Journal articles: 'Interactive Multiple Model algoritmus'

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Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.

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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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Liu, Ming Yong, Yang Li, and Xiao Jian Zhang. "A Research on the Weight of Interactive Multiple Model in Maneuvering Target Tracking." Applied Mechanics and Materials 568-570 (June 2014): 1008–11. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1008.

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The establishment of the target model is the key of maneuvering target tracking. The previous research on interactive multiple model, which is applied on tracking extensively, focused on the design of the model set and fusion with other algorithms, while there is less study on change mechanisms of the model weight. In light of this, the impetus behind this paper is to do some analysis which based on the model weight of different trajectories, reveal the change rule. Finally, the validity of the proposed approach is demonstrated by simulation.

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Funkhouser, Thomas, Seth Teller, Carlo Séquin, and Delnaz Khorramabadi. "The UC Berkeley System for Interactive Visualization of Large Architectural Models." Presence: Teleoperators and Virtual Environments 5, no. 1 (January 1996): 13–44. http://dx.doi.org/10.1162/pres.1996.5.1.13.

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Realistic-looking architectural models with furniture may consist of millions of polygons and require gigabytes of data—far than today's workstations can render at interactive frame rates or store in physical memory. We have developed data structures and algorithms for identifying a small portion of a large model to load into memory and render during each frame of an interactive walkthrough. Our algorithms rely upon an efficient display database that represents a building model as a set of objects, each of which can be described at multiple levels of detail, and contains an index of spatial cells with precomputed cell-to-cell and cell-to-object visibility information. As the observer moves through the model interactively, a real-time visibility algorithm traces sightline beams through transparent cell boundaries to determine a small set of objects potentially visible to the observer. An optimization algorithm dynamically selects a level of detail and rendering algorithm with which to display each potentially visible object to meet a userspecified target frame time. Throughout, memory management algorithms predict observer motion and prefetch objects from disk that may become visible during imminent frames. This paper describes an interactive building walkthrough system that uses these data structures and algorithms to maintain interactive frame rates during visualization of very large models. So far, the implementation supports models whose major occluding surfaces are axis-aligned rectangles (e.g., typical buildings). This system is able to maintain over twenty frames per second with little noticeable detail elision during interactive walkthroughs of a building model containing over one million polygons.

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Tanaka, Misato, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu. "Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/302573.

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We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there are multiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences.

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Čertický, Martin, Michal Čertický, Peter Sinčák, Gergely Magyar, Ján Vaščák, and Filippo Cavallo. "Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment." Sensors 19, no. 5 (February 26, 2019): 989. http://dx.doi.org/10.3390/s19050989.

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Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

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Chanyaswad, Thee, Changchang Liu, and Prateek Mittal. "RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (January 1, 2019): 26–46. http://dx.doi.org/10.2478/popets-2019-0003.

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Abstract A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple algorithms for a range of machine learning applications, including both unsupervised and supervised learning. Furthermore, we rigorously prove that (a) our algorithms satisfy the strong ɛ-differential privacy guarantee, and (b) RON projection can lower the level of perturbation required for differential privacy. Finally, we illustrate the effectiveness of RON-Gauss under three common machine learning applications – clustering, classification, and regression – on three large real-world datasets. Our empirical results show that (a) RON-Gauss outperforms previous approaches by up to an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.

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Okkan, Umut, and Umut Kirdemir. "Towards a hybrid algorithm for the robust calibration of rainfall–runoff models." Journal of Hydroinformatics 22, no. 4 (May 8, 2020): 876–99. http://dx.doi.org/10.2166/hydro.2020.016.

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Abstract In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method – analysis of variance (ANOVA) – was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low.

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SHINDE, AMIT, MOEED HAGHNEVIS, MARCO A. JANSSEN, GEORGE C. RUNGER, and MANI JANAKIRAM. "SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK." International Journal of Innovation and Technology Management 10, no. 05 (October 2013): 1340019. http://dx.doi.org/10.1142/s0219877013400191.

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A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

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Tavana, Madjid, Dawn A. Trevisani, and Jerry L. Dussault. "The Stability Model." International Journal of Information Technology Project Management 4, no. 2 (April 2013): 18–34. http://dx.doi.org/10.4018/jitpm.2013040102.

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The increasing complexity and tight coupling between people and technology in military Command and Control (C2) systems has led to greater vulnerability due to system failure. Although system vulnerabilities cannot be completely eliminated, the accidental or anticipated failures have to be thoroughly understood and guarded. Traditionally, the failure in C2 systems has been studied with resiliency and the concept of self-healing systems represented with reactive models or robustness and the concept of self-protecting systems represented with proactive models. The authors propose the stability model for simultaneous consideration of robustness and resiliency in C2 systems. Robustness and resiliency are measured with multiple criteria (i.e. repair-recovery times and repair-recovery costs). The proposed interactive framework plots the robustness and resiliency measures in a Cartesian coordinate system and derives an overall stability index for various states of the C2 system based on the theory of displaced ideals. An ideal state is formed as a composite of the best performance values and a nadir state is formed as a composite of the worst performance values exhibited by the system. Proximity to each of these performance poles is measured with the Euclidean distance. The C2 system should be as close to the ideal state as possible and as far from the nadir state as possible. The stability index is a composite measure of distance from the ideal and nadir states in the C2 system. The authors present a case study at the Air Force Research Laboratory to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (December 30, 2013): 288. http://dx.doi.org/10.12688/f1000research.2-288.v1.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (May 12, 2014): 288. http://dx.doi.org/10.12688/f1000research.2-288.v2.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (July 1, 2014): 288. http://dx.doi.org/10.12688/f1000research.2-288.v3.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available athttp://www.physiome.org/jsim/.

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Barlowe, Scott, Heather B. Coan, and Robert T. Youker. "SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment." PeerJ 5 (June 27, 2017): e3492. http://dx.doi.org/10.7717/peerj.3492.

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Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information.

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Sakawa, Masatoshi, and Kosuke Kato. "An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures." Advances in Operations Research 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/372548.

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We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving problems, we also propose genetic algorithms with decomposition procedures. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the proposed method.

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Vakil, Vahideh, and Wade Trappe. "Drug Combinations: Mathematical Modeling and Networking Methods." Pharmaceutics 11, no. 5 (May 2, 2019): 208. http://dx.doi.org/10.3390/pharmaceutics11050208.

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Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

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Jeong, G., N. Freitas, Y. Cho, and C. Han. "[AI-Machine Learning] Optimized Sensorless Human Pose Estimation for a Kpop Dance Application." Volume 5 - 2020, Issue 8 - August 5, no. 8 (September 2, 2020): 893–96. http://dx.doi.org/10.38124/ijisrt20aug003.

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There has been a great effort to use technology to make exercise more interactive, measurable and gamified. However, in order to optimize the detection accuracy, these efforts have always translated themselves into motion detection with multiple sensors including purpose specific hardware, which results in extra expenses on both the content production and consumption and induces limitations on the final mobility of the user. In this paper we aim to improve the accuracy, learning speed and detail range of Posenet’s AI sensorless human pose detection by using an artificial neural network to optimize its extraction and comparison algorithms, changing the current model that uses a ResNet convolutional neural network (CNN) to a model using DenseNet and developing a new algorithm for detailed corrections using relevant artificial neural networks. The findings here will be applied on a posture correction system for a dance and fitness application.

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Qin, Li, Hongyu Wang, Yazhou Yuan, and Shufan Qin. "Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task." Sensors 21, no. 11 (May 31, 2021): 3818. http://dx.doi.org/10.3390/s21113818.

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The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features’ estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster–Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task.

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Liu, Hongqiang, Zhongliang Zhou, and Lei Yu. "Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement." Mathematical Problems in Engineering 2018 (June 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/4984186.

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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

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Yang, Xiaohui, Jiating Long, Peiyun Liu, Xiaolong Zhang, and Xiaoping Liu. "Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm." Energies 11, no. 9 (September 10, 2018): 2381. http://dx.doi.org/10.3390/en11092381.

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Microgrid, taking advantage of distributed power generation technology, plays an important role in maximizing the utilization of renewable energy. Based on the problems of the energy crisis, environmental contamination and the high operating cost of the microgrid, the microgrid model can effectively ease energy pressure. We can dispatch the output of each part in the microgrid to obtain the optimal economy. Since many traditional optimization algorithms have limitations of local optimization, multiple iterations, and slow convergence speed, this paper proposes a method that applies the Water Cycle Algorithm (WCA) to optimize the dispatch of the microgrid to minimize the operating cost. The mathematical model of each distributed power is established. The interactive power between the microgrid and large grid is also considered. The lowest generation cost considering environmental benefits is taken as the objective function. Water cycle algorithm is implemented to obtain the optimal solution under various constraints. Some optimization algorithms such as Genetic Algorithm (GA), Interior Search Algorithm (ISA), and Differential Search Algorithm (DSA) were used for results evaluation. By comparing the results obtained from four different algorithms, a case study shows the WCA possesses the advancements of better convergence performance, faster calculation and higher precision compared to the other algorithms. The results demonstrate that the WCA applied to determine the optimal scheduling of the microgrid can achieve a better result than some other algorithms with an acceptable accuracy and efficiency.

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Zhao, Jin Xiao, Jian Qiu Zhang, and Dong Ming Zhou. "Polynomial Model Set and its Interacting Multiple Model Algorithms." Advanced Materials Research 562-564 (August 2012): 2038–44. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2038.

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From the maneuvering target orbit on the geometrical properties, according to different motion modes track corresponding to different order number polynomial curve, using the least squares fitting structure, this paper gives out a group of various motion modes matching the mathematical model—polynomial model set (PMS), and gives distinct mathematical process. PMS covers all the motion modes theoretically, easy to choose according to the practical situation and expand, especially suitable for single model can not accurately describe the complex sports scene. The model need not consider sampling interval, without lowering the filter performance at the same time, reduced prior knowledge dependence. Finally, simulation results indicate that the correctness and validity and practicality.

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Yan, Shuyuan, Bolin Ding, Wei Guo, Jingren Zhou, Zhewei Wei, Xiaowei Jiang, and Sheng Xu. "FlashP." Proceedings of the VLDB Endowment 14, no. 5 (January 2021): 721–29. http://dx.doi.org/10.14778/3446095.3446096.

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Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution its alternatives on real data.

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Semerdjiev, Emil, Ludmila Mihaylova, Tzvetan Semerdjiev, and Violeta Bogdanova. "Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based On New Ship Models." Information & Security: An International Journal 2 (1999): 122–37. http://dx.doi.org/10.11610/isij.0211.

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Shen, Ye, Stephen Vosti, Beatrice Rogers, and Patrick Webb. "Integrating Cost-Effectiveness into Nutrition Programming Decisions of Specialized Nutritious Foods: An Evidence-Informed Interactive Tool." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 906. http://dx.doi.org/10.1093/cdn/nzaa053_111.

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Abstract Objectives An interactive Food Assistance Cost-Effectiveness Tool (FACET) was created to support funders and implementing partners of selected nutrition programs involving specialized nutritious foods (SNF) to factor cost-effectiveness into programming decisions in development and humanitarian contexts. Methods Built with the Shiny package in R, the FACET interface guides users through the process of integrating user-provided data and scientific evidence into program decision-making under a cost-effectiveness framework. Users create a scenario by inputting choices or values for program, cost, and impact parameters, while the back-end algorithms calculate a list of quantity, cost, and cost-effectiveness indicators. Users can then create multiple scenarios to compare across alternative program designs, product choices, or procurement channels. Tool inputs and outputs of the scenarios can be saved and downloaded into a spreadsheet. Whenever possible, FACET provides data visualizations of, or direct links to, data sources and references such as USAID historical product procurement costs, scientific literature on program impacts, and other relevant guidance. Users have the flexibility to use their data sources to develop model parameters if deemed more appropriate. Throughout the FACET interface and user manual development, extensive feedback was gathered from USAID, international agencies, implementation organizations, research institutions, etc. Results User engagement has helped identify applications of the FACET tool: 1.Throughout the programming cycle, e.g., during proposal development (by implementing partners), during proposal reviews (by funders), for end-line reporting (by implementing partners), and for program reviews (by implementing partners and funders);2.New SNF product vetting, e.g., assessing the extent to which promising new products can ‘compete’ with existing products in terms of cost-effectiveness. Conclusions FACET brings together diverse expertise and available data and strengthens nutrition program funders and implementing partners’ decision-making capacity around cost-effectiveness. The development of FACET serves as an example of how the research community can facilitate bringing data/evidence into real-world decision-making. Funding Sources United States Agency for International Development.

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Horton, Pascal. "AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)." Geoscientific Model Development 12, no. 7 (July 12, 2019): 2915–40. http://dx.doi.org/10.5194/gmd-12-2915-2019.

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Abstract. Analog methods (AMs) use synoptic-scale predictors to search in the past for similar days to a target day in order to infer the predictand of interest, such as daily precipitation. They can rely on outputs of numerical weather prediction (NWP) models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated useful potential for application in several contexts. AtmoSwing is open-source software written in C++ that implements AMs in a flexible way so that different variants can be handled dynamically. It comprises four tools: a Forecaster for use in operational forecasting, a Viewer to display the results, a Downscaler for climate studies, and an Optimizer to establish the relationship between predictands and predictors. The Forecaster handles every required processing internally, such as NWP output downloading (when possible) and reading as well as grid interpolation, without external scripts or file conversion. The processing of a forecast requires low computing efforts and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a 3-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment with several levels of synthesis and detail. This allows for the provision of a quick overview of the potential critical situations in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows for the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a semiautomatic sequential approach, Monte Carlo simulations, and a global optimization technique, using genetic algorithms. Establishing a statistical relationship between predictors and predictands is computationally intensive because it requires numerous assessments over decades. To this end, the code was highly optimized for computing efficiency, is parallelized (using multiple threads), and scales well on a Linux cluster. This procedure is only required to establish the statistical relationship, which can then be used for forecasting or downscaling at a low computing cost.

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Zhao, Bi, Akila Katuwawala, Christopher J. Oldfield, A. Keith Dunker, Eshel Faraggi, Jörg Gsponer, Andrzej Kloczkowski, et al. "DescribePROT: database of amino acid-level protein structure and function predictions." Nucleic Acids Research 49, no. D1 (October 29, 2020): D298—D308. http://dx.doi.org/10.1093/nar/gkaa931.

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Abstract We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

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Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

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Zhou, Weidong, and Mengmeng Liu. "Robust interacting multiple model algorithms based on multi-sensor fusion criteria." International Journal of Systems Science 47, no. 1 (April 9, 2015): 92–106. http://dx.doi.org/10.1080/00207721.2015.1029566.

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Wang, Xiao Yan, Gui Xiang Shen, Ying Zhi Zhang, and Shu Guang Sun. "An Analytical Model of Interactive Coefficients of Multiple Systems." Advanced Materials Research 1030-1032 (September 2014): 1292–95. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1292.

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Dependent failures of machines may take many forms. Interactive failure is one type of the dependent failures happening among multiple subsystems. The interactive coefficient is represented by the degree of interaction between two subsystems, but it cannot be estimated in intricate interactive relationship among multiple subsystems by now. In order to solve the problem, the failure chains of the interactive failure are classified into five types in this paper and all the elements in it are defined. Then the interactive coefficient models are developed for all failure chains according to the relationship among the interactive failure rate, the independent failure rate and the dependent failure rate. The estimation of the interactive coefficient is based on failure rate equation. The results offer a solution to the problem that the interactive coefficient of multiple systems cannot be calculated. These findings provide a method to predict and design the reliability of mechanical equipment more accuracy as well as guide to optimize scheduled for the maintenance of mechanical equipment.

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Li, Zhen-Xing, Yun Wang, Jin-Mang Liu, Ni Peng, and Lin-Hai Gan. "Variable-structure interacting multiple-model estimation for group targets tracking with random matrices." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 7 (February 9, 2017): 1201–11. http://dx.doi.org/10.1177/0954410016688123.

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In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.

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Jovandic, Igor, Zeljko Djurovic, and Branko Kovacevic. "Adaptive filtering algorithms in target tracking applications." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 317–26. http://dx.doi.org/10.2298/fuee0303317j.

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Comparison of several target tracking algorithms is presented. Namely discrete noise level adjustment (DNLA), variable state dimension (VSD) and interacting multiple model (IMM) algorithms are discussed. Target trajectory, target models, filtering algorithms and simulation results are given. The cumulative estimation error criterion is used in order to compare the algorithms.

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Liu, Hong Jiang. "Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm." Applied Mechanics and Materials 190-191 (July 2012): 906–10. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.906.

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In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

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Tang, Fuhui, Xiankai Lu, Xiaoyu Zhang, Shiqiang Hu, and Huanlong Zhang. "Deep feature tracking based on interactive multiple model." Neurocomputing 333 (March 2019): 29–40. http://dx.doi.org/10.1016/j.neucom.2018.12.035.

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Wang, Zhenshu, Na Ji, Yangyang Ma, Zhanjie Liu, and Yu Wang. "Model selection mechanism of Interactive Multiple Load Modeling." International Journal of Electrical Power & Energy Systems 103 (December 2018): 58–66. http://dx.doi.org/10.1016/j.ijepes.2018.05.027.

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Ding, Zhen. "Static/dynamic distributed interacting multiple model fusion algorithms for multiplatform multisensor tracking." Optical Engineering 36, no. 3 (March 1, 1997): 708. http://dx.doi.org/10.1117/1.601268.

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Fu, X., Y. Jia, F. Yu, and J. Du. "New interacting multiple model algorithms for the tracking of the manoeuvring target." IET Control Theory & Applications 4, no. 10 (October 1, 2010): 2184–94. http://dx.doi.org/10.1049/iet-cta.2009.0583.

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Xue, Li, Yulan Han, and Chunning Na. "Robust Interacting Multiple Model Unscented Particle Filter for Navigation." Mathematical Problems in Engineering 2020 (November 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/8871358.

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In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

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Lim, Jaechan, Hun-Seok Kim, and Hyung-Min Park. "Interactive-Multiple-Model Algorithm Based on Minimax Particle Filtering." IEEE Signal Processing Letters 27 (2020): 36–40. http://dx.doi.org/10.1109/lsp.2019.2954000.

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Zhu, Meiyu, Guanyu Chen, and Weicun Zhang. "Maneuvering target tracking with improved interactive multiple model algorithm." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 373–76. http://dx.doi.org/10.5954/icarob.2021.os6-5.

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Koller, Tom L., and Udo Frese. "The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds." Sensors 21, no. 12 (June 17, 2021): 4164. http://dx.doi.org/10.3390/s21124164.

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Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a novel generalization of the interacting multiple filter and smoother to manifold state spaces, e.g., quaternions, based on the boxplus-method. As part thereof, we propose a linear approximation to the mixing of Gaussians and a Rauch–Tung–Striebel smoother for single models on boxplus-manifolds. The derivation of the smoother equations is based on a generalized definition of Gaussians on boxplus-manifolds. The three, novel algorithms are evaluated in a simulation and perform comparable to specialized solutions for quaternions. So far, the benefit of the more principled approach is the generality towards manifold state spaces. The evaluation and generic implementations are published open source.

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Zhou, Wei dong, Jia nan Cai, Long Sun, and Chen Shen. "An Improved Interacting Multiple Model Algorithm Used in Aircraft Tracking." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/813654.

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There are some problems in traditional interacting multiple model algorithms (IMM) when used in target tracking systems. For instance, the mode transition matrix is inaccurate and cannot be determined when the sojourn times are not known. To solve these problems, an optimal mode transition matrix IMM (OMTM-IMM) algorithm is proposed in this paper. The linear minimum variance theory is used to calculate the mode transition matrix which depends on the continuous system state rather than the sojourn times in this algorithm. Moreover, the correlation of the subfilter is considered; hence the covariance matrices are utilized to compute mode transition matrix. In this algorithm, the model probability is defined as a diagonal matrix which is combined with the filters outputs; thus the effects produced by each state can be distinguished. Finally, to verify the superiority of the new algorithm, the theoretical proof and simulation results are given. They show that the OMTM-IMM algorithm can improve the tracking accuracy and can be utilized in the complex environment.

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Cheng, Long, Sihang Huang, Mingkun Xue, and Yangyang Bi. "A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network." Sensors 20, no. 22 (November 19, 2020): 6634. http://dx.doi.org/10.3390/s20226634.

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With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.

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Liu, Ya Lei, and Xiao Hui Gu. "Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array." Applied Mechanics and Materials 300-301 (February 2013): 407–13. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.407.

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Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

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Shaikh, Bilal, Gnaneswara Marupilla, Mike Wilson, Michael L. Blinov, Ion I. Moraru, and Jonathan R. Karr. "RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats." Nucleic Acids Research 49, W1 (May 21, 2021): W597—W602. http://dx.doi.org/10.1093/nar/gkab411.

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Abstract Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

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Yun, Joongsup, and Chang-Kyung Ryoo. "Missile Guidance Law Estimation Using Modified Interactive Multiple Model Filter." Journal of Guidance, Control, and Dynamics 37, no. 2 (March 2014): 484–96. http://dx.doi.org/10.2514/1.61327.

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ATANASOV, Deyan. "TRACKING MANEUVERING TARGET WITH KALMAN FILTER AND INTERACTIVE MULTIPLE MODEL." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 21, no. 1 (October 8, 2019): 88–97. http://dx.doi.org/10.19062/2247-3173.2019.21.13.

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Yao, Yao, Yuanyuan Li, Xi Xiong, Yue Wu, Honggang Lin, and Shenggen Ju. "An interactive propagation model of multiple information in complex networks." Physica A: Statistical Mechanics and its Applications 537 (January 2020): 122764. http://dx.doi.org/10.1016/j.physa.2019.122764.

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Lotfi, Vahid, Theodor J. Stewart, and Stanley Zionts. "An aspiration-level interactive model for multiple criteria decision making." Computers & Operations Research 19, no. 7 (October 1992): 671–81. http://dx.doi.org/10.1016/0305-0548(92)90036-5.

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Vasuhi, S., and V. Vaidehi. "Target tracking using Interactive Multiple Model for Wireless Sensor Network." Information Fusion 27 (January 2016): 41–53. http://dx.doi.org/10.1016/j.inffus.2015.05.004.

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Bi, Suzhi, and Xiao Yi Ren. "MANEUVERING TARGET DOPPLER-BEARING TRACKING WITH SIGNAL TIME DELAY USING INTERACTING MULTIPLE MODEL ALGORITHMS." Progress In Electromagnetics Research 87 (2008): 15–41. http://dx.doi.org/10.2528/pier08091501.

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Held, Pascal, Alexander Dockhorn, and Rudolf Kruse. "On Merging and Dividing Social Graphs." Journal of Artificial Intelligence and Soft Computing Research 5, no. 1 (January 1, 2015): 23–49. http://dx.doi.org/10.1515/jaiscr-2015-0017.

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Abstract Modeling social interaction can be based on graphs. However most models lack the flexibility of including larger changes over time. The Barabási-Albert-model is a generative model which already offers mechanisms for adding nodes. We will extent this by presenting four methods for merging and five for dividing graphs based on the Barabási- Albert-model. Our algorithms were motivated by different real world scenarios and focus on preserving graph properties derived from these scenarios. With little alterations in the parameter estimation those algorithms can be used for other graph models as well. All algorithms were tested in multiple experiments using graphs based on the Barabási- Albert-model, an extended version of the Barabási-Albert-model by Holme and Kim, the Watts-Strogatz-model and the Erdős-Rényi-model. Furthermore we concluded that our algorithms are able to preserve different properties of graphs independently from the used model. To support the choice of algorithm, we created a guideline which highlights advantages and disadvantages of discussed methods and their possible use-cases.

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Journal articles on the topic 'Interactive Multiple Model algoritmus'

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