scholarly journals Wireless Sensor Network for Ignitions Detection: An IoT approach

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 893 ◽  
Author(s):  
Thadeu Brito ◽  
Ana I. Pereira ◽  
José Lima ◽  
António Valente
Keyword(s):  
Decision Making ◽  
Fire Detection ◽  
Barometric Pressure ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Data Simulation ◽  
Central System ◽  
Flame Ignition ◽  
Wide Scale ◽  
Point Data

Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 727
Author(s):  
Eric J. Ma ◽  
Arkadij Kummer
Keyword(s):  
Decision Making ◽  
High Throughput ◽  
Noisy Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Melting Temperatures ◽  
Hierarchical Bayesian Estimation ◽  
The Face ◽  
Point Data

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.

scholarly journals Fire Risk Assessment in Healthcare Settings: Application of FMEA Combined with Multi‐Criteria Decision Making Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fateme Omidvari ◽  
Mehdi Jahangiri ◽  
Reza Mehryar ◽  
Moslem Alimohammadlou ◽  
Mojtaba Kamalinia
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Fire Risk ◽  
Fire Detection ◽  
Healthcare Setting ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Healthcare Settings ◽  
Fire Risk Assessment ◽  
Risk Parameters

Fire is one of the most dangerous phenomena causing major casualties and financial losses in hospitals and healthcare settings. In order to prevent and control the fire sources, first risk assessment should be conducted. Failure Mode and Effect Analysis (FMEA) is one of the techniques widely used for risk assessment. However, Risk Priority Number (RPN) in this technique does not take into account the weight of the risk parameters. In addition, indirect relationships between risk parameters and expert opinions are not considered in decision making in this method. The aim is to conduct fire risk assessment of healthcare setting using the application of FMEA combined with Multi‐Criteria Decision Making (MCDM) methods. First, a review of previous studies on fire risk assessment was conducted and existing rules were identified. Then, the factors influencing fire risk were classified according to FMEA criteria. In the next step, weights of fire risk criteria and subcriteria were determined using Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) and different wards of the hospital were ranked using Interval-Valued Intuitionistic Fuzzy Combinative Distance-based Assessment (IVIFCODAS) method. Finally, a case study was performed in one of the hospitals of Shiraz University of Medical Sciences. In this study, fire alarm system (0.4995), electrical equipment and installations (0.277), and flammable materials (0.1065) had the highest weight, respectively. The hospital powerhouse also had the highest fire risk, due to the lack of fire extinguishers, alarms and fire detection, facilities located in the basement floor, boilers and explosive sensitivity, insufficient access, and housekeeping. The use of MCDM methods in combination with the FMEA method assesses the risk of fire in hospitals and health centers with great accuracy.

Enhancing Regulatory Decision Making for Postmarket Drug Safety

2021 ◽  
Author(s):  
Vishal Ahuja ◽  
Carlos A. Alvarez ◽  
John R. Birge ◽  
Chad Syverson
Keyword(s):  
Decision Making ◽  
Operations Management ◽  
The United States ◽  
Pharmaceutical Products ◽  
Unintended Consequences ◽  
Surveillance Systems ◽  
Veterans Health ◽  
Health Administration ◽  
Regulatory Decision ◽  
Increased Risk

The U.S. Food and Drug Administration (FDA) regulates the approval and safe public use of pharmaceutical products in the United States. The FDA uses postmarket surveillance systems to monitor drugs already on the market; a drug found to be associated with an increased risk of adverse events (ADEs) is subject to a recall or a warning. A flawed postmarket decision-making process can have unintended consequences for patients, create uncertainty among providers and affect their prescribing practices, and subject the FDA to unfavorable public scrutiny. The FDA’s current pharmacovigilance process suffers from several shortcomings (e.g., a high underreporting rate), often resulting in incorrect or untimely decisions. Thus, there is a need for robust, data-driven approaches to support and enhance regulatory decision making in the context of postmarket pharmacovigilance. We propose such an approach that has several appealing features—it employs large, reliable, and relevant longitudinal databases; it uses methods firmly established in literature; and it addresses selection bias and endogeneity concerns. Our approach can be used to both (i) independently validate existing safety concerns relating to a drug, such as those emanating from existing surveillance systems, and (ii) perform a holistic safety assessment by evaluating a drug’s association with other ADEs to which the users may be susceptible. We illustrate the utility of our approach by applying it retrospectively to a highly publicized FDA black box warning (BBW) for rosiglitazone, a diabetes drug. Using comprehensive data from the Veterans Health Administration on more than 320,000 diabetes patients over an eight-year period, we find that the drug was not associated with the two ADEs that led to the BBW, a conclusion that the FDA evidently reached, as it retracted the warning six years after issuing it. We demonstrate the generalizability of our approach by retroactively evaluating two additional warnings, those related to statins and atenolol, which we found to be valid. This paper was accepted by Vishal Gaur, operations management.

scholarly journals An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

2018 ◽  
Vol 10 (10) ◽  
pp. 102 ◽  
Author(s):  
Yi-Han Xu ◽  
Qiu-Ya Sun ◽  
Yu-Tong Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Forest Fire ◽  
Forest Fires ◽  
Network Architecture ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Fire Monitoring

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

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