Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimized Light-Load Efficiency Scheme for LLC Converters Based on Variable Ratio Transformer

The utilization of a dual-stage voltage regulator module (VRM) configuration is becoming increasingly popular in server applications. To ensure optimal efficiency during CPU sleep mode, the initial stage often incorporates an LLC converter due to its exceptional output efficiency and high power density. This study presents a transformer structure featuring a variable primary side winding that can dynamically adjust the primary side ratio based on the load condition. Under light load conditions, the Large Turn Ratio mode is employed to minimize core losses and enhance efficiency by reducing the maximum magnetic flux in the magnetic core. Simultaneously, for heavy load conditions, a mode with reduced primary side turn ratio is utilized to maintain optimal system efficiency. A highly efficient 48-12/9.6/5.3 V LLC DCX has been designed, achieving peak efficiency of up to 93.5%. Experimental results obtained from VRMs demonstrate significant enhancements in light-load efficiency resulting from these modifications.
Cutting Through the Noise: Machine Learning Proxies for High Dimensional Nested Simulation

Deep learning models have gained great success in many applications, but their adoption in financial and actuarial applications have been received by regulators with some trepidation. The lack of transparency and interpretability of these models leads to skepticism about their resilience and reliability, which are important factors to ensure financial stability and insurance benefit fulfillment. In this study, we use stochastic simulation as a data generator to examine deep learning models under controlled settings. Our study shows interesting findings in fundamental questions like "What do deep learning models learn from noisy data? " and "How well do they learn from noisy data? ". Based on our findings, we propose an efficient nested simulation procedure that uses deep learning models as proxies to estimate tail risk measures of hedging errors for variable annuities. The proposed procedure uses deep learning models to concentrate simulation budget on tail scenarios while maintaining transparency in the estimation.
Deep Learning Approach for Segmentation and Classification of COVID-19 in Lung CT Scan Images

The COVID-19 pandemic began in 2020 and is still spreading today, this disease has caused many patients to die. COVID-19 is caused by the severe acute respiratory syndrome 2 virus. COVID-19 has been declared a world pandemic by WHO because of the rapid spread and malignancy of this disease. An alternative to detecting COVID-19 is to use a CT scan, However, with the limited number of doctors to detect lesions in COVID-19 patients, an automated system is needed to segment and classify COVID-19 disease. Currently, research into COVID-19 detection using CT scans with deep learning is carried out using single-task learning so that the model can perform segmentation or classification. Some studies use feature extraction from segmentation to train other classification methods that require more time to train the model. The aim of this research is to utilize Swish U net and simplify the classification process. In this research, we proposed a model that can reduce memory usage, speed up the training process, and increase the performance, by using the proposed deep learning model for classification and segmentation with EfficientN et encoding and SwishUnet decoding, we managed to obtain good performance. Proposed model is trained using the dataset of the Italian Society of Medical and Interventional Radiology. For segmentation, sensitivity was obtained with a value of 75.31 % and specificity with a value of 99.44%. Meanwhile, classification succeeded in obtaining a precision performance of 97.72%, recall of 89.58%, and F-measure of 93.47%.
A Hybrid Integration Method for Low-Observable Micro-UAV Trajectory Tracking by 2D MIMO Radar

It is quite challenging for the micro-unmanned aerial vehicle (UAV) which is in low-observable environments and has minor radar cross section (RCS) to detect and track. Accordingly, a hybrid method of coherent and noncoherent integration is proposed for micro-UAV tracking under poor signal-to-noise ratio (SNR) surroundings by 2-dimensional (2D) multiple-input multiple-output (MIMO) radar with range, Doppler, azimuth and pitch. Firstly, for rectifying the range walk caused by long-term energy accumulation, this paper introduces keystone transform (KT) to eliminate the coupling terms of echo signals. On this foundation, for improving the detection and tracking performance of low-observable micro-UAV, the radar raw 4D measurements are directly utilized to integrate the detection and tracking processes aided by the track-before-detect (TBD) strategy, which avoids the information lack of weak targets caused by traditional tracking methods based on thresholds. Finally, simulation results indicate the approach in this paper corrects the range walk and has distinct superiorities over traditional approaches for trajectory tracking of micro-UAV under low SNR conditions.
Semantic Representations of Speech Production in Intracranial EEG

Speech neurprostheses have the potential to provide severely paralyzed patients with a means of communication. To enable the best possible decoding of speech processes from neural data, it is important to chose a representation of speech that is both meaningful to the decoding process and represented well in the neural recordings. Previously, acoustic, articulatory, and textual representations of speech have been decoded from neural recordings. Semantic representations of speech could add additional information about the content of the produced speech. In this study, we show that semantic embeddings for individual words, as extracted by a word2vec-model, can be used to reconstruct neural activity during speech production across wide-spread cortical and subcortical areas. We elucidate the temporal dynamics of reconstruction quality and show that a slight right hemisphere preference exists. These findings could be used to add semantic information into speech neuroprostheses in the future.
FPGA Implementation of Dehazing Model Based Low-Light Image Enhancement Algorithm

Low lighting conditions degrade the quality of the captured image by darkening some of its parts. This may lead to the loss of important information contained in the captured image in low-light. It is always desirable to improve the visual quality of low-light images. In this paper, a low-light image enhancement model based on the dehazing principle is proposed to address this issue. Further, its hardware architecture is proposed to meet the real-time system requirement. The proposed pixel-based non-linear transmission estimation technique prevent images from being over-enhanced. Additionally, it suppresses the artifacts around the edges. This curtails the need for an edge-preserving filter. The field-programmable gate array (FPGA) implementation of the proposed architecture requires only 694 LUTs, and it is capable of processing more than 60 images of resolution $1920\times 1080$ per second.
Two-Stage Lane Detection Based on Attention Mechanism and Post-Processing of Prediction

Existing lane detection methods have achieved remarkable performance in complex real-world scenarios, but many of them still have some problems, such as poor anti-interference ability, poor real-time performance, and difficult deployment at the vehicle end. In this work, we combine the characteristics of deep learning and traditional algorithms and propose a two-stage lane detection method: Firstly, the model uses the output of backbone based on line-anchor for feature pooling, and at the same time, an attention mechanism based on line-anchor is applied to extract global features. Then, the pooled features are integrated with the global features to solve the problems such as the occlusion of the lane lines. Then, the fused features are input into the classification branch and regression branch, and then the relevant position information such as the confidence degree and offset of the lane line are output. Finally, according to the characteristics of lane detection, a special predictive post-processing module, Post-processing of Prediction Results (PPR) is designed based on the output lane information. PPR can improve the detection performance without increasing the number of model training parameters. The experimental results show that the recognition time of one frame image by our method is only 0.012s, meanwhile the accuracy rate can reach 95.6%.
Predicting inﬂation in the Kingdom of Bahrain using ARIMA models

This research uses annual time series data on inflation rates in the Kingdom of Bahrain from 1966 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that Bahrain inflation series is I(1). The study presents the ARIMA (0, 1, 1). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 1) model is stable and acceptable for predicting inflation in the Kingdom of Bahrain. The results of the study apparently show that predicted inflation will be approximately 1.5% by 2020. Policy makers and the business community in the Kingdom of Bahrain are expected to take advantage of the anticipated stable inflation rates over the next decade.
An active disturbance rejection control for underactuated AUV

Aiming at the problems of uncertain parameters and sensitive external interference of the underactuated autonomous underwater vehicle(AUV), an active disturbance rejection controller (ADRC) is proposed. On the basis of arranging the transition process for the desired attitude, an extended state observer is designed to compensate the total perturbations real-time, which can overcome the external disturbances such as strong coupling, model uncertainty and water flow variation. In addition, the nonlinear state error feedback is designed to effectively suppress the tracking error. The stability control, disturbance immunity and robustness of the ADRC system are tested on the simulation platform, and the quantitative analysis is carried out with cascade PID control system. The results from the simulation prove that the designed active disturbance rejection controller can estimate and compensate the internal and external interference of the system well. Also it has strong robustness to the uncertainty of the parameters of the AUV. The performance index is obviously better than cascade PID.
Optimal Placement of Phasor Measurement Unit in Active Distribution Network considering Uncertainty and Soft Open Point

State estimation provides reliable status information supporting accurate dispatch of complicated active distribution systems. However, there are many buses in the distribution system, and state estimation can only be accomplished with an insufficient number of phase measurement units, with little confidence of accuracy. Moreover, the voltage instability by distributed generation uncertainties and power flow changes by soft open points bring new challenges to state estimation. Therefore, an optimal phasor measurement unit placement model considering economy, bus voltage out-of-limit weight and state estimation accuracy is investigated in the paper. First, Monte Carlo simulation calculates the bus voltage out-of-limit weights to approximate distribution generation uncertainties. Then, a virtual bus representing soft open point is introduced, displaying power flow changes with its own control signal in state estimation. Finally, placement locations are obtained from new optimization objective and constraints. Simulations and analyses on the modified IEEE 33-bus distribution network validate the proposed model.
Performance Evaluation of AI/ML Model to Enhance Beam Management in 5G-Advanced System

In fifth generation (5G) wireless communication systems, the millimeter wave (mmWave) is identified as a significant frequency band, offering extensive spectrum resources. Despite its broad bandwidth, mmWave experiences notable path loss. This challenge can be addressed using massive multiple-input multiple-output (MIMO) technology with beamforming. However, the extensive number of beams generated to counteract path loss necessitates excessive beam quality measurements and reports to ensure users connect to the optimal beam(s). Leveraging artificial intelligence (AI) and machine learning (ML) can enhance beam management. These technologies can predict the best beams, reducing the number for measurements and reports while keeping the same performance. This paper presents an evaluation of AI/ML approaches for determining the best beams in both spatial and temporal domains. Our findings highlight the performance of AI/ML models compared to traditional methods, the generalization capabilities of AI/ML models, and their resilience against measurement and quantization errors.
RTTAC-An Access Control Model Based on Blockchain and Role-Trusted Transfer

Aiming to address the data security issues in data sharing, current approaches such as RBAC, ABAC, and blockchain-based data sharing platforms suffer from problems like role redundancy, role abuse, complex authorization, and data user privacy leakage. In response, a role-based trusted transfer access control model based on trust and attributes is proposed. This model is built upon Attribute-Based Access Control (ABAC) while integrating Role-Based Access Control (RBAC). Users obtain roles based on their attributes and trust levels, and roles determine access permissions based on trust level and defined policies, and then determine the transferability of roles, at the same time, the transfer of roles is limited by trust levels. Encryption techniques and off-chain storage systems are utilized to protect data privacy and integrity, thereby alleviating the storage pressure on the blockchain. Finally, the performance and effectiveness of the proposed model were evaluated through experimental analysis, demonstrating its capability to achieve trustworthy role transfer and enhance the security and privacy of on-chain sharing schemes.
Intelligent Product Quality Prediction for Highly Customized Complex Production Adopting Ensemble Learning Model

End-product quality prediction is crucial in smart manufacturing, where reliable evaluation and parameter optimization are essential for ensuring high-quality outputs. This study presents a novel approach that combines adaptive machine learning and nonlinear regression to accurately predict the quality of highly customized end products using limited supply-chain data through digital transformation. The research was conducted in collaboration with a major power transformer manufacturer and its supply chain partners. The adaptive model was trained and validated using real datasets from key components provided by the supply chain, resulting in accurate predictions of end-product quality. The model incorporates the core loss parameter, obtained from the power transformer's key component, as an input dataset for training and testing. The proposed approach, called AdaBoost-Regression, combines adaptive boosting (AdaBoost) and Regression machine learning techniques. Experimental results demonstrate that the AdaBoost-Regression model outperforms simple AdaBoost and Regression models in predicting transformer quality. The model also exhibits superior performance in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE) during real-data verification. This approach has the potential to significantly reduce overall production costs by accurately predicting the quality of complex, expensive, and highly customized industrial products. It can be applied across various industrial sectors.
Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications

In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as “hesitation” in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.
The Research on the Effectiveness Evaluation Technology for the PD Sensing Components of the 220kV Cable Joint with Built-in Multi-Dimensional Sensing Elements

With the development of social economy, the scale of high-voltage cable equipment has developed rapidly, and at the same time, the online monitoring technology for high-voltage cables is widely applied. And the reliable integration of high-voltage cable equipment and state sensing components has become a current research hotspot. However, the integration level of high-voltage cable primary equipment and status sensing components remains relatively low at present stage. Therefore this paper proposes simulation calculations to explore the electrical performance of the high-voltage cable joint with built-in multi-dimensional sensing elements which was designed in the previous research. The results indicate that the built-in multi-dimensional sensing element in the 220kV cable joint did not affect its electrical performance, and the structural design is reasonable. Additionally, The Research on the effectiveness evaluation technology for the partial discharge (PD) sensing component is conducted in this paper. The on-site effectiveness validation results of the PD sensing component are favorable. This article provides a solution for the integration of primary and secondary equipment of high-voltage cable, and verifies the effectiveness of online monitoring technology. It can also better ensure the intrinsic safety of high-voltage cables.
Research Article Introducing Internet-Based Services in the Mountain Areas of Nepal: An Asset Pentagon Perspective

Information and communication technology for development (ICT4D) projects can only be considered successful if they lead to some kind of individual, social, or economic development. The beneats of introducing ICT4D projects in developing countries are yet to be realized, particularly those introduced in mountainous and remote areas. Our study addresses this knowledge gap by analyzing the Nepal Wireless Networking Project from the mountain areas in Nepal using the assets pentagon model (APM). The main contributions of this work are threefold: First, we illustrate and discuss the use and usefulness of introducing APM, addressing the call for more clarity as to how ICT4D projects add to development. Second, our study expands the research knowledge of the relationships among various capital assets. Finally, our study reports on an ICT4D project from the mountain areas of Nepal, representing a country and an area that have been scarcely reported in our research community.
Comparison of Machine Learning Based Methods Used in Bengali Question Classification

This paper work demonstrates phases of assembling a question answer type classification model. It is a step towards constructing a Question Answering (QA) system in Bengali Language. Question Answering (QA) has become a significant part of our developed life. As question classification is initial part of Question Answering (QA) system, it has become a concern for us to build question answer type classification system model. Question answer type classification system classifies the natural language questions. It categories the question asked by the user in natural language into correct Answer Category (AC). This paper presents the work to question classification in Bengali on machine learning approach using different types of algorithms such as Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD) by eliminating and without eliminating stop words.
Analyzing the Sleep and Anxiety States of an Individual with Anxiety Disorder by Utilizing Deep Neural Networks and Heart Rate Variability Based on Fingertip Pulse

Anxiety disorder is a prevalent psychological condition that can impact individuals' blood pressure and sleep patterns. This study employs a photoplethysmographic sensor to gather fingertip pulse data from a patient with an anxiety disorder over the course of one year. Both heart rate variability and deep learning methodologies are utilized to investigate the inherent correlations between the patient's anxiety levels, sleep quality, and blood pressure. A total of 824 five-minute samples of fingertip pulse data are utilized, and the data is categorized based on sleep quality and anxiety levels. After preprocessing steps such as noise reduction, both time-domain and frequency-domain heart rate variability indices are calculated. The analysis is carried out across various categories, illustrating that heart rate variability can effectively discern physiological states based on fingertip pulse data. Finally, the EfficientNetB0 neural network is employed to predict blood pressure values, and trends in blood pressure during different anxiety and sleep states are analyzed. The results indicate that blood pressure varies across different sleep and anxiety states. This research provides a non-invasive method for understanding individuals with anxiety disorders' sleep issues and anxiety states, offering valuable insights for clinical treatment and intervention.
Two-Swarm Cooperative Particle Swarm Optimization Including Prediction Using Gaussian Process Regression

This study presents a novel approach to enhance the performance of TCPSO by integrating Gaussian process regression into the solution search process. By leveraging Gaussian process regression to estimate the underlying function of the target problem, our proposed method aims to improve the algorithm's exploration capabilities and enable the discovery of more precise and reliable solutions. Through extensive experimentation and comparisons with existing approaches on diverse benchmark problems, we demonstrate the effectiveness and superiority of our approach in achieving improved search performance and solution quality.
High-Resistance Fault Localization Method for Underground Cables Based on Three-Dimensional Wideband Impedance Spectra

Locating cable faults is of significant importance for the stable operation of the power system. Currently, mainstream methods like partial discharge testing and time-domain reflectometry impact by issues like filtering distortions and rapid attenuation of high-frequency signals, making it difficult to accurately locate high-resistance cable faults. This paper introduces a three-dimensional wideband impedance localization method based on temperature gradients. The paper provides a theoretical analysis of impedance at cable joints and conducts experiments on a 57-meter cross-linked polyethylene medium-voltage cable, the experimental results demonstrates the effectiveness of the proposed three-dimensional wideband impedance method.