Academic literature on the topic 'Temporal graph exploration'
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Journal articles on the topic "Temporal graph exploration"
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Erlebach, Thomas, and Jakob T. Spooner. "Exploration of k-edge-deficient temporal graphs." Acta Informatica 59, no. 4 (August 2022): 387–407. http://dx.doi.org/10.1007/s00236-022-00421-5.
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AbstractA temporal graph with lifetime L is a sequence of L graphs $$G_1, \ldots ,G_L$$ G 1 , … , G L , called layers, all of which have the same vertex set V but can have different edge sets. The underlying graph is the graph with vertex set V that contains all the edges that appear in at least one layer. The temporal graph is always connected if each layer is a connected graph, and it is k-edge-deficient if each layer contains all except at most k edges of the underlying graph. For a given start vertex s, a temporal exploration is a temporal walk that starts at s, traverses at most one edge in each layer, and visits all vertices of the temporal graph. We show that always-connected, k-edge-deficient temporal graphs with sufficient lifetime can always be explored in $$O(kn \log n)$$ O ( k n log n ) time steps. We also construct always-connected, k-edge-deficient temporal graphs for which any exploration requires $$\varOmega (n \log k)$$ Ω ( n log k ) time steps. For always-connected, 1-edge-deficient temporal graphs, we show that O(n) time steps suffice for temporal exploration.
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Erlebach, Thomas, Michael Hoffmann, and Frank Kammer. "On temporal graph exploration." Journal of Computer and System Sciences 119 (August 2021): 1–18. http://dx.doi.org/10.1016/j.jcss.2021.01.005.
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Zhang, Zhiwen, Chenghao Shi, Pengming Zhu, Zhiwen Zeng, and Hui Zhang. "Autonomous Exploration of Mobile Robots via Deep Reinforcement Learning Based on Spatiotemporal Information on Graph." Applied Sciences 11, no. 18 (September 7, 2021): 8299. http://dx.doi.org/10.3390/app11188299.
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In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take full advantage of the spatiotemporal feature and historical information in the autonomous exploration task, we propose a novel network called Spatiotemporal Neural Network on Graph (Graph-STNN). Specifically, the proposed Graph-STNN extracts the spatial feature using graph convolutional network (GCN) and the temporal feature using temporal convolutional network (TCN). Then, gated recurrent unit (GRU) is performed to synthesize the spatial feature, the temporal feature, and the historical state information into the current state feature. Combined with DRL, our Graph-STNN helps estimation of the optimal target point through extracted hybrid features. The simulation experiment shows that our approach is more effective than the GCN-based approach and the information entropy-based approach. Moreover, Graph-STNN also performs better generalization ability than GCN-based, information entropy-based, and random methods. Finally, we validate our approach on the simulation platform Stage with the actual robot model.
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Madhavan, Advait, Matthew W. Daniels, and Mark D. Stiles. "Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations." ACM Journal on Emerging Technologies in Computing Systems 17, no. 3 (May 11, 2021): 1–27. http://dx.doi.org/10.1145/3451214.
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Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the various ad hoc mappings of algorithms into hardware rely on researcher ingenuity and result in custom architectures that are difficult to systematize. We propose to associate race logic with the mathematical field of tropical algebra, enabling a more methodical approach toward building temporal circuits. This association between the mathematical primitives of tropical algebra and generalized race logic computations guides the design of temporally coded tropical circuits. It also serves as a framework for expressing high-level timing-based algorithms. This abstraction, when combined with temporal memory, allows for the systematic exploration of race logic–based temporal architectures by making it possible to partition feed-forward computations into stages and organize them into a state machine. We leverage analog memristor-based temporal memories to design such a state machine that operates purely on time-coded wavefronts. We implement a version of Dijkstra’s algorithm to evaluate this temporal state machine. This demonstration shows the promise of expanding the expressibility of temporal computing to enable it to deliver significant energy and throughput advantages.
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Zhang, Zhen, Jiajun Bu, Zhao Li, Chengwei Yao, Can Wang, and Jia Wu. "TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks." Neural Networks 140 (August 2021): 13–26. http://dx.doi.org/10.1016/j.neunet.2021.02.016.
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Andrienko, Natalia, and Gennady Andrienko. "State Transition Graphs for Semantic Analysis of Movement Behaviours." Information Visualization 17, no. 1 (February 1, 2017): 41–65. http://dx.doi.org/10.1177/1473871617692841.
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A behaviour can be defined as a sequence of states or activities occurring one after another. A behaviour consisting of a finite number of reoccurring states/activities may be represented by a directed weighted graph with nodes and edges corresponding, respectively, to the possible states and transitions between them, while the weights represent the probabilities or frequencies of the state and transition occurrences. The same applies to multiple behaviours sharing the same set of possible states. In analysis of movement data, state transition graphs can be used to represent semantic abstractions of mobility behaviours, where states correspond to semantic categories of visited places (such as ‘home’, ‘work’, ‘shop’), activities of moving objects (‘driving’, ‘walking’, ‘exercising’, etc.) or characteristics of the movement (‘straight movement’, ‘sharp turn’, ‘acceleration’, ‘stop’, etc.). Such a representation supports the exploration and analysis of the semantic aspect (i.e. the meaning or purposes) of movement. For comprehensive analysis of movement data, state transition graphs need to be combined with representations reflecting the spatial and temporal aspects of the movement. This requires appropriate coordination between different visual displays (graphs, maps and temporal views) and appropriate reaction to analytical operations applied to any of the representations of the same data. We define in an abstract way the reactions of a graph display to analytical operations of querying, partitioning and direct selection. We also propose visual and interactive display features supporting comparisons between data subsets and between results of different operations. We demonstrate the use of the display features by examples of real-world and synthetic data sets.
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Kim, Sung-Kyun, Amanda Bouman, Gautam Salhotra, David D. Fan, Kyohei Otsu, Joel Burdick, and Ali-akbar Agha-mohammadi. "PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 652–62. http://dx.doi.org/10.1609/icaps.v31i1.16014.
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In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.
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Muoka, Pascal, Daniel Onwuchekwa, and Roman Obermaisser. "Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm." Electronics 11, no. 1 (December 24, 2021): 49. http://dx.doi.org/10.3390/electronics11010049.
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Adaptation in time-triggered systems can be motivated by energy efficiency, fault recovery, and changing environmental conditions. Adaptation in time-triggered systems is achieved by preserving temporal predictability through metascheduling techniques. Nevertheless, utilising existing metascheduling schemes for time-triggered network-on-chip architectures poses design time computation and run-time storage challenges for adaptation using the resulting schedules. In this work, an algorithm for path reconvergence in a multi-schedule graph, enabled by a reconvergence horizon, is presented to manage the state-space explosion problem resulting from an increase in the number of scenarios required for adaptation. A meta-scheduler invokes a genetic algorithm to solve a new scheduling problem for each adaptation scenario, resulting in a multi-schedule graph. Finally, repeated nodes of the multi-schedule graph are merged, and further exploration of paths is terminated. The proposed algorithm is evaluated using various application model sizes and different horizon configurations. Results show up to 56% reduction of schedules necessary for adaptation to 10 context events, with the reconvergence horizon set to 50 time units. Furthermore, 10 jobs with 10 slack events and a horizon of 40 ticks result in a 23% average sleep time for energy savings. Furthermore, the results demonstrate the reduction in the state-space size while showing the trade-off between the size of the reconvergence horizon and the number of nodes of the multi-schedule graph.
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Wang, Dongjie, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles E. Hughes, and Yanjie Fu. "Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4410–17. http://dx.doi.org/10.1609/aaai.v35i5.16567.
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In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.
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Luo, Xiao, Hui Hong, Shuyue Wang, Kaicheng Li, Qingze Zeng, Luwei Hong, Xiaocao Liu, et al. "Exploration of the Mechanism Underlying the Association of Incident Microinfarct and Motor Deficit: A Preliminary Functional MRI Study." Journal of Alzheimer's Disease 85, no. 4 (February 15, 2022): 1545–54. http://dx.doi.org/10.3233/jad-215227.
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Background: Cerebral microinfarcts (CMIs) might cause measurable disruption to brain connections and are associated with cognitive decline, but the association between CMIs and motor impairment is still unclear. Objective: To assess the CMIs effect on motor function in vivo and explore the potential neuropathological mechanism based on graph-based network method. Methods: We identified 133 non-demented middle-aged and elderly participants who underwent MRI scanning, cognitive, and motor assessment. The short physical performance battery (SPPB) assessed motor function, including balance, walking speed, and chair stand. We grouped participants into 34 incident CMIs carriers and 99 non-CMIs carriers as controls, depending on diffusion-weighted imaging. Then we assessed the independent CMIs effects on motor function and explored neural mechanisms of CMIs on motor impairment via mapping of degree centrality (DC) and eigenvector centrality (EC). Results: CMIs carriers had worse motor function than non-carriers. Linear regression analyses showed that CMIs independently contributed to motor function. CMIs carriers had decreased EC in the precuneus, while increased DC and EC in the middle temporal gyrus and increased DC in the inferior frontal gyrus compared to controls (p < 0.05, corrected). Correlation analyses showed that EC of precuneus was related to SPPB (r = 0.25) and balance (r = 0.27); however, DC (r = –0.25) and EC (r = –0.25) of middle temporal gyrus was related with SPPB in all participants (p < 0.05, corrected). Conclusion: CMIs represent an independent risk factor for motor dysfunction. The relationship between CMIs and motor function may be attributed to suppression of functional hub region and compensatory activation of motor-related regions.
Dissertations / Theses on the topic "Temporal graph exploration"
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Kerracher, Natalie. "Tasks and visual techniques for the exploration of temporal graph data." Thesis, Edinburgh Napier University, 2017. http://researchrepository.napier.ac.uk/Output/977758.
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This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same. The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way. While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated. Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques. The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified. The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems.
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Sanhes, Jeremy. "Contribution à la fouille de données spatio-temporelles : application à l'étude de l'érosion." Thesis, Nouvelle Calédonie, 2014. http://www.theses.fr/2014NCAL0065/document.
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Les événements spatio-temporels regroupent une large diversité de phénomènes comportant des caractéristiques propres. Par exemple, l’étude de flux migratoires se révèle ainsi très différente de l’étude de propagation de maladies. En effet, le domaine d’intérêt de la première porte sur le suivi des trajectoires, tandis que celui de la deuxième porte sur les facteurs de la propagation. De plus, chaque classe d’un problème spatio-temporel peut être abordée différemment, que l’on considère ou non un voisinage spatial, une caractérisation des objets d’étude unique ou multiple, ou bien une (in)dépendance entre les événements. Ainsi, les techniques de fouilles de données développées sont souvent restées spécifiques à une sous-classe de problème spatio-temporel, c’est-à-dire sous un ensemble restreint d’hypothèses.Or, pour réussir à dégager des connaissances nouvelles à partir de données, il est nécessaire d’élargir cet ensemble d’hypothèses, c’est-à-dire élargir le champs des possibles quant aux corrélations qu’il peut exister entre événements. Nous proposons donc une modélisation de ces phénomènes spatio-temporels permettant de prendre en compte plus de considérations que dans l’état de l’art. En outre, cette modélisation permet d’exprimer des événements qui existent dans les phénomènes d’érosion : un objet d’étude peut se diviser en plusieurs objets, ou fusionner avec d’autres objets pour n’en former qu’un seul. Plus précisément, nous modélisons les dynamiques spatio-temporelles sous la forme d’un unique graphe orienté, que la composante temporelle des problèmes rend acyclique, et dont les sommets sont attribués par plusieurs caractéristiquesSpatio-temporal events denote a large range of phenomena with different characteristics. For example, migration flows studies appear to be very different from disease spread studies. Indeed, interestingness of the first relies on tracking trajectories, whereas the second is about finding the factors of spread. Moreover, each class of a spatio-temporal problem can be tackled differently, depending on which parameters are considered: the studied spatial neighbourhood, the number of characteristics associated with the objects, or whether events are supposed correlated or independent. As a result, data mining techniques are often specificto a sub-class of spatio-temporal problem, that is to say, to a limited set of hypothesis.In order to bring out new knowledge from data, it seems to be necessary to enlarge this set of hypothesis, that is to say, to widen the field of possibilities regarding correlations that may exist between events. For this, we propose a new model that allows to take into account more considerations than existing studies. For example, this representation allows to model the complex spatio-temporal dynamic of erosion phenomenon: an object can be split up in several other objects, or can merge with other objects into one. More precisely, we use a single directed graph, that becomes acyclic thanks to the temporal component of the problem, and that is attributed by several characteristics
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Bach, Benjamin. "Connections, changes, and cubes : unfolding dynamic networks for visual exploration." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01020535.
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Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.
Book chapters on the topic "Temporal graph exploration"
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Erlebach, Thomas, Michael Hoffmann, and Frank Kammer. "On Temporal Graph Exploration." In Automata, Languages, and Programming, 444–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47672-7_36.
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Bumpus, Benjamin Merlin, and Kitty Meeks. "Edge Exploration of Temporal Graphs." In Lecture Notes in Computer Science, 107–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79987-8_8.
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Erlebach, Thomas, and Jakob T. Spooner. "Exploration of k-Edge-Deficient Temporal Graphs." In Lecture Notes in Computer Science, 371–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83508-8_27.
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Harbola, Shubhi, Martin Storz, and Volker Coors. "Augmented Reality for Windy Cities: 3D Visualization of Future Wind Nature Analysis in City Planning." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 241–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_15.
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AbstractEffective government management, convenient public services, and sustainable industrial development are achieved by the thorough utilization and management of green, renewable resources. The research and the study of meteorological data and its effect on devising renewable solutions as a replacement for nonrenewable ones is the motive of researchers and city planners. Sources of energy like wind and solar are free, green, and popularly being integrated into sustainable development and city planning to preserve environmental quality. Sensor networks have become a convenient tool for environmental monitoring. Wind energy generated through the use and maintenance of wind turbines requires knowledge of wind parameters such as speed and direction for proper maintenance. An augmented reality (AR) tool for interactive visualization and exploration of future wind nature analyses for experts is still missing. Existing solutions are limited to graphs, tabular data, two-dimensional space (2D) maps, globe view, and GIS tool designed for the desktop and not adapted with AR for easy, interactive mobile use. This work aims to provide a novel AR-based mobile supported application (App) that serves as a bridge between three-dimensional space (3D) temporal wind dataset visualization and predictive analysis through machine learning (ML). The proposed development is a dynamic application of AR supported with ML. It provides a user interactive designed approach, presenting a multilayered infrastructure process accessed through a mobile AR platform that supports 3D visualization of temporal wind data through future wind analysis. Thus, a novel AR visualization App with the prediction of wind nature using ML algorithms would provide city planners with advanced knowledge of wind conditions and help in easy decision-making with interactive 3D visualization.
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Khalgui, Mohamed, and Hans-Michael Hanisch. "Reconfiguration of Industrial Embedded Control Systems." In Behavioral Modeling for Embedded Systems and Technologies, 318–52. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-750-8.ch013.
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This research work deals with the development of safety reconfigurable embedded control systems following the international industrial component-based standard IEC61499. According to this standard, a function block (FB) is a functional unit of software and a control application a FB network that has to meet functional and temporal properties described in user requirements. We define in the book chapter a new semantic of the reconfiguration where a crucial criterion to consider is the automatic improvement of the system performance at run-time. If a reconfiguration scenario is applied at run-time, then the FB network implementing the system is totally changed or modified. To handle all possible reconfiguration forms, we propose thereafter an agent-based architecture that applies automatic reconfigurations to adapt the system according to well defined conditions and we model this agent with nested state machines according to the formalism of net condition/event systems which is an extension of the Petri net formalism. In order to satisfy user requirements, we specify the functional and temporal properties with the temporal logic CTL (as well as its extensions ECTL and TCTL) and we apply the model checker SESA to check the whole system behavior. To assign this reconfigurable system into the execution environment, we define thereafter an approach based on the exploration of reachability graphs to construct feasible OS tasks that encode the FB network corresponding to each reconfiguration scenario. Therefore, the system is implemented with sets of OS tasks where each set is to load in memory when the corresponding scenario is applied by the Agent. We developed the tool X-Reconfig to support these contributions that we apply on the FESTO and EnAS benchmark production systems available in our research laboratory.
Conference papers on the topic "Temporal graph exploration"
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Farrugia, Michael, and Aaron Quigley. "TGD: visual data exploration of temporal graph data." In IS&T/SPIE Electronic Imaging, edited by Katy Börner and Jinah Park. SPIE, 2009. http://dx.doi.org/10.1117/12.814921.
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Dal Col, Alcebiades, and Luis Gustavo Nonato. "Visual Analytics via Graph Signal Processing." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8295.
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This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Furthermore, we introduce in this dissertation a novel method for visual analysis of dynamic networks, which relies on the graph wavelet theory. Our method enables the automatic analysis of a signal defined on the nodes of a network. We use a fast approximation of the graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The wavelet coefficients naturally encode spatial and temporal variations of the signal, leading to an efficient and meaningful representation. This method allows for the exploration of the structural evolution of the network and their patterns over time. The effectiveness of our approach is demonstrated using different scenarios and comparisons involving real dynamic networks.