Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability prediction and optimization method based on material degradation and quality consistency

Designing and optimizing robust, low-cost, and highly reliable products to meet the requirements of industrial applications faces various challenges. Due to their complex structures with many components, poor quality consistency occurs during the manufacturing process, and performance degradation inevitably occurs during the application process, which also puts higher requirements on the product design and optimization process. However, at present, the effects of performance degradation and quality consistency on the robustness optimization results are seldom considered simultaneously in the product design optimization process. To address this problem, this paper proposes an adaptive robustness optimization method that simultaneously considers performance degradation and quality consistency. Firstly, a physical model of product failure is established using accelerated degradation test data, and its reliability is predicted, and then based on this, robustness optimization is carried out by considering cost, quality consistency and performance degradation at the same time. This paper takes an electromagnetic relay as an example to introduce and verify the effectiveness of the method in this paper.
Energy-Aware Planning of Heterogeneous Multi-Agent Systems for Serving Cooperative Tasks with Temporal Logic Specifications

We address a coordination problem for a team of heterogeneous and energy-limited agents to achieve cooperative tasks given as team-level spatio-temporal specifications. We assume that agents have stochastic energy dynamics and do not have identical capabilities. We define the team-level specification using Signal Temporal Logic (STL) with integral predicates, which can express tasks that can be completed collectively in an asynchronous way. We first abstract the environment as a graph using sampling-based methods. This abstraction includes the possible paths of different types of agents and ensures the availability of recharging within a certain distance. Then, we formulate a mixed-integer program over this abstraction to find the high-level paths of the agents. Finally, we steer the agents in the environment according to the nominal plan under stochastic energy consumption models and a recharging policy. Such stochastic energy dynamics cause deviations from the nominal plan and delays in completing the tasks. Accordingly, we define and evaluate the expected delay (temporal relaxation) in achieving the STL specification under the proposed solution.
A computational and experimental mechanical study of nanocomposites for 3D printed scaffolds with a new geometry

We present a combined study of the mechanical properties of 3D printed scaffolds made by nanocomposite materials based on polycaprolactone (PCL). The geometry and dimensions of the three different systems is the same. Τhe porosity is 50% for all systems. Distributions of von-Mises strains and stresses, and total deformations were obtained through Finite Element Analysis (FEA) for a maximum amount of force applied, in a compressive numerical experiment. Also compressive experiments were performed for both raw and 3D nanoconposite scaffolds.
Estimation of Lumbar Spine Loading of Low Back Pain Participant During Lifting Using an Open Source Musculoskeletal Model

Biomechanical modeling of spinal load during lifting in OpenSim has the potential for rehabilitation and clinical assessment. In the literature, several spinal models have been developed and validated with movement data from healthy individuals. Although these models are valid for predicting spinal load in healthy individuals, it is unknown whether these models are applicable for people with chronic low back pain (CLBP). This study aims to compare the application of the lifting full body (LFB) model between a healthy participant and a participant with CLBP. The participants performed the lifting activity, and the motion capture data was used to analyze how an open-source model predicts the loading of the lumbar spine. Peak spinal loading at L5/S1 joint was estimated as 3.9 kN for the healthy participant and 3.1 kN for the CLBP participant. The results suggest that a longer duration of lift and lower lumbar range of motion reduces lumbar spinal loading.
The final meltdown of dead-ice at the Holocene Thermal Maximum (8500–7400 cal. yr BP) in western Latvia, eastern Baltic

It is commonly assumed that the majority of buried ice-blocks in Europe melted at the end of Late Glacial period and during the first part of the early-Holocene. We show, however, that scattered dead-ice-blocks may have been preserved in the ground until 8500 cal. yr BP. We analysed thermokarst features in Lake Ķikuru, western Latvia, by means of a multi-proxy approach (pollen, non-pollen palynomorphs, plant macrofossils, diatoms, loss-on-ignition, magnetic susceptibility, C:N ratio, carbon accumulation rate and radiocarbon dating). Abiotic and biotic processes following the ice-block meltdown suggests abrupt development of a thermokarst from 8500 to 7400 cal. yr BP. Important changes in local vegetation occurred with the deepening of a kettle-hole during the transition from a fen to a lake that nearly coincided with the appearance of the first fish at 7800 cal. yr BP, thus forming a clear indication of a lacustrine environment. Our study shows that a thin peat layer formed at first and, due to the meltdown of the ice-block, it gradually lowered to the bottom of the kettle-hole, and gyttja begun to accumulate afterwards. Given that thermokarst arise when the mean summer air temperature gradually increases to a value above the present-day temperature, we must assume that the local conditions must have been exceptional to secure ice-block from the meltdown for so long. Therefore, the legacy of the last ice age was still evident even ca. 5500 years after the Weichselian ice retreat from the eastern Baltic.
Exploring 2D Representation and Transfer Learning Techniques for People Identification in Indoor Localization

Indoor localization is a crucial aspect of various disciplines in our daily lives. It enables efficient administration tasks and improves safety by identifying the position of items or people inside spaces, making it useful for activities like interior navigation, asset tracking, people rescue, and building security. However, traditional systems have limited performance due to various phenomena. In this paper, a novel system is proposed to identify users inside a building using a transfer learning algorithm and a received signal strength indicator signal as an image. The system utilizes pre-trained models and the scalogram technique to increase the performance of localizing the converted data RSSI to an image. The results demonstrate that the two models can recognize users with 90% accuracy for GoogleNet and 86% accuracy for SqueezNet model.
Enhancing 5G-Enabled Robots Autonomy by Radio-Aware Semantic Maps

Future robotics systems aiming for true autonomy must be robust against dynamic and unstructured environments. The 5th generation (5G) mobile network is expected to provide ubiquitous, reliable and low-latency wireless communications to ground robots, especially in outdoor scenarios. Empowered by 5G, the digital transformation of robotics is emerging, enabled by the cloud-native paradigm and the adoption of edge-computing principles for heavy computational task offloading. However, wireless link quality fluctuates due to multiple aspects such as the topography of the deployment area, the presence of obstacles, robots' movement and the configuration of the serving base stations. This directly impacts not only the connectivity to the robots but also the performance of robot operations, resulting in severe challenges when targeting full robot autonomy. To address such challenges, in this paper, we propose a framework to build a semantic map based on radio quality. By means of our proposed approach, mobile robots can gain knowledge on up-to-date radio context map information of the surrounding environment, hence enabling reliable and efficient robotics operations.
Supporting Physicians Through Prescriptive Process Monitoring

Physicians face increasing pressure to provide effective medical treatment. The complexity of the healthcare system, which is dynamic, multi-disciplinary, flexible, human-centered, and knowledge-intensive, makes it difficult for physicians to handle this pressure. Existing support systems for physicians often only provide basic documentation capabilities. Predictive process monitoring, a new area of research, offers techniques that can predict the future behavior of ongoing medical processes. This paper goes one step further by exploring the use of these predictions to make recommendations to physicians. We compare different neural network types, topologies, and input encodings with relevant benchmark techniques to identify the best options for next-activity recommendations in healthcare, based on accuracy, consistency, training speed, and prediction speed. We use data from three healthcare processes, including a real-life emergency medicine department, to evaluate the performance of these techniques.
Simulating A Fault-Tolerant System with Adjustable Reliability

The paper presents the approach and the algorithm for the simulation of a fault-tolerant system with adjustable reliability. Simulation is largely used to model dependable systems because of its flexibility, the ability to study various system characteristics and states, and the possibility to represent system details close to its real application. The properties of the fault-tolerant system with adjustable reliability necessitate a specific approach. The system is distributed and uses components with different hardware redundancy to adjust the system's reliability to the requirements of the application. The developed simulation algorithm derives the system's dependability properties and supports the investigation of different component redundancy distributions which achieve the desired system reliability.
Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.
Optimizing Real-Time Responsiveness in IIoT: A Dynamic Approach for WiFi OFDMA Uplink Transmissions

Addressing the key issues of WiFi OFDMA uplink transmissions, especially in terms of real-time responsiveness, is a vital concern for contemporary industrial scenarios. The challenges include rigid resource allocation and an inability to cater to varied user requirements effectively. This paper presents an advanced OFDMA-based WiFi uplink random access scheme, specifically tailored to enhance performance and optimize real-time responsiveness in industrial wireless networks. At the heart of our scheme is the implementation of unique association identifiers (AIDs) for real-time users, fostering quasi-scheduled access transmission for efficient data management. To complement this, we incorporate a state-aware resource allocation mechanism that dynamically refines resources in line with current network conditions. Furthermore, we leverage the maximal ratio combining (MRC) technique on resource units (RUs) transmitting identical data, an approach that significantly reduces consecutive frame loss and bolsters reliability. We validate our innovative approach via comprehensive simulations using the MATLAB WLAN toolbox, demonstrating its robustness and effectiveness. Our research aims to augment real-time responsiveness in industrial wireless networks.
Development of an Autonomous Modular Swimming Robot with Disturbance Rejection and Path Tracking

Here we present the development of an autonomous modular swimming robot. This robot, named µBot 2.0, was upgraded from our previous robot platform µBot and features onboard computing, sensing, and power. Its compact size and modularity render the robot an ideal platform for studying bio-inspired robot swimming. The robot is equipped with a micro controller in its head that communicates with external computers through Bluetooth Low Energy (BLE) and sends motor commands to the body segments via Inter-Integrated Circuit (I2C) protocol. Each body segment has a customized printed circuit board (PCB) that receives commands and controls the electromagnetic actuator for generating body movements. The robot head is also equipped with an Inertial Measurement Unit (IMU) to measure its heading and a battery for power. In this work, a µBot 2.0 with three actuators was assembled and the swimming performance was tested. The robot actuators were activated via rhythmic motor input from a central pattern generator (CPG). Experimental results showed that the swimming speed was highly sensitive to the frequency of the motor input, with a maximum swimming speed of 130 mm/s (equivalent to 0.7 body length per second) at 6 Hz. The robot also had the capability to correct its heading with IMU feedback and follow desired paths using a line-of-sight (LOS) guidance law with an overhead camera. Our results demonstrate the effectiveness of the robot's design and its potential in a variety of aquatic applications.
Optimal Bidding Strategy with Smooth Budget Delivery in Online Advertising

In this paper, the optimal bidding strategy with smooth budget delivery in a real-time bidding (RTB) platform is addressed. Feedback control theory plays an essential role in the performance enhancement of ad campaigns in online advertising industry. The objective is to determine the optimal bidding prices as control signals such that (i) the total number of clicks by visiting users is maximized, and (ii) the campaign budget in every episode is smoothly delivered without a premature finishing of the campaign budget or excessive spending rates. In this paper, the advertisers are regarded as the agents in a Markov decision process, where the rewards are chosen according to the main campaign objectives. An advertiser is supposed to select a sequence of bidding actions in terms of a control policy such that a long-term accumulation of the rewards is maximized. It is shown that the smooth bidding with real-time adaptation fits into the framework of reinforcement learning with dynamic programming. Accordingly, an approximation algorithm is proposed to solve the corresponding Bellman optimality equation. The results are utilized to form the bidding strategy with smooth budget delivery. Simulation results on a real-world dataset confirm that the proposed approach outperforms the state-of-the-art bidding strategies, by sustainable participation in the auctions, and maximizing the number of user clicks.
Developing a Superlens with High Resolution Using Quantum Dot Nano-Particles

In this paper, a new method is proposed to develop a superlens with ultra-high resolution and neglectable loss capable to produce sub-wavelength images. In this method, silver coated quantum dot nanoparticles are dispersed inside a polymeric medium to achieve a negative effective permittivity. By using CdSe/CdS quantum dots as cores of the nanoparticles, the loss of silver shell can be compensated resulting in a low-loss superlens. The designed superlens is used for sub-wavelength imaging and its resolution is numerically retrieved. The numerical simulations show a high resolution of λ $o$ /7 which is much beyond the diffraction limit.
From Social Robots to Autonomous Surface Vessels' Navigation

Maritime transportation plays a crucial role in global trade and commerce, but it faces challenges such as increasing demands, safety concerns, and environmental issues. To address these challenges, the development of collision-free autonomous surface vessels (ASVs) has gained significant interest. This paper presents a comprehensive investigation into the development of an autonomous navigation system for ASVs, focussing on the integration of predictive and reactive strategies. The navigation system is designed to ensure collision-free path planning and navigation in dynamic environments. The experimental results highlight the effectiveness of the hybrid model and its implications for the self-improving system integration (SISSY) problem, where autonomous and legacy systems interact within a shared environment. The research presented in this paper contributes to the advancement of autonomous navigation systems for ASVs, aiming to enhance safety, efficiency, and sustainability in maritime operations.
Proposed Dense Block for Reconstruction Hyperspectral Imaging of Milk Object

One of the techniques in image acquisition based on spatial information in the near infrared visible spectrum range is only hyperspectral imaging. Based on the advantages of hyperspectral, it can be seen that the use of hyperspectral is only among professionals with very expensive operational costs and complicated measurement designs, so a reconstruction is carried out using RGB imagery. This study aims to reconstruct RGB images from Hyperspectral images using objects from milk using the Proposed Algorithm Model Dense Block. RGB resulting from conversion using CIE 1931 Color Matching Function. The dataset used includes Training, Valdiation, and Testing with different target bands and spatial images to determine the best performance of the Reconstruction Algorithm. The test results of the performance model use an evaluation technique using the RMSE and MAE metrics. Band variations and segmentation can affect the performance of the Reconstruction Algorithm. The value of each RMSE and MAE is 0.01 and 0.023 from the value it can be seen that the reconstruction can be done with the proposed dense block.
Lymphoma Recognition in Histology Image of Gastric Mucosal Biopsy with Prototype Learning

Lymphomas are a group of malignant tumors developed from lymphocytes, which may occur in many organs. Therefore, accurately distinguishing lymphoma from solid tumors is of great clinical significance. Due to the strong ability of graph structure to capture the topology of the micro-environment of cells, graph convolutional networks (GCNs) have been widely used in pathological image processing. Nevertheless, the softmax classification layer of the graph convolutional models cannot drive learned representations compact enough to distinguish some types of lymphomas and solid tumors with strong morphological analogies on H&E-stained images. To alleviate this problem, a prototype learning based model is proposed, namely graph convolutional prototype network (GCPNet). Specifically, the method follows the patch-to-slide architecture first to perform patch-level classification and obtain image-level results by fusing patch-level predictions. The classification model is assembled with a graph convolutional feature extractor and prototype-based classification layer to build more robust feature representations for classification. For model training, a dynamic prototype loss is proposed to give the model different optimization priorities at different stages of training. Besides, a prototype reassignment operation is designed to prevent the model from getting stuck in local minima during optimization. Experiments are conducted on a dataset of 183 Whole slide images (WSI) of gastric mucosa biopsy. The proposed method achieved superior performance than existing methods.Clinical relevance— The work proposed a new deep learning framework tailored to lymphoma recognition on pathological image of gastric mucosal biopsy to differentiate lymphoma, adenocarcinoma and inflammation.
Artificial Intelligence Assisted Multi-modal Photoacoustic-Ultrasound Imaging for Studying Renal Tissue Function and Hemodynamics

Combined functional-anatomic imaging modalities, which integrate the benefits of visualizing gross anatomy along with the functional or metabolic information of tissue has revolutionized the world of medical imaging. However, such existing imaging modalities are very costly. An alternative option could be a hybrid modality combining contrast-enhanced ultrasound, doppler and photoacoustic imaging. In the current study, we propose an artificial intelligence assisted multi-modal imaging platform where we have used U-net model for segmenting the anatomical features from the ultrasound images obtained from an animal model study. The neural network has performed accurately for three different cases, each with a high dice score. The model was co-validated with doppler images. Further, blood perfusion and tissue oxygenation information from the predicted anatomical structures were also studied. The present findings confirm the feasibility of using this multimodal imaging modality facilitated by artificial intelligence for better understanding of the hemodynamics of the kidney.Clinical Relevance—A multi-modal imaging technique has been proposed which would provide anatomical and functional information to the clinicians for early detection and tracking of the disease prognosis. Unlike existing imaging modalities like PET-CT (Positron Emission Tomography- Computed Tomography), the proposed modality is much more costeffective and radiation free (non-ionizing nature).
In-Situ Measurement of Extrusion Width for Fused Filament Fabrication Process Using Vision and Machine Learning Models

Measuring geometry of the printing road is key for detection of anomalies in 3D printing processes. Although commercial 3D printers can measure the extrusion height using various distance sensors, measuring of the width in real-time remains a challenge. This paper presents a visual in-situ monitoring system to measure width of the printing filament road in 2D patterns. The proposed system is composed of a printable shroud with embedded camera setup and a visual detection approach based on a two-stage instance segmentation method. Each of the segmentation and localization stages can use multiple computational approaches including Gaussian mixture model, color filter, and deep neural network models. The visual monitoring system is mounted on a standard 3D printer and validated with the measurement of printed filament roads of sub-millimeter widths. The results on accuracy and robustness reveal that combinations of deep models for both segmentation and localization stages have better performance. Particularly, fully connected CNN segmentation model combined with YOLO object detector can measure sub-millimeter extrusion width with 90 μm accuracy at 125 ms speed. This visual monitoring system has potential to improve the control of printing processes by the real-time measurement of printed filament geometry.