Ground characterization and roof mapping: Online sensor signal-based change detection

Measurement while drilling systems are becoming an important part of excavation operations for rock characterization and ground support design that require reliable information on rock strength and location ＆ frequency of joints or voids. This paper focuses on improving rock characterization algorithms for instrumented roof-boRer systems. For this purpose, an improved void detection algorithm is proposed, where the underlying theory is built upon the concept of mean change detection based on the feed pressure signals. In addition, the application of acoustic sensing for void detection is examined and it is shown that the variance of the filtered acoustic signal is correlated to the strength of the material being drilled. The proposed algorithm has been validated on the data collected from full-scale drilling tests in various concrete and rock samples at the J. H. Fletcher facility.

Combined measurement of thyroid and plasma homocysteic acid for detecting the severity of vascular dementia

BACKGROUND: Recent researches demonstrate that onset of cerebral infarction always accompanies with obvious changes of function of thyroid axis; while, high-homocysteic acidemia has been proved as one of risk factors of vascular dementia and Alzheimer disease. Meanwhile, it is found that level of plasma homocysteic acid is positive correlation with injured degrees of cognitive function and brain damage. OBJECTIVE: To observe the changes of function of thyroid and level of homocysteic acid among patients with vascular dementia and compare with those patients without dementia cerebral infarction. DESIGN: Randomized grouping and contrast observation. SETTING: Department of Neurology, People’s Hospital Affiliated to Yunyang Medical College, South China Hospital of Wuhan University. PARTICIPANTS: A total of 38 patients with vascular dementia were hospitalized in the Department of Neurology, People’s Hospital Affiliated to Yunyang Medical College from February 2004 to December 2005. All patients met the diagnostic criteria of the Fourth Edition of Diagnostic and Statistical Manual of Mental Disorder (DSM-IV) established by American Psychiatric Association. Based on educational degrees, Mini-mental Status Examination (MMSE) was classified into illiteracy (≤ 17 points), education of primary school (educational duration ≤ 6 years, ≤ 24 points) and education of middle school or above (educational duration > 6 years, ≤ 24 points). Forty patients with non-dementia cerebral infarction were regarded as the control group and checked with CT examination. There were no significant differences of sex and age between the two groups. All patients and relatives were provided the consent. METHODS: Within 24 hours after hospitalization, patients with vascular dementia received MMSE scores, and the degrees were classified based on the scoring results: mild (20-24 points), moderate (10-19 points) and severe (below 10 points). Levels of thyroxine were measured with radioimmunodetection and content of homocysteic acid was measured with high performance liquid chromatogram (HPLC) electrochemical detection. MAIN OUTCOME MEASURES: Levels of homocysteic acid and thyroxine among patients with vascular dementia and non-dementia cerebral infarction. RESULTS: A total of 38 patients with vascular dementia and 40 patients with non-dementia cerebral infarction were involved in the final analysis. ① Levels of triiodothyronine (T3), thyroxine (T4) and free T3 (FT3) were (0.9±0.4) μg/L, (92.9±26.4) μg/L and (3.9±1.8) pmol/L in vascular dementia group respectively, which were higher than those in control group [(1.3±0.3) μg/L, (110.2±28.7) μg/L, (7.2±2.1) pmol/L, t =2.766 6-7.433 6, P < 0.01]; while, level of homocysteic acid was (29.57±7.12) μmol/L in vascular dementia group, which was higher than that in control group [(24.53±4.98) μmol/L, t =3.637 7, P < 0.01]. There were no significant differences of free T4 (FT4) and thyrotropic-stimulating hormone (TSH) between the two groups (P > 0.05). ② Levels of FT3 of patients with mild, moderate and severe vascular dementia were (1.0±0.2), (0.9±0.1) and (0.8±0.1) μg/L, respectively; levels of homocysteic acid were (26.52±4.84), (29.59±5.56) and (32.71±6.17) μmol/L, respectively. There were significant differences among patients at the three degrees of vascular dementia (F =3.59-32.4, P < 0.01). However, there were no significant differences of T4, FT4 and TSH among the three kinds of patients (P > 0.05). CONCLUSION: Levels of thyroxine of patients with vascular dementia decrease; however, levels of homocysteic acid increase. Therefore, the results can indirectly reflect severities of vascular dementia.

An Overview of Passive and Active Dust Detection Methods Using Satellite Measurements

In this paper,the methods to detect dust based on passive and active measurements from satellites have been summarized.These include the visible and infrared（VIR） method,thermal infrared（TIR） method,microwave polarized index（MPI） method,active lidar-based method,and combined lidar and infrared measurement（CLIM） method.The VIR method can identify dust during daytime.Using measurements at wavelengths of 8.5,11.0,and 12.0 fan,the TIR method can distinguish dust from other types of aerosols and cloud,and identify the occurrence of dust over bright surfaces and during night.Since neither the VIR nor the TIR method can penetrate ice clouds,they cannot detect dust beneath ice clouds.The MPI method,however,can identify about 85%of the dust beneath ice clouds.Meanwhile,the active lidar-based method,which uses the Cloud-Aerosol Lidar with Orthogonal Polarization（CALIOP） data and five-dimensional probability distribution functions,can provide very high-resolution vertical profiles of dust aerosols.Nonetheless,as the signals from dense dust and thin clouds are similar in the CALIOP measurements,the lidar-based method may fail to distinguish between them,especially over dust source regions.To address this issue,the CLIM method was developed,which takes the advantages of both TIR measurements（to discriminate between ice cloud and dense dust layers） and lidar measurements（to detect thin dust and water cloud layers）.The results obtained by using the new CLIM method show that the ratio of dust misclassification has been significantly reduced.Finally,a concept module for an integrated multi-satellites dust detection system was proposed to overcome some of the weaknesses inherent in the single-sensor dust detection.

Development of a low-cost measurement system for cutting edge profile detection

The use of a compact disk （CD） pickup head as a displacement measurement system is described. The components contained in a pickup head are explained and how they are combined to obtain the functionality of a pickup head. The application of measuring a knife edge profile is introduced. The results reveal some insuffiencies with the current system. The cutting edge＇s radius of curvature can be estimated.

Feedforward Control for Wind Turbine Load Reduction with Pseudo-LIDAR Measurement

A gain-scheduled feedforward controller, based on pseudo-LIDAR （light detection and ranging） wind speed measurement, is designed to augment the baseline feedback controller for wind turbine＇s load reduction in above rated operation. The pseudo-LIDAR measurement data are generated from a commercial software- Bladed using a designed sampling strategy. The nonlinear wind turbine model has been simplified and linearised at a set of equilibrium operating points. The feedforward controller is firstly developed based on a linearised model at an above rated wind speed, and then expanded to the full above rated operational envelope by employing gain scheduling strategy. The combined feedforward and baseline feedback control is simulated on a 5MW industrial wind turbine model. Simulation studies demonstrate that the proposed control strategy can improve the rotor and tower load reduction performance for large wind turbines.

Robust multitask feature learning for amnestic mild cognitive impairment diagnosis based on multidimensional surface measures

Previous studies have shown that amnestic mild cognitive impairment(aMCI)involves in the morphological abnormalities of multiple regions,including cortical thickness,sulcus depth,surface area,gray matter volume,jacobian metric and average curvature.All the measures have unique neuropathological and genetic meanings.However,most existing methods simply average or concatenate these measures when constructing the classifiers,which may include redundant information and ignore the relationships among them.In this study,we treat each measure as a task in our multitask learning framework.Considering the actual situation that we do not know the correlation between tasks in advance,we use a robust multitask feature learning(rMTFL)method to select a group of features among correlated measures and provide additional information by identifying outlier tasks at the same time.Then,we train several SVM classifiers and for each measure,we input the selected features into the corresponding SVM classifier.Finally,we use an ensemble classification strategy to combine the results of these classifiers based on the accuracy to make the final prediction.We use the leave-one-out cross-validation to evaluate our proposed method with 46 amnestic mild cognitive impairment(aMCI)and 52 normal controls(NC).The results show that rMTFL algorithm is superior to the group lasso method and average curvature is the outlier task based on multidimensional surface measures.