Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental platform.Based on such a platform,we first construct a real signal dataset with ten modulation methods.Then,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature classification.We also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated signals.Finally,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other algorithms.It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea

Wave energy resources assessment is a very important process before the exploitation and utilization of the wave energy. At present, the existing wave energy assessment is focused on theoretical wave energy conditions for interesting areas. While the evaluation for exploitable wave energy conditions is scarcely ever performed. Generally speaking, the wave energy are non-exploitable under a high sea state and a lower sea state which must be ignored when assessing wave energy. Aiming at this situation, a case study of the East China Sea and the South China Sea is performed. First, a division basis between the theoretical wave energy and the exploitable wave energy is studied. Next, based on recent 20 a ERA-Interim wave field data, some indexes including the spatial and temporal distribution of wave power density, a wave energy exploitable ratio, a wave energy level, a wave energy stability, a total wave energy density, the seasonal variation of the total wave energy and a high sea condition frequency are calculated. And then the theoretical wave energy and the exploitable wave energy are compared each other; the distributions of the exploitable wave energy are assessed and a regional division for exploitable wave energy resources is carried out; the influence of the high sea state is evaluated. The results show that considering collapsing force of the high sea state and the utilization efficiency for wave energy, it is determined that the energy by wave with a significant wave height being not less 1 m or not greater than 4 m is the exploitable wave energy. Compared with the theoretical wave energy, the average wave power density, energy level, total wave energy density and total wave energy of the exploitable wave energy decrease obviously and the stability enhances somewhat. Pronounced differences between the theoretical wave energy and the exploitable wave energy are present. In the East China Sea and the South China Sea, the areas of an abundant and stable exploitable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, east of Taiwan, China and north of Ryukyu Islands; annual average exploitable wave power density values in these areas are approximately 10-15 kW/m; the exploitable coefficient of variation （COV） and seasonal variation （SV） values in these areas are less than 1.2 and 1, respectively. Some coastal areas of the Beibu Gulf, the Changjiang Estuary, the Hangzhou Bay and the Zhujiang Estuary are the poor areas of the wave energy. The areas of the high wave energy exploitable ratio is primarily in nearshore waters. The influence of the high sea state for the wave energy in nearshore waters is less than that in offshore waters. In the areas of the abundant wave energy, the influence of the high sea state for the wave energy is prominent and the utilization of wave energy is relatively difficult. The developed evaluation method may give some references for an exploitable wave energy assessment and is valuable for practical applications.

A wave energy resource assessment in the China's seas based on multi-satellite merged radar altimeter data

Wave energy resources are abundant in both offshore and nearshore areas of the China＇s seas. A reliable assessment of the wave energy resources must be performed before they can be exploited. First, for a water depth in offshore waters of China, a parameterized wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power density. Second, wave heights and wind speeds on the surface of the China＇s seas are retrieved from an AVISO multi-satellite altim-eter data set for the period from 2009 to 2013. Three mean wave period inversion models are developed and used to calculate the wave energy period. Third, a practical application value for developing the wave energy is analyzed based on buoy data. Finally, the wave power density is then calculated using the wave field data. Using the distribution of wave power density, the energy level frequency, the time variability indexes, the to-tal wave energy and the distribution of total wave energy density according to a wave state, the offshore wave energy in the China＇s seas is assessed. The results show that the areas of abundant and stable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, southeast of Taiwan in the China＇s seas; the wave power density values in these areas are approximately 14.0–18.5 kW/m. The wave energy in the China’s seas presents obvious seasonal variations and optimal seasons for a wave energy utilization are in winter and autumn. Except for very coastal waters, in other sea areas in the China＇s seas, the energy is primarily from the wave state with 0.5 m≤Hs≤4 m, 4 s≤Te≤10 s whereHs is a significant wave height andTe is an energy period; within this wave state, the wave energy accounts for 80% above of the total wave energy. This characteristic is advantageous to designing wave energy convertors （WECs）. The practical application value of the wave energy is higher which can be as an effective supplement for an energy con-sumption in some areas. The above results are consistent with the wave model which indicates fully that this new microwave remote sensing method altimeter is effective and feasible for the wave energy assessment.

Undrained shear strength evaluation for hydrate-bearing sediment overlying strata in the Shenhu area, northern South China Sea

The undrained shear strength of shallow strata is a critical parameter for safety design in deep-water operations.In situ piezocone penetration tests(CPTU) and laboratory experiments are performed at Site W18-19 in the Shenhu area, northern South China Sea, where China's first marine hydrate exploitation operation is due to be located. The validation of the undrained shear strength prediction model based on CPTU parameters. Different laboratory tests, including pocket penetrometer, torvane, miniature vane and unconsolidated undrained triaxial tests, are employed to solve empirical cone coefficients by statistical and mathematical methods. Finally, an optimized model is proposed to describe the longitudinal distribution of undrained shear strength in calcareous clay strata in the Shenhu area. Research results reveal that average empirical cone coefficients based on total cone resistance, effective resistance, and excess-pore pressure are 13.8, 4.2 and 14.4, respectively. The undrained shear strength prediction model shows a good fit with the laboratory results only within specific intervals based on their compaction degree and gas-bearing conditions. The optimized prediction model in piecewise function format can be used to describe the longitudinal distribution of the undrained shear strength for calcareous clay within all depth intervals from the mud-line to the upper boundary of hydrate-bearing sediments(HBS). The optimized prediction result indicates that the effective cone resistance model is suitable for very soft to firm calcareous clays,the excess-pore pressure model can depict the undrained shear strength for firm to very stiff but gas-free clays,while the total cone resistance model is advantageous for evaluating the undrained shear strength for very stiff and gassy clays. The optimized model in piecewise function format can considerably improve the adaptability of empirical models for calcareous clay in the Shenhu area. These results are significant for safety evaluations of proposed hydrate exploitation projects.

Backstepping-based active fault-tolerant control for a class of uncertain SISO nonlinear systems

Active fault-tolerant control is investigated for a class of uncertain SISO nonlinear flight control systems based on the adaptive observer, feedback linearization and backstepping theory.Firstly an adaptive observer is constructed to estimate the fault in the faulty system.A new fault updating law is presented to simplify the assumption conditions of the adaptive observer.The asymptotical stability of the observer and the uniform ultimate boundedness of the fault estimation error are guaranteed by Lyapunov theorem.Then a backstepping-based active fault-tolerant controller is designed for the faulty system.The asymptotical stability of the closed-loop system and uniform ultimate boundedness of the tracking error are proved based on Lyapunov theorem.The effectiveness of the proposed scheme is demonstrated through the numerical simulation of a flight control system.

Robust Control Based on Feedback Linearization for Roll Stabilizing of Autonomous Underwater Vehicle Under Wave Disturbances

In the case of Autonomous Underwater Vehicle （AUV） navigating with low speed near water surface, a new method for design of roll motion controller is proposed in order to restrain wave disturbance effectively and improve roll stabilizing performance. Robust control is applied, which is based on uncertain nonlinear horizontal motion model of AUV and the principle of zero speed fin stabilizer. Feedback linearization approach is used to transform the complex nonlinear system into a comparatively simple linear system. For parameter uncertainty of motion model, the controller is designed with mixed-sensitivity method based on H-infinity robust control theory. Simulation results show better robustness improved by this control method for roll stabilizing of AUV navigating near water surface.

H_∞ Control Based on LMIs for a Class of Time-delay Switched Systems

H 稳定性分析和有在任意的切换的法律下面的推迟的状态的交换系统的控制合成的问题被考虑。借助于 Lyapunov 功能和线性矩阵不平等工具， H 稳定性的足够的状况以线性矩阵不平等被介绍。而且，经由州的反馈的柔韧的 H 控制合成和产量反馈被学习。最后，一个数字例子被给表明建议方法的有效性。

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column(DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control(MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol,n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function "gamultiobj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error(ISE).

Intelligent Identification of Remnant Ridge Edges in Region West of Yongxing Island,South China Sea

Edge detection enables identification of geomorphologic unit boundaries and thus assists with geomorphical mapping. In this paper, an intelligent edge identification method is proposed and image processing techniques are applied to multi-beam bathymetry data. To accomplish this, a color image is generated by the bathymetry, and a weighted method is used to convert the color image to a gray image. As the quality of the image has a significant influence on edge detection, different filter methods are applied to the gray image for de-noising. The peak signal-to-noise ratio and mean square error are calculated to evaluate which filter method is most appropriate for depth image filtering and the edge is subsequently detected using an image binarization method. Traditional image binarization methods cannot manage the complicated uneven seafloor, and therefore a binarization method is proposed that is based on the difference between image pixel values; the appropriate threshold for image binarization is estimated according to the probability distribution of pixel value differences between two adjacent pixels in horizontal and vertical directions, respectively. Finally, an eight-neighborhood frame is adopted to thin the binary image, connect the intermittent edge, and implement contour extraction. Experimental results show that the method described here can recognize the main boundaries of geomorphologic units. In addition, the proposed automatic edge identification method avoids use of subjective judgment, and reduces time and labor costs.

Submarine Landslides on the North Continental Slope of the South China Sea

Recent and paleo-submarine landslides are widely distributed within strata in deep-water areas along continental slopes, uplifts, and carbonate platforms on the north continental margin of the South China Sea(SCS). In this paper, high-resolution 3D seismic data and multibeam data based on seismic sedimentology and geomorphology are employed to assist in identifying submarine landslides. In addition, deposition models are proposed that are based on specific geological structures and features, and which illustrate the local stress field over entire submarine landslides in deep-water areas of the SCS. The SCS is one of the largest fluvial sediment sinks in enclosed or semi-enclosed marginal seas worldwide. It therefore provides a set of preconditions for the formation of submarine landslides, including rapid sediment accumulation, formation of gas hydrates, and fluid overpressure. A new concept involving temporal and spatial analyses is tested to construct a relationship between submarine landslides and different time scale trigger mechanisms, and three mechanisms are discussed in the context of spatial scale and temporal frequency: evolution of slope gradient and overpressure, global environmental changes, and tectonic events. Submarine landslides that are triggered by tectonic events are the largest but occur less frequently, while submarine landslides triggered by the combination of slope gradient and overpressure evolution are the smallest but most frequently occurring events. In summary, analysis shows that the formation of submarine landslides is a complex process involving the operation of different factors on various time scales.

Reconfigurability evaluation method for input-constrained control systems

This paper proposes a quantitative reconfigurability evaluation method for control systems with actuator saturation and additive faults from the perspective of system stability.Placing the saturated feedback law in the convex hull of a group of auxiliary linear controls,the sufficient reconfigurability conditions for the system under additive faults are derived using invariant sets.These conditions are then expressed as linear matrix inequalities(LMIs)and applied to quantify the degree of reconfigurability for the fault system.The largest fault magnitude for which the system can be stabilized,the largest initial state domain from which all the trajectories are convergent,and the minimum final state domain to which the trajectories will converge are investigated.The effectiveness of the proposed method is illustrated through an application example.

Geophysical Evidence for Carbonate Platform Periphery Gravity Flows in the Xisha Islands,South China Sea

High-resolution bathymetric and partial seismic data were utilized to determine the characteristics and distribution of mass-transport deposits(MTDs)in the Xisha area,North of the South China Sea.Many isolated carbonate platforms are found in this area,providing mass-wasting materials as a sediment source for MTDs.The MTDs in the study area were divided into three types(slide,slump,and debris flow)based on their rheological properties.The MTDs were mostly distributed at the toe of the carbonate platform slope,where a large amount of carbonate debris accumulated.A total of 32 MTDs were identified on the carbonate platform slopes.A systematic assessment of the MTD morphometric parameters showed that their total area covered 2225.2 km2,with the largest MTD covering 354.1 km2.These MTDs differ largely from the MTDs of other areas,especially in terms of sediment compositions and deposit processes.Gullies and channels on the slope and reefs on the top of the platform act as sediment conduits and carbonate-producing sources,respectively,playing vital roles in the distribution and generation of gravity flow deposits.The development model proposed in this work states that reefs disintegrate into carbonate debris and mix with seawater to form gravity flows,which scour the seafloor and accumulate at the downslope,eventually resulting in failure.Sediments from these failures turn into gravity flows,flowing into submarine canyons and channels.The results of this research further our understanding of the development pattern of MTDs in a carbonate setting.

Modeling and Control Strategy of Built-in Skin Effect Electric Tracing System

In order to ensure the safety of fluid flow in deep-water submarine pipelines,a safe and energy-saving built-in skin effect electric heat tracing technology was adopted as the thermal management strategy.The magnetic field distribution of built-in skin effect heating system is analyzed based on the mechanism of built-in skin effect heating system,so as to obtain the equivalent circuit model of built-in skin effect electric heating system.Meanwhile,heating power is introduced as an intermediate variable to establish the relationship between power supply frequency and built-in skin effect heating temperature.Aiming at the skin effect electric heating system,an Active Disturbance Rejection Control(ADRC)method is proposed macroscopically based on Hammerstein model.Firstly,the parameters of Hammerstein model are identified and optimized using the auxiliary model and standard particle swarm optimization algorithm.Then,the ADRC controller of linear link is designed,and the required heating temperature is used to solve the intermediate variable heating power.Finally,inversion calculation is applied in the nonlinear link to solve the required power frequency,so as to achieve the purpose of efficient heating and verify the feasibility and effectiveness of control strategy through simulation.

Control System Design of CSI Applied in the Battery Pack Testing System

This paper designed the loop-locked SVPWM control system of current source inverter (CSI) in the battery pack charging/discharging system. The battery pack is sensitive to the charging/discharging current ripple and orders the current posess fast transform speed. Because the battery pack has tiny inner resistance, back electromotive force and it acted as a comparative load. This made the system hard to design. The control system aimed at the specialty of the battery pack load and put forward a loop-locked control method based on dq coordinate conversion in SVPWM converters. It increases the transform efficiency by SVPWM and gets high power factor and high dynamic response quality by dq coordinate conversion.

Study on spiral winding swimming motion control of a slender legless creature model

Through the observation and analysis of the motion trajectory of spiral winding motion for slender legless biological creatures in water,V-REP software is adopted to build a dynamic simulator to study on the mechanism of spiral winding swimming(SWS)motion.By using the output of spiral function(SF),the dynamic simulation model of slender legless creature(SLC)realizes the SWS motion in water.The corresponding experiments under the control of different bending angle of SF to control the dynamic model are also carried out to analyze the water performance of SWS in still water.Combined with the output of two different bending angles of the spiral function,the dynamic model can be used to realize SWS and up/down motion.This work provides technical reserve and experimental platform for the corresponding study in related fields.

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.