Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.     

Solid Ink Laser Patterning for High-Resolution Information Labels with Supervised Learning Readout

Tagging, tracking, or validation of products are often facilitated by inkjet-printed optical information labels. However, this requires thorough substrate pretreatment, ink optimization, and often lacks in printing precision/resolution. Herein, a printing method based on laser-driven deposition of solid polymer ink that allows for printing on various substrates without pretreatment is demonstrated. Since the deposition process has a precision of <1 µm, it can introduce the concept of sub-positions with overlapping spots. This enables high-resolution fluorescent labels with comparable spot-to-spot distance of down to 15 µm (444,444 spots cm⁻²) and rapid machine learning-supported readout based on low-resolution fluorescence imaging. Furthermore, the defined thickness of the printed polymer ink spots can be used to fabricate multi-channel information labels. Additional information can be stored in different fluorescence channels or in a hidden topography channel of the label that is independent of the fluorescence.     

Direct Freeform Laser Fabrication of 3D Conformable Electronics

3D conformable electronic devices on freeform surfaces show superior performance to the conventional, planar ones. They represent a trend of future electronics and have witnessed exponential growth in various applications. However, their potential is largely limited by a lack of sophisticated fabrication techniques. To tackle this challenge, a new direct freeform laser (DFL) fabrication method enabled by a 5-axis laser processing platform for directly fabricating 3D conformable electronics on targeted arbitrary surfaces is reported. Accordingly, representative laser-induced graphene (LIG), metals, and metal oxides are successfully fabricated as high-performance sensing and electrode materials from different material precursors on various types of substrates for applications in temperature/light/gas sensing, energy storage, and printed circuit board for circuit. Last but not the least, to demonstrate an application in smart homes, LIG-based conformable strain sensors are fabricated and distributed in designated locations of an artificial tree. The distributed sensors have the capability of monitoring the wind speed and direction with the assistance of well-trained machine-learning models. This novel process will pave a new and general route to fabricating 3D conformable electronic devices, thus creating new opportunities in robotics, biomedical sensing, structural health, environmental monitoring, and Internet of Things applications.     

A matching game framework for users clustering and resource allocation with wireless power transfer in a CR-NOMA network

This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.     

Kernel-based adversarial attacks and defenses on support vector classification

While malicious samples are widely found in many application fields of machine learning, suitable countermeasures have been investigated in the field of adversarial machine learning. Due to the importance and popularity of Support Vector Machines (SVMs), we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper. The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier. Specially, we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers. Utilizing this vulnerability function, we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack. Our defense method is verified to be very effective on the benchmark datasets, and the SVM classifier becomes more robust after using our kernel optimization scheme.     

基于深度学习和灰度纹理特征的铁路接触网绝缘子状态检测

铁路接触网绝缘子状态检测对铁路行车安全有着 重大的意义,为解决目前人工对绝缘 子图像检测结果的不确定性,提出一种深度学习结合灰度纹理特征的检测方法。首先使用 Faster R-CNN (faster region-based convolutional neural network)目标检测算法对图像中绝缘子精确识别,再通过灰度共生矩阵对绝缘子纹理 特征进行分析提取,之后结合支持向量机将绝缘子分为正常绝缘子和异常绝缘子,实验数 据结果证明使用能量、熵、相关度3种纹理特征进行绝缘子状态分类时对实验数据中的正 常状态绝缘子的分类精度可达100%,异常状态绝缘子的分类精度达97.5%,最后依据绝缘 子图像灰度分布的周期性特点,利用灰度积分投影将异常绝缘子分为破损绝缘子和夹杂异 物绝缘子。实验结果表明所提方法可以有效对绝缘子状态进行检测分类。     

The investigation of the (p,ρ,T) and (ps,ρs,Ts) properties of {(1−x)CH3OH + xLiBr} for the application in absorption refrigeration machines and heat pumps

The (p,ρ,T) and (p_s,ρ_s,T_s) properties of {(1−x)CH₃OH + xLiBr} over a wide range of state parameters are reported for the first time. The experiments were carried out in a constant volume piezometer over a temperature range from 298.15 K to 398.15 K, at 0.08421, 0.13617, 0.19692, 0.23133 and 0.26891 mole fractions and from atmospheric pressure up to 60 MPa. The experimental uncertainties are ΔT=±3 mK for temperature, Δp=±5·10⁻² MPa for high pressure and Δp=±5·10⁻⁴ MPa for atmospheric pressure, Δρ=±3·10⁻² kg · m⁻³ for density. An equation of state was derived for correlation of the experimental data of the solutions.     

全自动电脑横机控制系统的设计

介绍了一种全自动电脑横机控制系统的新型分层体系设计.在该体系中,花型准备系统是基于Windows的开放式编程系统,通过编译自动生成编织指令数据文件,提供了独有的可扩充布纹库.下位NC硬件控制系统无需操作系统支持,便可完成对整个编织过程的实时监控,故障诊断,修改编织参数及测试执行部件等功能.新型的分层体系实现了上、下位机的功能独立,并保持了良好的人机交互界面和便捷的操作性,真正实现了全自动和智能化设计.     

MPOF切割机温度控制系统的设计

微结构聚合物光纤(MPOF)是一种新型的聚合物光纤,其主要特征是在包层中有形状、大小、排列不同的空气孔.因为材料的特性,聚合物光纤的切割以及端面处理要比石英光纤复杂得多,尤其对于具有微结构的聚合物光纤更是如此.影响光纤切割质量的因素很多,其中切割时切割机的温度是决定光纤切割质量的关键.因此,设计了一种以单片机作为核心部件的切割机温度精确控制系统.该系统工作稳定,性能可靠,能够满足实验需要.     

基于纹理的图像字符自动定位技术对比研究

随着信息技术的迅速发展,基于内容的图像检索技术引起了研究者的广泛关注.自动抽取图像/视频的语义内容是图像/视频检索中研究的重点和难点.本文通过分析多种字符定位技术,给出了基于纹理分析的字符定位算法的一般框架.对各种基于纹理分析的字符定位方法进行了对比研究,尤其深入地研究了纹理特征提取,分类器设计及边界位置确定对定位结果的影响.最后通过预分类的方法对算法进行改进,实验结果表明,该方法在保持正确定位率基本不变的情况下,可以大幅度提高处理速度.