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.     

Memory-based dynamic event-triggered control for networked interval type-2 fuzzy systems subject to DoS attacks

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.     

Multi-dimensional modeling of mixture preparation in a direct injection engine fueled with gaseous hydrogen

With the recent advances of direct injection (DI) technology, introducing hydrogen into the combustion chamber through DI is being considered as a viable approach to circumvent backfire and pre-ignition encountered in early generations of hydrogen engines. As part of a broader vision to develop a robust numerical model to study hydrogen spark ignition (SI) combustion in internal combustion (IC) engines, the present numerical investigation focuses on mixture preparation in a hydrogen DI SI engine. This study is carried out with a single hole injector with gaseous hydrogen injected at 100 bar injection pressure. Simulations are carried out for high and low tumble configurations and validated against optical data acquired from planar laser induced fluorescence (PLIF) measurements. Varying mesh configurations are investigated for the impact on in-cylinder mixture distribution. A particular emphasis is placed on the effect of nozzle geometry and mesh orientation near the wall. Overall, the computational model is found to predict the mixture distribution in the combustion cylinder reasonably well. The results showed that the alignment of mesh with the flow direction is important to achieve good agreement between numerical analysis and optical measurement data.     

注射模生产中智能化控制及应用

分析了注射模生产的现状，针对其生产中智能化调控应用方面的不足，提出模内参数的自适应调节方案，还介绍了自适应工作的原理、可调参数种类、逻辑推理等，并实际验证了基于注塑设备联网集成工艺数据下注射模成型工艺自适应调节的可行性。     

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.     

Experimental study and numerical modeling of mixing by air injection in yield stress fluids using the OpenFOAM software

Mixing by gas injection is an operation used in industrial processes such as wastewater treatment, metallurgy, or methanization in which pressurized gas is injected into a fluid in order to reduce concentrations and temperatures gradients. This study demonstrates how the CFD toolbox OpenFOAM can be used to simulate such flows. Experimental measurements and observations have been performed on a pilot-scale reactor where pressurized air is injected in a yield stress fluid. The volume of fluid method and an adaptive mesh with refinement at the interface have been used to track the gas inclusions. The numerical model accuracy has been assessed by comparing experimental and numerical results related to the bubble's frequency, dimensions, and rising velocities as well as the fluid recirculation, yielded, and unyielded regions in the tank. The influence of injection parameters such as the injection flow rate and the fluid rheological parameters has been quantified.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

汽车加油口双色注射模设计

分析了汽车加油口塑件结构,确定了注射成型方案、进胶方式,并介绍了通过滑块与开模动作配合实现塑件脱模的过程。     

Main trends on the simulation of the drying of refractory castables - Review

Refractory castables develop microstructures after curing that behave as partially saturated porous media. Upon heating (during its drying stage), the steam generated by the physical and chemically bond water can result in pore pressurization and explosive spalling. Numerical modeling can provide guidelines for designing safer heat-up profiles and also a better understanding of the mechanisms that lead to catastrophic damage. This work aims to review the fundamentals and models available, providing insightful thoughts on the current trends of the drying phenomena of ceramic compositions. The review also highlights that there are models better oriented to result in reasonable predictions of pore pressure values and others focused on a more accurate representation of the main physical phenomena that take place during heating. According to the findings, there are still various challenges to attain accurate models with high applicability capable of yielding safer and more efficient drying of refractory castables.     

Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.