Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning Control Strategy at Roundabout for i-CAR Autonomous Car

Institut Teknologi Sepuluh Nopember (ITS) has successfully implemented an Autonomous Car (i-CAR) as a mode of commuter transportation within the campus. One of the difficulties in implementing the maneuver is when passing roundabouts which are scattered on the i-CAR route. This paper discusses i-CAR’s maneuvers on roundabouts by implementing Deep Reinforcement Learning. The i-CAR vehicle is modeled in the CARLA simulation environment, then tested in a virtual environment in roundabouts with intersections and roundabouts without intersections that simulate U-turn maneuvers. The Deep Reinforcement method used is Deep Que Network (DQN) using various reward function configurations. Through experiments using CARLA simulations, it was obtained that Icar was able to pass roundabouts with and without intersections with an average deviation angle of 27.011 degrees and 30.068 degrees, respectively. The average time needed to pass the roundabout is 13.3 seconds and 7.9 seconds, with an average speed of 27.0 kmph and 28.5 kmph. This speed is still acceptable on campus, where the driving speed inside is limited to 40 kmph.
A Dual-Arm Manipulation System for Unfolding and Folding Rectangular Cloth

This paper describes a robot system that can perform several cloth manipulation tasks. Many commendable activities on deformable object manipulation are underway in the research community. One such activity is the Cloth Manipulation Track organized as part of the Robotic Grasping and Manipulation Competitions. Although limited to rectangular fabrics, the imposed tasks incorporate many of the basics of cloth manipulation and are well thought out. In order to realize these tasks, it is necessary to take into account the properties of the fabric, which can be deformed into various shapes. In this paper, we introduce our robot system that can perform corner detection, unfolding, and folding operations on rectangular fabric products of different sizes and types. We have devised an appropriate motion sequence based on our empirical knowledge of cloth deformation and adopted a feed-forward motion approach to realize these tasks. This approach was confirmed to be feasible for different types of cloth products.
Small-Signal Modeling and Control of three-phase Hybrid Transformer Considering Practical Impedances

This paper presents the small-signal models of a three-phase transformer. The derived small-signal models reflect the circuit’s practical impedances caused by the distribution and the low frequency magnetic transformers which are parts of the hybrid transformer. To simplify the model derivations, the impedances are incorporated in the LC filters which are attached to the input and the output of the back-to-back power converter. The state equations of the hybrid transformer are established, and the small-signal models are derived. Furthermore, the simplified models are suggested and compared with the originally derived small-signal models. Using the derived simplified models, the secondary constant voltage controller of the power stage is designed. The simulation and experimental results verify the effectiveness of the derived small-signal models and the controller design method.
A Novel Graph-Based Energy Efficient Sensor Selection Scheme in Edge Computing

IoT sensors are usually used for data collection and monitoring in various environments over the well-known edge computing architecture. However, the sensors' lifetime is a significant challenge when the sensors are battery-powered. To reduce energy consumption, this paper presents a novel graphbased sensor selection scheme to select working and sleeping sensors that maximizes the number of sleeping nodes while keeping the accuracy comparable with the state-of-the-art methods using a hierarchical prediction scheme. Moreover, an entropy-aware publishing method is proposed to reduce the edgeto-cloud transmissions to avoid transmitting predictable measurements to the cloud by utilizing the temporal correlation in a sensor measurement. The experimental results showed that the proposed method could surpass the previous approaches by turning off 14% more sensors on average under equal accuracy. Moreover, the entropy-aware publishing method could reach 44% average saving in data transmission.
A Single-Phase Buck-Boost Derived Common-Ground Inverter

This paper presents a new topology for the single-phase buck-boost derived common-ground inverter (1P-BBD-CGI). The 1P-BBD-CGI uses two extra switching devices besides conventional inverting buck-boost DC-DC converter to feature DC-AC operation. In this topology, the negative point of output load is directly connected to the ground of the DC input source. Hence, this topology can completely eliminate leakage current. Furthermore, having a lower component voltage rating and using film-capacitor make the proposed inverter have higher efficiency and reliability than other previous common-ground inverters. The proposed 1P-CDCGI has been verified by simulation and experimental results with the help of PLECS software and 300-W laboratory prototype.
Conditional Quantile Estimation for Truncated and Associated Data

Abstract In survival or reliability studies, it is common to have data which are not only incomplete but weakly dependent too. Random truncation and censoring are two common forms of such data when they are neither independent nor strongly mixing but rather associated. The focus of this paper is on estimating conditional distribution and conditional quantile functions for randomly left truncated data satisfying association condition. We aim at deriving strong uniform consistency rates and asymptotic normality for the estimators and thereby, extend to association case some results stated under iid and α-mixing hypotheses. The performance of the quantile function estimator is evaluated on simulated data sets.
A 3 kW 3.39 MHz DC/DC Inductive Power Transfer System with Power Combining Converters

A 3.39 MHz, 2 kW nominal power DC/DC inductive power transfer systems has been developed and tested experimentally. It achieves 95% peak efficiency at 3 kW output, with a novel parallel push-pull Class E inverter using 650 V, 60 A GaN devices and a full bridge Class DE rectifier with 1.2 kV, 20 A SiC Schottky diodes. The entire system design procedure of the magnetic link and the converters is provided in detail. Such high frequency wireless power transfer systems are targeted towards high power applications with low coupling, where requirements for lightweight and compact receiver sides are crucial.
An Ultra-Wideband SSL-FSS with Band Reject and Flexible Characteristics for Radome Applications

This work aims to synthesize a Frequency Selective Surface (FSS) with Band-reject characteristics over an ultra wideband operating range. A Swastika-Square-Loop (SSL) based FSS unit cell was designed to block the WiFi-6E, X and Ku-Bands. The designed conformal periodic structure provides band reject operation over a bandwidth of 12.78 GHz in the intended frequency range. The study of various bends conditions and the change in performance characteristics was studied and a regression based statistical analysis was carried out to establish a relationship between performance characteristics and bend radius. This proposed prediction model would enable the ready use of SSL-FSS based radome for various conformal applications like military equipment or Unmanned Aerial Vehicle (UAV).
Evaluation of Learning Management Systems Based on Usability and User Experience: A Systematic Literature Review

The use of a learning management system (LMS) in the world of education is encouraged to support and to optimize the teaching and learning process. Using the LMS, the learning process that has previously been performed in schools can be performed anywhere and anytime. A good LMS provides convenience and comfort to its users. However, to the best of our knowledge, there is no review study that focusses on the usability and user experience of LMS. Therefore, in this literature study, we surveyed several methods used to measure usability and user experience in the LMS. We managed to collect 40 papers discussing the evaluation of usability and user experience in LMS. Furthermore, we successfully answered four formulated research questions. Based on our study, we found that there are 9 methods used to measure usability and user experience, 5 stages carried out in evaluating usability and user experience, 25 characteristics measured, and there are 13 data analysis methods that can be used to analyze usability and user experience in LMS. These findings are important for academics and practitioners working on information technology to support the educational process.
Simultaneous Design of Double-D Pad Coil and Core Geometry by Neural Network Optimisation

This paper proposes a new design framework for an IPT pad for EV charging by optimisation with an artificial neural network. Many design parameters exist within an IPT pad, which affect magnetic performance, cost and mechanical robustness. The multivariate nature of the problem makes optimisation a suitable approach for semi-automated design of IPT pads. FEM simulation is used when ferrite cores are present, which slows the design process and makes it computationally expensive. Therefore, the proposed design approach replaces FEM with a neural network model, trading off accuracy for a faster search of the solution space. This paper uses genetic algorithm optimisation with neural network predictions, trained by FEM data, to design the coil and core layer geometry of a double-D primary pad. The neural network is capable of identifying multiple relationships between the design outputs and pad parameters, showing its ability to emulate the simulation physics. The selected optimal design replaces 48% ferrite with SMC over the typical matched full-ferrite primary, with a further 36% ferrite directly removed. The maximum leakage field has dropped by 30%, while the coupling has only lowered by 3%.
Diabetic Retinopathy Grading based on a Sparse Network Fusion of Heterogeneous ConvNeXt Models with Category Attention

Diabetic retinopathy (DR) is an eye disease caused by high blood sugar levels that may damage vessels in the retina, leading to partial or complete loss of vision in later stages. In recent years, convolutional neural networks (CNN) have been used to help diagnose the DR severity. However, due to the slight differences between each class and the imbalanced nature of the datasets, standard CNNs often struggle to distinguish accurately between different grades of DR. To overcome these challenges, we propose combining a novel CNN model (ConvNeXt) with category-attention blocks incorporated at multiple levels of the architecture. This generates different models that can effectively extract fine-grained features and minimize the impact of dataset imbalance. Finally, we introduce a Sparse Network Fusion technique that learns to combine the outputs of all models to consolidate their individual decisions. Extensive experiments on the challenging DDR dataset show that our proposal achieves a new state-of-the-art performance, improving by about 3% grading accuracy compared with existing methods.
Novel Switched-Capacitor DC-DC Converter Achieving Highest Rational Conversion Ratios Using Inter-Stage Feedback

This paper proposes an inter-stage feedback-based switched-capacitor (IFSC) converter that can be configured to realize any rational voltage-conversion ratio (VCR) using a minimum number of 2:1 switched-capacitor (SC) stages. The converter enables efficient dynamic voltage scaling (DVS) operation over a larger voltage range by significantly increasing the resolution of voltage levels. Additionally, a restructuring algorithm has been proposed to achieve all VCRs without requiring any additional dedicated voltage stages. The converter extends the idea of the recursive switched-capacitor (RSC) topology, which spans $2^{N}$-1 ratios with N 2:1 SC cells. Comparative analysis shows that the proposed converter outperforms the negative-output feedback-based converter in terms of bottom-plate parasitic loss for most VCRs. To validate the concept, a fully-reconfigurable hardware prototype has been developed.
Optimal Switching Sequence Direct Power Control for AC/DC Converters with Enhanced Converter Model for Lower Switching Frequencies

Grid-connected AC/DC power converters play a crucial role in integrating renewable energies, smart grids, and motor drives. Consequently, high-performance control techniques have constantly attracted the interest of researchers. Predictive direct power control (P-DPC) strategies are promising control techniques for these converters, due to their fast transient response. However, standard P-DPC strategies cannot effectively compensate for steady-state errors when the switching frequency is reduced, due to model inaccuracies. This work proposes a new optimal switching sequence direct power control (OSS-DPC) strategy, which enhances the converter discrete-time model by estimating the grid currents and voltages at each commutation instant within a sampling period. As a result, the proposed OSS-DPC provides a fast dynamic response with reduced overshoot and improved steady-state performance, outperforming the standard P-DPC strategies.
Evaluation of the Front-End AC/DC Converter Circuits for Medium-Voltage-Connected Power Supply Systems

For large power DC loads such as data centers, EV charging stations, light rail, etc., the medium-voltage-connected power supply is promising since it eliminates utility-frequency transformers, reduces power conversion stages and distribution loss and has a smaller size. In this paper, the front-end AC/DC conversion stage of the medium-voltage-connected power supply is investigated. Five AC/DC converter circuits are compared with respect to the efficiency and the power density by optimizing the switching frequency, the filter inductor and the heat sink.
Semantic Role Labeling for Information Extraction on Indonesian Texts: A Literature Review

The information extraction process includes Semantic Role Labeling (SRL) as one of its sub-tasks. SRL aims to determine the semantic role of each entity within a sentence by examining the meaning of the predicate. This helps construct the sentence structure by identifying the relationships between predicates and their corresponding arguments. SRL development is less common than Named Entity Recognition (NER) for information extraction because SRL annotation process is complicated, and labeling results are sometimes ambiguous. In event extraction problem, the use of NER alone is insufficient. Identifying location entities generated by NER is still inaccurate because geographic coordinates indicate locations irrelevant to actual events. On the other hand, SRL can detect locations precisely and in depth according to actual events. Even though the annotation process is complicated, the SRL can be adjusted according to the required domain and its ontology so that SRL can extract location entities down to the event level. This research aims to offer a comprehensive analysis concerning the advancement of Semantic Role Labeling (SRL) for extracting information from Indonesian texts. Indonesian is a low-resource language with a different character from English and only has very little literature, so it is interesting to study. The papers used for the review process came from IEEE, Science Direct, and Google Scholar from 2013 to 2023, and 15 papers were found that matched the research objectives. The study results show that most papers use Indonesian-language news articles as their dataset because they use formal language, which usually has a good language structure. The methods used in SRLs are mostly rule-based. A weakness of the rule-based development method is that the rules are very dependent on a particular language or problem domain. Thus, further work can use a transformer-based deep learning approach to perform SRL on Indonesian-language texts.
Linking Geomechanics and Seismics – Stress Effects on Time-Lapse Multicomponent Seismic Data

C011 Linking geomechanics and seismics: Stress effects on time-lapse multicomponent seismic data Abstract 1 Time-lapse stress effects on seismic data can have a similar magnitude as time-lapse effects caused by changes in saturation and pore pressure. Production-induced stress changes occur not only in the reservoir interval itself but are also manifested in the overburden. Furthermore because stress fields caused by reservoir production are directionally dependent the resulting changes in the elastic properties yield an anisotropic seismic response. To study effects in seismic data we have developed a workflow that allows the prediction/estimation of changes in (anisotropic) seismic observations from stress
ZPVehicles: a dataset of large vehicle 3D point cloud data

As for current datasets of vehicle 3D points cloud, there are some shortcomings like lack of large vehicles, sparseness of point cloud data and insufficiency of vehicle parts information. In this paper, a high-density points cloud dataset of large vehicles named ZPVehicles(Points cloud of vehicles by Zhejiang Institute of Metrology) is provided. The original data of ZPVehicles is collected by a LAMIoVP(Lidar-based Automatic Measuring Instrument of Vehicle Profile), which is developed by us and installed in a vehicle testing station in Hangzhou, China. ZPVehicles contains the high-density point cloud data of about 800vehicles, which consist of 11 vehicle types such as van, barn truck, fence truck, crane truck, semitrailer tractor, bus, mini-bus, garbage truck, watering truck, communication warehouse truck and fuel tank truck. At the same time, ZPVehicles also provides the standard profile size of each vehicle and every part of the vehicle is labeled. To prove the practicability of ZPVehicles, some usages have been verified and demonstrated by experiments.
Frequency Support Analysis in Heterogeneous Multi-machine System and Enhancement Frequency Control of The Grid-Forming Converter

With regard to the frequency response modelling of the heterogeneous multi-machine system which contains both a grid-forming converter and synchronous generator, a decoupled system frequency response (DSFR) model is derived in this paper, which is based on the distinction of time scaling of two intrinsic components, namely the averaging frequency and the differential frequency. It is found that the general motion of the frequency in each generation source can be well characterized by the dynamics of averaging frequency component, therefore the reduced-order frequency response model is further developed. By utilizing this reduced-order model, the parametric influence of GFM sources to the frequency response is revealed, including the contribution of inertia constant, droop coefficient and percentage of GFM sources, where it is found the system frequency response performance can be greatly improved by increasing the droop coefficient. Moreover, a dynamic frequency droop control (DFDC) is proposed, which features only increasing the droop coefficient of GFM converter at the initial stage of frequency response and vanishing eventually. The control performance and parameter robustness are verified by MATLAB/Simulink simulation.
Simple Controller Tuning for Unmanned Aerial Vehicles using Governors

A simple governor-based controller tuning is implemented and tested for the application to unmanned aerial vehicles (UAVs). We show that the governor-based tuning approach enables the high-level tuning of the existing controller of the UAVs by using a single tuning parameter only. We implement the approach on two UAV platforms of different sizes and controller frameworks to prove the versatility of the given approach. The validation is performed with experimental data collected for the UAVs flying in the supervised environment of an indoor flight laboratory.
Design of On-board Power Supply for Tether Drone Applications

This paper presents the design of an on-board power supply for the tether drone system used in commercial and military applications. The power supply is crucial for delivering DC power to the propulsion and control systems of the tethered drone. This paper proposes an interleaved LLC resonant converter as the main topology and presents the design and implementation of the converter and control circuits. The integrated magnetic design of the transformer and inductor is also proposed to improve the power density of the converter. The proposed techniques were applied to a 1.6kW converter with input/output voltages of 400V/50V, and experimental results are provided to verify the design of the proposed scheme.
Internet of Things-Based Telemonitoring System Design for Wrist Rehabilitation

Improving health services with a technological approach by remotely providing rehabilitation services is the implementation of telerehabilitation as a part of telemedicine. A device that utilizes Functional Electrical Stimulation combined with a single computer to store rehabilitation data locally has been designed to rehabilitate the wrist region due to monoplegia. Developing a telemonitoring system for a portable FES-based wrist joint movement rehabilitation device is suggested, where rehabilitative information, such as the wrist’s torque and degree of movement in response to the FES stimulus, may be kept in a structured database and shown through a web application. The study’s findings can be utilized to establish a telemonitoring system based on the Internet of Things (IoT). The system architecture includes a database that houses data on simulated wrist movement therapy, with data queries typically taking 0.0043 seconds for every 500 rows of data. Additionally, the generated graphs from the simulated rehabilitation data mirror those from the actual device obtained from real subjects. Developing a web-based application to exhibit rehabilitation data that one or more operators may access from any location at any time is also achievable. As for development in future studies, trial on actual wrist rehabilitation devices and database optimization is necessary for the proposed telemonitoring system design.
Low-Level Feature Aggregation Networks for Disease Severity Estimation of Coffee Leaves

This paper presents a deep learning-based approach for the severity classification of coffee leaf diseases. Coffee leaf diseases are one of the significant problems in the coffee industry, where estimating the health status of coffee leaves based on their appearance is crucial in the production process. However, there have been few studies on this task, and cases of misclassification have been reported due to the inability to detect slight color differences when classifying the disease severity. In this work, we propose a low-level feature aggregation technique for neural network-based classifiers to capture the discolored distribution of the entire coffee leaf, which effectively supports discrimination of the severity. This feature aggregation is achieved by incorporating attention mechanisms in the shallow layers of the network that extract low-level features such as color. The attention mechanism in the shallow layers provides the network with information on global dependencies of the color features of the leaves, allowing the network to more easily identify the disease severity. We use an efficient computational technique for the attention modules to reduce memory and computational cost, which enables us to introduce the attention mechanisms in large-sized feature maps in the shallow layers. We conduct in-depth validation experiments on the coffee leaf disease datasets and demonstrate the effectiveness of our proposed model compared to state-of-the-art image classification models in accurately classifying the severity of coffee leaf diseases.