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In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using it to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm's mechanical structure, electronics, and software are put to the test. The software is based on evolutionary software that implements genetic algorithms. The entire command system is also ported to FPGAs (to hardware) to increase speed and response time. [ABSTRACT FROM AUTHOR]

Distributed Energies Resources (DERs) can be controlled for supporting the voltage regulation at nodes of an Active Distribution Network (ADN) where they are connected. However, since the ADN is a Multi-Input Multi-Output (MIMO) system with coupled dynamics, the controller of a DER mutually interacts with all other controllers through the distribution lines. These interactions lead to operating conflicts which may drive the ADN to work close to its voltage stability boundaries. To achieve a stable voltage regulation without new investment in the existing ADNs, the present paper proposes a straightforward decentralized design of the multi-loop controllers based on the property of integral controllability. The main feature of the method is that the design problem can be expressed by a single parameter designed both for reducing the effects of the undesired coupling and for increasing the degree of robust stability in the presence of parameter uncertainty in the matrix plant. Simulation studies are developed to illustrate the design result and the performance achieved under different operating conditions. The performance is also compared with the one obtained by another method in terms of the integral absolute error. [ABSTRACT FROM AUTHOR]

The takeoff point in this paper is a random variable X for which large positive values are desired. When its probability distribution is highly skewed, the possibility of a long fat-tailed distribution can lead to an expected value, \mu =\mathbb E[ X] , which is unduly optimistic. For the reverse case when small values of X are desired, the ideas in this paper are applied to - X . This issue of over-optimism in the expected value is particularly important when a mission-critical random variable is involved. For example, when considering earthquake intensity or flood levels, reporting an understated expected value to a technically unsophisticated general public would be considered by many as highly undesirable. The Conservative Expected Value (CEV) of X , denoted by \mathbb CEV ( X ) is a new definition provided in this paper. It is a metric which we argue is particularly useful when risk aversion must be highly emphasized. At the same time, while being conservative, the CEV is also aimed at avoiding reflection of excessive pessimism. When, then \mathbb CEV ( X ), while being conservative, is defined in such a way so as not to be unduly pessimistic. In classical analysis, enhancement of a calculation often includes the variance \sigma ^ 2 \,\, = var( X ). However, when large X -values are desired, this may further distort one’s overview of the risk at hand; e.g., if X is profit, values above the mean should not be penalized. With these one-sidedness considerations in mind, we work with a new reward-risk pair, the CEV and the so-called Conservative Semi-Variance (CSV) of X , denoted by \mathbb CSV ( X ). Whereas the CEV definition is entirely new, the CSV definition is motivated by ideas in the area of finance. This paper also illustrates calculation of the CEV and CSV pair for a number of classical probability distributions and includes description of a number of properties of these metrics which suggest that this new theory is mathematically rich. Finally, we demonstrate the potential for application of the theory via two numerical examples. [ABSTRACT FROM AUTHOR]

In array processing, sparse arrays are capable of resolving $\mathcal {O}(N^2)$ uncorrelated sources with $N$ sensors. Sparse arrays have this property because they possess uniform linear array (ULA) segments of size $\mathcal {O}(N^2)$ in the difference coarray, defined as the differences between sensor locations. However, the coarray structure of sparse arrays is susceptible to sensor failures and the reliability of sparse arrays remains a significant but challenging topic for investigation. In the companion paper, a theory of the $k$ -essential family, the $k$ -fragility, and the $k$ -essential Sperner family were presented not only to characterize the patterns of $k$ faulty sensors that shrink the difference coarray, but also to provide a number of insights into the robustness of arrays. This paper derives closed-form characterizations of the $k$ -essential Sperner family for several commonly used array geometries, such as ULA, minimum redundancy arrays (MRA), minimum holes arrays (MHA), Cantor arrays, nested arrays, and coprime arrays. These results lead to many insights into the relative importance of each sensor, the robustness of these arrays, and the DOA estimation performance in the presence of sensor failure. Broadly speaking, ULAs are more robust than coprime arrays, while coprime arrays are more robust than maximally economic sparse arrays, such as MRA, MHA, Cantor arrays, and nested arrays. [ABSTRACT FROM AUTHOR]

In contrast to driverless cars and other three-wheeled and four-wheeled motorcycle vehicles, driverless two-wheeled motorcycles have the problem of maintaining balance. In this paper, we propose the design of an SGCMG frame angular velocity controller to realize the balance control of the motorcycle under static and dynamic working conditions. Meanwhile, since the roll angular acceleration of the actual body movement of the cross roll cannot be obtained directly, this paper proposes a Kalman filtering method based on the nonlinear dynamics model of the motorcycle to obtain a reliable angular acceleration signal. First, we modeled the dynamics of the motorcycle by analyzing the various types of moments generated by the motorcycle equipped with the SGCMG under static and dynamic conditions; Then, the design of the angular velocity control of the SGCMG frame was carried out with the feedback and through MATLAB/Simulink simulation to restore various types of actual working conditions to verify the controller has good robustness; Finally, we have completed the test of the controller using the above filtering method on the real vehicle with an embedded system and compared the effect with other controllers, obtained the results that the body is stable and balanced under static conditions and the applied load can automatically find a new balance point, so as to prove the effectiveness of the designed control. [ABSTRACT FROM AUTHOR]

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness. Currently, a popular research area related to artificial neural networks is adversarial attacks. The adversarial attacks on the image are not highly perceptible to the human eye, and they also drastically reduce the neural network's accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. We propose a technique to reduce the influence of high-frequency noise on the CNNs. We show that low-pass image filtering can improve the image recognition accuracy in the presence of high-frequency distortions in particular, caused by adversarial attacks. This technique is resource efficient and easy to implement. The proposed technique makes it possible to measure up the logic of an artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition. [ABSTRACT FROM AUTHOR]

Msplit estimation is a modern estimation method that has found various applications in processing geodetic data. Its basic variants were not meant to be robust against outliers; however, the practical applications showed that the method could be used in such a context. Therefore, there is a need to describe the robustness of different Msplit estimation variants. The paper uses the global breakdown point in an extended interval (GBdP- e) but also introduces the tolerance for growing errors of observations (TGE) to perform such an examination. It presents such measures obtained for the absolute Msplit estimation and robust Msplit estimation variants, which have not been shown before. The results prove that the absolute Msplit estimation predominates the squared Msplit estimation in such a context. Furthermore, the robust variants are much less sensitive to outliers than both basic variants mentioned. TGE not only describes how the method tolerates outliers but could also be applied to assume the most appropriate values of the steering parameters, which seems essential. The paper shows the theoretical relationship between basic Msplit estimation variants and respective M -estimation methods. It is a basis for introducing and deriving GBdP- e and also TGE for M -estimation. The paper shows that both measures are equivalent in the case of M -estimation. TGE could provide information about that estimation type's sensitivity to growing errors of observations (also robustness to outliers) that is unavailable by applying other measures, including classical breakdown points, influence functions, rejection points, or mean success rate. TGE presents the robustness potential of the M -estimation variants in a rather vivid and straightforward way, even for methods not classified as robust against outliers, e.g., the least-squares estimation. [ABSTRACT FROM AUTHOR]

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus–subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness. [ABSTRACT FROM AUTHOR]

To improve the anti-interference and robustness of the adaptive cruise control system in car-following mode, this paper designs a robust controller for the automobile adaptive cruise control system which contains two layers, the upper and lower structures, based on the μ control theory. On the one hand, the upper controller calculates the theoretical safety distance between two automobiles based on the current working conditions, and it calculates the expected acceleration of the vehicle through an optimal control method based on the safety distance and two vehicle speeds. On the other hand, this paper constructs the lower μ integrated controller of an automobile longitudinal dynamics system based on the performance requirements of an adaptive cruise control system and solves it in Matlab. Then, through calculation and simulation, it demonstrates that the designed dual-layer LQR-μ controller has good performance robustness and robust stability, which can significantly improve the anti-interference ability and driving safety performance of the vehicle during the following cruise process. [ABSTRACT FROM AUTHOR]

Simple Summary: Poultry meat is an affordable, lean source of animal protein. Where does it come from? Broilers (meat chickens) or turkeys raised on farms stem from core breeding families, which are crossed to generate commercial hybrids. It takes around four years for the birds from the core families to the birds on the farms. Over time, poultry breeding has developed from primarily looking at production (e.g., live weight and egg production) to balanced, holistic breeding, including a wide range of attributes (e.g., gait, leg health, robustness and cardiovascular health). All birds in the core breeding families are carefully measured for over forty items covering bird health and welfare, robustness, environmental impact, reproduction and production. Modern poultry breeding aims for the holistic improvement of all the traits included in a broad and balanced breeding goal. New and improved selection techniques and analytical tools are continuously developed to allow increases in the accuracy of selection's long-term progress. There are different broiler and turkey genotypes for different market requirements, and commercial portfolios will continue evolving with future markets and customer preferences. This paper provides a comprehensive overview of the history of commercial poultry breeding, from domestication to the development of science and commercial breeding structures. The development of breeding goals over time, from mainly focusing on production to broad goals, including bird welfare and health, robustness, environmental impact, biological efficiency and reproduction, is detailed. The paper outlines current breeding goals, including traits (e.g., on foot and leg health, contact dermatitis, gait, cardiovascular health, robustness and livability), recording techniques, their genetic basis and how trait these antagonisms, for example, between welfare and production, are managed. Novel areas like genomic selection and gut health research and their current and potential impact on breeding are highlighted. The environmental impact differences of various genotypes are explained. A future outlook shows that balanced, holistic breeding will continue to enable affordable lean animal protein to feed the world, with a focus on the welfare of the birds and a diversity of choice for the various preferences and cultures across the world. [ABSTRACT FROM AUTHOR]

This paper presents a high imperceptible and robust audio watermarking algorithm by optimizing the scaling parameter as per the imperceptible requirements with minimum bit error rate. The spread spectrum-based audio watermarking is used in this paper, where the scaling parameter is optimized to achieve trade-off between imperceptibility and robustness of watermarked audio. Primarily, the effect of scaling parameter on the perceptual quality of watermarked audio is investigated and the total error introduced in the host audio due to embedding the watermark is computed. The scaling parameter is optimized for maximizing the robustness by considering objective difference grade (ODG) score (for music signals), perceptual evaluation of speech quality (PESQ) score (for speech signals) as a constraint to meet the imperceptibility requirements. To solve this proposed optimization problem, two search algorithms are developed. The embedding is performed in low-pass framelet transform coefficients through SVD with the optimized scaling parameter. The experimental results show that the proposed algorithm achieves good imperceptibility with an average ODG score of −0.32 and PESQ score of 3.86 for music and speech signals, respectively, under various payload conditions. The proposed algorithm shows better robustness to the common signal processing attacks such as noise addition, filtering, resampling, MP3 compression, amplitude scaling, cropping, and requantization. [ABSTRACT FROM AUTHOR]

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print–scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print–scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print–scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution. [ABSTRACT FROM AUTHOR]

To deal with environmental uncertainty, organizations need resilience to respond to disruptions, such as changing market conditions or variations in demand or supply, while avoiding large scale adjustments. The concept of resilience is ambiguous, often explained as the capability of an organization or a supply chain to recover its original state, within an appropriate time frame, after being disrupted. Resilient supply chains have event handling capabilities, can provide efficient responses, and can return to their normal operating performance, after the disruptive event. To increase their resilience, companies often make changes or adjustments to their internal IT infrastructure, which may temporarily disrupt their smooth operation. As a result, contemporary IT infrastructures are mixed and include varied systems or technologies. Although new technologies, including blockchain, IoT and cloud-based solutions, may facilitate the handling of changes by providing secure, low cost and scalable solutions, more traditional systems may hinder such changes. Therefore, the relationship between IT and supply chain resilience is still unclear. The paper intends to examine the above issues by adopting a socio-technical approach to explain the concept of supply chain resilience and investigate the role of IT. More specifically, based on previous literature and on the appreciative systems thinking theoretical perspective, the paper develops a theoretical framework to analyse the organisational and/or supply chain resilience. It then uses this framework to examine and explain the impact of IT, by identifying important characteristics of an IT infrastructure and examining whether they may support or hinder business resilience. [ABSTRACT FROM AUTHOR]

Unreliable resources in conventional power systems have created new challenges for the users of these systems. These sources of production, including wind and solar sources, with a significant share in the power generation of many power systems, cause problems in the safe and stable operation of power systems due to uncertainty. Therefore, it is very important to provide a method or model for safe and stable operation of power systems. The main innovation of this paper is to present a new SCUC model to maximize the flexibility of the system along with reducing the operating cost in the form of a security constrained multi-level robust optimization problem in the presence of wind uncertainty sources. Also, a set of demand response programs and rapid response hybrid cycle units are used as sources of flexibility. Accordingly, the tolerable range of uncertainty and system slope reservation is developed with a low-cost combination and with the commitment to flexible covering resources. The desired optimization problem has been solved with the help of the NCCG algorithm and MILP method. The efficiency and reliability of this proposed model have been simulated and evaluated in IEEE 6-bus and 118-bus standard power systems, and the simulation results confirm the technical and economic efficiency of this model. [ABSTRACT FROM AUTHOR]

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects. [ABSTRACT FROM AUTHOR]

At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks. [ABSTRACT FROM AUTHOR]

The current article gives a new approach that is efficient for the design of a low-order H∞ controller over a finite time interval. The system under consideration is a linear discrete time system affected by norm bounded disturbances. The proposed method has the advantage that takes into account both robustness aspects and desired closed-loop characteristics, reducing the number of variables in Linear Matrix Inequalities (LMIs). Thus, reduced order H∞ controller parameters are given to guarantee a finite time H∞ bound (FTB-H∞) for a closed-loop system. The method of the finite time stability, that is proven in this paper by the Lyapunov theory, can be applied to a wide range of process models. Numerical examples demonstrating the effectiveness of the results developed are presented at the end of this paper. [ABSTRACT FROM AUTHOR]

The quadrotor aircraft has the characteristics of simple structure, high attitude maintenance performance and strong maneuverability, and is widely used in air surveillance, post−disaster search and rescue, target tracking and military industry. In this paper, a robust control scheme based on linear active disturbance rejection is proposed to solve the problem that the quadrotor is susceptible to various disturbances during the take−off process of non−horizontal planes and strong disturbances. Linear Active Disturbance Rejection Control (LADRC) is a product of a tracking differentiator (TD), a linear extended state observer (LESO) and an error feedback control law (PD) and is a control technique for estimating compensation for uncertainty. Radial Basis Function Neural Networks (RBFNN) is a well−performing forward network with best approximation, simple training, fast learning convergence and the ability to overcome local minima problems. Combined with the advantages and disadvantages of LADRC, Adaptive Control and Neural Network, the coupling force between each channel, gust crosswind disturbance and additional resistance of offshore platform jitter in the flight state of the quadrotor are optimized. In the control, the RBF neural network is designed, the nonlinear control signal is wirelessly approximated and the uncertain disturbance to the quadrotor is identified online. Finally, the real−time estimation and compensation are performed by LESO to realize the full−attitude take−off of the quadrotor. In addition, this paper uses adaptive control to optimize the parameters of LADRC to reduce the problem of many LADRC parameters and difficulty to integrate. Finally, the robust control system mentioned in this paper is simulated and verified, and the simulation results show that the control scheme has the advantages of simple parameter adjustment and stronger robustness. [ABSTRACT FROM AUTHOR]

Antifragility was introduced as a term no later than 10 years ago. As presented by Taleb, antifragility means that a system becomes more resilient and more robust with every harmful and/or stressful action it is confronted with. This paper is based on a study which aimed to use the concept of antifragility during the design stage of a self-improving system. This way, it is expected to obtain a fast adaptive system capable of functioning at optimal parameters even when it works under adverse conditions or faces unforeseen changes in the environment. Assuming that an antifragile system not only maintains its robust behavior when faced with stressful and harmful events but even benefits from them to optimize its performance, the paper offers a detailed description of the features that must be ensured when designing a self-improving antifragile manufacturing system. By ensuring the property of antifragility, complex manufacturing systems are much safer to exploit under uncertain conditions, which brings major benefits to the process management. Starting from consecrated solutions such as preventive maintenance (PvM) and predictive maintenance (PdM) and using techniques of artificial intelligence, we present the concept of antifragile maintenance (AfM). [ABSTRACT FROM AUTHOR]

This paper presents a robust deep learning method for fruit decay detection and plant identification. By addressing the limitations of previous studies that primarily focused on model accuracy, our approach aims to provide a more comprehensive solution that considers the challenges of robustness and limited data scenarios. The proposed method achieves exceptional accuracy of 99.93%, surpassing established models. In addition to its exceptional accuracy, the proposed method highlights the significance of robustness and adaptability in limited data scenarios. The proposed model exhibits strong performance even under the challenging conditions, such as intense lighting variations and partial image obstructions. Extensive evaluations demonstrate its robust performance, generalization ability, and minimal misclassifications. The inclusion of Class Activation Maps enhances the model's capability to identify distinguishing features between fresh and rotten fruits. This research has significant implications for fruit quality control, economic loss reduction, and applications in agriculture, transportation, and scientific research. The proposed method serves as a valuable resource for fruit and plant-related industries. It offers precise adaptation to specific data, customization of the network architecture, and effective training even with limited data. Overall, this research contributes to fruit quality control, economic loss reduction, and waste minimization. [ABSTRACT FROM AUTHOR]

When there is a mismatch in electrical power produced and the demanded electrical loading in an interconnected electrical power system, it results in deviations in frequencies in the constituent areas and in tie-line power as well. This issue is addressed by automatic generation control which when coupled with intelligent controller leads to fast recovery of the deviations both in tie-line power and frequencies from the assigned ranges associated with each of these areas. This paper is an attempt to utilize fuzzy logic-based fractional-order tilt-integral-derivative (Fuzzy FOTID) controller with gains/parameter optimized using the recent Coati Optimization Algorithm. Comparative studies are undertaken as a part of this work to exhibit the Fuzzy FOTID controller's supremacy over other popular controllers like proportional-integral-derivative, TID, and FOTID. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations. [ABSTRACT FROM AUTHOR]

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network's node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio. [ABSTRACT FROM AUTHOR]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

The Skolem problem is a long-standing open problem in linear dynamical systems: can a linear recurrence sequence (LRS) ever reach 0 from a given initial configuration? Similarly, the positivity problem asks whether the LRS stays positive from an initial configuration. Deciding Skolem (or positivity) has been open for half a century: the best known decidability results are for LRS with special properties (e.g., low order recurrences). On the other hand, these problems are much easier for “uninitialised” variants, where the initial configuration is not fixed but can vary arbitrarily: checking if there is an initial configuration from which the LRS stays positive can be decided by polynomial time algorithms (Tiwari in 2004, Braverman in 2006). In this paper, we consider problems that lie between the initialised and uninitialised variants. More precisely, we ask if 0 (resp. negative numbers) can be avoided from every initial configuration in a neighbourhood of a given initial configuration. This can be considered as a robust variant of the Skolem (resp. positivity) problem. We show that these problems lie at the frontier of decidability: if the neighbourhood is given as part of the input, then robust Skolem and robust positivity are Diophantine hard, i.e., solving either would entail major breakthroughs in Diophantine approximations, as happens for (non-robust) positivity. Interestingly, this is the first Diophantine hardness result on a variant of the Skolem problem. On the other hand, if one asks whether such a neighbourhood exists, then the problems turn out to be decidable in their full generality, with PSPACE complexity. Our analysis is based on the set of initial configurations such that positivity holds, which leads to new insights into these difficult problems, and interesting geometrical interpretations. [ABSTRACT FROM AUTHOR]

The reliability of AI-based object detection models has gained interest with their increasing use in safety-critical systems and the development of new regulations on artificial intelligence. To meet the need for robustness evaluation, several authors have proposed methods for testing these models. However, applying these methods in industrial settings can be difficult, and several challenges have been identified in practice in the design and execution of tests. There is, therefore, a need for clear guidelines for practitioners. In this paper, we propose a method and guidelines for assessing the robustness of AI-based 2D object detection systems, based on the Goal Question Metric approach. The method defines the overall robustness testing process and a set of recommended metrics to be used at each stage of the process. We developed and evaluated the method through action research cycles, based on two industrial cases and feedback from practitioners. Thus, the resulting method addresses issues encountered in practice. A qualitative evaluation of the method by practitioners was also conducted to provide insights that can guide future research on the subject. [ABSTRACT FROM AUTHOR]

Least Squares Support Vector Regression (LSSVR) which is a least squares version of the Support Vector Regression (SVR) is defined with a regularized squared loss without epsilon-insensitiveness. LSSVR is formulated in the dual space as a linear equality constrained quadratic minimization which can be transformed into solution of a linear algebraic equation system. As a consequence of this system where the number of Lagrange multipliers is half that of classical SVR, LSSVR has much less time consumption compared to the classical SVR. Despite this computationally attractive feature, it lacks the sparsity characteristic of SVR due to epsilon-insensitiveness. In LSSVR, every (training) input data is treated as a support vector, yielding extremely poor generalization performance. To overcome these drawbacks, the epsilon-insensitive LSSVR with epsilon-insensitivity at quadratic loss, in which sparsity is directly controlled by the epsilon parameter, is derived in this paper. Since the quadratic loss is sensitive to outliers, its weighted version (epsilon insensitive WLSSVR) has also been developed. Finally, the performances of epsilon-insensitive LSSVR and epsilon-insensitive WLSSVR are quantitatively compared in detail with those commonly used in the literature, pruning-based LSSVR and weighted pruning-based LSSVR. Experimental results on simulated and 8 different real-life data show that epsilon-insensitive LSSVR and epsilon-insensitive WLSSVR are superior in terms of computation time, generalization ability, and sparsity. [ABSTRACT FROM AUTHOR]

Is it possible to develop a robust crisis management response in a system where governance is characterized by coercive power and adversarial bargaining rather than the diversity, inclusion, and openness highlighted by extant scholarship as conducive factors for robustness? Using two instances of crisis in the European Union—the Eurozone crisis (2010‒2015) and COVID-19 pandemic (2020‒2022)—the paper argues that how actors reinterpret existing rules and institutions offers an important source of robustness in crisis management. Based on the employment of a disaggregation of robustness into degrees of robustness, as well as the concepts of ideational and institutional power, we show how actors can counter the coercive power of dominant coalitions and open up for rule adaptation through reinterpretations of existing rules that, at least in the short term, can solidify the functioning of existing institutions faced by turbulence. In the context of the Eurozone crisis, ideational and institutional power thus enabled a moderately robust response without treaty reform. In the case of the pandemic, it was possible to convince (particularly German) policymakers of the need to employ new ideas about common debt. This meant less need to employ ideational and institutional power by other actors, leading to significantly more effective crisis management than in the Eurozone crisis, what the paper terms maximal robustness. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for the solution of the electromagnetic interference impact problem in closed-loop control systems with proportional-integral-derivative (PID) controllers is proposed. The approach is based on the application of a combined controller that consists of three controllers: PID, PI and I, when only one of them is operating at a time, and which one is operating determines the control error value. The proposed combined controller allows for achieving better resistance of the control system to the noise signals produced by electromagnetic interference compared to PID and PID with low-pass filters in derivative term controllers without deteriorating the dynamic performance of the control system. The operation of the controller has been analysed using simulation and experiments with plants, the dynamics of which are described by first-order plus dead-time transfer functions. [ABSTRACT FROM AUTHOR]

DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and generalizable networks is a major challenge, especially when considering critical applications such as medical computer-aided diagnostics (CAD) and other medical imaging tasks. Within this context, a line of approaches incorporating prior knowledge domain information into deep learning methods has recently emerged. In particular, many of these approaches utilize known physics-based forward imaging models, aimed at improving the stability and generalization ability of DNNs for medical imaging applications. In this paper, we review recent work focused on such physics-based or physics-prior-based learning for a variety of imaging modalities and medical applications. We discuss how the inclusion of such physics priors to the training process and/or network architecture supports their stability and generalization ability. Moreover, we propose a new physics-based approach, in which an explicit physics prior, which describes the relation between the input and output of the forward imaging model, is included as an additional input into the network architecture. Furthermore, we propose a tailored training process for this extended architecture, for which training data are generated with perturbed physical priors that are also integrated into the network. Within the scope of this approach, we offer a problem formulation for a regression task with a highly nonlinear forward model and highlight possible useful applications for this task. Finally, we briefly discuss future challenges for physics-informed deep learning in the context of medical imaging. [ABSTRACT FROM AUTHOR]

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight's remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. [ABSTRACT FROM AUTHOR]

Intellectual property (IP) cores used in consumer electronics devices mandate protection against reverse engineering (RE) attacks. Functional obfuscation protects the design functionality by enhancing the complexity of RE attacks. This paper proposes a novel low cost (based on power-delay tradeoff) functional obfuscation methodology through employment of robust IP locking technique. In the proposed obfuscation methodology, several strong multipairwise secure IP locking block designs are presented that can only be actuated through application of valid serial key bits. As demonstration, this paper also shows a practical example of a functional obfuscated netlist structure of FIR filter. Proposed obfuscation on comparison with a recent approach for several DSP cores yielded a power reduction of ~10%, design cost reduction ~ 6.5% and security enhancement (strength of obfuscation) of $>\,\,4.29\,\,{e+9}$ times. [ABSTRACT FROM AUTHOR]

In the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model's effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT). [ABSTRACT FROM AUTHOR]

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility. [ABSTRACT FROM AUTHOR]

Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial for many real-world applications. Most of the current SOTA (state-of-the-art) models for ED are based on Graph Neural Networks (GNN). However, a few studies focus on the issue of GNN-based ED models' robustness towards text adversarial attacks, which is a challenge in practical applications of EDs that needs to be solved urgently. In this paper, we first propose a robustness analysis framework for an ED model. Using this framework, we can evaluate the robustness of the ED model with various adversarial data. To improve the robustness of the GNN-based ED model, we propose a new multi-order distance representation method and an edge representation update method based on attention weights, then design an innovative model named A-MDL-EEGCN. Extensive experiments illustrate that the proposed model can achieve better performance than other models both on original data and various adversarial data. The comprehensive robustness analysis according to experimental results in this paper brings new insights into the evaluation and design of a robust ED model. [ABSTRACT FROM AUTHOR]

This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions. We observe that the global temporal features are less generalizable, due to the temporal domain shift that videos from other unseen domains may have an unexpected absence or misalignment of the temporal relations. This finding has motivated us to solve video domain generalization by effectively learning the local-relation features of different timescales that are more generalizable, and exploiting them along with the global-relation features to maintain the discriminability. This paper presents the VideoDG framework with two technical contributions. The first is a new deep architecture named the Adversarial Pyramid Network, which improves the generalizability of video features by capturing the local-relation, global-relation, and cross-relation features progressively. On the basis of pyramid features, the second contribution is a new and robust approach of adversarial data augmentation that can bridge different video domains by improving the diversity and quality of augmented data. We construct three video domain generalization benchmarks in which domains are divided according to different datasets, different consequences of actions, or different camera views, respectively. VideoDG consistently outperforms the combinations of previous video classification models and existing domain generalization methods on all benchmarks. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive performance comparison between a current robust military waveform; namely, MIL-STD-188-110D, Waveform 0, and a filter bank multicarrier spread-spectrum (FBMC-SS) waveform proposed for communications through ionospheric/skywave HF channels. Waveform 0 is effectively a direct sequence spread spectrum waveform that uses Walsh multi-codes to enhance the information transmission rate. It may thus be referred to as Walsh-DSSS. FBMC-SS, on the other hand, makes use of filter banks to provide excellent performance when the received signal is subject to partial band interference. Successful application of FBMC-SS for communications across skywave HF channels has been previously demonstrated, both theoretically and through experimental work. However, very little has been done to contrast FBMC-SS against Walsh-DSSS. The goal of this paper is to first add new features to FBMC-SS to bring it on par with Walsh-DSSS. These features include: (i), introduction of multi-codes that achieve a comparable (or better) data rate to Walsh-DSSS; and (ii), addition of a scrambling step applied to the multi-codes to make the receiver detection robust against widely spread multipaths. With this established, in the second part of the paper, we examine the performance of the developed FBMC-SS against Walsh-DSSS when both are applied for communications across skywave HF channels. The two waveforms are compared both through a theoretical study and through experimental works across several skywave channels ranging from hundreds to thousands of kilometers. [ABSTRACT FROM AUTHOR]

Purpose: The banking sector in the MENA region is exposed to financial risks that originate from both the internal and external environment. Related studies in the literature have reached inconclusive determinants of the overall risks to banks. This paper examines the robustness of the determinants cited in the related literature. Design/methodology/approach: This paper examines the country-specific and bank-specific factors that affect banks' Z-score (being a proxy for the overall bank risks) in the MENA region. The sample banks consist of 33 listed commercial banks operating in six countries in the MENA region. Balanced panel data over 20 years (2000 to 2020) was examined, having a total of 660 observations. The Pooled Ordinary Least Square estimation (OLS) was used to carry out the empirical analysis. Findings: The findings of this paper showed that the robust determinants of overall bank risks are follows: (a) The unemployment rate had a negative effect on high overall bank risks in the period 2000–2010, (b) The financial crisis had a positive effect on the MENA overall bank risks in the period 2000–2010, but only for the low overall bank risks, (c) A robust and negative effect of cost/income ratio was observed in the period 2010–2020 only for high overall bank risk, (d) Low overall-risk banks were able to manage overall risks in a shorter time than high overall bank risks, and (e) In terms of the country-wide effect, the results for Egypt only showed that the overall bank risk had positive effects in the period 2000–2010, but negative and significant effects in the period 2011–2020 where overall bank risks reduced. Originality: This paper offers robust findings in the controversy around the determinants of overall bank risks in the MENA region, which is beneficial in light of the fact that the literature thus far has not reached a consensus regarding this issue. [ABSTRACT FROM AUTHOR]

This paper studies robust Bayesian persuasion of a privately informed receiver in a binary environment, where an ambiguity-averse sender with a maxmin expected utility function has limited knowledge about the receiver's private information source. We develop a novel method to solve the sender's information design problem. Our main result shows that the sender's optimal information structure can be found within the class of linear-contingent-payoff information structures. We also fully characterize the sender's optimal linear-contingent-payoff information structure and analyze the impact of ambiguity on the sender's payoff. [ABSTRACT FROM AUTHOR]

In order to enhance the performance of model-free adaptive control (MFAC) in solving the control problem caused by interference and improve the tracking speed, this paper focuses on the analysis and research of the system affected by interference using the MFAC method. This method is based on dynamic linearization technology, with system data which are represented by a full format dynamic linearization (FFDL) model that is very similar to actual industrial processes. In this work, a control law is derived by incorporating and assigning weights to both the output error and the output error rate (OER) as the performance index. Rigorous proofs are provided to establish convergence and stability. Considering the inherent complexity of actual systems, this paper also presents the MFAC-OER scheme for multiple-input–multiple-output (MIMO) systems. Furthermore, the effectiveness and practicality of the improved control strategy are evaluated through numerical arithmetic examples and control processes involving water level regulation in a circulating fluidized bed (CFB). Comparisons with conventional MFAC and PID control methods show that the enhanced control method is capable of quickly and accurately tracking the desired signal. Additionally, it exhibits superior anti-interference characteristics and is able to respond in a timely manner to changes in the operating conditions of the circulating fluidized bed system. As a result, it ensures the normal operation of the coal saver and water supply pipe without damage. [ABSTRACT FROM AUTHOR]

The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well as changes in the lighting environment, have a massive impact on the accuracy of the detection results. We improved the C2F module based on DenseBlock and proposed DCF to extract low-level features such as the edge texture of pests and diseases. Through the sensitivity of low-level features to the diversity of pests and diseases, the DCF module can better cope with complex detection tasks and improve the accuracy and robustness of the detection. The complex background environment of pests and diseases and different lighting conditions make the IP102 data set have strong nonlinear characteristics. The Mish activation function is selected to replace the CBS module with the CBM, which can better learn the nonlinear characteristics of the data and effectively solve the problems of gradient disappearance in the algorithm training process. Experiments show that the advanced Yolov8 algorithm has improved. Comparing with Yolov8, our algorithm improves the MAP50 index, Precision index, and Recall index by 2%, 1.3%, and 3.7%. The model in this paper has higher accuracy and versatility. [ABSTRACT FROM AUTHOR]

With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data. [ABSTRACT FROM AUTHOR]

The paper formulates conditions under which the roots closest to the imaginary axis (critical roots) of the characteristic equation of a linearized system are real for the maximum possible degree of stability of the closed-loop control system of a technological process with pure delay. For the parameters of the controllers corresponding to the maximum degree of stability, these roots are multiples. Their multiplicity order is one more than the number of coefficients in the transfer function of the controller. It is demonstrated that for a typical technological control object, these conditions are satisfied for all "serial" control laws. This allowed for obtaining analytical expressions for optimal settings and limiting degrees of stability as functions of object parameters for typical dynamic characteristics of technological processes. The paper considers the problem of robust stability for control systems with an object containing pure delay. It has been proven that in the maximum stability problem, the operations of maximizing over controller parameters and minimizing over the set of possible object parameters can be interchanged. Therefore, selecting robust settings amounts to determining the minimum of the maximum stability over the set of possible object parameter values. Controllers with such settings are suitable, without modification, for a whole class of technological processes. [ABSTRACT FROM AUTHOR]

With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. [ABSTRACT FROM AUTHOR]