Automatic diagnosis of strawberry water stress status based on machine vision

Water stress status of plants is very important for irrigation scheduling.However,plant water stress status monitoring has become the bottleneck of irrigation scheduling.In this study,an automatic water stress status monitoring method for strawberry plant was proposed and realized using combined RGB and infrared image information.RGB image and infrared images were obtained using RGB digital camera and infrared thermal camera,which were placed in a fixed shell in parallel.In the first experimental stage,three kinds of water stress treatments were carried out on three groups of strawberry plants,and each group includes three repetitions.Single point plant temperature,dry surface temperature,wet surface temperature were measured.In the second experimental stage,the infrared and visible light images of the canopy leaves were obtained.Meanwhile,plant temperature,dry surface temperature,wet surface temperature,and stomatal conductance were measured not only for single point but also for plant area temperature measurement.Fusion information of infrared image and visible light image was analyzed using image processing technology,to calculate the average temperature of plant areas.Based on single point temperature,area temperature,dry surface temperature and wet surface temperature of the plant,single point crop water stress index(CWSI)and area CWSI were calculated.Through analysis of variance(ANOVA),the experimental results showed that CWSI measured for plants under different treatments,were significantly different.Through correlation analysis,the experimental results showed that,determination coefficient between area CWSI and the corresponding stomatal conductance of three strawberry groups were 0.8834,0.8730 and 0.8851,respectively,which were larger than that of single-point CWSI and stomatal conductance.The results showed that area CWSI is more suitable to be used as the criteria for automatic diagnosis of plants.

A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

An Automated Approach for Software Fault Detection and Recovery

Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.

Classification of traditional Chinese medicine constitution based on facial features in color images

Objective:To explore the possible correlation between traditional Chinese medicine(TCM)constitution and facial features in color images and to improve the accuracy of automated constitution classification.Methods:Color images were taken of 5150 individuals of different professions.Automated face detection and key point positioning were performed on the collected images,which were then transformed into a standard size.The relationship between facial features and TCM constitution based on the red,green,blue(RGB)pixel and the local binary pattern(LBP)texture features was explored.Results:The overall accuracy rate and robustness of TCM constitution classification based on RGB features were low.Classification results of the phlegm-dampness,damp-heat,blood stasis,and balance constitutions achieved high accuracy rates.Classification accuracy rate using the LBP texture feature was higher than that of the RGB feature,with the best accuracy observed for the balance constitution.Conclusion:Application of computer image acquisition and processing of facial features may serve as an adjunct to the TCM diagnostic method of inspection.

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems.It calls for more research attention on three aspects:(i)application of control and detection uni ed framework to enhancing the diagnosis capability of feedback control systems,(ii)projection-based fault detection,and complementary and explainable applications of projection-and machine learning-based techniques,and(iii)system performance degradation detection that is of elemental importance for today's automatic control systems.Some ideas and conceptual schemes are presented and illustrated by means of examples,serving as convincing arguments for research e orts in these aspects.They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network

The smart water meter in water supply network can directly affect water production and usage when faults occur.The traditional method of fault detection is inefficient with time lagging,which is not helpful for modernization of water supply system.The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply.In this paper,an automatic fault diagnosis method for the smart device is proposed based on BP neural network.And it was applied on Google Tensorflow platform.Fault symptom vectors were constructed using water meter status data and were used to train the neural network model.In order to improve the learning convergence speed and fault classification effect of the network,a method of weighted symptom was also employed.Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.