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With the development of coastal region in Tianjin Binhai new district, environmental pollution, exhaustion of resources and ecological hazards are aggravated continuously. So for efficient utilization of resources and realization of sustainable development, study on the carrying capacity assessment of coastal region is the key means for guiding the management. This paper presents a Data-Driven Model...
Eutrophication has been considered as one of the most serious water quality problems of reservoirs in Taiwan. The back-propagation artificial neural network (ANN) was used to predict the water quality variation of the LungLuanTan Reservoir in southern Taiwan in current research. Three mathematical models were established and to predict the variation of parameters including total phosphorus (TP), secchi...
Strong correlation exists between river discharge and suspended sediment load. The relationship was used to estimate suspended sediment load by using linear regression model, power regression model, artificial neural network and support vector machine in this study. Records of river discharges and suspended sediment loads in Kaoping river basin were investigated as case study. Eighty-five percent...
This paper proposed an artificial neural network (ANN) approach based on Lagrangian multiplier method (Lagrangian ANN) to solve the problem of economic load flow in a power system. Operational requirements and transmission losses are also taken care by the proposed approach. Power plant operating costs are represented by exponential cost functions. Simulation on a test example with six generating...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
Solar energy is a green energy which is not only perennial but also accessible to every strata of the world. An easy way to convert solar energy into electric energy is to use Solar Photovoltaic (SPV) system. Solar panel is a power source having nonlinear internal resistance. As the intensity of light falling on the panel varies, its voltage as well as its internal resistance varies. To extract maximum...
Shading caused by surrounding objects is an important issue for solar energy system design and analysis. In the special case of building integrated photovoltaic (BIPV) systems, the prediction of the partial shading is critical in order to reduce losses due to poor Maximum Power Point Tracking (MPPT). This paper will present a technique that uses Artificial Neural Network to predict the output power...
Gross Domestic Product (GDP) is a benchmark for economic production conditions of a country. Estimates of economic growth in the coming year in a country has important roles, among others as a benchmark in determining business plans for business entities, and the basis for devising government fiscal policy. Artificial Neural Network (ANN) has been increasingly recognized as a good forecasting tool...
Neural Network is a network that resembles a human brain tissue, which may infer a result based on the facts or experience that happened. Many applications have implemented neural network. In this thesis, we compared the stock forecasting result of ANTM (PT Aneka Tam bang) using Artificial Neural Network and ARIMA. ARIMA is a technique of time-series forecasting, which means forecast based on the...
Energy plays a fundamental role in an economy. Turkey has the world's 15th largest GDP-Purchasing power parity and 17th largest Nominal GDP. Economists and political scientists classify Turkey as a newly industrialized country. In this study, an alternative model for Turkey's energy consumption is proposed for the time between 1980 and 2004. Artificial neural network based model (ANN) is preferred...
In this paper, an approach that combines HMM spectrum models and ANN prosody models is proposed to construct a speech synthesis system. Currently, a Mandarin corpus is used to show the feasibility of this approach. We hope that this approach can be used in other syllable prominent languages like Min-Nan and Hakka. In the training phase, DCC (discrete cepstrum coefficients) are computed for each frame...
A novel Neural Network Based Fuzzy Inference System for financial default forecast will be introduced. A wide range of financial forecasts is known. This method is focusing on the economical default forecast, but the method can be used generally for other financial forecasts as well, for example for calculating the Value at Risk. This hybrid method is combined by two classical methods: the artificial...
This paper innovatively proposes a hybrid intelligent system combining fuzzy comprehensive assessment approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. And also inducts the sensibility analysis to discriminate the importance of each index in the assessment index system. The effectiveness...
In this paper, the current forecast of storm surge based on BP is adapted to deal with the characteristics of storm surge. One main kind of fuzzy information in geology calamity predicting system is solved by information diffusion method. The whole process is as follows: Firstly, influential information is collected by single step predicting model and neural network predicting model separately to...
In order to satisfy the needs of human resources management and development, this study took R&D professionals as the research object and proposed an evaluation model for high-tech enterprise human resources based on artificial neural network, then trained and tested the neutral network for personnel evaluation. And the network was improved to be very effective to stimulate the evaluation of human...
Objective Forecast and analysis of cerebral infraction incidence rate are the basis and key work of cerebral infraction prevention and control. At present, forecast of cerebral infraction incidence rate is mainly based on traditional research approach or single artificial neural network technology. Recent study results show that combined forecast model approach enjoys more precise forecast than monomial...
Based on observed climate data and climate projection in the Songhuajiang River basin, streamflows at Jiamusi hydrological station under three emission scenarios during 2011-2050 are projected by applying artificial neural networks. The results show that annual streamflow will not change significantly and its decadal variations are also small, but there are significant seasonal variations under three...
In this experiment, by using the method of artificial neural network and DPS DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana...
Short term load forecasting for day ahead operations is an important task of an electric distribution company. Forecasting errors directly impact the economics of the distribution company in a market scenario. Many categories of methods like, expert system, artificial neural network and time series analysis, have been developed for short term load forecasting. We compare and contrast these methods...
The paper describes the training, validation and application of artificial neural network (ANN) models for prevalence of pneumoconiosis among workers in Yueyufeng iron and steel company (China). The models employed three input variables collected at several operational sites in 30 different iron and steel companies. The performance of the ANN models was assessed through the global error. The model...
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