Data Objects Research Articles

ІНТЕЛЕКТУАЛЬНІ ПРОЦЕДУРИ АНАЛІЗУ ТА КЛАСИФІКАЦІЇ НЕТОЧНО ВИЗНАЧЕНИХ ДАНИХ ТЕХНОЛОГІЧНИХ ПРОЦЕСІВ ТА ПРИРОДНЬОМОВНИХ ОБ’ЄКТІВ

The report presents the development of research on intelligent procedures of Hamming's fuzzy neural network. It also presents a software complex designed for the implementation of classification tasks of imprecisely defined data of technological processes and natural language objects. The means for automating classification procedures that was developed have shown their effectiveness in the implementation of the tasks of the optimal processes management forming in various service systems that had imprecisely defined and incomplete data on the current state of functioning. In the tasks of the data classification obtained during the research of the texts, the issues of establishing their authorship were resolved. The possibilities of reduction procedures (reducing the number of classification templates parameters) were investigated, and a comparison of different forms of parameters formation of classification templates was also performed.

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics.

In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.

Development of a vision system integrated with industrial robots for online weld seam tracking

This paper develops a laser-structured light vision system for the industrial welding manipulator. The designed structured light vision system consisting of a line laser projector and an industrial camera is a decent 3D object data acquisition platform. By applying the principle of triangulation, the depth value of the object can be obtained via the laser plane in the captured image. According to hand-eye calibration and the principle of triangulation, the 3D position of the objects in the robot coordinate system is calculated. Based on the discriminated convolution tracker and deep reinforcement learning that increases the accuracy and processing time of the tracker, allowing simultaneous scanning for seam data acquisition and welding along the weld path, a real-time seam tracking algorithm is developed to handle the square-groove butt joint with small gaps (i.e., <1 mm). Experiments are undertaken using a Yaskawa industrial welding robot integrating the designed laser-structured light vision system. Experiment results show the significant performance of the developed system in welding square-groove butt joints with curved profiles and small gaps, i.e., the welding absolute mean error is 0.31 mm, whereas in the case of using convolution tracker only, the welding absolute mean error 1.7 mm.

Clustering on heterogeneous IoT information network based on meta path.

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection.

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance.

RESEARCH ON THE IMPACT OF GREEN FINANCE ON GREEN TECHNOLOGY INNOVATION

The green technology innovation of enterprises cannot succeed without the support of capital. Financing constraints are the bottleneck of green technology innovation activities carried out by enterprises. In this regard, the government has adopted green finance-related policies, and the financial institutions have supported more efficient green technology innovation in enterprises with financial instruments like green credit and green bonds. This article, with the starting point of green finance, the study object of financial data of A-share listed companies from 2016 to 2021, and the mediator of financing constraints, studies the impact of green finance on green technology innovation through finding the correlation between enterprises’ annual ESG rating and financing constraints. This study finds: the impact of green finance on green technology innovation is positive; financing constraints are the mediator between green finance and green technology innovation; and there is a negative correlation between enterprises’ ESG management performance and their financing constraints.&#x0D; On account of the conclusion of this article, the suggestions are as follows: it is necessary for the government to design the development framework of the green economy in a top-down way and to encourage financial institutions and enterprises to work together in terms of green innovation; financial institutions should actively practice the philosophy of green development and broaden the supporting channels of green finance; and enterprises need to focus on the philosophy of ESG development in order to gain more support for green investment capital.

Random Sampling Method of Large-Scale Graph Data Classification

Graph data appears in broad real-world applications in modelling complex objects in big data. Effective analysis of graph data provides a deeper understanding of the data in data mining tasks, including classification, clustering, prediction, and recommendation systems. Mining a large number of graphs becomes a challenging task because state-of-the-art methods are not scalable due to the memory limit. To address this issue, we propose a novel approximate random sampling method for large-scale graph data classification. In this approach, we applied a representation method to encode each graph as a record of a vector string and a set of graphs as a set of N records in a file. Then, we partition the set of records into disjoint subsets of data blocks, making each data block a random sample of the data file. After that, we randomly select a subset of data blocks, each being a random sample of the graph dataset, and compute the different graph property distributions. Since the data blocks in this model are much smaller than the entire data set, it is more efficient to analyze them on a standalone small machine, and multiple data blocks can be analyzed on multiple nodes of the cluster in parallel. Finally, we classified the graphs of data blocks using the SVM algorithm. In experimental evaluation, our proposed method outperformed state-of-the-art graph kernels on graph classification datasets in terms of accuracy.

A Deep-Learning-Based System for Pig Posture Classification: Enhancing Sustainable Smart Pigsty Management

This paper presents a deep-learning-based system for classifying pig postures, aiming to improve the management of sustainable smart pigsties. The classification of pig postures is a crucial concern for researchers investigating pigsty environments and for on-site pigsty managers. To address this issue, we developed a comprehensive system framework for pig posture classification within a pigsty. We collected image datasets from an open data sharing site operated by a public organization and systematically conducted the following steps: object detection, data labeling, image preprocessing, model development, and training. These processes were carried out using the acquired datasets to ensure comprehensive and effective training for our pig posture classification system. Subsequently, we analyzed and discussed the classification results using techniques such as Grad-CAM. As a result of visual analysis through Grad-CAM, it is possible to identify image features when posture is correctly classified or misclassified in a pig image. By referring to these results, it is expected that the accuracy of pig posture classification can be further improved. Through this analysis and discussion, we can identify which features of pig postures in images need to be emphasized to improve the accuracy of pig posture classification. The findings of this study are anticipated to significantly improve the accuracy of pig posture classification. In practical applications, the proposed pig posture classification system holds the potential to promptly detect abnormal situations in pigsties, leading to prompt responses. Ultimately, this can greatly contribute to increased productivity in pigsty operations, fostering efficiency enhancements in pigsty management.

Process-Structure-Property Relationship Development in Large-Format Additive Manufacturing: Fiber Alignment and Ultimate Tensile Strength.

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber's size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with -34.64% and -53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process-structure-property relationships for large-format AM.

Demystifying Ceph: Unveiling the Power of Open-Source Software-Defined Storage: Systematic Literature Review

Ceph, a free and open-source software-defined storage (SDS) platform, offers a highly adaptable, distributed solution, accommodating various applications like cloud storage and virtualization. Deployment requires specific hardware, supported Linux distribution, and network reliability. Each node serves multiple roles, facilitating object storage, monitoring, and management. Configuration entails network setup, key generation, monitor map creation, and daemon configuration. While this abstract introduces Ceph's fundamentals, in-depth exploration mandates consulting official documentation for proper configuration and best practices. It delves into Ceph's architecture, features, and applications. Providing unified storage for object, block, and file data.

Covering variable precision fuzzy rough sets based on overlap functions and the application to multi-label classification

Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified.

The first spectroscopic IR reverberation programme on Mrk 509

ABSTRACT Near IR spectroscopic reverberation of Active Galactic Nuclei (AGN) potentially allows the infrared (IR) broad line region (BLR) to be reverberated alongside the disc and dust continua, while the spectra can also reveal details of dust astro-chemistry. Here, we describe results of a short pilot study (17 near-IR spectra over a 183 d period) for Mrk 509. The spectra give a luminosity-weighted dust radius of 〈Rd,lum〉 = 186 ± 4 light-days for blackbody (large grain dust), consistent with previous (photometric) reverberation campaigns, whereas carbon and silicate dust give much larger radii. We develop a method of calibrating spectral data in objects where the narrow lines are extended beyond the slit width. We demonstrate this by showing our resultant photometric band light curves are consistent with previous results, with a hot dust lag at &amp;gt;40 d in the K band, clearly different from the accretion disc response at &amp;lt;20 d in the z band. We place this limit of 40 d by demonstrating clearly that the modest variability that we do detect in the H and K band does not reverberate on time-scales of less than 40 d. We also extract the Pa β line light curve, and find a lag which is consistent with the optical BLR H β line of ∼70–90 d. This is important as direct imaging of the near-IR BLR is now possible in a few objects, so we need to understand its relation to the better studied optical BLR.

Rekonstruksi bentuk lambung kapal dalam format tiga dimensi (3D) dengan teknik fotogrametri

Fishing boat has unique characteristics, such as seaworthiness, maneuvering, boat’s velocity, displacement, fishing gear layout, and catch storage facilities. With increasing development of photographic technology, photography is not merely an art but it has become a field of mathematics, making it a technique that can be integrated with software technology designed to produce digital data objects in the form of 3D images called Photogrammetric technique. This study is to obtain the reconstruction of the boat's hull based on the Photogrammetric software, to determine the effect of photography to obtain a 3D image, and to know the weakness and strength of 3D reconstruction of the boat’s hull. The images in this study were obtained using a POCO F3 smartphone camera integrated with the PIX4Dcatch application and analyzed with Agisoft Metashape Professional (Trial Version). From this research, we concluded that 1) the reconstruction of the boat hull can be obtained by using Photogrammetric techniques in the PIX4Dcatch application followed by the Agisoft Metashape Professional (Trial Ver.) software analysis; 2). With photographic equipment and several data processing software, the boat’s shape could be produced in 3D images, and 3). There are several advantages in reconstructing the ship's hull with the Photogrammetric technique, namely producing the ship's reconstruction of 3D images, obtaining the principal dimensions of the boat, such as the area and volume of the boat, that could be as the basic data for boat resistance and ship stability calculation, and the work could be done by one person only.

ANALISIS MANAJEMEN WAKTU KONSTRUKSI PEMBANGUNAN GEDUNG KLINIK UNIBA DENGAN MENGGUNAKAN METODE CPM DAN APLIKASI QM FOR WINDOWS V5

Factors that influence the delay in the construction of the UNIBA clinic building is a lack of communication between labor and supervisor, delay shiping material, quality of job control, the intensity of rainfall. This research is to determine the analysis of the management planning of the construction. To find out the urgency of the use of barchart in the process of managing the construction of the UNIBA clinic building. Conclution in this problem the author uses the CPM method, barchart. In this CPM analysis there are several jobs that belong to the criticalpath, namely the critical path of the work of the UNIBA clinic building structure there are 10 jobs, namely preparatory work (A-7 days). Bored pilee (B-21 days) work. Pile cap (C-21 days). Floor plate work (H-14 days). Ladderwork (-3 days). Beam wor (N-21 days). Floor plate work (S-0-14 daays). Sanitair work (W-35). Ceiling work (S-42). Paint work (X-42). Conclution of the use of barchart in the process of managing the construction of th UNIBA clinic building timeliness, data objectivity, submitting information on time, to provide standards that can be used to measure or compare progress with status, to identify and isolate the most important information

AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions.

The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence lowering its accuracy. To alleviate these specific problems and increase the clustering accuracy, we propose two methods that use the statistical data from a given dataset's k-nearest neighbor density distribution in order to determine the optimal ɛ values. Our approach removes the burden on the users, and automatically detects the clusters of a given dataset. Furthermore, a method to identify the accurate border objects of separate clusters is proposed and implemented to solve the unpredictability of the original algorithm. Finally, in our experiments, we show that our efficient re-implementation of the original algorithm to automatically cluster datasets and improve the clustering quality of adjoining cluster members provides increase in clustering accuracy and faster running times when compared to earlier approaches.

Physical physiognomies of hybrid Karmarkar stars

Our current study intends to investigate the peculiar features of a Hybrid compact star that is both static and anisotropic, wherein the core consists of strange quark matter, while regular baryonic matter is distributed in its crust. The relationship between the pressure of strange quark matter and density within the stellar interior is described using the simplest form of the phenomenological MIT Bag model equation of state: Pq=13(ρq−4Bg). On the other hand, the radial pressure and matter density resulting from baryonic matter are correlated through a straightforward linear equation of state: Pr=mρ. Apart from that, we also incorporate the Karmarkar condition, which establishes a connection between the two components of the metric potentials, specifically grr and gtt. For one metric potential, we presumptively choose a specific model i.e. eλ=1+a2r2(1+br2)4, and the Karmarkar condition leads to us the second potential. Moreover, the Einstein field equations are solved by using Karmarkar technique, and additional identification of the unknown constants is carried out using some physical constraints. We employ both graphical and analytical techniques to assess the physical plausibility of our suggested framework. More specifically, we focused on seven compact stars as candidates for strange quark stars in this investigation. For this purpose, the existing observational data of the compact objects were compared to theoretical mass–radius estimates. Furthermore, a series of physical tests has been conducted to substantiate the physical feasibility and stability of our proposed model. These experiments encompass both analytical and graphical assessments and involve examining various aspects, such as the dynamic balance of applied forces, compactness factor, energy conditions, surface redshift, and other relevant parameters. In conclusion, our comprehensive analysis reveals that the present system fulfills all the essential physical requirements necessary for a realistic representation of the hybrid compact star. Moreover, the model demonstrates suitability for conducting in-depth investigations pertaining to strange quark stars. The adherence to physical prerequisites and the potential for further exploration make our current model a promising candidate for studying and understanding the intriguing properties of such astrophysical objects.

Visualisation and outlier detection for probability density function ensembles

AbstractExploratory data analysis (EDA) for functional data—data objects where observations are entire functions—is a difficult problem that has seen significant attention in recent literature. This surge in interest is motivated by the ubiquitous nature of functional data, which are prevalent in applications across fields such as meteorology, biology, medicine and engineering. Empirical probability density functions (PDFs) can be viewed as constrained functional data objects that must integrate to one and be nonnegative. They show up in contexts such as yearly income distributions, zooplankton size structure in oceanography and in connectivity patterns in the brain, among others. While PDF data are certainly common in modern research, little attention has been given to EDA specifically for PDFs. In this paper, we extend several methods for EDA on functional data for PDFs and compare them on simulated data that exhibit different types of variation, designed to mimic that seen in real‐world applications. We then use our new methods to perform EDA on the breakthrough curves observed in gas transport simulations for underground fracture networks.

Error‐controlled feature selection for ultrahigh‐dimensional and highly correlated feature space using deep learning

AbstractDeep learning has been at the center of analytics in recent years due to its impressive empirical success in analyzing complex data objects. Despite this success, most existing tools behave like black‐box machines, thus the increasing interest in interpretable, reliable, and robust deep learning models applicable to a broad class of applications. Feature‐selected deep learning has emerged as a promising tool in this realm. However, the recent developments do not accommodate ultrahigh‐dimensional and highly correlated features or high noise levels. In this article, we propose a novel screening and cleaning method with the aid of deep learning for a data‐adaptive multi‐resolutional discovery of highly correlated predictors with a controlled error rate. Extensive empirical evaluations over a wide range of simulated scenarios and several real datasets demonstrate the effectiveness of the proposed method in achieving high power while keeping the false discovery rate at a minimum.

A hybrid chaos-based algorithm for data object replication in distributed systems

One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms’ reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm’s performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach’s success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.

Extended scene deep-phase-retrieval Shack-Hartmann wavefront sensors

Context. Strong atmospheric turbulence has been a challenge for high-resolution imaging of solar telescopes. Adaptive optics (AO) systems are capable of improving the quality of imaging by correcting partial aberrations. Thus, the performance of Shack-Hartmann sensors in measuring aberrations generally determines the upper performance bound of AO systems. In solar AO, classic correlation Shack-Hartmann sensors only correct a small number of modal aberrations. Moreover, strong aberrations are difficult to measure stably by correlation Shack-Hartmann. In this context, the improvement in the performance of Shark-Hartmann sensors promises to enable higher-resolution imaging of extended objects for ground-based telescopes or Earth observation. Aims. We propose a new extended scene deep-phase-retrieval Shack-Hartmann wavefront sensing approach to improve the image quality of solar telescopes. It is capable of achieving high-accuracy measurements of high-spatial-resolution wavefronts on extended scene wavefront sensing. Moreover, it has great generalization when observing unknown objects from different fields of view of the telescope. Methods. Our proposed approach can extract features resembling the sub-aperture point spread function (PSF) from a Shack-Hartmann sensor image without any prior information. Then a convolutional neural network is used to establish a nonlinear mapping between the feature image and the wavefront modal coefficients. The extracted feature greatly eliminates the shape information of the extended object while maintaining more information related to aberrations. We verified the performance of the proposed method through simulations and experiments. Results. In the indoor experiment on the ground layer adaptive optics (GLAO) of the 1 m New Vacuum Solar Telescope, compared to the Shack-Hartmann correlation method, the proposed method reduces the correction errors by more than one third. When observing objects from different fields of view in the GLAO that differ from the object in the training data, the relative errors fluctuate within the range of 20% to 26%. The AO system with the proposed wavefront measurement method can obtain higher-resolution focal images of the simulated solar granulation after a round of offline correction. The average latency of the proposed method is about 0.6 ms.