Xueping Fan^*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu^*

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 69-83, 2021, DOI:10.32604/sdhm.2021.011922

Abstract This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application… More >

Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Mohamed Elhoseny³, Dac-Nhuong Le^4,5,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2555-2571, 2021, DOI:10.32604/cmc.2021.012941

Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More >

Kashif Iqbal^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Atifa Athar⁴, Muhammad Saleem Khan¹, Areej Fatima³, Gulzar Ahmad¹

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1595-1612, 2021, DOI:10.32604/cmc.2020.013231

Abstract The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study… More >

Ting Xu¹, Ming Zhao¹, Xin Yao^{1, *}, Kun He²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1605-1624, 2020, DOI:10.32604/cmc.2020.011458

Abstract With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network… More >

Zhiquan Huang^a,b, Yu Fu^a, Fuchu Dai^a

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 29-34, 2018, DOI:10.1080/10798587.2016.1267237

Abstract The rapid development and extensive application of geographic information system (GIS) and the advent of the age of big data bring about the generation of multi-resources spatial data, which makes data integration and fusion share more difficult due to the differences on data source, data accuracy and data modal. Meanwhile, study for multi-resources spatial data fusion methods has an important practical significance for reducing the production cost of geographic data, accelerating the updating speed of existing geographical information and improving the quality of GIS big data. To expound the formation and developing trends of multi-resources spatial data fusion methods systematically,… More >

Donghui Li¹, Cong Shen^2,*, Xiaopeng Dai¹, Xinghui Zhu¹, Jian Luo¹, Xueting Li¹, Haiwen Chen³, Zhiyao Liang⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1217-1231, 2019, DOI:10.32604/cmc.2019.06354

Abstract Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor. The data of water quality in the environment comes from different sensors, thus the data must be fused. In our research, self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value, temperature, oxygen dissolved and NH3 concentration of water quality environment. Based on the fusion, the Grubbs method is used to detect the abnormal data so as to provide data support for estimation, prediction and early warning of the water quality. More >

He Xufei¹, Deng Zhongmin², Song Zhitao¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 193-208, 2012, DOI:10.32604/sdhm.2012.008.193

Abstract This paper describes an information optimizing scheme which is developed by integrating rough set and hierarchical data fusion. The novel structural damage indices are extracted using the information from different sources and then imported into probabilistic neural network (PNN) for classification and health assessment. In order to enhance the accuracy of diagnosis, results from separate PNN classification are fused to achieve comprehensive decision. Rough set is employed to decrease the spatial dimension of data. The predictive accuracy of optimizing scheme is demonstrated on a helicopter, taken as an example, with varied sensors, for multiple damage identification. More >