Localization of WSN Using Fuzzy Inference System with Optimized Membership Function by Bat Algorithm

Hao Shi,Wanliang Wang,Liangjin Lu 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.5

Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system using the data of received signal strength in real indoor condition. The author has realized the algorithm on Zigbee platform and the experimental comparison on other different centroid localization algorithms indicates that Mamdani fuzzy inference adopting the membership function optimized by bat algorithm renders smaller mean localization errors.

Crowd Activity Classification Using Category Constrained Correlated Topic Model

( Xianping Huang ),( Wanliang Wang ),( Guojiang Shen ),( Xiaoqing Feng ),( Xiangjie Kong ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.11

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.

Hybrid CSA optimization with seasonal RVR in traffic flow forecasting

( Zhangguo Shen ),( Wanliang Wang ),( Qing Shen ),( Zechao Li ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

Managing Quality-of-Control and Requirement-of-Bandwidth in Networked Control Systems via Fuzzy Bandwidth Scheduling

Zuxin Li,Yunliang Jiang,Wanliang Wang 제어·로봇·시스템학회 2009 International Journal of Control, Automation, and Vol.7 No.2

There is an unavoidable tradeoff between the control performance and the quality of service in networked control systems with resource constraints. To address the impact of network resources availability on requirement of bandwidth (RoB) and quality of control (QoC), an intelligent control ap-proach to dynamic bandwidth management, namely fuzzy bandwidth management, is proposed based on fuzzy logic control technique. In order to guarantee the system’s stability, the lower and upper bound of the assignable bandwidth are evaluated in terms of linear matrix inequalities and the resource constraints, respectively. In addition, the normalizable criterions of QoC and RoB are also defined, which can estimate the performance of the whole networked control systems. Preliminary simulations are carried out to highlight the merits of the proposed approach. It is argued that the proposed approach can save significant bandwidth and simultaneously improve overall control performance in comparison with the fixed bandwidth allocation and optimal bandwidth allocation.

Joint MR image reconstruction and super-resolution via mutual co-attention network

Lv Tiantong,Wu Fei,Wang Wanliang 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1

In the realm of medical diagnosis, recent strides in deep neural network-guided magnetic resonance imaging (MRI) restoration have shown promise. Nevertheless, persistent drawbacks overshadow these advancements. Challenges persist in balancing acquisition speed and image quality, while existing methods primarily focus on singular tasks like MRI reconstruction or super-resolution (SR), neglecting the interplay between these tasks. To tackle these challenges, this paper introduces the mutual co-attention network (MCAN) specifically designed to concurrently address both MRI reconstruction and SR tasks. Comprising multiple mutual cooperation attention blocks (MCABs) in succession, MCAN is tailored to maintain consistency between local physiological details and global anatomical structures. The intricately crafted MCAB includes a feature extraction block, a local attention block and a global attention block. Additionally, to ensure data fidelity without compromising acquired data, we propose the channel-wise data consistency block. Thorough experimentation on the IXI and fastMRI dataset showcases MCAN’s superiority over existing state-of-the-art methods. Both quantitative metrics and visual quality assessments validate the enhanced performance of MCAN in MRI restoration. The findings underscore MCAN’s potential in significantly advancing therapeutic applications. By mitigating the trade-off between acquisition speed and image quality while simultaneously addressing both MRI reconstruction and SR tasks, MCAN emerges as a promising solution in the domain of magnetic resonance image restoration.

A dynamic multi-objective evolutionary algorithm based on prediction

Wu Fei,Chen Jiacheng,Wang Wanliang 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1

The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems; the main reasons are the objective’s conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the dynamic optimization issue. First, we introduce a decision variable clustering and screening strategy that clusters the decision space of the non-dominated solution set to find the cluster centroids and then employs a decision variable screening strategy to filter out solutions that have an impact on the distribution of individuals. This approach can broaden the range of dynamic multi-objective optimization algorithms. Second, an approach for predicting special points is suggested. The algorithm’s convergence is improved following environmental changes by forecasting the special point tracking Pareto front in the object space. Finally, the forward-looking center points are used to predict the non-dominated solution set and eliminate the useless individuals in the population. The prediction strategy can help the solution set converge while maintaining its diversity, which is compared with the four other state-of-the-art strategies. Our experimental results demonstrate that the proposed algorithm, DSSP, can effectively tackle DMOPs.