An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of outlier detection in water supply data.The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an output.The outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an outlier.For water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to zero.We set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers respectively.The data samples with reconstruction errors exceeding the thresholds are voted to be outliers.The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)curve.We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set.As a result,our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data.

Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation

Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.

Epigenetic combined with transcriptomic analysis of the m6A methylome after spared nerve injury-induced neuropathic pain in mice

Epigenetic changes in the spinal cord play a key role in the initiation and maintenance of nerve injury-induced neuro pathic pain.N6-methyladenosine(m6A)is one of the most abundant internal RNA modifications and plays an essential function in gene regulation in many diseases.However,the global m6A modification status of mRNA in the spinal cord at different stages after neuropathic pain is unknown.In this study,we established a neuropathic pain model in mice by preserving the complete sural nerve and only damaging the common peroneal nerve.High-throughput methylated RNA immunoprecipitation sequencing res ults showed that after spared nerve injury,there were 55 m6A methylated and diffe rentially expressed genes in the spinal cord.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway results showed that m6A modification triggered inflammatory responses and apoptotic processes in the early stages after spared nerve injury.Over time,the diffe rential gene function at postoperative day 7 was enriched in "positive regulation of neurogenesis" and "positive regulation of neural precursor cell prolife ration." These functions suggested that altered synaptic morphological plasticity was a turning point in neuropathic pain formation and maintenance.Results at postoperative day 14 suggested that the persistence of neuropathic pain might be from lipid metabolic processes,such as "very-low-density lipoprotein particle clearance," "negative regulation of choleste rol transport" and "membrane lipid catabolic process." We detected the expression of m6A enzymes and found elevated mRNA expression of Ythdf2 and Ythdf3 after spared nerve injury modeling.We speculate that m6A reader enzymes also have an important role in neuropathic pain.These results provide a global landscape of mRNA m6A modifications in the spinal cord in the spared nerve injury model at diffe rent stages after injury.

Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.

Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model

The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning

Intrusion Detection through DCSYS Propagation Compared to Auto-encoders

In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies.The presence of anomalies is also one of the disadvantages,internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on,this a case of true benign detected as false.Deep learning is very adept at dealing with such cases,but sometimes it has its own faults when dealing benign cases.Here we tend to adopt a dynamic control system(DCSYS)that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies.Its performance is compared with artificial neural network auto-encoders to define its predictive power.Results show that though physical systems can adapt securely,it can be used for network data packets to identify true benign cases.

Dynamic Prediction Method for Valuable Spare Parts Demand in Weaponry Equipment Based on Data Perception

Missile is an important weapon system of the army.The spare parts of missile equipment are significant effect on military operations.In order to improve the mission completion rate of missile equipment in wartime,this paper introduces data sensing method to forecast the demand of valuable spare parts of missile equipment dynamically.Firstly,the information related to valuable spare parts of missile equipment was obtained by data sensing,and the sample size was determined by Bernoulli uniform sampling probability.Secondly,according to the data quality of multi-source and multi-modal,the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained.Finally,according to the characteristics of the spare parts,the life of the spare parts was predicted,realizing the dynamic prediction of the demand for valuable spare parts of missile equipment.The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method,the accuracy is higher than 95%,and the real-time performance is more excellent.

子宫内膜不典型增生/早期子宫内膜癌患者保留生育功能治疗后IVF-ET妊娠结局及复发因素分析

目的分析子宫内膜不典型增生/早期子宫内膜癌(AH/EEC)患者保留生育功能治疗后接受体外受精-胚胎移植(IVF-ET)治疗的临床特点和预后,分析影响助孕妊娠结局和疾病复发的主要因素。方法回顾性分析2012年2月至2022年2月在北京协和医院接受AH/EEC生育保留治疗后进行IVF-ET治疗的78例患者的临床资料。总结分析纳入患者的临床特征、IVF-ET相关指标、妊娠结局和复发情况,以单因素和多因素分析临床妊娠率、活产率以及疾病复发的影响因素。结果78例患者中51例(65.38%)为AH患者,27例(34.62%)为EEC患者;开始IVF-ET周期的平均年龄为(34.17±3.70)岁。共有74例患者至少接受了1次移植,每移植周期的临床妊娠率和活产率分别为36.31%(65/179)和18.99%(34/179),累积妊娠率为72.97%(54/74)。多因素分析提示子宫内膜病变初次发病年龄是活产率的独立影响因素[OR=0.8794,95%CI(0.785,0.983),P=0.02]。纳入患者IVF-ET期间子宫内膜病变的总复发率为6.41%(5/78),多因素分析提示子宫内膜病变的病理类型和IVF-ET前复发史是疾病复发的危险因素(P<0.05)。结论AH/EEC患者保留生育功能治疗后的辅助生殖结局相对满意,在肿瘤治疗过程中,进行病变评估时应尽量保护内膜,减少损伤;在肿瘤治疗结束后,应尽快进行助孕治疗,以最大程度降低复发率。

逆行松解神经血管束且保留Retzius间隙机器人辅助腹腔镜根治性前列腺切除术的技术要点(“大家泌尿网”观看手术视频)

保留耻骨后间隙的前列腺癌根治术(RS-RARP)可以显著提高术后即刻尿控且不增加切缘阳性率。然而该术式学习曲线长,目前能掌握的学者不到10%,尚未推广。基于对前列腺解剖结构及手术方式的认识,本中心对RS-RARP进行优化。我们首创了完全后入路逆行松解神经血管束的RARP,最大限度保留神经血管束,简化手术操作,仅使用一根缝线即可完成尿道吻合,无需使用Hem-o-lok,减少相关并发症。我们在本中心常规开展该术式,通过多维度分析认为这是一种“肿瘤控制可、尿控保护好、性功能恢复快、并发症少、可操作性强”的手术方式。本文详细介绍该术式的关键步骤及操作体会。

基于主成分分析-BP神经网络的风电备件需求预测

风电机组具有结构复杂,运维困难,且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数,以确保风电场的平稳、高效运行。构建主成分分析-反向传播(principal component analysis-back propagation,PCA-BP)模型,针对受多因素影响的复杂备件,先利用PCA将影响风电备件的要素进行筛选,再利用BP神经网络算法,得到最为精确的预测结果。比较自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明:PCA能显著降低神经网络预测误差,预测的精度为93.94%,高于BP神经网络预测的88.39%和ARIMA模型的85.31%,所以PCA-BP神经网络模型的预测精度准确且有可靠结果,能够适用于风机备件的需求预测。

基于质量问题统计数据的某型车辆备件配置研究

基于交付用户使用后的某型特种车辆质量问题统计数据,对发生的质量问题进行了分类和分析,梳理出车辆易损件备件清单。最后对该产品备件配置存在的问题进行了探讨并提出了建议,以期对其他产品备件配置提供一定的借鉴。

基于飞机状态的备件动态规划技术

伴随民航业的竞争日趋激烈,航空公司迫切需要控制成本以提升效益。民机备件既是直接影响飞行安全的关键因素,备件成本又在航空公司可控成本中占比最高。因此,在保证飞行安全的前提下做好备件规划,对航空公司有至关重要的意义。根据民机备件的安全级别、故障修复时限等对备件分级设置保障率水平,采用符合备件故障特性的马尔可夫生灭过程,建立满足给定保障率水平的备件量计算模型,并在此基础上建立以保障成本最低为目标函数,满足保障率水平为约束函数的备件量动态规划模型。基于飞机状态分析历史故障数据、季节性、日利用率、机队规模对备件保障的影响,在所建模型中将故障率季节性差异、日利用率和飞机停场损失淡旺季差异进行评估以减小计算结果与实际需求的偏差。以H航空公司ERJ-190机型显示组件为例,对所建模型进行应用验证,计算结果与H航空公司运营实际相吻合,证明所建模型可为航空公司备件规划提供技术方法支持。

数据与知识双驱动的备件需求模糊预测模型

针对知识驱动型需求预测模型所需的专家知识稀缺、数据驱动型需求预测模型可解释性不足的问题,提出了数据与知识双驱动的备件需求模糊预测模型。该模型基于模糊聚类算法将数值型数据聚类为结构简单、可解释性强的规则库,运用模糊逻辑将领域专家知识表示为Mamdani型规则库。在此基础上,引入了一种新型智能计算理论——模糊网络理论对两类规则库进行合并运算,形成初始预测模型。采用遗传算法优化模型规则库的模糊集参数来提高模型预测准确性。通过与模糊聚类算法进行对比,提出的模型在可解释性以及准确性指标上均具有优势。

肾血管平滑肌脂肪瘤破裂出血的手术时机

目的:探讨不同的手术时机对肾血管平滑肌脂肪瘤(renal angiomyolipoma,RAML)破裂出血患者手术治疗效果的影响。方法:选择北京大学第三医院泌尿外科2013年6月至2023年2月收治的31例RAML破裂出血患者的病例资料进行回顾性分析,记录患者人口学和围手术期资料,将出血后小于7 d手术定义为近期手术组,出血后7 d至6个月手术定义为中期手术组,出血后超过6个月手术定义为远期手术组,比较组间的围手术期相关指标。结果:收集到行RAML破裂出血手术治疗的患者共31例,其中男性13例,女性18例,平均年龄(46.2±11.3)岁。近期手术组7例,中期手术组12例,远期手术组12例。肿瘤直径方面,远期手术组患者显著低于近期手术组[(6.6±2.4)cm vs.(10.0±3.0)cm,P=0.039];手术时间方面,远期手术组显著低于中期手术组[(157.5±56.8)min vs.(254.8±80.1)min,P=0.006],其余组间比较差异无统计学意义;出血量方面,远期手术组低于中期手术组[35(10,100)mL vs.650(300,1200)mL,P<0.001],其余组间比较差异无统计学意义;术中输血量方面,远期手术组显著低于中期手术组[0(0,0)mL vs.200(0,700)mL,P=0.014],其余组间比较差异无统计学意义;术后住院天数方面,远期手术组显著低于中期手术组[5(4,7)d vs.7(6,10)d,P=0.011],其余组间比较差异无统计学意义。结论:对于RAML破裂出血的患者,6个月以上再行手术是一个相对安全的时间,手术时间相对最短,术中出血量相对最少,因此更推荐待保守治疗血肿机化后再进行手术治疗。

备件需求预测中的不确定性问题研究综述

备件需求不确定性表现为随机性、多样性、时变性、信息不充分性,预测过程中很难精确地描述备件消耗与影响因素之间错综复杂的关系。以智能计算理论为代表的处理不确定性的各种方法和工具迅速发展。梳理处理备件需求预测不确定性的相关文献,按照不确定性的随机性、模糊性、不完全性、复合不确定性四大类属性,对每个大类分别进行了综述,并总结了相关研究的局限性与发展方向。研究成果可为装备备件管理提供参考。

退化系统的质量控制、状态维修与备件订购联合策略优化

针对关键生产系统退化过程中出现3种状态的问题,提出基于延迟时间理论的质量控制、状态维修与备件订购联合策略.首先,考虑系统退化状态与产品质量之间的函数关系,设计了在系统退化初期检查系统状态,而在退化后期检查产品质量的两阶段检测策略.其次,根据状态检测信息、产品质量信息及故障信息选择应采取的维修活动,结合系统更新时备件所处的状态确定检测周期内各种可能的事件,构建有限时域内的平均费用率模型.然后,基于离散事件仿真和响应曲面法设计优化算法以快速近似求解模型.最后,通过算例分析部分将所提联合策略与对比模型策略进行比较,验证了所提联合策略的有效性与可行性.