Grey GM Research Articles

Forecast of flood in Chaohu Lake basin of China based on Grey-Markov theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

Study on the Application of the Combined Prediction Modeling Method to Thermal Error Modeling on NC Machine Tools

Due to the complexity of machine tool thermal errors affected by various factors, a new combining prediction model, based on the theory of gery system GM (1,1) model, is applied to the trend prediction of machine tool thermal errors. The degree of smoothness of primary data sequence is first improved by function transform method and sequentially grey system GM (1,1) model is established; second, time series analysis model is established by remnant sequence of GM (1,1) model to amend the precision of grey system GM (1,1) model. Thus, the precision of combining prediction model is further improved. Through the prediction study on thermal error modeling in a spot NC turning center, testing results showed that combining prediction model can highly improve machine tool’s prediction precision and make it more effective for real-time compensation of machine tool thermal error.

A gray system model for studying the response to climatic change: The Liulin karst springs, China

Gray system theory uses a black-gray-white color spectrum to describe a complex system whose characteristics are only partially known or known with uncertainty. In this study, we use gray system theory to investigate the relation between precipitation and spring flows in a karst region in China. The gray incidence analysis was applied to the Liulin Springs, Shanxi Province, China to analyze the time-lag between spring flow and precipitation. The results showed that the average groundwater residence time at Liulin Spings is about 4 years. The gray system GM(1,2) model was subsequently used as a predictive tool for spring discharge. It was found that model predictions are in agreement with observed data. This study also shows that the discharge of the Liulin Springs primarily responds to climate change; anthropogenic impacts are secondary. The continuous decline of water level in the karst aquifer and waning of spring discharges in semi-arid regions of China might be largely a response of the groundwater system to the decline in regional precipitation over the past two decades.

The morbidity and mortality trend and prediction of lung cancer in residents of Nangang District of Harbin in China during the past 10 years

It is not clear yet about the secular changes of morbidity and mortality trend of lung cancer in residents of Nangang District of Harbin in China. The aim of this study is to estimate the trend of lung cancer morbidity and mortality in residents of Nangang District from 1992 to 2001 and to predict their levels in the future 5 years. Data were collected from the annual statistic reports on cancer death cause from Nangang District in Harbin. The classification of death cause was made according to the ICD-9. Predictions about morbidity and mortality were made by the gray system GM(1,1). During the past 10 years, the morbidity and mortality of lung cancer were placed in uptrend slowly. The average morbidity and mortality of lung cancer were 44.75 per 100000 and 41.37 per 100000 respectively, and lung cancer was the first leading cancer for both episode and death of malignant tumors. The proportions of lung cancer were 25.91% and 33.29% for episode and death in all malignant tumors respectively. A half patients with lung cancer was 20-64 years old. Predictive morbidity and mortality of lung cancer would be 47.79/100000 and 44.81/100000 for men and 45.80/100000 and 42.02/100000 for women respectively. The morbidity and mortality of lung cancer show a slowly increasing trend. Lung cancer is one of main malignant tumors among people of 20-64 years old. The gradually aging population, environmental pollution and individual unhealthy living habits are the important factors of lung cancer increasing.

Grey GM(1, 1) model with function‐transfer method and application to energy consuming prediction

The ability to forecast a trend is very important in energy consumption prediction and energy production planning. The principle, under which the grey systems theory is applied in our energy consumption prediction, is that the forecasting system can be considered as a grey system. In such a system, unknown system's information can be determined by using known information. Here, the known information consists of energy consumption data, development trend in the consumption system. Based on our study, we eventually make forecast and decisions regarding possible future development. Our method is a whitenization process of a grey course. The model developed is based on the division method established for general data modelling and estimation of parameters of GM(1,1) its standard error coefficient that was applied to judge the accuracy height of the model was put forward; further, the function transform to forecast energy consuming trend and assess GM(1, 1) parameter was established. These two models need not pre‐process the primitive data. It was not only suited for equal interval data modeling, but also for non‐equal interval data modeling. Its calculation was simple and used conveniently, and the oil consumption per unit output analysis was taken as an example. The example showed that the two models were simple and practical, it was worth expanding and applying in the energy consuming prediction and energy programming.

MRI volumetric analysis of multiple sclerosis: methodology and validation

We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.

A real-time shipboard fire-detection system based on grey-fuzzy algorithms

To improve on the performance of traditional ship fire alarm systems, this paper investigates a dual-sensor device employing a grey-fuzzy algorithm. The theoretical aspects of the device and its experimental evaluation are presented. In terms of the algorithm, first an adaptive fuzzy classification system with an automatically generated rule base is developed for accurate fire-detection response to the output of a sensor pair (one temperature K-type thermocouple and one analog photoelectric smoke detector). Second, two alternative grey GM(1,1) prediction models are developed for anticipating trends in real-time temperature and smoke data, thus allowing early fire warning. Finally, the fuzzy system is combined with the grey-prediction algorithms for final testing. In the engine room of a docked coastal fishing trawler, two experimentally controlled fires are created, one open flame and one smoldering, and results from the sensor pair are recorded. As-detected results for each fire are processed by computer which tests the response behaviour of the alternative fuzzy-grey options and selects the optimal options set, and also compares the dual-sensor pair as conventionally operated in a commercial detector. Results indicate grey-fuzzy algorithms combining fuzzy rule-based classification and grey GM(1,1) unified-dimensional new message modelling are feasible in real-time shipboard fire detection, allowing accurate fire alarm triggering from 30 to 60 s earlier than conventional methodology.

Applications of improved grey prediction model for power demand forecasting

Grey theory is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information and/or for which information is lacking. In this paper, an improved grey GM(1,1) model, using a technique that combines residual modification with artificial neural network sign estimation, is proposed. We use power demand forecasting of Taiwan as our case study to test the efficiency and accuracy of the proposed method. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.

A precise and user‐independent quantification technique for regional comparison of single volume proton MR spectroscopy of the human brain

The aim of this work was to study and correct the influence of varying coil load and local B1 field in single volume MR spectroscopy. A simple, precise, and user-independent way to adjust the transmitter gain has been developed and validated. It is based on a fit of the localized signal to flip angle variation around 90°. This method proved to be robust against B1 gradients and suitable for in vivo applications. Local B1 correction was combined with an external reference and decomposition of the volume into CSF and tissue to obtain a comprehensive absolute quantification of tissue water content and metabolite concentrations in human brain. STEAM localized spectra of parietal and insular gray matter and subparietal white matter (n = 11, TE = 30 ms) were analyzed using a linear combination of model spectra (LCModel). Coefficients of variation (CV) between 1.5% and 4% were obtained for the tissue water content (1–2% in a single subject). The CVs of major metabolite concentrations (4–21%) were dominated by the errors of the spectral analysis. The largest B1 variation in the in vivo experiments (range 30%) was due to changes in coil load. Differences in regional sensitivity due to B1 inhomogeneity (parietal: 8% and 9%; insular: 16%) were found to be the second largest source of variation. Correction for local B1 improved standard deviations and intra-subject reproducibility. On average, sensitivity was 9% less in insular than in parietal gray matter. If ignored, significant differences were introduced for water and N-acetyl-aspartate or were obscured for creatine and cholines. Hence, local sensitivity correction proved to be necessary for regional comparison of absolute metabolite concentrations. Copyright © 2000 John Wiley & Sons, Ltd. Abbreviations used: Cho choline containing compounds CSF cerebro-spinal fluid CV coefficient of variation ins GM insular gray matter pa WM parietal white matter pmp GM paramedian-parietal gray matter RF radio-frequency Rtra linear transmitter reference SD standard deviation SNR signal-to-noise ratio SPGR spoiled gradient echo STEAM stimulated echo acquisition mode tCr total creatine TG transmitter gain tNAA total N-acetyl aspartate TWC tissue water content VOI volume of interest

Complications of Primary Upper Gastrointestinal Lymphoma

This study reports the gastrointestinal complications of primary upper gastrointestinal lymphoma (PUGIL). of thirty-three patients with PUGIL, twenty-one (64%) had complications. These included upper gastrointestinal bleeding, narrowing of the lumen, malabsorption and obstructive jaundice in 11, 9, 5 and 4 patients, respectively. Perforation and fistula formation were encountered in one patient each. THe patients with complications were treated with chemotherapy alone (13), in combination with surgery (6), or combination with surgery and radiotherapy (2). Only in one of four patients was the mortality due to gastrointestinal complication in the form of severe upper gastrointestinal hemorrhage. The remaining three patients died of refractory septicemia. In conclusion, there was a high rate of gastrointestinal complications in patients with PUGIL in our population. Mortality was commonly unrelated to these gastrointestinal complications.