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An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

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Computers, Materials & Continua 2020年第7期64卷 325-344页

作者： Junxiang Wang Lin Huang Ying Zhang Yonghong Zhu Jiangqun Ni Yunqing Shi School of Mechanical and Electronic Engineering Jingdezhen Ceramic InstituteJingdezhen333403China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou510006China Department of Electronics and Computer Engineering New Jersey Institute of TechnologyNewark07102USA

To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking ***,conventional steganalysis schemes focused on the previous RDH algorithms,i.e.,some early spatial/pixel domain-based histogram-shifting(HS)schemes,which might cause great changes in statistical characteristics and thus be easy to be *** recent improved methods,such as some adaptive prediction error(PE)based embedding schemes,those conventional schemes might be invalid,since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of ***,a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for *** contributions of this paper could be summarized as follows.(1)By analyzing the characteristics for those adaptive HS-RDH,an effective“flat ground”based detection method is designed to fast identify whether the given image is used to hide secret data;(2)According to the empirical statistical model,double check mechanism is provided to improve the detection accuracy;(3)In addition,to further improve detection ability,some detailed information for secret data,i.e.,its content and embedding location are further *** with conventional steganalysis methods,experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.

关键词： Reversible data hiding steganalysis detection histogram shifting

Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 115-129页

作者： Xu Wang Tianyang Wang Anbo Ming Qinkai Han Fulei Chu Wei Zhang Aihua Li State Key Laboratory of Tribology Tsinghua UniversityBeijing 100084China High-Tech Research institute of Xi’an Xi’an 710025China

The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical *** to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed ***,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack ***,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR *** raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping ***,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct ***,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction *** on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR *** use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.

关键词： Bearing Remaining useful life Continuous wavelet transform Convolution neural network Gaussian process regression

prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

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Journal of Integrative Agriculture 2013年第12期12卷 2292-2299页

作者： LI Zhe-min CUI Li-guo XU Shi-wei WENG Ling-yun DONG Xiao-xia LI Gan-qiong YU Hai-peng Agricultural Information institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Information Service TechnologyMinistry of Agriculture

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in *** the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and ***,this model is applied to predict the retail prices of eggs and compared with *** result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of *** empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

关键词： chaos theory chaotic neural network neural network technology short-term prediction weekly retail price of eggs

prediction of the Slope Solute Loss Based on BP Neural Network

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Computers, Materials & Continua 2021年第12期69卷 3871-3888页

作者： Xiaona Zhang Jie Feng Zhiguo Yu Zhen Hong Xinge Yun School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Water Resources Research institute China Institute of Water Resources and Hydropower ResearchBeijing100044China Department of Geography and Environmental Sustainability University of OklahomaNorman73071USA

The existence of soil macropores is a common *** to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic *** this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil *** rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input *** network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer *** cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer *** rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this *** error analysis and the performance comparison between the proposed method and traditional gradient descent method are *** results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent *** addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.

关键词： Solute loss soil macropores improved BP neural network slope

Quantifying the impact of mineralogical heterogeneity on reactive transport modeling of CO_(2)+O_(2) in-situ leaching of uranium

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Acta Geochimica 2022年第1期41卷 50-63页

作者： Yun Yang Wenjie Qiu Zhengbang Liu Jian Song Jianfeng Wu Zhi Dou Jinguo Wang Jichun Wu School of Earth Sciences and Engineering Hohai University8 Focheng West Rd.NanjingChina Key Laboratory of Surficial Geochemistry Department of HydrosciencesSchool of Earth Sciences and EngineeringNanjing UniversityMinistry of Education163 Xianlin Rd.NanjingChina Beijing Research institute of Chemical Engineering Metallurgy BeijingChina

CO_(2)+O_(2) in-situ leaching(ISL)of sandstonetype uranium ore represents the third generation of solution mining in *** this study,reactive transport modeling of the interaction between hydrodynamic and geochemical reactions is performed to enable better prediction and regulation of the CO_(2)+O_(2) in-situ leaching process of *** reactions between mining solutions and rock,and the kinetic uranium dissolution controlled by O_(2)(aq)and bicarbonate(HCO_(3)-)are considered in the CO_(2)+O_(2) ISL reactive transport model of a typical sandstone-hosted uranium ore deposit in northern *** reactive leaching of uranium is most sensitive to the spatial distribution of the mineralogical properties of the uranium *** geostatistical models are used to represent the uncertainty on the spatial distribution of mineral grades.A Monte Carlo analysis was also performed to simulate the uranium production variability over an entire set of geostatistical *** ISL stochastic simulation performed with the selected geostatistical realizations approximates the uranium production variability *** simulation results of the ISL reactive transport model show that the extent of the uranium plume is highly dependent on mineralogical *** uncertainty analysis suggests the effect of uranium grade heterogeneity was found to be important to improve the accurate capture of the *** study provides guidance for the accurate simulation and dynamic regulation of the CO_(2)+O_(2) leaching process of uranium at the scale of large mining areas.

关键词： In-situ leaching Reactive transport Heterogeneity Stochastic geostatistical model Monte Carlo analysis Uranium grade

STUDY ON THE METEOROLOGICAL prediction MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

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Journal of Tropical Meteorology 2009年第1期15卷 83-88页

作者：吴建生金龙 Department of Mathematic and Computer Sciences Liuzhou Teachers College Guangxi Research institute of Meteorological Disasters Mitigation

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

关键词：混合粒子群优化算法 BP神经网络学习算法预测模型气象业务 PSO算法集合人工神经网络

A Short-Term Climate prediction Model Based on a Modular Fuzzy Neural Network

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Advances in Atmospheric Sciences 2005年第3期22卷 428-435页

作者：金龙金健姚才 Guangxi Research institute of Meteorological Disasters MitigationNanning 530022 Department of Computer Science East China Normal University Shanghai 200062

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

关键词： modular fuzzy neural network short-term climate prediction flood season

Geophysical-Geological Interpretation and Deep-Seated Gold Deposit Prospecting in Sanshandong-Jiaojia Area, Eastern Shandong Province, China

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Acta Geologica Sinica(English Edition) 2012年第3期86卷 640-652页

作者： SONG Mingchun WAN Guopu CAO Chunguo HE Chunyan Shandong Provincial Bureau of Geology and Mineral Resources Jinan 250013China Shandong institute of Geophysical and Geochemical Exploration Jinan 250013China

Integrated gravitational, electrical-magnetic surveys and data processing carried out in the Sanshandao-Jiaojia area, Eastern Shandong Province, northeast China, aim to illuminate the geological characteristics of this shallow-covered area and delineate deep-seated gold prospecting targets. In this region, altogether 12 faults exert critical control on distribution of three types of Early Precambrian metamorphic rock series, i.e. those in the metamorphic rock area, in the granitic rock area underlying the metamorphic rock, and in the remnant metamorphic rock area in granites, respectively. Additionally, the faults have major effects on distribution of four Mesozoic Linglong rock bodies of granite, i.e. the Cangshang, Liangguo, Zhuqiao-Miaojia and Jincheng granites. The Sanshandao and Jiaojia Faults are two well-known regional ore-controlling faults; they have opposite dip direction, and intersect at a depth of 4500 m. Fracture alteration zones have striking geophysical differences relative to the surrounding county rocks. The two faults extend down along dip direction in a gentle wave form, and appear at some steps with different dips. These steps comprise favorable gold prospecting areas, consistent with a step metallogenic model. Six deep-seated gold-prospecting targets are delineated, i.e. Jincheng-Qianchenjia, Xiaoxizhuang-Zhaoxian, Xiyou-Wujiazhuangzi, Xiangyangling-Xinlicun, Panjiawuzi and Miaojia-Pinglidian.

关键词： geophysical interpretation deep-seated gold deposit prospecting and prediction tectonic control Shandong Province

Adaptive forgetting factor OS-ELM and bootstrap for time series prediction

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第3期8卷 159-177页

作者： Jingzhong Liu Hunan institute of Technology School of MathematicsPhysics and Energy Engineering Hunan Hengyang 421101P.R.China

Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning *** OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for *** overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning ***,the current bootstrap is developed to fit time series prediction *** associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of *** of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series *** indicate the significant performances achieved by B-AFF-OS-ELM.

关键词： Online sequential extreme learning machine(OS-ELM) l2-regularization forecasting uncertainty prediction interval ensemble chaotic time series neural networks