Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient Stability Evaluation of Independent Microgrid based on Message Passing Graph Neural Network

Virtual synchronous generator is used to control independent microgrid to provide inertia and damping support, but it often brings transient stability problems. Due to the large number of nonlinear and non-smooth characteristics of highly electronic independent microgrid, it is difficult to analyze it by the traditional transient stability analysis method. In this paper, the independent microgrid controlled by virtual synchronization is taken as the object. Firstly, we compare the impact of detailed modeling of energy storage and simplified modeling on the transient stability, and then discuss the impact of current limiting link of virtual synchronous machine on the transient stability. Secondary, combined with the idea of graph neural network, we propose an independent microgrid transient stability evaluation model based on message passing graph neural network. The information corresponding to bus and transmission lines in microgrid is aggregated and updated by message passing mechanism, so as to depict the topological connection relationship of microgrid, and then consider the influence of topology structure on transient stability evaluation. Last, a WSCC-9 system with virtual synchronous machine is taken as an example. The results show that considering the energy storage detailed model and current limiting link can effectively improve the accuracy of stability analysis. The proposed method has stronger generalization ability and is more applicable to the microgrid system with variable topological structure.
Electric Load Forecasting Using Multiple Output Gaussian Processes and Multiple Kernel Learning

Electric load forecasting refers to forecasting the electricity demand at aggregated levels. Utilities use the predictions of this technique to keep a balance between electricity generation and consumption at each time and make accurate decision for power system planning, operations, and maintenance, etc. Based on prediction time horizon, electric load forecasting is classified to very short-term, short-term, medium-term, and long-term. In this paper, a multiple output Gaussian processes with multiple kernel learning is proposed to predict short-term electric load forecasting (predicting 24 load values for the next day) based on load, temperature, and dew point values of previous days. Mean absolute percentage error (MAPE) is used as a measure of prediction accuracy. By comparing MAPE values of the proposed method with the persistence method, it can been seen that the proposed method improves the persistence method MAPE up to 4%.
Implementation of Multi-Channel 600V-Class EHD Driver to Drive-Per-Nozzle of Hybrid Inkjet Printer

A multi-channel high voltage electrohydrodynamic (EHD) driver which can realize drive-per-nozzle (DPN) that can compensate the deviation of each nozzle is presented. The proposed driver features the use of a serial peripheral interface and a serial-to-parallel converter to realize DPN by generating high voltage pulse-width-modulation (PWM) signals with different widths and delays for each nozzle. In order to achieve a uniform ink state in the nozzle, these high voltage pulse can be adjusted for each nozzle and control the electric field applied to the nozzle. In this way, driving according to the state of each nozzle can be realized. We successfully design and implement the proposed EHD driver through 1um single-poly 3-metal high voltage technology using BCD-on-SOI process.
Fault Diagnosis of Quadrotor Actuators based on Digital Twin

This paper reports a fault diagnosis scheme for quadrotor actuators based on digital twin (DT) system. First, the component-level DT model of the quadrotor is established under MapleSim. Besides, models of two types of most frequently encountered actuator faults are built. Next, an algorithm which combines particle swarm optimization (PSO) and K-nearest neighbor regression (KNN) mechanisms is designed to perform update of fault parameters continuously. Finally, the effectiveness of the proposed method in terms of diagnosis of quadrotor actuators faults is demonstrated through simulation.
Integrated System for Real-Time Health Management in Rural Areas with Medicine Dispatcher Box

One of the India's largest issues is providing healthcare to rural people; in rural areas, where more than 70% of the population resides and few medical facilities, disease-related mortality rates are at an all-time high. This research aims to suggest a medicine dispatcher box with temperature, heart rate, and pressure sensors as a component of a comprehensive system for rural real-time health management. The suggested system intends to lessen rural populations' challenges accessing healthcare services by offering a holistic solution that blends health monitoring and medication dispensing functions. The system design consists of a wearable patient device, a medicine dispatcher box, which administers medication based on the received health data, and a gateway device that processes and communicates the data to a cloud-based platform. The proposed system will be usable in rural areas with minimal resources because it is made to be user-friendly and economical. The viability and effectiveness of the suggested system are assessed through a series of experiments. The results demonstrate that the suggested approach can potentially enhance healthcare outcomes and accessibility in remote locations.
Large-signal Stability Analysis of VSC with PLL for Distributed Renewable Power Grid-connected based on Takagi-Sugeno Fuzzy Model Theory

This paper investigates the large-signal stability of VSC with PLL by T-S fuzzy model theory. Taking the control loop dynamics and dimension disaster into account, the system-level stability analysis is completed includes the system parameters, control system of VSC and PLL. Proceeding from the T-S fuzzy model of FFTS, the calculation of a series of linear inequalities is introduced to construct the Lyapunov function and obtain according LS-DOA simply. Ultimately, it verifies the effectiveness of proposed stability analysis method through time-domain simulations results.
Statistical analysis of Continuous Haines Index and Fuel Moisture Index for pyrocumulonimbus and non-pyrocumulonimbus wildfires in southeast Australia

: Pyrocumulonimbus (pyroCb) are associated with some of the most extreme wildfire behaviours and some of Australia’s most notorious wildfire-related catastrophes. However, while an ever-increasing number of studies continue to shed more light on them, our overall understanding of pyroCb remains relatively limited. As pyroCb have become an increasingly common occurrence in Australia, with over one hundred events observed since 2003, understanding the environments in which they form is a high priority. In this study, we examined two fire indices in relation to the occurrence of pyroCb: the Continuous Haines Index (C-Haines), consisting of lower-tropospheric stability and dryness components, and the Fuel Moisture Index (FMI), a surface-based index originally developed as a simple alternative for representing fuel moisture content of fine dead fuels. ERA5 reanalysis data (Hersbach et al., 2020) was used to calculate values of C-Haines, its stability and dryness components, and FMI for both large standard (non-pyroCb-producing) wildfires and pyroCb event dates and locations across the southeast Australian mainland at 0200, 0400, and 0600 UTC (12:00, 14:00, and 16:00 AEST). We then explored the distributions of values of C-Haines, its components, and FMI, based on event type (standard wildfire versus pyroCb) and ran statistical tests to see if the two indices possess skill in differentiating pyroCb and standard wildfire environments. Relationships between C-Haines
Serious game in web programming learning: A systematic literature review

The importance of education has given rise to the search for different learning techniques and the use of information technology tools. One of the techniques currently used to achieve and encourage learning is serious games and gamification; these techniques allow students to achieve learning objectives while playing. This paper presents a systematic literature review about proposals for learning web programming based on serious games; 10 studies based on serious games are presented for learning content related to web programming, programming languages, and the topics addressed in teaching. This study contributes to web programming learning for students who want to learn web programming.
A Multi-attribute Decision-making based Importance Assessment Method of Sensor Nodes

In this paper, an air defense early warning sensor network node importance assessment method based on multi-attribute decision was proposed. Firstly, according to the characteristics of the air defense early warning sensor network, the factors that affect the node importance degree were analyzed, and the node importance degree was divided into three aspects, which were performance importance degree, function importance degree and structure importance degree. Secondly, three calculation methods of importance degree were introduced. Then the synthesis method of comprehensive importance degree based on TOPSIS method was proposed. Finally, the validity of the method and model proposed in this paper was verified.
The Space Radiation Detector onboard China Space Station

The radiation field in space is complex and consists of various cosmic rays, making it a serious threat to astronauts’ health and spaceships. Although prevented by many specific shield layers, the radiation dose inside the Space Station is still much higher than that on Earth, so specialized measurement instruments are still needed to monitor the radiation environment. This paper presents a radiation dose and energy measuring instrument, which has been applied onboard China Space Station, named as the Space Radiation Detector (SRD). It was designed for particle identification, energy spectrum measurement, and dose rate measurement of charged particles, neutrons, and gamma rays. A series of ground tests have been carried out, and the results showed that both the function and performance of SRD meet the design and requirements. Some actual measurement work has been carried out since the module was powered on onboard China Space Station as well, and the results showed that the equipment has been in working order and some real data on the radiation environment inside the cabin has been obtained already.
Research on aircraft detection method based on improved YOLO

This thesis presents an aircraft detection method based on the improved You Only Look Once (YOLO) algorithm. The traditional YOLO algorithm is efficient and accurate in target detection, but has some challenges in dealing with small and dense targets. To solve these problems, this paper proposes an aircraft detection method based on an improved YOLO algorithm.
Performance Evaluation of 5G MBS for Terrestrial Broadcasting Scenarios

This paper provides performance evaluations of 5G NR-MBS for terrestrial broadcasting environments. The physical layer performance of NR-MBS is analyzed through theoretical point of view and block error rate (BLER) performance over high-power high-tower (HPHT) infrastructure. Considering the future development of NR-MBS for terrestrial broadcasting services, this paper focuses on the performance evaluation of NR unicast numerology including length of cyclic prefix (CP) over HPHT single frequency network (SFN) environments. Specifically, it is demonstrated that the effect of CP length on the decoding performance depends on the transmission rate and the quality of channel estimation over HPHT SFN environments.
Detection Welding Performance of Industrial Robot Using Machine Learning

Automated welding robots have become an essential component in manufacturing industries due to their precision and increased productivity. Furthermore, they eliminate the need for human workers to be exposed to toxic fumes and bright light during welding processes, ensuring worker safety. However, any failure in welding robots can lead to poor product quality and impact a company's credibility. Therefore, it is critical to predict the welding performance of these machines to maintain product quality. This study proposes the use of spectrogram image classification to classify the quality of welding products. Using a TM-1400 WGIII welding robot, 180 welding datasets were collected to determine the conditions of the welding robot based on different welding wire lengths. Current and voltage signals of the welding robot were studied to identify suitable input parameters and machine learning algorithms were utilized to improve the accuracy of the predictive welding quality. The experiment resulted in an accuracy of 89% based on a 70% training sets and 30% testing sets of data, indicating that spectrogram image classification is an effective technique to monitor the condition of welding robots, improve welding quality, and increase product quality control. This method can be beneficial for both small- and large-scale industries such as automotive, energy, and construction.
Adaptive Quantized and Normalized MSA and Its Application for DTMB-A LDPC Codes

In practical implementation, the input and intermediate signals of Low-Density Parity-Check (LDPC) message passing decoders are quantized into fixed-point messages with finite bit-width. The quantization operation with limited bit-width enhances the data throughput and reduces the computation and storage overhead of LDPC decoder, but confronts the problem of performance degradation. Existing improved algorithms of min-sum algorithm (MSA) generally do not consider the effect of quantization, and thus lack the optimization of the quantization strategy. To fill this gap, we first propose adaptive asymmetric quantization strategy. Then, based on the proposed quantization strategy, combined with conventional adaptive normalization technique, we propose adaptive quantized and normalized MSA (AQNMSA), which significantly alleviates the performance degradation of fixed quantized and normalized MSA (NMSA) with negligible increment of computational complexity. Concurrently, we provide a novel look-up table design algorithm for AQNMSA based on the tool of multi-edge-type density evolution (MET-DE). Finally, we apply AQNMSA to DTMB-A LDPC codes with a very low quantization bit-width of 4 bits, and observe a superior threshold performance and lower error floor compared with its non-adaptive float-point counterparts.
Going-Private Transactions and Ex-Post Firm Behaviors: Evidence from Japanese Management Buyouts

This empirical examination assesses effects of ceasing the public trading of company stock, specifically the practice of “going-private,” on subsequent corporate behavior. More specifically, this report identifies effects of going private transactions on corporate restructuring, investment, and innovation activities (patents, R&D) using Japanese going-private-type management buy-out (MBO) data. Firms that conducted public-to-private MBOs are matched with firms that have similar attributes to clarify empirically whether going private promotes corporate innovation activities or restructuring. The study described herein also tests factors underlying changes that occur after going private using a hypothesis related to motives for going private. According to the results, restructuring behaviors are observed after going private, but firm innovation activities are not confirmed among the MBO firms. Buyout funds enhance investment of MBO firms after the buyouts are completed, but they exhibit no effect on innovation activities.
Reconfigurable CNN Accelerator Embedded in Instruction Extended RISC-V Core

The convolution neural network (CNN) is widely used in many aspects, such as Speech Recognition, Face Detection, and Image Classification. Utilizing a GPU is the traditional way of implementing CNN, which is fast but inefficient. In pursuit of lower power consumption and higher efficiency, we prefer application-specific hardware computing. This paper proposes a run-time reconfigurable CNN accelerator SoC (CNN-AS) architecture embedded in instruction-extended RISC-V. The application-specific extension instruction set is designed to accelerate high-frequency operations in CNN. To optimize the circuit structure, we created an 8-bit dynamic fixed-point (DFP) scheme within the CNN-AS. The accuracy of the DFP implementation is also compared with the TensorFlow float implementation. Furthermore, the corresponding software of RESNET and VGG16 is described and simulated with CNN-AS. Lastly, we compare the overall simulated results with other non-SoC FPGA designs in efficiency, throughput, and power.
Design of Circularly Polarized Dielectric Resonator Antenna for 5G NR Band Application

A right-hand circularly polarized cubic Dielectric Resonator (DR) Antenna (DRA) operating in the 5G NR FR1 n78 band $(3.3\sim 3.8\mathrm{GHz})$ is proposed. Two intersecting rings with unequal circumference are etched on the ground side of the dielectric substrate as coupling slots for the designed antenna. The central line of the annular slots is located on the diagonal of the substrate and fed by the $ 50\Omega$ microstrip line central coupling. Effective excitation of these two pairs of degenerate orthogonal modes, namely, $\mathbf{T E}^{y'}_{1}11$ and $\mathbf{T E}^{x'}_{1}11,\mathbf{T E}^{x'}_{1}11+\delta$ and $\mathbf{T E}^{y'}_{1}11+\delta$ ($\delta$ between 0 and 1), makes the antenna achieve circular polarization characteristic. The simulation results show that the $-10\mathrm{~dB}$ impedance bandwidth of the antenna is $3.20\sim 3.99\mathrm{GHz}$, covering the n78 band, and the $3\mathrm{~dB}$ axial ratio bandwidth is 3.38 $\sim 3.92\mathrm{GHz}$, including two axial-ratio resonance points. On the whole, the proposed antenna has stable gain and high radiation efficiency in the working band, and can be applied to 5G NR communication in the future.
Herbal Liqueur Effect Through Sublingual Vein Imaging Using Smartphone and Deep Learning

A method for visualizing the effects of consuming the herbal liqueur Yomeishu by acquiring images of sublingual veins using a smartphone camera with 50-megapixel resolution, the resolution of which has rapidly improved in recent years, is discussed. A simple configuration using a commercially available ring light and a polarizing film can be used to acquire clear images of sublingual veins. Using a ring light provides a stable light source, and by attaching a polarizing film orthogonally to the ring light and lens, it is possible to eliminates light reflection caused by saliva. We propose a method involves training a deep learning the sublingual veins before and after the experience of consuming Yomeishu. When a new image is inserted into the model, it generates an output value for the liquor effect. Analysis of the results obtained with the trained neural network reveals the sublingual position of the effect of taking Yomeishu.
Multipath Cluster-Based Scatterer Recognition by Object Detection Techniques Using Panoramic Images

Object detection is crucial in the field of wireless communication, as it helps in predicting the channel behavior and channel model parameters that are necessary for efficient communication. In recent years, various object detection techniques have been proposed for this purpose, ranging from traditional statistical methods to deep learning-based approaches. This paper provides a comprehensive review of object detection techniques for predicting wireless channel model parameters. Additionally, the paper discuss the advantages and limitations of different object detection frameworks such as YOLO, Faster R-CNN, and Mask R-CNN. Finally, the paper puts foward a conceptual introduction of a novel approach to utilize Faster R-CNN object detection technique andcomputer vision, to predict the scatterers and eventually estimate the channel characteristics of a wireless channel. The paper highlights the current challenges and future directions in object detection for wireless channel model parameters prediction. The dataset to train the deep learning model is generated from an example conference room environment panoramic images. The proposed approach can be applied in various wireless communication scenarios, such as 5G and beyond, to accurately predict the location of scatterers based on multipath clusters so as to optimize network design and improve the overall performance of the system.
Adaptive Propagation Model of Network Hotspot Events Based on SEIR

Internet hotspot events spread quickly and have a significant influence on the Internet, becoming the focus of monitoring public opinion. Due to the gradual fermentation of these events, the scope of transmission, the number of participants, and the event's influence constantly change. Therefore, a propagation model with fixed parameters cannot accurately describe the propagation law of hotspot events. To address these issues, this paper proposes an adaptive SEIR propagation model, called SEIR-A, which incorporates a dynamic infection rate. This model enhances the traditional SEIR model by considering susceptible, latent, infected, and cured individuals. Furthermore, it combines the model with the data assimilation method to capture the trend of hot topics. Experimental testing demonstrates that the model effectively describes the propagation trend of four network hotspot events from Weibo, thus proving its accuracy and applicability.
Analysis of Domestic Building Detection Based on the YOLO

Building detection is an important task for land monitoring, urban planning, and illegal building. Recently, research is increasing to apply deep learning for building detection. However, prior studies are difficult to apply domestically because the dataset has different geographic and building characteristics. In this paper, we created a dataset for domestic building detection by collecting high-resolution satellite imagery. And, the deep learning model (YOLOv5) that achieved SOTA (state-of-the-art) was trained with our dataset and detected building. Results obtained 89.7% accuracy carried on our building dataset. In addition, we compared the building detection results according to the resolution. The results showed that the lower the resolution, the lower the detection recognition.
Security Protection Method of Protective Control Information in Smart Substation Based on Message Identification

Aiming at problem of information security and precaution on protective control information of smart substation, this paper puts forward a three-defensive-line model for protective control information The model includes the first line focusing on invasion risk, the second line on propagation risk and the third line on invalidation risk. Furthermore, considering weak links of the second line, this paper proposes a security protection method and corresponding security data exchange model on propagation risk of protective control information, and establishes a simulation platform for this method. Simulation results show that this method can optimize message handling mechanism under normal circumstances, and guarantee reliable transmission of key message under abnormal situation. Therefore, the proposed security protection method can effectively raise anti-risk ability of smart substation.