Statistics Of Historical Data Research Articles

Disaggregation Model of Tigris River Inflow into a Proposed Makhol Reservoir Using Parametric Approach

Since Makhol dam is planned to be constructed on Tigris River to the north of Baiji discharge measurement station, it is essential to study the nature of inflow into this reservoir. The information concerning this inflow is of great help in operating and management of the prospective reservoir. From our point of view, it is necessary to know how these inflows are distributed and contributed to Tigris from different upstream sources. Disaggregation flow models are stochastic generation techniques, that used to divided data into lower time scales from higher time scales using parametric approaches with two main categories: spatial and temporal. In the streamflow disaggregation model, historical data statistics (mean, skewness, standard deviation, maximum, and minimum) can be preserved while distributing single-site values to several sites in space and time. In this study, the aggregated streamflows data at a key station will be disaggregated into a corresponding series of discharges at sub-stations that are statistically similar to those observed by applying Stochastic Analysis Modeling and Simulation (SAMS 2010) software. To investigate the appropriate the disaggregation method for modeling monthly flow data, we used the annual and monthly data flow of five gauging stations in the Tigres River in Iraq (Mosul Dam station on Tigris river, Asmawah on AlKhazir river, Eski Kalak on Upper Zab, Dibs Dam on Lower Zab, and Baiji station on Tigris river) for the duration 2000–2020. The application approach's statistical outcomes were contrasted with their historical counterparts and the results showed that most years and months at all stations were in good agreement with the historical data. Therefore, we argue that this method have ability to be used when making decisions about water management strategies in these regions which is essential for water resource managers and decision makers.

Online Distribution System Topology Monitoring With Limited Smart Meter Communication

Accurate topology monitoring is a prerequisite for advanced control and optimization of distribution power systems. However, online topology monitoring usually results in high communication facility investments and operating costs. This research proposes an online system topology monitoring method to reduce communication capacity and network traffic by adjusting the smart meter communication interval. Measurements from some critical buses are acquired with short communication intervals. In contrast, other non-critical buses can still maintain much longer communication intervals. We design a three-step angle-free optimization algorithm framework to track system topology and detect branch switching events online. First, we perform load forecasting to obtain active and reactive power pseudo measurements at non-critical buses. Power flow calculations are used to generate voltage pseudo measurements. Then, we perform the weighted least square method and an improved extended Kalman filter to calibrate both pseudo measurements and critical bus measurements. Finally, a topology tracking algorithm based on voltage estimation errors of critical buses is proposed to estimate and correct the current topology. In addition, we propose a critical bus selection method based on historical data statistics. The numerical results on IEEE 33-bus case show that our framework achieves a high online topology monitoring accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F1$</tex-math></inline-formula> score of 89.91% with only 10 critical buses. Further tests on a practical 108-bus system in China also demonstrate the effectiveness of our framework.

Design of air quality monitoring system based on light scattering sensor

The current air quality detector mainly detects the PM2.5, temperature and humidity of the environment at a single point, and the official website data is also a large range of regional data. The traditional cable transmission and wiring is complex, limited by the environment and high cost. In this paper, a data service air quality detection system based on light scattering sensor is proposed, which can detect PM2.5, temperature and humidity, and upload data to the Internet of things platform for real-time analysis and historical data statistics. It can also upload data to the Internet of things platform for real-time analysis and historical data statistics. The system collects three kinds of sensor modules to monitor indoor air environment in real time. Monitoring data is sent to mobile terminal equipment or PC terminal through WiFi. Its historical data can be viewed and called for data statistics through mobile phones. The system is stable and convenient to use. The paper also studies the detection characteristics and improvement methods of light scattering sensor.

Real‐Time Wind Power Forecast Error Estimation Based on Eigenvalue Extraction by Dictionary Learning

Because of the fluctuation and uncertainty characteristics of wind power, it is difficult to achieve a perfect wind power forecast. The forecast error may lead to an imbalance between the load demand and power supply. The object of recent research on forecast error is to achieve the probability distribution of forecast error based on the statistics of historical data. This statistical error achieved from a probability distribution cannot reveal the real-time condition of wind power. A real-time forecast Error estimate method based on dictionary learning (EEDL) was proposed. In EEDL, several coefficients that have strong relevance to the forecast error are computed. The dictionary learning method is used to extract the eigenvalues of forecast error from these coefficients. Based on the eigenvalues, a real-time error estimation model was built to obtain the forecast error. EEDL was compared to the estimation method based on a Probability distribution function (PDF). The performance of EEDL was also compared to the error estimation method based on a PDF while using different forecast techniques.

Conversion between 2 military combat-related injury coding systems.

Deployable medical systems(DEPMEDS) patient conditions (PCs) and Military Combat Injury Scale (MCIS) are 2 important military medical coding systems. However, both of them have defects when applied in military medical planning. Although each PC code contains information about treatment, intensity of care, treatment time, length of stay, and probability of disposition that is relevant to simulation, its description is too comprehensive and ambiguous to code historical military medical records. Therefore, conversion between PC and other medical coding systems applied in standard medical data is required when validity is required following simulation. The information linked to each PC code is based on subject matter expert opinion instead of standard medical data from the theater that need to be continuously updated depending on the results of medical data analysis. MCIS, a combat-related injury coding system, shows significant promise in coding real medical data, but it does not seek detailed information important for prediction and simulation unlike PCs. Therefore, MCIS cannot be used in planning tools directly. Thus, the effort to map MCIS to PCs is significant for medical logistic planning. We aim to identify whether conversion between PCs and MCIS is possible and to evaluate inter-coder reliability.Three senior coders assigned all possible MCIS codes to 187 combat-related PC codes. The data records were structured based on an earlier study. Inter-rater reliability was measured by using Cohen's k statistic and percent agreement.Low inter-rater reliability indicated the difficulty in conversion between PCs and MCIS.The injury descriptors of PCs should be expanded by referring to new standard medical data. The existing MCIS codes need to be modified to include more information on treatment brief, treatment time, length of stay, and other key information, and historical data statistics need to be developed.

Design and Development of Transformer Data Monitoring System Based on J2EE

The principle of the distribution transformer monitoring terminal and distribution transformer monitoring system is the base of the article. This paper establishes the implement programs of the distribution transformer monitoring system to strengthen the management of distribution transformer. This system provides many functions: real-time monitoring, graphs and reports display of historical data statistics of distribution transformer. This system uses formal J2EE framework, combining with eXtremeComponents (reporting system development tool) to develop customized reports.

Land Subsidence Disaster Survey and Its Economic Loss Assessment in Tianjin, China

Land subsidence is a kind of widespread geological disaster that occurs in China’s coastal cities. The Tianjin metropolis has suffered negative influence of land subsidence since the 1950s. Land subsidence disaster scenarios were field surveyed and done with archival research using a variety of sources to document the historical record of the disaster, with the results showing land subsidence lead comprehensive detrimental effects on society, economy, and the natural environment. The economic loss was assessed and evaluated in urban infrastructure, building, conservancy engineering, environmental loss, secondary disaster, etc., with help of various methods, such as historical data statistics, expert judgment, shadow project calculation, modeling techniques, etc. The outcome of economic loss is about RMB135.6 billion (US$18.03 billion) up to 2007. This result, although undoubtedly incomplete, subjective, and selective, provides a believable support for the administration’s decision making toward the implem...

Climate-Based Estimation of Hydrologic Inflow into Lake Okeechobee, Florida

This paper presents a comparative evaluation of methods for climate-based estimation of the net inflow rate into Lake Okeechobee, Fla. The estimated net inflow rate is used by the South Florida Water Management District (SFWMD) to support the management and operations of the Lake Okeechobee hydrologic system. The first method evaluated in this paper (Croley) uses rainfall outlooks provided by the National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) to calculate a weighed average of historical inflow values for each month. The second method evaluated in this paper (SFWMD Empirical) uses a linear regression on statistics of historical data to predict the net inflow rate. These two methods were developed and have been used operationally by the SFWMD since 2000. Three new methods are presented and comparatively evaluated to gauge their ability in estimating net inflow rates. The first two of these methods are based on CPC issued forecasts in decile probability density format. The ...