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This research aims to develop robust, resilient, and user-friendly over-the-air (OTA) software update mechanisms for advanced driver assistance systems (ADAS) in modern vehicles. The study conducts a comprehensive review on existing OTA update approaches, identifies security threats and vulnerabilities, and proposes an algorithm that incorporates cryptographic techniques, secure authentication, firmware validation, and rollback protection. The methodology utilized in this study involves secure package generation, distribution, installation, and monitoring, as well as adhering to automotive cybersecurity standards, such as ISO/SAE 21434 and UNECE WP.29. Extensive evaluation of OTA systems is important as it helps to assess their resilience against adversarial attacks, ensures regulatory compliance, and optimizes usability through user studies. The results demonstrate successful execution of secure OTA update mechanisms, preventing risks, fostering reliability and safety of ADAS software updates. The case studies highlight real-world deployments, best practices, and the effectiveness of the developed solution in improving automotive cybersecurity and functional safety. [ABSTRACT FROM AUTHOR]

This paper describes modified robust algorithms for a line clipping by a convex polygon in E2 and a convex polyhedron in E3. The proposed algorithm is based on the Cyrus-Beck algorithm and uses homogeneous coordinates to increase the robustness of computation. The algorithm enables computation fully in the projective space using the homogeneous coordinates and the line can be given in the projective space, in general. If the result can remain in projective space, no division operation is needed. It supports the use of vector-vector operations, SSE/AVX instructions, and GPU. [ABSTRACT FROM AUTHOR]

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control. [ABSTRACT FROM AUTHOR]

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a stable and accurate algorithm to compute all solutions of the inverse kinematics problem of a 6 revolute manipulator chain is presented. A system of equations is constructed based on the fundamental closure conditions, leading to a closed algebraic system of 20 equations involving 16 quantities, composed of trigonometric functions of five among the six unknown joint angles. Two among these five are stably eliminated using singular value decomposition (SVD) avoiding the need to consider special cases. The resulting system of equations involving three unknowns is solved by conversion to a generalized eigenvalue problem. The remaining three unknown angles are obtained using the previously computed pseudoinverse. In this formulation we exploit the inherently complex form of the system reducing it to 10 complex equations in 9 quantities, which substantially accelerates the SVD computation. The method’s robustness is demonstrated through a comparison to current methods and several examples including known problematic cases where some axis or link lengths vanish, or some joint angles are 180 degrees, as well as cases where multiple eigenvalues arise. [ABSTRACT FROM AUTHOR]

In this paper, Iterative Learning Control (ILC) combined with a Proportional Derivative (PD) regulator is proposed to deal with the problem of designing a control signal for motion control systems. The main idea in iterative learning control is to gradually improve the performance of the system by exploiting data from the previous iterations. The learning control algorithm can obtain a better tracking control performance for the next run and hence outperforms conventional control approaches such as Proportional Integral Derivative (PID) controller and feedforward control. The main area of application for ILC is control of industrial robots and CNC machine tool, printing, and other industrial applications. The learning algorithms can also be used in combination with other control techniques. For example, learning feedforward control is designed in the first iteration. Then iterative learning control is applied to improve performance in the subsequent iterations. In addition, the conventional feedback regulator is designed in combination with iterative control to deal with uncertainty. Simulation results demonstrate the potential benefits, sensitivity and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the development of a modified Fractional Order Proportional Integral Derivative (FOPID) controller to mitigate frequency deviation in a four-area thermal power system. Change in load demand and noisy power system environment can cause frequency deviation. Reducing high-frequency deviation is very paramount in load frequency control. This is because large frequency deviation can cause the transmission line to be overloaded, which may damage transformers at the transmission level, damage mechanical devices at the generating stations and also damage consumer devices at the distribution level. The conventional PID has been widely used for this problem. However, the parameter values of the various generating units of the power system like generators, turbines and governors keep changing due to numerous on/off witching in the load side. As such, it is essential that the control strategy applied should have a good capability of handling uncertainties in the system parameters and good disturbance rejection. Fractional order PID controller is known to give a higher phase margin resulting in very good disturbance rejection, robustness to high-frequency noise and elimination of steady-state error. A four-area power system was designed, and FOPID was used as the supplementary controller to mitigate frequency deviation. Ant Lion Optimizer (ALO) algorithm was used to optimize the gains of the FOPID controller by minimizing Integral Square Error (ISE) as the objective function. Results obtained outperformed other designed methods available in the literature in terms of reducing frequency deviation, tie-line power deviation and area control error. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize "contactless delivery" to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

DatalogMTL is an extension of Datalog with metric temporal operators that has recently found applications in stream reasoning and temporal ontology-based data access. In contrast to plain Datalog, where materialisation (a.k.a. forward chaining) naturally terminates in finitely many steps, reaching a fixpoint in DatalogMTL may require infinitely many rounds of rule applications. As a result, existing reasoning systems resort to other approaches, such as constructing large Büchi automata, whose implementations turn out to be highly inefficient in practice. In this paper, we propose and study finitely materialisable DatalogMTL programs, for which forward chaining reasoning is guaranteed to terminate. We consider a data-dependent notion of finite materialisability of a program, where termination is guaranteed for a given dataset, as well as a data-independent notion, where termination is guaranteed regardless of the dataset. We show that, for bounded programs (a natural DatalogMTL fragment for which reasoning is as hard as in the full language), checking data-dependent finite materialisability is ExpSpace-complete in combined complexity and PSpace-complete in data complexity; furthermore, we propose a practical materialisation-based decision procedure that works in doubly exponential time. We show that checking data-independent finite materialisability for bounded progams is computationally easier, namely ExpTime-complete; moreover, we propose sufficient conditions for data-indenpendent finite materialisability that can be efficiently checked. We provide also the complexity landscape of fact entailment for different classes of finitely materialisable programs; surprisingly, we could identify a large class of finitely materialisable programs, called MTL-acyclic programs, for which fact entailment has exactly the same data and combined complexity as in plain Datalog, which makes this fragment especially well suited for big-scale applications. [ABSTRACT FROM AUTHOR]

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal cascaded fractional order controller (CC-FOC) having disturbance observer (DOB) to minimize the frequency deviation of an autonomous wind-diesel-generator (WDG) system in the wake of load variation and random wind power output. The model of WDG consists of a variable-speed wind turbine (VSWT) and a diesel engine generator to match the ever-rising load demand. To regulate the frequency of the WDG system, fractional-order proportional-integral-derivative (FOPID) and tilt-integral-derivative (TID) controllers are simultaneously used as master and slave controllers, respectively, for developing the CC-FOC. An improved squirrel search algorithm (SSA) with quasi-oppositional-based learning (QOSSA) is applied to explore the optimum gains of the proposed non-integer controller. The performance of the developed controller is compared with the outputs of FOPID, TID, and PID controllers. The result demonstrates the mastery and effectiveness of the proposed controller against random load and wind power fluctuations. Finally, Kharitonov's stability theorem is applied to ensure the robustness margin of the studied WDG plant for a wide range of system parameters variation. [ABSTRACT FROM AUTHOR]

In this article, the authors study the utility of deep-learning approaches in statistical arbitrage under the generalized pairs-trading paradigm. Stock returns are regressed on a set of risk factors derived using principal component analysis, and the long short-term memory (LSTM) structure is employed to forecast directions of idiosyncratic residuals. Daily market-neutral trades are constructed based on the predicted signals. The authors compare their results with the influential relative value (RV) model by Avellaneda and Lee (2010) on the universe of S&P 500 Index (S&P 500) stocks. Model evaluations are performed on two distinct periods (2001–2007 and 2015–2021) to alleviate the survivorship bias resulting from the S&P 500 composition changes over time and to study the robustness of these two models in two distinct eras. Their findings suggest that the LSTM model consistently and significantly outperforms the RV model across the two periods when transaction costs are accounted for. However, in the transaction cost–free world, the outperformance is modest even though it is still consistent. [ABSTRACT FROM AUTHOR]

In the visual positioning of Unmanned Ground Vehicle (UGV), the visual odometer based on direct sparse method (DSO) has the advantages of small amount of calculation, high real-time performance and high robustness, so it is more widely used than the visual odometer based on feature point method. Ordinary vision sensors have a narrower viewing angle than panoramic vision sensors, and there are fewer road signs in a single frame of image, resulting in poor road sign tracking and positioning capabilities, and severely restricting the development of visual odometry. Based on these considerations, this paper proposes a binocular stereo panoramic vision positioning algorithm based on extended DSO, which can solve these problems well. The experimental results show that the binocular stereo panoramic vision positioning algorithm based on the extended DSO can directly obtain the panoramic depth image around the UGV, which greatly improves the accuracy and robustness of the visual positioning compared with other ordinary visual odometers. It will have widely application prospects in the UGV field in the future. [ABSTRACT FROM AUTHOR]

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12 % . [ABSTRACT FROM AUTHOR]

This paper presents a robust multi-objective optimal design (RMOP) of a passenger car with a semiactive suspension system. The mean-effective values of the root mean square of the passenger's head acceleration, suspension travel, and tire deflection are considered as design objectives. The passive components of the suspension and the design details of the Linear Quadratic Regulator (LQR) algorithm are used as design parameters. During the design, global sensitivity analysis is carried out using the Fourier Amplitude Sensitivity Test (FAST) to specify the elements of the model that can highly alter the design objectives. The mass of the passenger's head and upper body, the mass of the passenger's lower body and cushion, passenger and cushion's elastic properties, and the sprung mass of the vehicle are selected for the sensitivity analysis. Results show that the design criteria are very sensitive to the variations in the sprung mass of the vehicle as compared to the other parameters. As a result, the variations in this parameter and passive elements of the suspension system are considered. Constraints are applied on the objectives in compliance with the requirements of ISO 2631-1 on the design of car suspension systems. The optimization problem is solved by the NSGA-II (non-dominated sorting genetic algorithm II) and robust Pareto front and set are obtained. The Pareto set includes multiple design options from which the decision-maker can choose to implement. Responses of the passenger's head acceleration, suspension travel, and tire deflection show that the robust multi-objective design algorithm (RMOA) is very effective and guarantees less sensitivity to the suspension passive elements. [ABSTRACT FROM AUTHOR]

The ability to track vortices spatially and temporally is of great interest for the study of complex and turbulent flows. A methodology to solve the problem of vortex tracking by the application of machine learning approaches is investigated. First a well-known vortex detection algorithm is applied to identify coherent structures. Hierarchical clustering is then conducted followed by a unique application of the Hungarian assignment algorithm. Application to a synthetic flowfield of merging Batchelor vortices results in robust vortex labelling even in a vortex merging event. A robotic PIV experimental dataset of a canonical Ahmed body is used to demonstrate the applicability of the method to three-dimensional flows. [ABSTRACT FROM AUTHOR]

The present study aims to propose a robust method to detect the damage severity and location of the structural elements, focusing on the data type and acquisition method and promoting the model updating tools. The novelty of this method lies in its rotational mode shape acquisition that provides valuable information on the damage. In this method, the damaged elements were indirectly identified by detecting the healthy elements and eliminating them from the search space. Moreover, this method could minimize the modal strain energy difference between the damaged reference model and the numerical model using an optimization algorithm. An improved genetic algorithm was then employed to perform the optimization task. In this study, four numerical and two experimental damage scenarios were applied to a simply supported beam to examine the performance of the proposed method. Data acquisition systems were implemented using vision-based and accelerometer-based methods. The results indicated that this method could accurately identify the location and severity of damage using only the first mode shape since the rotational mode shapes were more sensitive to damage than the vertical mode shapes. [ABSTRACT FROM AUTHOR]

Industrial 3D models are usually characterized by a large number of hidden faces and it is very important to simplify them. Visible-surface determination methods provide one of the most common solutions to the visibility problem. This study presents a robust technique to address the global visibility problem in object space that guarantees theoretical convergence to the optimal result. More specifically, we propose a strategy that, in a finite number of steps, determines if each face of the mesh is globally visible or not. The proposed method is based on the use of Plücker coordinates that allows it to provide an efficient way to determine the intersection between a ray and a triangle. This algorithm does not require pre-calculations such as estimating the normal at each face: this implies the resilience to normals orientation. We compared the performance of the proposed algorithm against a state-of-the-art technique. Results showed that our approach is more robust in terms of convergence to the maximum lossless compression. [ABSTRACT FROM AUTHOR]

The paper addresses the Automatic Generation Control (AGC) of the interconnected two-area power system under deregulated environment. Area 1 includes thermal system, Distributed Generation (DG), and aggregate Electric Vehicle (EV) wheras area 2 contains thermal system, gas system, and aggregate electric vehicle. Nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB), Boiler Dynamics (BD), and Communication Delay (CD) are explored in the proposed test system to achieve a realistic approach. For considering cross coupling effect between excitation system and AGC, an exact model has been investigated. A novel cascade controller ð2 - DOF - PI - FOPIλDNÞ has been proposed to achieve the desired goal. For optimum values of controller, new novel Quasi Opposition Lion Optimization Algorithm (QOLOA) has been suggested and implemented for the studied system. The sensitivity analysis study was performed to assess the robustness of the proposed controller by varying system parameters. System parameters are varied by ±25%, ±35%, and ±50%, and the result shows the robustness of the controller and algorithm For the proposed controller, Figure of Demerit (FOD) is compared with various algorithms, and the Integral square Error (ISE) is taken as objective function. The optimistic results shows the effectiveness & superiority of proposed AGC in different scenario of deregulation. [ABSTRACT FROM AUTHOR]

State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, specifying and training RNNs is notoriously tricky, particularly as the common strategy to approximate gradients back in time, called truncated Back-prop Through Time (BPTT), can be sensitive to the truncation window. Further, domain-expertise--which can usually help constrain the function class and so improve trainability--can be difficult to incorporate into complex recurrent units used within RNNs. In this work, we explore how to use multi-step predictions to constrain the RNN and incorporate prior knowledge. In particular, we revisit the idea of using predictions to construct state and ask: does constraining (parts of) the state to consist of predictions about the future improve RNN trainability? We formulate a novel RNN architecture, called a General Value Function Network (GVFN), where each internal state component corresponds to a prediction about the future represented as a value function. We first provide an objective for optimizing GVFNs, and derive several algorithms to optimize this objective. We then show that GVFNs are more robust to the truncation level, in many cases only requiring one-step gradient updates. [ABSTRACT FROM AUTHOR]

In this work, two novel mixed-integer linear programming models for energy efficient scheduling of flexible job-shops with simultaneous consideration of machine switching off-on strategy are developed. While the first model is based on the unit-specific event-based approach, the other one uses the sequence-based approach. The computational results demonstrate that the proposed models, especially the unit-specific event-based model, are more robust and efficient than the existing models. To solve industrial-scale problems efficiently, a hybrid algorithm is developed through the combination of the existing eGEP algorithm and mathematical programming approach. The hybrid algorithm leads to up to 15 % more energy savings in comparison to the eGEP algorithm. [ABSTRACT FROM AUTHOR]

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A new evaluation scheme for double exposure three-dimensional particle-tracking velocimetry is proposed. Its main feature, a robust multi-pass matching algorithm, is presented and validated by investigating its performance when applied to a synthetic data set. To evaluate real measurement data, the approach is supplemented by an iterative triangulation scheme, in which the resulting particle positions are validated through the matching algorithm. The comparison with tomographic particle-image velocimetry data shows good agreement. The proposed algorithm allows this approach to be applied to volumetric measurements with seeding densities exceeding standard particle-tracking applications. Therefore, it can serve as a drop-in replacement for tomographic particle-image velocimetry at significantly reduced computational cost. [ABSTRACT FROM AUTHOR]

Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use. [ABSTRACT FROM AUTHOR]

The fuzzy c-means (FCM) clustering algorithm is an unsupervised learning method that has been widely applied to cluster unlabeled data automatically instead of artificially, but is sensitive to noisy observations due to its inappropriate treatment of noise in the data. In this paper, a novel method considering noise intelligently based on the existing FCM approach, called adaptive-FCM and its extended version (adaptive-REFCM) in combination with relative entropy, are proposed. Adaptive-FCM, relying on an inventive integration of the adaptive norm, benefits from a robust overall structure. Adaptive-REFCM further integrates the properties of the relative entropy and normalized distance to preserve the global details of the dataset. Several experiments are carried out, including noisy or noise-free University of California Irvine (UCI) clustering and image segmentation experiments. The results show that adaptive-REFCM exhibits better noise robustness and adaptive adjustment in comparison with relevant state-of-the-art FCM methods. [ABSTRACT FROM AUTHOR]

This paper presents a review of sliding mode control for autonomous underwater vehicles (AUVs). The AUVs are used under water operating in the presence of uncertainties (due to hydrodynamics coefficients) and external disturbances (due to water currents, waves, etc.). Sliding mode controller is one of the nonlinear robust controllers which is robust towards uncertainties, parameter variations and external disturbances. The evolution of sliding mode control in motion control studies of autonomous underwater vehicles is summarized throughout for the last three decades. The performance of the controller is examined based on the chattering reduction, accuracy (steady state error reduction), and robustness against perturbation. The review on sliding mode control for AUVs provides insights for readers to design new techniques and algorithms, to enhance the existing family of sliding mode control strategies into a new one or to merge and re-supervise the control techniques with other control strategies, in which, the aim is to obtain good controller design for AUVs in terms of great performance, stability and robustness. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel approach for robust topology optimization (RTO). The proposed approach integrates the hybrid polynomial correlated function expansion (H-PCFE) with the deterministic topology optimization (DTO) algorithm, where H-PCFE is used for computing the moments associated with the objective function in robust design optimization (RDO). It is argued that re-training the H-PCFE model at every iteration will make the overall algorithm computationally expensive and time-consuming. To alleviate this issue, an adaptive algorithm is proposed that automatically decides whether to retrain the H-PCFE model or to use the available H-PCFE model. Since RTO involves a large number of design variables, we also argue that, from a computational point of view, gradient-based optimization algorithms are better suited over gradient-free optimization techniques. However, the bottleneck of using gradient-based optimization algorithms is associated with the computation of the gradients. To address this issue, we propose a procedure for computing the gradients of the objective function in RTO. The proposed approach is highly flexible and can be integrated with the already available DTO codes from literature. To illustrate the performance of the proposed approach, three well-known benchmark problems have been solved; two of them being on the 2D MBB beam and one more on a 3D cantilever beam. As compared to the results of the DTO, RTO yields conservative results which are visible from the additional limbs appearing in the optimized topology of the structure. Furthermore, to justify the use of H-PCFE as a surrogate within the proposed framework, a comparative assessment of the proposed approach against the popular surrogate models has also been presented. It is observed that H-PCFE outperforms other popular surrogate models such as Kriging, radial basis function and polynomial chaos expansion, in both accuracy and efficiency. Highlights • A novel approach for robust topology optimization (RTO) is proposed. • H-PCFE is adaptively coupled with topology optimization algorithms. • Formula for computing topological derivative in RTO is proposed. • Proposed approach is efficient as well as accurate. [ABSTRACT FROM AUTHOR]

Abstract Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive. [ABSTRACT FROM AUTHOR]

Highlights • A new robust loss function is designed for imbalanced data sets. • The proposed model combines this loss with SVM. • The robustness of model is analyzed in theory. • The Bayes optimal solution is derived. • Alternative iterative algorithm is designed to reduce algorithm complexity. Abstract Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a robust SVM framework is presented which results in a Bayes optimal classifier. However, nonconvexity makes the model difficult to optimize. We develop an alternative iterative algorithm to solve the proposed model. What's more, we analyze the robustness of the proposed model theoretically from a re-weighted SVM viewpoint and the obtained optimal solution is consistent with Bayesian optimal decision rule. Furthermore, numerical experiments are carried out on databases that are drawn from UCI Machine Learning Repository and a practical application. With two different types of noise environments, one with label noise and one with feature noise, experiment results show that on these two databases the proposed method achieves better generalization results compared to other SVM methods. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system. [ABSTRACT FROM AUTHOR]

Summary: Novel robust finite‐time stabilizing algorithms are developed side by side for the state and output feedback designs. Being initially developed for a nonlinear cascade second‐order system, these algorithms are straightforwardly extendible to electromechanical systems of relative degree two. The proposed synthesis is based on the disturbance compensation, relying on the dirty differentiation and sliding mode approach, and it is applicable to a wider class of disturbances than that addressed in the literature. Simulation results illustrate the efficiency of the resulting synthesis procedure and support analytical results. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present an extension of the generalised supertwisting algorithm (STA) to the multivariable framework. We begin by introducing an algorithm that may be deemed as a linear, quasicontinuous, or discontinuous multivariable system, depending on the functions that define them. For the class represented by such an algorithm we prove the robust, Lyapunov stability of the origin and characterise the perturbations that preserve its stability. In particular, when its vector field is discontinuous or quasicontinuous our algorithm is endowed with finite‐time stability. Due to its resemblance to the scalar case, we denote such finite‐time stable systems as generalised multivariable STA. Furthermore, the class of finite‐time stable systems comprise the currently available versions of STAs. To finalise, by means of simulation examples, we show that our proposed finite‐time stable algorithms are well suited for signals online differentiation and highlight their dynamical traits. [ABSTRACT FROM AUTHOR]

Summary: The position and attitude control of a six‐degree‐of‐freedom vehicle is dealt with in this paper. The actuation system is assumed to consist of a set of mono‐directional devices suitably located and directed. The model of the system is characterized by a large amount of uncertainties, disturbances and measurement errors. The state is assumed fully available and the navigation problems generating smooth references for the state trajectories are supposed already solved. A new attitude guidance algorithm has been developed to enhance robustness with respect to a class of nonsmooth measurement errors. The use of the simplex‐based sliding mode methodology reveals to be simultaneously suitable for the design of the actuation system (position and orientation of the actuators) and the implementation of the control strategy. The chattering phenomenon is strongly attenuated by the introduction of integrators in the input channel and, consequently to this choice, a suitable mechanism to avoid the unbounded growth of the individual thrust of the actuators is designed, while at the same time, achieving a direct control of power losses. The performances of the proposed control scheme are demonstrated by simulation by using mathematical models available in the literature. [ABSTRACT FROM AUTHOR]

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models. [ABSTRACT FROM AUTHOR]

The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT's baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT's robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT's robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks. [ABSTRACT FROM AUTHOR]

Most recent technological proposals, such as cyber-physical systems or wireless sensor networks, consist of a collection of tiny nodes designed to be seamless integrated into daily living objects. These nodes then, due to their miniaturized configuration, use to present very limited processing capabilities. Because of that, in general complex algorithms, as which are employed today to secure communications, cannot be implemented in these new systems. Thus, new instruments for security are needed, with a special mention to hardware-supported solutions. Therefore, in this paper different robust hardware-supported cryptosystems based on Chua's circuit are proposed, studied ad compared. The described solutions are specifically designed to be employed in streaming communications among reduced computing power nodes. Moreover, an experimental validation is proposed comparing the performance of the proposed technologies and other existing solutions. [ABSTRACT FROM AUTHOR]

Allowing item review in computerized adaptive testing (CAT) is getting more attention in the educational measurement field as more and more testing programs adopt CAT. The research literature has shown that allowing item review in an educational test could result in more accurate estimates of examinees' abilities. The practice of item review in CAT, however, is hindered by the potential danger of test‐manipulation strategies. To provide review opportunities to examinees while minimizing the effect of test‐manipulation strategies, researchers have proposed different algorithms to implement CAT with restricted revision options. In this article, we propose and evaluate a new method that implements CAT without any restriction on item review. In particular, we evaluate the new method in terms of the accuracy on ability estimates and the robustness against test‐manipulation strategies. This study shows that the newly proposed method is promising in a win‐win situation: examinees have full freedom to review and change answers, and the impacts of test‐manipulation strategies are undermined. [ABSTRACT FROM AUTHOR]

The lack of adequate measures is often an impediment to robust policy evaluation. We discuss three approaches to measurement and data usage that have the potential to improve the way we conduct impact evaluations. First, the creation of new measures, when no adequate ones are available. Second, the use of multiple measures when a single one is not appropriate. And third, the use of machine learning algorithms to evaluate and understand programme impacts. We motivate the relevance of each of the categories by providing examples where they have proved useful in the past. We discuss the challenges and risks involved in each strategy and conclude with an outline of promising directions for future work. [ABSTRACT FROM AUTHOR]

The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results. [ABSTRACT FROM AUTHOR]

Abstract Correlation filter based trackers have achieved appealing performance and high efficiency in recent years. However, for long-term tracking where target objects undergo dramatic appearance variation due to heavy occlusion or out-of-view, conventional correlation filter based tracking algorithms would be distracted by irrelevant objects. Once the trained tracker loses its way, it is impossible to recover the information for the following frames as the model has drifted. In this paper, we decompose the long-term tracking task into tracking and detection. Tracker learns separate correlation filters for explicit translation and scale estimation. Specifically, in order to improve tracking accuracy, the convolutional features for translation filter are extracted, and the scale filter is learned using the target appearance sampled at different scales. Detector trains an online long-term filter and applies it to the entire frame to generate detection proposals. By exploiting these detection proposals, it helps the tracker to recover from problems such as temporary or persistent occlusions. In this way, the proposed approach could handle the model drifting problem effectively for long-term tracking with more accurate estimation of object scale and location. Extensive experimental results on large-scale benchmark sequences have shown the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the ℓ 2 -norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the ℓ 1 -norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the ℓ 1 -norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the ℓ 1 -norm. The ℓ 1 -norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Runtime verification (RV) is a pragmatic and scalable, yet rigorous technique, to assess the correctness of complex systems, including cyber-physical systems (CPSs). Modern RV tools also allow to measure the distance of a CPS behavior from a given formal requirement, thus, to quantify the robustness of a CPS with respect to perturbations caused by the physical environment. In this paper, we propose algebraic RV (ARV), a general, semantic framework for correctness and robustness monitoring. ARV implements an abstract monitoring procedure, in which the specification language (STL) can be instantiated with various qualitative and quantitative semantics. This allows us to expose the core aspects of RV, by separating the monitoring algorithm from the concrete choice of the STL and its semantics. We demonstrate the effectiveness of our framework on two examples from the automotive domain. [ABSTRACT FROM AUTHOR]

A two link planar rigid robotic manipulator is a highly nonlinear, coupled and multi-input multi-output system. For its effective control, an intelligent adaptive fractional order fuzzy sliding mode proportional integral and derivative controller (FOFSMCPID) has been presented in this work. Sliding mode controller (SMC) is designed by using exponential law and closed loop stability analysis is demonstrated by using Lyapunov theorem. The chattering in the SMC controller has been effectively reduced with the help of boundary layer along with the fuzzy logic. For tuning of the controller, a weighted sum of integral of absolute error and chatter has been considered as the objective function to be minimized using cuckoo search optimization algorithm. To demonstrate the efficacy of FOFSMCPID controller, the results have been compared with integer order fuzzy sliding mode proportional, integral and derivative controller (IOFSMCPID), integer order fuzzy sliding mode proportional and derivative controller (IOFSMCPD) and fractional order fuzzy sliding mode proportional and derivative (FOFSMCPD) controller for trajectory tracking, disturbance rejection, noise suppression and model uncertainties. The detailed presented investigations have demonstrated that FOFSMCPID controller exhibits much superior performance over IOFSMCPID, IOFSMCPD and FOFSMCPD controllers. [ABSTRACT FROM AUTHOR]

Numerous Fuzzy segmentation techniques have been proposed in the literature for Image segmentation. This paper proposes a new Novel Intuitionistic Fuzzy C-means (S-IFCM) incorporated with Spatial information to reduce noise/outliers influence. This new clustering algorithm uses City-block distance to compute the rank between two pixels. Yager's type fuzzy complement is used to compute non-membership and further hesitation degree is calculated. The new intuitionistic membership obtained is incorporated with spatial information of image for robustness to noise. Experiments are performed on various noisy images including MRI brain image, to assess the performance of the proposed algorithm. Comparison is done with existing hard, fuzzy and intuitionistic methods on the basis of entropy based segmentation accuracy and validity index. Experimental results show the effectiveness of the proposed method in contrast with other conventional methods. [ABSTRACT FROM AUTHOR]

Highlights • First study focuses on preserving the dynamic performance of HSV under varying pressure condition. • Adaptive switching of four different duty ratios of power source is achieved by current on-line analysis and feedback. • Current can be controlled at a desired value quickly to make the HSV's dynamic performance controllable and stable. Abstract High speed on/off solenoid valve (HSV) has often been used for pressure and flow control in hydraulic systems, which require faster and more stable dynamic performance of HSVs to improve control preciseness. For some kinds of HSVs which rely on the hydraulic pressure to operate, their dynamic performance is greatly influenced by the magnitude of the hydraulic pressure. In this paper, a self-correcting PWM control algorithm for high-speed on/off valves is proposed to improve and even maintain the HSVs' original dynamic performance under changing pressures. Using current feedback analysis based on critical switching currents, the operational state of the HSV is estimated and the adaptive switching of different duty ratios of power source is also realized to optimize the switching behavior of HSV. Finally, through numerical and experimental studies, it is demonstrated that with the proposed control algorithm, the dynamic characteristics of the HSV is not only greatly improved but adapted to pressure changes, and the variation range of the dynamic performance can be controlled within 7.4% (0.3 ms) under 4–20 MPa operating conditions. The presented control algorithm is effective and can be applied in HSV-controlled systems to improve the robustness of the target actuator. [ABSTRACT FROM AUTHOR]

Abstract This study involves proposing an assistive device using a wire-driven Series Elastic Actuator (SEA) for leg rehabilitation that can design and provide assistive force for exercises based on the physical ability of a patient. The proposed assistive device possesses a force estimation ability to measure the mass of the leg along with the force exerted by a user in real-time. In order to achieve precise human force estimation and assistive force generation, a compact Planetary-geared Elastic Actuator, which is a type of Series Elastic Actuator is applied in the proposed device. Various algorithms to provide robust assistive force and to estimate force and work performed by the user are proposed in the study. Pilot tests were conducted with an exercise scenario that fully utilizes the functions and algorithms proposed in this study, and the effectiveness of the proposed system was validated based on two types of experiments: a healthy subject case and a patient case. The results of pilot test approve that the proposed device and algorithm enable patients to perform the exercise with ease, and increase their physical capabilities effectively. [ABSTRACT FROM AUTHOR]

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems. [ABSTRACT FROM AUTHOR]

Abstract The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Highlights • Extend a parameter optimization method to controller design problems for MIMO FO-plants. • The methodology can be applied to fractional-order plants with or without time delays. • The robustness tests show that the controller is robust to the plant gain variations. [ABSTRACT FROM AUTHOR]