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Статті в журналах з теми "Trains – Conduite – Surveillance":

1

Lamhaut, L., C. M. Nivet, C. Dagron, L. Nace, F. Braun, and P. Carli. "Retour d’expérience des évacuations par train à grande vitesse de patients en syndrome de détresse respiratoire aiguë sur infection à Covid-19 : les missions Chardon." Annales françaises de médecine d’urgence 10, no. 4-5 (September 2020): 288–97. http://dx.doi.org/10.3166/afmu-2020-0275.

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Lors de la crise Covid-19 en France, il a fallu transférer des patients de zones où les lits de réanimation étaient saturés vers d’autres régions. Tous les moyens ont été utilisés : terrestre, aérien, maritime… Pour la première fois, des trains à grande vitesse (TGV) ont été utilisés. Le transport ferroviaire avait été utilisé largement pendant la Première Guerre mondiale. Ces transferts ont nécessité une collaboration extrêmement importante interservices : ministère, agences régionales de santé, hôpitaux, Samu zonaux, Samu, Smur associations de sécurités civiles, sapeurs-pompiers… L’une des collaborations des plus importantes a été celle avec la SNCF qui a permis une adaptation des rames, sécurisations des itinéraires, adaptation de la conduite… Chaque voiture transporte quatre patients intubés en syndrome de détresse respiratoire aiguë avec un médecin senior, un junior, quatre infirmiers et un logisticien pour la réalisation de la surveillance et des soins. Dans chaque rame, une équipe de régulation médicale est présente pour la coordination. Il y a eu dix évacuations sanitaires, qui ont transporté 197 patients sur 6 600 km (350‒950 km/TGV). Le transport le plus long a été de 7 h 14 min. On n’a pas relevé de complications majeures pendant les transferts. Plusieurs questions restent en suspens comme les critères de sélections des patients, la mise en place d’un train sanitaire aménagé permanent, un stock de matériel. Afin de mieux connaître les conséquences sur les patients, une étude est en cours. Les urgentistes ont une nouvelle corde à leur arc avec la possibilité d’effectuer des évacuations sanitaires en TGV pour des patients médicaux graves sur de longues distances.
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Andrejevic, Mark, Hugh Davies, Ruth DeSouza, Larissa Hjorth, and Ingrid Richardson. "Situating ‘careful surveillance’." International Journal of Cultural Studies 24, no. 4 (March 9, 2021): 567–83. http://dx.doi.org/10.1177/1367877921997450.

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In this article we explore preliminary findings from the study COVIDSafe and Beyond: Perceptions and Practices conducted in Australia in 2020. The study involved a survey followed by interviews, and aimed to capture the dynamic ways in which members of the Australian public perceive the impact of Covid practices – especially public health measures like the introduction of physical and social distancing, compulsory mask wearing, and contact tracing. In the rescripting of public space, different notions of formal and informal surveillance, along with different textures of mediated and social care, appeared. In this article, we explore perceptions around divergent forms of surveillance across social, technological, governmental modes, and the relationship of surveillance to care in our media and cultural practices. What does it mean to care for self and others during a pandemic? How does care get enacted in, and through, media interfaces and public interaction?
3

Wang, Kailao, Jinming Pan, Xiuqin Rao, Yefeng Yang, Fujie Wang, Rongjin Zheng, and Yibin Ying. "An Image-Assisted Rod-Platform Weighing System for Weight Information Sampling of Broilers." Transactions of the ASABE 61, no. 2 (2018): 631–40. http://dx.doi.org/10.13031/trans.12312.

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Abstract. The average weight and flock uniformity of broilers in group housing is important information that allows producers to know the flock growth conditions and determine the selling time. However, gathering weight information of chickens is not only labor-intensive for humans but also frightening for the birds. In this study, an image-assisted rod-platform weighing system was developed to automatically monitor the average weight and flock uniformity of broilers in chicken houses. This weighing system consists of a computer and several weighing scales. Each weighing scale contains a rod-platform weighing module and a surveillance camera module. The principle of the automated weighing system is to estimate population weight information using samples. The design of the rod-platform weighing module was based on the perching habit of birds to attract more broilers to stand on the rod platform and thus get more weight samples. The surveillance camera module is used to detect the number of broilers on the rod using image processing technology. A data processing method called PORWI, which includes elimination of redundant records and trim of non-redundant records, was designed to optimize the results of chicken number identification from images to improve the accuracy of the results. An experiment was done in two small groups of broilers with approximately 100 chickens and 8.58 m2 of area for each group. A weekly weighing was conducted, and three kinds of weight information were obtained, which included manual population weight information (MPWI), manual sample-based weight information (MSWI), and automated sampling weight information (ASWI). Each weight information set comprised the group average weight and flock uniformity, which were then used to evaluate accuracy. The perching rate of chickens using the rod platform reached an average of 60 times h-1, and the rate was retained with increasing age. Compared with the MPWI obtained by individual weighing, the manual sample-based measurement method provided results with errors of 0% to +5%, while our automated weighing system achieved accuracies within ±2% for average weight and ±1.5% for flock uniformity. Keywords: Automatic weighing, Average weight, Broiler, Chicken detection, Uniformity.
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Nora, Hastika Yanti, Muhammad Chaerul Latief, and Yuliyanto Budi Setiawan. "FUNGSI KOMUNIKASI MASSA DALAM TELEVISI (Studi Kasus Program Acara ’Bukan Empat Mata’ di TRANS 7)." Jurnal The Messenger 2, no. 1 (March 24, 2016): 10. http://dx.doi.org/10.26623/themessenger.v2i1.278.

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<em>This research is conducted to described and verified communication functions on television program called ’Bukan Empat Mata’ on Trans7. This research used survey method. Primary data gathered from questionnaires, while secondary data collected from related literatures. Research final gains, showing that ’Bukan Empat Mata’ serves two mass-communication functions, which are surveillance and linkage functions from media upon society, especially ’Bukan Empat Mata’ audiences come from Semarang University academics. </em>
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Salzberg, Navit T., Kasthuri Sivalogan, Quique Bassat, Allan W. Taylor, Sunday Adedini, Shams El Arifeen, Nega Assefa, et al. "Mortality Surveillance Methods to Identify and Characterize Deaths in Child Health and Mortality Prevention Surveillance Network Sites." Clinical Infectious Diseases 69, Supplement_4 (October 9, 2019): S262—S273. http://dx.doi.org/10.1093/cid/ciz599.

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Abstract Despite reductions over the past 2 decades, childhood mortality remains high in low- and middle-income countries in sub-Saharan Africa and South Asia. In these settings, children often die at home, without contact with the health system, and are neither accounted for, nor attributed with a cause of death. In addition, when cause of death determinations occur, they often use nonspecific methods. Consequently, findings from models currently utilized to build national and global estimates of causes of death are associated with substantial uncertainty. Higher-quality data would enable stakeholders to effectively target interventions for the leading causes of childhood mortality, a critical component to achieving the Sustainable Development Goals by eliminating preventable perinatal and childhood deaths. The Child Health and Mortality Prevention Surveillance (CHAMPS) Network tracks the causes of under-5 mortality and stillbirths at sites in sub-Saharan Africa and South Asia through comprehensive mortality surveillance, utilizing minimally invasive tissue sampling (MITS), postmortem laboratory and pathology testing, verbal autopsy, and clinical and demographic data. CHAMPS sites have established facility- and community-based mortality notification systems, which aim to report potentially eligible deaths, defined as under-5 deaths and stillbirths within a defined catchment area, within 24–36 hours so that MITS can be conducted quickly after death. Where MITS has been conducted, a final cause of death is determined by an expert review panel. Data on cause of death will be provided to local, national, and global stakeholders to inform strategies to reduce perinatal and childhood mortality in sub-Saharan Africa and South Asia.
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Santamaria, Amilcare, Pierfrancesco Raimondo, Mauro Tropea, Floriano De Rango, and Carmine Aiello. "An IoT Surveillance System Based on a Decentralised Architecture." Sensors 19, no. 6 (March 26, 2019): 1469. http://dx.doi.org/10.3390/s19061469.

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In the last few years, we witnessed numerous episodes of terrorist attacks and menaces in public crowded places. The necessity of better surveillance in these places pushed the development of new automated solutions to spot and notify possible menaces as fast as possible. In this work, we propose a novel approach to create a decentralized architecture to manage patrolling drones and cameras exploiting lightweight protocols used in the internet of things (IoT) domain. Through the adoption of the mist computing paradigm it is possible to give to all the object of the smart ecosystem a cognitive intelligence to speed up the recognition and analysis tasks. Distributing the intelligence among all the objects of the surveillance ecosystem allows a faster recognition and reaction to possible warning situations. The recognition of unusual objects in certain areas, e.g., airports, train stations and bus stations, has been made using computer vision algorithms. The adoption of the IoT protocols in a hierarchical architecture provides high scalability allowing an easy and painless join of other smart objects. Also a study on the soft real-time feasibility has been conducted and is herein presented.
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Fernández, Jessica, José M. Cañas, Vanessa Fernández, and Sergio Paniego. "Robust Real-Time Traffic Surveillance with Deep Learning." Computational Intelligence and Neuroscience 2021 (December 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/4632353.

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Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for object detection and classification, especially in the last years, made this area even more appealing due to the effectiveness of these methods. This study presents TrafficSensor, a system that employs deep learning techniques for automatic vehicle tracking and classification on highways using a calibrated and fixed camera. A new traffic image dataset was created to train the models, which includes real traffic images in poor lightning or weather conditions and low-resolution images. The proposed system consists mainly of two modules, first one responsible of vehicle detection and classification and a second one for vehicle tracking. For the first module, several neural models were tested and objectively compared, and finally, the YOLOv3 and YOLOv4-based network trained on the new traffic dataset were selected. The second module combines a simple spatial association algorithm with a more sophisticated KLT (Kanade–Lucas–Tomasi) tracker to follow the vehicles on the road. Several experiments have been conducted on challenging traffic videos in order to validate the system with real data. Experimental results show that the proposed system is able to successfully detect, track, and classify vehicles traveling on a highway on real time.
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Wan, Kim Sui, Peter Seah Keng Tok, Kishwen Kanna Yoga Ratnam, Nuraini Aziz, Marzuki Isahak, Rafdzah Ahmad Zaki, Nik Daliana Nik Farid, et al. "Implementation of a COVID-19 surveillance programme for healthcare workers in a teaching hospital in an upper-middle-income country." PLOS ONE 16, no. 4 (April 14, 2021): e0249394. http://dx.doi.org/10.1371/journal.pone.0249394.

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Introduction The reporting of Coronavirus Disease 19 (COVID-19) mortality among healthcare workers highlights their vulnerability in managing the COVID-19 pandemic. Some low- and middle-income countries have highlighted the challenges with COVID-19 testing, such as inadequate capacity, untrained laboratory personnel, and inadequate funding. This article describes the components and implementation of a healthcare worker surveillance programme in a designated COVID-19 teaching hospital in Malaysia. In addition, the distribution and characteristics of healthcare workers placed under surveillance are described. Material and methods A COVID-19 healthcare worker surveillance programme was implemented in University Malaya Medical Centre. The programme involved four teams: contact tracing, risk assessment, surveillance and outbreak investigation. Daily symptom surveillance was conducted over fourteen days for healthcare workers who were assessed to have low-, moderate- and high-risk of contracting COVID-19. A cross-sectional analysis was conducted for data collected over 24 weeks, from the 6th of March 2020 to the 20th of August 2020. Results A total of 1,174 healthcare workers were placed under surveillance. The majority were females (71.6%), aged between 25 and 34 years old (64.7%), were nursing staff (46.9%) and had no comorbidities (88.8%). A total of 70.9% were categorised as low-risk, 25.7% were moderate-risk, and 3.4% were at high risk of contracting COVID-19. One-third (35.2%) were symptomatic, with the sore throat (23.6%), cough (19.8%) and fever (5.0%) being the most commonly reported symptoms. A total of 17 healthcare workers tested positive for COVID-19, with a prevalence of 0.3% among all the healthcare workers. Risk category and presence of symptoms were associated with a positive COVID-19 test (p<0.001). Fever (p<0.001), cough (p = 0.003), shortness of breath (p = 0.015) and sore throat (p = 0.002) were associated with case positivity. Conclusion COVID-19 symptom surveillance and risk-based assessment have merits to be included in a healthcare worker surveillance programme to safeguard the health of the workforce.
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Maytin, Lauren, Jason Maytin, Priya Agarwal, Anna Krenitsky, JoAnn Krenitsky, and Robert S. Epstein. "Attitudes and Perceptions Toward COVID-19 Digital Surveillance: Survey of Young Adults in the United States." JMIR Formative Research 5, no. 1 (January 8, 2021): e23000. http://dx.doi.org/10.2196/23000.

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Background COVID-19 is an international health crisis of particular concern in the United States, which saw surges of infections with the lifting of lockdowns and relaxed social distancing. Young adults have proven to be a critical factor for COVID-19 transmission and are an important target of the efforts to contain the pandemic. Scalable digital public health technologies could be deployed to reduce COVID-19 transmission, but their use depends on the willingness of young adults to participate in surveillance. Objective The aim of this study is to determine the attitudes of young adults regarding COVID-19 digital surveillance, including which aspects they would accept and which they would not, as well as to determine factors that may be associated with their willingness to participate in digital surveillance. Methods We conducted an anonymous online survey of young adults aged 18-24 years throughout the United States in June 2020. The questionnaire contained predominantly closed-ended response options with one open-ended question. Descriptive statistics were applied to the data. Results Of 513 young adult respondents, 383 (74.7%) agreed that COVID-19 represents a public health crisis. However, only 231 (45.1%) agreed to actively share their COVID-19 status or symptoms for monitoring and only 171 (33.4%) reported a willingness to allow access to their cell phone for passive location tracking or contact tracing. Conclusions Despite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults.
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Maity, Sayan, Mohamed Abdel-Mottaleb, and Shihab S. Asfour. "Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video." Electronics 10, no. 9 (April 24, 2021): 1013. http://dx.doi.org/10.3390/electronics10091013.

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Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Дисертації з теми "Trains – Conduite – Surveillance":

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Sekak, Fatima. "Microwave radar techniques and dedicated signal processing for Vital Signs measurement." Thesis, Université de Lille (2018-2021), 2021. https://pepite-depot.univ-lille.fr/LIBRE/EDENGSYS/2021/2021LILUN033.pdf.

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Dans le contexte de la sécurisation des systèmes de transport, la surveillance à courte distance de l’activité des personnes, en particulier du conducteur dans un véhicule, constitue un enjeu majeur dans l’amélioration du système d’aide à la conduite. L’application visée dans ce travail concerne principalement le domaine du ferroviaire.Les fréquences respiratoire et cardiaque du conducteur sont des indicateurs clés pour l’évaluation de l’état physiologique. Les méthodes de mesure conventionnelles de ces signes vitaux reposent sur des capteurs opérant en contact direct avec la peau. Par conséquent, le caractère intrusif de ces solutions ne s’avère pas adapté au domaine du transport, en particulier du fait de la gêne induite. Dans le cadre de ces travaux, une solution radar hyperfréquence opérant à faible puissance est proposée pour la mesure en continue des signaux d’activités respiratoire et cardiaque. En particulier, les signaux physiologiques (battements du cœur, mouvement mécanique de la cage thoracique) sont des indicateurs de l’activité humaine qui peuvent être détectés à distance (jusqu’à une dizaine de mètres) au moyen d’ondes électromagnétiques hyperfréquences rayonnées.Bien que la littérature montre un engouement grandissant pour le développement de techniques radars dédiés à la surveillance des personnes, il n’existe pas, à ce jour, de dispositif commercial robuste, sensible et précis. Une analyse fine des paramètres électriques et géométriques de la technique radar est proposée dans ce travail afin d’identifier les sources d’incertitudes, de définir les paramètres optimaux, de valider expérimentalement la solution proposée. Un traitement de signal original, basé sur l’approche cyclostationnaire, est mis en œuvre afin d’extraire les paramètres d’intérêt dans des environnements de mesure de référence ou perturbés. Les solutions matérielles proposées associées à un traitement de signal optimal permettent d’entrevoir des architectures de radar adaptées aux contingences hors laboratoire
In the context of securing transportation systems, short-range monitoring of people's activity, in particular the driver's activity in a vehicle, is a major issue in the improvement of the driver assistance system. The application targeted in this work concerns mainly the railway domain.Respiratory and heart rates of the driver are key indicators for the evaluation of the physiological state. Conventional methods of measuring these vital signs rely on sensors operating in direct contact with the skin. Therefore, the intrusive character of these solutions is not suited for the transportation domain, especially because of the induced discomfort. In this work, a microwave radar solution operating at low power is proposed for the continuous measurement of respiratory and cardiac activity signals. In particular, physiological signals (heartbeat, mechanical movement of the rib cage) are indicators of human activity that can be detected at a distance (up to ten meters) using radiated microwave electromagnetic waves.Although the literature shows a growing interest in the development of radar techniques dedicated to the surveillance of people, there is no robust, sensitive and accurate commercial device available to date. A detailed analysis of the electrical and geometrical parameters of the radar technique is proposed in this work in order to identify the sources of uncertainties, to define the optimal parameters, to validate experimentally the proposed solution. An original signal processing, based on the cyclostationary approach, is implemented in order to extract the parameters of interest in reference or disturbed measurement environments. The proposed hardware solutions associated with an optimal signal processing allow to foresee radar architectures adapted to non-laboratory contingencies
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Dugast, Jérôme. "Essais en Microstructure des Marchés Financiers." Phd thesis, Jouy-en Josas, HEC, 2013. http://pastel.archives-ouvertes.fr/pastel-00940976.

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Cette thèse est composée de trois chapitres distincts. Dans le premier chapitre, je montre que les mesures de liquidités traditionnelles, telles que la profondeur du marché, ne sont pas toujours pertinentes pour mesurer le bien-être des investisseurs. Je construis un modèle de marché conduit par les ordres et montre qu'une offre de liquidité élevée peut correspondre à de mauvaises conditions d'exécution pour les fournisseurs de liquidité et à un bien-être relativement faible. Dans le deuxième chapitre, je modélise la vitesse des ajustements de prix à l'arrivée de nouvelles dans les marchés conduits pas les ordres, lorsque les investisseurs ont une capacité d'attention limitée. En raison de leur attention limitée, les investisseurs suivent imparfaitement l'arrivée de nouvelles. Ainsi, les prix s'ajustent aux nouvelles après un certain délai. Ce délai diminue lorsque le niveau d'attention des investisseurs augmente. Le délai d'ajustement des prix diminue également lorsque la fréquence à laquelle les nouvelles arrivent, augmente. Le troisième chapitre présente un travail écrit en collaboration avec Thierry Foucault. Nous construisons un modèle pour expliquer en quoi le trading à haute fréquence peut générer des "mini flash crashes" (un brusque changement de prix suivi d'un retour très rapide au niveau antérieur). Notre théorie est basée sur l'idée qu'il existe une tension entre la vitesse à laquelle l'information peut être acquise et la précision de cette information. Lorsque les traders à haute fréquence mettent en oeuvre des stratégies impliquant des réactions rapides à des événements de marché, ils augmentent leur risque à réagir à du bruit et génèrent ainsi des "mini flash crashes". Néanmoins, ils augmentent l'efficience informationnelle du marché.

Книги з теми "Trains – Conduite – Surveillance":

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Methodology for Evaluating National Arboviral Disease Prevention and Control Strategies in the Americas. Pan American Health Organization, 2022. http://dx.doi.org/10.37774/9789275124413.

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The IMS-Arbovirus is a model that provides a methodological framework for arboviral disease prevention and control. It divides the compendium of actions to be taken into the following components, which are not listed in their order of importance: management, epidemiology (with emphasis on health surveillance), laboratory, patient care (clinical), integrated vector management (IVM), and environment (with emphasis on water, sanitation, and hygiene). It also proposes common crosscutting themes for each component: operations research and health communication and promotion for behavioral change. Each component and crosscutting theme is overseen and executed by personnel trained for this purpose. The Integrated Management Strategy for Arbovirus Disease Prevention and Control in the Americas contains a group of indicators selected by the countries, and a trained professional regularly conducts an informal evaluation of the strategy. This evaluation may be based on what the coordinator for each component or the participants in the process report, often based only on their own experiences. Generically, this methodology attempts to organize ideas and the methodologies that should be followed for best performance in an evaluation. The IMS-Arbovirus currently includes monitoring and evaluation from the outset, thus systematically coordinating its planning, monitoring, and evaluation. The main objective is for monitoring and evaluation to serve as a good mechanism for management, course correction, and accountability to advance and improve the quality and impact of management with the preparation of the IMS Arbovirus. The specific objectives are as follows: determine the progress made and barriers implementing the IMS-Arbovirus, formulate recommendations to improve the IMS-Arbovirus Implementation process, and create a monitoring plan based on the evaluation's results.

Частини книг з теми "Trains – Conduite – Surveillance":

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Lu, Jia, and Wei Qi Yan. "Comparative Evaluations of Human Behavior Recognition Using Deep Learning." In Handbook of Research on Multimedia Cyber Security, 176–89. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2701-6.ch009.

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With the cost decrease of security monitoring facilities such as cameras, video surveillance has been widely applied to public security and safety such as banks, transportation, shopping malls, etc. which allows police to monitor abnormal events. Through deep learning, authors can achieve high performance of human behavior detection and recognition by using model training and tests. This chapter uses public datasets Weizmann dataset and KTH dataset to train deep learning models. Four deep learning models were investigated for human behavior recognition. Results show that YOLOv3 model is the best one and achieved 96.29% of mAP based on Weizmann dataset and 84.58% of mAP on KTH dataset. The chapter conducts human behavior recognition using deep learning and evaluates the outcomes of different approaches with the support of the datasets.

Тези доповідей конференцій з теми "Trains – Conduite – Surveillance":

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Tilaar, Revy, Asmarafia Asmarafia, Kurniati Kurniati, Ismi Maudilah Hardianti, and Rossi Sanusi. "Development of Surveillance for Stunting in Parigi-Moutong, Central Sulawesi." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.01.18.

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Background: The prevalence of stunting in Parigi-Moutong District (Parimo District) was 33.7% at 10 villages. Stunting prevention effort are structured into a response-surveillance system (SSR) that includes all stages of the natural course of child growth disorders (GPA) to detect (Dx) and provide action (Rx) on the agent-environment (primordial prevention) and the host (primary prevention to rehabilitation). This study aimed to examine the development of a stunting-response surveillance system in Parigi-Moutong District, Central Sulawesi. Subjects and Method: This was a qualitative study conducted at Faculty of Medicine, Public Health and Nursing, Gajah Mada University, Yogyakarta from November 26 to 30. The development of SSR Stunting in Parimo Regency was carried out by means of a Training of Trainer (ToT), the following stages: (1) ToT 1 in the health sector (secondary and tertiary prevention); (2) training in 10 locus villages, sub-districts and districts by trainer from parimo district; (3) establishing ssr stunting; (4) ToT 2 across sector (primordial prevention, primary prevention and rehabilitation); (5) training throughout parimo district; and, (6) establishing the sr system for priority diseases. Results: SSR officers are able to train Individual Health Effort (UKP) officers, Information technology (IT) officers, Surveillance-Response (SR) officers Conclusion: SSR follows the WHO SSR pattern which consists of four components: (1) Main functions; (2) Supporting functions; (3) Structure; and, (4) quality criteria. Keywords: stunting, SSR, surveillance Correspondence: Rossi Sanusi. Faculty of Medicine, Universitas Gadjah Mada. l. Farmako, Senolowo, Sekip Utara, Depok, Sleman, Daerah Istimewa Yogyakarta 55281. DOI: https://doi.org/10.26911/the7thicph.01.18
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Rooks, Tyler F., Andrea S. Dargie, and Valeta Carol Chancey. "Machine Learning Classification of Head Impact Sensor Data." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12173.

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Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.
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Mostoflei, Florin. "Forced vital capacity & oxygen consumption screening at students." In Fourth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/head18.2018.8063.

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This work was conducted with the support of 19-20 years old students during physical education classes across the 1st Semester of AY 2017/2018 at the Bucharest University of Economic Study. The study case starts with the premise that all subjects were under/medium level trained and it focuses on a cross-screening survey which includes body mass index, oxygen consumption, heart rate activity, oximetry, spirometry and caloric consumption rate. The participation of subjects was voluntary and for this they signed a written agreement which allows the results to be published. The surveillance process was made using approved devices and a previously tested methodology. The final results revealed that there is no correlation between VO2, BMI and FVC for the subjects of the group.
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Rothbaum, David, and Nabil Debsi. "Improving Rail Connectivity Through 3GPP Technology." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2342.

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In order to conduct rail operations safely and securely, operators need reliable train connectivity. With the advent of Intelligent Transport Systems, there are increased demands from this connectivity. This paper introduces the capabilities of 3GPP standard radio technology to meet the railway operator’s connectivity needs using a unified radio infrastructure. 3GPP is the 3rd Generation Partnership Project and provides the standards known to the public as 3G or 4G mobile telecommunications provided by the cellular phone operators. Recent developments in 3GPP standards make the technology suitable for use in both urban and mainline rail environments based on a private 4G LTE network owned and operated by the railroad agency. Since the 3GPP standard equipment ecosystem is shared by mobile operators, public safety agencies, utilities, and airports, the equipment cost is reduced compared to proprietary wireless techniques due to economies of scale. Using a dedicated radio network, all rail applications requiring wireless connectivity can traverse over a single radio infrastructure. These rail applications include: CCTV real time passenger surveillance, mission critical push-to-talk voice, train control signalling (CBTC, ETCS or PTC), train telemetry, including real-time condition-based monitoring and passenger information systems. Using Quality of Service prioritization and pre-emption inherent to the LTE radio system, the mission critical railway applications always receive priority over non-mission critical functions. The FCC has recently announced the availability of the 3.5 GHz band to the public based on spectrum sharing. Spectrum sharing could be an acceptable option for railway application, provided it was given priority for mission critical functions. Current advances in LTE radio, notably Massive MIMO (multiple input, multiple output) antenna arrays, can provide coverage in 3.5 GHz band to a distance of over 1400 yards — which is less than the typical distance between stations. this transit agency dedicated network. Alternately sharing spectrum with Firstnet 700 MHz band should be explored.
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Moreira, Letícia Karolina, Marcelo Romero, and Manassés Ribeiro. "Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-138.

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The quality of images obtained from video surveillance systems is a decisive aspect when performing investigations at the Forensic Science. Features such as scars, tattoos, and skin marks are great examples of details that allow to consolidate an investigation at certain scenarios in which there is the necessity to identify individuals captured in a video or image footage. However, the low quality of images could affect the results of the investigations. In this sense, this work proposes the study of a computational model to address the problem of increasing the resolution of Low-Resolution (LR) images, also known as the problem of super-resolution of images. The main idea is to train a Generative Adversarial Network (GAN) so that it can be able to enhance low-quality images. The hypothesis is that a variant model of a GAN, named Super-Resolution Generative Adversarial Network (SRGAN), is capable to produce High-Resolution (HR) images from LR ones. The proposed methodology is based on experimental research with the aid of the hypothetical deductive method, where two well-recognised state of art methods were used, which proposes the use of convolutional neural networks and deep learning. For the model validation, were conducted four different experiments: two to avail the capacity of the GAN to produce images with enhanced resolution and two other experiments to evaluate the quality of the results produced by the SRGAN. The quantitative results of our experiments are promising, with performances that are similar to those obtained by state-of-the-art approaches. Moreover, the qualitative results based on performing a visual analysis of the images produced by our approach suggest a interesting performance in terms of visual quality.

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