Different Types Of Objects Research Articles

Monitoring of radioactive pollution of the site and elimination of the consequences of natural and technogenic disasters

The article proposes an urgent task to increase the level of environmental safety of radioactively contaminated areas. The main tasks are to develop new methods of environmental monitoring; study of migration processes of radioactive elements in the soil cover in order to develop predictive models for timely identification of areas at risk of contamination; establishing the relationships between the state of soil contamination by radionuclides and their genesis and identifying patterns of pollutant distribution in the study environment. The urgency of the topic is due to the requirements of advanced automated systems for monitoring the radiation situation and ACS to reduce the processing time of data on radioactive contamination of the area and increase the reliability of the results of radiation detection. The aim of the work is to separate the tasks that are allocated in the development of a unified system of methods and algorithms for processing information about radioactive contamination in automated radiation monitoring systems, dangerous in terms of radiation. The paper formulates the problem of eliminating the consequences of natural and man-made disasters in Ukraine. The analysis of problems of management of technogenic safety where the emergency situation is fixed is carried out. A formal description of the studied system and its environment is obtained, based on the principles of the systems approach. The classification of sources of emergencies based on the causes, different types of objects of damage depending on the nature of behavior in extreme conditions, different types of protection resources that implement the necessary protective measures. The direct and inverse problems of finding the optimal plan are considered. The selection of objects for a direct task is carried out in order according to their priority. When solving the inverse problem, the selection of objects is carried out until the specified level of system effect, which accumulates in the selection process. Information on sources of radioactive contamination and meteorological information are used in forecasting.

Theoretical principles of spatial analysis of the history of administrative-territorial division

The article considers the theoretical aspects of spatial and spatio-temporal analysis of changes in the administrative-territorial division, its history. It is shown that despite the presence of a significant number of publications on the history of administrative-territorial division, the complex issue of its detailed spatial analysis has not been resolved. The main focus of the author of the article is on diachronic analysis. Levels of analysis are highlighted depending on which object from the history of territorial division is analyzed (one-dimensional linear objects – administrative boundaries, dynamic polygonal objects – administrative units, objects denoting changes, or arbitrary territories and territories with an identical history of administrative affiliation, within which the history of specific objects in the history of administrative-territorial division is summarized with the calculation of the corresponding aggregative indicator). The difference between the study of change as part of the history of specific objects of territorial organization and the study of change as individual spatial objects is described. The principles of allocation of areas of spatial changes, their varieties are described. For each level of analysis, the types of objects that can be units of mapping on analytical or synthetic maps from the history of administrative-territorial division are proposed and described in detail. Typical operations on space-time data on the history of territorial division are considered. Examples of specific indicators on the history of administrative-territorial division that can be calculated for the described objects are given. Differences in the calculation of similar in meaning indicators for objects of different types are described. Particular attention is paid to indicators-aggregates of the history of administrative-territorial division within individual territories. Classifications of such indicators are given. In particular, local aggregates, focal aggregates and aggregates of dynamic objects are distinguished. Examples of the analysis of the administrative reform of 2020 at the district level within the Dnipropetrovsk region are given. The features of comparative analysis of individual states or periods for different objects of change are briefly described.

A Meta Path Based Method for Entity Set Expansion in Knowledge Graph

Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction, query suggestion and new brand identification. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities. In addition, an automatic meta path generation method, called SMPG, has been designed to exploit the potential relations among entities. Heuristic learning and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE.

Using free satellite imagery to study the long-term evolution of intertidal bar systems

Intertidal bar systems (IBSs) are ubiquitous geomorphic features on beaches and play an important role in nourishing and protecting beaches. IBSs can also pose a significant threat to nearshore swimmers and ships. Therefore, studying the evolution of IBSs will yield valuable insight in understanding the dynamic nature of beaches, provide help in protecting beaches from current widespread erosion, and provide early warning of potential hazards. However, the data used by previous approaches are commonly site-specific and labor-intensive (or expensive), which restricts their application. This paper introduces a new approach for studying the long-term evolution of IBSs based on free satellite imagery. This satellite approach uses three methods, namely bar, trough, and breaker methods, to directly recognize the planar positions of IBSs. This approach has low requirements on satellite images, therefore it is conducive to making full use of the precious historical satellite images to study the highly dynamic evolution of IBSs in recent decades, which cannot be measured and photographed again. Based on the terabytes of free satellite imagery, this approach can be used on almost all beaches in the world, which will greatly expand the study object pool and facilitate the selection of study objects in different types of marine conditions. This paper also introduces how to use the satellite approach based on a case study. The case study quantitatively revealed the forming, migration, decay, and distribution of the IBSs around the Feng River mouth in China, evaluated the errors of the satellite-derived position and migration rate, analyzed the main factors affecting their evolution, and discussed the main factors influencing their evolution and their influence on the evolution of adjacent beaches. • A new satellite approach was provided to study the evolution of intertidal bar systems. • The approach uses bar, trough, and breaker methods to recognize the position of IBS. • The long-term evolution of IBSs around a river mouth was revealed and analyzed.

Low-Light Face Recognition and Identity Verification Based on Image Enhancement

After years of development, face recognition is now a relatively perfect technology. It is non-contact, intuitive, simple, accurate, and applicable to complex practical environments. To a certain extent, the application of deep learning has enhanced the accuracy of face recognition. But there are some defects with deep learning in detecting face objects of different types in different environments, calling for further explorations. Therefore, this paper explores the low-light face recognition and identity verification based on image enhancement. Specifically, light processing and Gaussian filtering were adopted to suppress and eliminate the low-light effect of low-light face images. The basic framework and objective function of the existing generative adversarial network (GAN) were modified. By learning the mapping of side and front faces in multi-pose face images in the image space, a cross-pose GAN was established to turn faces of different poses into front faces. The proposed model was proved effective through experiments.

APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars

Abstract We train a convolutional neural network, APOGEE Net, to predict T eff, log g , and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these parameters in a self-consistent manner not only for low-mass stars, (such as main-sequence dwarfs, pre-main-sequence stars, and red giants), but also high-mass stars with T eff in excess of 50,000 K, including hot dwarfs and blue supergiants. The catalog of ∼650,000 stars presented in this paper allows for a detailed investigation of the star-forming history of not just the Milky Way, but also of the Magellanic clouds, as different type of objects tracing different parts of these galaxies can be more cleanly selected through their distinct placement in T eff– log g parameter space than in previous APOGEE catalogs produced through different pipelines.

Improved SiamFC Target Tracking Algorithm Based on Anti-Interference Module

The SiamFC target tracking algorithm has attracted extensive attention because of its good balance between speed and performance, but the tracking effect of the SiamFC algorithm is not satisfactory in complex background scenes. When SiamFC algorithm uses deep semantic features for tracking, it has good recognition ability for different types of objects, but it has insufficient discrimination for the same types of objects. Therefore, we propose an effective anti-interference module to improve the discrimination ability of the algorithm. The anti-interference module uses another feature extraction network to extract the features of the candidate target images generated by the SiamFC main network. In addition, we set up the feature vector set to save the feature vectors of the tracking target and the template image. Finally, the tracking target is selected by calculating the minimum cosine distance between the feature vector of the candidate target and the vector in the feature vector set. A large number of experiments show that our anti-interference module can effectively improve the performance of SiamFC algorithm, and the performance of this algorithm can be comparable to the popular algorithms.

Mysterious odd radio circle near the large magellanic cloud – an intergalactic supernova remnant?

ABSTRACT We report the discovery of J0624–6948, a low-surface brightness radio ring, lying between the Galactic Plane and the large magellanic cloud (LMC). It was first detected at 888 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP), and with a diameter of ∼196 arcsec. This source has phenomenological similarities to odd radio circles (ORCs). Significant differences to the known ORCs – a flatter radio spectral index, the lack of a prominent central galaxy as a possible host, and larger apparent size – suggest that J0624–6948 may be a different type of object. We argue that the most plausible explanation for J0624–6948 is an intergalactic supernova remnant due to a star that resided in the LMC outskirts that had undergone a single-degenerate type Ia supernova, and we are seeing its remnant expand into a rarefied, intergalactic environment. We also examine if a massive star or a white dwarf binary ejected from either galaxy could be the supernova progenitor. Finally, we consider several other hypotheses for the nature of the object, including the jets of an active galactic nucleus (30Dor) or the remnant of a nearby stellar super-flare.

Multi-objective robust energy management for environment powered unmanned surface vehicles

The environment powered unmanned surface vehicle (EPUSV) is capable of achieving large-range and long-term marine operations by harvesting abundant environment energy. However, considering the intermittency of environment energy, limitation on battery capacity and varieties of loads, it is necessary to control and coordinate the electric devices of EPUSV to exploit energy efficiently. To address this issue, an optimization-based energy management strategy (EMS) is proposed in this paper to achieve optimal management for the devices of EPUSV. The operation constraints and relevant objectives of different types of devices are firstly modeled through mix-integer nonlinear programming (MINLP) method. The uncertainty parameters in the model are addressed by robust optimization method. Then various task modes are defined based on the preference for different objectives. Simulations indicate that the proposed method is able to achieve optimal scheduling for each device while satisfying the underlying operation constraints. Besides, the adaptability for different kinds of tasks and the robustness to the fluctuation of uncertainty parameters of the proposed EMS is analyzed and discussed.

Spatial Statistics for Understanding Tissue Organization.

Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

Using the Unity Game Engine to Develop a 3D Simulated Ecological System Based on a Predator–Prey Model Extended by Gene Evolution

In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of different types of objects. The simulation of ecosystems and the analysis of the data generated during the simulations can also be a starting point for further research, especially in relation to sustainability. Our system is publicly available, so anyone can customize and upload their own parameters, maps, objects, and biological species, as well as inheritance and behavioral habits, so they can test their own hypotheses from the data generated during its operation. The goal of this article was not to create and validate a model but to create an IT tool for evolutionary researchers who want to test their own models and to present them, for example, as animated conference presentations. The use of 3D simulation is primarily useful for educational purposes, such as to engage students and to increase their interest in biology. Students can learn in a playful way while observing in the graphical scenery how the ecosystem behaves, how natural selection helps the adaptability and survival of species, and what effects overpopulation and competition can have.

MSODANet: A network for multi-scale object detection in aerial images using hierarchical dilated convolutions

The object detection in aerial images is one of the most commonly used tasks in the wide-range of computer vision applications. However, the object detection is more challenging due to the following issues: (a) the pixel occupancy vary among the different scales of objects, (b) the distribution of objects is not uniform in aerial images, (c) the appearance of an object varies with different view-points and illumination conditions, and (d) the number of objects, even though they belong to same type, vary across the images. To address these issues, we propose a novel network for multi-scale object detection in aerial images using hierarchical dilated convolutions, called as mSODANet. In particular, we probe hierarchical dilated network using parallel dilated convolutions to learn the contextual information of different types of objects at multiple scales and multiple field-of-views. The introduced hierarchical dilated network captures the visual information of aerial image more effectively and enhances the detection capability of the model. Further, the extensive experiments conducted on three challenging publicly available datasets, i.e., Visdrone2019, DOTA (OBB & HBB), NWPU VHR-10, demonstrate the effectiveness of the proposed mSODANet and achieve the state-of-the-art performance on all three datasets.

GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification

With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network’s performance has been studied. It is shown how the network’s accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied.

Dynamic Selection of Optional Feature for Object Detection

To obtain the most intuitive pedestrian target detection results and avoid the impact of motion pose uncertainty on real-time detection, a pedestrian target detection system based on a convolutional neural network was designed. Dynamic Selection of Optional Feature (DSOF) module and a center branch were proposed in this paper, and the target was detected by an anchor-free method. Although almost all the most advanced target detectors use pre-defined anchor boxes to run through the possible positions, scales, and aspect ratios of search targets, their effectualness, and generalization ability are also limited by the anchor boxes. Most anchor-based detectors use heuristically guided anchor frames. Such a design is difficult to detect objects of different types and sizes, especially objects with highly overlapping boundaries. To solve this problem, the DSOF module is proposed in this paper, which selects for each instance the most appropriate feature layer through automatic feature selection. After using multi-level prediction, stacks of low-grade prediction bounding boxes will be generated far away from the target center. To eliminate these low-grade detections, we introduce a new center branch to predict the deviation of a pixel from its corresponding bounding box. This score is used to reduce the weight of the low-grade detection bounding box and merge the detection sequences into the Non-Maximum Suppression (NMS).

A Novel Hierarchical Shape Analysis based on Sampling Point-Line Distance for Regular and Symmetry Shape Detection

Regular and symmetry shapes occurred in natural and manufactured objects. Detecting these shapes are essential and still tricky task in computer vision. This paper proposes a novel hierarchical shape detection (HiSD) method, which consists of circularity and roundness detection, and regularity and symmetry detection phases. The first phase recognizes the circular and elliptical shapes using aspect ratio and roundness measurements. The second phase, the main phase in the HiSD, recognizes the regular and symmetry shapes using density distribution measurement (DDM) and the proposed sampling point-line distance distribution (SPLDD) algorithm. The proposed method presets effective with low computation cost shape detection approach which is not sensitive to specific category of objects. It enables to detect different types of objects involving the arbitrary, regular, and symmetry shapes. Experimental results show that the proposed method performs well compared to the existing state-of-the-art algorithms.

Reliable Template Matching for Image Detection in Vision Sensor Systems.

Template matching is a simple image detection algorithm that can easily detect different types of objects just by changing the template without tedious training procedures. Despite these advantages, template matching is not currently widely used. This is because traditional template matching is not very reliable for images that differ from the template. The reliability of template matching can be improved by using additional information (depths for the template) available from the vision sensor system. Methods of obtaining the depth of a template using stereo vision or a few (two or more) template images or a short template video via mono vision are well known in the vision literature and have been commercialized. In this strategy, this paper proposes a template matching vision sensor system that can easily detect various types of objects without prior training. To this end, by using the additional information provided by the vision sensor system, we study a method to increase the reliability of template matching, even when there is a difference in the 3D direction and size between the template and the image. Template images obtained through the vision sensor provide a depth template. Using this depth template, it is possible to predict the change of the image according to the difference in the 3D direction and the size of the object. Using the predicted changes in these images, the template is calibrated close to the given image, and then template matching is performed. For ease of use, the algorithm is proposed as a closed form solution that avoids tedious recursion or training processes. For wider application and more accurate results, the proposed method considers the 3D direction and size difference in the perspective projection model and the general 3D rotation model.

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances.

The possibilities of an effective method of two adjacent signals are investigated for the evaluation of Lamb waves phase velocity dispersion in objects of different types, namely polyvinyl chloride (PVC) film and wind turbine blade (WTB). A new algorithm based on peaks of spectrum magnitude is presented and used for the comparison of the results. To use the presented method, the wavelength-dependent parameter is proposed to determine the optimal distance range, which is necessary in selecting two signals for analysis. It is determined that, in the range of 0.17–0.5 wavelength where is not higher than 5%, it is appropriate to use in the case of an A0 mode in PVC film sample. The smallest error of 1.2%, in the distance greater than 1.5 wavelengths, is obtained in the case of the S0 mode. Using the method of two signals analysis for PVC sample, the phase velocity dispersion curve of the A0 mode is reconstructed using selected distances x1 = 70 mm and x2 = 70.5 mm between two spatial positions of a receiving transducer with a mean relative error , and for S0 mode, x1 = 61 mm and x2 = 79.7 mm with . In the case of the WTB sample, the range of 0.1–0.39 wavelength, where is not higher than 3%, is determined as the optimal distance range between two adjacent signals. The phase velocity dispersion curve of the A0 mode is reconstructed in two frequency ranges: first, using selected distances x1 = 225 mm and x2 = 231 mm with mean relative error ; and second, x1 = 225 mm and x2 = 237 mm with .

A visual object segmentation algorithm with spatial and temporal coherence inspired by the architecture of the visual cortex.

Scene analysis in video sequences is a complex task for a computer vision system. Several schemes have been addressed in this analysis, such as deep learning networks or traditional image processing methods. However, these methods require thorough training or manual adjustment of parameters to achieve accurate results. Therefore, it is necessary to develop novel methods to analyze the scenario information in video sequences. For this reason, this paper proposes a method for object segmentation in video sequences inspired by the structural layers of the visual cortex. The method is called Neuro-Inspired Object Segmentation, SegNI. SegNI has a hierarchical architecture that analyzes object features such as edges, color, and motion to generate regions that represent the objects in the scenario. The results obtained with the Video Segmentation Benchmark VSB100 dataset demonstrate that SegNI can adapt automatically to videos with scenarios that have different nature, composition, and different types of objects. Also, SegNI adapts its processing to new scenario conditions without training, which is a significant advantage over deep learning networks.

The Concept of Implementation of Multifrequency RFID System Industrial Involvement in Laboratory Conditions

People have been dealing with the correct identification of objects for a long time. In industry, we cannot avoid this area, whether it is to identify people, semi-finished products or final products. Therefore, this article deals with the design of a multifrequency RFID system for industry 4.0. The idea of the article is to implement one type of identification technology for tracking objects using the radio frequency spectrum at different wavelengths. We have based our design on the built industrial-assembly line in the SmartTechLab laboratory, where we have implemented LF, HF and UHF systems connected by an industrial PLC into a complex system. In this article, we gradually focus on the selection of RFID systems, their cooperation and the design of connection to one portable box. Using an RFID box, we can monitor different types of objects and verify RFID reading using a single reading device or by creating portal RFID gateways. The implemented system consists of four middleware and four independent antennas that can cooperate. For proper operation, there is necessary implement not only hardware but also necessary software. The system can identify RFID tags in the range of 1 cm to several meters. Also, the advantage of the design is that it identifies all types of tags (industry, label, ceramic, laundry, paper). One of the main benefits of the design is modularity, mobility and the creation of a robust design that can be used for measurements in companies and also for educating students in laboratory conditions. The whole system is designed to meet the requirements of Industry 4.0 and improve the competitiveness of businesses.

Image Recognition of Pledges of Capital Stock in Small- and Medium-Sized Enterprises Based on Partial Differential Equations

Image recognition is one of the core research directions in the field of computer vision research, which can be divided into general image recognition and fine-grained image recognition. General image recognition refers to the recognition of different types of objects; fine-grained image recognition refers to the recognition of different subclasses in the same broad class of objects, such as SME financing inventory pledge image recognition. In this paper, we propose a partial differential equation-based image recognition method for SME financing inventory pledges and conduct detailed analysis and experiments. Compared with general images, partial differential equation-based SME financing inventory pledges image recognition is difficult to recognize due to data characteristics such as small differences in features between classes, large differences in features within classes, and a small percentage of targets in the image. To address the problem that existing methods ignore the role of shallow features on fine-grained image recognition, this paper proposes a fine-grained image recognition method based on partial differential equations. By analyzing the important role of shallow features for fine-grained image recognition, a feature fusion method with adaptive weights is proposed. Using this method to fuse shallow and high-level semantic features for recognition, the role of shallow features in fine-grained image recognition is fully exploited. In addition, the proposed method does not change the order of magnitude of the model parameters and is highly transferable. The relevant experimental results verify the effectiveness of the proposed method.