A Systematic Review of Localization in WSN: Machine Learning and Optimization-Based approaches

In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year-wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy-efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN.     

Elastic-plastic analysis of a rolling disk by finite elements

An elastic-plastic finite element method is employed to determine the stress distribution in a circular disk rolling on a rigid track and subjected to hub loads. This model can be assumed to represent a railroad wheel. Of particular interest are the stresses that exceed the initial yield limit near the point of contact. The material constitutive laws considered herein represent elastic-perfectly plastic and strain hardening materials that obey Mises' yield condition. It is shown that unloading of the previously yielded elements due to rotation can be easily handled by the direct stiffness tangent modulus approach. Total and plastic strains, and the plastic work in the disk model are given for the entire rolling history of the disk. It is also demonstrated that the disk will shake down to a purely elastic steady state for load values beyond the initial yield if the material of the disk exhibits even a small amount of strain hardening.     

Influence of Initial Residual Stress on Material Properties of Bearing Steel During Rolling Contact Fatigue

Rolling contact fatigue (RCF) experiments were performed on AISI M50 bearing balls using a single ball test rig to investigate the evolution of the material properties within the RCF-affected subsurface region. Using a combination of micro-indentation and miniature compression testing methods, the influence of contact stress, initial residual stresses, and the number of contact cycles on the resulting evolution of material properties was investigated. It was found that the balls with initial residual compressive stresses show less change in material properties after RCF loading than the balls without such initial residual stresses. The formation of a light etching region (LER) is shown to not correlate with a decrease in material strength and hardness, but it does serve as a predictor for failure due to spall.     

Scheduling of FMSs with information delays: A simulation study

Practitioners and academicians throughout the world recognize the crucial role played by flexibility within manufacturing organizations, especially those engaged in small batch manufacture. However, although the concept of flexibility has begun to attract increased attention, its interaction with information integration and automation has not captured due attention. For example, it almost always has been assumed that a real-time control mechanism is available for exploiting routing flexibility on the shop floor. While this may be true for FMSs, it generally is not so for the vast majority of conventional manufacturing systems with varying levels of information integration and automation. The lack of a fully integrated and automated control mechanism within such semi-automated flexible manufacturing systems (SAFMSs) would eventually cause delays in the availability of shop status information. In this paper, we study the impact that defined modes of information delay have on the performance of a hypothetical SAFMS through detailed simulation experiments. Given that the level of routing flexibility is a controllable design parameter, our interest is in determining the impact that information delays have on decisions pertaining to the selection of appropriate levels of routing flexibility. To highlight the impact of information delays within the SAFMS, the Taguchi experimental design procedure is adopted as a performance evaluation and analysis vehicle, using makespan as a measure of performance. Simulation results indicate the presence of a system specific tolerance limit, operation below which minimizes performance loss.     

Study of processing variables on the electrical resistivity of conductive adhesives

In this paper, the authors explored the effects of processing variables, including carbon nanotube (CNT) concentration, assembly pressure, and processing temperature, on electrical conductivity of CNT-included electrically conductive adhesives (ECAs). The main effects of these variables were analyzed under specific range for each variable. Response surface methodology was used to investigate the cross-effects of these variables on ECA conductivity. By fitting the experimental data to the response function, minimum bulk resistivity of 1.5×10^?4 Ω cm was obtained at the optimum settings of processing variables (CNT concentration 2%, processing temperature 199 °C, pressure 6000 psi).     

磁流变阻尼器驱动的智能车辆悬挂控制及实验

针对由新型的磁流变阻尼器驱动的智能车辆悬挂设计要求,提出了一种基于天棚阻尼律实现不对称阻尼特性的半主动控制方案,能有效地抑制驱动电流的“开-关”冲击特性和磁流变阻尼器滞环特性产生的不良影响.将该控制器与四分之一车辆模型相结合,在变幅度谐波、平滑脉冲和随机信号激励下,对该半主动控制器的性能进行了仿真分析,并通过设计的硬件在环测试系统进行了系统的实验研究.结果验证了所提出的智能车辆悬挂设计能理想地实现车辆驾乘舒适性、与路面可靠接触、悬挂空间等多目标悬挂性能,以及对簧载质量变化的鲁棒特性.