10,636 research outputs found
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
A New Sample of Warm Extreme Debris Disks from the ALLWISE Catalog
Extreme debris disks (EDDs) are rare systems with peculiarly large amounts of warm dust that may stem from recent giant impacts between planetary embryos during the final phases of terrestrial planet growth. Here we report on the identification and characterization of six new EDDs. These disks surround F5-G9 type main-sequence stars with ages >100 Myr, have dust temperatures higher than 300 K, and fractional luminosities between 0.01 and 0.07. Using time-domain photometric data at 3.4 and 4.6 μm from the WISE all-sky surveys, we conclude that four of these disks exhibited variable mid-infrared (IR) emission between 2010 and 2019. Analyzing the sample of all known EDDs, now expanded to 17 objects, we find that 14 of them showed changes at 3-5 μm over the past decade, suggesting that mid-IR variability is an inherent characteristic of EDDs. We also report that wide-orbit pairs are significantly more common in EDD systems than in the normal stellar population. While current models of rocky planet formation predict that the majority of giant collisions occur in the first 100 Myr, we find that the sample of EDDs is dominated by systems older than this age. This raises the possibility that the era of giant impacts may be longer than we think, or that some other mechanism(s) can also produce EDDs. We examine a scenario where the observed warm dust stems from the disruption and/or collisions of comets delivered from an outer reservoir into the inner regions, and explore what role the wide companions could play in this process
Intelligent architecture to support second generation general accounting
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThis study aimed to innovate the world of accounting software. After so many years, accountants are faced
with an unbelievable amount of work, which is not always productive, effective and efficient for both the
accountant and the company that provided him with the data required to carry out the accounting. There is
already accounting software with various automation processes, from ornamentation to profitability analysis
and management reporting. There is also software that is updated in accordance with the accounting laws,
i.e., the platform changes its mechanisms according to the changes in the law.
Despite the existence of this software, manual work remains, and the amount of information accountants are
faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information
and data they have. One of the most common situations in the accounting world is undoubtedly the
miscalculation or forgetting of some financial or non-financial data found in accounting operations (income
statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree
and 100% reliable, an intelligent architecture has been developed to support second generation general
accounting. This architectural design was developed with a view to make the existing software smarter with
the help of artificial intelligence.
A study was carried out on accounting keys and concepts, on AI and main process automation techniques to
build the model. With these studies it was intended to acquire all possible requirements for the creation of the
architecture. Towards the end of the thesis the model was validated
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
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
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
How Sustainable is Machine Learning in Energy Applications? – The Sustainable Machine Learning Balance Sheet
Information Systems play a central role in the energy sector for achieving climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research has focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches should be extended to include the application’s energy consumption. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable Machine Learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews
AI and Robotics in the Fight Against COVID-19 Pandemic
The outbreak of the novel coronavirus and its disease, COVID-19 present an unprecedented challenge for humanity. Artificial Intelligence (AI) and robotics may help fighting
COVID-19. Potential applications of AI in this accelerating pandemic include, but are not limited to, early detection and diagnosis, massive agent modeling and simulation, data analytics, assistive robots, disinfection robots, public awareness and patrolling, contactless delivery services, virtual healthcare assistants, drug repurposing and vaccination discovery. This chapter sheds light on the roles AI and robotics can play in fighting this disastrous pandemic, and possible future ones. It highlights several potential applications to transform this challenge into opportunities. This chapter also discusses the ethical implications of AI and robotics during the pandemic and in the post-pandemic world
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
Modeling, Simulation and Data Processing for Additive Manufacturing
Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic
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