and theoretical innovation is also offered via the introduction of the ‘anticitizen’. The concept builds on Souza’s (2007) ‘subcitizen’ to account for the active pursuit of dangerous Others by judges casting themselves as crime fighters in a modern moral crusade. The findings point to the limited utility of human rights discourse – the normative approach to influencing judicial decision making around pre-trial detention – as a plurality of conceptualisations compete for dominance. This study has important implications for all actors aiming to reduce pre-trial detention in Brazil because unless underpinning colonial logics are addressed, every innovation risks becoming the next lei para inglês ver (law [just] for the English to see)

    The developing maternal-infant relationship: a qualitative longitudinal study

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    Aim The study aimed to explore maternal perceptions and the use of knowledge relating to their infant’s mental health over time using qualitative longitudinal research. Background There has been a growing interest in infant mental health over recent years. Much of this interest is directed through the lens of infant determinism, through knowledge regarding neurological development resulting in biological determinism. Research and policy in this field are directed toward individual parenting behaviours, usually focused on the mother. Despite this, there is little attention given to maternal perspectives of infant mental health, indicating that a more innovative approach to methodology is required. Methods This study took a qualitative longitudinal approach, and interviews were undertaken with seven mothers from the third trimester of pregnancy and then throughout the first year of the infant’s life. Interviews were conducted at 34 weeks of pregnancy, and then when the infant was 6 and 12 weeks, 6, 9, and 12 months, alongside the collection of researcher field notes—a total of 41 interviews. Data were analysed by creating case profiles, memos, and summaries, and then cross-comparison of the emerging narratives. A psycho-socially informed approach was taken to the analysis of data. Findings Three interrelated themes emerged from the data: evolving maternal identity, growing a person, and creating a safe space. The theme of evolving maternal identity dominated the other themes of growing a person and creating a safe space in a way that met perceived socio-cultural requirements for mothering and childcare practices. Participants’ personal stories give voice to their perceptions of the developing maternal-infant relationship in the context of their socio-cultural setting, relationships with others, and experiences over time. Conclusions This study adds new knowledge by giving mothers a voice to express how the maternal-infant relationship develops over time. The findings demonstrate how the developing maternal-infant relationship grows in response to their mutual needs as the mother works to create and sustain identities for herself and the infant that will fit within their socio-cultural context and individual situations. Additionally, the findings illustrate the importance of temporal considerations, social networks, and intergenerational relationships to this evolving process. Recommendations for practice, policy, and education are made that reflect the unique relationship between mother and infant and the need to conceptualise this using an ecological approach

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
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