Effect of flow pattern at pipe bends on corrosion behaviour of low carbon steek and its challenges

Recent design work regarding seawater flow lines has emphasized the need to identify, develop, and verify critical relationships between corrosion prediction and flow regime mechanisms at pipe bend. In practice this often reduces to an pragmatic interpretation of the effects of corrosion mechanisms at pipe bends. Most importantly the identification of positions or sites, within the internal surface contact areas where the maximum corrosion stimulus may be expected to occur, thereby allowing better understanding, mitigation, monitoring and corrosion control over the life cycle. Some case histories have been reviewed in this context, and the interaction between corrosion mechanisms and flow patterns closely determined, and in some cases correlated. Since the actual relationships are complex, it was determined that a risk based decision making process using selected ‘what’ if corrosion analyses linked to ‘what if’ flow assurance analyses was the best way forward. Using this in methodology, and pertinent field data exchange, it is postulated that significant improvements in corrosion prediction can be made. This paper outlines the approach used and shows how related corrosion modelling software data such as that available from corrosion models Norsok M5006, and Cassandra to parallel computational flow modelling in a targeted manner can generate very noteworthy results, and considerably more viable trends for corrosion control guidance. It is postulated that the normally associated lack of agreement between corrosion modelling and field experience, is more likely due to inadequate consideration of corrosion stimulating flow regime data, rather than limitations of the corrosion modelling. Applications of flow visualization studies as well as computations with the k-ε model of turbulence have identified flow features and regions where metal loss is a maximu

Audio-Video Event Recognition System For Public Transport Security

International audienceThis paper presents an audio-video surveillance system for the automatic surveillance in public transport vehicle. The system comprises six modules including in particular three novel ones: (i) Face Detection and Tracking, (ii) Audio Event Detection and (iii) Audio-Video Scenario Recognition. The Face Detection and Tracking module is responsible for detecting and tracking faces of people in front of cameras. The Audio Event Detection module detects abnormal audio events which are precursor for detecting scenarios which have been predefined by end-users. The Audio-Video Scenario Recognition module performs high level interpretation of the observed objects by combining audio and video events based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined audio, video and audio-video events specified using an audio-video event ontology

Survey on Hybrid Anonymization using k-anonymity for Privacy Preserving in Data Mining

K-anonymity is the one of the popular privacy preserving model. In the data mining there is multiple technique is available k-anonymity is one of the technique which is used for the protecting privacy in the database. In this paper our main approach is hybrid anonymization. The main thing of this technique is that it is the mixing of two techniques. We introduce hybrid anonymization with hybrid generalization which is formed by not only generalization but also the data relocation. Data relocation serves trade-off between truthfulness and utility. Using the hybrid anonymization we maintain the privacy standard such as k-anonymity. In the previous research we find that k-anonymity is not good work with multiple sensitive data and there is more information loss occurs for that issue we use hybrid anonymization on multiple dataset. We show that our model can decrease the information loss in minimum time period

A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second authorshi

Mitigation Strategies of Technostress on Supply Chain Management

Logistics managers work to create practices that reduce technostress, which is associated with diminished productivity in supply chain management. The purpose of this multiple case study was to explore the mitigation strategies that logistics managers at distribution centers used to reduce technostress with their employees in the Los Angeles County, California area. The conceptual framework included in this study was the sociotechnical systems theory. Semistructured interviews were conducted with 6 logistics managers from large distribution centers who implemented mitigation strategies that demonstrably reduced technostress with their employees. Public documents and physical artifacts reviewed in this study included productivity assessment tools, information and communication technology system training materials, technostress mitigation instruments, and information from technological devices. Data were analyzed through a process of pattern matching, cross-case synthesis, and systematic text condensation. The findings included 6 themes: reliance on internal information technology experts; hiring temporary experts; maintaining communication and training; using time management skills and organizing priorities; identification and understanding of employee differences; and implementing well-being, fitness, and health programs. These findings could contribute to positive social change by providing logistics managers with strategies to reduce technostress, which could lead to improved employee well-being, better work conditions, and increased productivity for greater company profitability that could produce a more thriving and prosperous community

BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning

In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multiphysics phenomena. To bridge this gap, we present the BubbleML Dataset \footnote{\label{git_dataset}\url{https://github.com/HPCForge/BubbleML}} which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multiphysics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.Comment: Submitted to Neurips Datasets and Benchmarks Track 202

Development of a tracking system using invisible markers for association football

Mestrado em Treino DesportivoNowadays in association football, obtaining information such as position and movements of the players is of great interest to coaches due to the potential to relate performance to tactics and to assist in planning training programs. Over the last decade, technological advances in this area included the introduction of more sophisticated systems that are being used in elite association football; however, the development of a fully automated system is still needed. The aim of this study is to contribute to the development of a non-intrusive, automatic, tracking system, using a marker invisible to humans. We select a marker that absorbs in the infrared region (IR) of the electromagnetic spectrum (Epolight 1110) and prepared solutions containing the marker. We tested the solutions in fabric samples to assess the tones of gray, as well as the resistance of the marker to water. T-shirts were soaked in the solutions created, and were used by football players in a 1vs2 in situ task where we proceeded to the tracking. The findings showed that this approach is a valid possibility to discriminate and track players. We concluded that it is possible to use IR markers to distinguish different players and, with the appropriate computer graphics’ algorithms, to automatically track the players.Actualmente no futebol, a obtenção de informações como posição e movimentos dos jogadores é de grande interesse por parte dos técnicos devido ao potencial para relacionar o desempenho à táctica e ajudar na elaboração dos programas de treino. Durante a última década, avanços tecnológicos nesta área incluíram a introdução de sistemas mais sofisticados que estão a ser utilizados no futebol de elite; no entanto, o desenvolvimento de um sistema totalmente automatizado ainda é necessário. O objectivo deste estudo é contribuir para o desenvolvimento de um sistema de tracking não-intrusivo, automático, usando um marcador invisível para os seres humanos. Seleccionámos um marcador que absorve na região do infravermelho (IV) do espectro electromagnético (Epolight 1110) e preparámos soluções contendo o marcador. Testámos as soluções em amostras de tecido para avaliar os tons de cinzento, assim como a resistência do marcador à água. Foram embebidas T-shirts com as soluções criadas, que foram usadas por jogadores de futebol numa situação in situ de 1vs2 onde se procedeu ao tracking dos mesmos. Os resultados mostraram que esta abordagem é válida para discriminar e acompanhar os jogadores. Concluímos que é possível usar marcadores IV para distinguir diferentes jogadores e, com algoritmos de computação gráfica adequados, é possível monitorizar automaticamente os jogadores