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Artykuły w czasopismach na temat "Link Activation Algorithms"
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Chlamtac, I., i A. Lerner. "Fair Algorithms for Maximal Link Activation in Multihop Radio Networks". IEEE Transactions on Communications 35, nr 7 (lipiec 1987): 739–46. http://dx.doi.org/10.1109/tcom.1987.1096847.
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Grenson, Pierre, i Eric Garnier. "Distortion reconstruction in S-ducts from wall static pressure measurements". International Journal of Numerical Methods for Heat & Fluid Flow 28, nr 5 (8.05.2018): 1134–55. http://dx.doi.org/10.1108/hff-06-2017-0232.
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Purpose This paper aims to report the attempts for predicting “on-the-fly” flow distortion in the engine entrance plane of a highly curved S-duct from wall static pressure measurements. Such a technology would be indispensable to trigger active flow control devices to mitigate the intense flow separations which occur in specific flight conditions. Design/methodology/approach Evaluation of different reconstruction algorithms is performed on the basis of data extracted from a Zonal Detached Eddy Simulation (ZDES) of a well-documented S-Duct (Garnier et al., AIAA J., 2015). Contrary to RANS methods, such a hybrid approach makes unsteady distortions available, which are necessary information for reconstruction algorithm assessment. Findings The best reconstruction accuracy is obtained with the artificial neural network (ANN) but the improvement compared to the classical linear stochastic estimation (LSE) is minor. The different inlet distortion coefficients are not reconstructed with the same accuracy. KA2 coefficient is finally identified as the more suited for activation of the control device. Originality/value LSE and its second-order variant (quadratic stochastic estimation [QSE]) are applied for reconstructing instantaneous stagnation pressure in the flow field. The potential improvement of an algorithm based on an ANN is also evaluated. The statistical link between the wall sensors and 40-Kulite rake sensors are carefully discussed and the accuracy of the reconstruction of the most used distortion coefficients (DC60, RDI, CDI and KA2) is quantified for each estimation technique.
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Syed, Zareen, Tim Finin i Anupam Joshi. "Wikipedia as an Ontology for Describing Documents". Proceedings of the International AAAI Conference on Web and Social Media 2, nr 1 (25.09.2021): 136–44. http://dx.doi.org/10.1609/icwsm.v2i1.18627.
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Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to predict concepts common to a set of documents. We describe several algorithms to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments demonstrate the feasibility of extending the category system with new concepts identified as a union of pages from the page link graph.
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Jablonska, Ewa, Patryk Gorniak, Weronika Prusisz, Przemyslaw Kiliszek, Maciej Szydlowski, Tomasz Sewastianik, Emilia Bialopiotrowicz i in. "Downregulation of Deptor By MiR-155 Promotes Cell Survival through Activation of PI3K/AKT and NFkB Signaling in ABC-Type Diffuse Large B-Cell Lymphomas". Blood 128, nr 22 (2.12.2016): 1761. http://dx.doi.org/10.1182/blood.v128.22.1761.1761.
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Abstract Introduction MiR-155 expression in DLBCLs is induced by NFkB and directly targets key regulators of B-cell maturation, motility and BCR signaling. In this study, we searched for new targets of miR-155 potentially involved in deregulation of BCR-PI3K/AKT and NFkB signaling in DLBCLs. We identified two new miR-155 targets: c-CBL (SYK ubiquitin E3 ligase) and DEPTOR (mTOR phosphatase). DEPTOR suppresses mTOR activity and in different tumors plays a role of either a tumor suppressor or an oncogene. Since the role of DEPTOR in DLCBLs cells has not been determined, we assessed the consequences of its inhibition in this malignancy. Methods Predicted miR-155 targets were validated with 3'UTR luciferase reporter assays. MiR-155 expression was modulated through transfection with miR-155 mimic or miR-155 inhibitor. The DEPTOR silencing in DLBCL cell was achieved with retroviral shRNA vector. DEPTOR mRNA expression and survival of DLBCL patients was determined using publicly available microarray data (Lenz et al, 2008, GEO accession GSE10846). Immunohistochemical assessment of DEPTOR expression was performed in 76 newly diagnosed DLBCL patients with available GCB/non-GCB designations based on Hans algorithm. Results Using miRNA target finding algorithms, we identified miR-155-matching sequences in 3'UTRs of two genes involved in SYK/PI3K/AKT pathway regulation: c-CBL and DEPTOR. MiR-155 suppressed luciferase activities of vectors containing 3'UTR fragments from c-CBL and DEPTOR genes with wild-type, but not mutant miR-155 seed sequence. To establish a link between miR-155 and c-CBL or DEPTOR, DLBCL cell lines were transfected with a control non-targeting miR or miR-155 mimic. Introduction of miR-155 resulted in decreased expression of c-CBL and DEPTOR, accompanied by a marked increase in phospho-AKT level. Inhibition of endogenous miR-155 in U2932 cell line by anti-miR-155 exhibited opposite effects. Using publicly available gene expression data (Lenz et al, 2008, GEO accession GSE10846), we found that miR-155 exhibited a reciprocal expression pattern with DEPTOR and c-CBL (r =-0.15; p=.002 and r=-0.189; p<.0001), indicating that miR-155 likely modulates expression of c-CBL and DEPTOR in primary tumors. Since the role of DEPTOR in DLBCL has not been previously addressed, we investigated its expression in a series of 76 newly diagnosed DLBCL patients by immunohistochemistry. Complete loss of DEPTOR expression was noted solely in non-GCB tumors (9 of 37; 24%). Additional 12 patients exhibited low expression of this protein. In contrast, none of the 39 GCB tumors stained negative for DEPTOR (Chi-square, p=.005). DEPTOR mRNA greater than mean in primary DLBCL biopsies was associated with longer overall survival (OS; P=0.016). To elucidate the function of DEPTOR in DLBCL cells, we silenced the expression of this protein with shRNA. Attenuated protein level of DEPTOR markedly enhanced AKT activity and promoted proliferation of DHL4 cells. DHL4 cells with silenced DEPTOR were also less prone to starvation- or SYK inhibition- induced apoptosis. Since AKT has been reported to stimulate activity of NFκB, we hypothesized that miR-155-induced decrease in DEPTOR expression would affect NFκB signaling. Consistent with this, inhibition of endogenous miR-155 in U2932 cells led to marked downregulation of NFĸB-controlled genes (CD40, BFL-1, RelB, IĸBα, A20, MIR155HG) and sensitized U2932 cells to ibrutinib, indicating that miR-155 amplifies NFĸB signaling in these cells. Conclusions Taken together, our data underscore the role of miR-155 in the regulation of AKT and NFkB prosurvival signaling in DLBCL. By targeting multiple negative regulators of PI3K/AKT pathway, miR-155 creates a feed - forward loop leading to increased NFkB activity. We also show that DEPTOR protein expression is decreased or lost selectively in a large fraction of ABC-DLBCLs. Since DEPTOR modulates AKT activity and its silencing promotes proliferation of DLBCL cells, these data suggest that DEPTOR functions as a tumor suppressor in ABC-DLBCLs. Disclosures Prochorec-Sobieszek: Roche: Other: travel, accommodation. Warzocha:BMS: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Juszczynski:Selvita S.A.: Consultancy, Membership on an entity's Board of Directors or advisory committees.
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CHINNASARN, KRISANA, CHIDCHANOK LURSINSAP i VASILE PALADE. "BLIND SEPARATION OF MIXED KURTOSIS SIGNED SIGNALS USING PARTIAL OBSERVATIONS AND LOW COMPLEXITY ACTIVATION FUNCTIONS". International Journal of Computational Intelligence and Applications 04, nr 02 (czerwiec 2004): 207–23. http://dx.doi.org/10.1142/s1469026804001239.
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Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity.
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Pellegrini, Riccardo, Andrea Serani, Giampaolo Liuzzi, Francesco Rinaldi, Stefano Lucidi i Matteo Diez. "Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches". Mathematics 8, nr 4 (7.04.2020): 546. http://dx.doi.org/10.3390/math8040546.
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The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.
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Guo, Yuan Hua, i Chun Lun Huang. "Functional Link Artificial Neural Networks Filter for Gaussian Noise". Applied Mechanics and Materials 347-350 (sierpień 2013): 2580–85. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2580.
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In this paper, FLANN(functional link ANN) filter is presented for Gaussian noise. FLANN is a singer layer with expanded input vectors and has lower computational cost than MLP(multilayer perceptron). Three types of functional expansion are discussed. BP(back propagation algorithm) for nonlinear activation function and matrix calculation for identical activation function are exploited for training FLANN. Simulation shows that convergence is not guaranteed in BP and related to the initial weight matrix and training images, and that linear FLANN trained by matrix calculation performs better than both nonlinear FLANN trained by BP and Wiener filter in detail region in environment of Gaussian noise
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Belcaid, A., i M. Douimi. "A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy". International Journal on Artificial Intelligence Tools 29, nr 06 (wrzesień 2020): 2050018. http://dx.doi.org/10.1142/s0218213020500189.
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In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.
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Bonn, B., E. Bourtsoukidis, T. S. Sun, H. Bingemer, L. Rondo, U. Javed, J. Li i in. "The link between atmospheric radicals and newly formed particles at a spruce forest site in Germany". Atmospheric Chemistry and Physics Discussions 13, nr 10 (24.10.2013): 27501–60. http://dx.doi.org/10.5194/acpd-13-27501-2013.
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Abstract. It has been claimed for more than a century that atmospheric new particle formation is primarily influenced by the presence of sulphuric acid. However, the activation process of sulphuric acid related clusters into detectable particles is still an unresolved topic. In this study we focus on the PARADE campaign measurements conducted during August/September 2011 at Mt. Kleiner Feldberg in central Germany. During this campaign a set of radicals, organic and inorganic compounds and oxidants and aerosol properties were measured or calculated. We compared a range of organic and inorganic nucleation theories, evaluating their ability to simulate measured particle formation rates at 3 nm in diameter (J3) for a variety of different conditions. Nucleation mechanisms involving only sulphuric acid tentatively captured the observed noon-time daily maximum in J3, but displayed an increasing difference to J3 measurements during the rest of the diurnal cycle. Including large organic radicals, i.e. organic peroxy radicals (RO2) deriving from monoterpenes and their oxidation products in the nucleation mechanism improved the correlation between observed and simulated J3. This supports a recently proposed empirical relationship for new particle formation that has been used in global models. However, the best match between theory and measurements for the site of interest was found for an activation process based on large organic peroxy radicals and stabilized Criegee intermediates (sCI). This novel laboratory derived algorithm simulated the daily pattern and intensity of J3 observed in the ambient data. In this algorithm organic derived radicals are involved in activation and growth and link the formation rate of smallest aerosol particles with OH during daytime and NO3 during nighttime. Because of the RO2s lifetime is controlled by HO2 and NO we conclude that peroxy radicals and NO seem to play an important role for ambient radical chemistry not only with respect to oxidation capacity but also for the activation process of new particle formation. This is supposed to have significant impact of atmospheric radical species on aerosol chemistry and should to be taken into account when studying the impact of new particles in climate feedback cycles.
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Bonn, B., E. Bourtsoukidis, T. S. Sun, H. Bingemer, L. Rondo, U. Javed, J. Li i in. "The link between atmospheric radicals and newly formed particles at a spruce forest site in Germany". Atmospheric Chemistry and Physics 14, nr 19 (15.10.2014): 10823–43. http://dx.doi.org/10.5194/acp-14-10823-2014.
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Abstract. It has been claimed for more than a century that atmospheric new particle formation is primarily influenced by the presence of sulfuric acid. However, the activation process of sulfuric acid related clusters into detectable particles is still an unresolved topic. In this study we focus on the PARADE campaign measurements conducted during August/September 2011 at Mt Kleiner Feldberg in central Germany. During this campaign a set of radicals, organic and inorganic compounds and oxidants and aerosol properties were measured or calculated. We compared a range of organic and inorganic nucleation theories, evaluating their ability to simulate measured particle formation rates at 3 nm in diameter (J3) for a variety of different conditions. Nucleation mechanisms involving only sulfuric acid tentatively captured the observed noon-time daily maximum in J3, but displayed an increasing difference to J3 measurements during the rest of the diurnal cycle. Including large organic radicals, i.e. organic peroxy radicals (RO2) deriving from monoterpenes and their oxidation products, in the nucleation mechanism improved the correlation between observed and simulated J3. This supports a recently proposed empirical relationship for new particle formation that has been used in global models. However, the best match between theory and measurements for the site of interest was found for an activation process based on large organic peroxy radicals and stabilised Criegee intermediates (sCI). This novel laboratory-derived algorithm simulated the daily pattern and intensity of J3 observed in the ambient data. In this algorithm organic derived radicals are involved in activation and growth and link the formation rate of smallest aerosol particles with OH during daytime and NO3 during night-time. Because the RO2 lifetime is controlled by HO2 and NO we conclude that peroxy radicals and NO seem to play an important role for ambient radical chemistry not only with respect to oxidation capacity but also for the activation process of new particle formation. This is supposed to have significant impact of atmospheric radical species on aerosol chemistry and should be taken into account when studying the impact of new particles in climate feedback cycles.
Rozprawy doktorskie na temat "Link Activation Algorithms"
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Madabhushi, Sireesha. "Optimal Routing and Data Transmission for Multi-Hop D2D Communications". Thesis, 2018. http://etd.iisc.ac.in/handle/2005/4145.
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Device-to-Device (D2D) communication, which enables direct link communication between nodes without going through the infrastructure, has received considerable attention in recent years, particularly in the context of Internet-of-Things (IoT) and machine-to-machine (M2M) communications. It is also slated to be part of the 5G communication standards. The primary advantages of D2D are enhanced spatial frequency reuse leading to better spectral efficiency, reduced out-of-cell interference, better cell-edge coverage, etc. Once implemented, the D2D mode of communication opens up new and interesting avenues for improving the spectral efficiency of a cellular system. A particularly interesting problem in this context is that of routing data over multiple D2D nodes to maximize the end-to-end throughput between a source and destination, while ensuring that the D2D nodes do not cause detrimental interference to the ongoing transmissions in the cellular network. This can potentially result in significantly higher D2D throughput compared to the hitherto-considered single-hop D2D communications paradigm. The main focus of this thesis is to determine a throughput-optimal route between a given source-destination pair under a probabilistic interference constraint imposed by the cellular network, and to analytically characterize the throughput improvement obtainable by multi-hop D2D communications.
The first part of the thesis proposes an easy-to-implement routing algorithm to maximize the end-to-end throughput of a given source-destination pair. Next, in order to further increase the throughput on the determined route, a link activation algorithm is proposed, which enables links opportunistically, based on the buffer states of the relay nodes and the link feasibility. Theoretical expressions for throughput, delay and system idle probability are derived by modeling the relay buffer state evolution as a Discrete Time Markov Chain (DTMC). Theory and simulation showcase the performance gains obtained by opportunistic link activation compared to the baseline sequential link activation scheme, where the links are enabled in a round-robin fashion.
The second part of the thesis proposes a routing algorithm to maximize the end-to-end throughput between D2D source-destination pair, under an additional per-hop delay constraint. The analytical expression derived for throughput on the multi-hop route facilitates the application of Dijkstra's algorithm, again leading to an easy-to-implement optimal routing algorithm. The tradeoff between throughput and delay is illustrated through simulations.
Overall, the results in the thesis are useful for (a) determining throughput optimal and delay-constrained throughput optimal routes between a given source and destination communicating in the D2D mode; (b) analytically characterizing the gains obtainable via multi-hop D2D communications; and (c) performance characterization and comparison of opportunistic link activation (which entails data buffering at the individual nodes) and sequential link activation, in terms of both delay and the throughput achieved
Części książek na temat "Link Activation Algorithms"
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Dağlarli, Evren. "Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models". W Advances and Applications in Deep Learning. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92172.
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The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.
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Flores-Monroy, Jonathan, Mariko Nakano-Miyatake, Hector Perez-Meana, Enrique Escamilla-Hernandez i Gabriel Sanchez-Perez. "Implementation of a CNN-Based Driver Drowsiness and Distraction Detector in Mobile Devices". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220258.
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Drowsiness and driver distraction are considered the main causes of traffic accidents in the world. Considering this situation, this paper proposes two important modifications to our previously proposed driver drowsiness and distraction detector for real-time implementation on handheld mobile devices, such as smartphones. The first modification is due to a large variation in the capacity of mobile devices. To adapt the proposed system to a wide range of mobile devices, we present two automatic threshold calculations, which are used to differentiate driver drowsiness from normal blinking and dangerous driver distraction from normal short-term distraction. The second modification is related to the alarm during a continuous dangerous situation of the driver. We introduce a new algorithm to ensure the continuous activation of the alarm while the dangerous situation continues. These improvements perform as the general algorithm, since when it was implemented in mobile devices with low computational power, as well as in devices that do not have these limitations, the alarm activation times were not affected; On the other hand, it was possible to increase the accuracy originally given by the first system with respect to Ground Truth by almost 25% on average, resulting in alarm activations not being affected to a great extent by the natural errors that the convolutional neural networks (CNN) may cause, these improvements are shown and supported by the implementation in real time through video links provided in this work.
Streszczenia konferencji na temat "Link Activation Algorithms"
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Iqbal, Kamran. "Optimal Realization of Endpoint Stiffness in Static Human Arm Postures". W ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3269.
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Abstract The endpoint stiffness, i.e., stiffness displayed by the wrist amid perturbations to the arm, has been used to assess the mechanical stability of the arm posture. The aim of this study is to develop an algorithm to optimally realize a desired end-point stiffness by minimizing muscle forces. The neuro-muscular behavior of the human arm during posture maintenance tasks is approximated by a two-link eight-muscle arm model. The model parameters reflect physiological data taken from published literature. The endpoint stiffness is shown to be a linear function of muscle activations. It is shown that the problem to minimize muscle activations while satisfying torque constraint at the joints can be solved by using non-negative least-squares method. Alternatively, linear programming can be used for this purpose. The biomechanical model is used to demonstrate how endpoint stiffness of desired magnitude, orientation, and eccentricity can be synthesized by activating arm muscles with minimal energy expenditure. Our simulation results suggest that bi-articular muscles play a major role in developing the desired endpoint stiffness. The model can be scaled up to three-dimensions by adding muscle groups.
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Perinetto, Aldo, Wilfrido Inchaustti, Luca Cernuzzi i Mario Bort. "Improve spreading activation algorithm using link assessment between actors from a mobile phone company network based on SMS traffic". W 2013 Latin American Computing Conference (CLEI). IEEE, 2013. http://dx.doi.org/10.1109/clei.2013.6670636.
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Shukla, Deepak, Darren M. Dawson i Frank W. Paul. "Partial State Feedback Based Real Time Neural Controller: Theory and Experiments". W ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0400.
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Abstract A partial state feedback based direct adaptive control scheme is developed using Orthonormal Activation Function based Neural Networks (OAFNN), for real time trajectory tracking control of a class of nonlinear systems. The OAFNNs are employed in the controller for feed-forward compensation of unknown system dynamics. The network weights are tuned on-line, in real time using Desired Compensation based Adaptive Laws (DCAL). The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis and a modified integral version of projection algorithm. The developed neural controllers are evaluated experimentally and the experimental results support theoretical analysis.
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Penninger, Charles L., Andre´s Tovar, Glen L. Niebur i John E. Renaud. "Signaling Pathways for Bone Resorption Predicted as a Hybrid Cellular Automaton Process". W ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39358.
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The bone remodeling process provides for various functions such as mineral homeostasis, damage repair, and adaptation to mechanical loading. At present, a clear link between the mechanical stimulation of bones and the biochemical response is not fully understood. Computational simulations can provide a means to test hypotheses and gain insight into processes that are difficult to examine experimentally. The objective of this work is to predict the effect of damage and strain as the stimulus for regulating the cellular signaling activity of remodeling. In this study, potential signaling pathways that mediate this cellular activity were incorporated in a hybrid cellular automaton (HCA) algorithm. Biological rules were implemented in this model to control recruitment, differentiation, and activation of osteoclasts. Prominent processes for describing recruitment and inhibition of the bone cells, as reported from experimental studies, are utilized. This work focuses on the resorption of a damaged site on a trabecular strut.
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Grecheneva, Anastasya, Nikolay Dorofeev i Maxim Goryachev. "An Intelligent Approach to Recognizing Human Movements Based on a Wearable Device". W 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-988-993.
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n this paper, we consider the possibility of distinguishing the movements of a person and people by their gait based on data obtained from the accelerometer of a wearable device. A mobile phone was used as a wearable device. The paper considers the features of recognizing human movements based on a wearable device. A recognition algorithm based on a neural network with preliminary data processing and correlation analysis is proposed. The volume of the training sample consisted of 32 subjects with various physiological characteristics. The sample size in the subgroup of four people ranged from 2000 to 3000 movements. The main motor patterns for classification were the movements performed when walking in a straight line and stairs with a load (a bag with a laptop weighing 3.5 kg) and without it. The direct propagation network is chosen as the basic structure for the neural network. The neural network has 260 input neurons, 100 neurons in one hidden layer, and 4 neurons in the output layer. When training the neural network, the gradient reverse descent function was used. Cross- entropy was used as an optimization criterion. The activation function of the hidden layer was a sigmoid, and the output layer was a normalized exponential function. The presented algorithm makes it possible to distinguish between subjects when performing different movements in more than 90% of cases. The practical application of the results of the work is relevant for automated information systems of the medical, law enforcement and banking sectors.
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Garci´a, Jaime, Jose´ Posada, Pedro Villalba i Marco Sanjuan. "Soft Sensor Design for Biodiesel Concentration in a Transesterification Reactor". W ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68697.
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Biofuels production is facing new challenges every day, related to better process control and quality monitoring. It is very important for the sustainability of these processes to implement strategies and alternatives in order to achieve a continuous production process and to control significant variables involved in the reaction. One of the most difficult variables to measure is the actual Biodiesel concentration inside the reactor. Neural networks have become a useful strategy to give solutions to complex problems; its application is growing faster at industries due to the inherent nonlinear behavior of the processes, modeled easily by this computational tool. The capacity of mapping a complex behavior trough input and output process data, without a complicated and hardly to obtain mathematical model, makes neural networks an attractive strategy to be implemented in most industries, in a soft sensor or a process model scheme. This investigation addresses the need to predict the concentrations of esters (biodiesel) when different triglycerides are reacting with alcohol. Concentration was estimated using an approach that uses a soft sensor that captures the dynamics of these variables through off line laboratory experiments. The soft sensor is actually a Random Activation Weight Neural Net (RAWN), which is a back propagation neural network with a fast training algorithm that does not need any iteration. Also, to reduce the complexity of the soft sensor an optimization procedure was carried out to determine the optimum number of neurons in the hidden layer. In this research Biodiesel was produced by transesterification of palm oil with ethanol and KOH as catalyst. During transesterification reaction the estimation of concentrations is determined by laboratory analysis at off line stages, these variables are very important to control the continuous process of a biodiesel plant.
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Zhu, Xian-Kui, William R. Johnson, Robert Sindelar i Bruce Wiersma. "Machine Learning Models of Burst Strength for Defect-Free Pipelines". W ASME 2022 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/pvp2022-84908.
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Abstract Burst strength of line pipes is essential to pipeline design and integrity management. The simple Barlow equation with the ultimate tensile strength (UTS) was often used to estimate burst strength of line pipes. To consider the plastic flow effect of ductile steels, Zhu and Leis (2006, IJPVP) developed an average shear stress yield criterion and obtained the Zhu-Leis solution of burst strength for defect-free pipelines in term of UTS and strain hardening exponent, n, of materials. The Zhu-Leis solution was validated by more than 100 burst tests for various pipeline steels. The Zhu-Leis solution, when normalized by the Barlow strength, is a function of strain hardening rate, n, only, while the experimentally measured burst strength, when normalized by the Barlow strength, is a strong function of n and a weak function of UTS and pipe diameter to thickness ratio D/t. Due to difficulty of three-parameter regressions, this paper adopts the machine learning technology to develop alternative models of burst strength based on a large database of full-scale burst tests. In comparing to the regression, the machine learning method works well for both single and multiple parameters by introducing an artificial neural network (ANN), activation functions and learning algorithm for the network to learn and make predictions. Three ANN models were developed for predicting the burst strength of defect-free pipelines. Model 1 has one input variable and one hidden layer with three neurons; Model 2 has three input variables and one hidden layer with five neurons; and Model 3 has three input variables and two hidden layers with three neurons for the first hidden layer and two neurons for the second hidden layer. Those ANN models were then validated by the full-scale test data and evaluated through comparison with the Zhu-Leis solution and the linear regression result. On this basis, the best ANN model is recommended.
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Kariyawasam, Shahani, Hong Wang, Boon Ong, Mohammad Al-Amin i Ning Zhang. "Quantitative System Specific Likelihood Algorithms for System Wide Risk Assessment". W 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33639.
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The System Wide Risk Assessment (SWRA) is an essential first step in the pipeline integrity management program. It is required by both Canadian and US regulators and is expected to estimate risk due to all threats, interaction of threats, and consequences. The main objective of the SWRA is to identify high risk segments so that segments with excessive risk can be mitigated. The SWRA models developed in this study employs quantified likelihood models and consequence models. A companion paper explains the consequence models. This paper presents the framework and rationale used to produce quantifiable measures of likelihood for each threat. The quantification enables sensible comparisons between threat likelihood values and also enables realistic combining of likelihood values to produce total likelihood of failure due to all threats. It also facilitates identification of key parameters that contribute to each threat. It is important to have a consistent risk framework that systematically applies to all the threats and accommodates all the different aspects and mitigative actions in each threat management process. For effective continuous improvement it is essential that the models are transparent and updatable. A consistent framework that is systematic, rigorous, transparent and updatable is utilized with explicit consideration to threat interactions. The main advantages of the likelihood models developed in this study are: • It is based on all evidence that is available for each threat (failure histories, observations from assessments, i.e., digs, HTs, and ILIs, and mechanistic understanding) • It considers all nine threat categories and relevant subcategories where causal factors are different (such as SCC and Circumferential SCC within the crack threat category) • It clearly considers all three types of threat interactions (Interacting coincident defects, Interacting-activating threats, and Interacting common-mode conditions) among all threat categories. • It is based on subsystem specific historical failure rates for each threat, where subsystem is defined as a subset of pipelines that have different performance characteristics with respect to at least one threat. This basis enables the failure frequencies predicted to be more in line with reality and consequently improves accuracy of predictions and appropriate quantification. • The subsystem specific historical failure rates are then calibrated to correlate to different mechanistic characteristics so that within-pipeline-subsystem variation due to changes in parameters is represented. • Finally assessments or observations are used to appropriately update threat likelihood with latest knowledge from measured local observations. All of the improvements mentioned above have helped the SWRA 2013 to produce more representative results. The comprehensive set of validation exercises verify that the results are realistic.