Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these data sets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation data sets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge.

This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning. It starts with an introductory chapter that describes notation used throughout the book and serve at a reminder of basic concepts in calculus, linear algebra and probability and also introduces some measure theoretic terminology, which can be used as a reading guide for the sections that use these tools. The introductory chapters also provide background material on matrix analysis and optimization. The latter chapter provides theoretical support to many algorithms that are used in the book, including stochastic gradient descent, proximal methods, etc. After discussing basic concepts for statistical prediction, the book includes an introduction to reproducing kernel theory and Hilbert space techniques, which are used in many places, before addressing the description of various algorithms for supervised statistical learning, including linear methods, support vector machines, decision trees, boosting, or neural networks. The subject then switches to generative methods, starting with a chapter that presents sampling methods and an introduction to the theory of Markov chains. The following chapter describe the theory of graphical models, an introduction to variational methods for models with latent variables, and to deep-learning based generative models. The next chapters focus on unsupervised learning methods, for clustering, factor analysis and manifold learning. The final chapter of the book is theory-oriented and discusses concentration inequalities and generalization bounds.

Autonomous mobile robots have many applications in indoor unstructured environment, wherein optimal movement of the robot is needed. The robot therefore needs to navigate in unknown and dynamic environments. This paper presents an implementation of fuzzy logic controller for navigation of mobile robot in an unknown dynamically cluttered environment. Fuzzy logic controller is used here as it is capable of making inferences even under uncertainties. It helps in rule generation and decision making process in order to reach the goal position under various situations. Sensor readings from the robot and the desired direction of motion are inputs to the fuzz logic controllers and the acceleration of the respective wheels are the output of the controller. Hence, the mobile robot avoids obstacles and reaches the goal position. Keywords: Fuzzy Logic Controller, Membership Functions, Takagi-Sugeno-Kang FIS, Centroid Defuzzification

With rapid advancements in exoskeleton hardware technologies, successful assessment and accurate control remain challenging. This study introduces a modular sensor-based system to enhance biomechanical evaluation and control in lower-limb exoskeletons, utilizing advanced sensor technologies and fuzzy logic. We aim to surpass the limitations of current biomechanical evaluation methods confined to laboratories and to address the high costs and complexity of exoskeleton control systems. The system integrates inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components function both independently and collectively to capture comprehensive biomechanical data, including the anteroposterior center of pressure and crutch ground reaction forces. This data is processed through a central unit using fuzzy logic algorithms for real-time gait phase estimation and exoskeleton control. Validation experiments with three participants, benchmarked against gold-standard motion capture and force plate technologies, demonstrate our system's capability for reliable gait phase detection and precise biomechanical measurements. By offering our designs open-source and integrating cost-effective technologies, this study advances wearable robotics and promotes broader innovation and adoption in exoskeleton research.

The article focuses on the urgent issue of femicide in Veracruz, Mexico, and the development of the MFM_FEM_VER_CP_2024 model, a mathematical framework designed to predict femicide risk using fuzzy logic. This model addresses the complexity and uncertainty inherent in gender based violence by formalizing risk factors such as coercive control, dehumanization, and the cycle of violence. These factors are mathematically modeled through membership functions that assess the degree of risk associated with various conditions, including personal relationships and specific acts of violence. The study enhances the original model by incorporating new rules and refining existing membership functions, which significantly improve the model predictive accuracy.

We introduced the Scorpion Hunting Strategy (SHS), a novel population-based, nature-inspired optimisation algorithm. This algorithm draws inspiration from the hunting strategy of scorpions, which identify, locate, and capture their prey using the alpha and beta vibration operators. These operators control the SHS algorithm's exploitation and exploration abilities. To formulate an optimisation method, we mathematically simulate these dynamic events and behaviors. We evaluate the effectiveness of the SHS algorithm by employing 20 benchmark functions (including 10 conventional and 10 CEC2020 functions), using both qualitative and quantitative analyses. Through a comparative analysis with 12 state-of-the-art meta-heuristic algorithms, we demonstrate that the proposed SHS algorithm yields exceptionally promising results. These findings are further supported by statistically significant results obtained through the Wilcoxon rank sum test. Additionally, the ranking of SHS, as determined by the average rank derived from the Friedman test, positions it at the forefront when compared to other algorithms. Going beyond theoretical validation, we showcase the practical utility of the SHS algorithm by applying it to six distinct real-world optimisation tasks. These applications illustrate the algorithm's potential in addressing complex optimisation challenges. In summary, this work not only introduces the innovative SHS algorithm but also substantiates its effectiveness and versatility through rigorous benchmarking and real-world problem-solving scenarios.

The worldwide impact of the recent COVID-19 pandemic has been substantial, necessitating the development of accurate forecasting models to predict the spread and course of a pandemic. Previous methods for outbreak forecasting have faced limitations by not utilizing multiple sources of input and yielding suboptimal performance due to the limited availability of data. In this study, we propose a novel approach to address the challenges of infectious disease forecasting. We introduce a Multilateral Attention-enhanced GRU model that leverages information from multiple sources, thus enabling a comprehensive analysis of factors influencing the spread of a pandemic. By incorporating attention mechanisms within a GRU framework, our model can effectively capture complex relationships and temporal dependencies in the data, leading to improved forecasting performance. Further, we have curated a well-structured multi-source dataset for the recent COVID-19 pandemic that the research community can utilize as a great resource to conduct experiments and analysis on time-series forecasting. We evaluated the proposed model on our COVID-19 dataset and reported the output in terms of RMSE and MAE. The experimental results provide evidence that our proposed model surpasses existing techniques in terms of performance. We also performed performance gain and qualitative analysis on our dataset to evaluate the impact of the attention mechanism and show that the proposed model closely follows the trajectory of the pandemic.

The application of autonomous robotics to close-contact healthcare tasks has a clear role for the future due to its potential to reduce infection risks to staff and improve clinical efficiency. Nasopharyngeal (NP) swab sample collection for diagnosing upper-respiratory illnesses is one type of close contact task that is interesting for robotics due to the dexterity requirements and the unobservability of the nasal cavity. We propose a control system that performs the test using a collaborative manipulator arm with an instrumented end-effector to take visual and force measurements, under the scenario that the patient is unrestrained and the tools are general enough to be applied to other close contact tasks. The system employs a visual servo controller to align the swab with the nostrils. A compliant joint velocity controller inserts the swab along a trajectory optimized through a simulation environment, that also reacts to measured forces applied to the swab. Additional subsystems include a fuzzy logic system for detecting when the swab reaches the nasopharynx and a method for detaching the swab and aborting the procedure if safety criteria is violated. The system is evaluated using a second robotic arm that holds a nasal cavity phantom and simulates the natural head motions that could occur during the procedure. Through extensive experiments, we identify controller configurations capable of effectively performing the NP swab test even with significant head motion, which demonstrates the safety and reliability of the system.

Offline reinforcement learning (RL) learns policies from a fixed dataset, but often requires large amounts of data. The challenge arises when labeled datasets are expensive, especially when rewards have to be provided by human labelers for large datasets. In contrast, unlabelled data tends to be less expensive. This situation highlights the importance of finding effective ways to use unlabelled data in offline RL, especially when labelled data is limited or expensive to obtain. In this paper, we present the algorithm to utilize the unlabeled data in the offline RL method with kernel function approximation and give the theoretical guarantee. We present various eigenvalue decay conditions of $\mathcal{H}_k$ which determine the complexity of the algorithm. In summary, our work provides a promising approach for exploiting the advantages offered by unlabeled data in offline RL, whilst maintaining theoretical assurances.

Control affine assumptions, human inputs are external disturbances, in certified safe controller synthesis approaches are frequently violated in operational deployment under causal human actions. This paper takes a human-in-the-loop human-in-the-plant (HIL-HIP) approach towards ensuring operational safety of safety critical autonomous systems: human and real world controller (RWC) are modeled as a unified system. A three-way interaction is considered: a) through personalized inputs and biological feedback processes between HIP and HIL, b) through sensors and actuators between RWC and HIP, and c) through personalized configuration changes and data feedback between HIL and RWC. We extend control Lyapunov theory by generating barrier function (CLBF) under human action plans, model the HIL as a combination of Markov Chain for spontaneous events and Fuzzy inference system for event responses, the RWC as a black box, and integrate the HIL-HIP model with neural architectures that can learn CLBF certificates. We show that synthesized HIL-HIP controller for automated insulin delivery in Type 1 Diabetes is the only controller to meet safety requirements for human action inputs.