Distance Measurement Method Research Articles

Distance Measurement of Contra-Rotating Rotor Blades with Ultrasonic Transducers.

Coaxial rotor helicopters have great potential in civilian and commercial uses, with many advantages, but challenges remain in the accurate measurement of rotor blades' distance to prevent blade collision. In this paper, a blade tip distance measurement method based on ultrasonic measurement window and phase triggering is proposed, and the triggering time of the transmitter is studied. Due to the complexity of the measured signal, bandpass filtering and a time-of-flight (TOF) estimation based on the power density of the received signal are utilised. The method is tested on an experimental test platform with a pair of 200 kHz ultrasonic transducers. The experimental results show that the maximum ranging error is less than 1.0% for the blade tip distance in a range of 100-1000 mm. Compared with the amplitude threshold method, the proposed TOF estimation method works well on the received signal with a low SNR and improves the ranging accuracy by about 5 mm when the blade tip distance is larger than 500 mm. This study provides a good reference for the accurate measurement of rotor blade tip distance, and gives a solution for ranging high-speed rotating objects.

Fault Distance Measurement Method Based on Wavelet Energy Spectrum and BWO Algorithm Optimized CNN-GRU Hybrid Neural Network

Aiming at the problems such as noise interference in the current fault ranging methods for flexible DC transmission lines, a single-ended intelligent fault ranging method is proposed as a hybrid neural network based on wavelet energy spectrum and BWO algorithm to optimize the multi-head attention mechanism of CNN combined with gated recurrent unit GRU. First, the correlation between wavelet spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the model input of the neural network. Second, the hybrid neural network model is constructed and trained to mine the deep fault information in the time series, and the parameters of the hybrid neural network model with added multi-head attention are optimized using the iterative optimization search of the beluga algorithm to achieve fast and accurate positioning of the fault distance. Finally, the network is trained using the constructed four-terminal Zhangbei flexible DC transmission system model to collect data, and the experimental results prove that the method has high distance measurement accuracy, strong anti-interference ability and generalization ability, and a certain degree of resistance to transition resistance.

Measurement method of virtual image distance for a head-mounted display based on a variable-focus liquid lens

The distance from the virtual image to the human eye is an important factor in measuring the comfort of a head-mounted display (HMD). However, accurately measuring their distance is challenging due to the dynamic changes in virtual presence and distance. In this paper, we proposed a virtual image distance measurement prototype based on a variable-focus liquid lens and derived a virtual image distance calculation model. We built a variable-focus liquid lens experimental platform to verify the method’s correctness. In addition, we proposed an improved optimization algorithm that can efficiently and accurately search for the optimal focal length corresponding to the maximum sharpness moment of the virtual image within the focal length value space. Verified in an experimental scene of 0.5 m to 3.5 m, we observed that the error between the object image distance and the virtual image distance at the same focal length is about 5 cm. The proposed virtual image distance measurement method can accurately measure the distance value of the virtual image in the HMD. This method can be widely used in virtual and augmented reality, especially in the task of constructing realistic scenes.

Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software

Measuring the vegetation structure of a plant with the help of 3D images is a non-destructive method that can be used to monitor the plant’s status during the growing season. Structure from motion (SfM) is a popular optical distance measurement method that uses 2D images to create 3D models. Identifying factors that influence the precision of SfM is important due to its increasing use in phenotyping. The primary objectives of this study were, i) to scrutinize the impact of imaging parameters—including, color checkerboard location, overlap size of images, and background filtering—on the quality indices of 3D models, and ii) to attain precise measurements of leaf area, stem diameter, and height through the combination of images captured from different camera shooting angles. The three nursery tomato plants were photographed at every five-degree rotation of the turntable, five different locations of color checkerboards, and five camera shooting angles. Results indicated the color checkerboard’s presence and position affect the quality indices of the 3D models. Visually and by the lowest mean keypoint size value, the least noisy 3D model was obtained when the wall of the plant pot was covered with a checkerboard. When the base-to-distance (b/y) ratio as the image overlap index increased from 0.1 to 0.2, the generation time of the 3D models decreased by more than two times. Optimal Mean Absolute Percentage Error (MAPE) values of 1.08 %, 1.01 %, and 1.38 % were achieved for the estimation of leaf area, stem diameter, and height, respectively. This was recorded by using images taken at 0°, 30°, and 105° vertical azimuth angles. Constructing a 3D model by integrating images captured from other angles or more azimuth angles represents numerous visual challenges and leads to problems of point mismatch, which affects the accuracy of measurements.

A New Distance Measurement Method for Perceptual Image Hashing

A large number of perceptual image hashing schemes have been developed in recent decades, and they usually calculated the hash distances using Euclidean distance, correlation coefficient, normalized Hamming distance, etc. These hash distances are not customized measurements for hash sequences, which are all single values. To this end, a new distance measurement method, i.e. a distance vector instead of a single value, is proposed in this paper. Since a hash sequence can be regarded as the compressed representation of image perceptual content, the correlation between images is maintained between hash sequences. In our method, the correlation between hash sequences is captured by residuals and co-occurrence matrix to form a distance vector. The correlation differences between perceptually similar and distinct images can be captured in the obtained distance vectors. Using a binary classifier, it is easy to classify the distance vectors of perceptually similar image pairs and perceptually distinct image pairs. As a result, content authentication accuracy of perceptual image hashing can be effectively increased, which is verified by experimental results.

A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively.

Fault distance measurement method for wind power transmission lines based on improved NSGA II

A fault distance measurement method that is suitable for long-distance wind power transmission lines is proposed by this paper. By combining the voltage and current at both ends of the line during normal operation, a dynamic line parameter correction method based on improved NSGA II is proposed to ensure measurement errors caused by parameter changes are avoided. Based on the construction of the fault distance measurement function, a global optimization method that is based on adaptive golden section search is proposed, where only the power frequency measurement data at both ends of the bus is required and synchronization is not required for the measurement of fault distance. A large number of experimental results demonstrate that the method this paper proposes is highly adaptable, with a relative distance measurement error of less than 0.2 km, and high accuracy is maintained in distance measurement even with certain degrees of noise interference.

A Method for Inspiring Radical Innovative Design Based on Cross-Domain Knowledge Mining

The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device.

Application of time series analysis to classify therapeutic breathing patterns

ObjectiveCompare various methods for measuring time series similarity in order to classify referenced therapeutic breathing patterns (BP) used in respiratory disorder rehabilitation. MethodsThis experimental study involved the collection of respiratory signals during specified breathing exercises conducted with healthy volunteers. The study employed a screening phase using a k-NN classifier and eight distance measurement methods, including Minkowski Distance, Dynamic Time Warping-DTW (including FastDTW and constrained-cDTW variations), Longest Common Subsequence-LCSS, Edit Distance on Real Sequences-EDR, Time Warp Edit Distance-TWEED, and Minimum Jump Costs-MJC. Two distinct approaches were employed for classifying therapeutic BP based on time series similarity: (1) using the k-Shape algorithm for clustering, and 2) integrating methods to represent therapeutic BP and classify test curves using the most relevant measurement methods obtained from the first approach. ResultsAmong the two tested approaches, the combination of the cDTW algorithm and Minkowski distance (p = 2), using the 1-NN classifier, achieved the highest scores in this study, closely matching the metrics obtained from visual inspection conducted by human evaluators. ConclusionThe use of combined classification methods in the analysis of flow curves referring to therapeutic breathing patterns improves the classification results, with metrics closely aligned with those obtained through visual evaluation conducted by individuals. SignificanceTime series analysis methods proved to be sensitive to classify respiratory flow curves equivalent to therapeutic breathing patterns used in respiratory disorder rehabilitation. This methodology can be used to monitor respiratory curves in new applications and implementation in devices for evaluating and treating the ventilatory pattern.

Comprehensive evaluation of green mine construction level considering fuzzy factors using intuitionistic fuzzy TOPSIS with kernel distance.

With increasing concerns about climate change and resource-environmental limitations, the green development of the mining industry has become mainstream and gained much support. Driven by the concept of sustainable and green development, China has made the advancement of green mine construction a crucial part of establishing an eco-society and has put forward the overall goal of green mines. An important future strategy is to evaluate a large number of mines. However, developing scientific, reliable, and comprehensive index systems and evaluation methods is extremely difficult because of the objective complexity of green mine evaluation and the fuzziness of some indicators. The kernel method and intuitionistic fuzzy set can effectively handle these problems. This study proposed a comprehensive evaluation index system and a hybrid evaluation method based on the kernel distance measure and intuitionistic fuzzy TOPSIS method. The index system contains 22 indicators considering six aspects: mining area environment, resource development mode, comprehensive utilization of resources, energy saving and emission reduction, technical innovation, and corporation management. The hybrid evaluation method was applied to the practical assessment of ten green mines in Panxi, China. Comparative analyses were carried out to demonstrate its applicability and sensitivity. The results verify that the hybrid method can fully depict the construction achievements of green mines in all aspects with strong reliability and stability. This approach is a valuable reference for evaluators and decision-makers in government departments.

An adaptive Copula function-based framework for fault detection in semiconductor wafer fabrication

Wafer fabrication is a complex manufacturing system. Timely identification of the key influencing factors associated with the cause of defects is critical to wafer yield enhancement. However, the massive wafer fabrication process dataset has various data types, uncertain data distribution, and complex correlation relationships. These issues make the statistical sense of correlation association measures, and fault detection (FD) methods face many challenges. Therefore, this study proposes an adaptive Copula function-based framework for FD in semiconductor wafer fabrication. Firstly, the marginal distribution of the wafer fabrication process data was determined. After that, a joint distribution model based on the Copula function was designed for the nonlinear relationship and the nonnormal distribution characteristics. Based on the model mentioned above, the traditional Copula function selection method has a high-dimensional spatial failure problem based on the Euclidean distance theory. Therefore, a high-dimensional spatial distance measurement method based on improved kernel distance measurement (IKDM) is designed to realize the adaptive selection of Copula functions. Afterward, the key influencing factors associated with wafer yield are identified through feature sensitivity analysis and validated with multiple prediction models for comparison. Finally, a case study using actual data from a semiconductor wafer fabrication system is used to validate the effectiveness of the proposed methodological framework.

A comparative analysis of the price explosiveness in Bitcoin and forked coins

This study employs the PSY method to detect the price bubble of Bitcoin (BTC) and its forked coins. The statistical relationship between BTC and its forked coins is then calculated using Pearson correlation coefficients and six distance measurement methods. The findings indicate that (1) BTC has more bubbles with longer periods. In contrast, certain forked coins do not have bubbles; (2) The correlation between the price bubble of BTC and several forked coins is significant; (3) From the distance of binary time series, Bitcoin Cash and BTC are the closest, while Bitcoin SV is the farthest from BTC.

A High-Precision Joint Smoothing Method for Inter-Spacecraft Radio Frequency Doppler and Distance Measurements using P-Spline Approximation

A High-Precision Joint Smoothing Method for Inter-Spacecraft Radio Frequency Doppler and Distance Measurements using P-Spline Approximation

Pattern Clustering of Symmetric Regional Cerebral Edema on Brain MRI in Patients with Hepatic Encephalopathy.

Metabolic abnormalities in hepatic encephalopathy (HE) cause brain edema or demyelinating disease, resulting in symmetric regional cerebral edema (SRCE) on MRI. This study aimed to investigate the usefulness of the clustering analysis of SRCE in predicting the development of brain failure. MR findings and clinical data of 98 consecutive patients with HE were retrospectively analyzed. The correlation between the 12 regions of SRCE was calculated using the phi (Φ) coefficient, and the pattern was classified using hierarchical clustering using the φ2 distance measure and Ward's method. The classified patterns of SRCE were correlated with clinical parameters such as the model for end-stage liver disease (MELD) score and HE grade. Significant associations were found between 22 pairs of regions of interest, including the red nucleus and corpus callosum (Φ = 0.81, p < 0.001), crus cerebri and red nucleus (Φ = 0.72, p < 0.001), and red nucleus and dentate nucleus (Φ = 0.66, p < 0.001). After hierarchical clustering, 24 cases were classified into Group I, 35 into Group II, and 39 into Group III. Group III had a higher MELD score (p = 0.04) and HE grade (p = 0.002) than Group I. Our study demonstrates that the SRCE patterns can be useful in predicting hepatic preservation and the occurrence of cerebral failure in HE.

Shortest Route Navigation Indoors Using Digital Maps

Digital maps have revolutionized our ability to navigate to desired locations by providing the shortest routes, primarily in open spaces. However, this functionality is limited to outdoor environments. This study aims to extend this capability by enabling the determination of the shortest routes within indoor spaces. The research employs the Haversine method for distance measurement and integrates the Dijkstra algorithm for route determination. The findings demonstrate the feasibility of implementing the Haversine method and the Dijkstra algorithm for route determination within enclosed spaces. The research results show that searching for the shortest route indoors be done and the route can be displayed on a digital map. It was observed that the routing machine between route nodes did not perform optimally, prompting its replacement with a polyline.

Catadioptric omnidirectional monocular visual distance measurement method integrating structured light

Catadioptric omnidirectional monocular visual distance measurement method integrating structured light

Data‐Driven Decision Algorithm for Open Caisson Foundation Construction

The accuracy of instructions for the extraction of large open caissons directly affects the construction quality and safety. This study is focused on developing a smart construction methodology for predicting open caisson excavation instructions. The proposed approach aims to reduce the errors in excavation instructions attributable to the subjective and varying experiences of decision‐makers. The multilabel classification task of predicting the instructions of large open caisson excavation is accomplished through the problem analysis. The study conducted a comparative analysis of a multilayer perceptron model, a classifier chain model, and a multilabel K‐nearest neighbor model (MLKNN) based on Hamming loss and accuracy. The results revealed that MLKNN exhibits significant differences from the other models and is most suitable for predicting open caisson excavation instructions. Furthermore, the traditional MLKNN model was improved by refining the normalization and distance measurement methods for the open caisson scenario. The prediction accuracy of the improved MLKNN model is 89.04% for the excavation instructions and 98.63% for the nonexcavation decisions. The successful prediction of excavation instructions for a large open caisson can compensate for the lack of data mining capability of the existing monitoring system. This can reduce construction risks. Finally, this study discussed the robustness of the improved MLKNN in a real project, highlighting the model’s high accuracy during the midstage of open caisson construction.

Product innovation based on the host gene and target gene recombination under the technological parasitism framework

In the rapidly changeable market, product innovation is critical for enterprises to improve their product competitiveness. Product innovation can be described as an evolutionary process of technical systems where the relationship between technologies follows the evolution from parasitism to symbiosis in technological parasitism theory. The existing research on the technological parasitism theory mainly focuses on explaining and verifying the evolution law of product systems from parasitism to symbiosis in the product innovation process. However, these research activities are a post-hoc summary, which limits the guidance for enterprises to proactively realize the evolution of products from parasitism to symbiosis in an ex-ante perspective. For the development of innovative products in the engineering field, enterprises lack an effective ex-ante design method to proactively find the appropriate parasitic technology, and integrate it with the host technology for the product system evolving towards a symbiotic state. To fill this gap, this paper proposes a product innovation design process based on the recombination of host and target genes under the technological parasitism framework. Product scenario analysis is used to explore the potential function needs to determine the search direction of parasitic technologies. The min-complement distance measure method is introduced to identify the appropriate parasitic technology. Based on four recombination operations, the construction process of new product genome through the recombination of host and target genes is proposed. The concept of new product is formed through the transcription of the product genome. The final scheme is developed objectively by using the coefficient variation method and Dempster combination rule. The proposed method is applied in the ex-ante design of a Chinese medicine dispensing machine for its feasibility and effectiveness.