Metallurgical behavior and variation of vibro-acoustic signal during preheating assisted friction stir welding between AA6061-T6 and AA7075-T651 alloys

The present work investigated the effects of pin profiles(cylindrical and square),pin eccentricity(0.5 mm and 1 mm)in cylindrical tool and preheating(secondary heating)on metallurgical behavior,variation of vibro-acoustic signal pattern and joint strength during friction stir welding(FSW)between AA6061-T6 and AA7075-T651 alloys.The eccentric tool pins were observed to provide good flowability and intermixing between dissimilar metals,increased the size of stir zone,and the grains in stir zone were sufficiently finer with eccentric tool pin than concentric pin.The magnitude of vibro-acoustic signal increased when shoulder plunging started and drop in signal was noted when the tool shoulder reached its desired depth.The signal magnitude was noted to be higher in welding stage compared to tool plunging stage as the tool took in fresh material during tool movement along the weld path.Preheating the workpiece prior to pin plunging and during welding notably influenced the flow behavior and mixing pattern,and the grains in stir zone were slightly coarser than those in specimen without preheating.Significant reduction in the magnitude of the signal was also observed after preheating.Tensile and flexural strength of joints were also improved slightly when additional heating was employed.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Optimal multi-sensor Kalman smoothing fusion for discrete multichannel ARMA signals

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using white noise estimators, an optimal fusion distributed Kalman smoother is given for discrete multi-channel ARMA （autoregressive moving average） signals. The smoothing error cross-covanance matrices between any two sensors are given for measurement noises. Furthermore, the fusion smoother gives higher precision than any local smoother does.

Diffusion-weighted magnetic resonance imaging reflects activation of signal transducer and activator of transcription 3 during focal cerebral ischemia/reperfusion

Signal transducer and activator of transcription（STAT）is a unique protein family that binds to DNA,coupled with tyrosine phosphorylation signaling pathways,acting as a transcriptional regulator to mediate a variety of biological effects.Cerebral ischemia and reperfusion can activate STATs signaling pathway,but no studies have confirmed whether STAT activation can be verified by diffusion-weighted magnetic resonance imaging（DWI）in rats after cerebral ischemia/reperfusion.Here,we established a rat model of focal cerebral ischemia injury using the modified Longa method.DWI revealed hyperintensity in parts of the left hemisphere before reperfusion and a low apparent diffusion coefficient.STAT3 protein expression showed no significant change after reperfusion,but phosphorylated STAT3 expression began to increase after 30 minutes of reperfusion and peaked at 24 hours.Pearson correlation analysis showed that STAT3 activation was correlated positively with the relative apparent diffusion coefficient and negatively with the DWI abnormal signal area.These results indicate that DWI is a reliable representation of the infarct area and reflects STAT phosphorylation in rat brain following focal cerebral ischemia/reperfusion.

HIGH RESOLUTION RANGE PROFILE FORMATION BASED ON LFM SIGNAL FUSION OF MULTIPLE RADARS

This paper presents a new method of High Resolution Range (HRR) profile formation based on Linear Frequency Modulation (LFM) signal fusion of multiple radars with multiple frequency bands. The principle of the multiple radars signal fusion improving the range resolution is analyzed. With the analysis of return signals received by two radars,it is derived that the phase difference between the echoes varies almost linearly with respect to the frequency if the distance between two radars is neg-ligible compared with the radar observation distance. To compensate the phase difference,an en-tropy-minimization principle based compensation algorithm is proposed. During the fusion process,the B-splines interpolation method is applied to resample the signals for Fourier transform imaging. The theoretical analysis and simulations results show the proposed method can effectively increase signal bandwidth and provide a high resolution range profile.

DISTRIBUTED CFAR SIGNAL DETECTION BASED ON AREA FUSION

The multisensor detection area partitioning is considered. An approach is presented to the fusion in each detection area where the sensor uses different thresholds and then at system level. The expressions of the detection probability and false alarm probability are given. An application of the method is illustrated to distributed CFAR detection systems. The result shows that the system detection probability may be improved by setting different thresholds for a detector.

Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

基于Fusion的感应同步器的信号处理

通过鉴别感应同步器激磁信号和感应信号的相位差来检测感应同步器的转子相对定子的角度.采用Actel的Fusion集成的FPGA、A/D转换和CoreMP7的ARM7内核,实现正、余弦信号的产生、数据采集和数据处理的单芯片SOC系统,同时,在激磁信号0°相位时采集感应信号,通过FFT计算感应信号的初相位,进而实现感应同步器的位置检测.

中药治疗腰椎椎体间融合术后发热的炎症信号通路

背景:术后发热是后路腰椎椎体间融合后常见临床症状之一,目前没有明确的病因病机,患者术后体温易反复,病程较久,西药治疗效果欠佳,影响患者术后恢复。目的:综述总结炎症信号通路和术后发热的关联及中药防治后路腰椎椎体间融合后发热的机制,为研究术后发热继续探索新的治疗药物。方法:检索CNKI中国期刊全文数据库、万方数据库和PubMed数据库2006年6月至2023年12月收录的文献,中文检索词为“中药,术后发热,炎症,白细胞介素1,白细胞介素6,白细胞介素8,干扰素γ,肿瘤坏死因子,前列腺素E2,p38丝裂原活化蛋白激酶,核因子κB,Toll样受体,Janus激酶/信号转导和转录激活因子,Notch信号通路”;英文检索词为“medicinal herb,postoperative fever,inflammation,interleukin-1,interleukin-6,interleukin-8,γ-interferon,tumor necrosis factor,Prostaglandin E2,p38 mitogen-activated protein kinase,nuclear factor-κB,toll-like receptor,janus kinases/signal transducer and activator of transcription,notch signaling pathway”。阅读文章剔除研究内容不相关、质量差的文献,纳入100篇文献进行归纳总结。结果与结论:(1)手术造成软组织创伤促使机体释放炎症因子,促炎因子作用于下丘脑前部的体温调节中心,促使发热;(2)术后切口内出血及渗液等的吸收,吞噬细胞吞噬坏死细胞的蛋白分解产物后,产生内生致热原(白细胞介素、肿瘤坏死因子、干扰素等)导致发热;(3)细胞因子通过炎症信号通路发挥致炎作用,促使发热;(4)中药或中药复方可以调控炎症信号通路发挥抗炎解热作用防治术后发热;目前中药作用机制尚无定论,应深入研究阐明中医药防治术后发热的相关信号通路,运用现代技术把细胞分子技术与中医药药理作用机制联系起来,以便指导临床医生用药,促进患者术后恢复;(5)中药联合西药治疗术后发热是未来的研究热点,应充分发挥中医药优势,明确不同证型应用中药单体或有效成分提取物和中药汤剂的靶点作用机制。

Investigation of Image Fusion Between High-Resolution Image and Multi-spectral Image

On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China. The experimental results show that a perfect image fusion can be built up by using the image analytical solution and re-construction in the image frequency domain based on the physical characteristics of the image formation. The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.

Noninvasive Blood Glucose Monitoring System Based on Distributed Multi-Sensors Information Fusion of Multi-Wavelength NIR

In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root- Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coefficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri- buted multi-sensors, shows better prediction accuracy.

Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and uncertain.Traditional data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data.Data fusion is the process of integrating multiple data sources.Data filtering means examining a dataset to exclude,rearrange,or apportion data according to the criteria.Different sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional methods.The advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional methods.However,many open issues still exist as machine learning algorithms are used for data fusion.From the literature,nine issues have been identified irrespective of any application.The decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these issues.It helps to get the weights for each corresponding issue and rank issues based on these calculated weights.The most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted 0.141.The least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

Gait is a biological typical that defines the method by that people walk.Walking is the most significant performance which keeps our day-to-day life and physical condition.Surface electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent.Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment.Several approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)approaches.This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG signals.The EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is fused.In addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait recognition.At last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR technique.The results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor

In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion

In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.

Role of the STAT3/survivin signaling pathway in the EML4-ALK-positive lung adenocarcinoma cell line H2228 before and after crizotinib-induced resistance

Objective This study investigated the role of the STAT3/survivin signaling pathway in the EML4-ALK- positive lung adenocarcinoma cell line H2228 before and after crizotinib-induced resistance. The mecha- nism of resistance was studied. Methods Cell viability was determined using the MTT assay. Crizotinib-induced apoptosis in H2228 and H2228 crizotinib-resistant cells treated with the indicated doses of crizotinib was measured at different times （24 h, 48 h, 72 h） using flow cytometry. The levels of p-ALK, ALK, p-STAT3, STAT3, and survivin after treatment of cells with 0, 0.3, and 1 pM crizotinib for 72 h were determined using Western blot analysis. DNA sequencing was used to identify mutations in H2228 crizotinib-resistant cells. Results The crizotinib IC50 values in H2228 and H2228 crizotinib-resistant cells at 72 h were 334.5 nM and 3418 nM, respectively. The resistance index of 1-12228 crizotinib-resistant cells was 10.20. Crizotinib induced apoptosis in H2228 cells and reduced the levels of p-ALK, p-STAT3, and survivin. In contrast, no changes in the levels of p-ALK, p-STAT3, and survivin were observed in H2228 crizotinib-resistant cells. The mutations 2067G--,A and 2182G--,C in EML4-ALK were present in the H2228 crizotinib-resistant cells. Conclusion Crizotinib decreased the viability of H2228 cells in a dose- and time-dependent manner. In the STAT3/survivin pathway, downregulation of p-ALK, p-STAT3, and survivin might contribute to crizo- tinib-induced apoptosis in H2228 ceils. However, the STAT3/survivin pathway in H2228 crizotinib-resistant cells was unaffected by crizotinib treatment. Acquired resistance in H2228 cells might be related to ALK mutations.

Study on key technologies of GNSS-based train state perception for traincentric railway signaling

The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

基于AESL-GA的BN球磨机滚动轴承故障诊断方法

针对基于知识的贝叶斯网络(BN)构建方法存在不完全和不精确的缺点,提出一种基于知识引导和数据挖掘的BN结构构建方法。针对单一信号故障诊断结果不精确的问题和故障信息中存在的不确定性问题,将电流信号与振动信号融合建立BN的特征节点,分别提取2种信号的故障特征参数,利用区分度指标法进行特征筛选,将其作为BN结构特征层的节点。将专家知识构建的初始BN结构结合自适应精英结构遗传算法(AESL-GA)进行结构优化,通过自适应限制进化过程中的搜索空间,减少自由参数的数量,提高其全局搜索能力,得到最优BN结构。通过MQY5585溢流型球磨机滚动轴承实测数据和Paderborn University轴承数据集对所提方法进行验证,结果证明了所提方法的有效性。

基于多传感数据融合的变速运行齿轮异常振动故障诊断

变速运行齿轮异常振动故障诊断性能过差会增加汽车维护成本,缩短齿轮使用寿命。为了及时识别齿轮故障,保证汽车变速器总成具有良好的振动特性,提出基于多传感数据融合的变速运行齿轮异常振动故障诊断方法。通过分析多传感器数据融合技术,掌握变速运行齿轮异常振动故障诊断的理论框架,并以此为基础,参考传感器融合模块、特征级并行多神经网络局部诊断模块和终端分类模块,结合变分模态分解、多通道加权融合和单隐层前馈神经网络训练算法,从信号采集、信号特征提取和信号特征分类3个步骤实现变速运行齿轮异常振动故障诊断。实验结果表明:在齿轮发生轻度磨损时,磨损振动信号的幅值在20~40 mV之间,磨损振动信号的频率在0~4000 Hz区间;中度磨损时,信号的幅值在30~55 mV之间,信号频率在3000~7000 Hz区间;重度磨损时,信号幅值在50~70 mV之间,信号频率在6000~12000 Hz区间,且各阶段诊断结果均与故障程度的实际转折点吻合。由此可知在各样本数量均相同的情况下,提出的故障诊断方法预测值与真实值均相同,故障程度和故障类型的诊断性能均较好。