scholarly journals Techniques for reducing false alarms in infrared forest-fire automatic detection systems

1999 ◽  
Vol 7 (1) ◽  
pp. 123-131 ◽  
Author(s):  
A. Ollero ◽  
B.C. Arrue ◽  
J.R. Martinez ◽  
J.J. Murillo
1993 ◽  
Vol 16 (1) ◽  
pp. 35-50 ◽  
Author(s):  
F. Andreucci ◽  
M. V. Arbolino

1993 ◽  
Vol 16 (1) ◽  
pp. 51-65 ◽  
Author(s):  
F. Andreucci ◽  
M. V. Arbolino

2021 ◽  
Vol 19 (6) ◽  
pp. 952-960
Author(s):  
Gisela De La Fuente Cortes ◽  
Jose Alejandro Diaz-Mendez ◽  
Guillermo Espinosa Flores-Verdad ◽  
Victor Rodolfo Gonzalez-Diaz

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.


Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).


2021 ◽  
Vol 4 (3) ◽  
pp. 1-56
Author(s):  
Agathe Balayn ◽  
Jie Yang ◽  
Zoltan Szlavik ◽  
Alessandro Bozzon

The automatic detection of conflictual languages (harmful, aggressive, abusive, and offensive languages) is essential to provide a healthy conversation environment on the Web. To design and develop detection systems that are capable of achieving satisfactory performance, a thorough understanding of the nature and properties of the targeted type of conflictual language is of great importance. The scientific communities investigating human psychology and social behavior have studied these languages in details, but their insights have only partially reached the computer science community. In this survey, we aim both at systematically characterizing the conceptual properties of online conflictual languages, and at investigating the extent to which they are reflected in state-of-the-art automatic detection systems. Through an analysis of psychology literature, we provide a reconciled taxonomy that denotes the ensemble of conflictual languages typically studied in computer science. We then characterize the conceptual mismatches that can be observed in the main semantic and contextual properties of these languages and their treatment in computer science works; and systematically uncover resulting technical biases in the design of machine learning classification models and the dataset created for their training. Finally, we discuss diverse research opportunities for the computer science community and reflect on broader technical and structural issues.


Author(s):  
Рустам Зайтунович Сунагатуллин ◽  
Антон Михайлович Чионов ◽  
Семен Васильевич Петренко

Автоматизированные системы управления используются в нефтепроводном транспорте с целью автоматизации технологических процессов транспортировки нефти и нефтепродуктов, при этом основной задачей является обеспечение надежности и безопасности перекачки, что невозможно без контроля целостности трубопровода. В связи с этим актуальной остается тема обнаружения утечек, требуют продолжения исследования в области повышения надежности автоматизированных систем обнаружения утечек (СОУ). При эксплуатации СОУ особую важность представляет описание процессов заполнения и опорожнения участков трубопровода с безнапорным течением. Скорость установления стационарного режима работы таких участков и участков с полным сечением существенно отличается. Слабые возмущения давления могут приводить к значительному дебалансу расхода нефти и, как следствие, вызывать ложные срабатывания СОУ. Авторами представлен алгоритм вычисления скорости изменения запаса нефти на участке трубопровода при медленном изменении размера самотечной полости, на основании которого предложен способ корректировки уравнения баланса вещества. Показано использование разработанного алгоритма для повышения чувствительности СОУ и уменьшения количества ложных срабатываний. During the operation of leak detection systems (LDS), it is of great importance to describe the processes of filling and emptying pipeline free flow sections. The speed of establishing a stationary operation mode of such sections and full sections is significantly different. Weak pressure perturbations can lead to significant imbalance in the oil flow rate and, as a consequence, cause false LDS positives. The authors present an algorithm for calculating rate of change in oil reserve in the pipeline section with a slow change in the size of gravity cavity, on the basis of which a method for adjusting the substance balance equation is proposed. The use of a developed algorithm is shown to increase the sensitivity of LDS and reduce the number of false alarms.


Author(s):  
Mingtao Wu ◽  
Young B. Moon

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.


2019 ◽  
Vol 11 (7) ◽  
pp. 806 ◽  
Author(s):  
Ingri Soldal ◽  
Wolfgang Dierking ◽  
Anton Korosov ◽  
Armando Marino

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.


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