An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.     

An efficient initialization approach of Q-learning for mobile robots

This article demonstrates that Q-learning can be accelerated by appropriately specifying initial Q-values using dynamic wave expansion neural network. In our method, the neural network has the same topography as robot work space. Each neuron corresponds to a certain discrete state. Every neuron of the network will reach an equilibrium state according to the initial environment information. The activity of the special neuron denotes the maximum cumulative reward by following the optimal policy from the corresponding state when the network is stable. Then the initial Q-values are defined as the immediate reward plus the maximum cumulative reward by following the optimal policy beginning at the succeeding state. In this way, we create a mapping between the known environment information and the initial values of Q-table based on neural network. The prior knowledge can be incorporated into the learning system, and give robots a better learning foundation. Results of experiments in a grid world problem show that neural network-based Q-learning enables a robot to acquire an optimal policy with better learning performance compared to conventional Q-learning and potential field-based Qlearning.     

Parameter identification and design of a robust attitude controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> methodology for the raptor E620 small-scale helicopter

Rigorous control synthesis for an unmanned aerial vehicle necessitates the availability of a good, reasonable model for such a vehicle. The work reported in this paper focuses on the modeling of a rotary unmanned aerial vehicle (RUAV) and the development of a robust controller that can handle parameter uncertainties and disturbances. The parameters of the plant model are obtained through the use of the prediction error method with real flight data. The response of the identified linear model shows a good match with the measured flight data. The H _∞ control scheme is applied to obtain a robustly stable controller using the identified model. The proposed controller is analyzed using frequency-domain analysis and time-domain simulations. The performance of the proposed H _∞ controller is better than that of the conventional proportional derivative controller in that the proposed controller has a shorter settling time and less overshoot. Furthermore, the degradation of the proposed controller performance is negligible and stability is maintained when the input gains to the plant are doubled, which demonstrates the good performance and robustness of the controller.     

分布式计算环境中的协同分配任务调度仿真系统

协同分配是在分布式计算环境中进行资源分配的一种重要技术,用于把一个应用程序分解为多个子作业,然后将其分配到多个资源上同时处理来满足特定的性能要求。本文提出了一个离散事件驱动的网格资源协同分配仿真系统,实现了对用户、调度器、协同分配器、协同预留器等协同分配相关实体的仿真,实现了FCFS、FPFS、Backfill等主要的协同分配调度算法和策略,可用于资源协同分配相关的分布式计算环境的资源管理和调度算法的仿真和研究。     

信息融合技术在变压器油气识别故障诊断中的研究

变压器故障分为放电性故障和过热性故障两大类别,它们均会在变压器油中有所反映。本文通过对变压器油中主要气体的分析,判断变压器的故障类型。具体方法是:利用改进算法的BP网络和信息融合技术,以变压器油中五种主要特征气体作为神经网络的输入,以六种变压器状态作为相应的输出,通过加入动量因子,可以提高学习率系数,充分发挥改进算法的BP网络具有自适应学习能力的优势。仿真测试结果表明,本方法能够在较大范围内准确有效地进行变压器的故障诊断。     

基于DSP嵌入式技术的无刷直流电动机智能控制系统的研究

本文以TI公司的数字信号处理器TMS320F2812作为核心控制芯片,设计出了一款低成本具有足够功能的无刷直流电动机的调速控制系统实验平台。详细介绍了该控制系统的软硬件结构及控制策略。通过对该控制系统实验结果的分析,表明该系统的动态性能和稳态性能好,抗干扰能力强的特点,且在该实验平台上可进行一些先进控制算法的研究。     

考虑客户画像和能量枢纽的配电网多目标抢修重构模型

随着大规模风电以及能量枢纽接入配电网,配电网故障后抢修重构面临新挑战,提出一种考虑客户画像和能量枢纽的配电网多目标多故障抢修重构模型。通过对配电网用电客户多因素精准画像,确定权重,建立兼顾客户画像和能量枢纽的配电网多目标抢修重构模型,提出一种改进NSGA-II算法以适应对模型求解,从而获得Pareto解集,并利用模糊最大满意度进行决策。算例仿真表明所提模型正确有效,与传统模型相比更加精细而客观。     

Enhanced Photoelectrochemical Performance of Hematite Photoanode by Decorating NiCoP Nanoparticles Through a Facile Spin Coating Method

Catalysis Letters - Hematite (α-Fe2O3) is a potential photoanode material for photoelectrochemical (PEC) water splitting, but its short hole diffusion length and low water oxidation kinetics...     

冠脉造影图像血管增强及减影的优化方法

针对于传统X射线冠状动脉造影序列图像的血管的减影包含严重伪影以及减影结果不完整等问题,提出了冠脉造影图像血管增强以及减影优化的方法。基于冠脉造影序列图像的时空特性,将相同时相的图像匹配进行减影,得到初步减影结果。针对初步减影部分缺失,对有导管遮挡没有被识别的单根血管进行二次增强处理。增强方法为:运用区域生长确定被导管遮挡的血管部分,以此部分为基础按规定模板扩展,形成覆盖导管区域,区分血管和导管,改变两部分的灰度值,增大对比度。最后采用灰度线性变换、中值滤波等步骤,得到优化的冠脉血管减影。实验表明,本减影及优化方法可以从冠脉造影序列图中提取较为完整清晰的冠脉血管并且具有很好的可控性和鲁棒性。     

Design of an Ultra-Compact and Highly-Sensitive Temperature Sensor Using Photonic Crystal Based Single Micro-Ring Resonator and Cascaded Micro-Ring Resonator

Silicon - In the present report, a photonic crystal based micro-ring resonator (MRR) structure is proposed which is very compact in size and has very fast response and is employed for temperature...