Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

基于改进Autoformer模型的短期电力负荷预测

针对短期电力负荷预测因受天气、温度、节假日等多重不确定性因素影响而造成精度低的问题,提出一种基于改进Autoformer模型的短期电力负荷预测模型。改变序列分解预处理的惯例,设计深度模型的内部分解模块,该模块提取模型中隐藏状态的内在复杂时序趋势,使得模型具有复杂时间序列的渐进分解能力;提出Nystrom自注意力机制,该机制利用Nystrom方法来逼近标准的自注意力机制。某地电力负荷预测实验结果表明,所提模型比基于标准Autoformer模型的短期电力负荷预测模型的时间复杂度更低,准确率更高。

New Issue to Modeling Intentionality in the Field of Consciousness

Brentano in 1870s was the first to introduce intentionality to mean “conscious of”. At the end of the 1960s, a version of this view was developed by analytic American philosophy to construct a theory of meaningful language. That led Dennett to claim that intentionality was mainly a feature of sentence, not mental states. In contrast, Searle in 1990s rejected the Brentanian thesis and explained intentionality by a biological naturalism. Thereafter, radical eliminativists such as Churchland claimed that all philosophical arguments merited replacement by neuroscientific knowledge. Unfortunately, very few neurophysiological studies attempted to scientifically tackle the problem raised by intentionality. The issue now emerging is a new conception of intentionality based on phenomenological, neurobiological and quantum theories, such as: 1) the notion of “intentional arc” proposed in the philosophy of Merleau-Ponty;2) the neurobiological and quantum model of Freeman, in which self-organizing pathways are accompanied by quantum transitions in controlling intentionality in brain;3) the recent hypothesis that some visuo-motor neurons would be involved in controlling these self-organized pathways;4) the quantum models of Vitiello and Globus, in which a thermofield (dissipative) system governs the dynamic dialog of dual quantum modes between environment and brain. Based on this conception of mind-world interactions, it implicitly appears that intentionality might be a fundamental force which draws us irreversibly towards the future. An alternative hypothesis based on this promising proposal is argued.

Expansion modelling of discrete grey model based on multi-factor information aggregation

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

A UNIFIED ENERGY APPROACH TO A CLASS OF MICROMECHANICS MODELS FOR COMPOSITE MATERIALS

Several micromechanics models for the determination of composite moduli are investigated in this paper,including the dilute solution,self-consistent method,generalized self-consistent method,and Mori-Tanaka's method.These mi- cromechanical models have been developed by following quite different approaches and physical interpretations.It is shown that all the micromechanics models share a common ground,the generalized Budiansky's energy-equivalence framework.The dif- ference among the various models is shown to be the way in which the average strain of the inclusion phase is evaluated.As a bonus of this theoretical development,the asymmetry suffered in Mori-Tanaka's method can be circumvented and the applica- bility of the generalized self-consistent method can be extended to materials contain- ing microcracks,multiphase inclusions,non-spherical inclusions,or non-cylindrical inclusions.The relevance to the differential method,double-inclusion model,and Hashin-Shtrikman bounds is also discussed.The application of these micromechanics models to particulate-reinforced composites and microcracked solids is reviewed and some new results are presented.

A Kinetic Study of Anaerobic Biodegradation of Food and Fruit Residues during Biogas Generation Using Initial Rate Method

A kinetic study of biogas production from Urban Solid Waste (USW) generated in Dar es Salaam city (Tanzania) is presented. An experimental bioreactor simulating mesophilic conditions of most USW landfills was developed. The goal of the study was to generate the kinetic order of reaction with respect to biodegradable organic waste and use it to model biogas production from food residues mixed with fruit waste. Anaerobic biodegradation was employed under temperature range of 28℃ - 38℃. The main controls were leachate recirculation and pH adjustments to minimize acid inhibitory effects and accelerate waste biodegradation. The experimental setup comprised of three sets of bioreactors. A biodegradation rate law in differential form was proposed and the numerical values of kinetic order and rate constant were determined using initial rate method as 0.994 and 0.3093 mol0.006·day-1, respectively. Results obtained were consistent with that found in literature and model predictions were in reasonable agreement with experimental data.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

Moment Analysis of a Rice-Pile Model

Large scale simulations of a rice-pile model are performed. We use moment analysis techniques to evaluate critical exponents and data collapse method to verify the obtained results. The moment analysis yields well-defined avalanche exponents, which show that the rice-pile model can be coherently described within a finite size scaling framework. The general picture resulting from our analysis allows us to characterize the large scale behavior of the present model with great accuracy.

Analytical Expressions of Concentration of VOC and Oxygen in Steady-State in Biofilteration Model

Mathematical models of steady-state biofilteration are discussed. The theoretical results are much useful for the design of biofilters. This model is based on the system of non-linear reaction/diffusion equations contains a non-linear term related to Monod kinetics, Andrews kinetics, interactive model from Monod kinetics and Andrews kinetics. Analytical expression of concentration of VOC (Volatile organic compounds) and oxygen are derived by solving the system of non-linear equations using Adomian decomposition method (ADM) method. Our analytical results are also compared with the simulation results. Satisfactory agreement is noted.

Hidden Markov Models to Estimate the Lagged Effects of Weather on Stroke and Ischemic Heart Disease

The links between low temperature and the incidence of disease have been studied by many researchers. What remains still unclear is the exact nature of the relation, especially the mechanism by which the change of weather effects on the onset of diseases. The existence of lag period between exposure to temperature and its effect on mortality may reflect the nature of the onset of diseases. Therefore, to assess lagged effects becomes potentially important. The most of studies on lags used the method by Lag-distributed Poisson Regression, and neglected extreme case as random noise to get correlations. In order to assess the lagged effect, we proposed a new approach, i.e., Hidden Markov Model by Self Organized Map (HMM by SOM) apart from well-known regression models. HMM by SOM includes the randomness in its nature and encompasses the extreme cases which were neglected by auto-regression models. The daily data of the number of patients transported by ambulance in Nagoya, Japan, were used. SOM was carried out to classify the meteorological elements into six classes. These classes were used as “states” of HMM. HMM was used to describe a background process which might produce the time series of the incidence of diseases. The background process was considered to change randomly weather states, classified by SOM. We estimated the lagged effects of weather change on the onset of both cerebral infarction and ischemic heart disease. This fact is potentially important in that if one could trace a path in the chain of events leading from temperature change to death, one might be able to prevent it and avert the fatal outcome.

Wavelet time series MPARIMA modeling for power system short term load forecasting

The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.

A new model of foraging behavior in ant system

Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.

Cellular automation model of faults and algorithmic complexity

In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it isshown that both the existence and their mutual arrangement of faults could obviously influence the overallcharacters of earthquake process. Then the characters of each stage of model evolution are explained withself-organized critical state theory. Finally, earthquake sequences produced by the models are analysed interms pf algorithmic complexity and the result shows that AC-values of algorithmic complexity could be usedto study earthquake process and evolution.

Hidden Markov Models and Self-Organizing Maps Applied to Stroke Incidence

Several studies were devoted to investigate the effects of meteorological factors on the occurrence of stroke. Regression models had been mostly used to assess the correlation between weather and stroke incidence. However, these methods could not describe the process proceeding in the back-ground of stroke incidence. The purpose of this study was to provide a new approach based on Hidden Markov Models (HMMs) and self-organizing maps (SOM), interpreting the background from the viewpoint of weather variability. Based on meteorological data, SOM was performed to classify weather patterns. Using these classes by SOM as randomly changing “states”, our Hidden Markov Models were constructed with “observation data” that were extracted from the daily data of emergency transport at Nagoya City in Japan. We showed that SOM was an effective method to get weather patterns that would serve as “states” of Hidden Markov Models. Our Hidden Markov Models provided effective models to clarify background process for stroke incidence. The effectiveness of these Hidden Markov Models was estimated by stochastic test for root mean square errors (RMSE). “HMMs with states by SOM” would serve as a description of the background process of stroke incidence and were useful to show the influence of weather on stroke onset. This finding will contribute to an improvement of our understanding for links between weather variability and stroke incidence.

An Emergence of a Quantum World in a Self-Organized Vacuum—A Possible Scenario

We have explored a model of vacuum self-organization based on dissipative dynamics and recurrent self-interactions. The initial state of the vacuum is assumed as self-interacting vacuum dust. The medium is dispersive and resembles dark-energy vacuum as described by general relativity. Beside self-diffusion, vacuum dust endowed with self-attraction, resembling Newton’s gravity. We explored what would happen with this medium when the strength of self-gravitation progressively increases. We observed a cascade of phase transitions. First transition occurs when self-attraction reaches the point when it can balance self-diffusion. A vortex-cellular structure emerges. Vortexes operate as self-sustained oscillators and tend to synchronize their dynamics. They form a synchronized network that possesses a universal time scale and, after zooming out, its structure acquires a form of fiber-bundle structure of electromagnetic field. With increasing self-gravitation strength, the system experiences another phase transition. The fiber-bundle structure becomes resembling that of weak nuclear field. Vacuum cells acquire spinorial dynamics. Electric charges emerge. When synchronized, the weakly interacting cells create lepton-like molecules. Oscillating charges in spinorial cells give a birth to current loops, which magnetic moment linked to the particle spin. During the next phase transition, the cell dynamics experiences another topological transformation, which is accompanied by creation of three color charges. The acquired fiber-bundle structure form resembles that of strong nuclear field. Synchronized strongly interacting vacuum cells create quark-like particles that carry color charges. We associate their complex synchronization patterns with particle flavors. We also explored statistical distributions of vacuum cells as functions of self-gravitation strength. We found that the distribution spectrum is essentially discrete, and the vacuum cells group around the states that we call super-attractive. Discrete cell distribution implies charge quantization. Synchronization transforms initial Boltzmann-like distribution into quantum-like distributions. During phase transitions, cell distributions experience transformations that can be encoded in the chemical potentials of the corresponding states. We found that chemical potentials apparently relate to the coupling constants and mixing angles and amplitudes in the standard model.

Local Modeling模式及其在月径流预测中的应用

LocalModeling方法是一种动力系统预测方法,将其应用于河西内陆区黑河干流出山口莺落峡水文站月平均流量的中长期预测预报,取得了较为理想的成果。预测试验的结果表明,该预测模型有较高计算精度,尤其适用于非主汛期各月的月平均流量的预测;对于主汛期6～9月的月平均流量的预测,在考虑前期来水与预见期内降水的影响后,亦可获到较为理想的预测结果。可以认为,该方法的预报精度达到了水文情报预报规范的要求,LocalModeling方法的应用,将为西北干旱地区河川径流的中长期预报提供了一个新的途径。

Local Modeling模型及其在黄河上游月径流预测中的应用

基于黄河上游有关水文气象台站的降水径流资料,将LocalModeling方法应用于龙羊峡水库月入库径流预报的中长期水文预报模型.模型的检验和应用结果表明,该方法有着稳健性好、数学物理意义明确、对数据系列要求不高和容易操作等优点,在非汛期各月的径流预测中具有较高的准确性,并且在考虑了降水的影响后,对汛期径流的计算精度亦基本符合水文情报预报规范和实际应用的要求.该模型在黄河上游水量预报和调度工作中具有良好的应用前景.