Robustness Requirements Research Articles

High-speed imaging of giant unilamellar vesicle production in cDICE.

Giant unilamellar vesicles (GUVs) are cell-sized containers that are commonly used as three-dimensional model membranes in biophysics, as in vitro model systems in synthetic biology, and even as cargo carriers in various other research fields. Despite their ubiquitous use, there is still no one-size-fits-all GUV production method. Over the years, numerous methods have been developed, attempting to meet the demanding requirements of robustness, reliability, and high yield while simultaneously achieving robust encapsulation. Double emulsion-based methods are often praised for their apparent simplicity and good yields; hence, methods like continuous droplet interface crossing encapsulation (cDICE) that make use of this principle, have gained popularity in recent years. In cDICE, aqueous droplets that originate from a capillary orifice are continuously forced through an oil-water interface by centrifugal force, thereby forming a lipid bilayer and thus GUVs. Although cDICE and related methods are frequently used in the field, the complexity of the underlying principles and fluid dynamics has not been considered previously, and how exactly the GUVs are being formed remains unknown. To elucidate the process of GUV formation in cDICE, we have developed a high-speed microscopy setup that allows us to visualize GUV formation in real time. We focused on the capillary orifice, where initial droplet formation occurs, and on the oil-water interface, where droplets are converted into GUVs. Our experiments reveal a complex droplet formation process at the capillary orifice and suboptimal droplet transfer through the water-oil interface, which we explain using fluid dynamics and theoretical modeling. Our results are a first step towards explaining the widely observed variation in encapsulation efficiency and size polydispersity in cDICE. Ultimately, these results will contribute to a better understanding of GUV formation processes in cDICE and in extension, in double emulsion-based methods in general.

Controlled manufacturing of black and white flower-like dendritic micropattern tags for product unclonable security identification

Anti-counterfeiting tags with physical unclonable functions (PUFs) are critical for ensuring the security of products. In general, PUFs exhibit unique random physical patterns as a result of a stochastic minutiae that generates a large number of robust tags. In the current study, a novel flower-like dendritic micropattern security tag is created in rapid batch production by a simple one-step spray method and a comprehensive examination of the entire security identification system is conducted, from signage to literacy. A silicate condensation reaction was controlled to tune the morphology, resulting in six-class micropatterns with regular geometry. Additionally, the recognition feature of tags was derived directly from the shape contour and distribution characteristics of the pattern. Thus, the micropatterns have both stochastic minutiae and rule-based aspects. The unpredictability of the micropatterns’ size and position distribution contributes to the tags’ high robustness and the structural regularity has potential utility in error correction schemes. The article also discusses the mechanism involved in the formation and evolution of six-class patterns and conducts a systematic analysis of the mathematical foundation of the uniqueness of micropatterns. The experimental statistics indicate that, by appropriately limiting the deviation of the curing parameters and the image capture position, the production and identification of tags remain controllable and stable. Additionally, the tag identification exhibits significant intraclass correlation and interclass uncorrelation, indicating that they meet the requirements for high robustness of anti-counterfeiting tags. These tags have significant potential for application in security management of customized products, high-value goods and forensic purposes.

A Course Controller with Finite-Time Stability for Unmanned Surface Vehicle Thruster System

Aiming at the situation that small unmanned surface vehicle (USV) encounters unknown disturbance during low speed sailing, a course controller with finite time stability is designed. To solve this problem, we construct an undisturbed ideal navigation model which simply meets the stability requirements, and constructs an adaptive sliding mode surface. The control under finite time approach law is also introduced. The model under perturbation can land on the sliding mode surface in finite time and then synchronize with the ideal navigation model. The adaptive control was applied in the implementation of power control for the thruster structure, so as to ensure the tracking of the desired course within the finite time, and satisfy the needs for the stable system performance. Lyapunov direct method is used to strictly prove that the designed controller can ensure the system which converges to the steady state value in a given time period. Simulation results show that the designed adaptive finite-time controller can ensure the stable course tracking of the USV with thruster structure at low speed, and meet the requirements of the course robustness of the USV under dynamic conditions.

Integrity and authenticity verification of printed documents by smartphones

To this day, most important documents are still issued on paper. The security is based on the fact that the cost of creating a counterfeit must be unattractive for counterfeiters in relation to the expected profit. This results typically in using expensive printing equipment and substrate. This work introduces an approach which evaluates paper documents using any internet enabled device with a camera and a web browser like smartphones and tablets. Optical character recognition (OCR) is used to make text machine readable after the document is recognized and rectified. Digital signatures are then used to verify the authenticity and integrity of the data. Beyond that, the requirements of privacy, robustness and usability are satisfied. By using JAB Code, a high-capacity matrix code, the data to be verified can be stored directly on the document without having to use a database. This brings key advantages compared to database-bound systems in terms of security and privacy. The use of OCR achieves high usability.

Robust Full-Screen Wavelength Calibration Algorithm

In spectrometer measurement, it is very important to accurately calibrate the wavelength of all target characteristic spectra. Although wavelength calibration methods have long been investigated, no techniques have been designed for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer, to the best of our knowledge. Based on the grating diffraction equation and experimental results, a mathematical model of wavelength calibration was established for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer. In this study, a robust, full-screen, wavelength calibration algorithm is proposed, based on the related working principle and the requirements of both accuracy and robustness. The detailed steps are as follows. First, we established a wavelength calibration model at central pixels, following the grating diffraction equation. Then, according to the relationship between the difference in pixels and the feedback values of the grating ruler, a model was established to show the association between these factors. Finally, we combined the two models and built a full-screen wavelength calibration model. We theoretically and experimentally demonstrate that the proposed calibration algorithm is an excellent calibration tool, which can conveniently and accurately calibrate the wavelengths of central and non-central pixels at the same time.

FlexibleNet: A New Lightweight Convolutional Neural Network Model for Estimating Carbon Sequestration Qualitatively Using Remote Sensing

Many heavy and lightweight convolutional neural networks (CNNs) require large datasets and parameter tuning. Moreover, they consume time and computer resources. A new lightweight model called FlexibleNet was created to overcome these obstacles. The new lightweight model is a CNN scaling-based model (width, depth, and resolution). Unlike the conventional practice, which arbitrarily scales these factors, FlexibleNet uniformly scales the network width, depth, and resolution with a set of fixed scaling coefficients. The new model was tested by qualitatively estimating sequestered carbon in the aboveground forest biomass from Sentinel-2 images. We also created three different sizes of training datasets. The new training datasets consisted of six qualitative categories (no carbon, very low, low, medium, high, and very high). The results showed that FlexibleNet was better or comparable to the other lightweight or heavy CNN models concerning the number of parameters and time requirements. Moreover, FlexibleNet had the highest accuracy compared to these CNN models. Finally, the FlexibleNet model showed robustness and low parameter tuning requirements when a small dataset was provided for training compared to other models.

Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.

Разработка программно-аппаратного комплекса для управления группой беспилотных транспортных средств на основе робастных алгоритмов компьютерного зрения

The widespread adoption of automated robotic systems requires their confirmed efficiency and safety. To test these criteria, experimental testbeds that simulate an external environment under the conditions similar to the operating environment are developed. Systems focused on performing tasks under real conditions should be robust against the dynamic external environment. In this work, we develop a combination of software and hardware components for controlling a group of unmanned vehicles (UVs) based on computer vision algorithms that would be robust to external environmental factors. As part of our investigation, UV prototypes and their testing environment are developed. The action decision-making of each UV is based on a computer vision system, which incorporates algorithms for positioning, movement correction, other vehicle and obstacle detection. To verify the developed system, we conduct an empirical study into how the UV decisionmaking is affected by such external environmental factors, as changes in the level and tone of lighting, as well as the appearance of the surface. The experimental results show that the developed and implemented UV computer vision system meets the established robustness and stability requirements regardless of the tested environmental conditions. The developed system minimizes deviations from the planned route, reduces the localization error to 10 % at most, provides the accuracy of 84 % of other UVs detection, and maintains UVs operation without failures for two hours. The developed hardware and software bundle and testbed can be used for security and smart city control algorithms verification, as well as for other tasks requiring UVs prototyping.

Identification of Surrogate Models for the Prediction of Degrees of Freedom within a Tolerance Chain

The computation of assembly tolerance information is necessary to fulfill robust design requirements. This assembly is computationally costly, with current calculations taking several hours. We aim to identify surrogate models for predicting degrees of freedom within a tolerance chain based on point connections between assembly components. Thus, replacing part of the current computation workflow and consequently reduce computation time. We use manufacturing tolerances set by norms and industrial standards to identifly these surrogate models, which define all relevant features and resulting output variables. We use black-box modeling methods (artificial neural networks and gradient boosted trees), as well as white-box modeling (symbolic regression by genetic programming). We see that these three models can reliably predict the degrees of freedom of a tolerance chain with high accuracy (R2 > 0.99).

Lightweight Semantic-Aided Localization With Spinning LiDAR Sensor

Autonomous driving demands robust and precise vehicle localization in complex environments with limited on-board computational resources. Incorporating reliable semantic information with localization algorithms can increase accuracy remarkably, however, the process of extracting semantic information from LiDAR point clouds and matching it to semantic maps is computationally intensive. Moreover, pure semantic localization cannot achieve the robustness requirements for safe self-driving as the necessary quantity of semantic landmarks cannot be guaranteed under extreme conditions. In this paper, we present a lightweight semantic-aided localization method that improves upon traditional techniques in two ways. First, we propose a highly efficient pipeline to extract three semantic classes from a LiDAR scan. Second, instead of semantic 3D point cloud registration, map matching is performed through 2D key point matching. We then integrate these two functions into a dynamic semantic aided localization framework. Our on-road experiments demonstrate that the proposed method achieves both the high accuracy of semantic localization and the robustness of non-semantic localization. With our algorithm consuming under 10% of CPU resources, we observe reduced positioning error, especially peak error, when comparing to non-semantic counterparts.

Weighted-leader search: A new choice in metaheuristic and its application in real-world large-scale optimization

Aiming at the robustness and real-time requirements of engineering applications, this paper develops a novel metaheuristic algorithm with a simple mathematical framework for numerical optimization, namely the weighted-leader search (WLS). With the WLS, a weighted mean point of the top solutions is developed to evolve the population. Additionally, several simple local search mechanisms are introduced to prevent the algorithm from premature convergence. Moreover, the WLS possesses fewer constant coefficients to reduce the parameter sensitivity. The performance of the WLS is evaluated with the CEC 2017 test suite with 10D, 30D, 50D and 100D problems, the CEC 2008 test suite with 100D, 500D and 1000D problems, large-scale benchmarks with 100D, 500D and 1000D (or beyond) problems and constrained engineering design problems. The statistical results of the experiments indicate that the WLS exhibits an outstanding ability among the state-of-the art algorithms in different competitions, which verifies the accuracy and efficiency of our algorithm in solving complex problems, as well as its robustness in large-scale evaluation. Moreover, the WLS is employed to address the cooperative path planning problem of an unmanned aircraft vehicle (UAV) swarm with 100 members. Through problem model construction, the path planning issue is transformed into a constrained optimization problem and solved by the WLS. The experimental results show that the proposed WLS is able to solve the path planning problem in real time within the constraints and significantly outperforms the other competitors, demonstrating the accuracy and time savings of the algorithm in solving real-world optimization problems.

Comparison of Modern Control Methods for Soft Robots

With the rise in new soft robotic applications, the control requirements increase. Therefore, precise control methods for soft robots are required. However, the dynamic control of soft robots, which is required for fast movements, is still an open topic and will be discussed here. In this contribution, one kinematic and two dynamic control methods for soft robots are examined. Thereby, an LQI controller with gain scheduling, which is new to soft robotic applications, and an MPC controller are presented. The controllers are compared in a simulation regarding their accuracy and robustness. Additionally, the required implementation effort and computational effort is examined. For this purpose, the trajectory tracking control of a simple soft robot is studied for different trajectories. The soft robot is beam-shaped and tendon-actuated. It is modeled using the piecewise constant curvature model, which is one of the most popular modeling techniques in soft robotics. In this paper, it is shown that all three controllers are able to follow the examined trajectories. However, the dynamic controllers show much higher accuracy and robustness than the kinematic controller. Nevertheless, it should be noted that the implementation and computational effort for the dynamic controllers is significantly higher. Therefore, kinematic controllers should be used if movements are slow and small oscillations can be accepted, while dynamic controllers should be used for faster movements with higher accuracy or robustness requirements.

Handling and Stability Integrated Control of AFS and DYC for Distributed Drive Electric Vehicles Based on Risk Assessment and Prediction

How to improve the trajectory following ability and lateral stability under extreme conditions is an important research problem for distributed drive electric vehicles (DDEVs). This paper proposes a novel integrated control architecture of active front steering control (AFS) system and direct yaw moment control (DYC) system for DDEVs. First, to deal with the future instability problem caused by driver’s misoperation or delayed control, online risk assessment and prediction models, including self-regulating phase plane stability judgment and future driving state prediction of vehicles, is designed to provide decision commands for actuators in advance under extreme conditions. Then, on the basis of comprehensive consideration of system chattering, robustness and control constraint index requirements, an integrated control method based on robust sliding mode predictive control (SMPC) to put forward to solve the multi-objective and multi-constraint optimization problem for multi-subsystem integration. Finally, the simulation and experimental results show that the proposed control architecture can effectively assist drivers improve the trajectory following ability and handling stability of DDEVs, as to ensure the maneuverability and safety of emergency obstacle avoidance under extreme conditions.

OP27 Health Technology Assessment Processes, Characteristics, And Key Differences In High, Middle, And Low-Income Countries Of Asia Pacific Region

IntroductionThe healthcare sector in the Asia Pacific (APAC) region is in a period of rapid growth and exciting innovation. This has led to an increase in the number of APAC countries adopting and implementing health technology assessment (HTA) to assess the clinical, ethical, economic, and societal aspect of healthcare technology. The aim of this study is to provide an updated snapshot of the status of HTA and key differences in selected countries of APAC region.MethodsHTA robustness and gap requirements, including its structure, process, use in decision-making and resource allocation processes were assessed through a review of published and gray literature for each of the selected country. A qualitative analysis was carried out by using a set of 15 principles of an International Working Group for HTA Advancement to identify the robustness and key differences in HTA based decision-making process in scoping countries.ResultsThe finding of this study reveals that maturity of HTA determined by country-specific factors, such as presence of independent HTA agency, healthcare funding and expenditure, etcetera, and varies across the high-, middle-, and low-income countries of APAC region. Based on the study’s results, HTA ecosystem of selected countries categorized into rising HTA followed by advancing and mature HTA categories. In addition to the differences in HTA structure, the influence of stakeholder engagement differs among HTA bodies. The variation in the time frame of HTA decisions was significant among countries, with a general lack of awareness and transparency among health policy decision-makers and resulted in longer time for assessment for rising HTA category compared to the advance and mature HTA categories.ConclusionsThe vision of a comprehensive and robust HTA system can be achieved by implementing a transparent, independent, decision-making, and strongly integrated HTA process in the region. We recommend that efforts should be directed to promote a transparent and sustainable HTA, throughout the low- and middle-income countries of APAC region which eventually, lead to more effective HTA ecosystem.

International Briefs

International Briefs

Frequency Domain Design Method of the Aeroengine Fuel Servo Constant Pressure Difference Control System with High Performance

The constant pressure difference regulating mechanism is widely used in aeroengine fuel servo metering systems, and it almost decides the metering precision. However, the design theory and design method of the available constant pressure difference regulating mechanism are unclear, and it is difficult to follow the high stability, high accuracy, and high robustness requirements of the modern aeroengine fuel servo metering system. In this paper, the design theory of the constant pressure difference regulating mechanism is revealed, and it indicates that it consists of two basic control units: a state feedback stabilization controller to ensure the asymptotic stability and disturbance rejection performance; and a servo and feed-forward compensator to ensure the asymptotic tracking ability. In addition, based on the frequency domain analysis method, the decisive influences about the control gains of the two control units on the dynamic performance and stability are analyzed. On this basis, a frequency domain design method of the two core control gains is proposed to complete the design task of the closed-loop system. The simulation results show that, under the adverse conditions of 1 MPa strong step disturbance of the inlet pressure and 50 mm2 strong step disturbance of the variable inlet flow area, the steady-state working range of the controlled pressure difference meets 0.92 ± 0.01 MPa, the steady-state error is not more than 1%, the regulation time is not more than 0.01 s, the dynamic overshoot is not more than 10%, and the designed phase margin is more than 70°.

Fast-response hot-wire flow sensors for wind and gust estimation on UAVs

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard an UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector’s direction and magnitude. The MAST’s performance was evaluated in a wind tunnel at speeds up to 5 m s−1 and orientations of 0∘–360∘. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m s−1 for speed and 1.6∘ for direction. Furthermore, 95% of measurements are within 0.36 m s−1 for speed and 5.0∘ for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.

Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines

Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.

Implementation and characterization of flash‐based hardware security primitives for cryptographic key generation

AbstractHardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off‐the‐shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware‐based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min‐entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high‐entropy requirements, such as cryptographic key generation for IoT devices.

PPT: A privacy-preserving global model training protocol for federated learning in P2P networks

The concept of Federated Learning (FL), which is vital to the development of machine learning, has emerged as a convergence of machine learning, information, and communication technology. However, general FL settings cannot meet requirements in decentralized environments, especially in peer-to-peer (P2P) networks, where a fully connected central server is unavailable due to limited communication ranges. In this paper, to satisfy the requirements of security, privacy preservation, and robustness in the context of FL in P2P networks, we propose a decentralized global model training protocol, named PPT. Particularly, PPT aggregates local model update parameters in a single-hop manner and uses the symmetric cryptosystem to ensure secure communications between network nodes where an enhanced Eschenauer-Gligor (E-G) scheme is proposed for secure key distribution. Further, PPT generates random noise for privacy preservation without reducing the model accuracy since the noise is eliminated ultimately. PPT also adopts game theory to resist collusion attacks. In addition, PPT has elaborate designs in terms of communication efficiency and dropout-robustness. Through extensive analysis, we demonstrate that PPT can resist various security threats and preserve user privacy. Ingenious experiments on Trec05, Trec06p, Trec07, and SMS Spam Collection v.1 confirm the 20× and 12× improvement of computation efficiency that PPT achieves compared to Google’s Secure Aggregation and Local Differential Privacy (LDP)-based FL methods. More importantly, the global model trained by PPT is better than that trained by LDP-based FL methods in terms of prediction performance (about 14% and 1% improvement in convergence rate and accuracy).