Distance Measurement Method Research Articles

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

AbstractAs an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments.

Dual-biomimetic curved compound-eye camera system for multi-target distance measurement in a large field of view

Biomimetic curved compound-eye cameras (BCCECs) have attracted great attention for their potential applications in a variety of fields such as target recognition, monitor and three-dimensional localization in military due to their unique optical properties such as large field of view (FOV) and small size. In this work, we proposed a multi-target distance measurement method based on a dual-BCCEC system in a large FOV. To guarantee the precise measurement of the distance of multiple targets, a feature point searching and matching algorithm is developed for the dual-BCCEC system to improve the localizing efficiency of common feature points. In addition, a CALibration Tag (CALTag) self-recognition calibration method is also developed to calibrate ommatidia of the BCCEC with a high efficiency. Based on these two methods, the coordinates of multiple targets with clear feature points can be obtained after the distortion correction in sub-images and thus the distances of multiple targets with clear feature points can be achieved simultaneously with a single compound-eye raw image. The experiment results show that the dual-BCCEC system has a high distant measurement accuracy with an error of less than 6.80% for at least ten different targets in the a working distance ranging from 400 to 600 m in a quite large FOV of 98°×98°. The method demonstrated in this work can pave the way for multi-targets tracking in those related areas with high security monitoring requirements.

Simulation Research on the Dual-Electrode Current Excitation Method for Distance Measurements While Drilling

Based on a comprehensive analysis of the existing methods for measuring adjacent well distances, along with their advantages and disadvantages, this study employs theoretical analysis, simulation experiments, and other comprehensive research methods to investigate a distance measurement method based on current excitation. In response to the need for measuring and controlling the connection of relief wells, a method utilizing dual-electrode current excitation during drilling is proposed. This approach facilitates synchronous excitation measurements while drilling, significantly reducing both time and costs while ensuring safety and efficiency, making it particularly suitable for the connection operation of relief wells that involve safety risks. Firstly, this paper establishes a drilling with measurement model corresponding to the excitation mode, which derives the calculation formulas for the target casing current amplitude attenuation, as well as the induced magnetic field distribution within the formation. Additionally, it provides the calculation methods for determining the target well distance and azimuth direction. Lastly, the impact levels of various key factors are verified through numerical calculations and simulation analyses, which confirm the correctness and effectiveness of this distance measurement method. The findings from this research establish both a core theoretical foundation and a technological basis for the real-time measurement of adjacent well distances during relief well operations.

PERBANDINGAN JARAK EUCLIDEAN, CITYBLOCK, MINKOWSKI, CANBERRA, DAN CHEBYSHEV DALAM SISTEM TEMU KEMBALI CITRA BATIK

Batik is a highly valuable cultural heritage in Indonesia, showcasing a rich diversity of motifs with deep meaning and aesthetics. To enhance the accessibility and utilization of batik collections, an efficient image retrieval system is essential. This study compares distance measurement methods in a batik image retrieval system: Euclidean, Cityblock, Minkowski, Canberra, and Chebyshev, using a combination of color and texture features. The dataset comprises 50 types of batik images. The results show that the Cityblock method achieves the highest Mean Average Precision (MAP) of 97.71, followed by Canberra with MAP 96.87. The Euclidean method also performs well with a MAP of 94.56, while Minkowski and Chebyshev have lower MAP values of 92.93 and 90.89, respectively. Chebyshev experiences the largest MAP drop when images are rotated (5.98), while Cityblock demonstrates the best resistance to rotation with the smallest MAP drop (1.51). This research successfully developed a Content-Based Image Retrieval (CBIR) system with a GUI in MATLAB and suggests integrating the latest image processing and machine learning techniques for further enhancement.

Diagnostic value of rEBUS-TBLB combined distance measurement method based on ultrasound images in bronchoscopy for peripheral lung lesions

Traditional imaging methods have limitations in the diagnosis of peripheral lung lesions. The aim of this study is to evaluate the diagnostic value of the distance measurement method based on ultrasound image-based inverted electrostrain (rEBUS) combined with thoracoscopic lung biopsy (TBLB) for peripheral lung lesions. A group of patients with peripheral lung lesions were recruited for the study, and rEBUS examination was performed simultaneously during TBLB. Using rEBUS ultrasound images combined with electrostrain information, evaluate the morphological characteristics of peripheral lung lesions and the elastic properties of internal tissues. By comparing with pathological examination results, both rEBUS-D-TBLB and rEBUS-GS-TBLB have a higher positive diagnostic rate for PPL under bronchoscopy. However, rEBUS-D-TBLB is more effective in diagnosing benign PPL with ≥ 3 cm PPL than rEBUS-GS-TBLB. The rEBUS-TBLB combined ranging method has shown high accuracy and sensitivity in diagnosing peripheral lung lesions. Ultrasound images provide clear morphological features of the tumor, while the electrical strain information of rEBUS provides elastic information of the internal tissue of the tumor, further improving the accuracy of diagnosis.

A Novel Clark Distance-Based Decision-Making Algorithm on Intuitionistic Fuzzy Sets

Fuzzy sets possess remarkable abilities in expressing and handling information uncertainty, which has resulted in their widespread application in various fields. Nevertheless, distance measurement between IFSs for quantitating their differences and levels of differentiation has remained an open problem that deserves attention. Despite the development of various metrics, they either lack intuitive insight or do not satisfy the axioms of distance measurement, leading to counterintuitive results. To address these issues, this paper proposed a distance measurement method based on Clark divergence, which satisfies the distance measurement axioms and exhibits nonlinearity. Numerical examples demonstrate that our method effectively distinguishes different indicators, yielding more reasonable results. Moreover, when comparing relative differences of the results, our method demonstrated superior adaptability to complex environmental decision-making, providing decision-makers with more accurate and confidential judgments. In our numerical and pattern classification application tests, we achieve an accuracy of 98%, a 40% increase in computing time efficiency and a relative diversity improvement of 35%. The pattern classification algorithm designed in this paper will offer a promising solution to inference problems.

Research on fault location and distance measurement methods for low current grounding systems in short distances

Research on fault location and distance measurement methods for low current grounding systems in short distances

Defect Rate Prediction in Manufacturing Process Using K-Nearest Neighbor Algorithm

The efficiency and effectiveness in the manufacturing industry are significantly impacted by artificial intelligence technology. An important application involves the improvement of product quality, which is measurable through the defects occurring during the production process. This research is aimed at predicting defects in the manufacturing process using the K-Nearest Neighbor (KNN) algorithm with various distance measurement methods, namely Euclidean, Minkowski, and Manhattan distances. The research methodology is composed of four stages: dataset collection, data preprocessing, modeling, and evaluation. The focus of this research is on the optimal K value and the conditions that yield the highest accuracy, considering various scenarios of training and test data splitting ratios and different random state values. The test results indicate that the Minkowski distance method, with a data division ratio of 80% for training data, 20% for test data, and a random state value of 32, provides the best performance, with an optimal K value of 10 and an accuracy of 86.41%.

Major technical barriers and a high-precision distance measurement method for frequency modulated continuous wave LiDAR

Frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) is considered as one of the most promising 3D imaging technologies by virtue of its single-photon detection sensitivity, high interference immunity, and high precision. In this paper, major technical barriers that limit the precision of FMCW LiDAR range are summarized and analyzed, and solutions to improve the measurement precision are presented. In addition, a novel method for high precision ranging of FMCW LiDAR is proposed, which can be applied to measure target distance in both active and passive environments. The proposed method has a precision of 11 μm in passive environments.

The Relationships of Dust and Total Masses of Luminous Infrared Galaxies with Their Far Infrared and Total Luminosities Using Two Methods of Distance Measurements

This research aims to determine the nature of the relationships among luminous infrared galaxies (LIRGs) by using different methods of distance measurement (distance modulus “dm” and luminosity distance “dl”). We used data from the following sources: NASA Extragalactic Database (NED) and the HYPERLEDA database, where this data was used to study the nature of the relationships and the extent to which they relate to each other. The program (statistica-win-program) was employed, which helped us know the strength of the association between the variables. The results of the statistical analysis showed that there was a strong correlation between dust mass (Mdust dm & Mdust dl) and luminous infrared (LFIRdm, LIRdm) & (LFIRdl, LIRdl), where the partial correlation coefficient was equal to (R⋍0.7) and the slope was linear. There was also a positive correlation between (LFIRdm, LFIRdl)& ( ) where the partial correlation coefficient was (R⋍0.4) with a linear slope. The relationship between (LIR)& ( ) was a positive relation in the modulus distance case (dm), where its correlation was equal to (R⋍0.4), but in another case (luminosity distance), the relation was very weak, and the correlation was (R⋍0.2). The relationship between total masses ( ) & (LFIRdm, dl­­) in both cases of distance (dm) & (dl) was good, and the partial correlation coefficient was equal to (R⋍0.4) with a linear slope. The relationship between (LIR) & ( ) in both cases of distance was also positive, and the partial correlation coefficient was equal to (R⋍0.3). There was an inverse relationship between the ratio fluxes (F100/F60) & ( ), as well as a very tight partial correlation coefficient, which was equal to (R⋍-0.9) with a slope of (-1).

A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering

To successfully market to automotive parts customers in the Industrial Internet era, parts agents need to perform effective customer analysis and management. Dynamic customer segmentation is an effective analytical tool that helps parts agents identify different customer groups. RFM model and time series clustering algorithms are commonly used analytical methods in dynamic customer segmentation. The original RFM model suffers from the problems of R index randomness and ignoring customers’ perceived value. For most existing studies on dynamic customer segmentation, time series clustering techniques largely focus on univariate clustering, with less research on multivariate clustering. To solve the above problems, this paper proposes a dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. Firstly, this method represents each customer behavior as a time series sequence of the Length, Recency, Frequency, Monetary and Satisfaction variables. And then, we apply a multi-dimensional time series clustering algorithm based on three distance measurement methods called DTW-D, SBD, and CID to carry out customer segmentation. Finally, an empirical study and comparative analyses are conducted using customer transaction data of parts agents to verify the effectiveness of the approach. Additionally, a detailed analysis of different customer groups is made, and corresponding marketing suggestions are provided.

Secure computation protocol of Chebyshev distance under the malicious model

Secure multi-party computation of Chebyshev distance represents a crucial method for confidential distance measurement, holding significant theoretical and practical implications. Especially within electronic archival management systems, secure computation of Chebyshev distance is employed for similarity measurement, classification, and clustering of sensitive archival information, thereby enhancing the security of sensitive archival queries and sharing. This paper proposes a secure protocol for computing Chebyshev distance under a semi-honest model, leveraging the additive homomorphic properties of the NTRU cryptosystem and a vector encoding method. This protocol transforms the confidential computation of Chebyshev distance into the inner product of confidential computation vectors, as demonstrated through the model paradigm validating its security under the semi-honest model. Addressing potential malicious participant scenarios, a secure protocol for computing Chebyshev distance under a malicious model is introduced, utilizing cryptographic tools such as digital commitments and mutual decryption methods. The security of this protocol under the malicious model is affirmed using the real/ideal model paradigm. Theoretical analysis and experimental simulations demonstrate the efficiency and practical applicability of the proposed schemes.

Natural gas pipeline leak diagnosis based on manifold learning

Natural gas pipeline leakage is a common safety hazard, which can have a great impact on the economy and the environment. This paper proposed a novel manifold learning-enabled feature extraction method for natural gas pipeline leakage diagnosis. Firstly, the natural gas pipeline working condition signal is decomposed and denoised by Variational mode decomposition (VMD). Secondly, the denoised pipeline signals were constructed into a form expressed by the Symmetric positive definite matrix (SPD) using the VMD reconstruction technique, and the geodesic distance measurement method was applied to the SPD matrix to make the data located on the SPD manifold. Then feature extraction is carried out by Local linear embedding (LLE) method based on asymmetric distance. Finally, pattern recognition of the features extracted in this paper by Support vector machine (SVM) can achieve 100% recognition accuracy. By enabling faster and more accurate leak detection, the method minimizes gas loss, as well as mitigating the environmental risks caused by this potent greenhouse gas.

Efficient semi-supervised clustering with pairwise constraint propagation for multivariate time series

Semi-supervised clustering is an effective method, which improves the clustering performance based on pairwise constraints. However, state-of-the-art methods suffer from two issues: 1) due to the high dimensionality and multiple variables of multivariate time series (MTS) data, the competitive similarity approach DTW is time consuming on large-scale MTS data. 2) Traditional semi-supervised clustering methods could not make full use of pairwise constraints information, which affects clustering performance. To deal with these issues, we propose an efficient semi-supervised clustering with pairwise constraint propagation for MTS data. First, two approximate distance measure methods are designed based on dynamic time warping (DTW) from the perspectives of boundary and dimension reduction, which greatly improve the efficiency of clustering without sacrificing its accuracy. Then, a graph-based clustering method with pairwise constraints propagation (GCPCP) is advanced on multivariate time series data. In GCPCP, the pairwise constraint propagation matrix and the affinity matrix are jointly optimized to exploit the dependence between them, which finally improves the clustering performance. Experimental results on 12 multivariate time series datasets show the effectiveness and efficiency of our proposed method.

Guided-wave Dynamic Probability Model-based Crack Quantification Method for Metal Structures

In the process of applying guided-wave-based aircraft online structural health monitoring, the characteristics of guided wave signals used to represent structural status exhibit random uncertainty due to the interference of loads on guided wave propagation. The trend of signal feature changes caused by damage is often obscured, making the quantification of cracks under loads one of the challenges in online structural health monitoring for aircraft. To address this challenge, this paper proposes a crack quantification method using an adaptive Gaussian mixture model (GMM) and optimized probability migration distance measurement. This method effectively mitigates the uncertainty caused by loads by constructing an adaptive Gaussian mixture model to track the probability distribution changes of guided wave signal features under different structural health conditions. Subsequently, an optimized probability migration distance measurement method is employed to characterize the structural state, further enhancing the correlation between features and the extent of structural damage. Finally, an optimized probability migration distance relationship with crack length is established to estimate the crack length of new structural specimens. The effectiveness of this method is validated using load-induced crack propagation experiments on metal structures.

Risk factors for preoperative neurological impairment in patients with spinal meningioma: A retrospective multicenter study

BackgroundPatients with spinal meningioma may present preoperatively with paralysis and sensory deficits. However, there is a paucity of detailed evaluations and a lack of consensus regarding imaging findings that are predictive of neurological symptoms in patients with spinal meningioma. MethodsHerein, a total of 55 patients who underwent surgical resection of spinal meningiomas in eight hospitals between 2011 and 2021 were enrolled. Patient characteristics, degree of muscle weakness, sensory disturbances, and the presence of bowel/bladder dysfunction (BBD) before surgical treatment were evaluated using medical records. Patients with American Spinal Injury Impairment Scale grades A-C and the presence of BBD were classified into the paralysis (+) group. Patients with sensory disturbances were assigned to the sensory disturbance (+) group. Based on magnetic resonance (MR) and computed tomography images, the tumor location was classified according to the spinal level and its attachment to the dura mater. To evaluate tumor size, the tumor occupation ratio (OR) was calculated using the area and distance measurement method in horizontal MR images, and the maximum length and area of the tumor in the sagittal plane were measured. ResultsOf all patients, 85 % were women. The mean age of patients at surgery was 69.7 years. Twenty-eight (51 %) and 41 (75 %) patients were classified into the paralysis (+) and sensory disturbance (+) groups, respectively. The average tumor length and area in the sagittal plane were 19.6 mm and 203 mm2, respectively; OR-area and diameters were 70.3 % and 72.3 %, respectively. In univariate analyses, tumor length and area in the sagittal plane were significant risk factors for paralysis. OR-diameter, symptom duration, and a low MIB-1 index correlated with sensory disturbances. Multivariate logistic regression analysis demonstrated that the area and length of the tumor in the sagittal plane were significantly correlated with paralysis, whereas the OR-diameter and symptom duration significantly correlated with sensory disturbances. The cut-off values for the area and length of the tumor in the sagittal plane to predict paralysis were 243 mm2 and 20.1 mm, respectively. ConclusionsPreoperative paralysis in patients with spinal meningiomas was significantly associated with sagittal tumor size than with high tumor occupancy in the horizontal plane. Sensory disturbances were associated with high occupancy in the horizontal plane. Patients with spinal meningiomas > 20 mm in length or 243 mm2 in area in the sagittal plane are at risk of developing paralysis and could be considered for surgery even in the absence of paralysis.

Enhancing autonomous driving through intelligent navigation: A comprehensive improvement approach

In this paper, an intelligent navigation system is developed to achieve accurate and rapid response to autonomous driving. The system is improved with three modules: target detection, distance measurement, and navigation obstacle avoidance. In the target detection module, the YOLOv7x-CM model is proposed to improve the efficiency and accuracy of target detection by introducing the CBAM attention mechanism and MPDioU loss function. In the obstacle distance measurement module, the concept of an off-center angle is introduced to optimize the traditional monocular distance measurement method. In the obstacle avoidance module, acceleration jump and steering speed constraints are introduced into the local path planning algorithm TEB, and the TEB-S algorithm is proposed. Finally, this paper evaluates the performance of the system modules using the KITTI dataset and the BDD100K dataset. It is demonstrated that YOLOv7x-CM improves the mAP @ 0.5 metrics by 5.3% and 6.8% on the KITTI dataset and the BDD100K dataset, respectively, and the FPS also increases by 35.4%. Secondly, for the optimized monocular detection method, the average relative distance error is reduced by 9 times. In addition, the proposed TEB-S algorithm has a shorter obstacle avoidance path and higher efficiency than the normal TEB algorithm.

A new distance measure between two basic probability assignments based on penalty coefficient

Uncertainty information processing is a challenging topic in information science. Dempster-Shafer evidence theory shows excellent performance when dealing with uncertainty in information representation, fusion, and processing. The measurement of the distance between the basic probability assignments (BPA) in Dempster-Shafer evidence theory is a powerful tool that deals with uncertainty in evidential information processing. Most of the existing methods are based on the difference in the assessment of evidential degree for different hypotheses, with a notable example Belief-Jensen-Shannon (BJS) divergence. However, these methods ignore the information that is carried by different units in the power sets that represent the hypotheses, i.e., the difference on the hypotheses themselves that include in the evidential degree assessment of different BPA should be considered in the distance measurement. Therefore, in this paper, a novel method grounded in BJS divergence and incorporating a penalty coefficient to rectify the distance between basic probability assignments is proposed. This methodology showcases commendable properties, including symmetry, boundedness, and adherence to the triangle inequality. To demonstrate the efficiency of the proposed distance measurement method between BPAs, we apply it to two common-used machine learning data sets: Iris data set and Dry Beans data set. Compared to the represented existing evidence distance measurement approaches, the proposed method yields superior performance in both data sets with respect to classification accuracy.

ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

Robust distance measurement using illumination map estimation and MAHNet in underground coal mines

An effective binocular stereo distance measurement method is proposed to address challenges posed by low brightness and weak texture of images captured in underground coal mines for the machine vision method. This approach is based on illumination map estimation and the MobileNetV3 attention hourglass stereo matching network (MAHNet) model. First, a binocular stereo vision system is established in which infrared LEDs are uniformly distributed on both sides of the belt conveyor bracket as visual feature points. Second, images are preprocessed using illumination map estimation, and the optimization of inhomogeneous brightness image enhancement is achieved by adopting adaptive Gamma correction. Third, the YOLOv5 target detection network and Gaussian fitting fusion algorithm are utilized to detect infrared LED feature points. Fourth, the MAHNet model is employed to generate the cost volume and perform disparity regression, resulting in the acquisition of accurate disparity images. Finally, triangulation is applied to determine the depth of feature points. The experimental results of distance measurement demonstrate that an average relative ranging accuracy of 1.52% within the range of 50.0 cm to 250.0 cm can be achieved by the optimized method, thereby validating the effectiveness of this binocular distance measurement method in underground coal mines.