Journal articles: 'Domain-transfer problem'

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Bassok, Miriam. "Transfer of domain-specific problem-solving procedures." Journal of Experimental Psychology: Learning, Memory, and Cognition 16, no. 3 (May 1990): 522–33. http://dx.doi.org/10.1037/0278-7393.16.3.522.

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Bejiga, Mesay Belete, Farid Melgani, and Pietro Beraldini. "Domain Adversarial Neural Networks for Large-Scale Land Cover Classification." Remote Sensing 11, no. 10 (May 14, 2019): 1153. http://dx.doi.org/10.3390/rs11101153.

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Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%.

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Kangro, Ilmārs, Harijs Kalis, Ērika Teirumnieka, and Edmunds Teirumnieks. "SPECIAL SPLINE APPROXIMATION FOR THE SOLUTION OF THE NON-STATIONARY 3-D MASS TRANSFER PROBLEM." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 17, 2021): 69–73. http://dx.doi.org/10.17770/etr2021vol2.6577.

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In this paper we consider the conservative averaging method (CAM) with special spline approximation for solving the non-stationary 3-D mass transfer problem. The special hyperbolic type spline, which interpolates the middle integral values of piece-wise smooth function is used. With the help of these splines the initial-boundary value problem (IBVP) of mathematical physics in 3-D domain with respect to one coordinate is reduced to problems for system of equations in 2-D domain. This procedure allows reduce also the 2-D problem to a 1-D problem and thus the solution of the approximated problem can be obtained analytically. The accuracy of the approximated solution for the special 1-D IBVP is compared with the exact solution of the studied problem obtained with the Fourier series method. The numerical solution is compared with the spline solution. The above-mentioned method has extensive physical applications, related to mass and heat transfer problems in 3-D domains.

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Kalis, H., and I. Kangro. "SIMPLE METHODS OF ENGINEERING CALCULATION FOR SOLVING HEAT TRANSFER PROBLEMS." Mathematical Modelling and Analysis 8, no. 1 (March 31, 2003): 33–42. http://dx.doi.org/10.3846/13926292.2003.9637208.

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There are well-known numerical methods for solving the initial‐boundary value problems for partial differential equations. We mention only some of them: finite difference method (FDM), finite element method (FEM), boundary element method (BEM), Galerkin type methods and others. In the given work FDM and BEM are considered for determination a distribution of heat in the multilayer media. These methods were used for the reduction of the 1D heat transfer problem described by a partial differential equation to an initial‐value problem for a system of ordinary differential equations (ODEs). Such a procedure allows us to obtain a simple engineering algorithm for solving heat transfer equation in multilayered domain. In a stationary case the exact finite difference scheme is obtained. An inverse problem is also solved. The heat transfer coefficients are found and temperatures in the interior layers depending on the given temperatures inside and outside of a domain are obtained.

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KLAR, A., and N. SIEDOW. "Boundary layers and domain decomposition for radiative heat transfer and diffusion equations: applications to glass manufacturing process." European Journal of Applied Mathematics 9, no. 4 (August 1998): 351–72. http://dx.doi.org/10.1017/s0956792598003490.

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In this paper domain decomposition methods for radiative transfer problems including conductive heat transfer are treated. The paper focuses on semi-transparent materials, like glass, and the associated conditions at the interface between the materials. Using asymptotic analysis we derive conditions for the coupling of the radiative transfer equations and a diffusion approximation. Several test casts are treated and a problem appearing in glass manufacturing processes is computed. The results clearly show the advantages of a domain decomposition approach. Accuracy equivalent to the solution of the global radiative transfer solution is achieved, whereas computation time is strongly reduced.

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Tan, Chuanqi, Fuchun Sun, Bin Fang, Tao Kong, and Wenchang Zhang. "Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141984086. http://dx.doi.org/10.1177/1729881419840860.

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The brain–computer interface-based rehabilitation robot has quickly become a very important research area due to its natural interaction. One of the most important problems in brain–computer interface is that large-scale annotated electroencephalography data sets required by advanced classifiers are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed with the test data. It can be considered a powerful tool for solving the problem of insufficient training data. There are two basic issues with transfer learning, under transfer and negative transfer. We proposed a novel brain–computer interface framework by using autoencoder-based transfer learning, which includes three main components: an autoencoder framework, a joint adversarial network, and a regularized manifold constraint. The autoencoder framework automatically encodes and reconstructs data from source and target domains and forces the neural network to learn to represent these domains reliably. The joint adversarial network aims to force the network to learn to encode more appropriately for the source domain and target domain simultaneously, thereby overcoming the problem of under transfer. The regularized manifold constraint aims to avoid the problem of negative transfer by avoiding geometric manifold structure in the target domain being destroyed by the source domain. Experiments show that the brain–computer interface framework proposed by us can achieve better results than state-of-the-art approaches in electroencephalography signal classification tasks. This is helpful in aiding our rehabilitation robot to understand the intention of patients and can help patients to carry out rehabilitation exercises effectively.

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Mishuris, Gennady, and Michał Wróbel. "Coupled FEM-BEM Approach for Axisymetrical Heat Transfer Problems." Defect and Diffusion Forum 273-276 (February 2008): 740–45. http://dx.doi.org/10.4028/www.scientific.net/ddf.273-276.740.

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This work deals with a stationary axisymmetrical heat transfer problem in a combined domain. This domain consists of half-space joined with a bounded cylinder. An important feature of the problem is the possible flux singularity along the edge points of the transmission surface. Domain decomposition is used to separate the subdomains. The solution for an auxiliary mixed boundary value problem in the half space is found analytically by means of Hankel integral transform. This allows us to reduce the main problem in the infinite domain to another problem defined in the bounded subdomain. In turn, the new problem contains a nonlocal boundary conditions along the transmission surface. These conditions incorporate all basic information about the infinite sub-domain (material properties, internal sources etc.). The problem is solved then by means of the Finite Element Method. In fact it might be considered as a coupled FEM-BEM approach. We use standard MATLAB PDE toolbox for the FEM analysis. As it is not possible for this package to introduce directly a non-classical boundary condition, we construct an appropriate iterative procedure and show the fast convergence of the main problem solution. The possible solution singularity is taken into account and the corresponding intensity coefficient of the heat flux is computed with a high accuracy. Numerical examples dealing with heat transfer between closed reservoir (filled with some substance) and the infinite foundation are discussed.

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Paul, A., F. Rottensteiner, and C. Heipke. "TRANSFER LEARNING BASED ON LOGISTIC REGRESSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 145–52. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-145-2015.

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In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.

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Yan, Zhenhao, Guifang Liu, Jinrui Wang, Huaiqian Bao, Zongzhen Zhang, Xiao Zhang, and Baokun Han. "A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults." Entropy 23, no. 8 (August 16, 2021): 1052. http://dx.doi.org/10.3390/e23081052.

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The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.

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Peng, Jian She, Guang Bing Luo, and Liu Yang. "Solving Transient Heat Transfer Problems by the Convolution Type Semi-Analytical DQ Method." Applied Mechanics and Materials 204-208 (October 2012): 4315–19. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4315.

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The convolution-type Gurtin variational principle is known as the only variational principle that is, from mathematics point of view, totally equivalent to the initial value problem system. In this paper, the governing equation of bars is first transformed to a new equation containing initial conditions by using convolution method. Then, a convolution-type semi-analytical DQ approach, which involves differential quadrature (DQ) approximation in space domain and an analytical series expansion in time domain, is proposed to obtain the transient response solution. The transient heat transfer examples show the proposed method is a very useful and efficient tool in transient heat transfer problems.

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Meng, Jiana, Yingchun Long, Yuhai Yu, Dandan Zhao, and Shuang Liu. "Cross-Domain Text Sentiment Analysis Based on CNN_FT Method." Information 10, no. 5 (May 1, 2019): 162. http://dx.doi.org/10.3390/info10050162.

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Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

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Tahmoresnezhad, Jafar, and Sattar Hashemi. "An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (February 2016): 1651003. http://dx.doi.org/10.1142/s0218001416510034.

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One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of dataset. In this paper, we exploit jointly transfer learning and class discrimination to cope with domain shift problem on which the distribution difference is considerably large. We therefore put forward a novel transfer learning and class discrimination approach, referred to as RandOm k-samplesets feature Weighting Approach (ROWA). Specifically, ROWA reduces the distribution difference across domains in an unsupervised manner where no label is available in the test set. Moreover, ROWA exploits feature-label relation to separate various classes alongside the domain transfer, and augments the relation of selected features and source domain labels. In this work, we employ disjoint/overlapping small-sized samplesets to iteratively converge to final solution. Employment of local sets along with a novel optimization problem constructs a robust and effective reduced representation for adaptation across domains. Extensive experiments on real and synthetic datasets verify that ROWA can significantly outperform state-of-the-art transfer learning approaches.

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Wu, Lan, Chongyang Li, Qiliang Chen, and Binquan Li. "Deep adversarial domain adaptation network." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142096464. http://dx.doi.org/10.1177/1729881420964648.

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The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.

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Yuan, Weiwei, Jiali Pang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, and Mohammed Al-Dhelaan. "Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning." Complexity 2019 (February 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/4906903.

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Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model.

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Lee, Kihoon, Soonyoung Han, Van Huan Pham, Seungyon Cho, Hae-Jin Choi, Jiwoong Lee, Inwoong Noh, and Sang Won Lee. "Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis." Applied Sciences 11, no. 5 (March 7, 2021): 2370. http://dx.doi.org/10.3390/app11052370.

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Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.

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Li, Xudong, Jianhua Zheng, Mingtao Li, Wenzhen Ma, and Yang Hu. "Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis." Sensors 21, no. 2 (January 10, 2021): 450. http://dx.doi.org/10.3390/s21020450.

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

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Li, Xudong, Jianhua Zheng, Mingtao Li, Wenzhen Ma, and Yang Hu. "Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis." Sensors 21, no. 2 (January 10, 2021): 450. http://dx.doi.org/10.3390/s21020450.

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

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Li, Weigui, Zhuqing Yuan, Wenyu Sun, and Yongpan Liu. "Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions." MATEC Web of Conferences 319 (2020): 03001. http://dx.doi.org/10.1051/matecconf/202031903001.

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Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

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Hyun, Jaeguk, ChanYong Lee, Hoseong Kim, Hyunjung Yoo, and Eunjin Koh. "Learning Domain Invariant Representation via Self-Rugularization." Journal of the Korea Institute of Military Science and Technology 24, no. 4 (August 5, 2021): 382–91. http://dx.doi.org/10.9766/kimst.2021.24.4.382.

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Unsupervised domain adaptation often gives impressive solutions to handle domain shift of data. Most of current approaches assume that unlabeled target data to train is abundant. This assumption is not always true in practices. To tackle this issue, we propose a general solution to solve the domain gap minimization problem without any target data. Our method consists of two regularization steps. The first step is a pixel regularization by arbitrary style transfer. Recently, some methods bring style transfer algorithms to domain adaptation and domain generalization process. They use style transfer algorithms to remove texture bias in source domain data. We also use style transfer algorithms for removing texture bias, but our method depends on neither domain adaptation nor domain generalization paradigm. The second regularization step is a feature regularization by feature alignment. Adding a feature alignment loss term to the model loss, the model learns domain invariant representation more efficiently. We evaluate our regularization methods from several experiments both on small dataset and large dataset. From the experiments, we show that our model can learn domain invariant representation as much as unsupervised domain adaptation methods.

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MATSUMOTO, Toshiro, Masataka TANAKA, and Ryosuke TOKUDA. "261 Boundary integral treatment of domain integral for initial condition in time-domain BEM for transient heat transfer problem." Proceedings of The Computational Mechanics Conference 2001.14 (2001): 245–46. http://dx.doi.org/10.1299/jsmecmd.2001.14.245.

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Mochnacki, B., and M. Ciesielski. "Sensitivity of transient temperature field in domain of forearm insulated by protective clothing with respect to perturbations of external boundary heat flux." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 3 (September 1, 2016): 591–98. http://dx.doi.org/10.1515/bpasts-2016-0066.

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AbstractThe problem discussed in the paper is numerical modeling of thermal processes in the domain of biological tissue secured by a layer of protective clothing being in thermal contact with the environment. The cross-section of the forearm (2D problem) is treated as non-homogeneous domain in which the sub-domains of skin tissue, fat, muscle and bone are distinguished. The air gap between skin tissue and protective clothing is taken into account. The process of external heating is determined by Robin boundary condition and sensitivity analysis with respect to the perturbations of heat transfer coefficient and ambient temperature is also discussed. Both the basic boundary-initial problem and the sensitivity problems are solved by means of control volume method using Voronoi polygons.

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He, Jun, Xiang Li, Yong Chen, Danfeng Chen, Jing Guo, and Yan Zhou. "Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis." Shock and Vibration 2021 (May 19, 2021): 1–16. http://dx.doi.org/10.1155/2021/6687331.

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In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

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Wu, Bi-Xiao, Chen-Guang Yang, and Jun-Pei Zhong. "Research on Transfer Learning of Vision-based Gesture Recognition." International Journal of Automation and Computing 18, no. 3 (March 8, 2021): 422–31. http://dx.doi.org/10.1007/s11633-020-1273-9.

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AbstractGesture recognition has been widely used for human-robot interaction. At present, a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains. For each new domain, it is required to collect and annotate a large amount of data, and the training of the algorithm does not benefit from prior knowledge, leading to redundant calculation workload and excessive time investment. To address this problem, the paper proposes a method that could transfer gesture data in different domains. We use a red-green-blue (RGB) Camera to collect images of the gestures, and use Leap Motion to collect the coordinates of 21 joint points of the human hand. Then, we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning. This paper compares the effects of three classification algorithms, i.e., support vector machine (SVM), broad learning system (BLS) and deep learning (DL). We also compare learning performances with and without using the joint distribution adaptation (JDA) algorithm. The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion. In addition, we found that when using DL to classify the data, excessive training on the source domain may reduce the accuracy of recognition in the target domain.

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Allaire, Grégoire, and Zakaria Habibi. "Homogenization of a Conductive, Convective, and Radiative Heat Transfer Problem in a Heterogeneous Domain." SIAM Journal on Mathematical Analysis 45, no. 3 (January 2013): 1136–78. http://dx.doi.org/10.1137/110849821.

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Yang, Haitian. "A PRECISE ALGORITHM IN THE TIME DOMAIN TO SOLVE THE PROBLEM OF HEAT TRANSFER." Numerical Heat Transfer, Part B: Fundamentals 35, no. 2 (March 1999): 243–49. http://dx.doi.org/10.1080/104077999275974.

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Chakraborty, Suman, and Pradip Dutta. "Analytical Solutions for Heat Transfer During Cyclic Melting and Freezing of a Phase Change Material Used in Electronic or Electrical Packaging." Journal of Electronic Packaging 125, no. 1 (March 1, 2003): 126–33. http://dx.doi.org/10.1115/1.1535445.

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In this paper, we develop an analytical heat transfer model, which is capable of analyzing cyclic melting and solidification processes of a phase change material used in the context of electronics cooling systems. The model is essentially based on conduction heat transfer, with treatments for convection and radiation embedded inside. The whole solution domain is first divided into two main sub-domains, namely, the melting sub-domain and the solidification sub-domain. Each sub-domain is then analyzed for a number of temporal regimes. Accordingly, analytical solutions for temperature distribution within each sub-domain are formulated either using a semi-infinity consideration, or employing a method of quasi-steady state, depending on the applicability. The solution modules are subsequently united, leading to a closed-form solution for the entire problem. The analytical solutions are then compared with experimental and numerical solutions for a benchmark problem quoted in the literature, and excellent agreements can be observed.

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Zhang, Kai, Hefu Zhang, Qi Liu, Hongke Zhao, Hengshu Zhu, and Enhong Chen. "Interactive Attention Transfer Network for Cross-Domain Sentiment Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5773–80. http://dx.doi.org/10.1609/aaai.v33i01.33015773.

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Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common features between domains. Unfortunately, they cannot fully consider the effects of the aspect (e.g., the battery life in reviewing an electronic product) information of the sentences. In order to better solve this problem, we propose an Interactive Attention Transfer Network (IATN) for crossdomain sentiment classification. IATN provides an interactive attention transfer mechanism, which can better transfer sentiment across domains by incorporating information of both sentences and aspects. Specifically, IATN comprises two attention networks, one of them is to identify the common features between domains through domain classification, and the other aims to extract information from the aspects by using the common features as a bridge. Then, we conduct interactive attention learning for those two networks so that both the sentences and the aspects can influence the final sentiment representation. Extensive experiments on the Amazon reviews dataset and crowdfunding reviews dataset not only demonstrate the effectiveness and universality of our method, but also give an interpretable way to track the attention information for sentiment.

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Sawicki, Dominik, and Eugeniusz Zieniuk. "Parametric Integral Equations Systems Method In Solving Unsteady Heat Transfer Problems For Laser Heated Materials." Acta Mechanica et Automatica 9, no. 3 (September 1, 2015): 167–72. http://dx.doi.org/10.1515/ama-2015-0028.

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Abstract One of the most popular applications of high power lasers is heating of the surface layer of a material, in order to change its properties. Numerical methods allow an easy and fast way to simulate the heating process inside of the material. The most popular numerical methods FEM and BEM, used to simulate this kind of processes have one fundamental defect, which is the necessity of discretization of the boundary or the domain. An alternative to avoid the mentioned problem are parametric integral equations systems (PIES), which do not require classical discretization of the boundary and the domain while being numerically solved. PIES method was previously used with success to solve steady-state problems, as well as transient heat transfer problems. The purpose of this paper is to test the efficacy of the PIES method with time discretization in solving problem of laser heating of a material, with different pulse shape approximation functions.

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Lawson, Michael J. "Critique: The Case for Instruction in the Use of General Problem-Solving Strategies in Mathematics Teaching: A Comment on Owen and Sweller." Journal for Research in Mathematics Education 21, no. 5 (November 1990): 403–10. http://dx.doi.org/10.5951/jresematheduc.21.5.0403.

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Owen and Sweller (1989) question the wisdom of recent moves to allocate time in mathematics teaching to instruction in the use of general problem-solving strategies because they doubt that such instruction will help overcome problems in the transfer of learning. According to Owen and Sweller transfer failure is more likely to be the result of a lack of appropriate schema or insufficient automation of rules. They imply that attention allocated to general problem-solving strategies would be more appropriately diverted to instruction concerned with domain-specific knowledge and practice with worked examples and goal-modified problems. Because curricula in several countries are in the process of being modified to incorporate explicit consideration of the nature of general problem-solving strategies, Owen and Sweller's view that the evidence on the efficacy of such instruction is “very sparse” deserves examination.

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Baddour, Natalie. "Multidimensional Wave Field Signal Theory: Transfer Function Relationships." Mathematical Problems in Engineering 2012 (2012): 1–27. http://dx.doi.org/10.1155/2012/478295.

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The transmission of information by propagating or diffusive waves is common to many fields of engineering and physics. Such physical phenomena are governed by a Helmholtz (real wavenumber) or pseudo-Helmholtz (complex wavenumber) equation. Since these equations are linear, it would be useful to be able to use tools from signal theory in solving related problems. The aim of this paper is to derive multidimensional input/output transfer function relationships in the spatial domain for these equations in order to permit such a signal theoretic approach to problem solving. This paper presents such transfer function relationships for the spatial (not Fourier) domain within appropriate coordinate systems. It is shown that the relationships assume particularly simple and computationally useful forms once the appropriate curvilinear version of a multidimensional spatial Fourier transform is used. These results are shown for both real and complex wavenumbers. Fourier inversion of these formulas would have applications for tomographic problems in various modalities. In the case of real wavenumbers, these inversion formulas are presented in closed form, whereby an input can be calculated from a given or measured wavefield.

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Rezvaya, Ekaterina, Pavel Goncharov, and Gennady Ososkov. "Using deep domain adaptation for image-based plant disease detection." System Analysis in Science and Education, no. 2 (2020) (June 30, 2020): 59–69. http://dx.doi.org/10.37005/2071-9612-2020-2-59-69.

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Crop losses due to plant diseases isa serious problem for the farming sector of agricultureand the economy. Therefore, a multi-functional Plant Disease Detection Platform (PDDP) was developed in the LIT JINR. Deep learning techniques are successfully used in PDDP to solve the problem of recognizing plant diseases from photographs of their leaves. However, such methods require a large training dataset. At the same time, there are number of methods used to solve classification problems in cases of a small training dataset, asfor example,domain adaptation(DA)methods.In this paper, a comparative study of three DA methods is performed:Domain-Adversarial Training of Neural Networks (DANN), two-steps transfer learning and Unsupervised Domain Adaptation with Deep Metric Learning (M-ADDA).The advantage of the M-ADDA methodwas shown, which allowed toachieve 92% ofclassification accuracy.

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Rostami, Mohammad, Soheil Kolouri, Eric Eaton, and Kyungnam Kim. "Deep Transfer Learning for Few-Shot SAR Image Classification." Remote Sensing 11, no. 11 (June 8, 2019): 1374. http://dx.doi.org/10.3390/rs11111374.

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The reemergence of Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper, we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for a huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data are easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide a theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other competing learning approaches.

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CALABRÒ, FRANCESCO, and PAOLO ZUNINO. "ANALYSIS OF PARABOLIC PROBLEMS ON PARTITIONED DOMAINS WITH NONLINEAR CONDITIONS AT THE INTERFACE: APPLICATION TO MASS TRANSFER THROUGH SEMI-PERMEABLE MEMBRANES." Mathematical Models and Methods in Applied Sciences 16, no. 04 (April 2006): 479–501. http://dx.doi.org/10.1142/s0218202506001236.

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In this work we address a problem governed by linear parabolic partial differential equations set in two adjoining domains, coupled by nonlinear interface conditions of Neumann type. In particular, we address the existence and uniqueness of strong solutions by applying the strong maximum principle, the Schauder fixed point theorem and the fundamental solutions of linear parabolic partial differential equations.In the first part of this work, we consider the properties of a linear parabolic partial differential equation set on a single domain with a nonlinear boundary condition. After having addressed the well-posedness and some comparison results for the problem on one domain, in the second part of this work we address the case of coupled problems on adjoining domains. In both cases, we complete the understanding of the behavior of the solution of the problems at hand by means of numerical simulations.The theoretical results obtained here are applied to study the behavior of a biological model for the transfer of chemicals through thin biological membranes. This model represents the dynamics of the concentration u of a chemical solution separated from the exterior by a semi-permeable membrane.The analysis of the two-domain problem that we carry out could also be used to investigate the convergence property of iterative substructuring methods applied to the approximation of multidomain problems with nonlinear coupling of Neumann type.

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Shapiro, Daniel G., Hector Munoz-Avila, and David Stracuzzi. "The Special Issue of AI Magazine on Structured Knowledge Transfer." AI Magazine 32, no. 1 (March 16, 2011): 12. http://dx.doi.org/10.1609/aimag.v32i1.2328.

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This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.

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Fernandez, Susana, Ricardo Aler, and Daniel Borrajo. "Knowledge Transfer between Automated Planners." AI Magazine 32, no. 2 (March 16, 2011): 79. http://dx.doi.org/10.1609/aimag.v32i2.2334.

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In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

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Frąckowiak, Andrzej, and Michał Ciałkowski. "Application of discrete Fourier transform to inverse heat conduction problem regularization." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 1 (January 2, 2018): 239–53. http://dx.doi.org/10.1108/hff-09-2017-0381.

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Purpose This paper aims to present the Cauchy problem for the Laplace’s equation for profiles of gas turbine blades with one and three cooling channels. The distribution of heat transfer coefficient and temperature on the outer boundary of the blade are known. On this basis, the temperature on inner surfaces of the blade (the walls of cooling channels) is determined. Design/methodology/approach Such posed inverse problem was solved using the finite element method in the domain of the discrete Fourier transform (DFT). Findings Calculations indicate that the regularization in the domain of the DFT enables obtaining a stable solution to the inverse problem. In the example under consideration, problems with reconstruction constant temperature, assumed on the outer boundary of the blade, in the vicinity of the trailing and leading edges occurred. Originality/value The application of DFT in connection with regularization is an original achievement presented in this study.

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Wang, Peng (Edward), and Matthew Russell. "Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1137.

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Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.

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Du, Yan, Aiming Wang, Shuai Wang, Baomei He, and Guoying Meng. "Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network." Shock and Vibration 2020 (May 4, 2020): 1–18. http://dx.doi.org/10.1155/2020/1274380.

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Fault diagnosis plays a very important role in ensuring the safe and reliable operations of machines. Currently, the deep learning-based fault diagnosis is attracting increasing attention. However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. This problem is also unavoidable in deep learning-based fault diagnosis methods. This paper contributes to the ongoing investigation by proposing a new approach for the fault diagnosis under variable working conditions based on STFT and transfer deep residual network (TDRN). The STFT was employed to convert vibration signal to time-frequency image as the input of the TDRN. To address the domain discrepancy problem, the TDRN was developed in this paper. Unlike traditional deep convolutional neural network (DCNN) methods, by combining with transfer learning, the TDRN can make a bridge between two different working conditions, thereby using the knowledge learned from a working condition to achieve a high classification accuracy in another working condition. Moreover, since the residual learning is introducing, the TDRN can overcome the problems of training difficulty and performance degradation existing in traditional DCNN methods, thus further improving the classification accuracy. Experiments are conducted on the popular CWRU bearing dataset to validate the effectiveness and superiority of the proposed approach. The results show that the developed TDRN outperforms those methods without transfer learning and/or residual learning in terms of the accuracy and feature learning ability for the fault diagnosis under variable working conditions.

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Feng, Yuntian, Guoliang Wang, Zhipeng Liu, Runming Feng, Xiang Chen, and Ning Tai. "An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning." Algorithms 12, no. 12 (December 16, 2019): 271. http://dx.doi.org/10.3390/a12120271.

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Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

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Gourary, Mark, Sergey Rusakov, Mikhail Zharov, and Sergey Ulyanov. "Computational Algorithms for Reducing Rational Transfer Functions’ Order." SPIIRAS Proceedings 19, no. 2 (April 23, 2020): 330–56. http://dx.doi.org/10.15622/sp.2020.19.2.4.

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A problem of reducing a linear time-invariant dynamic system is considered as a problem of approximating its initial rational transfer function with a similar function of a lower order. The initial transfer function is also assumed to be rational. The approximation error is defined as the standard integral deviation of the transient characteristics of the initial and reduced transfer function in the time domain. The formulations of two main types of approximation problems are considered: a) the traditional problem of minimizing the approximation error at a given order of the reduced model; b) the proposed problem of minimizing the order of the model at a given tolerance on the approximation error. Algorithms for solving approximation problems based on the Gauss-Newton iterative process are developed. At the iteration step, the current deviation of the transient characteristics is linearized with respect to the coefficients of the denominator of the reduced transfer function. Linearized deviations are used to obtain new values of the transfer function coefficients using the least-squares method in a functional space based on Gram-Schmidt orthogonalization. The general form of expressions representing linearized deviations of transient characteristics is obtained. To solve the problem of minimizing the order of the transfer function in the framework of the least squares algorithm, the Gram-Schmidt process is also used. The completion criterion of the process is to achieve a given error tolerance. It is shown that the sequence of process steps corresponding to the alternation of coefficients of polynomials of the numerator and denominator of the transfer function provides the minimum order of transfer function. The paper presents an extension of the developed algorithms to the case of a vector transfer function with a common denominator. An algorithm is presented with the approximation error defined in the form of a geometric sum of scalar errors. The use of the minimax form for error estimation and the possibility of extending the proposed approach to the problem of reducing the irrational initial transfer function are discussed. Experimental code implementing the proposed algorithms is developed, and the results of numerical evaluations of test examples of various types are obtained.

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Paul, A., K. Vogt, F. Rottensteiner, J. Ostermann, and C. Heipke. "A COMPARISON OF TWO STRATEGIES FOR AVOIDING NEGATIVE TRANSFER IN DOMAIN ADAPTATION BASED ON LOGISTIC REGRESSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 845–52. http://dx.doi.org/10.5194/isprs-archives-xlii-2-845-2018.

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In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset.

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Kalis, H., and I. Kangro. "SIMPLE METHODS OF ENGINEERING CALCULATION FOR SOLVING STATIONARY 2 –D HEAT TRANSFER PROBLEMS IN MULTILAYER MEDIA." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 26, 2006): 359. http://dx.doi.org/10.17770/etr2003vol1.1991.

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There are well-known different numerical methods for solving the boundary value problems for partial differential equations. Some of them are: finite difference method (FDM), finite element method (FEM), boundary element methods (BEM), and others. In the given work two methods FDM and BEM for the mathematical model of stationary distribution of heat in the multilayer media are considered. These methods were used for the reduction of the two-dimensional heat transfer problem described by a partial differential equation to a boundary – value problem for a system of ordinary differential equations. (ODEs). Such a procedure allows obtaining simple engineering algorithms for solving heat transfer equation in mulyilayer domain. In the case of three layers the system of ODEs is possible for solving analytically.

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Hollingsworth, Maurice, and John Woodward. "Integrated Learning: Explicit Strategies and Their Role in Problem-Solving Instruction for Students with Learning Disabilities." Exceptional Children 59, no. 5 (March 1993): 444–55. http://dx.doi.org/10.1177/001440299305900507.

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This study investigated the effectiveness of an explicit strategy as a means of linking facts, concepts, and problem solving in an unfamiliar domain of learning. Participants were 37 secondary students with learning disabilities. All students were taught health facts and concepts, which they then applied to problem-solving exercises presented through computer-simulation games. Students in the experimental group were taught an explicit strategy for solving the problems; the comparison group was given supportive feedback and encouraged to induce their own strategies. The explicit strategy group performed significantly better on two transfer measures, including videotaped problem-solving exercises.

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Chebotarev, Alexander Yu, and Andrey E. Kovtanyuk. "Boundary Design of Reflection Properties of a Steady-State Complex Heat Transfer Model." Key Engineering Materials 685 (February 2016): 90–93. http://dx.doi.org/10.4028/www.scientific.net/kem.685.90.

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A boundary multiplicative control problem for a nonlinear steady-state heat transfer model accounting for heat radiation effects is considered. The aim of control consists in obtaining a prescribed temperature or radiative intensity distributions in a part of the model domain by controlling the boundary reflectivity. The solvability of this control problem is proved, and optimality conditions are derived.

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Sanjosé López, V., Joan Josep Solaz Portolés, and Tomás Valenzuela. "Inter-domain transfer in problem solving: an instructional methodology based on the «algebraic translation» process." Enseñanza de las Ciencias. Revista de investigación y experiencias didácticas 27, no. 2 (June 9, 2009): 169. http://dx.doi.org/10.5565/rev/ensciencias.3729.

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OPITZ, KARSTEN, and HELMUT POTTMANN. "COMPUTING SHORTEST PATHS ON POLYHEDRA: APPLICATIONS IN GEOMETRIC MODELING AND SCIENTIFIC VISUALIZATION." International Journal of Computational Geometry & Applications 04, no. 02 (June 1994): 165–78. http://dx.doi.org/10.1142/s0218195994000112.

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The application of shortest path calculations on polyhedra in different problem domains is discussed. A technique is introduced which allows to transfer problems defined on a curved domain surface into a plane. The technique is based on an approximation to the inverse exponential map. It is applied to gradient estimation for scattered data on surfaces and for the construction of local surface approximations from unorganized points.

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Bohaienko, Vsevolod, and Volodymyr Bulavatsky. "Fractional-Fractal Modeling of Filtration-Consolidation Processes in Saline Saturated Soils." Fractal and Fractional 4, no. 4 (December 16, 2020): 59. http://dx.doi.org/10.3390/fractalfract4040059.

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To study the peculiarities of anomalous consolidation processes in saturated porous (soil) media in the conditions of salt transfer, we present a new mathematical model developed on the base of the fractional-fractal approach that allows considering temporal non-locality of transfer processes in media of fractal structure. For the case of the finite thickness domain with permeable boundaries, a finite-difference technique for numerical solution of the corresponding one-dimensional non-linear boundary value problem is developed. The paper also presents a fractional-fractal model of a filtration-consolidation process in clay soils of fractal structure saturated with salt solutions. An analytical solution is found for the corresponding one-dimensional boundary value problem in the domain of finite thickness with permeable upper and impermeable lower boundaries.

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Lubyshev, F. V., and M. E. Fairuzov. "Approximation of a mixed boundary value problem." Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva 20, no. 4 (December 30, 2018): 429–38. http://dx.doi.org/10.15507/2079-6900.20.201804.429-438.

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The mixed boundary value problem for the divergent-type elliptic equation with variable coefficients is considered. It is assumed that the integration domain has a sufficiently smooth boundary that is the union of two disjoint pieces. The Dirichlet boundary condition is given on the first piece, and the Neumann boundary condition is given on the other one. So the problem has discontinuous boundary condition. Such problems with mixed boundary conditions are the most common in practice when modeling processes and are of considerable interest in the development of methods for their solution. In particular, a number of problems in the theory of elasticity, theory of diffusion, filtration, geophysics, a number of problems of optimization in electro-heat and mass transfer in complex multielectrode electrochemical systems are reduced to the boundary value problems of this type. In this paper, we propose an approximation of the original mixed boundary value problem by the third boundary value problem with a parameter. The convergence of the proposed approximations is investigated. Estimates of the approximations’ convergence rate in Sobolev norms are established.

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Fitriansyah, Muhammad Nurul. "Problem Multikultural Pendidikan Islam." Tsamratul Fikri | Jurnal Studi Islam 14, no. 1 (June 13, 2020): 43. http://dx.doi.org/10.36667/tf.v14i1.378.

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Education is a tool in forming someone's character. An education system that only emphasizes the transfer of knowledge alone makes education no longer meaningful and has a positive effect on students. Education that does not touch the affective domain makes students less moral so that it will cause social conflict. Multicultural education is a concept of education as a solution to socio-cultural conflicts in plural societies. In the course of multicultural education began to enter the area of religious education by carrying out the mission of religious pluralism, relativism and secular humanism. In the Islamic view, the concept of multicultural education tries to deconstruct the concept of God, which should be instilled in students. Therefore, Islamic education should apply the concept that was born from the teachings of Islam itself, namely the concept of tasamuh. This research uses literature review method by searching various sources of literature that are relevant to the research subject.

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Yin, Yuqing, Xu Yang, Peihao Li, Kaiwen Zhang, Pengpeng Chen, and Qiang Niu. "Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments." Sensors 21, no. 3 (February 2, 2021): 1015. http://dx.doi.org/10.3390/s21031015.

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Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

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Journal articles on the topic 'Domain-transfer problem'

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