Improved CoSaMP Reconstruction Algorithm Based on Residual Update

A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of selecting the fixed number of indexes improves the reconstruction efficiency, it also brings the problem of low index selection accuracy. Based on the full study of the theory of compressed sensing, we propose a dynamic indexes selection strategy based on residual update to improve the performance of the compressed sampling matching pursuit algorithm (CoSaMP). As an extension of CoSaMP algorithm, the proposed algorithm adopts a residual comparison strategy to improve the accuracy of backtracking selected indexes. This backtracking strategy can efficiently select backtracking indexes. And without increasing the computational complexity, the proposed improvement algorithm has a higher exact reconstruction rate and peak signal to noise ratio (PSNR). Simulation results demonstrate the proposed algorithm significantly outperforms the CoSaMP for image recovery and one-dimensional signal.

MLSI-RT: memorize LOS range measurements identified residual test location algorithm and performance analysis

The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.

Extending the Network Lifetime of Wireless Sensor Networks Using Residual Energy Extraction—Hybrid Scheduling Algorithm

Wireless sensor networks (WSNs) are mostly deployed in a remote working environment, since sensor nodes are small in size, cost-efficient, low-power devices, and have limited battery power supply. Because of limited power source, energy consumption has been considered as the most critical factor when designing sensor network protocols. The network lifetime mainly depends on the battery lifetime of the node. The main concern is to increase the lifetime with respect to energy constraints. One way of doing this is by turning off redun-dant nodes to sleep mode to conserve energy while active nodes can provide essential k-coverage, which improves fault-tolerance. Hence, we use scheduling algorithms that turn off redundant nodes after providing the required coverage level k. The scheduling algorithms can be implemented in centralized or localized schemes, which have their own advantages and disadvantages. To exploit the advantages of both schemes, we employ both schemes on the network according to a threshold value. This threshold value is estimated on the performance of WSN based on network lifetime comparison using centralized and localized algorithms. To extend the network lifetime and to extract the useful energy from the network further, we go for compromise in the area covered by nodes.

The Residual Potential of Bottom Water Reservoir Based upon Genetic Algorithm for the Relative Permeability Inversion

X oilfield has successfully adopted horizontal wells to develop strong bottom water reservoirs, as a typical representative of development styles in the Bohai offshore oilfield. At present, many contributions to methods of inverting relative permeability curve and forecasting residual recoverable reserves had been made by investigators, but rarely involved in horizontal wells’ in bottom water reservoir. As the pore volume injected was less (usually under 30 PV), the relative permeability curve endpoint had become a serious distortion. That caused a certain deviation in forecasting residual recoverable reserves in the practical value of field directly. For the performance of water cresting, the common method existed some problems, such as no pertinence, ineffectiveness and less affecting factors considered. This paper adopts the streamlines theory with two phases flowing to solve that. Meanwhile, based on the research coupling genetic algorithm, optimized relative permeability curve was calculated by bottom-water drive model. The residual oil saturation calculated was lower than the initial’s, and the hydrophilic property was more reinforced, due to improving the pore volume injected vastly. Also, the study finally helped us enhance residual recoverable reserves degree at high water cut stage, more than 20%, taking Guantao sandstone as an example. As oil field being gradually entering high water cut stage, this method had a great significance to evaluate the development effect and guide the potential of the reservoir.

A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

Correcting dynamic residual aberrations of conformal optical systems using AO technology

This paper analyses the dynamic residual aberrations of a conformal optical system and introduces adaptive optics （AO） correction technology to this system. The image sharpening AO system is chosen as the correction scheme.Communication between MATLAB and Code V is established via ActiveX technique in computer simulation.The SPGD algorithm is operated at seven zoom positions to calculate the optimized surface shape of the deformable mirror.After comparison of performance of the corrected system with the baseline system,AO technology is proved to be a good way of correcting the dynamic residual aberration in conformal optical design.

Research on classification diagnosis model of psoriasis based on deep residual

Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of diagnosis.Methods Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of psoriasis.Finally,we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.

Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

With the advent of Machine and Deep Learning algorithms,medical image diagnosis has a new perception of diagnosis and clinical treatment.Regret-tably,medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques.However,the presence of noise images degrades both the diagnosis and clinical treatment processes.The existing intelligent meth-ods suffer from the deficiency in handling the diverse range of noise in the ver-satile medical images.This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alle-viate this challenge.The proposed deep learning architecture exploits the advan-tages of the capsule network,which is used to extract correlation features and combine them with redefined residual features.Additionally,thefinal stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate,even for noisy and complex images.Extensive experimen-tation has been conducted using different medical images.Various performances such as Peak-Signal-To-Noise Ratio(PSNR)and Structural-Similarity-Index-Metrics(SSIM)are compared with the existing deep learning architectures.Addi-tionally,a comprehensive analysis of individual algorithms is analyzed.The experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

With the advent of Machine and Deep Learning algorithms,medical image diagnosis has a new perception of diagnosis and clinical treatment.Regret-tably,medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques.However,the presence of noise images degrades both the diagnosis and clinical treatment processes.The existing intelligent meth-ods suffer from the deficiency in handling the diverse range of noise in the ver-satile medical images.This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alle-viate this challenge.The proposed deep learning architecture exploits the advan-tages of the capsule network,which is used to extract correlation features and combine them with redefined residual features.Additionally,the final stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate,even for noisy and complex images.Extensive experimen-tation has been conducted using different medical images.Various performances such as Peak-Signal-To-Noise Ratio(PSNR)and Structural-Similarity-Index-Metrics(SSIM)are compared with the existing deep learning architectures.Addi-tionally,a comprehensive analysis of individual algorithms is analyzed.The experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

基于XGBoost算法的电力虚假数据注入攻击残差检测

电力系统中的负载波动、电压失调等不确定性因素会影响电力虚假数据注入攻击残差检测的准确性,导致电力虚假数据注入攻击电力系统,产生电力系统误判或失控现象,为此设计基于XGBoost算法的电力虚假数据注入攻击残差检测方法。根据XGBoost算法原理完成对电力虚假数据的辨识;构造完整的虚假数据集合,通过分析虚假数据注入攻击行为的方式,确定残差检测系数的取值范围,实现基于XGBoost算法的电力虚假数据注入攻击残差检测。实验结果表明,所提方法可以较为准确地检测出虚假数据的实时注入量,从而将检测结果与真实注入量之间的数值差控制在低水平状态,以便实现对电力虚假数据注入攻击行为的有效控制。

基于多数据融合的短时交通流量预测算法研究

针对单一模型无法分析复杂、非线性交通流数据的问题,在多平台数据融合的基础上提出了一种面向短时交通流量的组合预测算法。对于交通流数据具备的时间性和空间性特征,首先使用径向基函数(RBF)神经网络对相邻节点的空间交通流数据进行分析,然后利用ResNet对RBF神经网络效率低的缺陷加以改进,最后通过双向长短期记忆(LSTM)网络的时间序列分析能力提取交通流数据的时间特征。同时,引入了萤火虫算法对时空模型的参数进行优化。对基于公共数据集获取到的车辆、天气、高速公路等多平台信息进行了试验。相较于对比算法,所提算法的均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)指标均最优,预测值与实际流量值最接近。该算法的综合性能较理想,且鲁棒性较强。

Mode Shape Identification Using Residues of Measured Offshore Structure Data

Compared to traditional mode shape identification methods such as eigensystem realization algorithm(ERA),this article proposes a mode shape identification method based on estimated residues of measured data and the theoretical relationship between the estimated residues and the mode shapes from the state space model is obtained by defining a coefficient matrix.A mass-spring model with five degrees of freedom(DOFs) is utilized to demonstrate the approach.The numerical results indicate that the estimated residues are the mode shapes of structures,but with a coefficient matrix to maintain consistency with the mode shapes from the ERA.Using MATLAB a complicated numerical jacket platform is built to further study the proposed method.The results show that mode shapes consistent with those from the ERA could be obtained by taking the defined coefficient matrix into account,which is also demonstrated by a physical beam model that was built at Ocean University of China.

AN ITERATIVE PARALLEL ALGORITHM OF FINITE ELEMENT METHOD

In this paper, a parallel algorithm with iterative form for solving finite element equation is presented. Based on the iterative solution of linear algebra equations, the parallel computational steps are introduced in this method. Also by using the weighted residual method and choosing the appropriate weighting functions, the finite element basic form of parallel algorithm is deduced. The program of this algorithm has been realized on the ELXSI-6400 parallel computer of Xi'an Jiaotong University. The computational results show the operational speed will be raised and the CPU time will be cut down effectively. So this method is one kind of effective parallel algorithm for solving the finite element equations of large-scale structures.

Cleaner production for continuous digester processes based on hybrid Pareto genetic algorithm

Pulping production process produces a large amount of wastewater and pollutant emitted, which has become one of the main pollution sources in pulp and paper industry. To solve this problem, it is necessary to implement cleaner production by using modeling and optimization technology. This paper studies the modeling and multi\|objective genetic algorithms for continuous digester process. First, model is established, in which environmental pollution and saving energy factors are considered. Then hybrid genetic algorithm based on Pareto stratum\|niche count is designed for finding near\|Pareto or Pareto optimal solutions in the problem and a new genetic evaluation and selection mechanism is proposed. Finally using the real data from a pulp mill shows the results of computer simulation. Through comparing with the practical curve of digester,this method can reduce the pollutant effectively and increase the profit while keeping the pulp quality unchanged.

Hardware Architecture for RSA Cryptography Based on Residue Number System

A parallel architecture for efficient hardware implementation of Rivest Shamir Adleman(RSA) cryptography is proposed.Residue number system(RNS) is introduced to realize high parallelism,thus all the elements under the same base are independent of each other and can be computed in parallel.Moreover,a simple and fast base transformation is used to achieve RNS Montgomery modular multiplication algorithm,which facilitates hardware implementation.Based on transport triggered architecture(TTA),the proposed architecture is designed to evaluate the performance and feasibility of the algorithm.With these optimizations,a decryption rate of 106 kbps can be achieved for 1 024-b RSA at the frequency of 100 MHz.

A QMR-Type Algorithm for Drazin-Inverse Solution of Singular Nonsymmetric Linear Systems

In this paper, we propose DQMR algorithm for the Drazin-inverse solution of consistent or inconsistent linear systems of the form Ax=b where is a singular and in general non-hermitian matrix that has an arbitrary index. DQMR algorithm for singular systems is analogous to QMR algorithm for non-singular systems. We compare this algorithm with DGMRES by numerical experiments.

A Multi-Hop Dynamic Path-Selection (MHDP) Algorithm for the Augmented Lifetime of Wireless Sensor Networks

In Wireless Sensor Networks (WSN), the lifetime of sensors is the crucial issue. Numerous schemes are proposed to augment the life time of sensors based on the wide range of parameters. In majority of the cases, the center of attraction will be the nodes’ lifetime enhancement and routing. In the scenario of cluster based WSN, multi-hop mode of communication reduces the communication cast by increasing average delay and also increases the routing overhead. In this proposed scheme, two ideas are introduced to overcome the delay and routing overhead. To achieve the higher degree in the lifetime of the nodes, the residual energy (remaining energy) of the nodes for multi-hop node choice is taken into consideration first. Then the modification in the routing protocol is evolved (Multi-Hop Dynamic Path-Selection Algorithm—MHDP). A dynamic path updating is initiated in frequent interval based on nodes residual energy to avoid the data loss due to path extrication and also to avoid the early dying of nodes due to elevation of data forwarding. The proposed method improves network’s lifetime significantly. The diminution in the average delay and increment in the lifetime of network are also accomplished. The MHDP offers 50% delay lesser than clustering. The average residual energy is 20% higher than clustering and 10% higher than multi-hop clustering. The proposed method improves network lifetime by 40% than clustering and 30% than multi-hop clustering which is considerably much better than the preceding methods.

Investigation on Three Types of Residues in Phase-unwrapping

In phase unwrapping, how to control the residues and cut lines is critical to unwrap ill-state wrapped phase map successfully. Some new concepts in phase unwrapping field are proposed. Firstly, the residues in three types, i.e., singular residues, normal residues and virtual residues, are classified, which leads to some rational unwrapping paths. Secondly, some differences between cut-line and curved line are analyzed. And finally, experiment is carried out to compare our new enhanced Goldstein algorithm with the original Goldstein algorithm.

结合蜉蝣算法和响应面的结构静力损伤识别

提出了一种结合蜉蝣智能搜索算法和静位移响应面的结构损伤识别方法.首先构造了基于灵敏度的静力损伤识别方程残差和位移残差指标的正则化优化目标函数,然后采用人工智能领域的蜉蝣算法来识别结构单元级别的损伤.在优化过程中,为了解决静力加载点和位移测点不一致的问题,利用模型凝聚和位移扩展方法重新构建了损伤识别方程.同时,利用静位移响应面计算目标函数中的位移残差,避免了耗时的有限元计算,提高了优化效率.简支梁的数值算例结果表明,本文方法在寻优速度和精度方面均优于传统的粒子群法和差分进化算法;与单独基于静力损伤识别方程残差或位移残差指标的方法相比,识别损伤更为准确.铝合金悬臂梁损伤识别的静载实验进一步验证了本文方法的高效率和有效性.