Automatic Update Research Articles

Visual Analytics of Trajectories with RoseTrajVis

We propose RoseTrajVis, a visual analytics system for studying commuting behaviors using classic rose diagrams, which, given a location in a map, aggregate trajectories by direction, show additional summary information, such as average speed bands, and allow the application of spatial-temporal filters. The rose diagrams also include colored arrows on the border that point to the origin and destination of the trajectories and show the average speed. To support analytical work, the user can also adjust the aggregation radius, move a location marker to a different position on the map with automatic update of the corresponding rose diagram, and create multiple diagrams for the same location with different filters applied. We developed two prototypes of RoseTrajVis, which were evaluated through user studies covering various types of analytical tasks with trajectories. Results suggest that the concept of rose diagrams was well understood, that the system was easy to use, and reveal that most tasks were executed with no errors. RoseTrajVis provides an innovative way of aggregating trajectories and offers features that enable the analysis of commuting movements and other exploratory tasks on a map, to support urban planning and operation.

Automatic Key Update Mechanism for Lightweight M2M Communication and Enhancement of IoT Security: A Case Study of CoAP Using Libcoap Library.

The ecosystem for an Internet of Things (IoT) generally comprises endpoint clients, network devices, and cloud servers. Thus, data transfers within the network present multiple security concerns. The recent boom in IoT applications has accelerated the need for a network infrastructure that provides timely and safe information exchange services. A shortcoming of many existing networks is the use of static key authentication. To enable the use of automatic key update mechanisms in IoT devices and enhance security in lightweight machine-to-machine (M2M) communications, we propose a key update mechanism, namely, double OTP (D-OTP), which combines both one-time password (OTP) and one-time pad to achieve an IoT ecosystem with theoretically unbreakable security. The proposed D-OTP was implemented into the Constrained Application Protocol (CoAP) through the commonly used libcoap library. The experimental results revealed that an additional 8.93% latency overhead was required to obtain an unbreakable guarantee of data transfers in 100 CoAP communication sessions.

The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps

Timely and accurate large-scale mapping of the spread of winter wheat ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\emph {Triticum aestivum}$</tex-math></inline-formula> ) is crucial to guarantee food security, study climate change, and monitor operational agriculture. Traditional winter wheat mapping frameworks are constrained by insufficient spatial resolution and heavy dependence on field surveys, while traditional machine learning models excessively rely on subjective judgment. Furthermore, collecting sufficient field samples covering a large area is expensive and time consuming. In this context, an automatic label update deep learning solution is developed to produce 10 m resolution winter wheat maps using Sentinel-2 data and existing coarse-resolution (30 m) winter wheat mapping products. In particular, a label update module considering the unique phenological (seasonal) characteristics of winter wheat is designed to update labels in the training phase. The results indicate that our method yields a satisfactory classification result with an overall accuracy exceeding 92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${F}_{1}$</tex-math></inline-formula> score greater than 0.85 for all validation samples, even when no field survey data were used for training. Additionally, a 10 m spatial resolution winter wheat map for the entire Shandong province is generated, showing a significant correlation between the computed winter wheat map and the agricultural statistical land, with correlation coefficients of 0.95 and 0.78 at the municipal and county levels, respectively. The proposed methodology can serve as a viable and promising method for high-resolution, operational agricultural monitoring over large areas.

The place of impositions in the meaning typology

The paper reviews the notion of the imposition introduced in the paradigm of dynamic semantics. An imposition is an informative and automatic update of the common ground or the context set. This type of context update is associated with appositive clauses, evidential markers, parenthetical constructions. An imposition encodes the discourse behavior of its bearer, which allows us to examine the interplay between the semantic representation of an expression, its discourse change potential, the broader context, and the speaker’s intentions behind the information packaging. The paper also compares the notion of the imposition with the more traditional notions of conventional implicature and presupposition.

Integrating the Reconstructed Scattering Center Feature Maps With Deep CNN Feature Maps for Automatic SAR Target Recognition

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

Automatic update method of GIS platform drawing model based on machine learning

Aiming at the problem that the number of data bytes in the traditional automatic update technology of GIS platform is small, a method of automatic update of GIS platform graph model based on machine learning is studied. Firstly, the data of the GIS platform model is convolved by the iso-linear feature detection operator in the automatic updating technology of the GIS platform model, and the calculated data of the GIS platform model is expressed as spatial data. A reasonable updating criterion is established, the spatial relationship of GSI data is reconstructed by the measure of updating criterion, the data vector of GIS platform model updated within the updating time range is calculated, and the regional data elements in the space are constantly changed to complete the data updating of GIS platform model. The experimental results show that compared with the automatic updating method of GIS platform model, the proposed method can update more data bytes with the same number of data bytes.

Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

User experience research on social robot application

PurposeSocial robot, a subtype of robots that is designed for the various interactive services for human, which must deliver superior user experience (UX) by expressing human-like social behavior or service and emotional sensitivity. This study develops a social robot app called the “Music Buddy” in ASUS Zenbo that provides a situational music based on the users' electroencephalogram (EEG) data. The research uses this app to explore its UX criteria and the prioritization of human robot interaction (HRI).Design/methodology/approachThe research methodologies include the both system development and decision analysis for the social robot. The first part is to design and develop a social robot app. The second part is to investigate the criteria of HRI through the Analytic Hierarchy Process (AHP) from UX aspects.FindingsIn view of the results of the AHP, the first-layer criteria consist of personalized function, easy-to-use the system and intelligent process. In terms of prioritization of multi-criteria, the overall ranking discloses the nine criteria in order including autonomy for robot, easy-to-use EEG device, accurate music preference, simple operations for brainwave device and easy-to-use applications, active music recommendation, automatic updates of music and easy-to-use robot as well as fast detection for emotion.Originality/valueThis research includes a self-developed social robot app and its UX research using AHP. This paper contributes to the improvement and innovation of the social robot design according to the results of UX research on HRI of social robot.

DESIGNING DISTRIBUTED AUTOMATIC SOFTWARE UPDATE SYSTEM

This article offers the reader a practical guide to the design and development of a distributed client-server system designed to perform automatic software updates using the example of non-trivial functionality. For comparison, examples of existing systems and solutions used to perform software updates are given and justifications for their functional unsuitability in the given circumstances are provided.

The Application of Blockchain-Based Life Cycle Assessment on an Industrial Supply Chain

Life cycle assessment (LCA) is a widely recognized tool used to evaluate the environmental impacts of a product or process, based on the environmental inventory database and bills of material. Data quality is one of the most significant factors affecting the analysis results. However, currently, most datasets in inventory databases are generic, i.e., they may represent the material and energy flow of a process at a market average, instead of a specific process used by a manufacturer. As a result, stockholders are unable to track their supply chain to find out the actual environmental impact from each supplier and to compare the environmental performance of alternative options. In this paper, we developed a new framework, i.e., blockchain-based LCA (BC-LCA), where blockchain technology is adapted to secure and transmit inventory data from upstream suppliers to downstream manufacturers. With BC-LCA, more specific data can be acquired along the supply chain in a real-time manner. Moreover, the availability, accuracy, privacy, and automatic update of inventory data can be improved. A case study is provided based on an industrial supply chain to demonstrate the utilization of BC-LCA.

ONQUADRO: a database of experimentally determined quadruplex structures.

ONQUADRO is an advanced database system that supports the study of the structures of canonical and non-canonical quadruplexes. It combines a relational database that collects comprehensive information on tetrads, quadruplexes, and G4-helices; programs to compute structure parameters and visualise the data; scripts for statistical analysis; automatic updates and newsletter modules; and a web application that provides a user interface. The database is a self-updating resource, with new information arriving once a week. The preliminary data are downloaded from the Protein Data Bank, processed, annotated, and completed. As of August 2021, ONQUADRO contains 1,661 tetrads, 518 quadruplexes, and 30 G4-helices found in 467 experimentally determined 3D structures of nucleic acids. Users can view and download their description: sequence, secondary structure (dot-bracket, classical diagram, arc diagram), tertiary structure (ball-and-stick, surface or vdw-ball model, layer diagram), planarity, twist, rise, chi angle (value and type), loop characteristics, strand directionality, metal ions, ONZ, and Webba da Silva classification (the latter by loop topology and tetrad combination), origin structure ID, assembly ID, experimental method, and molecule type. The database is freely available at https://onquadro.cs.put.poznan.pl/. It can be used on both desktop computers and mobile devices.

VentQsys: Low-cost open IoT system for CO_2 monitoring in classrooms

In educational context, a source of nuisance for students is carbon dioxide (CO_2) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide (CO_2), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

Polygonization method for automatic generation of indoor and outdoor pedestrian navigation path for smart city

Smart mobility cannot be supported by smart technologies alone. It has to be supported by geospatial data with navigation lines. Manual methods for creating and updating them are not only costly but also time-consuming. Automatic extraction and updating of geospatial navigation lines from GIS and indoor layout plans are much needed. The navigation path is often stored and conceived as centreline graphs in GIS. However, navigation paths in which the centrelines are located can be considered as a combination of basic navigation polygons. This paper proposed a generic polygonization method that can automatically generate and update navigation centrelines from GIS and BIM. By constructing basic navigation polygons from GIS and decomposing path polygons in street layouts and floor plans from GIS and BIM into basic navigation polygons, indoor and outdoor pedestrian navigation paths and centrelines can be extracted and generated to be used for indoor and outdoor pedestrian navigation.

An innovative hybrid technique for road extraction from noisy satellite images

Remote sensing takes an important role in the detection and monitoring of natural resources. It is also used in abundant fields, including geography, topographical survey, and geoscience disciplines such as land monitoring, forest management, crop identification, soil mapping, identification of ocean resources, road extraction, etc. This road extraction has a vital application in which it plays a key role in the development of GIS. Automatic updating of GIS information has become very important in our daily life. Semi-automated road extraction had a problem of computational complexity, high rate; consume a large amount of manpower and time. A fully Automated process is better than this but due to the quality of RSI, pre-processing techniques, segmentation, etc., the extraction differs. This paper proposes a new hybrid technique for the extraction of roads with better accuracy. Gaussian filter and CLAHE are used to improve the quality of the image, and to reinforce the features with the neighboring pixels. This approach of extraction from RSI images is based on Histogram equalizer, fuzzy – c mean algorithm. This is achieved by mat-lab and applies to high-resolution satellite images.

A Moving Object Detection and Predictive Control Algorithm Based on Deep Learning

In traffic scene, a low detection rate of dynamic target is caused by interference features of background area and fast speed of detected moving target. In this paper, an optimal target detection and prediction algorithm is proposed. Firstly, the algorithm of real-time motion parameters (speed, direction, etc.) detection of moving objects (vehicles) based on the depth theory is studied. The original prediction problem is transformed into the problem of automatic updating rate of uncertain parameters and the problem of minimizing the maximum path distance. Secondly, by on-line estimation of the automatic update rate, the proposed trajectory generated is guaranteed to be the minimum length. The allocation strategy minimizes the maximum distance traveled in the collision free. Then, the stability of the estimation error system is guaranteed base on deep learning algorithm. Finally, the simulation shows that the proposed algorithm can be used for moving target detection and path prediction accurately. The performance of the control system is improved.

Non-technical losses detection using missing values’ pattern and neural architecture search

The fast growth of non-technical loss (NTL) has gradually become one of the main concerns for distribution network operators (DNOs). Electricity theft which constitutes the main part of NTL not only brings losses to the DNOs, but also reduces the quality of the supply. A traditional detection method relies on utility workers’ experience and consumes a large amount of manpower. Thanks to the emerging of advanced metering infrastructure (AMI), utility companies can now collect detailed data reflecting consumers’ electricity usage, which enabled algorithms-based non-technical loss detection. The current data-based methods focus on the characteristics of electricity consumption thereby less efficient when dealing with rapidly changed electricity theft techniques. This article introduced a new data set, the location information of missing values, to improve the accuracy of non-technical loss detection. The relationship between missing values and electricity theft techniques is analyzed and a neural network model is built through neural architecture search (NAS). The improved model achieved an excellent Area Under Curve (AUC) value around 0.926 which verified the close link between missing values and electricity theft techniques. The nature of neural architecture search allows automatic model update which makes it a user-friendly tool even for engineers without any neural network expertise. A case study was carried out in which the missing value pattern was analyzed through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm.

High-accuracy continuous mapping of surface water dynamics using automatic update of training samples and temporal consistency modification based on Google Earth Engine: A case study from Huizhou, China

Surface water is experiencing significant changes due to anthropogenic activities and climate change. Recent studies have explored long-term surface water dynamics with the development of multi-source remote sensing data and mapping technology. However, cost-effective and time-saving reliable references for mapping surface water from remote sensing images at multiple times are still lacking. Further, negligence of abnormal changes in time-series surface water maps seriously affects surface water mapping accuracy. In this study, we propose a high-accuracy method for long-term mapping of surface water by automatic update of training samples and temporal consistency modification in surface water sequences. Taking the surface water in Huizhou from 1986 to 2020 as a case study, the method is applied and tested. Temporal training samples were updated through Robust Mahalanobis distances and statistical filtering. Combined with the random forest algorithm and the Google Earth Engine platform, annual surface water maps were generated. A method for temporal consistency detection in moving window and abnormal changes modification rule with intra-annual information are proposed to improve annual surface water mapping accuracy. The accuracy of surface water dynamics and abnormal water modifications were quantified as 89.8% and 95.7%, respectively. With our proposed method, the resultant maximum annual water areas of Huizhou were detected, with a statistically significant upward trend and net increase of 103.1 km2 over 35 years. Reservoir construction and aquaculture expansion constitute the main sources of increased waters in this case study. It is demonstrated that our proposed method offers important advantages in terms of the accuracy of annual surface water maps and especially change monitoring.

Nonlinear finite element analysis of PBL shear connectors in hybrid structures

The load-bearing capacity of perfobond Leisten (PBL) shear connectors in hybrid structures is represented by the shear-friction effect between the shear surfaces caused by the interlocking of aggregates when the fracture surface of the concrete dowel slips under an external load. To carry out an accurate numerical simulation of the bearing capacity and shear transmission mechanism of the PBL shear connectors, based on the secondary development function of the user subroutine, user-defined field variable subroutine (USDFLD), the effective simulation of the transverse shear expansion effect of the concrete dowel is achieved. A comparison with the results of the test data shows that the concrete damage plasticity (CDP) model demonstrates good robustness and convergence for the prediction and simulation of the damage characteristics of concrete structures. Based on the user subroutine USDFLD's automatic update function of material points and element integration points to field variables, this model effectively simulated the functional gradient model of the concrete dilatancy angle, the load on the shear connectors is always transmitted from the near load end to the far one, and the bearing capacity is clearly affected by the boundary conditions.

Guiding the evolution of product-line configurations

A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated for configurations consisting of selected features. Product-line evolution can lead to unintended changes to product behavior. We illustrate that updating configurations after product-line evolution requires decisions of both, domain engineers responsible for product-line evolution as well as application engineers responsible for configurations. The challenge is that domain and application engineers might not be able to interact with each other. We propose a formal foundation and a methodology that enables domain engineers to guide application engineers through configuration evolution by sharing knowledge on product-line evolution and by defining automatic update operations for configurations. As an effect, we enable knowledge transfer between those engineers without the need for interactions. We evaluate our methodology on four large-scale industrial product lines. The results of the qualitative evaluation indicate that our method is flexible enough for real-world product-line evolution. The quantitative evaluation indicates that we detect product behavior changes for up to 55.3% of the configurations which would not have been detected using existing methods.

Crafting the Next Generation of Web-based Learning Tools for Manuscript Artefacts in the Time of the COVID-19 Pandemic. A Focus oN sCiENCE, TECHNoLoGY, AND ENGiNEERiNG CoDiCEs, WoRLD MAPs, AND ARcHiVAL DocuMENTs iN EXHiBiTioN sETTiNGs

This paper starts with a review of the secondary literature published in English about the strengths and weaknesses of the web-based learning tools available in museum systems as brought forward by the outbreak of the COVID-19 pandemic in 2020. Next, this review contextualises the solutions designed and developed within the Engineering-Historical-Memory online interactive system to fully access permanent and temporary exhibitions and discover the knowledge embedded in manuscript artefacts via the visitor's smart device or institutional interactive screens. With these solutions, the original artefacts, or their physical and digital replicas, become gateways to online interactive applications. These applications provide high-definition 2D and 3D reproductions of the artefacts and make them explorable by free navigation or via graphs and geospatial views with an automatic real-time update for relevant scholarly publications, images, and videos. © 2021. All Rights Reserved.