A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

3-D Reconstruction and Visualization of Laser-Scanned Trees by Weighted Locally Optimal Projection and Accurate Modeling Method

This paper presents a method to reconstruct 3-D models of trees from terrestrial laser scan(TLS)point clouds.This method uses the weighted locally optimal projection(WLOP)and the AdTree method to reconstruct detailed 3-D tree models.To improve its representation accuracy,the WLOP algorithm is introduced to consolidate the point cloud.Its reconstruction accuracy is tested using a dataset of ten trees,and the one-sided Hausdorff distances between the input point clouds and the resulting 3-D models are measured.The experimental results show that the optimal projection modeling method has an average one-sided Hausdorff distance(mean)lower by 30.74%and 6.43%compared with AdTree and AdQSM methods,respectively.Furthermore,it has an average one-sided Hausdorff distance(RMS)lower by 29.95%and 12.28%compared with AdTree and AdQSM methods.Results show that the 3-D model generated fits closely to the input point cloud data and ensures a high geometrical accuracy.

DISTRIBUTED OPTIMAL LOCAL DOUBLE LOOP NETWORK

A distibuted optimal local double loop(DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter (d) and average hop distance (a) for this class of networks are [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.

Local Optimization and Adversarial Consideration: Agent Skills in Everest Simulation Team

Skilled individual agents are firm basis of a strong soccer team. The skills available to Everest 2002 (agents) include kicking, dribbling, forwarding, ball interception and tackling. These intermediate sub goals are implemented by a combination of local optimization which hopes to determine the optimal primitive action from a local perspective and adversarial consideration which takes into account opponents and limitations imposed by simulation environment. Everest 2002 RoboCup simulation teams, building on 11 skilled agents and an on-line coach, won the 2nd place in RoboCup 2002 simulation league.

Developing high-efficiency base editors by combining optimized synergistic core components with new types of nuclear localization signal peptide

The clustered regularly interspaced short palindromic repeats(CRISPR)–CRISPR-associated protein(Cas) system has been widely used for genome editing. In this system, the cytosine base editor(CBE) and adenine base editor(ABE) allow generating precise and irreversible base mutations in a programmable manner and have been used in many different types of cells and organisms. However, their applications are limited by low editing efficiency at certain genomic target sites or at specific target cytosine(C) or adenine(A) residues. Using a strategy of combining optimized synergistic core components, we developed a new multiplex super-assembled ABE(sABE) in rice that showed higher base-editing efficiency than previously developed ABEs. We also designed a new type of nuclear localization signal(NLS) comprising a FLAG epitope tag with four copies of a codon-optimized NLS(F4NLS^(r2)) to generate another ABE named F4NLS-sABE. This new NLS increased editing efficiency or edited additional A at several target sites. A new multiplex super-assembled CBE(sCBE) and F4NLS^(r2) involved F4NLS-sCBE were also created using the same strategy. F4NLS-sCBE was proven to be much more efficient than sCBE in rice. These optimized base editors will serve as powerful genome-editing tools for basic research or molecular breeding in rice and will provide a reference for the development of superior editing tools for other plants or animals.

Learning Bayesian network structure with immune algorithm

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

Two-Phase Genetic Algorithm Applied in the Optimization of Multi-Modal Function

This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions, usually we can obtain all the global optimal solutions.

Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users’ expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA)-II is developed to perform multi-objective optimization by considering the power load profile, the users’charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the real-time optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy.

OPTIMAL INTERIOR AND LOCAL ERROR ESTIMATES OF A RECOVERED GRADIENT OF LINEAR ELEMENTS ON NONUNIFORM TRIANGULATIONS

We examine a simple averaging formula for the gradieni of linear finite elemelitsin Rd whose interpolation order in the Lq-norm is O(h2) for d < 2q and nonuniformtriangulations. For elliptic problems in R2 we derive an interior superconvergencefor the averaged gradient over quasiuniform triangulations. Local error estimatesup to a regular part of the boundary and the effect of numerical integration arealso investigated.

Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side

Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.

Optimal two-impulse space interception with multiple constraints

We consider optimal two-impulse space interception problems with multiple constraints.The multiple constraints are imposed on the terminal position of a space interceptor,impulse and impact instants,and the component-wise magnitudes of velocity impulses.These optimization problems are formulated as multi-point boundary value problems and solved by the calculus of variations.Slackness variable methods are used to convert all inequality constraints into equality constraints so that the Lagrange multiplier method can be used.A new dynamic slackness variable method is presented.As a result,an indirect optimization method is developed.Subsequently,our method is used to solve the two-impulse space interception problems of free-flight ballistic missiles.A number of conclusions for local optimal solutions have been drawn based on highly accurate numerical solutions.Specifically,by numerical examples,we show that when time and velocity impulse constraints are imposed,optimal two-impulse solutions may occur;if two-impulse instants are free,then a two-impulse space interception problem with velocity impulse constraints may degenerate to a one-impulse case.

Whole-process design and experimental validation of landing gear lower drag stay with global/local linked driven optimization strategy

Landing gear lower drag stay is a key component which connects fuselage and landing gear and directly effects the safety and performance of aircraft takeoff and landing. To effectively design the lower drag stay and reduce the weight of landing gear, Global/local Linked Driven Optimization Strategy(GLDOS) was developed to conduct the overall process design of lower drag stay in respect of optimization thought. The whole-process optimization involves two stages of structural conceptual design and detailed design. In the structural conceptual design, the landing gear lower drag stay was globally topologically optimized by adopting multiple starting points algorithm. In the detailed design, the local size and shape of landing gear lower drag stay were globally optimized by the gradient optimization strategy. The GLDOS method adopts different optimization strategies for different optimization stages to acquire the optimum design effect. Through the experimental validation, the weight of the optimized lower dray stay with the developed GLDOS is reduced by 16.79% while keeping enough strength and stiffness, which satisfies the requirements of engineering design under the typical loading conditions. The proposed GLDOS is validated to be accurate and efficient in optimization scheme and design cycles. The efforts of this paper provide a whole-process optimization approach regarding different optimization technologies in different design phases, which is significant in reducing structural weight and enhance design tp wid 1 precision for complex structures in aircrafts.

PARBLO:Page-Allocation-Based DRAM Row Buffer Locality Optimization

DRAM row buffer conflicts can increase memory access latency significantly. This paper presents a new pageallocation-based optimization that works seamlessly together with some existing hardware and software optimizations to eliminate significantly more row buffer conflicts. Validation in simulation using a set of selected scientific and engineering benchmarks against a few representative memory controller optimizations shows that our method can reduce row buffer miss rates by up to 76% （with an average of 37.4%）. This reduction in row buffer miss rates will be translated into performance speedups by up to 15% （with an average of 5%）.

Mesh simplification algorithm based on edge curvature metrics and local optimization

Aiming at the problem that the mesh simplification algorithm loses the geometric features of the model in large-scale simplification,an improved half-edge collapse mesh simplification algorithm is proposed.The concept of approximate measurement of edge curvature is introduced,and the edge curvature is added to the error measure,so that the order of half-edge collapse of the mesh is changed,and the simplified details of the mesh model can be preserved accurately.At the same time,by analyzing the quality of simplified triangular mesh,optimizing triangular mesh locally,reducing the amount of narrow triangles,the quality of the simplified model is improved.The proposed algorithm was tested on Cow model,Car model and Bunny model,and compared with another three algorithms,one of them is a classical mesh simplification algorithm based on edge collapse,the other is an improved algorithm of the classical one.The experimental results show that the improved algorithm can better retain the detail features of the original model at the same reduction ratio,and has reasonable mesh allocation,fast execution speed and small error.

Optimized Local Trigonometric Bases with Nonuniform Partitions

The authors provide optimized local trigonometric bases with nonuniform partitions which efficiently compress trigonometric functions. Numerical examples demonstrate that in many cases the proposed bases provide better compression than the optimized bases with uniform partitions obtained by Matviyenko.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

FasFast Triangulation and Local Optimization for Scan Data of Laser Radar

In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular network.Based on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay rule.Meanwhile,a new judging rule is proposed to contrast the triangulation before and after the optimization on the network.The result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.

The Application of ODE Method in Constrained Optimization

in this paper,a new method to solve the general constrained optimization problem is proposed,theproblem of finding the local optimal points of the nonlinear programming problem with equality and inequality constraints is considered by solving the ODE d.e.Ordinary oprential Equation)with aPPropriatenumerical procedure.Moreover,the rate of conveyance to optimal points is quadratic.Some numerical resultis given to show the efficiency of the method proposed in this poper.

Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

Abstract This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle （UAV） through modified particle swam optimization （PSO）. The proce- dure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems （MEMS） inertial mea,mring element and a global positioning system （GPS） receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration （SRPSO）. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.