Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Double Deep Q Network for Active Distribution Network Equivalent Modeling

As more and more distributed renewable energy generation is connected to the active distribution network (ADN), the power interaction between the ADN and the backbone network becomes more complex. In addition, due to the lack of numerous branches of power measurement units, it is difficult to accurately simulate the power flow in ADN. Therefore, we proposes an equivalent model of ADN, consisting of a motor and a synthetic ZIP load. Equivalent modeling utilizes a Hybrid double deep Q-network (HDDQN) to track the dynamics of the original ADN, using a small amount of power measurements in the boundary to achieve high accuracy. In HDDQN, select the types of ZIP loads and motors from the load pool to obtain the dynamic equivalent modeling of the first layer ADN. Through the powerful feature extraction ability of deep learning and the optimization decision-making ability of reinforcement learning, determine the weight of the model, and obtain the $PQ$ components of the motor and ZIP loads in the second layer. The results indicate that HDDQN can accurately equivalent ADN compare with DQN.
Aeroengine Life Prediction Based on CNN-BiLSTM Model and Attention Mechanism

One of the core technologies in predictive maintenance is equipment life prediction. The aeroengine serves as the main power source for aircraft, and its reliability is crucial for ensuring safe flight missions. Using equipment life prediction can better manage the use and replacement of aircraft engines, which plays a very important role in the area of improving their availability and enhancing aircraft safety. To better predict the remaining service life of aircraft engines under various working conditions, this study proposes an aeroengine life prediction artificial intelligent model based on a series of Convolutional Neural Network (CNN) as well as the Bidirectional Long-Short-Term Memory network (BiLSTM). The attention mechanism is arranged into the designed network to enhance the feature weight of the data that need to pay attention to, thereby improving the prediction accuracy. Experiments were conducted on the NASA C-MAPSS simulation dataset. In addition, results of experiments are compared at the same time with several traditional methods. The contrast of the different results demonstrate that the model is able to predict the remaining life of aeroengines with higher accuracy than traditional methods.
Circulation pump bearing fault diagnosis research based on BAM-CNN model and CWT processing time-frequency map

This article introduces a vibration-based method for diagnosing faults in centrifugal pump bearings. Traditional vibration signals contain a large amount of noise and interference, and can only provide information in the time domain, making it difficult to capture local features of the signal. To address this issue, the original vibration signal is transformed into a time-frequency representation using the Continuous Wavelet Transform (CWT) method, overcoming the limitations of time and frequency domains, enhancing the capability of feature extraction, and eliminating noise and interference. Furthermore, considering the limitations of traditional Convolutional Neural Networks (CNN) in capturing global context information due to their local receptive field of convolutional layers and the down-sampling operation of pooling layers, which can affect the model's performance, this study proposes a CNN with a Bottleneck Attention Module (BAM-CNN). The BAM module integrates the attention mechanism to leverage global information for weighting the feature maps, enhancing the model's generalization capability. Experimental results demonstrated that employing CWT to convert the original vibration signal into a time-frequency representation and feeding it into the BAM-CNN model yielded remarkable generalization performance. The model achieved a fault recognition accuracy of up to 98.4% on the test set.
Interharmonics Suppression Scheme in PV System with Reference Phase Correction MPPT Algorithm

In general, the conventional perturbation and observation maximum power point tracking (P&O MPPT) algorithm has been widely used to ensure the maximum power output of photovoltaic (PV) arrays. When the maximum power point is found, the P&O MPPT method will generate a three-point oscillation at the maximum power point, which introduce interharmonics injection. And it can lead to a deterioration in power quality, and when multiple PV inverters are connected in parallel, the deterioration will be exacerbated. Especially, the most serious situation will occur when the MPPT of each battery in all parallel inverters is disturbed in phase, which caused large interharmonics in the grid. To address the above issues, a reference phase correction scheme for interharmonics control of PV systems was proposed in this paper. when the PV system operates stably at a maximum power point (MPP). The disturbance phase of its DC-link MPPT was calculated by one parallel unit, then sends it to another parallel unit. The shift phase required to offset the disturbance of the first parallel unit was calculated by the receiving parallel unit based on the received phase and its own MPPT disturbance phase. In addition, due to communication delays, a phase compensation method is adopted, which is compensating phase required was calculated by using delay measurements on the communication channel. The compensating phase and previously calculated shift phase are added, the final phase adjustment of the second parallel unit is determined. After phase adjustment, effective mitigation of equivalent total DC disturbances is reduced, and interharmonics injection was suppressed effectively. The effectiveness of the proposed control scheme was shown by simulation results.
Feasibility Study of Hydrogen Fuel Cell and Battery Freight Locomotive using EMR and Simulink

Currently, initiatives are made to implement zerocarbon technologies and replace trains that use fossil fuels. Transportation is one of the significant sources of carbon emissions. This work investigates the applicability of fuel cell and battery technology to freight railway lines on the pit-to-port manganese line between Hotazel and Port of Ngqura in South Africa. Energetic macroscopic representation (EMR) and MATLAB/Simulink are used to control, represent and simulate interactions and power transfer of the system. The sizing of onboard sources (fuel cells and batteries) is done such that, the retrofit space constraint is respected. The results show the required traction energy peak of 89 MWh and reduced to 65 MWh due to regenerated energy. Battery charged to 74% at the end of the one-way trip. However, due to constraints on battery capacity, a substantial amount of the regenerated energy is wasted as heat. Furthermore, it was found that the size constraint sources are feasible to deliver the required payload.
A 6-18-GHz GaN High Power Amplifier with Excellent Gain Flatness

This paper presents a monolithic microwave integrated circuit (MMIC) reactive matching power amplifier (RMPA) designed using a 150 nm AlGaN/GaN on SiC technology, showcasing its exceptional gain flatness of only 3dB across the 6-18 GHz band. The three-stage RMPA operates in pulsed mode with a pulse-width of 100-µsec and a duty cycle of 10%, delivering impressive performance metrics. It achieves a maximum output power of 43.5 dBm (equivalent to 22.4 watts) and a maximum efficiency of 25%, while maintaining a linear gain of 22 dB under a bias condition of 28 V drain voltage and 620 mA quiescent drain current. The MMIC PA has a small dimension of 4.5 mm × 4.05 mm.
Fault diagnosis of asynchronous motors based on 2DCNN-GRU network optimization

The current paper proposes a method of asynchronous motor fault diagnosis based on the combination of Two-Dimensional Convolutional Neural Network (2DCNN) and Gated Recurrent Unit (GRU) with optimised network parameters to address the issues of low accuracy of the existing single model based on neural network in motor fault diagnosis. Firstly, the 2DCNN and GRU networks are used to extract the spatial and temporal characteristics of the asynchronous motor fault signals, respectively. Next, the main parameters of the 2DCNN-GRU network model are optimised using the Weighted Mean Of Vectors Algorithm (INFO), and then the SoftMax function is used to fault classification. The proposed method is experimentally evaluated in this study using a three-phase asynchronous motor fault diagnosis, and the findings demonstrate the high accuracy of the suggested model.
The Making and Remaking of Portland: The Archaeology of Identity and Landscape at the Portland Wharf, Louisville, Kentucky

OF DISSERTATION THE MAKING AND REMAKING OF PORTLAND: THE ARCHAEOLOGY OF IDENTITY AND LANDSCAPE AT THE PORTLAND WHARF The town of Portland, Kentucky was founded over 200 years ago as a speculative venture to profit from its advantageous location at the base of the Falls of the Ohio River. The Portland Wharf was the economic and cultural heart of the town. Throughout its history, the community has experienced much change. These changes are visible in the landscape of the Portland Wharf which reflected changes in the community’s identity. Identity and landscape are topics that have been of great interest to archaeologists and this dissertation builds on previous works to examine identity as something that is reflected in the practices of people and can be unconscious, as well as overt. Identity can only become visible through contrast of differing aspects of culture, which is often created by researchers. The landscape is one place where the contrast necessary for making identity visible takes place, as it is where identities can be created, modified, and maintained. This study utilized archaeological, historical, and ethnographic data to examine changes to Portland’s identity and landscape over time. The archaeological analysis of deposits at two house lots at the Portland Wharf has allowed for a reconstruction of Portland’s historic landscape that when compared to that of Louisville created the necessary contrast to expose Portland’s independent identity. This identity was developed amongst Portland’s contentious and symbiotic relationship with Louisville and manifested in the landscape and the way privies were constructed. The process of identity continues present day, as the people of Portland reach into their past to deploy versions of history that are loosely based on events that are no longer materialized in the landscape when their identity is threatened. As the community plans to reanimate the Portland Wharf landscape to create and maintain identities based on the community’s past, archaeologists must recognize their role in the process of identity and I argue that such responsibility can and should be used for activist goals for the good of the community.
Prediction of Student Performance by Using Machine Learning Techniques

Each person’s learning process is distinct and diversified. Therefore, using the right teaching techniques is crucial to improving student performance and academic achievement. Numerous educational institutions, including schools and universities, make an effort to anticipate their students’ academic performance in order to improve the effectiveness of the educational systems. In educational technology and the recent learning systems, discovering useful hidden patterns in learner data is significantly valuable. In this paper, several machine learning classifiers were applied such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB), to predict the student’s performance. Furthermore, a hybrid model was used to reduce data dimensionality and improve the overall performance of the classifiers used. A real dataset obtained from the UCI machine learning repository is used to evaluate the proposed hybrid model. The reported results show the performance of CNN-RNN outperforms other methods with an accuracy equal to 89.1%.
AFALog: A General Augmentation Framework for Log-based Anomaly Detection with Active Learning

Log-based anomaly detection is becoming more and more important for maintaining the availability of modern microservice systems. Existing supervised/semi-supervised log anomaly detection models require a large amount of human-labeled logs for training which are hard to collect in real-world systems. Unsupervised models often perform poorly without explicit anomaly labels. To improve the performance of unsupervised models, in this paper, we first make an empirical study of existing unsupervised models to tackle the reason why they often produce unsatisfied results. We find that anomaly detection results produced by existing unsupervised models are significantly affected by two key problems including Not-Cover (NC) problem and Suspicious-Noise (SN) problem. To solve these problems, we propose a novel augmentation framework called AFALog. AFALog leverages the idea of active learning to incorporate human knowledge so as to augment data quality. It can support almost all existing unsupervised models and improve their performance. Our experiments on two open datasets and one dataset collected from a real-world microservice system demonstrate that DALog improves the F1-score by an average of 6.61%, with only 5.9% labeled training data.
Evaluation of the Indoor Space Temperature Distribution by Point Measurements for the Comfort Level Control Problem

An approach to studying the dynamic characteristics of the indoor environment in individual public rooms for the microclimate control problem is proposed. A numerical procedure combining computational fluid dynamics and network airflow methods makes it possible to simulate the operation of a measuring complex, design an expedient sensor network, and reduce the time of building inspection when deploying a centralized microclimate control system.
Directional Relay of Wind Farm Transmission Line Based on Traveling Wave Polarity Comparison

The amplitude and phase of the equivalent positive sequence impedance of the wind farm considerably fluctuate during a fault, and the extraction of the sequence fault component is affected by the frequency deviation. As a result, the operating performance of traditional directional relay is affected. This paper proposes a directional relay based on the polarity comparison of traveling waves. First, the polarities of the voltage and current traveling waves are analyzed under the forward and reverse faults. Second, an improved wavelet transform algorithm based on the complementary detail coefficients of adjacent windows is proposed to detect the fault traveling wave. This algorithm eliminates the signal loss problem of wavelet transform. Finally, the Pearson correlation coefficient is introduced to measure the correlation between voltage and current traveling waves, and the action criterion of the directional relay is constructed to realize fault direction discrimination. PSCAD/ETMDC simulation results show that the proposed directional relay can identify the fault direction correctly.
Analysis of the trade-off between voltage gain and frequency response of OTA designed using experimental data of omega-gate nanowire SOI MOSFETs

In this paper the Omega-gate nanowire (NW) transistor applied for designing Operational Transconductance Amplifier (OTA) is studied, focusing mainly on the trade-off between voltage gain (Av) and gain-bandwidth product (GBW). One option to determine a good compromise between gain and frequency response is to get a bias condition where the product Av * GBW reach the maximum values. For NW nMOSFET and pMOSFET the maximum product occurs for gm/ID = 11 V−1 and 10 V−1 respectively. OTA simulations were realized based on experimental data using Verilog A language with look up table method. The circuit was created using Cadence’s Spectre showing great results for the omega-gate nanowire as a voltage gain of 1122 V/V and a GBW of 602 MHz for a gm/ID of 8 V−1. Our work shows that the best results for OTA circuit was obtained when the transistor efficiency is gm/ID = 8 V−1, different of the values got from individual transistors.
Performance Estimation and Prototyping of Reconfigurable Near-Memory Computing Systems

The concept of near-memory computing (NMC) has emerged as a promising solution to address the memory wall challenges faced by future computing architectures. By utilizing modern systems that integrate 3D-stacked DRAM memory, the NMC paradigm minimizes unnecessary data movement between the memory subsystem and the CPU. FPGA vendors have incorporated 3D-stacked memories into their products to meet the increasing bandwidth requirements of memory-intensive applications, enabling FPGAs to compete with GPU solutions in terms of speed and energy efficiency. Recent NMC proposals focus on different data processing workloads, including graph processing and machine learning. This work addresses the research questions of how to leverage the full bandwidth of 3D-stacked high-bandwidth memory and how to facilitate the adoption of the near-memory computing paradigm.
Advanced Modeling and Analysis of Individual and Combined TSN Shapers in OMNeT++

The selection of a Time-Sensitive Networking (TSN) shaping mechanism is a crucial design decision that impacts the Quality of Service (QoS) of applications and configuration complexity. However, current research has mainly evaluated TSN shapers individually, despite them being designed to work together in an egress port. Hence, the lack of investigation of the mixed TSN shaping mechanism is a major limitation of the current state of the art. Combined TSN traffic shaping provides greater flexibility to improve QoS than individual shapers, making it particularly beneficial for real-time applications. This paper aims to bridge this research gap by implementing the Asynchronous Traffic Shaper (ATS) in a plug-and-play manner, enabling its use individually or in combination with other TSN shapers. We propose various models of mixed TSN shaper architectures and implement the frozen and non-frozen credit behavior of the Time Aware Shaper (TAS) + Credit Based Shaper (CBS) during the guard band (GB) using OMNeT++. Furthermore, we compare the simulation results of ATS and CBS with the Network Calculus (NC) upper bounds. Our results indicate that the simulated delays (SMDs) were significantly lower than the theoretical worst-case delays (WCDs) obtained from the NC, indicating the need for tighter theoretical upper bounds, particularly for higher network loads. To the best of our knowledge, we are the first to provide simulation-based performance analysis of the combined $\text{TAS}+\text{ATS}+\text{CBS}$ and $\text{TAS}+\text{ATS}+\text{Strict}$ Priority (SP) architecture. Overall, this paper highlights the benefits of combining TSN shapers and encourages further research into the potential advantages of utilizing multiple shapers simultaneously to decrease reliance on TAS and CBS.
Human Mobility Patterns: A Source of Geospatial Knowledge

The study and analysis of human mobility patterns and their relationship with the environment can provide us with a better understanding of certain aspects of human behaviour. New, ubiquitous and non-intrusive devices have made it easier to place sensors in mobile phones, Personal Digital Assistants (PDAs) or even clothing, providing us with relatively low cost means of gathering highly detailed data on the movement of people and the environments in which they move. However, new spatio-temporal models and geovisualization techniques need to be developed to handle the large volumes of data generated by these technologies, and to detect, analyse and visualize mobility patterns so as to understand the interplay between human mobility and the environment, while at the same time maintaining the individuals’ right to privacy. The findings from such studies will help to lay the foundations for more sustainable human mobility.
Rolling bearing fault diagnosis with variable load and few samples based on multifeature fusion meta-learning

Faced with variable working conditions and limited samples, the fault diagnosis of rotating machinery rolling bearings in the actual industrial process will result in reduced accuracy of intelligent fault diagnosis methods based on deep learning. In this paper, to solve the existing problems faced by bearing fault diagnosis, a fault diagnosis model based on multi-feature fusion Model-agnostic meta-learning is proposed in a few-shot variable load scenario, and the bearing multi-modal feature fusion and channel attention mechanism are introduced. Through domain adaptation experiments on bearing data set provided by Case Western Reserve University, the effectiveness and accuracy of the method in bearing fault classification are verified.
Short-Term Load Prediction Based on Typical Daily Feature Selection and Time Convolutional Neural Network

Accurate load forecasting is the guarantee of safe and economic operation of the power system. With the progress of the power system, data acquisition and analysis equipment continue to improve, which increases the amount of data involved in the process of load forecasting, improves the feature dimension, and increases the complexity of load forecasting when massive power load data and feature data increase available information. At the same time, the prediction accuracy will be reduced due to the existence of redundant data. Therefore, this paper uses k-means method to cluster historical loads based on load feature clustering, uses feature selection method based on maximum information coefficient to screen input variables for typical days of different types of loads, trains TCN time convolution network respectively, and uses different input variables according to different types of days during forecasting, so as to enhance the adaptability of the model and improve the forecasting accuracy.
A Spinwave Time-Multiplexed Ising Machine

We present a spinwave-based time-multiplexed Ising Machine (SWIM) where artificial Ising spins are formed with ns-long spinwave RF pulses propagating in a delay line based on $5-\mu m$ thick Yttrium Iron Garnet (YIG) film. The phase of artificial Ising spins is binarized using an off-the-shelf phase-sensitive microwave amplifier and the coupling between spins is implemented with microwave delay cables. Thanks to the very low spinwave group velocity, the 7-mm long YIG delay line can host an 8-spin MAX-CUT combinatorial optimization problem and solve it in less than 4 $\mu s$ while consuming only 7 $\mu J$.
Effect of Humidity on Head Smear Generation in Head-Assisted Magnetic Recording

The causes of head smears deposited on the magnetic head surface due to laser heating in heat-assisted magnetic recording need to be investigated to achieve higher recording densities for hard disk drives. Factors that affect smear formation in hard disk drives include outgassing from components and humidity. In this study, we focused on the effect of relative humidity in the environment on smears generated due to laser heating. The lubricant films (dipped, UV-treated, and rinsed) were deposited on the samples using three different methods, and the amount of smear generated on a glass pin was evaluated using a pin-on-disk tester with a laser heating system. As a result, it was confirmed that the amount of smear increased as the humidity increased. The lubricant film with a higher chemical bonding fraction showed less smear generation and a smaller dependence on humidity.