Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application

A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.

Over-excavation forecast of underground opening by using Bayes discriminant analysis method

A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.

Auto recognition of carbonate microfacies based on an improved back propagation neural network

Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.

A Comprehensive Evaluation of Industrial Land Intensive Use in Hubei Province Based on Typical Industrial Enterprises

Here, we used Bayes methods to evaluate land use intensity in 365 typical industrial enterprises across nine manufacturing industries in Hubei, China and calculated the potentiality of enterprises with moderate and low intensive land use. We did this by constructing an evaluation index system of intensive land use for industrial enterprises comprised of four sub-objective layers including land use structure, land use intensity, land input and land output, and nine element layers. We found that among 365 enterprises, 82 enterprises use land intensively, accounting for 22.47% of our sample; 215 enterprises use land moderately or have low use, accounting for 58.90% of our samples. Enterprises with intensive use tended to be metal smelting and rolling processing industries and communication equipment, computer and other electronic equipment manufacturing industries. Enterprises with moderate and low use tended to be from the special equipment manufacturing industry, pharmaceutical manufacturing industry and chemical raw materials and chemical industry. The potential area for enterprises with moderate and low land use is approximately 865.70 ha and accounts for 42.38% of total current land approved, indicating that their potentiality for intensive use is large.

Prehospital Identification of Stroke Subtypes in Chinese Rural Areas

Background： Differentiating intracerebral hemorrhage （ICH） from cerebral infarction as early as possible is vital tbr the timely initiation of different treatments. This study developed an applicable model for the ambulance system to differentiate stroke subtypes. Methods： From 26,163 patients initially screened over 4 years, this study comprised 1989 consecutive patients with potential first-ever acute stroke with sudden onset of the focal neurological deficit, conscious or not, and given ambulance transport for admission to two county hospitals in Yutian County of Hebei Province. All the patients underwent cranial computed tomography （CT） or magnetic resonance imaging to confirm the final diagnosis based on stroke criteria. Correlation with stroke subtype clinical features was calculated and Bayes＇ discriminant model was applied to discriminate stroke subtypes. Results： Among the 1989 patients, 797,689, 109, and 394 received diagnoses of cerebral infarction, ICH, subarachnoid hemorrhage, and other forms of nonstroke, respectively. A history of atrial fibrillation, vomiting, and diabetes mellitus were associated with cerebral infarction, while vomiting, systolic blood pressure _〉180 mmHg, and age 〈65 years were more typical of ICH. For noncomatose stroke patients, Bayes＇ discriminant model for stroke subtype yielded a combination of multiple items that provided 72.3% agreement in the test model and 79.3% in the validation model; for comatose patients, corresponding agreement rates were 75.4% and 73.5%. Conclusions： The model herein presented, with multiple parameters, can predict stroke subtypes with acceptable sensitivity and specificity before CT scanning, either in alert or comatose patients. This may facilitate prehospital management for patients with stroke.