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1.
Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks. 相似文献
2.
The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods. 相似文献
3.
针对锅炉飞灰含碳置影响因素复杂.实时测量难度大.精度低,提出了一种基于遗传神经网络的预测方法,并用于某电厂,结果表明,该方法是有效的。 相似文献
4.
对溢洪闸门自动监控系统中几个问题的探讨 总被引:1,自引:1,他引:0
结合某水电站介绍了溢洪闸门自动监控系统的结构、功能,讨论了该研制过程中的几个问题的解决办法. 相似文献
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In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort. 相似文献
7.
针对液压电梯在工作过程中出现的启动时间长、运行速度不平稳、停止阶段轿厢抖动等不足,借助多物理领域仿真软件Simscape对液压电梯系统进行了综合建模,并提出了遗传算法(GA)整定PID对液压电梯运行速度进行控制的新方法。介绍了液压电梯的速度控制原理以及遗传算法的控制策略,针对液压电梯的工作特点对遗传要素进行了合理规划,并对不同工况下的液压电梯进行了仿真研究。仿真结果表明:在载重量为0 kg,500 kg,1000 kg的工况下,遗传算法整定的PID控制器在动态特性、跟踪特性等方面都具有明显的优势。最后通过实验证明了基于遗传算法PID整定在提高液压电梯运行性能方面的有效性。 相似文献
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一种基于混合决策树的调度知识获取算法* 总被引:1,自引:1,他引:0
提出了一种基于混合决策树的调度知识获取算法。将模拟退火算法融入遗传算法中,作为一种具有自适应变概率的变异操作,构成一种混合优化方法。利用这种混合方法求解在不同调度目标下制造系统的最优特征子集,同时确定控制决策树规模的最优参数;使用决策树评价混合方法中染色体编码的适应度,在得到不同调度目标下的最优特征子集和最优决策树参数后,生成调度知识。仿真实验结果表明,该算法在性能上优于其他算法。 相似文献
10.