Distance Measurement Algorithm Research Articles

Research on SLAM Localization Algorithm for Orchard Dynamic Vision Based on YOLOD-SLAM2

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

Locality-constraint Representation with Minkowski distance metric for an effective Face Hallucination

Face hallucination (FaceH), the domain specific super resolution technique has significant importance in the field of image processing and computer vision. These methods provide visually convincing high-resolution face images using the low-resolution input images. To do this, all the state-of-the-art methods make use of distance measure algorithms. The performance of distance measure is one of the core components that impact the performance of FaceH. However, there are many k-NN distance measure algorithms, to the best of our knowledge, the most commonly used metric is the Euclidean distance, though it is the best, a little deviation may result in large elevation on estimating the reconstructed weight of the pixel. To overcome this, it is necessary to find an alternative method and this motivated to develop Minkowski Locality-constrained Representation (MinLcR) FaceH that makes use of Minkowski distance and after which it is integrated with Locality-constrained Representation (LcR) that incorporates the contextual information of the neighboring pixel for better hallucination. The main idea of MinLcR is to make a difference in the distance metric and compare it with methods that make use of Euclidean distance. Experimental analysis carried out using FEI database shows that the proposed method has an effective improvement over other methods.

Prospects for realtime characterization of core-collapse supernova and neutrino properties

Core-collapse supernovae (CCSNe) offer extremely valuable insights into the dynamics of galaxies. Neutrino time profiles from CCSNe, in particular, could reveal unique details about collapsing stars and particle behavior in dense environments. However, CCSNe in our galaxy and the Large Magellanic Cloud are rare and only one supernova neutrino observation has been made so far. To maximize the information obtained from the next Galactic CCSN, it is essential to combine analyses from multiple neutrino experiments in real time and transmit any relevant information to electromagnetic facilities within minutes. Locating the CCSN, in particular, is challenging, requiring disentangling CCSN localization information from observational features associated with the properties of the supernova progenitor and the physics of the neutrinos. Yet, being able to estimate the progenitor distance from the neutrino signal would be of great help for the optimisation of the electromagnetic follow-up campaign that will start soon after the propagation of the neutrino alert. Existing CCSN distance measurement algorithms based on neutrino observations hence rely on the assumption that neutrino properties can be described by the Standard Model. This paper presents a swift and robust approach to extract CCSN and neutrino physics information, leveraging diverse next-generation neutrino detectors to counteract potential measurement biases from Beyond the Standard Model effects.

A Gaussian Process Regression-Based Multitarget Orientation Compensation Algorithm for Distance Measurement

A Gaussian Process Regression-Based Multitarget Orientation Compensation Algorithm for Distance Measurement

A New Fault Distance Measurement Scheme for DC Distribution Network

To solve the problem of fault location in the DC distribution network, this paper proposes a new fault distance measurement scheme based on the impedance measurement algorithm against fault transition resistance and the improved Moore voting algorithm. Firstly, signals of different frequencies are injected into the DC circuit in different periods, after the converter is blocked due to the DC side fault. Then, through the distance measurement algorithm against fault transition resistance, several groups of fault distance data are obtained. The improved Moore voting algorithm is used to process these fault distance values so that the accurate fault distance values are determined. Finally, simulation tests verify the reliability and accuracy of the proposed scheme. The proposed scheme requires a low sampling frequency, and it can not be interfered with by the fault transition resistance and occasional large errors. These advantages help to improve the efficiency and accuracy of troubleshooting.

Object Detection and Distance Measurement Algorithm for Collision Avoidance of Precast Concrete Installation during Crane Lifting Process

In the construction industry, the process of carrying heavy loads from one location to another by means of a crane is inevitable. This reliance on cranes to carry heavy loads is more obvious when it comes to high-rise building construction. Depending on the conditions and requirements on-site, various types of construction lifting equipment (i.e., cranes) are being used. As off-site construction (OSC) is gaining more traction recently, cranes are becoming more important throughout the construction project as precast concrete (PC) members are major components of OSC calling for lifting work. As a result of the increased use of cranes on construction sites, concerns about construction safety as well as the effectiveness of existing load collision prevention systems are attracting more attention from various parties involved. Besides the inherent risks associated with heavy load lifting, the unpredictable movement of on-site workers around the crane operation area, along with the presence of blind spots that obstruct the crane operator’s field-of-view (FOV), further increase the accident probability during crane operation. As such, the need for a more reliable and improved collision avoidance system that prevents lifted loads from hitting other structures and workers is paramount. This study introduces the application of deep learning-based object detection and distance measurement sensors integrated in a complementary way to achieve the stated need. Specifically, the object detection technique was used with the application of an Internet Protocol (IP) camera to detect the workers within the crane operation radius, whereas ultrasonic sensors were used to measure the distance of surrounding obstacles. Both applications were designed to work concurrently so as to prevent potential collisions during crane lifting operations. The field testing and evaluation of the integrated system showed promising results.

Risk assessment of bolt-gasket-flange connection (BGFC) failures at hydrogen transfer stations based on improved FMEA

Failure Modes and Effects Analysis (FMEA) is an engineering technique widely used to identify, evaluate, and eliminate known or potential failures, problems, and errors and inform risk management decisions. Traditional FMEA uses the risk priority number (RPN) to identify potential failure modes and their levels. However, it displays shortcomings in determining risk factor weights, ranking risk priorities of failure modes, and dealing with uncertainties during the risk assessment process. To address these issues, this paper proposed an improved FMEA risk assessment method by integrating FMEA, the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the cloud model (CM) to determine a more reasonable failure mode level. First, the CM was used to improve the FMEA framework structure to comprehensively assess ambiguity and randomness during the evaluation process. Second, the cloud weights of the risk factor severity (S), occurrence (O), and detectability (D) were calculated using AHP and CM to determine the relative risk factor importance. Then, the cloud distance measurement algorithm and TOPSIS were combined into the TOPSIS-CM risk ranking method to objectively identify the failure modes requiring improvement. Finally, a case study of a flange connection system at a hydrogen blending transmission site in northern China was conducted to verify the adaptability of the proposed method in failure mode assessment. Compared with traditional FMEA methods, the decision scores obtained using the improved FMEA were more discriminating and distinguished the risk of each failure mode, providing more accurate information to decision makers (DMs).

Editorial Issue 34.2

Editorial Issue 34.2

Acoustic Sensing Based on Online Handwritten Signature Verification

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2% AUC and 1.25% EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc.

Far-Field 3-D Localization of Radioactive Hotspots via Four-Eyes Stereo Gamma Camera

In the fields of radiation environment monitoring and unknown radioactive materials search, locating the far-field gamma-ray sources with high accurate coordinates is an important and necessary capability for a gamma imager. However, traditional coded gamma-ray imaging systems only provide 2-D radiation images without distance information. In this work, a four-eyes stereo gamma camera is proposed, which consists of four independent coded aperture detectors with an adjacent spacing of 286 mm. Each detector is comprised of pixelated scintillators with a pixel array of 44 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times44$ </tex-math></inline-formula> , forming a global detection area of 140 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times140$ </tex-math></inline-formula> mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The ranging capability is related to the resolution and the adjacent spacing of the detectors. With the setup of the above device, the traditional binocular-based ranging method has a short measurement range and a large measurement deviation. Here, a min-area distance measurement algorithm based on a four-eyes stereo gamma camera is proposed, combined with “manual” fine sampling and Gaussian fitting method to successfully achieve the high-precision localization of the far-field source. The simulation results show that compared with the traditional binocular-based ranging method, the min-area method based on a four-eyes stereo camera lengthens the range from 20 m to more than 50 m and improves the ranging accuracy within 20 m by more than six times. The accuracy of 20 and 50 m is less than 1.4 m (7.13%) and 10.5 m (21%), respectively. The reliability of the simulation results has been verified by experiments. Furthermore, the ranging capability of this algorithm for multipoint sources in the far field has been verified.

DCR‐Net: Dilated convolutional residual network for fashion image retrieval

AbstractFashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods.

China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach

China’s commercial Bank shares have become the backbone of the capital market. The prediction of a bank's stock price has been a hot topic in the investment field. However, the stock price is always unstable and non-linear, challenging the traditional statistical models. Inspired by this problem, a novel hybrid deep learning approach is proposed to improve prediction performance. By modifying the distance measurement algorithm into DTW, an improved K-means clustering algorithm is proposed to cluster out banks with similar price trends. Then those clustered stocks are used to train a long and short-term memory (LSTM) neural network model for static and dynamic stock price prediction. Besides, by transforming the output of the LSTM network into multi-step output to predict multi-time intervals at one time, the performance of the long-term forecasts is improved. Through experiments, it is found that the hybrid model performs better than the single model in generalization ability and accuracy(i.e. R-SQUARE, MAE, MSE). Moreover, the multi-step output static prediction outperforms the dynamic rolling prediction for long-term prediction. In summary, this approach can predict stock prices more accurately and help investors and companies to make more profitable decisions.

A novel algorithm for distance measurement using stereo camera

Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras. The stereo vision system is generally composed of two cameras to mimic the human binocular vision. In this paper, a Python-based algorithm is proposed to find the parameters of each camera, rectify the images, create the disparity maps and finally use these maps for distance measurements. Experiments using real-time images, which were captured from our stereo vision system, of different obstacles positioned at multiple distances (60–200 cm) prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate. The accuracy of distance measurement is up to 99.83%. The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.

Development and Performance Evaluation of Distance Measurements Detection Using 3 Axis Binocular Vision System

The world currently entering industry 4.0, which the industrial processes become automated and remotely control by computer with little input from human operators. Binocular vision is the result of signal sent to brain from eyes simultaneously which having advantage of depth perception. This project is about distance measurement of the object in 3-axis by using the advantages of binocular vision system. In this project, two webcams were used as binocular vision system to capture image and detect the distance measurement of the sample objects in 3-axis. The cameras must be calibrated before start to computing the distance measurement due to the fish eyes effect caused by the lens. In this project, the cameras were calibrated by using chessboard pattern with the size of 9x6 internal corners due to the simple geometry. The distance measurement algorithm will be executed once the cameras are calibrated, and the distance measurement is computed by using trigonometry concept.

A Sub-Cycle phase angle distance measure algorithm for power transformer differential protection

Power transformer differential protection may confront mal-functioning in authentic discrimination between inrush and internal faults. To tackle the latter mal-functioning, a new two-stages algorithm based on phase content of the current signal of the current transformers (CTs) is put forward. The proposed algorithm is designed based on the fact that the fundamental phase angle of a fault signal ideally remains constant during the fault. However, during inrush cases, the phase angle varies. Also, during the internal fault, the phase angles of the current signals of CTs are in phase while during the external fault, the phase angles of the current signals of CTs are 180° out of phase. In the first stage, the proposed algorithm calculates the fundamental phase angles of the current signals of the CTs using sub-cycle modified recursive least squares (MRLS). Afterward, normalized mean residue (NMR) is employed to measure distance between the estimated phase angles of the CT’s currents. MRLS and NMR algorithms require limited samples (i.e. 10 and 5 samples respectively) for executing their calculations. Performance evaluation with simulated and experimental recorded current signals shows the ability of the proposed method in discrimination of the internal faults from inrush and external fault currents.

Causality Distance Measures for Multivariate Time Series with Applications

In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

Modeling of TST Using Biological Signals and KL-Hausdorff Distance Similarity Measure for Gene Based Parkinson’s Disease Recognition

Genetic research experienced drastic transformation since past decades, which benefits the biological area eventually for the detection of neurodegenerative ailment like Parkinson’s disease (PD). Recently, rigorous investigate had been conceded out for of PDs detection instigated through-sequence -and recessive auto-somal-of dominant-genes such as PARK2, LRRK2, SNCA, PARK7 and PINK1. Several inherent based similarity degree representations such as Cosine similarity and Hamming Distance model were introduced for the detection of these genes. However, these representations detect 2 to 3 gene sequence barely by maximum Root Mean Square Error (RMSE) and minimum accuracy rate. The ratio of misclassification is too great for prevailing scheme. To perceive PD through low RMSE and high accuracy a Kullback-Leibler Hausdroff distance (KL-H) similarity measure model is proposed so as to discover the affected patient pattern efficiently. It works in two phases, in first, protein sequence of amino acid is determined with the use of model transcription, splicing and translation (TST). The second stage in turn distinguish PD that depends on the model of similarity measure which comprise assessment of template sequence and specified sequence with the use of Hausdorff distance and KL-distance process. The property of nucleotide density in KL distance measure algorithm was employed. The result analysis and comparative study were presented among the proposed and existing system. We attained maximum accuracy of 88%, with sensitivity 67.86%, specificity 93.81%, precision 76%, F1 score 71.69%, minimum RMSE (12%) and FPR (6.19%)in comparison to the prevailing similarity measurement model.

A robust discriminative multi-atlas label fusion method for hippocampus segmentation from MR image

Accurate and automatic segmentation of the hippocampus plays a vital role in the diagnosis and treatment of nervous system diseases. However, due to the anatomical variability of different subjects, the registered atlas images are not always perfectly aligned with the target image. This makes the segmentation of the hippocampus still face great challenges. In this paper, we propose a robust discriminative label fusion method under the multi-atlas framework. It is a patch embedding label fusion method based on conditional random field (CRF) model that integrates the metric learning and the graph cuts by an integrated formulation. Unlike most current label fusion methods with fixed (non-learning) distance metrics, a novel distance metric learning is presented to enhance discriminative observation and embed it into the unary potential function. In particular, Bayesian inference is utilized to extend a classic distance metric learning, in which large margin constraints are instead of pairwise constraints to obtain a more robust distance metric. And the pairwise homogeneity is fully considered in the spatial prior term based on classification labels and voxel intensity. The resulting integrated formulation is globally minimized by the efficient graph cuts algorithm. Further, sparse patch based method is utilized to polish the obtained segmentation results in label space. The proposed method is evaluated on IABA dataset and ADNI dataset for hippocampus segmentation. The Dice scores achieved by our method are 87.2%, 87.8%, 88.2% and 88.9% on left and right hippocampus on both two datasets, while the best Dice scores obtained by other methods are 86.0%, 86.9%, 86.8% and 88.0% on IABA dataset and ADNI dataset respectively. Experiments show that our approach achieves higher accuracy than state-of-the-art methods. We hope the proposed model can be transferred to combine with other promising distance measurement algorithms.

Image recognition and blind-guiding algorithm based on improved YOLOv3

YOLOv3, a target detection algorithm based on deep learning, is widely applied in object recognition, especially in guiding the blind. The existing products of assisting the blind based on YOLOv3 can already achieve high-precision, high-real-time object recognition. But YOLOv3 also has many limitations, such as the inability to measure distances or it’s hard to recognize objects correctly in fog or haze. For these deficiencies, this paper proposes a road barrier monitoring method based on improved YOLOv3, using an image downsampling algorithm based on the dark channel to defog the image, and then with the binocular distance measurement algorithm to calculate the obstacles from the distance of the camera according to the width and height of the obstacles. The experimental results show that the improved product retains the advantages of high accuracy and fast recognition speed of YOLOv3. At the same time, it also owns the new functions of obstacle ranging and bad weather identification. The improved algorithm can meet the requirements of portability, real-time, and practicality of guide products.

Sustainable Management of the Supply Chain Based on Fuzzy Logic

Companies face a number of management challenges, and decision makers must adopt new approaches that consider sustainable development in their operations. In this context, the article aims to address sustainable supply chain management and propose solutions based on a fuzzy logic. Three distance measurement algorithms are applied to evaluate and classify suppliers in a consumer goods industry. The results demonstrate the usefulness of the algorithms in decision-making and bring a contribution to the sustainable development of companies. Furthermore, it supports future studies on sustainable management, the supply chain, and the application of algorithms to sustainability.