Intelligent Measurement Method Research Articles

Intelligent measurement method of transition resistance of insulation fasteners in urban rail transit system based on SSA-optimized algorithm

Rail-to-earth transition resistance characterizes the amplitude of stray current leakage in urban rail transit system using electric traction, which is critical for evaluating failure risk of metal infrastructures caused by electrochemical corrosion. However, detection method commonly used in the field exhibit measurement errors resulting from the limitation of measuring principle and measuring distance, which will seriously affect the accuracy of electrical insulation performance evaluation of the whole system. In view of this, this paper proposes a data-driven method for accurately measuring rail-to-earth transition resistance in urban rail transit system based on CDEGS-based simulation and SSA-optimized algorithm. CDEGS model was built to construct database for machine learning of SSA-RF network structure. SSA algorithm was used in the measurement model to optimize the topology of the random forest (RF) structure. Proposed ensemble in this paper was proved to conduct transition resistance regression with a mean relative accuracy rate of 98.85 %. Results of sensitivity analysis indicate that proposed measurement method shows acceptable robustness, which allows the algorithm parameters to fluctuate within a certain range when applied in the field. Besides, in the case of uniform and non-uniform insulation degradation, the proposed method shows good performance in assessing insulation performance. This provides a feasible foundation for future applications of the proposed method in failure risk evaluation of buried infrastructures.

Dynamic identification of coupler force of heavy haul locomotive: An effective and long-term intelligent measurement method

Longitudinal impulse, as a common dynamic problem of heavy haul trains, could induce the lateral instability of the coupler and the separation of the locomotive and wagon connection and thus seriously hinder the train running safety. The coupler force undergoes complex changes during longitudinal impulse transmission and exhibits strong nonlinear characteristics, which poses significant challenges to the identification of the coupler force. To address these issues, this paper reports a novel quantitative method for the coupler force identification of heavy haul locomotives. Firstly, the moving average filtering method is used to preprocess the signal of longitudinal relative displacement between the coupler and carbody (LRDCC), and the approximate relative velocity (ARV) feature is also extracted from the preprocessed signal. Then, a Convolutional Neural Network (CNN) model is constructed to extract high-level features from LRDCC and ARV. The model with randomly initialized weight parameters does not require additional training and thus reduces the computational cost. Finally, an Extreme Learning Machine (ELM) is used as the regression layer to process the extracted high-level features and further improve the identification performance. Additionally, the dung beetle algorithm is used to obtain the optimal model parameters to improve the coupler force identification performance. Not only a dynamics simulation but a field test is carried out to show that the proposed method could accurately detect the coupler force of heavy haul locomotives. Furthermore, the proposed method shows promising prospects in replacing the traditional monitoring method under the actual long-term working conditions of trains.

A High-Confidence Intelligent Measurement Method for Aero-Engine Oil Debris Based on Improved Variational Mode Decomposition Denoising

This paper presents an effective method for measuring oil debris with high confidence to ensure the wear monitoring of aero-engines, which suffers from severe noise interference, weak signal characteristics, and false detection. First, an improved variational mode decomposition algorithm is proposed, which combines wavelet transform and interval threshold processing to suppress the complex noise interference on the signal. Then, a long-short-term memory neural network with deep scattering spectrum preprocessing is used to identify the signal characteristics under the multi-resolution analysis framework. The optimal hyperparameters are automatically configured using Bayesian optimization to solve the problem of weak, distorted, and hard-to-extract signal characteristics. Finally, a detection algorithm based on multi-window fusion judgment is applied to improve the confidence of the detection process, reduce the false detection and false alarm rate, and calculate the debris size information according to the sensor principle. The experimental results show that the proposed method can extract debris signals from noise with a signal-to-noise ratio improvement of more than 9 dB, achieve a high recognition accuracy of 99.76% with a missed detection rate of 0.24%, and output size information of debris to meet the need for aero-engine oil debris measurement.

Deep learning–based intelligent measurement methods and system for CMM

Extensive manual intervention and management are typically required when using coordinate measuring machines (CMMs) for inspections in production lines leading to low efficiency. This study presents a deep learning–based intelligent measurement method and system for measuring typical features (including holes, cylinders, balls, steps, and slots) of common components to improve inspection efficiency. This method combines vision sensors and a trigger probe. The You Only Look Once algorithm was employed to learn and achieve intelligent detection of features. An image-matching algorithm based on image inverse perspective transformation was designed, and the ant colony algorithm was implemented to optimize the measurement sequence. Then, an automatic approach for feature measurement path planning was designed. The presented system was tested using CMM, and a component with multiple typical features was measured. Results show that this method and system can be efficaciously implemented for intelligent measurement of typical features.

High-accuracy thermophysical property measurement technique based on Labview-3ω method

High-accuracy thermophysical property measurement technique plays a significant role in the development of new materials and structures. Especially with the emerging nanomaterials, techniques that can provide thermophysical property measurement for nanoscale structures become urgent needed. Here, a Labview-3ω technique with automatic data acquisition and thermal measurement functions on the basis of the conventional 3ω technique is developed, which greatly improves the measurement speed, accuracy, and uncertainty for a wide range of nanomaterials. This high-speed, automatic, and intelligent measurement method provides great application potential for a thermophysical property measurement of various materials.

Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation.

To explore the application of the proposed intelligent image processing method in the diagnosis of aortic coarctation computed tomography angiography (CTA) and to clarify its value in the diagnosis of aortic coarctation based on the diagnosis results. Fifty-three children with coarctation of the aorta (CoA) and forty children without CoA were selected to constitute the study population. CTA was performed on all subjects. The minimum diameters of the ascending aorta, proximal arch, distal arch, isthmus, and descending aorta were measured using manual and intelligent methods, respectively. The Wilcoxon signed-rank test was used to analyze the differences between the two measurements. The surgical diagnosis results were used as the gold standard, and the diagnostic results obtained by the two measurement methods were compared with the gold standard to quantitatively evaluate the diagnostic results of CoA by the two measurement methods. The Kappa test was used to analyze the consistency of intelligence diagnosis results with the gold standard. Whether people have CoA or not, there was a significant difference (p < 0.05) in the measurements of the minimum diameter at most sites using the two methods. However, close final diagnoses were made using the intelligent method and the manual. Meanwhile, the intelligent measurement method obtained higher accuracy, specificity, and AUC (area under the curve) compared to manual measurement in diagnosing CoA based on Karl's classification (accuracy = 0.95, specificity = 0.9, and AUC = 0.94). Furthermore, the diagnostic results of the intelligence method applied to the three criteria agreed well with the gold standard (all kappa ≥ 0.8). The results of the comparative analysis showed that Karl's classification had the best diagnostic effect on CoA. The proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.

An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network

In the laboratory, using traditional methods to measure the resonant frequency of transducers is inefficient and costly. For this, in this study, the feature variables determining the resonant frequency of transducer was firstly analyzed with the equivalent circuit method. Based on the input–output mapping function of back propagation (BP) neural network, the nonlinear relationship model between feature variables and the resonant frequency was established. Then, particle swarm optimization (PSO) was used to optimize the initial weight and threshold of BP neural network to obtain the best parameter combination, thereby improving the output accuracy of BP neural network. The experimental data showed that it is effective to measure the resonant frequency of the transducer by PSO-BP neural network, and the measurement error was 1 Hz. Compared to traditional measurement method, the intelligent method has the advantages of low cost, high efficiency, fast response speed, and strong robustness.

RETRACTED ARTICLE: An adaptive QoS computation for medical data processing in intelligent healthcare applications

Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4–10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (− 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.

Research on Intelligent Measurement Methods of Land Area Inundated by Seawater in Coastal Cities under Rainy Season Conditions

Huang, H., 2018. Research on intelligent measurement methods of land area inundated by seawater in coastal cities under rainy season conditions. In: Ashraf, M.A. and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Responses to Human and Climatic Changes throughout Asia.At present, intelligent measurement methods for accurately measuring land area submerged by seawater with GIS is difficult. Therefore, this article proposes a kind of intelligent measuring method for land area submerged by seawater in a coastal city based on improved Zernike moment within a rainy season environment. Based on Gauss projections, our research used the arithmetic mean method and the Pauta criterion to build the measurement model, and then used the edge detection operator to detect the land edge that was submerged by seawater, through a gray-weighted operation, which provided calculations of the basic parameters of the land submerged by the sea through interlayer interpolation and the arc tangent extrapolation methods. Experimental result shows that the method proposed in this article has a high degree of measurement accuracy.

Intelligent measurement methods in technical diagnosis and quality assurance - a comparison

Faults in technical systems cannot be measured directly. Thus, a variety of intelligent measurement methods has been developed to detect faults. Two widely used procedures-the vibration analysis and the parameter estimation method, and their applications to technical diagnosis - will be described and compared. The paper deals with the application of the vibration analysis to the fault detection in a gear and the application of the parameter estimation to the fault detection in an electric drive system. The paper shows that faults in auxiliary systems canbe detected better by the vibration analysis, whereas faults in the drive system can be detected by the parameter estimation method. A combination of both methods provides better test results than conventional procedures.