Reviewerly/exHarmony huggingface dataset:
Nonlinear constructive observer design for direct homography estimation

Feature-based homography estimation approaches rely on extensive image processing for feature extraction and matching, and do not adequately account for the information provided by the image. Therefore, developing efficient direct techniques to extract the homography from images is essential. This paper presents a novel nonlinear direct homography observer that exploits the Lie group structure of SL(3) and its action on the space of image maps. Theoretical analysis demonstrates local asymptotic convergence of the observer. The observer design is also extended for partial measurements of velocity under the assumption that the unknown component is constant or slowly time-varying. Finally, simulation results demonstrate the performance of the proposed solutions on real images.
DyClee-N&amp;C: a clustering algorithm for heterogeneous data based situation assessment

In data-based situation assessment applications, the proliferation of data acquired and recorded on current technological systems is a key issue in that data remain unlabeled because labeling would require too much time and implies prohibitive costs. The data should therefore speak for itself. The different situations, e.g., normal or faulty, must hence be learned only from the data. Clustering methods, also named unsupervised classification methods, can be used for that purpose. These methods are designed to cluster the samples according to some similarity criterion. The different clusters can be associated to different situations whose discrimination may be relevant to obtain a proper diagnosis. Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to categorical data. This is the case of the algorithm DyClee, named DyClee-N in the paper. However, in many application domains, qualitative features are key to properly describe the different situations. DyClee-N was recast to produce a version, named DyClee-C that accepts categorical features, but only categorical features. This paper presents DyClee-N&C that subsumes both the numeric and categorical feature based algorithms DyClee-N and DyClee-C respectively. DyClee-N&C is applied to a data set of the literature for the evaluation of risk in the automobile domain and compared to state of the art clustering methods.
A study on data augmentation optimization for data-centric health prognostics of industrial systems

Factory of the Future and Industry 4.0 bring new challenges for manufacturing systems. At the same time, new maintenance strategies have become a major pillar to keep manufacturing systems performances and competitiveness. Such strategies allow to anticipate failure thanks to prognostics process. As such, promising prognostics approaches use data-driven machine learning techniques, however the recorded data set for learning is often "small" as failure occurrences are rare. Therefore, this study investigates data augmentation methods for improving prognostics by adding new samples generated from existing ones. This paper use a public prognostics data set to assess the performances of data transformation methods based on transformation hyper-parameters optimization using cross-validation.
Dynamic Supplier Selection Based on Fuzzy Cognitive Map

In this paper, the principle of fuzzy cognitive map is adapted to identify the main important criteria for a given period of time and then to select suppliers dynamically. The main idea come from the fact that a given criterion, like for example "a supplier who is ecofriendly", could be described and decomposed by various sub-criteria or factors, like "low emission of pollutants", "low emission of carbon dioxide", "low energy consumption", "respectful of biodiversity", "recyclable and/or recycled products", noted that these factors can go in the same direction or on the contrary be contradictory. Furthermore, an important factor for the selection of suppliers at a given period could be less interesting at another time because of dynamical changes and crises (economic, wars, national or international politics, competition...). Therefore, a timely prediction of important criteria based on a fuzzy cognitive map associated with nonlinear Hebbian algorithm is proposed. The fuzzy cognitive map is defined with the help of experts and tested for a simple pharmaceutical supply chain. Results reveal the performance of the proposed method.
Named Entity Recognition in Persian Language based on Self-attention Mechanism with Weighted Relational Position Encoding

Named-entity Recognition (NER) is challenging for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER Model. We propose a customized model based on linguistic properties to compensate for this lack of resources in low-resource languages like Persian. According to pronoun-dropping and subject-object-verb word order specifications of Persian, we propose new weighted relative positional encoding in the self-attention mechanism. Using the pointwise mutual information factor, we inject co-occurrence information into context representation. We trained and tested our model on three different datasets: Arman, Peyma, and ParsTwiNER, and our method achieved 94.16%, 93.36%, and 84.49% word-level F1 scores, respectively. The experiments showed that our proposed model performs better than other Persian NER models. Ablation Study and Case Study also showed that our method can converge faster and is less prone to overfitting.
Distributed Parameter Estimation under Gaussian Observation Noises

In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown d-dimensional parameter, which might be subject to Gaussian random noises. They aim to infer true value of the unknown parameter by cooperating with each other. To this end, we first generalize the so-called dynamic regressor extension and mixing (DREM) algorithm to stochastic systems, with which the problem of estimating a d-dimensional vector parameter is transformed to that of d scalar ones: one for each of the unknown parameters. For each of the scalar problem, an estimation scheme is given, where each sensor fuses the regressors and measurements in its in-neighborhood and updates its local estimate by using least-mean squares. Particularly, a counter is also introduced for each sensor, which prevents any (noisy) measurement from being repeatedly used such that the estimation performance will not be greatly affected by certain extreme values. A novel excitation condition termed as local persistent excitation (Local-PE) condition is also proposed, which relaxes the traditional persistent excitation (PE) condition and only requires that the collective signals in each sensor's in-neighborhood are sufficiently excited. With the Local-PE condition and proper step sizes, we show that the proposed estimator guarantee that each sensor infers the true parameter in mean square, even if any individual of them cannot. Numerical examples are finally provided to illustrate the established results.
Decision Tree based diagnosis for hybrid model-based/data-driven fault detection and exclusion of a decentralized multi-vehicle cooperative localization system*

Cooperative navigation systems are one of the main topics of interest in multi-robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (DJS) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data from three Turtlebot3 burger robot equipped with wheel encoders (for prediction), a gyroscope (for the yaw angle) and a Marvelmind (for the absolute position). A ground truth is also recorded using optitrack system.
Interpretation of Explainable AI Methods as Identification of Local Linearized Models

Artificial intelligence (AI) models are increasingly ubiquitous in daily life, their unparalleled predictive and decision-making capabilities are being utilized for applications of all magnitudes, ranging from minor decisions to those with significant impacts on individuals and society. However, many of these models feature a multitude of redundant parameters, which render them incomprehensible to human understanding. The lack of transparency raises concerns about the reliability and fairness of the decisions made by AI models motivating a new field of research called eXplainable AI (XAI), which aims to elucidate complex AI model outcomes and develop tools to enable human understanding. Given the increasing impact of machine learning in the fields of data-driven estimation and control, it becomes crucial to integrate XAI tools with control theory to better comprehend the decisions made by AI models in estimation and control. In this article, we propose the use of an XAI method called Local Interpretable Model-Agnostic Explanations (LIME) to explain the mechanisms behind a black-box estimation algorithm processing time-series data. Moreover, we demonstrate that LIME can be used to identify a local linearized model that approximates the complex machine learning algorithm. We show that the identified local linearized model can shed light on the dynamic of the model that generated the training data.
Calculation and Analysis of Technical and Economic Indicators of the NPP Power Unit

Technical and economic indicators (TEI) characterize the efficiency, reliability, and durability of power equipment during its operation. Based on automating the determination of TEI directly during the production of electricity, objective information can be obtained on the efficiency of the flow of technological processes, as well as on the actual state of power equipment. The conditions for optimal operation of power plants are determined in the process of analyzing its technical and economic indicators. Therefore, the analysis of indicators is one of the most important tasks of automated process control systems (APCS) of nuclear power plants, which largely determines the efficiency of the power unit.
Distributed MARL with Limited Sensing for Robot Navigation Problems

This paper proposes a Multi-Agent Reinforcement Learning (MARL) algorithm for the multi-robot navigation problem. Most of the proposals in the literature requires some form of information sharing and communications among agents to coordinate their action in order to complete the overall task. The proposed paper, named Limited Sensing MARL (LS-MARL), assumes that each robot decisions rely on local information and is provided with sensor, which can be switched on for the localization of the robots within a given range. Besides the navigation task, each agent aims at limiting the use of the sensor as much as possible (i.e., to be as independent as possible) for energy saving or safety reasons. The algorithm is evaluated by simulations and favourably compares to the one proposed in (Yu et al. (2015)), that assumes a similar setup in which the neighbouring agents share their positioning information.
Multi-maneuver algorithms for multi-risk collision avoidance via nonconvex quadratic optimization

In this article, the problem of modeling and computing the optimal collision avoidance maneuvers for multiple short-term encounters is presented. Several metrics defining the overall collision risk are analyzed. These include imposing lower bounds on the miss-distance or the Mahalanobis distance at each corresponding time of closest approach (TCA) or upper-bounds on the collision probability. The avoidance maneuvers are modeled as impulsive ones in a single direction of the local frame and for a priori fixed dates for operational motivations. Station-keeping constraints are also imposed via linear inequalities on the relative states at each TCA. The specific nature of the imposed constraints is dictated by a practical framework provided by the French Space Agency (CNES). This results in formulating the maneuver design problem as an optimization problem with linear objective and non-convex quadratic constraints. Different algorithms are presented to solve this problem. Finally, the relative efficiency of the proposed approaches is evaluated and analyzed on a realistic conjunction built from data extracted from the CNES database and on academic random examples of higher complexity.
Zonotopic-tube-based LPV Motion Planner for Safety Coordination of Autonomous Vehicles

In this paper an optimization-based solution to the collision avoidance challenge for autonomous vehicles is proposed. The presented approach consists in an online motion planner designed to define a feasible and efficient path which implicitly guarantees safety manoeuvres in dynamic surroundings. The fact of considering moving obstacles inside the motion planner increases the complexity of the problem while forces it to be executed more frequently as others. To reduce its computational complexity, this approach proposes a two stages translation of the commonly used non-linear optimization-based structure into a QP formulation which can be easily solved. The first stage is based on the use of LPV matrices in the dynamic constraints of the vehicle. The second stage consists in computing linear expressions by set propagation to obtain the set of permitted inputs and reachable states which guarantee safety conditions.
Domain Knowledge in CRISP-DM: An Application Case in Manufacturing

To keep up with shifting technology trends and remain competitive, more manufacturing companies are investigating how to utilize data analytics to improve their processes. An issue these companies often face today is the need for more competence to perform advanced analytics projects within their departments. By using a human-in-the-loop approach and efficiently utilizing current domain knowledge in combination with data analytics, the higher success of implementation can be achieved. A common approach today to perform data analytics projects is to use the general Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. This methodology does not consider the challenges specific to manufacturing and how to include domain expertise. This paper, therefore, suggests how the CRISP-DM methodology can be adapted to compensate for these issues. The adapted methodology is demonstrated in a case study for improving quality in the machining process by using interpretable machine learning models that can be used to assist experts when performing root cause analysis. This contributes to showing how to use domain experts' knowledge better and how data analytics can be used in conjunction with domain-specific methods.
Consensus-Based Distributed Batch Estimation in Asynchronous Wireless Sensor Networks

In this paper, to address the distributed estimation problem over an asynchronous wireless sensor network (aWSN), an average consensus-based distributed batch estimation (DBE) method is proposed. The DBE seeks to update the global posterior with a predefined global update period (GUP) and is implemented with a local filter (LF) and a fusion filter (FF). For LF, we develop two different asynchronous batch estimation approaches to align and compute the asynchronous local posteriors of multiple nodes in an aWSN. At FF, an average consensus filter is adopted to compute the global posterior via a proposed DBE fusion rule. Numerical results show that the proposed DBE method has high target-tracking accuracy and is robust to strong asynchronism. Besides, the optimality of DBE fusion can be approximately achieved with a sufficiently large number of particles and consensus iterations.
6D Object Pose Tracking with Optical Flow Network for Robotic Manipulation

In this paper, we design a novel 6-DOF object pose tracking framework for robotic manipulation tasks. The framework takes a RGB-D video stream as input observations and outputs a 6D pose estimate corresponding to each frame for the interested object to be tracked. The novelty lies in the pose change estimation where we leverage a segmentation network and an optical flow network to estimate the pose change between previous and current frames. The final 6D pose estimate is the multiplication of the 6D pose matrix in previous frame and the pose change. Unlike most tracking networks, our pose tracking model does not require any object 3D model as auxiliary input. We take two consecutive frames as the input and estimate their optical flow map by using a pre-trained optical flow network. Our framework is a keypoint based estimation method. The estimated flow map can extract the temporal motion information that can be used to generate keypoint candidates. Then an iterative keypoint refinement scheme is used to validate the selected keypoints. Our experimental results show that our framework can outperform some existing works or achieve comparable results in three selected datasets.
Automatic Generation of Control Lyapunov Functions for Vehicle Guidance on Complex Paths

In this paper, we propose a new method to construct systematically a control Lyapunov function (CLF) for vehicle guidance and obstacle avoidance. The obtained CLF is expressed by some parameters, and the exact values of the CLF and its gradient can be computed online. The rough route of the vehicle to the goal point is designed by a directed acyclic graph (DAG). The asymptotic stability of the controller based on the designed CLF is mathematically guaranteed. The method can be applied to cases with obstacles having complex shapes. By modifying the DAG and some values, we can change the rough route of the vehicle as well as the goal point.
Speed and Range Parameterised State Estimators for 3D underwater angles-only target tracking problem

An angles-only underwater target tracking problem in three-dimensional space is considered in this work. The uncertainty in the initial range and speed parameters of the target can lead to a loss of accuracy. Hence, a parametrised state estimation approach is opted to achieve better estimation performance for the nonlinear filtering algorithms. To achieve this, the unscented Kalman filter (UKF) and new sigma point Kalman filter (NSKF) has been reformulated by incorporating the speed and range parametrisation concept. A comparison analysis considering different sampling intervals is performed based on root mean square error (RMSE) in position, percentage track loss, and bias norm, for a moderately nonlinear tracking scenario.
Reinforcement Learning with Partial Parametric Model Knowledge

We adapt reinforcement learning methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete information from a partial model and retains RL's data-driven adaption towards optimal performance. The linear quadratic regulator provides a case study; numerical experiments demonstrate the effectiveness and resulting benefits of the proposed method.
Ensuring utility while enforcing current-state opacity

In this paper, we deal with current-state opacity and we address one of the main criticisms of opacity enforcement through obfuscation techniques raised in the literature: in order to conceal the secret behavior of the system from the intruder, additional information is obfuscated to the legitimate receiver. We then propose an Opacity-Enforcer that misleads the intruder to never infer that the secret behavior has been executed (opacity enforcement) while the legitimate receiver must always acknowledge when the utility behavior has occurred (ensuring utility). We also provide necessary and sufficient conditions to model the behavior of the Opacity-Enforcer, together with an algorithm for its realization, when possible.
The Simple Solution for Nonlinear State Estimation of Ill-Conditioned Systems: The Normalized Unscented Kalman Filter

An easy-to-implement method for nonlinear state estimation for ill-conditioned systems is proposed. By propagating standard deviations and correlations instead of the covariance in the unscented Kalman filter (UKF), the condition numbers of relevant matrices are reduced. The reduction in the condition number is related to the scaling of the problem. Hence, what we propose is a normalization method that acts as an "auto-scaler". Compared to other methods in state estimation for ill-conditioned systems, our proposed method factors the covariance matrix into physically meaningful statistics which can be used to check for filter divergence online. The method is compared to a standard UKF in a case study and shows a significant reduction in the condition number.
Stochastic Differential Equations with State Dependent Diffusion - 2 Order Statistics and State Estimation

Estimation of states in stochastic differential equations with state dependent diffusion is known to be difficult. Previous research recommend the higher order extended Kalman filter or the Lamperti transform method for this case. This paper shows that a new developed method, based on the unscented Kalman filter, is superior for two simulated stochastic differential equation systems.
Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel way of manipulating the grid, leading to the time-update in form of a convolution, is proposed. This reduces the PMF time complexity from quadratic to log-linear with respect to the number of grid points. Furthermore, the number of unique transition probability values is greatly reduced causing a significant reduction of the data storage needed. The proposed PMF prediction step is verified in a numerical study.
Self-driving Electric Wheelchair in Crowded Environments Using a Fuzzy Potential Model Predictive Control

In the recent development of small autonomous vehicles, such as electric wheelchairs, one of the challenging environments to be operated is a congested area in which vehicles are mixed with pedestrians. In addition to prompt avoidance in response to the surrounding non-stationary environment, optimal maneuvering must be conducted between the multiple feasible trajectories. In the present study, the fuzzy potential method is combined with model predictive control to avoid obstacles based on predictions of future behavior. Furthermore, the global search for a solution and flexible switching of avoidance paths were achieved utilizing the Monte Carlo optimization in model predictive control. This also enables the optimization of a nonlinear and discontinuous evaluation function including membership functions for fuzzy inference. The method's effectiveness was verified through simulations and real-time experiments for a self-driving electric wheelchair equipped with a LiDAR, and verification confirmed flexible obstacle avoidance.