DETECTION METHOD OF SPOT WELDING BASED ON MULTI-INFORMATION FUSION AND FRACTAL

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.

A homological multi-information fusion method for processing gastric tumor tissue pathological images

A homological multi-information image fusion method was introduced for recognition of the gastric tumor pathological tissue images.The main purpose is that fewer procedures are used to provide more information and the result images could be easier to be understood than any other methods.First,multi-scale wavelet transform was used to extract edge feature,and then watershed morphology was used to form multi-threshold grayscale contours.The research laid emphasis upon the homological tissue image fusion based on extended Bayesian algorithm,which fusion result images of linear weighted algorithm was used to compare with the ones of extended Bayesian algorithm.The final fusion images are shown in Fig 5.The final image evaluation was made by information entropy,information correlativity and statistics methods.It is indicated that this method has more advantages for clinical application.

Contact detection with multi-information fusion for quadruped robot locomotion under unstructured terrain

Reliable foot-to-ground contact state detection is crucial for the locomotion control of quadruped robots in unstructured environments.To improve the reliability and accuracy of contact detection for quadruped robots,a detection approach based on the probabilistic contact model with multi-information fusion is presented to detect the actual contact states of robotic feet with the ground.Moreover,a relevant control strategy to address unexpected early and delayed contacts is planned.The approach combines the internal state information of the robot with the measurements from external sensors mounted on the legs and feet of the prototype.The overall contact states are obtained by the classification of the model-based predicted probabilities.The control strategy for unexpected foot-to-ground contacts can correct the control actions of each leg of the robot to traverse cluttered environments by changing the contact state.The probabilistic model parameters are determined by testing on the single-leg experimental platform.The experiments are conducted on the experimental prototype,and results validate the contact detection and control strategy for unexpected contacts in unstructured terrains during walking and trotting.Compared with the body orientation under the time-based control method regardless of terrain,the root mean square errors of roll,pitch,and yaw respectively decreased by 60.07%,54.73%,and 64.50%during walking and 73.40%,61.49%,and 61.48%during trotting.

Safety Protection Method of Rehabilitation Robot Based on fNIRS and RGB-D Information Fusion

In order to improve the safety protection performance of the rehabilitation robot,an active safety protection method is proposed in the rehabilitation scene.The oxyhemoglobin concentration information and RGB-D information are combined in this method,which aims to realize the comprehensive monitoring of the invasion target,the patient’s brain function movement state,and the joint angle in the rehabilitation scene.The main focus is to study the fusion method of the oxyhemoglobin concentration information and RGB-D information in the rehabilitation scene.Frequency analysis of brain functional connectivity coefficient was used to distinguish the basic motion states.The human skeleton recognition algorithm was used to realize the angle monitoring of the upper limb joint combined with the depth information.Compared with speed and separation monitoring,the protection method of multi-information fusion is safer and more comprehensive for stroke patients.By building the active safety protection platform of the upper limb rehabilitation robot,the performance of the system in different safety states is tested,and the safety protection performance of the method in the upper limb rehabilitation scene is verified.

Application of Traditional Chinese Medicine Four-diagnostic auxilia-ry apparatus in evaluation of health status and clinical treatment

OBJECTIVE: To evaluate the application of Traditional Chinese Medicine(TCM) Four-diagnostic Auxiliary Apparatus in disease diagnosis.METHODS: The liver cancer patients and healthy controls were recruited from Shanghai Integrated Chinese and Western Medicine Hospital and Beijing University of Traditional Chinese Medicine, respectively. Then, the included subjects were diagnosed by the Four-diagnostic auxiliary apparatus.RESULTS: Thirty liver cancer patients and 30 paired healthy controls were enrolled in this study. Based on the apparatus, the pulse wave velocity was significantly higher in patients compared with controls(P < 0.05). The number of patients with purple tongue and ecchymosis were more than controls(P < 0.05). The number of patients(10%) with yellow tongue coating were higher than the controls(0%). Patients were inclined to be with water type and fire type constitution.CONCLUSION: TCM Four-diagnostic auxiliary apparatus can be applied in clinical diagnosis of body constitution and health status of subjects. It promotes the accuracy and speed for disease diagnosis and TCM standardization.