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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 dose rate and radiological spectrum. The ORMS also combines multimodal data transmission and various programmed software for data processing, signal transmission, and system control. Therefore, the proposed configuration system has potential application in terms of performance and maintenance.
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Glavič-Cindro, Denis, Drago Brodnik, Toni Petrovič, Matjaž Vencelj, Dušan Ponikvar, Steven James Bell, Lynsey Keightley, and Selina Woods. "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, H. Dombrowski, W. Harms, I. Hellmann, R. Luff, B. Prommer, S. Seifert, and F. Weiler. "THE GERMAN DOSE RATE MONITORING NETWORK AND IMPLEMENTED DATA HARMONIZATION TECHNIQUES." Radiation Protection Dosimetry 183, no. 4 (January 10, 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 allowing the continuous optimization of all relevant components. A probe characterization and quality assurance and control program are in place. Operational and technical aspects of the network and data harmonization techniques are described. The latter allows for calculating of the terrestrial and net ADER combined with uncertainties mainly from site specific effects. Harmonized data are finally used as input to the German emergency management system and the European radiological data exchange platform.
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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 (July 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 a timely and comprehensible manner within Swayimane, KwaZulu-Natal, South Africa. The system is composed of an electrical field meter and a lightning flash sensor with warnings disseminated via audible and visible alarms on site and with a remote server issuing short message services (SMSs) and email alerts. Twelve months of data (February 2018–February 2019) were utilized to evaluate the performance of the EWS’s detection and warning capabilities. Diurnal variations in lightning activity indicated the influence of solar radiation, causing convective conditions with peaks in lightning activity occurring during the late afternoon and early evening (between 1400 and 2100) coinciding with students being released from school and when most workers return home. In addition to detecting the threat of lightning, the EWS was beneficial in identifying periods that exhibited above-normal lightning activity, with two specific lightning events examined in detail. Poor network signals in rural communities presented an initial challenge, delaying data transmission to the central server until rectified using multiple network providers. Overall, the EWS was found to disseminate reliable warnings in a timely manner.
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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 (January 19, 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 population of compact binary merger observations. In particular, we inject 100 000 such signals in Gaussian noise, with component masses $m_1 \in \left[1, 60 \right] \, \mathrm{M}_{\odot }$ and $m_2 \in \left[1, 3 \right] \, \mathrm{M}_{\odot }$. We consider three scenarios involving ground-based detectors: the fifth (O5) observing run of the Advanced LIGO-Virgo-KAGRA network, its projected Voyager upgrade, as well as a proposed third-generation (3G) network. We find that for fixed early-warning times of 20–60 s, the inclusion of the higher modes can provide localization improvements of a factor of ≳2 for up to ${\sim}60{{\ \rm per\ cent}}$ ($70 {{\ \rm per\ cent}}$) of the neutron star–black hole (NSBH) systems in the O5 (Voyager) scenario. Considering only those NSBH systems that can produce potential electromagnetic counterparts, such improvements in the localization can be expected for ${\sim}5\!-\!35{{\ \rm per\ cent}}$ $(20\!-\!50{{\ \rm per\ cent}})$ binaries in O5 (Voyager). For the 3G scenario, a significant fraction of the events have time gains of a minute to several minutes, assuming fiducial target localization areas of 100–1000 deg2.
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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 (August 2009): 205–11. http://dx.doi.org/10.3139/124.110032.
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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 (April 5, 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 challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics.
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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 (January 5, 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 hydrological stations have been built in VGTB river basin. To capture changes in weather and stream flow in the basin, the 5-minute and half-hour recording frequency options were set for meteorological and hydrological variables, respectively. All HM data was transmitted every 30 minutes to the data server at the data processing centre via Global System for Mobile Communications (GSM)/General Packet Radio Service (GPRS) network. These data were then input into hydrological-hydraulic models for inundation simulation in the basin. The results showed that the performance of flood simulation at hourly time step has significantly improved during flood events in September and November 2015. Overall, near-real-time HM data recording from an automatic monitoring network proved beneficial for an flood early warning system.
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Shukla, Shraddhanand, Daniel McEvoy, Mike Hobbins, Greg Husak, Justin Huntington, Chris Funk, Denis Macharia, and James Verdin. "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 (November 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 climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from the National Centers for Environmental Prediction CFSv2 model and the National Aeronautics and Space Administration GEOS-5 model. The skill evaluation, using deterministic and probabilistic scores, focuses on the December–February (DJF), March–May (MAM), June–August (JJA), and September–November seasons. The results indicate that ET0 forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation > 0.35 at leads of 2 months) include the western United States, northern parts of South America, parts of the Sahel region, and southern Africa. The FEWS NET regions where forecasts are most skillful (correlation > 0.35 at lead 3) include northern sub-Saharan Africa (DJF; dry season), Central America (DJF; dry season), parts of East Africa (JJA; wet season), southern Africa (JJA; dry season), and central Asia (MAM; wet season). A case study over parts of East Africa for the JJA season shows that ET0 forecasts in combination with the precipitation forecasts would have provided early warning of recent severe drought events (e.g., in 2002, 2004, 2009) that contributed to substantial food insecurity in the region.
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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 (September 13, 2006): 190–208. http://dx.doi.org/10.1093/rpd/ncl112.
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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 (February 13, 2021): 1449–61. http://dx.doi.org/10.1007/s00603-020-02314-w.
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Sun, Jiaqi, Jiarong Wang, Zhicheng Hao, Ming Zhu, Haijiang Sun, Ming Wei, and Kun Dong. "AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM." Remote Sensing 14, no. 13 (July 4, 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 data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.
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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 (December 15, 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 through determining the influencing factors in establishing a monitoring base, namely the purpose of installation at the location (safety and security), demographics, legal subjects, resources, type (technology) detectors, and environmental radioactivity. To increase the national content for the use of the product, the problem statement of this paper focuses on developing technical specifications for the type of low-resolution gamma spectrometer-based monitor (detector) following the analytical method developed by the authors for the determination of alarms triggered by radiation from facilities and equipment. This study aims to develop IRDMS technical specifications following the needs of nuclear control and bridge the gap (transition) of acceptance of national content before the parties can accept it as SNI. This proposed technical specification was adopted from the international standard IEC 61017:2016 and modified to suit the proposed alarm determination analysis method and Indonesian conditions, including consultation with interested parties. The content of this technical specification is relatively broad in scope. It is hoped that it can be adopted by parties who must carry out environmental monitoring following regulatory criteria and with the ability to provide alarms by increasing radiation doses equivalent to natural events (especially by rain). Keywords: environmental monitoring, gamma spectrometer, regulatory oversight, early warning
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Li, Jiawei, Maren Böse, Max Wyss, David J. Wald, Alexandra Hutchison, John F. Clinton, Zhongliang Wu, Changsheng Jiang, and Shiyong Zhou. "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 (January 28, 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 regional onscale strong-motion waveforms of the 2008 Mw 7.9 Wenchuan, 2013 Mw 6.6 Lushan, and 2017 Mw 6.5 Jiuzhaigou earthquakes to study the performance of FinDer for the current layout of the China Strong Motion Network. Overall, the FinDer line-source models agree well with the observed spatial distribution of aftershocks and models determined from waveform inversion. However, because FinDer models are constructed to characterize seismic ground motions (as needed for EEW) instead of source parameters, the rupture length can be overestimated for events radiating high levels of high-frequency motions. If the strong-motion data used had been available in real time, 50%–80% of sites experiencing intensity modified Mercalli intensity IV–VII (light to very strong) and 30% experiencing VIII–IX (severe to violent) could have been issued a warning with 10 and 5 s, respectively, before the arrival of the S wave. We also show that loss estimates based on the FinDer line source are more accurate compared to point-source models. For the Wenchuan earthquake, for example, they predict a four to six times larger number of fatalities and injured, which is consistent with official reports. These losses could be provided 1/2∼3 hr faster than if they were based on more complex inversion rupture models.
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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 (June 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 locations belonging to the Greek Early Warning System Network (which is based on Reuter-Stokes ionization chambers) for three periods, averaging 5 months each. In the same locations, in situ gamma spectrometry measurements were performed with portable germanium detectors. Gamma ambient dose equivalent rates were deduced by the in situ gamma spectrometry measurements and by soil sample analysis. The mean daily electret potential drop (in Volts) was compared with the mean daily ambient dose equivalent, measured with a portable HPGe detector and Reuter-Stokes high-pressure ionization chambers. From these measurements, ‘field’ calibration factors (electret’s voltage drop due to gamma radiation in terms of ambient dose equivalent) were deduced and found in very good agreement with the values deduced in Laboratory. The influence of cosmic radiation and the intrinsic voltage loss when performing long-term environmental gamma measurements with EICs, was estimated.
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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 in previous studies. The main challenge in developing an accurate prediction model lies in modeling the influencing factors that accurately simulate the effect of external environmental conditions on long-term horizontal displacement. To enhance the prediction accuracy in the proposed study, the surrounding environmental effects by considering the relationship between the current and past displacements in addition to air temperature, thermal inertia, and solar radiation are modeled as critical influencing factors. In addition, a data-driven method based on an artificial neural network (ANN) integrated with Bayesian optimization (BO) is employed to model and predict long-term horizontal displacement with the adopted critical influencing factors. An overpass bridge equipped with bearing displacement monitoring and temperature sensors is used to validate the robustness and effectiveness of the proposed method. The analysis of the results concludes that the proposed method can generate an accurate and robust long-term horizontal displacement prediction model that supports a reliable anomaly detection approach for early warning systems of bridge structures.
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Long, Qi, Fei Wang, Wenyan Ge, Feng Jiao, Jianqiao Han, Hao Chen, Fidel Alejandro Roig, Elena María Abraham, Mengxia Xie, and Lu Cai. "Temporal and Spatial Change in Vegetation and Its Interaction with Climate Change in Argentina from 1982 to 2015." Remote Sensing 15, no. 7 (April 3, 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 and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change.
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Hashim, NurIzzah M., Norazian Mohamed Noor, Ahmad Zia Ul-Saufie, Andrei Victor Sandu, Petrica Vizureanu, György Deák, and Marwan Kheimi. "Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models." Sustainability 14, no. 13 (June 29, 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 the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.
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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 (May 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) spectrometers were tested in order to measure Ambient gamma Dose Equivalent Rate (ADER). The NaI(Tl) scintillators were irradiated in a homogeneous field with 662 keV photons with different ADER values from 0.17 to 100 μSv h−1 at 0° incidence (irradiation field perpendicular to the detector’s front window) and at 90° incidence. For each irradiation, the measured ADER by the spectrometers and the ‘true’ ADER values (provided by the IRCL) were compared. In addition, the angular dependence (0–359°) of the ADER response of the spectrometers was studied with a 152Eu source placed at 1, 2 and 3 m from the spectrometers. The ADER dependence as function of the distance from the 152Eu source (at 0° incidence) measured by the two detectors was compared with the theoretical one. In the field studies, ADER was measured by the spectrometers at seven locations belonging to the Greek Early Warning System Network (which is based on Reuter-Stokes ionization chambers). These locations have different ADER values ranging from 20 to 120 nSv h−1. In these locations, gamma ADER were also deduced (1) by in situ gamma spectrometry measurements with portable Germanium HPGe detectors and (2) by the Reuter-Stokes ionization chambers (by subtraction of the cosmic radiation). Gamma dose measurements were also performed with the InSpector 1000 and RIIDEYE M-G3 detectors in 25 locations (beaches) of Northern Greece and compared with the ADER values deduced by sand sample analysis with gamma spectroscopy. Beaches with sand are good candidates for such type of measurements since they are commonly flat and in principle the natural radionuclides are homogenously distributed.
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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 is responsible for forecasting these specific meteorological conditions with 72 h in advance and possibly issue the early warning. In particular, an accurate wind simulation is required. Unfortunately, numerical weather prediction models suffer from errors, especially for what concerns near-surface fields. These errors depend primarily on uncertainties in the initial and boundary conditions provided by global models and secondly on the model formulation, in particular the physical parametrizations used to represent processes such as turbulence, radiation exchange, cumulus and microphysics. In our work, we tried to compensate for the latter limitation by using different Planetary Boundary Layer (PBL) parameterization schemes. Five combinations of PBL and Surface Layer (SL) schemes were considered. Simulations are implemented in a real-time configuration since our intention is to analyze the same configuration implemented by ARPA-PUGLIA for operational runs; the validation is focused over a time range extending from 49 to 72 h with hourly time resolution. The assessment of the performance was computed by comparing the WRF model output with ground data measured at a weather monitoring station in Taranto, near the steel plant. After the analysis of the simulations performed with different PBL schemes, both simple (e.g. average) and more complex post-processing methods (e.g. weighted average, linear and nonlinear regression, and artificial neural network) are adopted to improve the performances with respect to the output of each single setup. The neural network approach comes out as the most promising method.
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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 (June 27, 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 and homogenized typology of microseismic signals at clayey landslides. A thorough knowledge of the various microseismic signals generated by slope deformation is crucial for the future development of automatic detection systems to be implemented in landslide early-warning systems. Detected signals range from short-duration (< 2 s) quake-like signals to a wide variety of longer-duration tremor-like radiations (> 2 s – several min). The complexity of seismic velocity structures, the low quantity and low quality of available signal onsets and non-optimal seismic network geometry severely impedes the source location procedure; thus, rendering source processes characterization challenging. Therefore, we constrain sources' locations using the prominent waveform amplitude attenuation pattern characteristic of near-source area (< about 50 m) landslide-induced microseismic events. A local magnitude scale for clayey landslides (ML−LS) is empirically calibrated using calibration shots and hammer blow data. The derived ML−LS returns daily landslide-induced microseismicity rates that positively correlate with higher average daily displacement rates. However, high temporal and spatial resolution analyses of the landslide dynamics and hydrology are required to better decipher the potential relations linking landslide-induced microseismic signals to landslide deformation.
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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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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 themselves.
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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 many advantages. The combination of the simulated annealing algorithm with multithreading, data compression, and segmentation greatly improves the efficiency of the algorithm and shortens the running time. Using the logistic regression early warning model and BP neural network early warning model and 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 perspective of model accuracy and variable importance. The results show that the three index dimensions of the BP neural network optimized by the simulated annealing algorithm have good discrimination ability to financial status. The BP neural network early warning model optimized based on the simulated annealing algorithm has good prediction accuracy and good practical significance.
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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 accordance with the system hardware, which is designed with a data collector, gateway, man-machine interface, fault analysis module, disaster early warning module, and expert decision support module. According to the system hardware and software design, the design of a distribution network disaster early warning and production decision support system based on multisource data is realized. The simulation results show that the system has high accuracy and a short warning time.
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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 system early-warning research of solving resource economy sustainable development.
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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 warning mechanisms in order to confirm the validity and applicability of the educational network early warning mechanism. The experimental results demonstrate that the accuracy of the educational network early warning mechanism in this paper is higher than that of the conventional early warning mechanism, which is 9.87 percent, at up to 95.02 percent. The proposed model, however, clearly excels in terms of early warning adaptability, model fitting level, and information overload processing effectiveness. In general, this paper successfully applies the Markov model to the early warning system of the college education network. For the study of the college education network’s early warning system, it has some reference value.
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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 methods such as difficult to deal with highly non-linear and lack of adaptive capacity.
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Li, Guiliang, Bingyuan Hong, Haoran Hu, Bowen Shao, Wei Jiang, Cuicui Li, and Jian Guo. "Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network." Energies 15, no. 9 (April 29, 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-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.
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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, Claus Milkereit, Matteo Picozzi, Kevin Fleming, Mihal Brumbulli, Björn Lichtblau, and Ingmar Eveslage. "A wireless mesh sensing network for early warning." Journal of Network and Computer Applications 35, no. 2 (March 2012): 538–47. http://dx.doi.org/10.1016/j.jnca.2011.07.016.
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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 of a ballistic missile in boost phase, and the model has been verified by simulation based on the data of a two stage ballistic missile.
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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 (June 17, 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. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.
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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 of the experiment shows that this early warning model is applicable to forecast the risk of operating ability of electric power bureaus.
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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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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 traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.
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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 above statistical methods. This article proposes a method of selecting macroeconomic early-warning indicators using self-organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self-organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early-warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self-organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self-organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability.
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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 (October 2, 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 (September 30, 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 humidity sensor to detect humidity in the environment and vibration sensor to detect movement in the soil was implemented. The data captured by the sensing element is transmitted to a microcontroller which sends early warning signal represented by LED, Buzzer and sending of SMS or call. Through the setup, it is found that the sensitivity of the moisture sensor is rather limited however the range of moisture content detected by the sensor is functional. The vibration sensor can be tuned to have high sensitivity to the movement of the soil however, a number of false positive outcome may be detected. The early warning system is still possible by using multiple sensing element and averaging over their outcomes. The early warning system using low cost components as prototype has been successfully demonstrated. This setup can be scaled up to a real field implementation after careful tuning.
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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 better surge early warning margin performance and engineering practicability.
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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 of distribution network information physical system is proposed, and then the early warning method of distribution network cyber physical system based on Hidden Markov model is established. Finally, an example is given to verify that the proposed strategy can accurately and efficiently early warn the fault.
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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 host hardware. Combined with the spatial convolution neural network, fuzzy neural network (logarithmic depth iterative regression neural network, polynomial depth iterative regression neural network, transfinite learning machine, and binary neural network) and other technologies finally realize high geological disaster early warning sensitivity and long early warning advance. The system will become an alternative to the previous geological disaster early warning system based on the direct data of geological embedded probes.
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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 that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.
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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 (June 12, 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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Zhong, Yihua, Yuxin Liu, Xuxu Lin, and Shiming Luo. "The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9564801.
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Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.
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Bakry, Gema Nusantara, and Rizki Nurislaminingsih. "Information network on Twitter regarding early warning of mount Semeru eruption." Jurnal Kajian Komunikasi 11, no. 2 (December 31, 2023): 306. http://dx.doi.org/10.24198/jkk.v11i2.50537.
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Background: Indonesia is a country that is highly susceptible to volcanic disasters. One potential measure the community can take is to utilize social media platforms to participate in disaster mitigation efforts. The hashtag #Semeru exemplifies the utilization of social media in disseminating information regarding volcanic disasters. It became a trending topic on Twitter regarding the information on the eruption of Mount Semeru at the end of 2021. Purpose: The primary objective of this research is to examine the operational mechanisms of the Mount Semeru eruption early warning system on Twitter. Furthermore, the objective is to determine the key actors responsible for disseminating early warning information on Twitter. Methods: This study employed the Social Network Analysis (SNA) method. Results: The findings show that the network distribution pattern of the Semeru eruption early warning system has a radial communication network pattern with indicators of low network density levels. The actors @fiersabesari, @bnonews, @asumsico, @disclose.tv, @jawafess, and @insiderpaper have a proximity centrality value of 0 due to their lack of acquaintance. On the other hand, two actors possess a closeness centrality value: @melodiysore with a value of 0.8 and @daryonoBMKG with a value of 0.2. This study highlighted that the actors involved in disaster management and mitigation had a level of popularity that ranked outside the top 10. Conclusions: The information network system for the early warning of the Mount Semeru eruption on Twitter forms a network distribution with a radial communication pattern that is concentrated at one point and acts as a key actor. Eight key actors play a role in disseminating early warning messages, specifically @fiersabesari, @daryonoBMKG, @bnonews, @asumsico, @disclose.tv, @theinsiderpaper, @melodiysore, and @jawafess (community). Implications: This study demonstrates the benefits of using Twitter as a timely indicator for disasters, notably the eruption of Mount Semeru. It can effectively engage the community and government in disseminating early-warning information about volcanic eruptions.
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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 the focus of this paper. Research on early warning mechanism and the construction of the main problems are the establishment of an information transmission link. Therefore, based on the transmission characteristics of information transmission network, we carry on the division for the metropolitan area network level. Through the analysis of whole network resources deployment, we put forward a reasonable solution according to the performance index, making the whole of early warning mechanism has shown more rationality, but also provides a theoretical method and technical support for the establishment of a similar warning mechanism.
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Leon, Ernesto, Cristian Alberoni, Miguel Wister, and Jose Hernández-Nolasco. "Flood Early Warning System by Twitter Using LoRa." Proceedings 2, no. 19 (October 24, 2018): 1213. http://dx.doi.org/10.3390/proceedings2191213.
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In this paper, a sensor network architecture is presented. This work proposes an early warning system for river overflows. The sensor network consists of a river level sensor node that measures the distance between the sensor and the mass of water using a precision ultrasonic sensor. The recorded information is transmitted to a receiving node by radio frequency (915 MHz) using LoRa modulation. The receiving node is implemented in a Raspberry Pi, it processes the information in real time and publishes the alert using a social network (Twitter). Finally, a prototype of the river level node was tested, obtaining a measurement range from 20 cm to 2 m. The receiving node was located 500 m away from the sensor node, that received the data packets sent without loss of data.
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Wang, Rongxia, Malik Bader Alazzam, Fawaz Alassery, Ahmed Almulihi, and Marvin White. "Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System." Mobile Information Systems 2021 (November 19, 2021): 1–8. http://dx.doi.org/10.1155/2021/3773688.
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Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.
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Fleming, K., M. Picozzi, C. Milkereit, F. Kuhnlenz, B. Lichtblau, J. Fischer, C. Zulfikar, and O. Ozel. "The Self-organizing Seismic Early Warning Information Network (SOSEWIN)." Seismological Research Letters 80, no. 5 (September 1, 2009): 755–71. http://dx.doi.org/10.1785/gssrl.80.5.755.
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