344 research outputs found

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

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields 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 modified Proportional Conflict 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 classifiers, 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, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. 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 classification, and hybrid techniques mixing deep learning with belief functions as well

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Emergent quality issues in the supply of Chinese medicinal plants: A mixed methods investigation of their contemporary occurrence and historical persistence

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    Quality issues that emerged centuries ago in Chinese medicinal plants (CMP) were investigated to explore why they still persist in an era of advanced analytical testing and extensive legislation so that a solution to improve CMP quality could be proposed. This is important for 85% of the world’s population who rely on medicinal plants (MP) for primary healthcare considering the adverse events, including fatalities that arise from such quality issues. CMP are the most prevalent medicinal plants globally. This investigation used mixed-methods, including 15 interviews with CMP expert key informants (KI), together with thematic analysis that identified the main CMP quality issues, why they persisted, and informed solutions. An unexplained case example, Eleutherococcus nodiflorus (EN), was analysed by collection of 106 samples of EN, its known toxic adulterant Periploca sepium (PS), and a related substitute, Eleutherococcus senticosus (ES), across mainland China, Taiwan and the UK. Authenticity of the samples was determined using High-performance thinlayer chromatography. Misidentification, adulteration, substitution and toxicity were the main CMP quality issues identified. Adulteration was found widespread globally with 57.4% EN found authentic, and 24.6% adulterated with cardiotoxic PS, mostly at markets and traditional pharmacies. The EN study further highlighted that the reason CMP quality issues persisted was due to the laboratory-bound nature of analytical methods and testing currently used that leave gaps in detection throughout much of the supply chain. CMP quality could be more effectively tested with patented analytical technology (PAT) and simpler field-based testing including indicator strip tests. Education highlighting the long-term economic value and communal benefit of delivering better quality CMP to consumers was recommended in favour of the financial motivation for actions that lead to the persistence of well-known and recurrent CMP quality issues

    Analiza sadržaja revizorskih izveštaja javnih društava: Blagovremenost i vrsta mišljenja

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    Zainteresovanost istraživača za oblast revizije finansijskih izveštaja proizilazi iz brojnih specifičnosti sa kojim serevizori susreću u tom procesu, a koji mogu biti predmet istraživanja, kao i značajnosti koju rezultati analizesadržaja revizorskih izveštaja mogu imati za privredu posmatranu u celini. Samim tim, cilj ove disertacije jedvojak, prvi je analiza sadržaja revizorskih izveštaja javnih društava sa aspekta varijabli koje utiču na kašnjenjeu dostavljanju izveštaja, a drugi je analiza vrste mišljenja i potencijalnih varijabli koje su u korelaciji sa vrstommišljenja. Korisnost istraživanja se ogleda u činjenici da bi rezultati trebalo da budu od pomoći upravi javnihdruštava, kao klijenata revizije, u sagledavanju faktora koji su povezani sa izdatom vrstom mišljenja ili savremenskim periodom u okviru kojeg mogu očekivati revizorski izveštaj. Sa druge strane, rezultati istraživanjamogu biti od koristi i potencijalnim i postojećim investitorima u analizi nivoa rizika ulaganja, a vezano zapouzdanost informacija prikazanih u finansijskim izveštajima i značaja blagovremenog finansijskog izveštavanja.Problem istraživanja ogleda se u determinisanju ključnih faktora i prirode uticaja koji oni imaju na periodkašnjenja i vrstu mišljenja revizorskih izveštaja javnih društava iz Republike Srbije. Postavlja se pitanje kojifaktori imaju značajan uticaj na period kašnjenja revizorskih izveštaja i izdatu vrstu mišljenja javnih društava uRepublici Srbiji. Takođe, imajući u vidu da je blagovremenost dostavljanja revizorskih izveštaja zakonskiregulisan aspekt revizorskog izveštaja, postavlja se pitanje da li javna društva u Republici Srbiji dobijajurevizorske izveštaje sa datumom koji je u okviru zakonske regulative i da li dužina perioda dobijanja revizorskihizveštaja odstupa u značajnoj meri od perioda u razvijenim ekonomijama. Na osnovu definisanog cilja i hipotezaistraživanja koncipirano je i sprovedeno empirijsko istraživanje. Uzorak istraživanja se sastojao od 241 javnogdruštva posmatranih po periodima istraživanja (2016-2019), što je ukupno činilo 964 jedinice posmatranja.Vrednosti varijabli za posmatrane periode izračunate su na osnovu podataka preuzetih iz javno obelodanjenihfinansijskih i revizorskih izveštaja uzorkovanih javnih društava. Rezultati istraživanja pokazuju da od ukupno 29varijabli odabranih za ispitivanje, polovina se može dovesti u vezu sa periodom kašnjenja revizorskog mišljenjaili vrstom revizorskog mišljenja javnih društava iz Republike Srbije. U disertaciji su prikazani rezultati opsežnestatističke analize koji otkrivaju smer i jačinu povezanosti između posmatranih varijabli. Rezultati sprovedenogistraživanja pokazuju da su skoro sva uzorkovana društva dobila revizorski izveštaj u zakonski predviđenom roku.Takođe, otprilike polovina izdatih mišljenja je nemodifikovana, dok mišljenje sa rezervom preovlađuje kaomodifikovano mišljenje. Negativno mišljenje je zastupljeno sa oko svega 2% u proseku u posmatranom periodu.Prosečno javno društvo koje dobija nemodifikovano mišljenje je ono koje je profitabilno, likvidno i sa nižomstopom zaduženosti. Ujedno, ovakvo javno društvo može očekivati dobijanje revizorskog izveštaja u kraćemvremenskom periodu u odnosu na druge klijente revizije. Pored toga, rezultati ukazuju da internacionalnarevizorska društva, uključujući Veliku četvorku, verovatno usled činjenice da ih biraju klijenti sa višim nivoomkvaliteta finansijskog izveštavanja, češće izdaju nemodifikovana mišljenja, dok takav uticaj ne postoji kada je upitanju period kašnjenja revizorskog izveštaja. Zanimljiv rezultat istraživanja se odnosi na činjenicu da su revizorimuškog i ženskog pola podjednako zastupljeni, kao i da njihov izbor nema presudni uticaj kako na vrstu mišljenja,tako i na period kašnjenja. Posmatrano sa ova dva aspekta rezultati dokazuju da rotacija revizora (društva) kaoinstrument održavanja profesionalnog skepticizma uspešno realizuju tu ulogu. Naposletku, može se primetiti daizdavanje modifikovanog mišljenja zahteva više resursa u vidu broja dana koje je neophodno utrošiti zaprikupljanje revizorskih dokaza i izražavanje mišljenja. Na osnovu prethodnog, preporuka regulatornim telimabila bi usmerena ka pooštravanju uslova vezano za kotiranje javnih društava u zavisnosti od vrste dobijenogrevizorskog mišljenja. Na taj način bi potencijalni investitori bili u prilici da dobiju ažurnije računovodstveneinformacije koje bi bile kvalitetan input u procesu donošenja investicionih odluka

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Physics-guided Machine Learning for Small Data Sets

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    In order to avoid costly machine breakdowns, proactive schedules are often put in place to substitute wear parts regularly. Currently, the contrary approach of Predictive Maintenance is receiving a lot of attention, as it promises needs-based maintenance. Currently, successful implementations are mainly found in highly standardized industries with a vast history of failure data. These conditions are not fulfilled for custom-built machines, namely here bottling machines. This thesis proposes an approach of combining machine learning with physical knowledge to compensate for missing error data. The approach is applied to bottle transport error cases in filling machines. First, a physical intuition for the machine and the possible error cases is obtained by creating an analytical physical model, avoiding the need for extensive numerical simulations. Second, errors are detected via one-shot semi-supervised anomaly detection, guided by the physical intuition to narrow down suitable algorithms. The one-shot setup involves a particularly short training phase, with only a single healthy sample. The results of the scoring process are anomaly probabilities that are calculated by comparing new samples with the training sample. Samples with high anomaly probabilities continue into the third step, the classification. The anomalous patterns are compared to error sketches, which are drawn by domain experts and enriched by physical knowledge. This approach has so far not been reported in literature. This thesis demonstrates that this strategy can pave the way to Predictive Maintenance for custom-built machines. It creates reliable results and allows transfer learning to similar machines naturally. It also allows feedback to domain experts in order to improve the machine construction

    Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

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    Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

    Real-time Ultrasound Signals Processing: Denoising and Super-resolution

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    Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies

    A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

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    Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels
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