Robustness Requirements Research Articles

Corrosion fatigue mechanisms and evaluation methods of high‐strength steel wires: A state‐of‐the‐art review

AbstractCorrosion significantly degrades the fatigue performance of high‐strength steel wires. This study conducts an extensive review of corrosion fatigue in corroded steel wires based on relevant literature. It outlines corrosion fatigue evolution mechanisms and discusses various test methodologies. Six distinct approaches are employed to assess corrosion fatigue life. A three‐stage multiscale corrosion fatigue evolution process involves pitting formation and growth, short crack propagation, and long crack propagation. Advanced fatigue assessment methods like the theory of critical distances (TCD) and the strain energy density (SED) are explored for predicting the corrosion fatigue life for corroded steel wires. A model utilizes an elastoplastic corrosion fatigue damage framework to comprehensively represent the complex interactions. The physics‐data‐driven method has garnered increasing attention due to its requirement of fewer sample data and good robustness. Moreover, the reliability method underscores its pivotal role in guaranteeing the longevity of suspenders and cables in practical settings.

Co-design of adaptive event-triggered mechanism and controller for networked control systems with uncertainty and time-varying delay

ABSTRACT This paper investigates the co-design problem of event-triggered mechanisms and state feedback controllers for networked control systems with parameter uncertainty and network-induced delay. Firstly, to overcome the network bandwidth limitation, an adaptive event-triggered mechanism is used to filter the required sampling signals to reduce the network resource occupancy. Second, establish a unified model subject to event triggering mechanisms, parameter uncertainty, and network delay constraints, and derive the stability conditions of the system for the model by combining Lyapunov stability theory and the linear matrix inequality method. Then, the state feedback controller is designed to satisfy the event-triggering condition and certain robust performance requirements, and an optimisation method is proposed to compromise network resource consumption and control performance. Finally, the effectiveness of the proposed method is verified by a numerical example.

Ultrastrong and Reusable Solar‒Thermal‒Electric Generators by Economical Starch Vitrimers.

In frigid regions, it is imperative to possess functionality materials that are ultrastrong, reusable, and economical, providing self-generated heat and electricity. One promising solution is a solar‒thermal‒electric (STE) generator, composed of solar‒thermal conversion phase change composites (PCCs) and temperature-difference power-generation-sheets. However, the existing PCCs face challenges with conflicting requirements for solar‒thermal conversion efficiency and mechanical robustness, mainly due to monotonous functionalized aerogel framework. Herein, a novel starch vitrimer aerogel is proposed that incorporates orientational distributed carboxylated carbon nanotubes (CCNT) to create PCC. This innovative design integrates large through-holes, mechanical robustness, and superior solar‒thermal conversion. Remarkably, PCC with only 0.8 wt.% CCNT loading achieves 85.8MPa compressive strength, 102.4°C at 200mW cm-2 irradiation with an impressive 92.9% solar-thermal conversion efficiency. Noteworthy, the STE generator assembled with PCC harvests 99.1W m-2 output power density, surpassing other reported STE generators. Strikingly, even under harsh conditions of -10°C and 10mW cm‒2 irradiation, the STE generator maintains 20°C for PCC with 325mV output voltage and 45mA current, showcasing enhanced electricity generation in colder environments. This study introduces a groundbreaking STE generator, paving the way for self-sufficient heat and electricity supply in cold regions.

Design and analysis of an adaptive radiation resilient RRAM subsystem for processing systems in satellites

Among the numerous benefits that novel RRAM devices offer over conventional memory technologies is an inherent resilience to the effects of radiation. Hence, they appear suitable for use as a memory subsystem in a computer architecture for satellites. In addition to memory devices resistant to radiation, the concept of applying protective measures dynamically promises a system with low susceptibility to errors during radiation events, while also ensuring efficient performance in the absence of radiation events. This paper presents the first RRAM-based memory subsystem for satellites with a dynamic response to radiation events. We integrate this subsystem into a computing platform that employs the same dynamic principles for its processing system and implements modules for timely detection and even prediction of radiation events. To determine which protection mechanism is optimal, we examine various approaches and simulate the probability of errors in memory. Additionally, we are studying the impact on the overall system by investigating different software algorithms and their radiation robustness requirements using a fault injection simulation. Finally, we propose a potential implementation of the dynamic RRAM-based memory subsystem that includes different levels of protection and can be used for real applications in satellites.

Polarization self-compensation in a laser-driven interferometric fiber optic gyroscope with high long-term stability

We present a laser-driven interferometric fiber optic gyroscope (IFOG) with polarization self-compensation to achieve high scale-factor stability, sensitivity, and long-term stability. Coherent light with 200kHz linewidth is employed to keep the scale factor stable. The optical scheme ensures polarization reciprocity as well as the optimal working point for good sensitivity. Furthermore, a hybrid machine learning loop (MLL) method, combining the advantages of PID fast response and artificial neural network (ANN) dynamic search, can control a liquid crystal rotator (LCR) to dynamically compensate for slow drift induced by polarization coupling. In open environment, when the sensitivity is 0.005∘/h, the bias instability (BI) is significantly optimized from 0.6723°/h at 60s (PID) to 0.3869°/h at 200s (MLL), which is close to the Sagnac interferometric limit (SIL). Such IFOG can meet the real-time and robust requirements for inertial navigation systems in long-term measurement.

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

Learning stability of partially observed switched linear systems

This paper deals with learning stability of partially observed switched linear systems under arbitrary switching. Such systems are widely used to describe cyber–physical systems which arise by combining physical systems with digital components. In many real-world applications, the internal states cannot be observed directly. It is thus more realistic to conduct system analysis using the outputs of the system. Stability is one of the most frequent requirement for safety and robustness of cyber–physical systems. Existing methods for analyzing stability of switched linear systems often require the knowledge of the parameters and/or all the states of the underlying system. In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data. The proposed algorithm essentially relies on an output-based Lyapunov stability framework and returns an estimate of the joint spectral radius (JSR). We also prove a probably approximately correct error bound on the quality of the estimate of the JSR from the perspective of statistical learning theory.

Unsupervised Layer-Wise Score Aggregation for Textual OOD Detection

Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number of AI-based systems. Existing OOD textual detectors often rely on anomaly scores (\textit{e.g.}, Mahalanobis distance) computed on the embedding output of the last layer of the encoder. In this work, we observe that OOD detection performance varies greatly depending on the task and layer output. More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results can be achieved, provided that an oracle selects the best layer. We propose a data-driven, unsupervised method to leverage this observation to combine layer-wise anomaly scores. In addition, we extend classical textual OOD benchmarks by including classification tasks with a more significant number of classes (up to 150), which reflects more realistic settings. On this augmented benchmark, we show that the proposed post-aggregation methods achieve robust and consistent results comparable to using the best layer according to an oracle while removing manual feature selection altogether.

Usability test and implications of an activity allocation mapping in complex organizations: perceptions about a software by emergency room agents

PurposeProperly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.Design/methodology/approachTest sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.FindingsParticipants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.Practical implicationsMAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.Social implicationsA more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.Originality/valueThe ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

A 0.59 nW/kHz Clock Circuit with High-Precision Clock Calibration for Passive Internet of Things Chips

The high precision and low power consumption of the clock generator are critical in passive RFID transponders and passive IoT chips, but fluctuations in PVT can cause considerable degradation in the precision of the chip’s internal clocks. This paper proposes a high-precision clock circuit with a single-shot calibration method to addresses this issue in a low-power clock solution. Based on the reference timespan in the preamble of the down-link RF envelope, a TDIF (Time-digital to current-frequency) calibration method was implemented with both a streamlined procedure and customized circuits. By computing the difference between the time counts and applying it to an ultra-low-power, current-starved oscillator, the current change ratio can be linearly controlled. Compared to the traditional integer frequency division scheme used by passive tags for a 160 k bits up-link data rate, the required frequency for the clock generator was reduced from 960 kHz to 320 kHz, the calibration error was reduced from ±10% to ±3% for ±25% frequency deviation, the calibration time was 133.3 μs for a single shot in this work, and the power consumption was 158 nW after the calibration was completed. This leads to an excellent power efficiency of 0.59 nW/kHz and meets the requirements of low power, low cost, and PVT robustness in the RF-powered passive IoT chips. By appropriately increasing the number of calibration digits and the duration, this calibration approach could also be used for other ultra-low-power passive IoT chips that require higher-precision clocking without the use of off-chip crystals.

An Investigation of Representative Customer Load Collectives in the Development of Electric Vehicle Drivetrain Durability

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.

Robot Indoor Navigation: Comparative Analysis of LiDAR 2D and Visual SLAM

<span lang="EN-US">Robot indoor navigation has become a significant area of research and development for applications such as autonomous robots, smart homes, and industrial automation. This article presents an in-depth comparative analysis of LiDAR 2D and visual sensor simultaneous localization and mapping (SLAM) approaches for robot indoor navigation. The increasing demand for autonomous robots in indoor environments has led to the development of various SLAM techniques for mapping and localization. LiDAR 2D and visual sensor-based SLAM methods are widely used due to their low cost and ease of implementation. The article provides an overview of LiDAR 2D and visual sensor-based SLAM techniques, including their working principles, advantages, and limitations. A comprehensive comparative analysis is conducted, assesing their capabilities in terms of robustness, accuracy, and computational requirements. The article also discusses the impact of environmental factors, such as lighting conditions and obstacles, on the performance of both approaches. The analysis’s findings highlight each approach’s strengths and weaknesses, providing valuable insights for researchers and practitioners in selecting the appropriate SLAM method for robot indoor navigation based on specific requirements and constraints</span>

Tensor-Based Confident Information Coverage Reliability of Hybrid Internet of Things

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this paper, we define Confident Information Coverage Reliability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CICR</i> ) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CICR</i> . The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.

A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.

Active disturbance rejection control of flexible industrial manipulator: A MIMO benchmark problem

Robotic manipulators are widely used in modern industry. In order for robot manipulators to achieve trajectory tracking control, the end-effectors must move precisely along the given trajectories. The nonlinearity, strong coupling, and uncertainty of the system make trajectory tracking very complicated and difficult. An approach for robust feedback control is presented in this paper as a solution to this problem. The design relies on active disturbance rejection control to estimate unknown disturbances affecting the plant. The controlled system is an uncertain two-link manipulator with elastic gear transmissions, described by nonlinear friction and elasticity. It is based on an industrial benchmark problem. The model has two significant uncertainties: parametric uncertainty as well as external disturbances that affect tools and motors. A simulation-based evaluation of the proposed control method and a comparison of its performance, considering specific robustness requirements and trajectory tracking, are presented. The comparison includes the baseline controller, the QFD (Quality Function Deployment) method, and PD-CNN (PD Control Compensation based on the Cascade Neural Network). Computer simulations of the proposed controller are carried out to validate the ABB (Asea Brown Boveri) industrial benchmark. The simulation results demonstrate superiority in meeting stability and target value requirements. The stability and ultimate boundedness of the tracking error of ADRC (Active Disturbance Rejection Control) are demonstrated.

Constructing universal phenomenology for biological cellular systems: an idiosyncratic review on evolutionary dimensional reduction

The possibility of establishing a macroscopic phenomenological theory for biological systems, akin to the well-established framework of thermodynamics, is briefly reviewed. We introduce the concept of an evolutionary fluctuation–response relationship, which highlights the tight correlation between the variance in phenotypic traits caused by genetic mutations and by internal noise. We provide a distribution theory that allows us to derive these relationships, which suggests that the changes in traits resulting from adaptation and evolution are considerably constrained within a lower-dimensional space. We explore the reasons behind this dimensional reduction, focusing on the constraints posed by the requirements for steady growth and robustness achieved through the evolutionary process. We draw support from recent laboratory and numerical experiments to substantiate our claims. The universality of evolutionary dimensional reduction is presented and potential theoretical formulations for it are discussed. We conclude by briefly considering the prospects of establishing a macroscopic framework that characterizes biological robustness and irreversibility in cell differentiation, as well as an ideal cell model.

Analisis Adopsi Teknologi Tanpa Uang Tunai Di Unit X PT Y

This research delves into the adoption of non-cash payment technology at X Unit of PT Y. Its benefits encompass streamlined income reporting, transaction security, and efficiency. However, challenges include additional infrastructure costs, robust internet connectivity requirements, occurrences of double payments, and reconciliation issues in financial reports. Proposed solutions include installing additional ATMs, collaborating with banks, and hiring Information and Technology (IT) personnel. Applying the Technology, Organization, and Environment (TOE) theory, the research analyzes factors influencing technology adoption in the tourism industry. In conclusion, introducing non-cash payment systems to tourists is essential to enhance service effectiveness and financial management at Prambanan. This study significantly contributes to understanding obstacles and solutions in adopting non-cash payment technology in the tourism industry, offering a strategic framework for sustainable development in the future.

Refined sliding mode disturbance observer‐based finite‐time composite control for Euler–Lagrange systems with multiple disturbances

AbstractA wide range of practical control systems such as robot manipulators and spacecrafts can be modeled by Euler–Lagrange systems. On one hand, the requirements for system control accuracy, response speed, and robustness are ever increasing. On the other hand, the control performances are inevitably degraded by multiple disturbances. In this paper, a novel finite‐time composite control law is proposed for a class of Euler–Lagrange systems subject to multiple heterogeneous disturbances including a neutrally stable disturbance and a norm‐bounded disturbance. More specifically, a refined sliding mode disturbance observer (RSMDO) is proposed to estimate and reject the neutrally stable disturbance in the feedforward channel, while an adaptive nonsingular backstepping control (ANBC) is designed in the feedback channel to stabilize the system and attenuate the observer error as well as the norm‐bounded disturbance. The proposed RSMDO can not only fully utilize the disturbance information, but also quantify the convergence time and steady‐state accuracy of the estimation error. The finite‐time stability analysis is carried out for the closed‐loop system. Finally, an example of spacecraft attitude control system is given to demonstrate the effectiveness of the proposed methods.

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

During the Keyhole Tungsten Inert Gas (K-TIG) welding process, a significant amount of information related to the weld quality can be obtained from the weld pool and the keyhole of the topside molten pool image, which provides a vital basis for the control of welding quality. However, the topside molten pool image has the unstable characteristic of strong arc light, which leads to difficulty in contour extraction. The existing image segmentation algorithms cannot satisfy the requirements for accuracy, timing, and robustness. Aiming at these problems, a real-time recognition method, based on improved DeepLabV3+, for identifying the molten pool more accurately and effectively was proposed in this paper. First, MobileNetV2 was selected as the feature extraction network with which to improve detection efficiency. Then, the atrous rates of atrous convolution layers were optimized to reduce the receptive field and balance the sensitivity of the model to molten pools of different scales. Finally, the convolutional block attention module (CBAM) was introduced to improve the segmentation accuracy of the model. The experimental results verified that the proposed model had a fast segmentation speed and higher segmentation accuracy, with an average intersection ratio of 89.89% and an inference speed of 103 frames per second. Furthermore, the trained model was deployed in a real-time system and achieved a real-time performance of up to 28 frames per second, thus meeting the real-time and accuracy requirements of the K-TIG molten pool monitoring system.

A 0.55-mm2 8-bit 32-GS/s TI-SAR ADC with optimized hierarchical sampling architecture

This paper analyzes the bandwidth of the time-interleaved analog-to-digital converter (TI-ADC) with hierarchical sampling and presents an 8-bit 32-GS/s TI-ADC in 28-nm CMOS. The front-end sampler is implemented by a two-stage hierarchical sampling architecture to extend the analog input bandwidth. The 32-way sub-ADCs are realized with 2b/cycle successive approximation register (SAR) architecture and non-binary search strategy, which meet the requirements of high speed, medium resolution, and strong robustness. Multi-phase low-jitter clock tree is designed based on the delay-locked loop (DLL). Calibration methods for timing skew, offset and gain mismatches are introduced to improve accuracy. The proposed TI-SAR ADC achieves an analog input bandwidth of 23.7 GHz. It occupies an active area of 0.55 mm2 and consumes 356 mW at 1.8/1 V supply.