Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty

Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.

Image Rain Removal Using Conditional Generative Networks Incorporating

The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.

Feedback Stabilization over Wireless Network Using Adaptive Coded Modulation

In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteristics of varying rate, interference,and routing in wireless transmission channels,the concepts of equivalent delay (ED) and networked condition index (NCI) are introduced.Also,the analytic lower and upper bounds of EDs are obtained.Furthermore,we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission.Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI). Numerical results show the validity and advantage of our proposed control strategies.

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

Optimal paths planning in dynamic transportation networks with random link travel times

A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.

Intelligent Controller for UPQC Using Combined Neural Network

The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.

基于神经网络与NSGA-Ⅱ算法的方舱温控优化设计

综合考虑车载方舱外部传热与舱内设备的功率负载等因素对舱内热环境的影响,以车载空调的布局与送风角等作为设计变量,以舱内电子设备耐受温度与乘员舒适度为目标函数,采用BP神经网络建立设计变量与目标的映射关系,利用改进型非支配排序遗传算法对舱内的气流布局与环境温度梯度进行优化,取得Pareto前沿解。优化结果表明,在制冷功率一定的前提下,最优解集主要集中在空调较接近电子设备且前出风角较平直时。此方法节省仿真优化成本,对方舱内温度与流场环境控制具有重要工程指导意义。

基于ISSA-LSTM的热舒适短期预测模型

为解决在测试日内的短期预测过程中,农村城镇人体热舒适中建筑惰性及人员等随机因素使人体感受变化的样本对预测结果影响大而导致预测精准度低的问题,提出基于改进麻雀搜索算法(ISSA)优化长短期记忆神经网络(LSTM)的方法建立新型户用空调热舒适短期预测模型;首先,对测试日气象数据进行动态性分析,对数据进行有效性验证并构建多种热舒适预测模型;随后选用新型用户热舒适短期预测模型(ISSA-LSTM)对热舒适进行预测。结果表明,模型的最高预测均方误差(MSE)比麻雀搜索算法(SSA)和蜣螂优化算法(DBO)优化LSTM分别提高了0.02296和0.10827,采用ISSA-LSTM方法后改善了短期热舒适预测的精度问题,并提高了分体式空调通过热舒适来控制温度的性能。

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

Enhancing Pneumonia Detection in Pediatric Chest X-Rays Using CGAN-Augmented Datasets and Lightweight Deep Transfer Learning Models

Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.

纺纱空调系统运行仿真及机器学习辅助调控

针对纺纱车间采用传统的PID控制车间温湿度不稳定问题,采用机器学习的BP神经网络对后台历史数据进行监督学习,并建立空调设备自控参数的专家库。当PID调控产生温湿度较大波动时采用信息库建立好的空调控制参数直接介入辅助控制。基于热量平衡、湿量平衡以及风量平衡开发了纺纱空调仿真系统,并分析了风机、水泵、二回风窗、新风窗对于车间温湿度控制的影响。为验证辅助调控的效果,将BP神经网络预测的空调控制参数介入车间温湿度控制,并与传统PID控制车间的温湿度进行了对比。结果表明:采用BP神经网络辅助调控空调系统的参数,车间温湿度稳定性优于传统PID控制。认为:机器学习辅助调控纺纱空调系统可达到稳定控制温湿度的目的。

面向脉冲负载的基于超级电容储能UPQC设计及控制策略研究

针对带有脉冲负载电网的电压畸变和电流畸变问题,设计了带有超级电容储能的三电平统一电能质量调节器,提出了基于人工神经网络的方法对串联和并联补偿单元进行控制,基于双闭环PI控制的方法对超级电容储能单元进行控制。其中,串联补偿单元进行电压补偿,以维持负载电压的稳定,保证负载的用电需求;并联补偿单元进行电流补偿,以维持电源电流的稳定,避免电网受到持续大幅冲击;超级电容储能单元对直流侧进行充放电,以维持直流侧电压的恒定,保证串、并联侧的正常工作。所提方法省去了繁琐的坐标变换过程,避免了多个滤波器造成相位滞后的问题。仿真实验结果表明,所提拓扑和控制策略有助于改善带有脉冲负载电网的电能质量。

基于机会约束规划的含智能楼宇主动配电网分布式能量管理策略

为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其次,综合考虑楼宇侧与网络侧的运行约束,建立基于Dist Flow的集成IBs的ADN数学模型;然后,考虑到光伏(photovoltaic,PV)出力与外界温度的不确定性,利用机会约束规划将集成IBs的ADN优化问题转化为混合整数二阶锥规划(mixed integer second-order cone programming,MISOCP)问题;最后,为了保护配电网运营商与用户的隐私性,利用交替方向乘子法(alternating direction method of multipliers,ADMM)实现了集成IBs的ADN的分布式能量管理。基于ADMM的解耦机制,原MISOCP问题可以被分解为楼宇侧的混合整数线性规划(mixed-integer linear programming,MILP)子问题以及网络侧的二阶锥规划(second-order cone programming,SOCP)子问题进行求解。结果表明,在保障各主体信息隐私性的前提下,所提策略利用IBs灵活性实现了集成IBs的ADN全局最优能量管理。

基于神经网络的变频空调控制系统

基于神经网络的变频控制空调可以根据实际环境与室内需求的不同,连续地、动态地、适时地按需要输出,改进了一般定速空调器在实际应用中室内机的输出滞后于压缩机,而室内空气参数的滞后则更大的不足。采用了神经网络的误差反传(BP)算法,可以快速、准确的对从实际环境中获得的数据进行综合、分析,得出正确的结论,从而通过控制单元调节压缩机、风机和电子膨胀阀,使其根据现状迅速地做出反应,达到智能控制的效果,具有先进性、经济性、和实用性。

基于无线模式的中央空调节能监控网络应用研究

基于信息节能的理念,构建了一个基于无线模式的中央空调节能监控网络,实现了对大型公共建筑或建筑群中央空调系统的节能监测与控制。所研发的现场节能监控装置以PLC为核心,配以人机界面、GPRS模块等。过程控制系统采用以负荷预报为基准的节能监控策略。现场监控装置与系统监控中心采用GPRS无线模式进行组网与通信,通过GPRS无线网络,将设备运行数据上传给监控中心,监控管理中心可以对各节能监控节点进行远程监控管理。

配网统一电能质量控制器直流电容的容量计算与分析

子模块的直流电容是影响MMC-UPQC装置的成本和体积的重要因素之一,直流电容容量选择过大会影响MMC-UPQC在配网应用中的实用性。为减小直流电容容量,提出在MMC-UPQC装置补偿配网馈线电压暂降时MMC-UPQC的并联变换器吸收适量有功功率的控制策略。分析了采用不同电压暂降补偿策略的MMC-UPQC子模块直流电容容量选择方法,指出所提电压暂降补偿策略为较优策略,并通过仿真研究验证了该电压暂降补偿策略的正确性。MMC-UPQC装置采用该电压暂降补偿策略可减小直流电容容量,从而减少MMC-UPQC装置成本和体积,提高MMC-UPQC配网应用的实用性。

医院网络机房精密空调的运行与维护

精密空调是保证机房恒温、恒湿和除尘的重要设备,对于交换机、服务器等设备的正常运行起着十分关键的作用。通过介绍精密空调运行的一般原理及主要设置参数,总结了该设备常见的告警及处理办法,提出了精密空调的常规巡检及特殊维护方案。

基于ZigBee技术的空调控制系统

为了达到节约能源的目的,利用ZigBee技术实现了空调控制系统的分散控制和集中管理。首先,在介绍ZigBee网络技术及其拓扑结构的基础上,给出了本空调控制系统的整体框架结构;接着,针对方案中相关ZigBee节点的软硬件实现做了详细介绍。研究结果表明,采用该控制系统可以合理地设定空调的运行参数,在满足人体对环境舒适度要求的同时,降低了空调系统运行时间,节省了运行费用,应用效果良好。