Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Architecture for Multisensor Fault Detection, Isolation, and Accommodation in Natural-Gas Pipelines

A significant quantity of sensors distributed throughout the natural gas pipeline is susceptible to errors. Timely diagnosis of sensor faults in such scenarios holds great significance in averting catastrophic failures. This article proposes a novel approach termed model-based multisensor fault detection, isolation, and accommodation (MM-SFDIA) technique to mitigate multiple sensor faults occurring simultaneously in large-scale distributed systems. The proposed approach leverages a distributed filtering framework, employing multiple local ensemble Kalman filters (EnKFs). Each individual local filter generates a distinct local state estimation using a distinct set of sensor measurements. By analyzing the differences among these local state estimates, a strategy based on state consistency, the faulty sensors are identified. Furthermore, an adaptive thresholding technique is devised to ensure resilient fault detection and identification. Compared with the existing state-of-the-art techniques, the proposed approach offers a lower computational burden and is applicable to high-dimensional nonlinear systems with numerous sensor faults. Moreover, the results affirm the effectiveness of the proposed architecture, demonstrating a high accuracy and low execution time in detecting and isolating multiple sensor faults.
Demonstration of 1200-V E-Mode GaN-on-Sapphire Power Transistor With Low Dynamic ON-Resistance Based on Active Passivation Technique

This letter demonstrates a 1200-V E-mode GaN-on-sapphire power transistor based on active passivation technique. The active passivation concept utilizes a thin p-GaN layer extending from the p-GaN gate towards near the drain to screen the surface traps. The fabricated active-passivation HEMT (AP-HEMT) with <inline-formula> <tex-math notation="LaTeX">${L}_{\text {GD}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$27 ~\mu \text{m}$ </tex-math></inline-formula> exhibits a low <inline-formula> <tex-math notation="LaTeX">${R}_{\text {ON}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$16.9 \Omega \cdot $ </tex-math></inline-formula>mm, corresponding to a specific <inline-formula> <tex-math notation="LaTeX">${R}_{\text {ON}}$ </tex-math></inline-formula> <inline-formula> <tex-math notation="LaTeX">$({R}_{\text {ON, SP}})$ </tex-math></inline-formula> of 6.42 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm2. A breakdown voltage (BV) over 2000 V is obtained for the AP-HEMT. Besides, the AP-HEMT showcased excellent dynamic performance due to the surface shielding effect provided by the active passivation. Dynamic ON-resistance was characterized <inline-formula> <tex-math notation="LaTeX">$120 ~\mu \text{s}$ </tex-math></inline-formula> after a 10-ms VDS-OFF stress. At VDS-OFF = 1200 V, the ratio of dynamic <inline-formula> <tex-math notation="LaTeX">${R}_{\text {ON}}$ </tex-math></inline-formula> to static <inline-formula> <tex-math notation="LaTeX">${R}_{\text {ON}}$ </tex-math></inline-formula> is 1.09. The results highlight the superior capabilities of active passivation technique for 1200-V GaN power transistors.
Segmentation of Cardiac MRI Images Based on nnU-Net

Cardiac MRI image segmentation is a critical task in medical image analysis, as it assists in the diagnosis of various cardiovascular diseases. The traditional segmentation methods, such as thresholding and clustering, are limited in accuracy and efficiency. Deep learning-based approaches have shown promising results in medical image segmentation. The nnU-Net model is a state-of-the-art deep learning architecture that has achieved excellent performance in various medical image segmentation tasks. In this study, we applied the nnU-Net model to cardiac MRI image segmentation and evaluated its robustness on the ACDC cardiac dataset. Our experiment results demonstrate that nnU-Net outperforms several other models, such as R50-Unet, R50-Attn Unet, and VIT, in terms of Dice value. The application of the nnU-Net model for cardiac MRI image segmentation has significant value in clinical practice, contributing to improved diagnostic accuracy and saving clinicians' time. The results of this study prove the feasibility of using the nnU-Net framework to process cardiac MRI data comprehensively.
EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs.
Search and Rescue System for Maritime Distressed Persons Based on UAV

Maritime search and rescue (MSAR) is an important aspect of maritime traffic safety. Currently, China's MSAR system mainly relies on GPS or Beidou localization devices carried by persons in distress, as well as boats and helicopters, however, it has the following problems: Due to the limitation of ship height and speed, the scope of search and rescue operations will be greatly reduced. Moreover, the exact position of persons in distress cannot be received if the GPS or Beidou signal is interrupted. Considering the high efficiency and convenience of UAV, this paper proposes a scheme of MSAR system for persons in distress based on UAV, which is mainly divided into three modules: The first is the wireless location module, which mainly performs two-phase wireless location of the distress signal sent by the beacon on the persons in distress, and can obtain the search and rescue range of the target in the case of GPS or Beidou failure; The second is the drift track prediction module, taking into account the impact of ocean current on the location of persons in distress, and realizes the further search and rescue scope; Finally, the target detection module detects and identifies targets of persons in distress on the nearby sea. Through a series of simulation experiments, the proposed search and rescue scheme, integrating key technologies for MSAR, can effectively achieve the task of searching and rescuing persons in distress at sea, and has great research value and significance.
Electric Vehicle Charger Performance from Grid to Vehicle and Vehicle to Grid

When it comes to the rising cost of fuel and environmental issues, electric vehicles offer the most suitable form of mobility. In a plug-in hybrid electric vehicle, batteries are used to supply the electric motor with the energy it needs to operate. Different types of fuels, including diesel and petrol, are used to generate power in cars using internal combustion engines. Plug-in electric vehicles can run in one of two ways: either grid-to-vehicle or vehicle-to-grid facility. In first mode of operation,vehicle exercise grid energy to charge its batteries. In vehicle-to-grid facility status of operation, the grid gets the necessary power by drawing power from a vehicle’s battery. The acquainting of the owners of PEVs and grid operators alike are becoming more curious about the vehicle-to-grid conception. In order to analyse advantages of the operation on the overall performance of the vehicles, this research primarily involves simulating the V2G and G2V operation of the single and three phase electric vehicle chargers in MATLAB/Simulink.
Suspicious Naevi Classification Using Auxiliary Classifier Generative Adversarial Network

A naevus is a collection of melanocytes / naevus cells which form a benign skin lesion and appear in different shapes, sizes and colours. They can occur in different parts of the human body and can be more prevalent in areas exposed to sun exposure. Studying naevi is important, as the number of naevi is the strongest melanoma risk predictor. The main aim of this study is to generate realistic looking naevi and to classify them as suspicious and non-suspicious naevi using generative adversarial networks (GANs). This study could be an efficient approach to the early detection of melanoma by identifying suspicious naevi. Two GAN models were explored to implement this research including Deep Convolutional generative adversarial network (DCGAN), Auxiliary Classifier Generative Adversarial Network (ACGAN). We show that an Auxiliary Classifier GAN (ACGAN) achieved high average accuracy, specificity, sensitivity, precision, and AUC with and without augmenting the suspicious naevi images while being able to generate high quality and realistic looking naevi. The ACGAN model also has achieved higher classification outcomes even compared to those incorporating pretraining.
Digital Twin Based Fault-Tolerance Framework for RRAM Based Neural Computing Systems

An RRAM-based computing system (RCS) is a cutting-edge hardware platform that shows significant promise for implementing vector-matrix multiplication in analog form for neuromorphic computing, which can significantly reduce energy consumption. However, one major issue is that current fabrication processes lead to high rates of hard faults and limited endurance of RRAMs, which can cause a drastic reduction in the accuracy and lifespan of RCS. Moreover, training large neural networks on modern computing systems can be time-consuming, further impeding progress in the field. To overcome these obstacles, we introduce a digital-twin-based framework for RCS that is both cost-effective and fault-tolerant. This approach utilizes the natural fault-tolerance of neural networks to with-stand faults during subsequent training phases without requiring additional hardware. To facilitate faster processing, we have devised a solution that integrates fault-tolerance algorithms such as significant classification and remapping, transfer learning, and threshold training into the virtual twin of the digital-twin. To assess the efficacy of our proposed solution, we conducted tests on the CIFAR-10 dataset using VGG16, Alexnet, and ResNet20 networks. The results demonstrate that our proposed framework significantly reduces training time by 90% when compared to current state-of-the-art methods. Moreover, our framework is scalable, secure, and offers 55% lower Total Cost of Ownership (TCO), making it an attractive solution for the future development of RCS.
Semantic Segmentation Algorithm for Remote Sensing Images Based on Attention Mechanism

This paper propose to combine the attention mechanism with the U-Net model to improve the performance and accuracy of semantic segmentation tasks. The attention mechanism can better focus on task-relevant regions and features, thereby enhancing the model's perceptual and expressive capabilities. By introducing attention modules into the U-Net semantic segmentation model, fine adjustments and attention to image features are achieved, thereby improving the effectiveness of semantic segmentation. Experimental results also demonstrate that the algorithm proposed in this paper greatly improves the accuracy of semantic segmentation for remote sensing images.
Enhancing Cooperative Communications via Reconfigurable Intelligent Surface-Assisted Strategies and the Integration of Low-Density Parity-Check Codes

Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reconfigurable Surfaces (IRS), stand as one of the most promising technologies in wireless communications. Their remarkable attributes, including energy efficiency, low latency, and enhanced spectral efficiency, have drawn significant attention. This paper presents a novel approach to harnessing RIS for cooperative communications, seeking to replace traditional relay systems such as Decode and Forward (DF), Amplify and Forward (AF), and Coded Cooperation (CC). In this study, the RIS serves as a passive element within the communication system. The information originates from the sources and is transmitted to the RIS and destination in a single timeslot. RIS plays its role by reflecting the messages and redirecting them toward their intended destination. Additionally, this paper proposes an innovative combination of the RIS system with Low-Density Parity Check (LDPC) codes to enhance the overall system performance. Encoding of messages takes place at the source, while decoding occurs at the destination, facilitating the retrieval of the original information bits. Furthermore, the paper incorporates the Maximal Ratio Combining (MRC) technique to effectively combine signals for joint iterative decoding at the destination. The entire simulation was conducted using MATLAB software, with a focus on assessing key performance parameters (KPIs), namely the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). Our proposed system was subjected to comparative analysis against various benchmarks, including a system without RIS, a system featuring coded cooperation relay, and several RIS-based systems with varying numbers of RIS surfaces. The objective was to not only demonstrate the enhanced BER performance brought about by our proposed system but also to determine the optimal conditions under which it operates most effectively. This research showcases the potential of RIS in transforming cooperative communications and paving the way for more efficient and reliable wireless networks.
Seismicity of Croatia in the period 1993-1996 and the Ston-Slano earthquake of 1996

Seismic activity in Croatia and surrounding areas in the 1993-1996 period was mostly confined to the previously identified seismically active areas. Nine events (excluding aftershocks) with the magnitude equal to or exceeding 4.5 occurred during that time. The most important earthquake sequence is the one that started on September 5, 1996 (Ml=6.0, Imax=VIII MSK) in the Ston-Slano area (greater Dubrovnik region). Microseismic locations of hypocentres of thousands of aftershocks, as well as the best double-couple CMT solution for the main-shock indicate that the earthquakes occurred on the NW-SE striking reverse fault system dipping towards NE.
Holistic ARDS Prognosis Evaluation Framework Utilizing Data Governance and Ensemble Feature Selection

Acute respiratory distress syndrome (ARDS) prognosis has become integral to modern critical care models aimed at determining expected patient outcomes, optimizing clinical pathways, and improving resource allocation. However, existing clinical studies of ARDS prognosis face severe challenges due to the redundancy of multiple sources of heterogeneous data in current healthcare datasets, the high dimensionality of patient features, and the wide variation in the importance of each feature. In this paper, we propose a holistic ARDS prognostic framework for assessing ARDS prognosis through a standardized data governance process and an integrated feature selection approach based on embedded algorithms. Specifically, we extract ARDS patient data from medical data sources by various criteria and normalize the features. Then we apply multiple embedded feature selection methods to obtain a decision matrix based on the data-governed dataset. We conducted extensive experiments to demonstrate the efficiency and superiority of our proposed framework. The experimental results show that our proposed framework performs well in both data governance and feature selection and has wide clinical application potential.
Development of a Plant Monitoring System Using a Drone-Equipped Agricultural Robot*

This study proposes a plant monitoring system to automate agricultural works by robots in plant factories. This system uses a drone-equipped agricultural robot to monitor plants. This robot has a camera-equipped drone tethered with a power supply cable. Therefore, the drone can fly without worrying about its battery capacity. As a case study, this system targets a strawberry greenhouse. YOLO (You Look Only Once) v4 is used to detect strawberry plants. Created detection models were evaluated by four-fold cross-validation, the average accuracies of flowers, immature and mature fruits were 0.826, 0.841 and 0.920, respectively. As the performance of this system, the numbers of strawberry plants were counted based on the detection results, and the counting results were evaluated by comparing with the actual numbers. The accuracies of flowers, immature and mature fruits were 0.963, 0.961, and 0.972 respectively. Even if strawberry plants cannot be detected from one viewpoint, if they can be detected from another viewpoint, the task of detecting them is achieved. The results of this experiment demonstrated the effectiveness of a plant monitoring system using a drone-equipped agricultural robot.
A Taguchi customized multi-objective particle swarm optimization method for emergency repair of distribution network

The distribution network is the last link of power supply, but in the face of extreme weather, failures are inevitable. How to complete the repair work of the distribution network at low cost and efficiently has become a research hotspot this year. Since many factors need to be considered in the emergency repair of distribution network faults, single-objective planning alone is not competent. Therefore, this paper solves the emergency repair problem of distribution network based on multi-objective optimization particle swarm optimization algorithm (MOSPO). In order to improve the performance of MOPSO, this paper uses the Taguchi method to customize the optimal parameters of the MOPSO algorithm, and proposes Ti-MOPSO. This paper uses NAGA II, IDBEA and Ti-MOPSO to conduct comparative tests. The test results show that the Ti-MOPSO proposed in this paper has excellent performance; in the simulation test, Ti-MOPSO can excellently complete the emergency repair plan planning of the distribution network.
Application of stochastic load flow considering generator and branch outages under uncertainties

The intermittent nature of renewable energy sources such as wind generation and imprecise loads necessitates the incorporation of uncertainty into conventional power flow models. This paper proposes a probabilistic load flow (PLF) method for incorporating generator and line contingencies. The proposed PLF method provides the output variables distribution as probability density function (PDF) in the presence of uncertainties, which supports existing decision-making methods. Moreover, the incorporation of uncertain wind in a failing system has been investigated. Considering generator and line outages, the approach has been tested on IEEE-300 bus systems with wind turbine generators. For IEEE-300 bus system, the generators are modelled as single-state and multi-state with five states. Of the total 55 generator units, 46 generation units have been modelled as multi-state units and rest are modelled as generators having single-state.
Curvature Tracking of a Double Section Soft Bending Actuator Using MPC Method

The movement control of soft robot manipulators with pneumatic actuators is an emerging and interesting new topic in automatically controlled robots. In this paper, the Model Predictive Control (MPC) method, is proposed to solve the curvature tracking problem of a two link soft robot with bending pneumatic actuators and to further reduce the steady state error and oscillations. First, we calculate a proper mapping matrix between the soft robot and the rigid manipulator with equal dynamics characteristics. After that, the MPC method is used to realize the curvature tracking control of the robot in question. Stability of the proposed MPC method is proven using lyapunov theory of stability. To evaluate the performance of the MPC controller, simulations are performed and the results are compared to the ones obtained from a tuned PID controller. This verifies the effectiveness of the control approach mentioned above for the dynamic control of the soft robot manipulators.
Model-based Predictive Direct Power Control of Single Phase Active Front End Converter using a Finite Control Set

The paper proposes a Model-based Predictive Direct Power Control (MP-DPC) of Single-Phase Active Front End converter operating under Unity Power Factor using a Finite Control Set (FCS). The values of One-Sample Ahead (OSA) grid current have been calculated using the discrete-time model of the system. Based on the possible switching states of the converter, the Two-Sample Ahead (TSA) grid currents and the associated power drawn from the grid for each switching state are calculated. The peak current reference generated from the outer DC link voltage control loop is used to estimate the reference power drawn from the grid. The optimal switching state, selected according to the error minimization function is applied to the converter in the next switching instant. The power drawn by the converter from the grid is controlled directly leading to faster transient response of the converter compared to Instantaneous Current Control schemes (ICC). The elimination of linear current controllers in the inner loop is an added advantage. The results from tests conducted on a 5-kW laboratory set-up have been presented to validate the proposed control algorithm.
Design of Anthropomimetic Robotic Wrist Joint and Forearm

In this study, we propose a design methodology for anthropomimetic robotic wrists and forearms. Conventional robotic wrists and forearms have few examples of human mimicry, and they have been simplified. The ligaments and tendons of the robotic forearm proposed in this study were replicated using chain-stitched wires and arranged in a manner similar to the human anatomy. Motion capture and goniometer measurements were used to measure the range of motion (ROM) of the wrist and forearm, driven by 16 servomotors. The results of the experiment were compared with the human ROM reported in previous studies, and it was found that the developed robotic forearm could achieve a ROM similar to that of humans. The use of this robotic forearm in functional verification experiments will enhance our understanding of the human structure. The integration of mechanical mechanisms with structures created through evolution is expected to improve the functionality of future robots.
Path Tracking and Obstacle Avoidance Control Using Deep Reinforcement Learning for Autonomous Vehicles

Autonomous Vehicles (AVs) can significantly contribute to environmental benefits by optimizing driving patterns and traffic flow. The process of training AVs in real-world conditions can be risky as it involves real traffic, pedestrians, and unpredictable scenarios. This makes accidents and safety concerns a genuine issue during the learning phase of autonomous systems. However, simulated environments offer a safer way to test autonomous systems in diverse scenarios, including rare and dangerous situations, without risking real-world accidents or injuries. We used Proximal Policy Optimization (PPO), a type of deep reinforcement learning algorithm, to train our driving agent by using Light Detection and Ranging (Lidar), and a camera sensor. We used Lidar for path tracking and a combination of Lidar and a camera sensor for obstacle avoidance. Results show that using the PPO is an effective technique to train AVs for path tracking and obstacle avoidance in a simulated environment. Results also show that the improved PPO is performing way better than the default PPO.