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Journal articles on the topic 'Radiation early warning network'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Tran Duc Tan. "DEVELOPMENT OF A SMART OCEAN RADIATION MONITORING SYSTEM." Journal of Military Science and Technology, no. 75A (November 11, 2021): 38–45. http://dx.doi.org/10.54939/1859-1043.j.mst.75a.2021.38-45.

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Ocean radiation monitoring systems (ORMSs) are an essential component in the radiation early warning network that monitors radiation exposure and estimates radioactive propagation induced by nuclear activities or nuclear accidents in the sea. Numerous systems have been developed and installed in the radiation warning network in different countries. However, there is not any similar product that has been studied and developed in Vietnam. This paper presents a complete process in designing and manufacturing a marine buoy integrated with a radiation sensor. The radiation detector can measure both

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Glavič-Cindro, Denis, Drago Brodnik, Toni Petrovič, et al. "Compact radioactive aerosol monitoring device for early warning networks." Applied Radiation and Isotopes 126 (August 2017): 219–24. http://dx.doi.org/10.1016/j.apradiso.2016.12.036.

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Stöhlker, U., M. Bleher, H. Doll, et al. "THE GERMAN DOSE RATE MONITORING NETWORK AND IMPLEMENTED DATA HARMONIZATION TECHNIQUES." Radiation Protection Dosimetry 183, no. 4 (2018): 405–17. http://dx.doi.org/10.1093/rpd/ncy154.

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Abstract Environmental radiation monitoring networks have been established in Europe and world-wide for the purpose of protecting population and environment against ionizing radiation. Some of these networks had been established during the cold war period and were improved after the Chernobyl accident in 1986. Today, the German Federal Office for Radiation Protection (BfS) operates an early warning network with roughly 1800 ambient dose equivalent rate (ADER) stations equally distributed over the German territory. The hardware and software of all network components are developed in-house allow

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Mahomed, Maqsooda, Alistair D. Clulow, Sheldon Strydom, Tafadzwanashe Mabhaudhi, and Michael J. Savage. "Assessment of a Ground-Based Lightning Detection and Near-Real-Time Warning System in the Rural Community of Swayimane, KwaZulu-Natal, South Africa." Weather, Climate, and Society 13, no. 3 (2021): 605–21. http://dx.doi.org/10.1175/wcas-d-20-0116.1.

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AbstractClimate change projections of increases in lightning activity are an added concern for lightning-prone countries such as South Africa. South Africa’s high levels of poverty, lack of education, and awareness, as well as a poorly developed infrastructure, increase the vulnerability of rural communities to the threat of lightning. Despite the existence of national lightning networks, lightning alerts and warnings are not disseminated well to such rural communities. We therefore developed a community-based early warning system (EWS) to detect and disseminate lightning threats and alerts in

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Singh, Mukesh Kumar, Shasvath J. Kapadia, Md Arif Shaikh, Deep Chatterjee, and Parameswaran Ajith. "Improved early warning of compact binary mergers using higher modes of gravitational radiation: a population study." Monthly Notices of the Royal Astronomical Society 502, no. 2 (2021): 1612–22. http://dx.doi.org/10.1093/mnras/stab125.

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ABSTRACT A gravitational wave early warning of a compact binary coalescence event, with a sufficiently tight localization skymap, would allow telescopes to point in the direction of the potential electromagnetic counterpart before its onset. Use of higher modes of gravitational radiation, in addition to the dominant mode typically used in templated real-time searches, was recently shown to produce significant improvements in early-warning times and skyarea localizations for a range of asymmetric mass binaries. We perform a large-scale study to assess the benefits of this method for a populatio

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Faleschini, J., H. Mayer, M. Bielz, W. Hackl, and T. Schulz. "Early warning against airborne radioactivity in Bavaria: Measuring network for radioactive immissions." Kerntechnik 74, no. 4 (2009): 205–11. http://dx.doi.org/10.3139/124.110032.

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Nguyen, Liem D., Hong T. Nguyen, Phuong D. N. Dang, Trung Q. Duong, and Loi K. Nguyen. "Design of an automatic hydro-meteorological observation network for a real-time flood warning system: a case study of Vu Gia-Thu Bon river basin, Vietnam." Journal of Hydroinformatics 23, no. 2 (2021): 324–39. http://dx.doi.org/10.2166/hydro.2021.124.

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Abstract This paper presents an interdisciplinary approach, along with Vietnam's legal frameworks, to design an automatic hydro-meteorological (HM) observation network for a real-time flood warning system in Vu Gia-Thu Bon (VGTB) river basin, Vietnam. The automatic HM monitoring network consists of weather-proof enclosures containing data loggers, rechargeable batteries, sensors for air temperature, air humidity, solar radiation, wind speed, water level with attached solar panels and mounted upon masts located at fixed ground stations. A total of 20 meteorological stations and five hydrologica

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Zhao, Enyu, Nianxin Qu, Yulei Wang, and Caixia Gao. "Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network." Remote Sensing 16, no. 7 (2024): 1284. http://dx.doi.org/10.3390/rs16071284.

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Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the

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Shukla, Shraddhanand, Daniel McEvoy, Mike Hobbins, et al. "Examining the Value of Global Seasonal Reference Evapotranspiration Forecasts to Support FEWS NET’s Food Insecurity Outlooks." Journal of Applied Meteorology and Climatology 56, no. 11 (2017): 2941–49. http://dx.doi.org/10.1175/jamc-d-17-0104.1.

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AbstractThe Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ET0) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by FEWS NET to support food insecurity early warning. The ET0 reforecasts span the 1982–2009 period and are calculated following the American Society for Civil Engineers formulation of the Penman–Monteith method driven by seasonal cl

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Toader, Victorin-Emilian, Constantin Ionescu, Iren-Adelina Moldovan, Alexandru Marmureanu, Iosif Lıngvay, and Andrei Mihai. "Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)." Applied Sciences 15, no. 13 (2025): 7396. https://doi.org/10.3390/app15137396.

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The National Institute of Earth Physics (NIEP) in Romania has upgraded its seismic monitoring stations into multifunctional platforms equipped with advanced devices for measuring gas emissions, magnetic fields, telluric fields, solar radiation, and more. This enhancement enabled the integration of a seismic forecasting system designed to extend the alert time of the existing warning system, which previously relied solely on seismic data. The implementation of an Operational Earthquake Forecast (OEF) aims to expand NIEP’s existing Rapid Earthquake Early Warning System (REWS) which currently pro

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Sáez-Vergara, J. C., I. M. G. Thompson, R. Gurriarán, H. Dombrowski, E. Funck, and S. Neumaier. "The second EURADOS intercomparison of national network systems used to provide early warning of a nuclear accident." Radiation Protection Dosimetry 123, no. 2 (2006): 190–208. http://dx.doi.org/10.1093/rpd/ncl112.

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Sun, Jiaqi, Jiarong Wang, Zhicheng Hao, et al. "AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM." Remote Sensing 14, no. 13 (2022): 3221. http://dx.doi.org/10.3390/rs14133221.

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Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by d

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Di, Yangyang, and Enyuan Wang. "Rock Burst Precursor Electromagnetic Radiation Signal Recognition Method and Early Warning Application Based on Recurrent Neural Networks." Rock Mechanics and Rock Engineering 54, no. 3 (2021): 1449–61. http://dx.doi.org/10.1007/s00603-020-02314-w.

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Akhmad, Yus Rusdian, Angga Kautsar, Taruniyati Handayani, Judi Pramono, and Aditia Anamta. "Pengembangan spesifikasi teknis sistem pemantau radiasi lingkungan berbasis spektrometer gama untuk pengawasan ketenaganukliran di Indonesia." Jurnal Pengawasan Tenaga Nuklir 1, no. 2 (2021): 1–10. http://dx.doi.org/10.53862/jupeten.v1i2.013.

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THE INDONESIAN RADIATION DATA MONITORING SYSTEM (IRDMS) IS A NETWORK CATEGORIZED AS COMPLEX PROBLEMS WITH INFLUENCING FACTORS INTO A SINGLE UNIT AS MULTIPLE PROBLEMS THAT MUST SOLVE THROUGH VARIOUS APPROACHES OPTIMALLY. One of the approaches required is the application of optimization. For example, optimization is needed between the detection sensitivity of the radiation source and the number of false alarms due to the permissible background radiation by determining the operating parameters of the monitor. In addition, optimization is needed between costs and data (information) obtained throug

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Li, Jiawei, Maren Böse, Max Wyss, et al. "Estimating Rupture Dimensions of Three Major Earthquakes in Sichuan, China, for Early Warning and Rapid Loss Estimates." Bulletin of the Seismological Society of America 110, no. 2 (2020): 920–36. http://dx.doi.org/10.1785/0120190117.

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ABSTRACT Large earthquakes, such as Wenchuan in 2008, Mw 7.9, Sichuan, China, provide an opportunity for earthquake early warning (EEW), as many heavily shaken areas are far (∼50 km) from the epicenter and warning times could be sufficient (≥5 s) to take preventive action. On the other hand, earthquakes with magnitudes larger than ∼M 6.5 are challenging for EEW because source dimensions need to be defined to adequately estimate shaking. Finite-fault rupture detector (FinDer) is an approach to identify fault rupture extents from real-time seismic records. In this study, we playback local and re

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Leontaris, F., A. Boziari, A. Clouvas, M. Kolovou, and J. Guilhot. "PROCEDURES TO MEASURE MEAN AMBIENT DOSE EQUIVALENT RATES USING ELECTRET ION CHAMBERS." Radiation Protection Dosimetry 190, no. 1 (2020): 6–21. http://dx.doi.org/10.1093/rpd/ncaa061.

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Abstract The capabilities of electret ion chambers (EICs) to measure mean ambient dose equivalent rates were investigated by performing both laboratory and field studies of their properties. First, EICs were ‘calibrated’ to measure ambient gamma dose equivalent in the Ionizing Calibration Laboratory of the Greek Atomic Energy Commission. The EICs were irradiated with different gamma photon energies and from different angles. Calibration factors were deduced (electret’s voltage drop due to irradiation in terms of ambient dose equivalent). In the field studies, EICs were installed at eight locat

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Asad, Ali Turab, Byunghyun Kim, Soojin Cho, and Sung-Han Sim. "Prediction Model for Long-Term Bridge Bearing Displacement Using Artificial Neural Network and Bayesian Optimization." Structural Control and Health Monitoring 2023 (July 14, 2023): 1–22. http://dx.doi.org/10.1155/2023/6664981.

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Bridge bearings are critical components in bridge structures because they ensure the normal functioning of bridges by accommodating the long-term horizontal movements caused by changing environmental conditions. However, abnormal structural behaviors in long-term horizontal displacement are observed when the structural integrity of bridge structures is degraded. This study aims to construct an accurate prediction model for long-term horizontal displacement under varying external environmental conditions to support the reliable assessment of bridge structures which has not been fully explored i

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Ervianto, Edy, Noveri Lysbetti Marpaung, Abu Yazid Raisal, et al. "Assessing the Efficacy of the UV Index in Predicting Surface UV Radiation: A Comprehensive Analysis Using Statistical and Machine Learning Methods." Indonesian Review of Physics 6, no. 2 (2023): 99–121. http://dx.doi.org/10.12928/irip.v6i2.8216.

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The study investigated the relationship between the UV Index and measurements of ultraviolet A (UVA) and ultraviolet B (UVB) radiation to evaluate the effectiveness of the UV Index in predicting and understanding UV radiation at the surface. The implications of this study are significant for public health policies and UV protection strategies. This study used a variety of statistical analyses and modelling techniques, including ANOVA, Naive Bayes classification, decision trees, artificial neural networks, support vector machines (SVM), and k-means clustering, to examine relationships and predi

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Long, Qi, Fei Wang, Wenyan Ge, et al. "Temporal and Spatial Change in Vegetation and Its Interaction with Climate Change in Argentina from 1982 to 2015." Remote Sensing 15, no. 7 (2023): 1926. http://dx.doi.org/10.3390/rs15071926.

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Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring a

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Frankemölle, Jens Peter Karolus Wenceslaus, Johan Camps, Pieter De Meutter, and Johan Meyers. "A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks." Geoscientific Model Development 18, no. 6 (2025): 1989–2003. https://doi.org/10.5194/gmd-18-1989-2025.

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Abstract. Detector networks that measure environmental radiation serve as radiological surveillance and early warning networks in many countries across Europe and beyond. Their goal is to detect anomalous radioactive signatures that indicate the release of radionuclides into the environment. Often, the background ambient dose equivalent rate H˙*(10) is predicted using meteorological information. However, in dense detector networks, the correlation between different detectors is expected to contain markedly more information. In this work, we investigate how the joint observations by neighbourin

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Hashim, NurIzzah M., Norazian Mohamed Noor, Ahmad Zia Ul-Saufie, et al. "Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models." Sustainability 14, no. 13 (2022): 7936. http://dx.doi.org/10.3390/su14137936.

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Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in

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Clouvas, A., S. Xanthos, A. Boziari, F. Leontaris, I. Kaissas, and M. Omirou. "PERFORMANCE OF HANDHELD NAI(TL) SPECTROMETERS AS DOSIMETERS BY LABORATORY AND FIELD DOSE RATE MEASUREMENTS." Radiation Protection Dosimetry 194, no. 4 (2021): 233–48. http://dx.doi.org/10.1093/rpd/ncab098.

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Abstract In the framework of the IAEA Coordinated Research Project (CRP) J02012 on ‘Advancing Radiation Detection Equipment for Detecting Nuclear and Other Radioactive Material Out of Regulatory Control’, the properties of two commercial instruments (1) InSpector 1000 analyzer (Canberra), with a 2″ × 2″ NaI(Tl) scintillator and (2) RIIDEYE M-G3 analyzer (Thermo Scientific), with a 3″ × 3″ NaI(Tl) scintillator, were evaluated as dosimeters by laboratory and field measurements. In the Ionizing Radiation Calibration Laboratory (IRCL) of the Greek Atomic Energy Commission, the NaI(Tl) spectrometer

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Tateo, Andrea, Mario Marcello Miglietta, Francesca Fedele, Micaela Menegotto, Alfonso Monaco, and Roberto Bellotti. "Ensemble using different Planetary Boundary Layer schemes in WRF model for wind speed and direction prediction over Apulia region." Advances in Science and Research 14 (April 28, 2017): 95–102. http://dx.doi.org/10.5194/asr-14-95-2017.

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Abstract. The Weather Research and Forecasting mesoscale model (WRF) was used to simulate hourly 10 m wind speed and direction over the city of Taranto, Apulia region (south-eastern Italy). This area is characterized by a large industrial complex including the largest European steel plant and is subject to a Regional Air Quality Recovery Plan. This plan constrains industries in the area to reduce by 10 % the mean daily emissions by diffuse and point sources during specific meteorological conditions named wind days. According to the Recovery Plan, the Regional Environmental Agency ARPA-PUGLIA i

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Eguagie-suyi, Precious, Boluwatife Dada, and Emmanuel Chilekwu Okogbue. "Machine Learning Based Drought Prediction Using the Standardized Precipitation Evapotranspiration Index (SPEI) in Kebbi State, Nigeria." Journal of Atmospheric Science Research 8, no. 2 (2025): 1–21. https://doi.org/10.30564/jasr.v8i2.8220.

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Drought represents a major threat to livelihoods and economic stability in regions prone to its occurrence. This paper aims to address the gap in applying machine learning techniques for enhanced meteorological drought prediction to support resilience and preparedness. The study focuses on Kebbi State, located in northwest Nigeria, which experiences droughts with devastating agricultural, ecological and humanitarian impacts. The Standardized Precipitation Evapotranspiration Index (SPEI) was used to calculate different drought severity based on rainfall deficit, over varying accumulation period

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Vouillamoz, Naomi, Sabrina Rothmund, and Manfred Joswig. "Characterizing the complexity of microseismic signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France) and Pechgraben (Upper Austria) case studies." Earth Surface Dynamics 6, no. 2 (2018): 525–50. http://dx.doi.org/10.5194/esurf-6-525-2018.

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Abstract. Soil and debris slides are prone to rapid and dramatic reactivation. Deformation within the instability is accommodated by sliding, whereby weak seismic energies are released through material deformation. Thus, passive microseismic monitoring provides information that relates to the slope dynamics. In this study, passive microseismic data acquired at Super-Sauze (southeastern France) and Pechgraben (Upper Austria) slow-moving clay-rich debris slides (“clayey landslides”) are investigated. Observations are benchmarked against previous similar case studies to provide a comprehensive an

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Pan, Zhangrong, Xiufeng Tian, Weidong Zhang, and Jie Yuan. "Analysis on Early Warning Capability of Gansu Earthquake Early Warning Stations Network." IOP Conference Series: Earth and Environmental Science 304 (September 18, 2019): 042027. http://dx.doi.org/10.1088/1755-1315/304/4/042027.

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Chen, Ying. "BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model." Computational Intelligence and Neuroscience 2021 (October 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/4034903.

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Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspectives of model accuracy and variable importance. Through the comparative analysis of the empirical results of the three methods, it can be seen that the simulated annealing algorithm has ma

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Li, Shan, Bin Feng, Wei Zhang, Yubin Feng, and Zhidu Huang. "Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data." Mathematical Problems in Engineering 2023 (May 26, 2023): 1–10. http://dx.doi.org/10.1155/2023/8929066.

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Aiming at the problems of long warning time and low warning accuracy in the traditional distribution network disaster early warning and production decision support systems, a distribution network disaster early warning and production decision support system based on multisource data is designed. The forecast information is collected through the data collector, the wind load and lightning trip rate of the line are calculated, all of the information is integrated together for multisource data fusion processing, and the distribution network disaster early warning model is constructed in accordanc

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Song, Min, and Xue Min Liu. "Early-Warning Research on Resource Economy Sustainable Development Based on BP Artificial Neural Network - The Case of Yulin of Shaanxi Province." Advanced Materials Research 524-527 (May 2012): 3070–74. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3070.

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Resource economy sustainable development degree of Yulin, Shaanxi Province during Year 2000-2014 was estimated and pre-warned by building BP neural network early-warning model and applying the written Matlab7.1 calculation program and AHP method. The early-warning results indicated that, economy sustainable development tendency of Yulin, Shaanxi during Year 2000-2014 is under huge warning, serious warning, medium warning and light warning these four states, respectively; early-warning model based on BP neural network has strong simulation ability, which is more appropriate for non-linear syste

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Gostilo, Vladimirs, Andrey Vlasenko, Vasily Litvinsky, and Igors Krainukovs. "Development of nuclear radiation monitors for radiation early warning systems." Nuclear Technology and Radiation Protection 37, no. 3 (2022): 193–200. http://dx.doi.org/10.2298/ntrp2203193g.

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The results of the development of modern precision monitors of alpha, beta and gamma ray radiation for setting up early warning systems for radioactive contamination in the atmosphere and rapid assessment of emerging threats, are presented. Proportional counters, scintillation SrI (Eu) crystals and semiconductor Si, CdZnTe, and HPGe detectors are used for 2 the development. The designed monitors provide information both on dose rate values in real time and on the activity of specific radionuclides. The software controls the measurement mode, as well as diagnoses the condition of the monitors t

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You, Lianghai. "Construction of Early Warning Mechanism of University Education Network Based on the Markov Model." Mobile Information Systems 2022 (July 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7302623.

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This paper proposes and builds an early warning mechanism model of the college education network using the Markov model. This paper proposes a method to determine the observation value of Markov model based on the flow control principle and TCP/IP model in an effort to address the issue that the observation value of Markov model is challenging to determine when it is applied to intrusion detection. The detection model also employs an adaptive sliding detection window algorithm to further increase the system’s detection rate. The mechanism developed in this paper is compared to other early warn

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Zhou, Hong Xiao, and Sai Hua Xu. "Application of Artificial Neural Network in Corporate Financial Risk Early-Warning." Applied Mechanics and Materials 336-338 (July 2013): 2476–79. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2476.

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The traditional financial risk warning model are all based on probability theory and statistical analysis, but the precisions of the results are usually not satisfied in practice. This paper studies the application of artificial neural network in corporate financial risk early-warning. It designs an early warning model of financial risk based on BP neural network. And then selects financial data from 30 enterprises as samples to train and test the network. The result indicates that the risk early warning model is very effective. It can solve some problems of the traditional early warning metho

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Ding, Guo Zhen, Zhan Yue Zhang, Lei Wang, and Zhe Zhang. "Modeling and Simulation of the Dynamical Infrared Radiation of Ballistic Missile in Boost Phase." Applied Mechanics and Materials 568-570 (June 2014): 933–37. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.933.

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With development of the Missile technology, there are more and more challenges for the technology of early-warning satellite detection. The early-warning satellite can compute the data of launch point and impact point of a missile by detecting the changing infrared radiation of the missile. Therefore, the research on the infrared radiation of ballistic missile in boost phase is important for developing the detecting technology of early-warning satellite. In this paper, the dynamical infrared radiation model has been constructed based on the characteristics of trajectory and infrared radiation

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Li, Guiliang, Bingyuan Hong, Haoran Hu, et al. "Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network." Energies 15, no. 9 (2022): 3278. http://dx.doi.org/10.3390/en15093278.

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Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-ty

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Meng, Fanqiang. "Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network." Symmetry 13, no. 6 (2021): 1082. http://dx.doi.org/10.3390/sym13061082.

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Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Fir

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Hu, Xiaoya. "Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition." Advances in Multimedia 2022 (July 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/6049649.

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Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditi

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Chen, Feng, Wei Wei Xu, Zong Heng Wang, Tao Yang, and Hong Yang Huang. "A research on early warning method of Distribution Network Cyber Physical System." E3S Web of Conferences 248 (2021): 02054. http://dx.doi.org/10.1051/e3sconf/202124802054.

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The high integration of cyber and physics is the development trend of intelligent distribution network in the future, but the cyber system not only supports the stable operation of the physical system, also brings some security risks to the cyber physical system of distribution network. Aiming at the requirements of real-time, accuracy, efficiency and other characteristics of distribution network monitoring, this paper proposes an early warning method of distribution network cyber physical system based on Hidden Markov model. Firstly, the online monitoring and early warning system architecture

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Qiu, Xiaohong, and Jiali Chen. "Algorithm of axial compressor stall warning based on BP neural network and fuzzy logic." MATEC Web of Conferences 355 (2022): 03007. http://dx.doi.org/10.1051/matecconf/202235503007.

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Stall warning of axial compressor is very challenging and the existing warning margin is not enough. A algorithm based on BP neural network fusion fuzzy logic is proposed. Firstly, BP neural network is used for training recognition, next the identification results are fused with fuzzy logic reasoning to form the result judgment of time sequence, finally the stall early warning of axial compressor is realized. The simulation results of the experimental data show that the stall data at all speeds are at least 0.1s in advance of the early warning. Compared with other methods, this method has a be

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Chen, Xianjun. "Early Warning of Regional Landslide Disaster and Development of Rural Ecological Industrialization Based on IoT Sensor." Scientific Programming 2022 (March 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/9535488.

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Regional landslide disaster is actually the instability caused by the movement of ground or slopes, etc. If a landslide occurs in a habitat area where people live, it can cause a great deal of damage. So, in order to improve the early warning effect of regional geological landslide disaster, the study abandoned the conventional geological probe data, directly used the tilt photography data provided by the fixed camera, introduced the camera clock synchronization control system supported by the high-sensitivity atomic clock timing function, and used the data warehouse hardware and computing hos

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Yan, Xue, Xiangwu Deng, and Shouheng Sun. "Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network." Complexity 2020 (November 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/8838468.

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Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation (BP) neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things

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Maguire, Oliver R., Albert S. Y. Wong, Jan Harm Westerdiep, and Wilhelm T. S. Huck. "Early warning signals in chemical reaction networks." Chemical Communications 56, no. 26 (2020): 3725–28. http://dx.doi.org/10.1039/d0cc01010c.

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Many natural and man-made complex systems display early warning signals when close to an abrupt shift in behaviour. Here we show that such early warning signals appear in a complex chemical reaction network.

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Wang, Xiaoxuan, Jingjing Wang, Ying Zhang, and Yixing Du. "Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network." Journal of Mathematics 2022 (February 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/7880652.

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At present, the commonly used index selection methods for macroeconomic early-warning research include K-L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due to the existence of statistical errors, these methods are difficult to perform. Therefore, this paper proposes to use a self-organizing competitive neural network to select early warning indicators. Its self-learning and adaptive characteristics and fault tolerance overcome the limitations of the abo

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Le, Xiang, and Yong Zhao. "An Early Warning Model for Industrial Network Security Issues: A Crafted Strategy for High Accuracy Based on Machine Learning Approach." Information Technology and Control 54, no. 2 (2025): 629–42. https://doi.org/10.5755/j01.itc.54.2.39543.

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An industrial network has become an important infrastructure. As industrial networks develop, their cybersecurity problems become more and more prominent. The attacks currently realized to networks turn out to be advancing quicker than ever, and their destructive force also continuously gets bigger. Thus, the available early warning technology for industrial network security issues requires more accuracy and timeliness since a serious amount of delays occurs in real cases. The article proposes a strategy with high accuracy based on a machine-learning algorithm. Nonlinear high-dimensional data

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Wang, Mo Yu, Jie Chen, Xiao Liu Shen, and Gui Lin Yu. "Early Warning Model of Enterprise Operating Ability Using BP Neural Network." Applied Mechanics and Materials 20-23 (January 2010): 948–53. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.948.

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With the increasing risk in electric power bureaus, warning risk of enterprise operating ability in advance is an important work. However it is very difficult to establish stable functions to describe the mapping relationship between operating ability and associated causal influences. Hence, early warning of the operating ability is harder. In this paper, an early warning model based on BP neural network is designed and put forward to forecast the risk of operating ability of an electric power bureau. In addition, illustration by the experiment is given. The stable and accurate analysis result

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马, 硕. "Early Warning of Diseases Based on Network Resilience." Advances in Applied Mathematics 10, no. 02 (2021): 617–31. http://dx.doi.org/10.12677/aam.2021.102067.

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Fischer, Joachim, Jens-Peter Redlich, Jochen Zschau, et al. "A wireless mesh sensing network for early warning." Journal of Network and Computer Applications 35, no. 2 (2012): 538–47. http://dx.doi.org/10.1016/j.jnca.2011.07.016.

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Wang, Hui. "Research on Analytical Model of Network Transmission Based on Topological Structure of Logical Layer." Applied Mechanics and Materials 513-517 (February 2014): 2360–63. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2360.

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With the continually expanding of city construction scale in china, metropolitan area network transmission system is established at present. But with the increase of network users, the network capacity can not meet the requirements of our normal life, and 4G network technology is to expand the capacity of the network developed. According to its size, building the appropriate model for the metropolitan area network is the current focus issues of experts and scholars. Based on the internet information technology, the establishment of a metropolitan area network crisis early warning mechanism is

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Al Saleh, Mohammed, Béatrice Finance, Yehia Taher, Rafiqul Haque, Ali Jaber, and Nourhan Bachir. "Introducing artificial intelligence to the radiation early warning system." Environmental Science and Pollution Research 29, no. 10 (2021): 14036–45. http://dx.doi.org/10.1007/s11356-021-16771-5.

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Chapagai, Kamal K. "Sensor Network Based Testbench Implementation of Landslide Early Warning System." Environmental and Earth Sciences Research Journal 8, no. 3 (2021): 134–39. http://dx.doi.org/10.18280/eesrj.080304.

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The paper presents an implementation of prototype based Early Warning System (EWS) to detect and provide early warning of Landslide activities. The main aim of this work is to implement the prototype with low cost sensor network. A simulation setup and a table based prototype setup was implemented to study the capability and effectiveness of the system. The setup consists of table based setup of landslide with multiple/changing angle from 30° to 90°. Multiple sensing elements including rain sensor to detect presence of rain, soil moisture sensor to detect the moisture content, temperature and

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Hutapea, Zulkifli Yacub, and Lukas Lukas. "IMPLEMENTASI SENSOR NETWORK UNTUK MONITORING BASE TRANSCEIVER STATION ( BTS )." Komputika : Jurnal Sistem Komputer 8, no. 1 (2019): 13–20. http://dx.doi.org/10.34010/komputika.v8i1.1650.

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Sensor Network yang diusulkan dibagunan menggunakan perangkat mikrokontroler yang diintergrasikan dengan router OpenWRT. Perangkat sensor network kemudian diintergrasikan pada jaringan data yang ada pada sistem seluler yang dimonitor. Pada bagian Server dibangun sistem pengolahan data yang untuk menghasilkan informasi early warning bagi pihak operator. Kata kunci : Shelter BTS, Early Warning System, Temperatur, Humidity, Power Supply.

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