Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency Control in the Presence of Dynamic Model of Induction Motor Based on Virtual Inertia

In this article, a fifth-order dynamic model is introduced for electrical loads of the induction motor type, which is further reduced to a model of the third and first orders. In the following, it is shown that with the increase of motor inertia, the error of low-order models increases compared to the main model. Of course, with an acceptable approximation, the first-order model can be chosen as the engine model. By adding the induction motor as a dynamic load to a microgrid, it has been shown that, except some special cases, a fixed coefficient cannot be utilized to simulate and represent the induction motor. The next goal of this article is to design virtual inertia as the primary controller to reduce and control the frequency changes of a microgrid. Considering that wind turbine tracks the highest power output at different wind speeds, it cannot engage in the mode of frequency control of the microgrid, therefore, for the participation of the wind turbine in frequency regulation, virtual inertia has been brought to turbine to lessen the frequency adjustments and with growing the virtual inertia model of an induction motor may be decreased to first-order.
High-Precision Geometric Positioning of Optical Satellite Images Assisted by LiDAR Data

To achieve automated optical high-precision geometric positioning of satellite images, this study proposes a LiDAR point cloud-assisted method for high-precision automatic positioning of satellite stereo images, taking advantage of the complementary characteristics of LiDAR data and optical satellite images in terms of geometric positioning. Firstly, we match the LiDAR depth image and the optical satellite image with the AWOG methods and take the corresponding points as virtual control points. Then, the Rational Function Model (RFM)-based regional network adjustment is employed to calculate the Rational Polynomial Coefficients (RPC) correction parameters. By utilizing the optimized positioning parameters of the satellite imagery, the geometric orientation accuracy of the satellite stereo imagery is improved. Experimental results based on LiDAR data and GF7 satellite stereo imagery demonstrate that the positioning error is controlled within 3 meters, meeting the requirements of 1:50000 mapping accuracy. This indicates that the method proposed in this paper can achieve high-precision geometric positioning to assist optical satellite stereo imagery using LiDAR data.
Invariant Safe Contingency Model Predictive Control for Intersection Coordination of Mixed Traffic

This paper addresses the coordination challenge at intersections of mixed traffic involving both Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). To strike a balance between coordination performance and safety guarantees, we propose an invariant safe Contingency Model Predictive Control (CMPC) framework. The CMPC framework incorporates two parallel horizons for the ego vehicle: a nominal horizon optimized for performance based on the most likely prediction of the opponent HDV, and a contingency horizon designed to maintain an invariant safe backup plan for emergencies. In the contingency horizon, we consider the worst-case behavior of the human driver and formulate safety constraints using the forward reachable sets of the HDV within the planning horizon. These safety constraints are complemented by maximal invariant safe sets as terminal constraints. The two horizons are tied together by enforcing equality of the feedback inputs at the beginning of the horizons. We provide theoretical evidence supporting the recursive feasibility and persistent performance improvement of the invariant safe CMPC compared to our previously proposed nominal invariant safe Model Predictive Control (MPC). Through simulation studies, we evaluate the proposed method. The simulation results demonstrate that the CMPC approach achieves enhanced performance by reducing conservatism while simultaneously preserving the invariant safety property.
A High Boost Cascaded Multilevel Inverter Topology Using Switched Capacitor Converters

A new switched capacitor converter (SCC) is proposed for switched capacitor-based multilevel inverters (SCMLI). The SCC can be assembled in cascades and does not have any ‘not connected’ state. It requires fewer external circuits because of the capacitors’ ability for self-voltage balancing. The basic SCC unit is used first to construct a triple-boost seven-level inverter (TBSLI). Then, a larger range of voltage levels is produced by combining two SCC units. This extended version can operate in symmetric or asymmetric modes and produce a voltage output having 13 levels and 49 levels, respectively, depending on the values of the two dc voltage sources. Next, the proposed topology is supported by detailed comparisons with recent literature and simulation findings. The advantages of this design include a three-fold increase in the input voltage, greater efficiency, and a reduction in the requirement for the number of dc voltage sources and switches. The suggested structure can be used in commercial applications to significantly increase the performance of the existing dc-ac converters.
Research on Travel Paths Based on Car-Hailing Services Connecting with Rail Transit

Urban rail transit has become the primary mode of transportation for most commuters, while car-hailing services have gained popularity as a new transportation option in recent years. Based on the relatively stable scheduling of subway trains and the distribution patterns of car-hailing services, this study proposes a combination of car-hailing services and rail transit for efficient travel. By analyzing and processing car-hailing passenger flow data, extracting origin-destination (OD) pairs, and constructing road network information, the study determines the attraction areas of rail transit stations. An improved Sarsa algorithm is employed to search for optimal travel paths based on the principle of connection travel, providing passengers with better travel decision-making. Compared to the Q-learning algorithm, the Sarsa algorithm demonstrates a 35% improvement in convergence speed, an 11% reduction in optimal path length, and an 8% decrease in minimum travel costs. Sarsa exhibits stronger adaptability to complex and unknown environments.
Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey

The rise of new complex attacks scenarios in Internet of things (IoT) environments necessitate more advanced and intelligent cyber defense techniques such as various Intrusion Detection Systems (IDSs) which are responsible for detecting and mitigating malicious activities in IoT networks without human intervention. To address this issue, deep reinforcement learning (DRL) has been proposed in recent years, to automatically tackle intrusions/attacks. In this paper, a comprehensive survey of DRL-based IDS on IoT is presented. Furthermore, in this survey, the state-of-the-art DRL-based IDS methods have been classified into five categories including wireless sensor network (WSN), deep Q-network (QP), healthcare, hybrid, and other techniques. In addition, the most crucial performance metrics, namely accuracy, recall, precision, false negative rate (FNR), false positive rate (FPR), and F-measure, are detailed, in order to evaluate the performance of each proposed method. The paper provides a summary of datasets utilized in the studies as well.
Moving Object Detection with Photometric Monocular SLAM on a Moving Ego-Platform

This paper describes a novel method for detecting moving objects using wide field of view monocular cameras and Direct Simultaneous Localization And Mapping (D-SLAM) on a moving platform. The main idea is to use (1) the quality of photometric patch matching and (2) deviation from the epipolar 3D constraint matching to separate static from dynamic objects. The result of the SLAM-based detection is a set of points, which are clustered together as boxes and tracked using an Extended Kalman filter. In order to relate the track to car coordinates, a ground detection algorithm used to extend the tracking box to the ground level for a proper distance estimation. No training data is required for the process and the algorithm has been tested on several different cameras from different manufacturers. A demonstration system running in different automotive platforms has been developed and evaluated, where detection accuracy is significantly improved compared to previous arts as demonstrated through precision-recall graphs. The algorithm is runnable at full frame rate at 30Hz on a Raspberry Pi ARM or an automotive TI TDA4 processor, suitable to be deployed in real automotive applications.
Fibonacci Series Based 21 Level Switched Capacitor Inverter Topology Using Hysteresis Band

Single phase transformer less inverter with multi level AC output is proposed and discussed in this paper. Recent applications, switched capacitor inverters are used widely for high efficiency and less losses. A 21 level output voltage is generated using switched capacitor topology with the components of 2 flying capacitors, 8 switches and 2 diodes is designed. Proposed inverter is configured by charging and discharging of capacitors based on hysteresis bandwidth. These capacitors are charged to reach 21 levels of output voltage magnitude by Fibonacci series of count 8. i.e., (1, 1, 2, 3, 5, and 8). The switched capacitor mulitlevel inverter (SCMLI) with symmetric multi band hysteresis current control (MBHCC) is used to control switches by multiphase pulse generation. The 21-level output voltage is achieved by comparing the reference current and the load current. Simulated results obtained are used to validate the strategy, and different load is considered and analysed each stages of SCMLI.
Tomato leaf diseases classification with convolutional neural network

In recent years, convolutional neural network (CNN) has developed rapidly and been widely used in plant disease detection. Tomato leaf disease is an important diseases of plant diseases. Therefore, it is necessary to improve the classification results of tomato leaf diseases. In this paper, a new CNN framework is proposed to classify tomato leaf diseases. The proposed framework includes several key deep-learning-based techniques: dilated convolution, batch normalization (BN), and the dropout after each convolutional block. Specifically, we apply dilated convolution to tomato leaf disease classification, which enlarges the receptive field without the reduction of image size. In addition, BN and the dropout are used to accelerate model convergence and reduce the redundant features generated by each convolutional block. Experiments show that the proposed model achieved the best results in both classification accuracy and the number of parameters compared with different CNN models.
The Transfer Learning Influence on YOLO v8 Performance For Motorbike Detection

The number of motorbike theft case in Indonesia has increasing rapidly. This phenomenon led to the increasing of the criminal number especially motorbike theft case. Nowadays, the use of CCTV (Closed Circuit Television) camera has been widely applied. Nevertheless, CCTV does not has the ability to prevent criminal acts such motorbike theft case. Therefore, there is a need of a technology based to prevent motorbike theft acts. This research proposed an initial research of motorbike theft detection system that focused on the process of motorbike detection using Deep Learning approach. In this research, the YOLO architecture was utilized to detect the motorbike object inside an image frame. There are two testing scenarios for motorbike detection process, using trained model, and using pre-trained model. The trained model obtain better result rather than using pre-trained model by achieving 0.93 of its accuracy that proven the YOLO v8 model are working well especially when it is applied with transfer learning process for its based model.
Multiple Gain Adaptations for Improved Neural Networks Training

A two stage algorithm developed called Multiple Gain Adaptations for Improved Networks (MGAIN) is presented. MAGAIN alternatively finds output weights and uses several gain factors to update the input weights in a Multi-Layer Perceptron. The gain factors are computed using Newtons method. Our method dynamically adjusts the quantity of gain factors calculated to maximize the reduction in loss with each epoch. The results demonstrate that our approach outperforms existing second order algorithms across the majority of diverse datasets.
Advancing Quality and Detail: Enhanced-LapSRN for Chip Socket Image Super-Resolution

Recently, image super-resolution reconstruction models based on deep neural networks have achieved remarkable success. This paper introduces a network named Enhanced-LapSRN, designed using the classic CNN structure. This network not only effectively utilizes the pyramid structure from the LapSRN to construct high-resolution image residuals but also incorporates the architectural concept of DenseNet to facilitate feature reuse and prevent information loss. Additionally, we employ dilated convolution and convolution cascade techniques to expand the receptive field and introduce a convolution block attention module(CBAM) to focus more on high-frequency information in the image. This allows for the comprehensive integration of image features at different levels and results in comprehensive feature extraction. Further experiments confirm the strong performance of our method on public datasets, with a notable 0.49dB improvement in SET5, as well as an 8\% enhancement in positioning accuracy on our self-constructed chip socket datasets. Furthermore, our method is capable of real-time processing, ensuring high efficiency.
A Machine Learning Method for Real-Time Traffic State Estimation from Probe Vehicle Data

Reliable Traffic State Estimation (TSE) is an important precursor to developing sophisticated traffic controls for intelligent transportation systems (ITS). Historically, TSE is calculated using stationary sensors with occasional vehicle probe data as supplementary data. However, even with recent developments that apply machine learning to TSE calculations, the literature reports having to fuse probe data with stationary data or focus solely on freeways where the penetration is greater. This work proposes and analyzes an Ordinal Regression model developed using XGBoost to compute TSE exclusively from probe data that can be used for real-time model predictive control on signalized corridors. Our results show our model to have an mean absolute error of less than half a class and show promising preliminary results in a real-world control experiment.
Career Cocoons: Analyzing Occupational Mobility with Graph Embedding Model

The Information cocoon is discovered in social media, which is a phenomenon of biased information acquisition and opinion reinforcement. We find that occupational mobility is analogous to information exchange in social media and the mechanism of career choice may cause an information cocoon-like effect. The study identifies and measures the information cocoon-like effect on occupational mobility, and names this effect the career cocoon. We first construct a complex network to model occupational mobility using the real-world dataset, where the edges of the network represent connection tendencies between occupations and the weights of edges represent selection preferences of individuals for occupations. The information cocoon-like effect is identified based on four characteristics: separation, homogeneity, polarization, and system efficiency. Subsequently, the graph embedding model is used to map the occupational mobility network into a vector space. We design indicators to evaluate the ability of career trajectories to span the occupational space, thus enabling the quantification of career cocoons. This study introduces information cocoons into occupational research for the first time, and utilizes data-driven approaches as well as intelligent techniques to measure career cocoons, providing new insights into occupational mobility research.
Reinforcement learning based traffic signal control considering the railway information in Japan

Reinforcement learning (RL) has recently gained significant attention as a dynamic method for controlling traffic signals based on the real-time traffic flow. However, previous studies paid little attention to the coefficients of the reward function, and there has been limited research investigating the effect of the railway information. To address these research gaps, this study investigates the RL-based traffic signal control considering the railway information. To achieve this, the SUMO (Simulation of Urban Mobility) software was used to establish an intelligent transportation system (ITS) field consisting of two signalized intersections and two railway crossings. Two RL algorithms, namely PPO and DQN, were employed to establish the RL-SOMO model for simulation experiments under given traffic conditions. Simulation results demonstrate that the proposed RL-based traffic signal control method performs significantly better compared to the fixed control method, which indicates the promising application of RL-based traffic control in real traffic flow control.
Comparative Analysis of Extreme Learning Machine and Backpropagation for Faults Diagnosis in the Tennessee Eastman Process

This paper explores a comparative analysis of two learning algorithms: the Extreme Learning Machine (ELM) and the Back-Propagation (BP), specifically applied to the Single Layer Feed-Forward (SLFF) neural network. To conduct this investigation, we have selected the Tennessee Eastman Process (TEP) as a case study. The evaluation criteria encompass the time demands and accuracy levels during both the learning and testing phases.The results reveals that SLFF, when trained using BP, achieves superior accuracy and demonstrates faster performance in both the learning and testing phases when compared to its training with ELM.
Towards Better 3D Visual Lane Detection in Traffic Scenes via Leveraging Object Semantics

3D lane detection plays a significant role in autonomous driving applications. However, existing methods fail to fully consider the influence of other traffic participants on lane estimation, resulting in reduced prediction accuracy. To address this limitation, we propose a novel method called Semantic Gen-LaneNet that integrates semantic information from other traffic objects into a deep learning based 3D visual lane detection model. Our approach incorporates semantic masks of moving objects in a new branch and integrates the semantic features with the visual features using a feature fusion module. The fused feature map is utilized to predict top view lane lines, and real 3D lane points are obtained directly through geometric projection. Comprehensive experiments on the Apollo dataset demonstrate that our proposed method achieves at most 3.6% improvement in average precision (AP). In far-range detection, our method even exceeds the original model by up to 24%. Furthermore, various object segmentation algorithms can be successfully applied to our method in practical applications.
Neural Network Model Predictive Control Based on PSO Approach: Applied to DC Motor

A control approach based on neural network identification and particle swarm optimization algorithm is depicted in this work. The proposed control technique, called neural network model predictive control optimized by the particles swarm optimization algorithm, has been tested in simulation, to control the rotation speed of a direct current motor. This control technique proved its effectiveness in terms of accuracy (analysis of the value of the root mean square error, the mean square error, and the absolute mean square error) and overshoot through a comparative study between the simulation results obtained by the proposed controller and a PID controller tuned by the teaching learning based optimization algorithm, using a multi-step and sinusoïdal reference trajectories to represent the desired rotational speed.
BanglaMedNER: A Gold Standard Medical Named Entity Recognition Corpus for Bangla Text

Medical Named Entity Recognition (MNER) has gained much attention recently and aims to recognize medical terms from text, such as proteins, diseases, symptoms, drugs, chemicals, and human anatomy. However, MNER for Bangla text has yet to progress significantly due to various challenges. One of these challenges is the need for a large and standardized corpus in this domain for Bangla. This work presents the annotated Bangla corpus for medical named entity recognition in standard IOB2 format consisting of more than 117000 tokens. The annotation process involved collecting texts from diverse Bangla health and drug-related domains. This corpus includes three distinct entity classes: Chemicals and Drugs (CD), Disease and Symptom (DS), and Anatomy (ANAT). Multiple machine-learning techniques were explored for medical named entity recognition, and the Bidirectional Long Short-Term Memory (BiLSTM) model, coupled with a Bidirectional Conditional Random Field (BI-CRF) layer, outperformed all the other models in our dataset with an outstanding F1 macro average score of 75%. We also employed class weights to account for the biases in our dataset.
Incomplete Trajectory Recovery for Supporting Urban Link Travel Time Distribution Considering Spatial-Temporal Correlation

Travel time distribution (TTD) estimation on urban arterial links with sparse trajectory data is a practically important while substantially challenging subject. To this end, this paper makes full use of ubiquitous incomplete trajectories that only traverse part of the link and incorporates them with complete trajectories to alleviate the data sparsity problem. The focus of this study is to develop a framework of estimating link TTDs based on incomplete and complete trajectories. Three major steps are involved: first, we consider a simple trajectory imputation method for missing GPS points in order to improve the input data quality and serve as a basis for measuring the similarity between incomplete and complete trajectories; then, spatiotemporal K-nearest neighbor (KNN) algorithm is proposed to estimate virtual link travel times of incomplete trajectories for the purposes of increasing travel time sample size; finally, Bayesian estimation method via Gibbs sampling is introduced to approximate the posterior distribution of link travel times by incorporating historical trends and enhanced data. A case study was conducted on a major arterial in Nanjing, China. The results indicate that the proposed approach with the augmented data can achieve promising performance, compared to the baseline method in terms of effectiveness and adaptiveness.
A Novel Modified Improved Propotionnate Fast NLMS Algorithm for Acoustic Echo Cancellation

The Modified Improved Proportionate Normalized Least Mean Square (m-IPNLMS) algorithm enhanced the performance of the original IPNLMS algorithm by establishing a relationship between an important parameter known as the proportional term and the sparseness measure built on the $\boldsymbol{\ell}_2$ and $\boldsymbol{\ell}_1$ norms. To further improve the performance of the m-IPNLMS algorithm, we propose first, to extend the Fast NLMS concept to the m-IPNLMS algorithm and then, by using the sparseness measure based on the $\boldsymbol{\ell}_1, \boldsymbol{\ell}_2$ and $\boldsymbol{\ell}_\infty$ norms, which provide adequate representations of both dispersive and sparse impulse responses, we also replace the quadratic output filtering error with a recursive estimation version. The proposed algorithm, called Novel-Modified IPNLMS (Novel-m-IPNLMS) algorithm is superior in terms of convergence speed, tracking ability and steady-state mean square error (MSE) for sparse and dispersive systems. Simulation results are presented to validate the effectiveness of the suggested algorithm.