Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Cooperative Control Algorithm for Regional Power Grid Based on Long and Short-Term Memory Networks

With the increasing proportion of new energy resources connected, the power fluctuation of the regional power grid presents higher stochasticity property, and the real-time power balance faces greater challenges. To address this problem, a cooperative control model is constructed for real-time power balance in the regional grid with the high participation of multiple frequency regulation resources. In order to quickly generate the high- quality of cooperative control strategies, a long and short-term memory network (LSTM) is proposed to learn knowledge of the historical cooperative control strategies. Meanwhile, an infeasible solution correction method based on the ideal point method is proposed to modify the infeasible solution created by the network into a high-quality feasible solution. Finally, based on the extension model of the IEEE standard two-area frequency control model, the effectiveness and performance of the proposed method are verified by comparing various cooperative control optimization algorithms.
Ensuring privacy in provenance information for images

Current technologies facilitate the generation of falsified images that spread misinformation and endanger trust between users and media. Technological and academic institutions have contributed towards building frameworks that try to combat the aforementioned issue by establishing verifiable mechanisms to annotate metadata regarding the creation and modifications of an image (i.e., provenance). However, this type of metadata might conceal information about a specific person or place which threatens the privacy of individuals and locations. To address this problem, we propose the extension of existing provenance schemas in order to provide privacy-enabling features that allow users to gain control over the verifiable data that they intend to share. The proposal examines the provenance schema of the Coalition for Content Authenticity and Provenance (C2PA) specification and extends it using the JPEG Privacy and Security standard (ISO/IEC 19566-4) in order to support the protection of privacy-related information in images. Part of this work has been submitted as a contribution to the standardization activities of the ISO/IEC JTC 1/SC 29/WG 1 "JPEG Coding of digital representations of images" committee.
Analysis of Cross-Slot Antenna at ISM band for Hyperthermia application

For use in biomedical applications, a unique cross-slot antenna operating at 2.45 GHz in the ISM band is presented. The proposed antenna can achieve an effective size reduction at a fixed operating frequency by incorporating the cross slot and rounding the patch's corners on both sides. The dimension of the proposed antenna is 50× 50 × 1.6mm3. In light of this, it is a viable and appropriate contender for biological applications.
EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics

We propose EMS®, a cloud-enabled massive computational experiment management system supporting high-throughput computational robotics research. Compared to existing systems, EMS® features a sky-based pipeline orchestrator which allows us to exploit heterogeneous computing environments painlessly (e.g., on-premise clusters, public clouds, edge devices) to optimally deploy large-scale computational jobs (e.g., with more than millions of computational hours) in an integrated fashion. Cornerstoned on this sky-based pipeline orchestrator, this paper introduces three abstraction layers of the EMS® software architecture: (i) Configuration management layer focusing on automatically enumerating experimental configurations; (ii) Dependency management layer focusing on managing the complex task dependencies within each experimental configuration; (iii) Computation management layer focusing on optimally executing the computational tasks using the given computing resource. Such an architectural design greatly increases the scalability and reproducibility of data-driven robotics research leading to much-improved productivity. To demonstrate this point, we compare EMS® with more traditional approaches on an offline reinforcement learning problem for training mobile robots. Our results show that EMS® outperforms more traditional approaches in two magnitudes of orders (in terms of experimental high throughput and cost) with only several lines of code change. We also exploit EMS® to develop mobile robot, robot arm, and bipedal applications, demonstrating its applicability to numerous robot applications.
Distributed H2 Controller Synthesis for Multi-Agent Systems with Stochastic Packet Loss

In practical networking scenarios, communication links can rarely be considered to be deterministic, yet the influence of stochastic interconnections on multi-agent systems is neglected most of the time. To bridge this gap, this paper develops synthesis conditions for distributed state- and output-feedback controllers that guarantee an upper bound on the closed-loop H2-norm under the effect of Bernoulli distributed packet loss. Utilizing the frameworks of Markov jump linear system and decomposable systems, the synthesis problem is formulated as a linear matrix inequality problem with complexity that scales linearly with the number of agents. Finally, the closed-loop performance is assessed in simulation studies with a signal-to-interference-plus-noise ratio based packet loss model for communication between autonomous underwater vehicles.
An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation – Frequency Modulation multi-scale feature sets

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models.
Reinforcement Learning for Underwater Spatiotemporal Path Planning, with Application to an Autonomous Marine Current Turbine

This paper presents a reinforcement learning (RL) framework applied for an autonomous underwater vehicle (AUV) path planning, focusing on a specific type of energyharvesting AUV, entitled marine current turbine (MCT). The proposed RL-based approach improves a classical path planning to adopt with an underwater environment prone to spatiotemporal uncertainties. The path planning problem is formulated to achieve the goal of maximizing the harnessed energy from the MCT subject to the agent dynamics and the spatiotemporal environment constraints. Three RL algorithms, including Q-learning, deep Q-network (DQN), and proximal policy optimization (PPO), are nominated to deal with the path planning over both discrete gridded and continuous underwater environments modeling. The experimental results demonstrate the efficiency of the RL-based approaches in seeking the optimal path in the underwater environment, where further discussion is presented to generalize the proposed approach to other energy-harvesting autonomous vehicles operating in the spatiotemporally varying environment, such as airborne wind turbines.
Picking by Tilting: In-Hand Manipulation for Object Picking using Effector with Curved Form

This paper presents a robotic in-hand manipulation technique that can be applied to pick an object too large to grasp in a prehensile manner, by taking advantage of its contact interactions with a curved, passive end-effector, and two flat support surfaces. First, the object is tilted up while being held between the end-effector and the supports. Then, the end-effector is tucked into the gap underneath the object, which is formed by tilting, in order to obtain a grasp against gravity. In this paper, we first examine the mechanics of tilting to understand the different ways in which the object can be initially tilted. We then present a strategy to tilt up the object in a secure manner. Finally, we demonstrate successful picking of objects of various size and geometry using our technique through a set of experiments performed with a custom-made robotic device and a conventional robot arm. Our experiment results show that object picking can be performed reliably with our method using simple hardware and control, and when possible, with appropriate fixture design.
Clustering of Point-to-Point Ethernet Transmission Wireless Backhaul Links

Clustering is bundling physical links to construct a single logical link between peers to increase bandwidth and overcome failover issues. While this definition holds true, it is imperative to know that its implementation is hard with certain key factors to maintain those links along with a single control and data plane. With 5G becomes more ubiquitous, Point-to-Point (P2P) wireless Ethernet transmission backhaul links play a vital role in backbone networks, it is essential to make data rates capability of Ethernet transmission links as high as possible. Presently, backhaul links suffers from poor resiliency, performance, scalability, maintainability, and manageability. This paper proposed a stateful clustering algorithm, which improves the failover mechanism, increase bandwidth, and maintains a single control and data plane for upstream and downstream devices for the aggregated links. According to our best knowledge this is the first comprehensive, stateful, and failover resilience protocol for clustering wireless backhaul links.
Generalizing a Small Facial Image Dataset Using Facial Generative Adversarial Networks for Stroke’s Facial Weakness Screening

Stroke is a medical emergency resulting from disruption of blood supply to different parts of the brain which leads to facial weakness and paralysis as the brain is the control center. Stroke is the leading cause of long-term disability which significantly changes the patient’s life. This paper introduces the use of facial image dataset containing neutral and smiling expressions to classify facial weakness which is a common sign of stroke. Our “real facial image dataset” comprises of face images of normal subjects and stroke patients. However, to increase the dataset, we added another dataset known as “FaceGAN dataset”. This additional dataset contains a pair of neutral and smiling facial image synthesized from public datasets which were augmented to generate two additional smiling images at eight different age groups. The faces were divided into left and right side using facial landmark detection technique and corrected for geometric distortions through affine transformation matrix from Delaunay triangulation. An autoencoder model composed of ConvNeXt encoder and ConvNet decoder was trained and used to fine-tune a facial weakness classification model from our proposed architecture. Results from four-fold cross validation showed that the model validation was less prone to overfitting when used with the FaceGAN dataset, with an average AUC of 0.76 and F1-score of 71.19%, compared to without FaceGAN data which only achieved an F1-score of 61.54%. This study shows that the FaceGAN can efficiently generalize models for programs with a small dataset for use with stroke detection. This work can be further improved and optimized for clinical application in the future.
Retrospective-Cost-Based Model Reference Adaptive Control of Nonminimum-Phase Systems

This paper presents a novel approach to model reference adaptive control inspired by the adaptive pole-placement technique of Elliot and based on retrospective cost optimization (RC-MRAC). RC-MRAC is applicable to nonminimum-phase (NMP) systems assuming that the NMP zeros are known. Under this assumption, the advantage of RC-MRAC is a reduced need for persistency.
Terahertz Band Transmittance Modeling Tools: A Comparative Study

In this paper, we investigate three terahertz (THz) band simulation tools: International Telecommunication Union’s Attenuation model (ITU model), Line-By-Line Radiative Transfer Model (LBLRTM), and Atmospheric Model (am). Transmittance and molecular absorption loss (attenuation) values are obtained from these tools for the available frequency range at distances from 1 m to 100 m and altitudes from 0m to 1000 m with tropical weather conditions. The results exhibit that the ITU model, LBLRTM, and am are highly consistent with each other.
Load Forecasting in Nordic Residential Buildings

Renewable power production technologies, such as solar photovoltaics, are becoming increasingly popular in the residential sector. Due to the volatility of renewable power production, and mismatch between peak production and consumption hours, it can be beneficial to augment local power production with energy storage systems, such as batteries. Connection to mains grid also facilitates selling overproduction, especially when the price of electricity is high. However, such a system requires automated decision making, which would highly benefit from knowing the state of electrical load, price and solar power production in advance. This study focuses specifically on the task of load forecasting, applied to Nordic multifamily residential buildings, which are mainly heated by district heating. While it is common to utilize weather data for load forecasting, this work shows by properly choosing and optimizing the forecast model, omitting the weather data can in this case improve the accuracy of the forecasts.
Adaptive output synchronization of networked positive real infinite dimensional systems using adaptive functional estimation

This paper considers the synchronization of identical structurally perturbed infinite dimensional systems. Due to the structured perturbations, represented by nonlinear functions of output signals, a standard synchronization via standard consensus protocols cannot provide any form of agreement unless one removes the presence of structured perturbations. When the structured perturbations are estimated adaptively, via an adaptive functional estimation scheme, the inclusion of their adaptive estimates facilitates and enhances the synchronization of the networked systems. Adaptive consensus protocols are applied to both the synchronization controllers ensuring that all networked systems agree with each other, and to the functional estimates ensuring that all functional estimates are synchronized.
Adaptive Robust Tracking Control for First-Order Linear Systems with Input Delay and Lipschitz Nonlinear Disturbance

In this paper, an adaptive robust tracking controller is proposed for first-order linear systems with input delay, unknown plant parameters and Lipschitz nonlinear disturbance. The controller employs the predictor feedback to compensate for the effect of input delay, the robust feedback to deal with uncertainties, the model compensation for trajectory tracking, and projection-type adaptation laws are designed. By the stability analysis with a Lyapunov function in integral form, the closed-loop system is locally stable in the sense that the tracking error is bounded above by a known function which exponentially converges to a specified accuracy provided that the initial states and control parameters meet certain conditions. Furthermore, when the disturbance is reduced to a constant, the controller guarantees the semi-global stability that the tracking error asymptotically converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.
Structural Sparsity in Networked Control Systems

A great deal of research is being conducted to define control strategies that involve a level of sparsity either in the communication network, number of sensors, or the number of active actuators during the control process. These sparse control strategies are typically formulated as an optimization problem that has a criterion with a component associated with the dynamics of the controlled system, and a component based on the $\ell _{{1}}$ -norm that induces sparsity on the optimization variables. Although this approach addresses the problem of obtaining sparse approximations, solving this optimization problem can be seen as a design process that is conducted simultaneously along with a variable selection process that does not take into account the inherent structural relationships between the components of the networked system to be controlled, such as spatial relations associated with the physics of the system or communication constraints. In this paper, we incorporate the concept of structural sparsity into the mathematical framework of control design in networked systems. Under this extended framework, it is possible to define control strategies that lead to sparse designs considering a prior knowledge on the structural relations between the variables of the system. This can potentially improve the system’s performance, the consistency of the solution with physical constraints, and the interpretability of the design. First, we show how structural sparsity inducing norms can be constructed as a combination of $ \ell _{{1}}$ - and $\ell _{{2}}$ -norms, encoding structures, such as overlapping groups, nonoverlapping groups, and hierarchies. Also, we show how these norms can induce both individual and group variable selection. Then, we present two different control design techniques, where structural sparsity is induced during the design process: linear feedback control design and dynamic resource allocation. We show through practical applications and simulation results the usefulness of structural sparsity in the control system design process.
Multilabel Classifier Using DenseNet-169 for Alzheimer's disease

Alzheimer's disease is one of the most recurrent type of mental illness leading to dementia amongst old-age people. With age, the chances of getting this brain disease are: 17% people between the age group 65 to 74,47% of individuals between age group 75 to 84 and 36% with age 85 or over suffered from this disease. Each year an estimated 44 million people are diagnosed with Alzheimer's worldwide. A total of 3.7 million people in India have Alzheimer's disease and related problems, and the number is likely to double by 2030. Alzheimer's disease is not completely cured but if diagnosed early, it can help to delay the progression of the disease and prevent further damage to the brain tissue. In the proposed work, a Deep Learning Classifier using Convolutional Neural Network is proposed for classifying the stage of Alzheimer's disease in a patient by using the DenseNet-169 CNN classifier with an accuracy of 91.80%. The model is trained on a MRI image database that contains MRI images of patients diagnosed with Alzheimer's disease.
Displacement damage and predicted non-ionizing energy loss in GaAs

Large-scale molecular dynamics (MD) simulations, along with bond-order interatomic potentials, have been applied to study the defect production for lattice atom recoil energies from 500 eV to 20 keV in gallium arsenide (GaAs). At low energies, the most surviving defects are single interstitials and vacancies, and only 20% of the interstitial population is contained in clusters. However, a direct-impact amorphization in GaAs occurs with a high degree of probability during the cascade lifetime for Ga PKAs (primary knock-on atoms) with energies larger than 2 keV. The results reveal a non-linear defect production that increases with the PKA energy. The damage density within a cascade core is evaluated, and used to develop a model that describes a new energy partition function. Based on the MD results, we have developed a model to determine the non-ionizing energy loss (NIEL) in GaAs, which can be used to predict the displacement damage degradation induced by space radiation on electronic components. The calcu.
A Case Study of LTE Coverage Extension for Rural Malaysia

LTE service demand is increasing for the rural areas since Work from Home (WFH) and online learning becoming the norm for Malaysians. However, the LTE coverage expansion mainly focus at highly populated areas or high ROI market, leaving some rural areas under-connected. Instead of deploying a new e-NB, coverage extension method is one way forward for rural mobile connectivity. Mobile Network Operator (MNO) may benefits huge savings on the CAPEX. In this paper, a case study of LTE coverage extension for 2 rural sites is presented. Low profile pico repeater system has been deployed to prove the benefits of LTE coverage extension method. The findings from the coverage mapping and user experience test after repeater deployment are presented. From the field trial, it has been proven that coverage extension method with proper planning can provide LTE coverage with stable service.
Anti-windup Compensation for Stable and Unstable Systems with Quantized and Saturated Inputs*

It is well known that actuator saturation can cause destabilization and degradation in performance; similar problems are faced when actuation is quantized. This paper proposes the design of an anti-windup compensator for systems with actuators that are limited to a finite number of quantization levels. This combination of discrete level actuation and saturation poses a unique anti-windup problem that has not yet been solved. To surmount this combined issue, an anti-windup compensator is proposed which provides ultimate-boundedness of the system state within a prescribed region, and also guarantees that the state does not stray outside a larger compact set. A numerical simulation example illustrates the effectiveness on a rigid-body system which inspired this work.
Towards a Finned-Swimming Exoskeleton: A Robotic Flutter Kicking Testbed and its Corresponding Thrust Generation

While lower limb exoskeletons for above-ground locomotion have been emerging, few attempts have been made to develop an exoskeleton to augment human swimming. Such efforts are hindered by a lack of knowledge surrounding the kinematics and kinetics of human swimming. This paper presents the design of a robotic platform to be used as a finned swimming testbed; describes a controller to generate finned swimming movement; and presents experiments and associated experimental results conducted to explore thrust production resulting from a flutter kick swimming motion.
Fixed-time Terminal Sliding-mode Control for Free-Floating Space Manipulator

This article focuses on the tracking problem for n-link free-floating space manipulators (FFSM) subject to uncertainties. Based on a non-singular terminal sliding mode, a fixed time controller is well-designed, where the sliding surface is supposed to be reachable and the irrelevance between convergence time and system states’ initial value is proved. Meanwhile, the uncertainties during modeling are well suppressed through the proposed robust control algorithm. At last, the Lyapunov stability theory is adopted to verify the fixed-time convergence of the closed-loop FFSM system. Simulation experiments taking a 6-DOF FFSM validate the effectiveness of the proposed control scheme.