Data Objects Research Articles

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 >40 d in the K band, clearly different from the accretion disc response at <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.

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Exploring Celestial Object Characteristics: An In-depth Analysis of Quasars, Stars, and White Dwarfs Using the Sloan Digital Sky Survey (SDSS) Dataset

This research utilizes the Sloan Digital Sky Survey (SDSS) dataset, examining 12,884 observations to explore quasars, stars, and white dwarf objects. Magnitude data and coordinates across five filter bands are analyzed, revealing unique features through statistical methods. The identification of 77,429 quasars with 15 dimensions enhances the dataset. Thorough analyses of stellar and white dwarf classes, coupled with visualization techniques, unveil variable relationships. Residual validation and Gaussian kernel density plots confirm significant class differences. Non-linear regression and a normal distribution mixture model depict complex variable relationships. A parallel coordinates plot aids in interpreting data patterns, while predictive modeling via regression exposes meaningful coefficients. Logistic regression effectively classifies astronomical objects in the SDSS training data. This research contributes to understanding celestial object characteristics, offering valuable insights for astronomers and astrophysicists in analyzing large-scale astronomical datasets.

Analisis Sistem Akuntansi Keuangan Daerah Pada Badan Pengelolaan Keuangan Dan Aset Daerah (BPKAD) Kabupaten Sarolangun

This research aims to analyze the regional financial accounting system at the Sarolangun district regional financial and asset management agency office. This research lasted for 3 months. This research is qualitative research, while the data sources used in this research consist of secondary data sourced from information at the research site, scientific journals and books related to the research object and primary data obtained directly from respondents or those who are sample in this study. Data analysis in this research used the Miles & Huberman Interactive analysis technique. The results of this research show that in carrying out the main tasks and functions in the field of effective, accurate and transparent regional financial management, the Regional Financial & Asset Management Agency uses the SIMDA Finance Application and Microsoft Excel which helps manage regional government financial data. The implementation of the regional financial accounting system at BPKAD Sarolangun Regency is in accordance with Minister of Home Affairs Regulation Number 70 of 2019 concerning Regional Government Information Systems Articles 2 and 20. Preparation of BPKAD Sarolangun Regency Financial Reports consisting of: 1) Budget Realization Report, 2) Operational Report 3) Balance Sheet , 4) Equity Change Report 5) Cash Flow Report and 6) Notes to Financial Reports where the format and description of estimates have been classified in accordance with Government Regulation Number 71 of 2010 concerning Government Accounting Standards (PSAP 01) and Minister of Home Affairs Regulation Number 64 of 2013 Concerning the Implementation of Accrual-Based Government Accounting Standards in Regional Governments.

Design and Characterization of a Powered Wheelchair Autonomous Guidance System.

The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver's feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.

A Digital 4D Information System on the World Scale: Research Challenges, Approaches, and Preliminary Results

Numerous digital media repositories have been set up during recent decades, each containing plenty of data about historic cityscapes. In contrast, digital 3D reconstructions of no longer extant cityscapes have so far almost solely been performed for specific cases and via manual modelling techniques. Within the Jena4D research group, we are investigating and developing methods and technologies for transferring historical media and their contextual information into 4D models. The next step will be to automate this process and extend it to the world scale. Our team are working on different facets of that endeavor: retrieving images, text, and object data from public sources; crowdsourcing and collecting qualitative data from contests and pupil labs; processing historical plans and photographs to extract spatial data; location-based mobile visualization; and collection-browsing interfaces. This article is intended to highlight (1) current challenges, (2) the state of the art, (3) our approach to 4D modelling and visualization on the world scale, and (4) results from testing demo cases in Dresden, Jena, and Amsterdam.

Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas

Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.

Hyperspectral image classification based on convolutional neural network

Hyperspectral remote sensing is the acquisition of spatial and spectral data of objects by detecting the electromagnetic waves reflected from them using a hyperspectral sensor. It enables the evaluation and identification of material composition, morphology, structure, and other aspects. Currently, hyperspectral imaging is widely applied in fields such as agriculture, environmental monitoring, forestry, medicine, and geology. However, traditional hyperspectral image classification encounters the problem of high complexity and overfitting when dealing with small sample sizes, requiring the use of advanced convolutional neural network models to address this issue. This paper primarily employs literature analysis, review, and comparative analysis methods to summarize the main challenges encountered when using convolutional neural networks for hyperspectral image classification. It also selects popular models in recent years to introduce, namely 3D-CNN model, hyperspectral pyramidal ResNet model and HybridSN model. This article focuses on whether these three models can solve problems that traditional classification models cannot solve, and what room for improvement there is, which will provide a reference for future research.