Dynamic Update Algorithm Research Articles

Matrix-based approximation dynamic update approach to multi-granulation neighborhood rough sets for intuitionistic fuzzy ordered datasets

With the explosive growth and rapid changes in the use of data, information systems are constantly evolving. Timely dynamic updates have become imperative with real-time monitoring of data increasingly common. Effectively characterizing the approximation space in dynamic environments is of significant concern. This paper investigates a dynamic update mechanism for generalized multi-granulation neighborhood dominant rough sets, based on a matrix form, in an intuitionistic fuzzy ordered information table. We first define support and inclusion functions to construct the model of generalized multi-granulation neighborhood dominant rough sets. Additionally, we analyze the dynamic update process in which objects are added or removed in matrix form. Corresponding dynamic update algorithms are proposed based on generalized multi-granulation neighborhood dominant rough sets. Finally, to validate the effectiveness of the matrix-based dynamic approximation update algorithm, eight UCI datasets are used to perform experiments. The results verify that our matrix-based dynamic update algorithm is effective in approximating updates for dynamic intuitionistic fuzzy ordered information datasets.

Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction

Support vector regression (SVR) has been widely used for reliability modeling and prediction in various engineering practices. In order to improve the accuracy and robustness of SVR models, this paper proposes a hierarchical Bayesian support vector regression (HBSVR) model, which can be used for dynamic high-dimensional reliability modeling with small data sets. First, a hierarchical Bayesian model is developed to obtain the posterior inference of HBSVR model parameters. Then, a step-size adaptive accelerated Markov Chain Monte Carlo (SAA-MCMC) method is proposed and combined with Sequential Minimal Optimization (SMO) for model parameter calibration. In addition, a HSBVR dynamic update algorithm is proposed to readjust the current parameters of HBSVR by using the updated dataset and SAA-MCMC to avoid repeated modeling during online prediction. Finally, an active learning algorithm is applied to guide the selection of new samples to improve the model accuracy continuously. Eight numerical examples and two real engineering cases are conducted. The results show that the proposed HBSVR outperforms other methods in terms of prediction accuracy and robustness in small sample reliability prediction tasks.

A double scrambling-DNA row and column closed loop image encryption algorithm based on chaotic system.

In this paper, a dynamic update algorithm of double scrambling-DNA row and column closed loop based on chaotic system is proposed. The classical scrambling and diffusion structure are used in the whole process. In the scrambling stage, a new pixel reconstruction method is proposed by combining the Hilbert curve with Knuth-Durstenfeld shuffle algorithm to overcome the shortcoming of nearby storage of Hilbert curve. This method reconstructs the pixel matrix of one-dimensional vector according to the Hilbert curve coding method, and achieves good scrambling effect, while reducing its time complexity and space complexity. In the diffusion stage, combining the plaintext row, the ciphertext row and the key row, and taking advantage of the parallel computing power and high storage density of the DNA encoding, the existing block diffusion operation is improved, and the two-round diffusion of the DNA encoding is proposed. When the last line of ciphertext is generated, the first line of ciphertext is updated and the closed-loop dynamic update of the encryption system is realized. Finally, SHA-256 is used to give the secret key and calculate the initial value of the chaotic system. The simulation results show that the “double scrambling-DNA row and column closed loop dynamic” update algorithm proposed in this paper can effectively improve the efficiency of information transmission and have high security.

From WASD to BLS with application to pattern classification

The single-hidden-layer feedforward neural network (SLFN) exhibits an excellent approximation ability, despite its simple structure. This study examines two neural network models developed based on the general structure of the SLFN, i.e., the weights-and-structure-determination (WASD) neural network and broad learning system (BLS). The BLS and WASD neural networks exhibit many similarities in terms of their structure and algorithm, while BLS incorporates certain distinct ideas that render it an improved version of the WASD neural network. The research described in this paper is the first approach to establish a connection between the BLS and WASD neural networks. Moreover, pattern classification experiments on a foot dataset and several benchmark datasets are conducted using these two kinds of neural network models, and the performance of different models is compared and analyzed.Experimental results show that dividing the feature nodes into one group is the best choice for our foot dataset, and that the cross-layer connectivity and sparse coding of BLS can effectively improve the classification accuracy. In addition, it is shown that the dynamic update algorithm of BLS can improve the accuracy and save a lot of time when updating the model structure, but the final accuracy depends on the initial structure and parameters. Comparative experiments show that BLS achieves 89.12% classification accuracy for the foot dataset, which is the highest among all the demonstrated WASD neural networks and two other SLFN models. Finally, extended experiments on five benchmark datasets show that BLS can achieve satisfactory accuracy on multiple types of datasets, while WASD neural networks are possibly inadequate in processing image datasets in contrast.

Social event detection with retweeting behavior correlation

Event detection over microblogs has attracted great research interest due to its wide application in crisis management and decision making etc. In natural disasters, complex events are reported in real time on social media sites, but these reports are invisible to crisis coordinators. Detecting these crisis events helps watchers to make right decisions rapidly, reducing injuries, deaths and economic loss. In sporting activities, detecting events helps audiences make better and more timely game viewing plans. However, existing event detection techniques are not effective at handling complex social events that evolve over time. In this paper, we propose an event detection method that takes advantage of retweeting behavior for handling the events evolution. Specifically, we first propose a topic model called RL-LDA to capture the social media information over hashtag, location, textual and retweeting behavior. Using RL-LDA, a complex event can be well handled by exploring the correlation between retweeting behavior and the event. Then to maintain the RL-LDA in a dynamic environment, we propose a dynamic update algorithm, which incrementally updates events over real time streams. Experiments over real-world datasets show that RL-LDA detects the temporal evolution of complex events effectively and efficiently.

Decentralized Coordination of Controlled Loads and Transformers in a hierarchical structure

The paper considers the coordination of controlled loads in a setting where the loads are connected to the distribution network through transformers. Our objective is to design a decentralized control method that can motivate selfish loads to achieve global benefits. This problem has a hierarchical structure. At the lower level, each transformer broadcasts a price signal to the loads connect to it, under which loads establish their best usage strategies. At the upper level, transformers communicate with the distribution network and obtain a price reflecting the system generation cost. The price broadcast by each transformer includes this system price together with another component that reflects individual loading characteristics. By proposing a dynamic update algorithm, it is shown that the system converges to the unique and efficient solution with fast convergence rate.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences.