基于BP神经网络的智能BIT故障诊断系统研究

论述了智能BIT的设计、检测、诊断、决策四个方面的主要研究内容,分析了BP神经网络的网络模型及工作原理,构建了基于BP神经网络的智能BIT故障诊断系统,并用某雷达录取终端的故障实际数据进行了验证。结果表明将神经网络与智能BIT结合是一种有效的诊断方法,解决了传统BIT故障诊断能力不足,导致系统虚警率过高、自适应性能差等问题,使被测系统具有更高的故障诊断能力。

基于BP神经网络的BIT虚警抑制技术

从BIT设计、工作可靠性上论述了虚警产生的原因,并以此提出了BIT虚警抑制的方法。分析了BP神经网络的网络模型及工作原理,构建了基于BP神经网络的故障诊断系统,并对导弹测试数据进行了实验,结果表明将BP神经网络用于故障诊断能够有效地抑制虚警产生,使被测系统具有更高的故障诊断能力。

遗传BP网络在雷达装备BIT虚警抑制中的应用研究

针对现有故障诊断方法不能有效抑制雷达装备BIT虚警的现象,提出一种遗传算法优化后的BP神经网络技术抑制雷达装备BIT虚警的方法;首先介绍经遗传算法优化后BP神经网络的基本结构和学习算法,再结合雷达装备BIT的特点,以某火控系统雷达发射机作为被故障诊断对象,采用9个具有代表性的雷达发射机故障特征和8个典型故障,以Matlab作为开发工具进行仿真实验;实验结果表明该方法能准确对故障进行定位,有效抑制BIT虚警,提高雷达系统故障诊断能力。

An Image Encryption Method Based on Bit Plane Hiding Technology

A novel image hiding method based on the correlation analysis of bit plane is described in this paper. Firstly, based on the correlation analysis, different bit plane of a secret image is hided in different bit plane of several different open images. And then a new hiding image is acquired by a nesting ＂Exclusive-OR＂ operation on those images obtained from the first step. At last, by employing image fusion technique, the final hiding result is achieved. The experimental result shows that the method proposed in this paper is effec rive.

A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender&Age Assessment

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills.Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images.The permutation and combination of these features realized satisfactory accuracies for a set of limited groups.In this paper,assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images.A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process.Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image.A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female.The experimentations are conducted on the datasets of Radiological Society of North America(RSNA)of about 12442 images.Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%.Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.

A Reversible Data Hiding Algorithm Based on Image Camouflage and Bit-Plane Compression

Reversible data hiding in encrypted image(RDHEI)is a widely used technique for privacy protection,which has been developed in many applications that require high confidentiality,authentication and integrity.Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image.Moreover,the ciphertext form of encrypted image in RDHEI framework is easy to cause the attention of attackers.This paper proposes a reversible data hiding algorithm based on image camouflage encryption and bit plane compression.A camouflage encryption algorithm is used to transform a secret image into another meaningful target image,which can cover both secret image and encryption behavior based on“plaintext to plaintext”transformation.An edge optimization method based on prediction algorithm is designed to improve the image camouflage encryption quality.The reversible data hiding based bit-plane level compression,which can improve the redundancy of the bit plane by Gray coding,is used to embed watermark in the camouflage image.The experimental results also show the superior performance of the method in terms of embedding capacity and image quality.

An Electronic Image Stabilization Algorithm Based on Efficient Block Matching on the Bitplane

This paper proposes an electronic image stabilization algorithm based on efficient block matching on the plane. This algorithm uses a hexagonal search algorithm, and uses the bit-planes to estimate and compensate for the translational motion between video sequences at the same time;As for the rotary motion vector generated in the video sequences, in order to highlight the intensity change of the image sequence, the algorithm firstly conducts Laplace transform for the reference frame, then select a number of characteristics at the image edge to make block matching with the current frame, calculate and compensate for the rotational movement that may exist finally. Through theoretical analysis and simula-tion, we prove that, as for a mixed translational and rotational motion video sequences, the proposed algorithm can reduce required time for block matching computation ,while improving the accuracy of the electronic image stabilization.

Position Optimization for Mobile Relay Communications with Constrained Energy over a Whole Horizontal Plane

This paper concerns the position optimization problem of a mobile relay over a whole horizontal plane.This problem is important because the position of a mobile relay directly affects the end-to-end performance,e.g.,reliability,connectivity,and data rate.In this paper,we propose a new position optimization scheme of a mobile relay over a whole horizontal plane based on the one-bit feedback information from the destination node,which improves the performance over the prior scheme whose position of the mobile relay is optimized over a fixed orbit.In the proposed scheme,the mobile relay is equipped with merely one single onboard antenna.Moreover,no prior information about the positions of both the source node and the destination node is required.Thus,the proposed scheme can work at low network resources scenario,which is particularly suitable for mobile relay communication with constrained energy,e.g.,the communications in a disaster area where the infrastructure is heavily damaged,volcano monitoring,and wireless powered communication networking.According to the characteristics of the proposed scheme,we further design two heuristic implementations to calculate the optimal position of a mobile relay over a whole horizontal plane.The first implementation has better steady performance whereas the second implementation has better convergence speed.We implement the proposed scheme and conduct an extensive performance comparison between the proposed scheme and prior schemes to verify the advantages of the proposed scheme.

一种基于BPCS的高容量可逆信息隐藏算法

提出了一种基于位面复杂度的高容量可逆信息隐藏方法。首先通过计算低位平面中位平面复杂度分割(BPCS)复杂度筛选出最佳位平面,其次将筛选出的位平面进行实块和非实块划分,对块进行重构组成新的位平面,将辅助信息和秘密信息嵌入,最后通过块置乱、像素置乱和混沌加密对图像进行加密。基于这种算法,一定程度上优化了位平面嵌入选择,可以实现高容量的信息嵌入和图像恢复。

8 bit灰度图像分层信息量研究

灰度图像按比特位从高到低可以分为8 bit。其中,高比特平面包含大量有视觉意义的数据,而低比特平面只含有少量有视觉意义的数据。这个特性在图像识别和压缩等领域有重要的应用。对此,应用信息论的方法证明其中原因。首先,用直方图数据说明低比特平面灰度直方图的分布比高比特平面平滑,从而说明视觉信息与熵值有关;其次,从数学上严谨地证明,同一图像比特平面从高到低,其熵值单调递增,从而证明比特平面包含的视觉信息是单调递减的;最后,通过实验对不同图像的比特平面进行熵值计算,得到的结果与之前的证明完全一致。

基于位平面分解的量子彩色图像加密方案

一般的量子图像加密算法虽然能达到加密效果,但需要的量子比特数较多,计算复杂度较高,为优化这一问题,提出了一种位平面分解的量子彩色图像加密方案。首先,采用一种位平面序信息单独编码的彩色数字图像量子表示模型(quantum representation model of color digital image,QRCI)来表示图像,利用量子交换门设计一种量子位交换操作,并作用于彩色图像中,对图像像素进行置乱,同时再对图像进行颜色通道交换操作。其次,对位平面序列进行反序操作,进一步加强置乱效果。接着,利用安全散列算法(secure hash algorithm 256,SHA-256)产生的哈希值来确定混沌系统的初始值。最后,利用混沌系统产生的序列对图像进行扩散,完成加密过程,形成加密图像。数值分析表明,相较于以往的加密方案,此方案大大降低了彩色图像存储时所需的量子比特位数,同时其密钥空间大,灵敏度高。仿真结果表明,该方案加密效果好,能抵御大部分常见的攻击。

对空域BPCS密写的统计分析

位面复杂度分割(BPCS)密写法将复杂度较高的位平面小块用秘密信息替换,可以嵌入较多的秘密信息并维持较好的隐蔽性,但含密图像的位平面小块复杂度直方图中存在两个明显的不连续点·据此,提出了针对BPCS密写的分析方法,由复杂度直方图的不连续性可以判断秘密信息的存在性,并确定秘密信息块的复杂度范围,准确测算秘密信息嵌入量·这种分析方法的原理同样适用于变换域位面复杂度分割密写·

针对BPCS隐写的隐写分析法

为实现对位平面复杂度分割(BPCS)隐写的有效检测,分析了图像复杂度直方图在像素块替换前后的变化,针对载密图像的位平面复杂度直方图出现明显的不连续点,定义了新的直方图不连续性测度,并提出了改进的检测算法.与已有算法相比,改进算法的虚警率和漏检率降低20%以上,并且易于实现,有利于实现实时检测.

基于BP神经网络的红外焦平面非均匀性校正技术

在传统BP神经网络非均匀性校正理论的基础上,结合红外焦平面阵列读出通道的时变特性,提出一种新的输出期望估计方法.该方法利用当前像素邻域和读出通道估计输出期望值.实验表明,算法能有效抑制焦平面固定图案噪音,提高场景目标的分辨能力,达到较好的视觉效果.

一种基于位平面和位运动估计的视频数据压缩算法

高分辨率和高刷新率使得带宽传输的压力加倍。通常,视频压缩被用来解决这个问题。但传统的视频压缩方法不根据位平面进行编码和解码,会增加存储区域的成本。因此本文首先介绍了一种位恰可察觉失真(BJND)模型,计算了结合视点因子的位平面恰可察觉失真模型的阈值,并将亮度因子和纹理因子的恰可察觉失真(JND)阈值映射到位平面上,综合得出了位恰可察觉失真模型。然后提出了一种基于位运动估计算法的视频压缩方案。该方案使改进后的搜索范围不仅包括时间维度,还包括灰度维度。根据人眼的视觉特点和概率统计分析,方案新增了补充匹配块来替代残差数据,从而保证了压缩比的恒定性。实验结果表明,所提方案对低5位平面压缩具有最佳的综合效果,压缩比为1.38,数据传输量恒定,且恢复后的图像与原始图像并无明显差异,更加符合人眼的直观感受。

抗统计分析的BPCS隐写

基于复杂度直方图的隐写统计分析法能有效地察觉以BPCS隐写的秘密信息的存在,因此为提高隐写算法的安全性,本文提出了一种改进的BPCS隐写算法,即在不同位平面设置不同的复杂度阈值;并构造一个二次函数来衡量区域的明暗度,利用小块所处区域的明暗度和复杂度相结合来选择位面小块嵌入信息.实验结果表明,该算法能有效地抗基于复杂度直方图的统计分析,同时隐写图像质量比BPCS方法有较大的改善.

基于BP&MATLAB的平面加工CAPP的研究

基于人工神经网络中的BP网络建立了零件表面加工方案模型。把一系列推理规则转化为网络权值,并设计BP网络。然后利用MATLAB软件中的神经网络工具箱编程,对网络进行训练,从而得到了零件平面加工工艺顺序。

基于岩石抗钻特性的BP神经网络钻头选型方法

地层岩石抗钻特性是影响钻头使用效果最重要的一个因素,传统的钻头选型方法大多只是定性分析地质层位和钻头的对应关系,定量地计算分析岩石抗钻特性与钻头使用效果之间的关系较少。在分析现有钻头选型方法的基础上,提出一种基于岩石抗钻特性的BP神经网络钻头选型方法,并在大港油田进行了应用。首先基于测井资料和室内实验建立大港油田地层抗钻特性评价模型,并且利用模型计算得到的岩石力学参数数据和现场钻头使用数据编码成神经网络学习样本,然后建立完钻井地层抗钻特性和对应最佳钻头类型的BP神经网络模型,最后输入学习样本完成神经网络训练。利用该模型进行选型时需提供待钻井段的岩石力学参数预测数据,神经网络经过计算之后会输出最佳钻头类型。该选型方法在港深18-17井应用效果不错,优选的钻头在港深18-17井沙一段相比于邻井同一层段平均机械钻速提高了25%。

智能水下机器人BP神经网络S面控制

针对智能水下机器人传统S面控制器参数设置过程依赖经验且设置不当将严重影响运动控制效果的问题,设计了BP神经网络S面控制器,由神经网络正向传播输出S面控制器参数,并在反向传播中实现参数的在线整定。采用某微小型智能水下机器人模型仿真实验的结果表明,该控制器能够自主完成控制参数初始化与调整,具有收敛速度快、超调与稳态误差小、干扰条件下能够迅速恢复稳定等优点,可以为实际工程中运动控制器设计提供参考。