Browsing by Subject "Computational modeling"

Now showing 1 - 15 of 15

Open Access
Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts
(Institute of Electrical and Electronics Engineers Inc., 2019) Tian, R.; Li, S.; Li, N.; Kolmanovsky, İ.; Girard, A.; Yıldız, Yıldıray; Teel, A. R.; Egerstedt, M.
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.
Open Access
Architectural requirements for energy efficient execution of graph analytics applications
(IEEE, 2015-11) Özdal, Muhammet Mustafa; Yeşil, Şerif; Kim, T.; Ayupov, A.; Burns, S.; Öztürk, Özcan
Intelligent data analysis has become more important in the last decade especially because of the significant increase in the size and availability of data. In this paper, we focus on the common execution models and characteristics of iterative graph analytics applications. We show that the features that improve work efficiency can lead to significant overheads on existing systems. We identify the opportunities for custom hardware implementation, and outline the desired architectural features for energy efficient computation of graph analytics applications. © 2015 IEEE.
Open Access
Behavioral and computational investigation of the effect of prior knowledge on visual perception
(2021-01) Ürgen, Buse Merve
Visual perception results from the dynamic interaction of bottom-up and topdown processes. Top-down prior knowledge and expectations can guide us to predict upcoming events and even determine what we see in an ambiguous or noisy sensory stimulus. Despite the well-established facilitating effects of expectations on recognition or decision-making, whether and how early sensory processes are affected by expectations remain unclear. This dissertation attempts to investigate the effect of expectations on early visual processes. To this end, we used behavioral experiments to examine the effects of expectation on visual perception at the threshold level and implemented a recursive Bayesian model and a recurrent cortical model to unravel the computational mechanisms underlying those effects. In the behavioral experiments, we systematically manipulated expectation’s validity in separate sessions and measured duration thresholds, which is the shortest presentation time sufficient to achieve a certain success level. Our behavioral findings showed that valid expectations do not reduce the thresholds, rather unmet expectations lead them to increase. Next, using a recursive Bayesian updating scheme, we modeled the empirical data obtained in the behavioral experiments. Model fitting showed that higher thresholds observed in the unmet expectations are not due to a change in the internal parameters of the system. Instead, additional computations are required by the system to complete the sensory process. Finally, within the predictive processing framework, we implemented a recurrent cortical model to explain the behavioral findings and discuss possible neural mechanisms underlying the observed effects. The cortical model findings were in agreement with the Bayesian model results, revealing that longer processing is needed when expectations are not met. Overall, the computational models that are proposed in this study provide a parsimonious explanation for the observed behavioral effects. The proposed experimental paradigm and the computational models offer a novel framework that can be extended and used in other stimuli, tasks, and sensory modalities.
Open Access
Computational modeling of vehicle radiators using porous medium approach
(InTech Open, 2017) Çetin, Barbaros; Güler, K.; Aksel, M. H.; Murshed, M. S.; Lopes, M. M.
A common tool for the determination of thermal characteristics of vehicle radiators is the experimental testing. However, experimental testing may not be feasible considering the cost and labor-time. Basic understanding of the past experimental data and analytical/ computational modeling can significantly enhance the effectiveness of the design and development phase. One such computational modeling technique is the utilization of computational fluid dynamics (CFD) analysis to predict the thermal characteristics of a vehicle radiator. However, CFD models are also not suitable to be used as a design tool since considerable amount of computational power and time is required due to the multiple length scales involved in the problem, especially the small-scale geometric details associated with the fins. Although fins introduce a significant complexity for the problem, the repetitive and/or regular structure of the fins enables the porous medium based modeling. By porous modeling, a memory and time efficient computational model can be developed and implemented as an efficient design tool for radiators. In this work, a computational methodology is described to obtain the hydrodynamic and thermal characteristics of a vehicle radiator. Although the proposed methodology is discussed in the context of a vehicle radiator, the proposed methodology can be implemented to any compact heat exchanger with repetitive fin structures which is an important problem for many industrial applications.
Open Access
Designing tunable composites with general interfaces
(Elsevier, 2019) Saeb, S.; Steinmann, P.; Javili, Ali
In this manuscript, we employ interface enhanced computational homogenization to explore and detail on a number of unfamiliar characteristics that composites can exhibit at different length scales. Here, the interface between the constituents is general in the sense that both displacement and traction jumps across the interface are admissible. We carry out numerous computational investigations using the finite element method for a broad range of various material parameters. Our numerical results reveal that the effective response of a microstructure embedding general interfaces is intuitively unpredictable and highly complex. In particular, for certain ranges of material parameters the overall response shows insensitivity with respect to either microstructure size or stiffness-ratio between inclusion and matrix. This unique behavior is observed likewise for two- and three-dimensional unit-cells. Our findings provide a valuable guideline to design tunable composites utilizing interfaces.
Open Access
Electromagnetic scattering solution of conducting strips in layered media using the fast multipole method
(Institute of Electrical and Electronics Engineers, 1996-08) Gürel, Levent; Aksun, M. I.
The fast multipole method (FMM) is applied to the solution of the electromagnetic scattering problems in layered media for the first time. This is achieved by using closed-form expressions for the spatial-domain Green's functions for layered media. Until now, the FMM has been limited to the homogeneous-medium problems. An integral equation based on the two-dimensional scalar Helmholtz equation is solved to compute the electromagnetic scattering from sample geometries of conducting strips in layered media in order to demonstrate the accuracy and the efficiency of the new method.
Open Access
FAME: Face association through model evolution
(IEEE, 2015-06) Gölge, Eren; Duygulu, Pınar
We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the models associated to a name to evolve. The idea is based on capturing discriminative and representative properties of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with different characteristics. On benchmark datasets, our results are comparable to or better than the state-of-the-art studies for the task of face identification. © 2015 IEEE.
Open Access
Fast-dissolving electrospun gelatin nanofibers encapsulating ciprofloxacin/cyclodextrin inclusion complex
(Elsevier, 2019) Aytaç, Zeynep; İpek, Semran; Erol, I.; Durgun, Engin; Uyar, Tamer
Electrospun gelatin nanofibrous matrix encapsulating ciprofloxacin (CIP)/hydroxypropyl-beta-cyclodextrin (HPβCD)-inclusion complex (IC) was produced via electrospinning method. Computational modeling indicated that van der Waals forces are the most significant driving forces for the complexation and hydrophobic moiety (piperazinyl) of CIP, which was included in the cavity of HPβCD. The FTIR and XRD studies indicated the formation of CIP/HPβCD host/guest complexation, FTIR also suggested that hydrophobic moiety of CIP is in the HPβCD cavity in parallel with the computational modeling results. The phase solubility diagram demonstrated that the solubility of CIP was enhanced after complexation with HPβCD. SEM images showed that electrospun gelatin nanofibers encapsulating CIP/HPβCD-IC have bead-free morphology with a diameter of ˜90 nm. The gelatin nanofibrous mat loaded with CIP/HPβCD-IC has exhibited fast-dissolving character in water compared to gelatin/CIP nanofibrous mat due to the enhanced wettability of the nanofibrous mat by HPβCD and improvement achieved in the solubility of CIP.
Open Access
Fast-dissolving, prolonged release, and antibacterial cyclodextrin/limonene-inclusion complex nanofibrous webs via polymer-free electrospinning
(American Chemical Society, 2016) Aytac Z.; Yildiz, Z. I.; Kayaci-Senirmak, F.; S. Keskin, N. O.; Kusku, S. I.; Durgun, Engin; Tekinay, T.; Uyar, Tamer
We have proposed a new strategy for preparing free-standing nanofibrous webs from an inclusion complex (IC) of a well-known flavor/fragrance compound (limonene) with three modified cyclodextrins (HPβCD, MβCD, and HPγCD) via electrospinning (CD/limonene-IC-NFs) without using a polymeric matrix. The experimental and computational modeling studies proved that the stoichiometry of the complexes was 1:1 for CD/limonene systems. MβCD/limonene-IC-NF released much more limonene at 37, 50, and 75 °C than HPβCD/limonene-IC-NF and HPγCD/limonene-IC-NF because of the greater amount of preserved limonene. Moreover, MβCD/limonene-IC-NF has released only 25% (w/w) of its limonene, whereas HPβCD/limonene-IC-NF and HPγCD/limonene-IC-NF released 51 and 88% (w/w) of their limonene in 100 days, respectively. CD/limonene-IC-NFs exhibited high antibacterial activity against E. coli and S. aureus. The water solubility of limonene increased significantly and CD/limonene-IC-NFs were dissolved in water in a few seconds. In brief, CD/limonene-IC-NFs with fast-dissolving character enhanced the thermal stability and prolonged the shelf life along with antibacterial properties could be quite applicable in food and oral care applications.
Open Access
Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems
(IEEE, 2016) Li, N.; Oyler, D.; Zhang, M.; Yıldız, Yıldıray; Girard, A.; Kolmanovsky, İ.
A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach.
Open Access
Multivariate time series imputation with transformers
(IEEE, 2022-11-25) Yıldız, A. Yarkın; Koç, Emirhan; Koç, Aykut
Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.
Open Access
A new method for nonlinear circuit simulation in time domain: NOWE
(Institute of Electrical and Electronics Engineers, 1996-03) Ocalı, O.; Tan, M. A.; Atalar, Abdullah
A new method for the time-domain solution of general nonlinear dynamic circuits is presented. In this method, the solutions of the state variables are computed by using their time derivatives up to some order at the initial time instant. The computation of the higher order derivatives is equivalent to solving the same linear circuit for various sets of dc excitations. Once the time derivatives of the state variables are obtained, an approximation to the solution can be found as a polynomial rational function of time. The time derivatives of the approximation at the initial time instant are matched to those of the exact solution. This method is promising in terms of execution speed, since it can achieve the same accuracy as the trapezoidal approximation with much smaller number of matrix inversions.
Open Access
A novel approach to 3-dimensional holographic television display: principles and simulations
(IEEE, 1992-03) Bozdağı, Gözde; Onural, Levent; Atalar, Abdullah
The authors present a new technique for the display end of a holographic three-dimensional television system and describe the computer simulations. The technique is based on the reproduction of the desired pattern, in this case the hologram, using traveling surface waves. The proposed method is simpler and more efficient than the methods available in the literature and it solves the display resolution and refreshing rate problems completely. Simulations show that the proposed system will work as desired when implemented in real time.
Open Access
A reputation-based trust management system for P2P networks
(IEEE, 2004) Selçuk, Ali Aydin; Uzun, Ersin; Pariente, Mark Reşat
The open and anonymous nature of a P2P network makes it an ideal medium for attackers to spread malicious content. In this paper, we describe a reputation-based trust management protocol for P2P networks where users rate the reliability of parties they deal with, and share this information with their peers. The protocol helps establishing trust among good peers as well as identifying the malicious ones. Results of various simulation experiments show that the proposed system can be highly effective in preventing the spread of malicious content in P2P networks.
Open Access
Time-aware and context-sensitive ensemble learning for sequential data
(Institute of Electrical and Electronics Engineers, 2023-09-26) Fazla, Arda; Aydın, Mustafa E.; Kozat, Suleyman Serdar
We investigate sequential time series data through ensemble learning. Conventional ensemble algorithms and the recently introduced ones have provided significant performance improvements in widely publicized time series prediction competitions for stationary data. However, recent studies are inadequate in capturing the temporally varying statistics for non-stationary data. To this end, we introduce a novel approach using a meta learner that effectively combines base learners in both a time varying and context-dependent manner. Our approach is based on solving a weight optimization problem that minimizes a specific loss function with constraints on the linear combination of the base learners. The constraints are theoretically analyzed under known statistics and integrated into the learning procedure of the meta-learner as part of the optimization in an automated manner. We demonstrate significant performance improvements on real-life data and well-known competition datasets over the widely used conventional ensemble methods and the state-ofthe-art forecasting methods in the machine learning literature. Furthermore, we openly share the source code of our method to facilitate further research and comparison.