Distance Method Research Articles

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building's refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

EFFICIENT SCHEMES OF CLASSIFIERS FOR REMOTE SENSING SATELLITE IMAGERIES OF LAND USE PATTERN CLASSIFICATIONS

Land use pattern classification of remote sensing imagery data is imperative to research that is used in remote sensing applications. Remote sensing (RS) technologies were exploited to mine some of the significant spatially variable factors, such as land cover and land use (LCLU), from satellite images of remote arid areas in Karnataka, India. Four diverse classification techniques unsupervised, and supervised (Maximum likelihood, Mahalnobis Distance, and Minimum Distance) are applied in Bellary district in Karnataka, India for the classification of the raw satellite images. The developed maps are then visually compared with each other and accuracy evaluations make using of ground-truths are carried out. It was initiated that the Maximum likelihood technique gave the finest results and both Minimum distance and Mahalnobis distance methods overvalued agricultural land areas. In spite of missing a few insignificant features due to the low resolution of the satellite images, a high-quality accord between parameters extracted automatically from the developed maps and field observations was found. Keywords: Remote sensing (RS), land cover and land use (LCLU).

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It not only provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model's input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning techniques and proposes a new model, the DLL model for multiple wells based on Transfer Learning and Multidimensional Feature Fusion (TMFF). Initially, the Euclidean distance and Maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining transfer learning techniques with the MFF model, the TMFF model is established. The model's capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Intelligent prediction of rail corrugation evolution trend based on self-attention bidirectional TCN and GRU

Analyzing the evolution trend of rail corrugation using signal processing and deep learning is critical for railway safety, as current traditional methods struggle to capture the complex evolution of corrugation. This present study addresses the challenge of accurately capturing this trend, which relies significantly on expert judgment, by proposing an intelligent prediction method based on self-attention (SA), a bidirectional temporal convolutional network (TCN), and a bidirectional gated recurrent unit (GRU). First, multidomain feature extraction and adaptive feature screening were used to obtain the optimal feature set. These features were then combined with principal component analysis (PCA) and the Mahalanobis distance (MD) method to construct a comprehensive health indicator (CHI) that reflects the evolution of rail corrugation. A bidirectional fusion model architecture was employed to capture the temporal correlations between forward and backward information during corrugation evolution, with SA embedded in the model to enhance the focus on key information. The outcome was a rail corrugation trend prediction network that combined a bidirectional TCN, bidirectional GRU, and SA. Subsequently, a multi-strategy improved crested porcupine optimizer (CPO) algorithm was constructed to automatically obtain the optimal network hyperparameters. The proposed method was validated with on-site rail corrugation data, demonstrating superior predictive performance compared to other advanced methods. In summary, the proposed method can accurately predict the evolution trend of rail corrugation, offering a valuable tool for on-site railway maintenance.

Modified crystal plasticity constitutive model considering tensorial properties of microstructural evolution and creep life prediction model for Ni-based single crystal superalloy with film cooling hole

Accurate assessment of the creep life of film cooling hole structures is critical for long-life design and safe operation of aero engines and gas turbines. Firstly, through the high temperature creep experiment of nickel-based single crystal superalloy with film cooling hole, the microstructure evolution process under multiaxial stress state around film hole is characterized. Then, considering the directional effect of rafting structure and the influence of multiaxial stress, a fourth-order tensor is used to describe the evolution of γ phase width, and the microstructure evolution model accounting for multi-axial stress states is established. The microstructure evolution is coupled into the crystal plasticity constitutive model by Orowan stress. Meanwhile, based on continuous damage mechanics, a new multiaxial damage evolution law is established by introducing a multiaxial ductility factor into the constitutive model. The improved crystal plasticity constitutive model can effectively predict the microstructural evolution under multiaxial stress conditions. Furthermore, the combination of the modified crystal plasticity constitutive model and the critical distance method considering stress gradients is used for life prediction of film cooling hole structures. The prediction results show the effectiveness and necessity of considering the microstructure evolution in the life prediction.

Letters of Historians of the 2nd Half of the 20th Century as a Channel of Scholarly and Business Communications (Based on Letters from Siberia to the Capital)

The article analyzes the letters of specialists in Russian history of the 17th – 19th centuries who sent correspondences to their colleagues in Moscow. The reason these letters are being analyzed is because, due to the distance and communication methods available at the time, correspondence was the main way for Siberian scholars to communicate with the representatives of the important scientific centers, to stay updated on the latest research trends and discoveries. During this era, authors were comparatively unrestricted in their statements, and their internal and external interactions were diverse and significant. Historians from Siberia and Moscow used to correspond on scientific and organizational matters through letters. The topics of discussions included participation in scientific conferences, invitations, opportunities for business trips, publications, cooperation, and reviews. The scholars shared their professional thoughts, ideas, and plans, characterized the sources they found and analyzed, expressed hypotheses and explained preliminary conclusions, reported the outcomes of research, and sometimes talked about the progress in their research projects and the development in their understanding of scientific problems. The letters often described events in the scientific careers of the authors. The content and emotional tone of the correspondence were significantly influenced by the author's character and relationship with the addressee. The letters often contained information of both scientific and personal nature. Sometimes, Siberian scientists requested assistance and help from their colleagues from the capital. The article concludes that letters were a reliable and effective communication channel among the professional community of historians. They were used to convey scientific, business, and occasionally personal information. The recipients of these letters were typically scholars from Moscow who were older in age and higher in status. As a reference group, they played a significant role in determining the specifics of the letters addressed to them.

Insights into the genetic divergence in Asiatic cotton (Gossypium arboreum L.) germplasm for fibre-quality traits

Abstract Asiatic cotton (Gossypium arboreum L.) has evolved in the Indian subcontinent and is known for its adaptability to low-input management conditions. In the present study, 300 diverse G. arboreum lines, including 100 Nandyal arboreum breeding lines (NAB), 132 Arboreum germplasm collections (AGC) and 68 long-linted arboreum genotypes (LLA), were evaluated for fibre quality to assess the diversity among them and to identify promising genotypes with desirable fibre traits. Significant variations were observed among the genotypes for the studied fibre-quality traits. Principal component analysis showed that the traits micronaire (Mic) and elongation percentage (E%) followed by upper half mean length (UHML) and bundle tenacity (tenacity) were the most significant contributors to variation. Cluster analysis based on the Euclidian distance method showed 16 clusters among 300 G. arboreum genotypes. The genotypes in cluster 4 have desirable UHML, tenacity and UI (uniformity index) traits, and cluster 12 has Mic and E% traits. Furthermore, the number of genotypes with desirable fibre-quality traits was found to be higher in the AGC group than in the LLA and NAB groups. The trait tenacity followed by the UHML showed relatively higher Shannon–Weiner diversity index values across different genotypic groups. Based on the superior performance, the genotypes PA 847, PA 809, PA 837, PA 863, NDLA 3147-2, NDLA 2974 and NDLA 3081 were found to be having desirable fibre traits. The identified promising genotypes are valuable genetic resources for improving fibre quality in G. arboreum cotton.

Research on Multi-Objective Optimization Methods of Urban Rail Train Automatic Driving Based on NSGA-II

In order to improve the control performance of automatic train operation (ATO) in urban rail trains, five typical operating sequences of urban rail trains were studied. Under the condition of meeting the safety and comfort principles of train operation, a train dynamics model was established to achieve the goals of low energy consumption, short running time, and high stopping accuracy in urban rail transit trains. In the process of finding a multi-objective solution to this problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used with an elite retention strategy, and the optimal Pareto multi-objective solution set was sought. In the process of optimal solution weight assignment, the hierarchical analysis Mahalanobis distance method, which combines subjective and objective analysis, was used. Finally, taking the Beijing Yizhuang subway line as the background design example, the simulation verified the effectiveness and feasibility of the algorithm and obtained high-quality automatic train driving curves under various working conditions. This research has important reference significance for the actual operation of automatic driving in urban rail trains.

Near‐Infrared Spectroscopy‐Based Chilled Fresh Lamb Quality Detection Using Machine Learning Algorithms

ABSTRACTTraditionally methods for assessing mutton quality rely on physical and chemical examination analyses that necessitate precise experimental environment conditions and specialized knowledge, often resulting in the compromise of the sample's structural integrity. To address these challenges, this study explores the application of near‐infrared spectroscopy (NIR) as a non‐destructive alternative for mutton quality evaluation, leveraging its operational simplicity, rapid analysis capabilities, and minimal requirement for technical expertise. Among various spectral data preprocessing techniques evaluated, multiple scattering correction (MSC) was found to significantly enhance model detection performance. Furthermore, principal component analysis (PCA) combined with the Mahalanobis Distance method was utilized for outlier identification. Finally, a mutton freshness detection model is constructed based on stacking ensemble learning, yielding an impressive accuracy rate of 0.976, outperforming other advanced approaches. In conclusion, our findings establish a robust theoretical framework for the rapid and non‐destructive assessment of meat freshness, contributing to advancements in meat quality detection.

Strong convergence of Bregman projection algorithms for solving split feasibility problems

Bregman distance methods play a key role in solving problems in nonlinear analysis and optimization theory, since the Bregman distance is a useful substitute for the metric. The main purpose of this paper is to investigate two new iterative algorithms based on the Bregman distance and the Bregman projection for solving split feasibility problems in real Hilbert spaces. The algorithms are constructed around these methods: Byrne’s CQ method, Polyak’s gradient method, Halpern method, and hybrid projection method. The proposed methods involve inertial extrapolation terms and self-adaptive step sizes. We prove that the proposed iterations converge strongly to the Bregman projection of the initial point onto the solution set. Some numerical examples are provided to illustrate the computational effectiveness of our algorithms. The main results extend and improve the recent results related to the split feasibility problem.

Linear polarization study of open clusters in the anticenter direction: Signature of the spiral arms

Aims. Our objective is to investigate the distribution of dust and associated large-scale structures of the Galaxy using optical linear polarization measurements of various open clusters located at different distances in the Galactic anticenter direction. Methods. We present R-band linear polarization observations of stars toward five open clusters: Kronberger 1, Berkeley 69, Berkeley 71, Berkeley 19, and King 8 in the anticenter direction. The polarization observations were carried out using the ARIES (Aryabhatta Research Institute of Observational Sciences) IMaging POLarimeter mounted on the 104 cm Sampurnanand telescope of ARIES, Nainital, making it the first study to target the polarization observations toward distant clusters (~6 kpc). We combined the observed polarization data with the distance information from the Gaia space telescope to infer the dust distribution along the line of sight. Results. The variation in the degree of polarization and extinction with distance reveals multiple dust layers in each cluster direction. In addition, common foreground-dust layers detected toward different cluster directions highlight global features such as spiral arms. Our results show that the dust clouds at 2 kpc toward Berkeley 69 and Berkeley 71 coincide with the Perseus arm, while the dust layer at ~4 kpc toward the distant clusters Berkeley 19 and King 8 indicates the presence of the Outer arm. The large-scale dust distribution obtained by combining our polarization results with previous polarization studies of nearby open clusters suggests that the anticenter direction is characterized by a low-extinction homogeneous dust distribution with a somewhat uniform orientation of the plane-of-sky component of the magnetic field along the line of sight. Conclusions. Our study demonstrates that polarization is useful as a tool for studying the large-scale dust distribution and structural features where kinematic distance methods are inadequate and cannot provide accurate distances to the dust clouds. The global dust distribution in the anticenter direction shows signatures of the intervening spiral arms.

Genetic variability and diversity analysis for some agronomic traits of a sweet potato (Ipomoea batatas L.) collection: Insights for breeding superior genotypes

A study was conducted during the 2017 cropping season to assess the genetic variability of selected agronomic traits in sweet potato. A total of 355 sweet potato genotypes (351 from different countries of the world and four check varieties) were evaluated at Tuber Crops Research Sub Centre, Bangladesh Agricultural Research Institution, Bogura, Bangladesh. The experiment was conducted using an augmented randomized complete block design. Findings revealed positive correlations among five agronomic traits, namely the weight of marketable storage root per plant (MRW), weight of storage root per plant (RW), fresh weight of foliage per plant (FW), number of marketable storage root per plant (MRN) and number of storage root per plant (RN) suggesting their suitability for phenotypic selection. There was a positive skewness in most studied traits (FW, MRW, MRW and RW), indicating the presence of a few genotypes with exceptionally high values for those traits. By employing principal component analysis, the most influential traits in sweet potato genotypes were identified for first two components (Dim) showing significant contributions to Dim-1 and Dim-2 collectively accounting for 84.7 % of the total variance. Through cluster analysis, two main clusters (Cluster I and Cluster II) were identified among the studied genotypes, each characterized by distinct trait patterns. Cluster I demonstrated superior performance in all traits, while Cluster II exhibited lower values for all traits. Further sub-clustering within Cluster II (sub-clusters IIA and IIB) revealed additional variations of the collection. The Euclidean distance for inter-cluster (ranging from 7.94 to 11.39) and intra-cluster (ranging from 3.17 to 10.38) were determined utilizing the complete distance method. In restricted maximum likelihood (REML) model, all the traits were significantly influenced by genetic effects, with broad-sense heritability ranging from 32 % to 70 %. The REML displayed high selection accuracy, with values exceeding 91 % for studied traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) identified 18 genotypes, with Moz1.15 emerging as the best-performing genotype under a 5 % selection pressure. Among the selected genotypes, trait uniqueness was highest for FW (54 %), followed by RN (50 %), MRN (18 %), MRW (14 %), and RW (5 %). The selection gain for these top-ranked genotypes varied between 23.2 % and 69.2 %. Findings established the existence of useful genetic variability for selected agronomic traits of sweet potato that could be exploited for breeding, genetics study and conservation.

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

Convergence of Peaceman-Rachford splitting method with Bregman distance for three-block nonconvex nonseparable optimization

Abstract It is of strong theoretical significance and application prospects to explore three-block nonconvex optimization with nonseparable structure, which are often modeled for many problems in machine learning, statistics, and image and signal processing. In this article, by combining the Bregman distance and Peaceman-Rachford splitting method, we propose a novel three-block Bregman Peaceman-Rachford splitting method (3-BPRSM). Under a general assumption, global convergence is presented via optimality conditions. Furthermore, we prove strong convergence when the augmented Lagrange function satisfies Kurdyka-Łojasiewicz property. In addition, if the association function possessing the Kurdyka-Łojasiewicz property exhibits a distinctive structure, then linear and sublinear convergence rate of 3-BPRSM can be guaranteed. Finally, a preliminary numerical experiment demonstrates the effectiveness.

A Sensitivity Test on the Modifiable Areal Unit Problem in the Spatial Aggregation of Fossil Data

In paleobiology and macroevolution research, the spatial aggregation of fossil data can be influenced by the modifiable areal unit problem (MAUP), wherein the selection of different grid-cell sizes for data aggregation can lead to variations in statistical results. This study presents a case analysis focused on the spatial extent of marine bivalves and brachiopods over time across three Areas of Interest (AOIs) to evaluate the potential impact of the MAUP in grid-based fossil data processing. By employing rectangular grid matrices with cell sizes of 50, 100, 200, and 400 km, this research assesses the MAUP-related sensitivity of two commonly used grid-based proxies for species’ spatial distribution. The results reveal that the proxy based on the number of occupied grid cells (OGCs) is particularly sensitive to changes in cell size, whereas the proxy based on minimum-spanning-tree distance (MST distance) demonstrates greater robustness across varying grid scales. This study underscores that when constructing proxies for species’ spatial distribution ranges using grid matrices, the OGC method is more susceptible to MAUP effects than the MST distance method, warranting increased caution in studies employing the OGC approach.

Shift Pruning-Based SCL Decoding for Polar Codes

In the context of the high-speed development of 5G communications, high-performance decoding schemes for polar codes are a hot spot in channel coding research. Shift pruning successive cancellation list (SP-SCL) decoding aims to recover the correct path by shift pruning in the extra SCL decoding. However, the current SP-SCL decoding is inflexible in determining the shift positions. In this paper, a flexible shift pruning SCL (FSP-SCL) decoding is proposed. Firstly, the reasons for movement and the eliminated states of the correct path are analyzed in detail using the path metric range (PMR), and on this basis, the validity of the method adopted in this paper for determining the shift priority of the information bits is verified. Secondly, the FSP-SCL decoding proposes two methods for determining the shift positions. One is the log-likelihood ratio (LLR) threshold method, which compares the LLR values of the eliminated paths on the shift bit with the corresponding LLR threshold to determine the shift positions. The other is the path distance method. It combines the minimum distance between the eliminated paths and the received vector with the path metrics to determine the shift positions. Both methods are more flexible and practical, as they can calculate the corresponding shift positions online based on a specific shift bit, avoiding the high complexity caused by the simulation method. Finally, this paper designs various experimental schemes to verify the decoding performance of the FSP-SCL. The experimental results show that in terms of error-correction performance, the LLR threshold-based FSP-SCL (FSPL (LLR threshold)) decoding, the path distance-based FSP-SCL (FSPL (path distance)) decoding and the existing SP-SCL decoding are roughly equal overall. In terms of decoding complexity, FSPL (LLR threshold) decoding is slightly better than FSPL (path distance) decoding, and the decoding complexity of both is lower than that of SP-SCL decoding, with the difference being more pronounced in the medium to high SNR regions.

A novel asymmetric extension of power XLindley distribution: properties, inference and applications to engineering data

It is impossible to overstate the importance of using trigonometric functions appropriately in distribution theory. The main contribution of the research is to construct a flexible trigonometric extension of the power XLindley distribution. More specifically, we build an innovative two-parameter lifetime distribution known as the sine power XLindley distribution (SPXLD) using characteristics from the sine-generated family of distributions. As the main motivational fact, it provides an attractive alternative to the power Lindley, power XLindley, weighted Lindley, and extended power Lindley distributions; it may be better able to model lifetime phenomena presenting data of leptokurtic and platkurtic nature. In contrast to the increasing, decreasing, and reversed-j-shaped hazard rate function, the density exhibits asymmetric shapes with varying peakedness levels. Several significant characteristics are illustrated, including moments, the quantile function, the probability density function in series representation, the stress-strength reliability, and incomplete moments. To analyze the behavior of the suggested distribution, sixteen estimation techniques are applied, such as the maximum likelihood, percentiles, some methods of minimum distances, some methods based on minimum and maximum spacing distances, and the Kolmogorov method. After that, an extensive simulation study and the examination of two survival real datasets are used to show the viability, usefulness, and adaptability of the SPXLD. Relevant goodness of fit criteria demonstrates that the SPXLD fits several current distributions.

Evaluation of Complex Shear Modulus of Recycled Asphalt Mortars Based on Euclidean Distance Method in the Mixing Process

Evaluation of Complex Shear Modulus of Recycled Asphalt Mortars Based on Euclidean Distance Method in the Mixing Process

Classification of kimchi using laser-induced breakdown spectroscopy and k-nearest neighbors modeling

This study introduces a novel application of Laser-Induced Breakdown Spectroscopy (LIBS) combined with k-nearest neighbors (KNN) modeling to classify the origin of kimchi. Using the spectral intensities of Mg II at 279 nm and K I at 766 nm, we achieved a classification accuracy of 92.8 %. This method effectively leverages regional differences in salt supply chains impacting kimchi's elemental composition. The innovation lies in applying the interclass distance method for variable selection in LIBS analysis, enhancing the interpretability and accuracy of food classification. Compared to traditional elemental analysis techniques, LIBS offers a practical, cost-effective solution for rapid field analysis with minimal sample preparation. This study not only demonstrates the potential of LIBS for food authenticity but also provides insights for developing accurate methods for detecting Mg and K in various food products, contributing to advancements in food quality control.

Investigation of sustainable operation oriented- economic, process and environment based multi-criteria optimization of large scale methanol production plant

Methanol is one of the main raw materials and a building block for many essential chemicals in the industry, and it is widely produced from the synthesis gas. It is used as a fuel, solvent, inhibitor, antifreeze agent, and in producing a variety of chemicals including olefins and paraffin. The global demand for methanol is 70 million tons per year and it is estimated to grow by a factor of 6–8% per year so it is necessary to develop a methanol production process, which is economical, and environment friendly. The multi-objective optimization (MOO) approach is used to optimize the large scale Lurgi two-step methanol synthesis process using the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto front solutions involving the process, economic, and environmental-based objectives. Methanol production rate, total energy consumption, total annual CO2 emissions, and profit are considered as objectives. The five decision variables include syngas molar flow, reactor feed pressure, reactor 1 temperature, reactor 2 temperature, and by-product mass flow in the methanol distillation column. Two constraints are set on ethanol mole fraction in the methanol stream, and methanol mole flow in water to the treatment stream. Three optimization cases (two bi-objective and one tri-objective) are developed. In the Case 1 study, CO2 emissions decreased by 65.83%, the methanol production rate increased by 10.11%, and total energy was reduced by 57.55%. The Pareto ranking is carried out using the Preference Ranking On the Basis of Ideal-average Distance (PROBID) method and the best optimal solution is reported for all cases.