The island coal face arises in coal mines with the purpose of preventing gas explosion or maintaining the balance between mining and tunneling. However, its particular stress conditions in the surrounding rock may inc...The island coal face arises in coal mines with the purpose of preventing gas explosion or maintaining the balance between mining and tunneling. However, its particular stress conditions in the surrounding rock may increase the difficulty of stress control in the coal face and in its mining roadways, especially when the coal seam, the roof, and the floor have rock-burst propensities, The high energy accumulated in the island coal face and in its roof and floor will intensify rock-burst propensity or even induce rock burst, which further result in great casualties and financial losses. Taking island coal face 2321 in Jinqiao coal mine as a case, we propose a method for the prediction of rock-burst-threatened areas in an island coal face with weak rock-burst propensity. Based on the anaHysis of the movement of the overlying roof and characteristics of stress distribution, this method combined numerical simulation with drilling bits to ensure the prediction accuracy. The effects of coal pillars with different widths on the mitigation of stress concentration in the coal face and on the prevention of rock burst are analyzed together with the mech- anism behind. Finally, corresponding measures against the rock burst in the island coal face are proposed.展开更多
Forecasts of record values are usually avoided unless expected to occur with great confidence within less than 48 hours, or in association with an extreme event such as a hurricane. Otherwise the risk of a high visibi...Forecasts of record values are usually avoided unless expected to occur with great confidence within less than 48 hours, or in association with an extreme event such as a hurricane. Otherwise the risk of a high visibility false alarm outweighs the benefit of a correct early hit. Yet automated forecasts may occasionally include record values beyond day 2, which forecasters may choose to downplay, or not. In Canada, forecasters keep their focus on high impact weather for days l and 2, so that forecasts for day 3 and beyond are mostly automated and usually released after a quick glance. So a process was designed to bring up cases where automated temperature forecasts exceed known records for a number of sites, with the sole purpose of alerting the forecasters who may decide whether or not modifications are needed before release. As a by-product it is found that some record temperature forecasts are issued every day in Canada, even more records are actually observed, and in recent years there have been twice as many new high records as low ones. We discuss the origin of the process, its logics, its current status, interesting findings, and possible improvements.展开更多
Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic...Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.展开更多
In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surr...In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B first discovers x＇s k nearest neighbours, and then applies progressive sampling to the selected neighbours to train a set of base classifiers, by using a given very weak （VW） learner. At the last stage, x is labeled as the most frequently voted class of all base classifiers. Finally, we apply the proposed L3B to computer forensic.展开更多
基金provided by the National Natural Science Foundation of China (Nos.51304208 and 51474208)
文摘The island coal face arises in coal mines with the purpose of preventing gas explosion or maintaining the balance between mining and tunneling. However, its particular stress conditions in the surrounding rock may increase the difficulty of stress control in the coal face and in its mining roadways, especially when the coal seam, the roof, and the floor have rock-burst propensities, The high energy accumulated in the island coal face and in its roof and floor will intensify rock-burst propensity or even induce rock burst, which further result in great casualties and financial losses. Taking island coal face 2321 in Jinqiao coal mine as a case, we propose a method for the prediction of rock-burst-threatened areas in an island coal face with weak rock-burst propensity. Based on the anaHysis of the movement of the overlying roof and characteristics of stress distribution, this method combined numerical simulation with drilling bits to ensure the prediction accuracy. The effects of coal pillars with different widths on the mitigation of stress concentration in the coal face and on the prevention of rock burst are analyzed together with the mech- anism behind. Finally, corresponding measures against the rock burst in the island coal face are proposed.
文摘Forecasts of record values are usually avoided unless expected to occur with great confidence within less than 48 hours, or in association with an extreme event such as a hurricane. Otherwise the risk of a high visibility false alarm outweighs the benefit of a correct early hit. Yet automated forecasts may occasionally include record values beyond day 2, which forecasters may choose to downplay, or not. In Canada, forecasters keep their focus on high impact weather for days l and 2, so that forecasts for day 3 and beyond are mostly automated and usually released after a quick glance. So a process was designed to bring up cases where automated temperature forecasts exceed known records for a number of sites, with the sole purpose of alerting the forecasters who may decide whether or not modifications are needed before release. As a by-product it is found that some record temperature forecasts are issued every day in Canada, even more records are actually observed, and in recent years there have been twice as many new high records as low ones. We discuss the origin of the process, its logics, its current status, interesting findings, and possible improvements.
基金Project(2013CB036004)supported by the National Basic Research Program of ChinaProject(51378510)supported by the National Natural Science Foundation of China
文摘Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.
基金the National High Technology Research and Development Program(863) of China(No.2007AA01Z456)the National Natural Science Foundation of China(No.60703030)
文摘In this paper, we study the problem of employ ensemble learning for computer forensic. We propose a Lazy Local Learning based bagging （L3B） approach, where base learners are trained from a small instance subset surrounding each test instance. More specifically, given a test instance x, L3B first discovers x＇s k nearest neighbours, and then applies progressive sampling to the selected neighbours to train a set of base classifiers, by using a given very weak （VW） learner. At the last stage, x is labeled as the most frequently voted class of all base classifiers. Finally, we apply the proposed L3B to computer forensic.
