Miracle-dz/UniFAR-FTUs huggingface dataset:
LHCP and RHCP enhancement on 30-pointed star antenna with cross slot

Circular polarized antennas of fer the advantage of minimizing polarization mismatch, as they can effectively align with the polarization of the received signal disregarding of the orientation of the receiving antenna. This paper introduces a wideband antenna design featuring a cross-slot 30-pointed star configuration to enhance the circular polarization. The incorporation of the cross-slot enhances the antenna’s axial ratio bandwidth and gain at 2.4 GHz, enabling it to produce both left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). The dimensions of the slot, including its length and width, are examined to determine their impact on RHCP and LHCP.
A Standardized Edge Computing Infrastructure of LoRaWAN Using IEEE 2668

LoRaWAN is an overwhelmingly popular wide area networking protocol that is capable of deploying city-wide Internet of Things (IoT) networks. Increase on the deployment of LoRaWAN in the last few years has led to exponential increase on the number of LoRa sensory and actuating end devices and results in more congestion in LoRaWAN network traffic. Moreover, the evolving AI-IoT (AIoT) applications (e.g., AI-powered environment monitoring system) create needs for more computing power with low latency requirements. To mitigate the high latency issue and the shortage of computing power due to continuously demanding quality of service (QoS) requirements of IoT applications, the Edge-Cloud server structure has been explored in a thorough manner in research area in recent years. An IEEE 2668 standardized IoT infrastructure system is presented in this work focusing on analyzing the performance of applying Edge-Cloud network server structure to LoRaWAN networks and provide standardized edge computing LoRaWAN infrastructure. This work uses queuing network to quantitively analyze the performance of an edge computing infrastructure applied to LoRaWAN and uses IEEE 2668 to quantitively present the performance and applicability of the Edge-Cloud LoRaWAN infrastructure.
Comparative Study of Coupling Characteristics Between Machine- and Grid-side Subsystems of PMSG-based WTGs under Different Control Modes

The permanent magnet synchronous generator (PMSG)-based wind turbine generator (WTG) has become the mainstream in wind power generation. However, the wideband oscillation issues have become increasingly serious due to the interaction of PMSG-based WTGs and the grid. It is generally considered that the interaction characteristics almost completely depend on the grid-side subsystem (GSS), so that the machine-side subsystem dynamics (MSSD) are usually ignored in existing researches. The rationality of such simplification method has not been systematically studied. Therefore, the comparative study of coupling characteristics between machine- and grid-side subsystems of PMSG-based WTGs under four types of control modes is carried out in this paper. Firstly, the detailed impedance models of PMSG-based WTGs under these control modes are developed and validated. Based on these impedance models, the comparative analysis of coupling characteristics is carried out qualitatively and quantitatively. Some useful findings are obtained. Finally, the case study verifies the impact of such coupling effects on the stability analysis of gird-connected PMSG-based WTGs.
A Vision-Based Algorithm of Slope Angle Estimation by Using Cooperation of Snake Robots

When snake robots perform tasks, their ability of sensing the environment is very important. This paper proposes an algorithm for estimating the slope angle based on the cooperation of two snake robots by using the visual system. Through the movement of two snake robots, the corresponding geometric models will be created. After that, the angle of the slope is calculated by substituting the coordinates that are recorded by the visual system. Finally, the experiments are conducted to verify the performance of the algorithm. The results show the errors are within 2° when the slope range is between 15° and 75°.
Factors Affecting Purchase Intention of Hanfu: Considering Product Identification, Cultural Motivation, and Perceived Authenticity

With the trend of national cultural confidence and the growing appreciation for aesthetic diversity, traditional apparel from different countries or regions has become a driving force in the clothing industry. Hanfu, an emerging trend that industrializes traditional culture, has garnered increasing attention from consumers. Thus, with the objective of exploring the psychological antecedents of Hanfu consumers’ purchase intentions from the perspectives of product identification, cultural motivation, and consumers’ perceived authenticity, the present study was empirically conducted with a sample of 823 respondents. Partial least squares-structural equation modeling (PLS-SEM) was employed to examine the proposed research model. The results demonstrate that consumers’ identification with Hanfu and cultural motivation positively influence object-based and existential authenticity, as well as purchase intention. Furthermore, the mediating effect of perceived object-based authenticity is confirmed, indicating its significance in shaping consumers’ purchase intentions, while the mediating effect of existential authenticity is found to be insignificant. Research findings could contribute to the understanding of the psychological mechanisms driving consumers’ purchase intentions towards traditional clothing and highlight the importance of consumers’ perceived object-based authenticity in the market of traditional cultural clothing.
Virtual Plant Design: Automatic Sortation and Warehouse System for Distribution Intralogistics based on Petri-Net Method

Warehousing has a pivotal role in the distribution of goods across the supply chain. However, many manual processes still exist in intralogistics distribution, relying on employees to operate the system. This emphasizes the need for an automated intralogistics distribution system that incorporates automatic object sorter, which then feeds into palletizer followed by the use of cranes. The system utilizes a programmable logic controller (PLC) as a controller, with ladder diagrams as the programming language. The current approach to designing ladder diagrams lacks structure and can be challenging to solve problems efficiently. To address this, the petri net method is employed as a guide for programming, enabling the design of a more structured ladder diagram, and facilitating the characterization of discrete event simulation models. The ladder diagram’s results were recreated through simulation by employing a virtual plant implemented via Factory I/O, providing a close depiction of the logistics process within the manufacturing sector. The design result for intralogistics distribution in virtual plant involves the integration of 53 sensors and 68 actuators. The recorded time for distributing 25 palletized products to the warehouse averages at 17 minutes and 38 seconds.
A Compact Triple-Band Rectifier and Dual-Band Rectenna for IoT Applications

This article presents an original design of a compact and adaptive triple band rectifier for IoT wireless Applications intended to be used as a rectenna. The particularity of this proposed design is the tunability of the rectifier through resonance generation thanks the capacitance cancellation with lumped inductors in order to target different operating frequencies. Its global size of 25 mm x 15 mm was optimized with the use on lumped component in order to be easily integrated in the antenna to form a compact rectenna. The rectifier was operated on frequencies allocated to applications: UHF 900 MHz, 2.4 GHz and 5 GHz. Thus, the prototype finds its use for RF signal from RFID, Bluetooth, and WIFI applications. The design has been accurately simulated and the prototype has been fully characterized in the three bands targeted. A rectenna was later prototyped with a small double band antenna consisting of modified two-layer bowtie and rectangular rings. The results of the dual band rectenna is also presented.
Key Indicators for the Discrimination of Wines by Electronic Noses

In the food industry, and especially in wines as products thereof, ethanol and sulfur dioxide play an equally important role. Both substances are important wine quality characteristics as they influence the taste and odor. As both substances comprise volatile matter, electronic noses should be applicable to discriminate the different qualities of wines. Our study investigates the influence of alcohol and sulfur dioxide on the discrimination ability of wines (especially those of the same grape variety) using two different electronic nose systems. One system is equipped with metal oxide sensors and the other with quartz crystal microbalance sensors. Contrary to indications in literature, where the alcohol content is discussed to have a large influence on e-nose results, it was shown that a difference of 1 % ethanol was not sufficient to allow accurate discrimination using Linear Discriminant Analysis by any system. On the positive side, the analyzed concentrations of ethanol (about 12 %) did not superimpose other volatile information. So difference in sulfur dioxide content gave an accuracy for sample discrimination of up to 90.6 % with MOS nose. Thus, we are so far partially able to discriminate wines with electronic noses based on their volatile imprint.
Pavement Diseases Detection Using Improved YOLOv5

Pavement diseases have a negative impact on traffic safety and ride comfort. With the rapid development of autonomous vehicle, the demand for rapid and accurate detection of pavement diseases is becoming more and more urgent. Previous pavement detectors have the contradiction between accuracy and speed. To address the above issue, a pavement disease detection model based on YOLOv5 is proposed. To improve the detection accuracy, we combine SPPF with attention mechanism, decouple the YOLOv5 detection head and use depthwise separable convolution. By using K-means to adjust the anchors, the convergence process of the model is smoother. The strategy of label smoothing is used to improve the generalization ability. Experiments on RDD2020 data set show that our method improves the accuracy of pavement diseases detection compared with the original YOLOv5 under the premise of maintaining real-time performance. Also the detection performance is better than EfficientDet, Faster RCNN and other series.
Gaming Behaviors and Its Correlation with Internet Gaming Disorder among Indonesian Young Adults

Internet Gaming Disorder (IGD) is a worldwide issue that may result in negative consequences for individuals. Many studies investigated the prevalence, gaming behavior, and risk factors of IGD in many countries. Indonesia is one of the Southeast Asia countries with a high number of video game players. However, there is no valid data available on the IGD prevalence and gaming behaviors, particularly among Indonesian young adults on a nationwide scale. This study aims to examine the gaming behaviors among Indonesian young adults, investigate whether the criteria of IGD are present in this population, and examine the correlation between gaming behaviors and IGD. A total of 1451 individuals aged 18 – 24 years old (mean age = 20.29, SD = 1.879) participated in this study. Gaming disorder was assessed using the nine-item Internet Gaming Disorder Scale–Short Form (IGDS9-SF). The correlation between gaming behaviors and IGD was assessed using Hierarchical Regression Analysis. The study found that 11.85% of Indonesian young adults were indicated to have IGD. The results also showed that time spent gaming and game genre significantly predicted IGD. The findings were anticipated to be the basis for further prevention and intervention efforts, particularly for those who are most vulnerable to IGD.
GRAFCET Reduction Techniques for Model Checking

Model checking of GRAFCET, an IEC standardized specification language, is typically performed by a translation of GRAFCET into a different (target) formalism. However, analyzing instances of considerable sizes quickly becomes unfeasible due to the state space explosion problem. We propose three techniques to reduce a GRAFCET instance depending on the property to be evaluated, resulting in a smaller state space. These techniques can be employed, even in combination, before transformation into a formalism suitable for model checking.
PWM-PFM hybrid modulation for DAB converter based on RL algorithm

This paper proposes a hybrid modulation method to control a dual active bridge(DAB) converter. To improve the efficiency of the system, the proposed hybrid modulation is consisted with pulse width modulation(PWM) and pulse frequency modulation(PFM) in addition to single phase shift(SPS) control which is widely used in DAB converter. By adopting the proposed method, the RMS current of the switching device can be reduced while keeping ZVS operation capability. Therefore, the system can accomplish the high efficiency. Also, this method can be used to a wide output voltage range and light load which is traditionally referred to as a drawback of the DAB converter. To realize optimized control variables for hybrid modulation, reinforcement learning(RL) method is adopted.To verify the effectiveness of the proposed method, a 2 kW DAB converter is built and tested. The efficiency results shows that the efficiency is increased in 1.05% at full load.
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts

Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.
Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data

This study evaluates the effectiveness of transfer learning models in industrial surface defect detection using few-shot learning. Surface defect detection is a critical task in various industrial applications, where accurately detecting and classifying defects can improve product quality and increase manufacturing efficiency. However, data scarcity is a considerable challenge: obtaining and labelling defect samples is a costly, time-consuming process and difficult due to their infrequent occurrence. Few-Shot learning aims to effectively train models using only a limited number of labelled samples, thus mitigating the impact of data scarcity. This study compares the performance of transfer learning models pre-trained on three different data sets for few-shot learning in the context of surface defect detection. On the one hand, transfer learning models pre-trained on the ImageNet data set yield the best overall results in terms of accuracy. On the other hand, our results indicate that the DAGM data set, an industrial optical inspection data set which is close to the target domain, is particularly effective for training models to clearly detect surface defects in a few-shot learning scenario.
Digital Gate Driver IC with Real-Time Gate Current Change by Sensing Drain Current to Cope with Operating Condition Variations of SiC MOSFET

A digital gate driver IC with real-time gate current (IG) change by sensing drain current (ID) is applied to SiC MOSFETs, and it is demonstrated that the IC always reduces switching loss and switching noise by always performing appropriate active gate driving even when the operating conditions of SiC MOSFETs, such as load current and junction temperature, change. The IC integrates all of a current-source based digital gate driver which changes IG in 6 bits, a dID/dt sensor to detect IG switching timing, and a controller into a single chip. In the turn-on measurement of an SiC MOSFET at 600 V and 25 °C, when the load current changes to 20 A, 70 A, and 120 A, compared with the conventional single-step gate drive, the active gate drive using the developed IC reduced the switching loss by 17 %, 12 %, and 11 % under ID overshoot-aligned condition, respectively.
Smith Predictor-based Cross-Coupling Correction for Voltage Source Converters under Mixed Reference Frames

Voltage Source Converters (VSC) have been under the spotlight of scientific research due to the development of the distributed generation system. Synchronous Reference Frame (SRF)-based controllers are readily used for the voltage and current control of these converters due to its simplicity and clear physical significance. However, in machine drive or more electric aircraft (MEA) scenarios, the low switching-to-fundamental (S2F) frequency ratio leads to more significant cross-coupling between d and q axis, which will affect the control accuracy and even jeopardize the system stability. This work proposes a Smith Predictor-based current control under mixed reference frames that is able to eliminate the cross-coupling effect without a sophisticated parameter design process. Experimental results are given to further verify its performance under MEA grid scenario.
Improve Sparse Implicit Projection Via Incomplete Cholesky Factorization

Model order reduction is widely used to speed up the simulation of complex RC network with large number of terminals. Eliminating the internal nodes is a class of important reduction methods for RC network with large number of terminals. However, the reduced system matrices would be dense ones due to the introduction of fill-ins during elimination of internal nodes. In this paper, we propose to introduce incomplete Cholesky factorization into the elimination-based approach Sparse Implicit Projection (SIP) to produce a sparser reduced RC network with fewer passive elements. Compared with traditional approaches, our proposed method can achieve sparser reduction results with acceptable accuracy, and experiments show the benefits in the subsequent simulation.
Semi-supervised Variational Autoencoders for Regression: Application to Soft Sensors

We present the development of a semi-supervised regression method using variational autoencoders (VAE) for soft sensing of process quality variables. Recently, use of VAEs was proposed for regression applications based on variational inference. In this work, We extend this approach of supervised VAEs for regression to make it learn from both labelled and unlabelled data leading to a semi-supervised VAE for regression (SSVAER) formulation. The probabilistic regressor resulting from the variational approach makes it possible to estimate the variance of the predictions simultaneously, which provides a means for online uncertainty quantification for soft sensors. We provide an extensive comparative study of SSVAER with previously proposed semi-supervised learning methods on two soft sensing benchmark problems using fixed-size datasets, where we vary the percentage of labelled data available for training. In these experiments, SSVAER achieves the lowest test errors in 11 of the 20 studied cases, compared to other methods where the second best method gets 4 lowest test errors out of the 20.
Triple-Mode Current-Fed Resonant DC-DC Converter for Wide Input Voltage Range with Extended Asymmetric Modulation

This paper proposes a triple-mode current-fed resonant converter operating over a wide input voltage range for renewable energy systems. First, when the input voltage is lower than the nominal voltage, the proposed converter operates in a high boost mode. Next, when the input voltage is equal to the nominal voltage, it operates in a nominal boost mode. Last, when the input voltage is higher than the nominal voltage, it operates in a low boost mode. In contrast to conventional symmetric modulation techniques, this converter uses an extended asymmetric modulation technique that can lower hard switching turn-on loss. Accordingly, the proposed converter achieves high step-up ratio and power density over a wide input voltage range. A 400-W prototype is constructed and experimental tests were carried out to demonstrate the performance of the proposed converter.
Ebublio: Edge-Assisted Multiuser 360° Video Streaming

As one of the most important manifestations of virtual reality (VR), 360° panoramic videos in recent years have experienced booming development due to the desire for immersive and interactive experiences. Compared to traditional videos, 360° videos are featured with uncertain user Field of View (FoV), more sensitive delay tolerance, and much higher bandwidth requirement, bringing unprecedented challenges to 360° video streaming. Meanwhile, the development of 5G and mobile edge computing starts to pave the way for high-bandwidth low-latency video streaming. Some preliminary works focus on either individual FoV prediction or multiuser Quality of Experience (QoE) oriented cache strategy design, while how to design a holistic solution toward optimizing the overall user QoE with considerations over fairness and long-term system cost remains a nontrivial problem. In this article, we propose Ebublio, a novel intelligent edge caching framework to address the aforementioned challenges in 360° video streaming. Ebublio consists of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module to jointly optimize the long-term user QoE and system cost. The former module integrates the features of video content, user trajectory, and other users’ records for combined prediction. The latter one employs the Lyapunov framework and a subgradient optimization approach toward the optimal caching replacement policy. Our trace-driven evaluation demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE at similar traffic consumption.
Integration of Reinforcement Learning into Fluid Control Systems

Reinforcement Learning (RL) is becoming increasingly important in closed-loop controller design. RL controllers adaptively adjust to time-variant and nonlinear system/environmental characteristics and learn automatic controller parameterization without plant modeling. This paper investigates whether RL can be used as a control strategy for a pneumatic pressure control unit in time-pressure dosing (TPD) systems. These nonlinear systems are characterized by actuator hystereses and discrete-event driven changes in system dynamics depending on the system state. Further challenge in control arises from manipulating two actuators by one single control signal. We apply the Deep Deterministic Policy-Gradient (DDPG) agent and perform the training in loop with a first principles simulation model of the system dynamics in Simulink. We introduce a reward function to achieve the required steady-state accuracy and eliminate oscillating actuation. The trained RL controller is implemented on a 32-bit STM32F405 microcontroller by automatic code generation and is evaluated against an existing PI controller. The results show that the RL controller can control the pressure of TPD systems with the existing nonlinearities and discrete-event changes in system dynamics. Although the time constants of the real system differ from those of the simulation model, the RL controller still meets the requirements of the control loop. Compared to the PI controller, the RL controller improves the closed-loop dynamics by achieving lower time constants.