Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

双亲纳米SiO2颗粒的制备及提高渗吸采收率性能

以正辛基三乙氧基硅烷和3-巯基丙基三乙氧基硅烷为改性剂，以双氧水为氧化剂，在水基环境下对亲水纳米SiO2颗粒表面进行改性，得到具有磺酸基和辛基的双亲纳米SiO2颗粒，并通过红外和热重对其化学结构和热稳定性进行分析。将双亲纳米SiO2颗粒分散在地层水中制备纳米流体，并评价纳米流体的稳定性、界面性质和渗吸效率。利用核磁共振技术探究纳米流体渗吸过程中岩心孔隙内原油运移规律。结果表明，纳米流体储存30 d未出现分层现象，表现出良好的稳定性；经纳米流体处理的岩心亲水性增强。此外，双亲纳米SiO2颗粒将油水界面张力降低至1.7 mN/m；纳米流体渗吸采收率高达22.6%，渗吸初始阶段小孔隙中的原油被动用，而在渗吸后期阶段大孔隙中的原油才被动用。     

三门核电土石方工程中深孔爆破法的应用

浙江三门核电平基工程中采用深孔台阶爆破进行土石方开挖,对底板的平整度和完整性有很高的要求,超挖不得超过20cm,不容许欠挖。为此,根据可爆性对开挖区内的岩石进行分级,分别采用不同的钻爆参数,同时采取了预留保护层爆破、无超深爆破、底部空气间隔装药等技术措施,确保了底板的开挖质量。     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Effect of the particle size ratio on macro- and mesoscopic shear characteristics of the geogrid-reinforced rubber and sand mixture interface

As a new type of material for civil engineering projects, the rubber and sand mixture is widely used in roadbed fillers, offering environmental benefits over traditional tyre disposal methods. This study uses a large-scale direct shear apparatus to examine the interface shear properties of the geogrid-reinforced rubber and sand mixture, considering different particle size ratios (r), rubber contents, and normal stresses. Based on indoor tests, direct shear models of the mixture with different values of r are established in PFC^3D, revealing the meso-mechanical mechanism of the mixture in the direct shear process. The results show that when r is greater than 1, incorporating a certain amount of rubber particles can increase the shear strength of the mixture. The r values of 15.78, 7.63, and 3.98 correspond to an optimal rubber content of 30%, 10%, and 20%, respectively. When r is less than 1, mixing rubber particles can only reduce the shear strength of the mixture. When the rubber content is low, the smaller the value of r, the greater is the thickness of the shear band. Furthermore, the normal and tangential contact forces are greater. The fabric anisotropy evolution law of the mixture is consistent with the change in the contact force distribution.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

异种铁素体耐热钢接头界面组织及其影响

综述了铁素体与铁素体异种金属焊缝（dissimilar metal welds，DMWs）接头界面组织及其影响。结果表明，在焊后热处理或运行温度下的铁素体钢DMWs接头的不均匀界面组织中，通常会形成脱碳层和增碳层。在铁素体钢DMWs焊接接头界面组织影响因素中，焊缝金属的化学成分有重要影响；焊后热处理规范（温度和时间）、工作温度下运行时间的影响较为突出；焊接工艺参数的影响亦不可忽略。异种钢接头界面处近缝区裂纹的产生，以及接头的蠕变强度随Larson Miller 参数增大而下降等不利影响，均为异种钢界面碳迁移行为所导致。焊缝成分控制法是接头界面组织控制或改善的必要条件，而脱碳层部位转移法能有效防止裂纹发生，亦是接头安全运行的重要工艺措施之一。