Miracle-dz/UniFAR-FTUs huggingface dataset:
MRAS-based Integral Sliding Mode Control of Electric Vehicles under Speed and Load Torque Fluctuations

The performance of Electric vehicles (EVs) depends on the conditions like terrains, sudden start/stop, and other uncertain scenarios which create an imbalance between load torque and electromagnetic torque. Due to this imbalance, severe torsional vibrations occur which causes an uncomforting situation for passengers and put extra mechanical stress on EVs. The sensorless, reactive power-based Model Reference Adaptive System (Q-MRAS) is suitable for precise speed estimation and control but in low-speed regions and with uneven load conditions, its performance degrades. To mitigate these issues and make the system robust against parametric variations, external disturbance, and operational uncertainties, an improved Model Reference Adaptive System based Integral Sliding Mode Control (MRAS-ISMC) is proposed. The MRAS integrated with the ISMC makes the drive simply robust against parametric variation by balancing the load torque and electromagnetic torque. The proposed MRAS-ISMC topology is verified on various periodic, non-periodic load torque disturbances and varying reference speeds.
CO Emission in the Radio-Loud Quasar 3C 48

We have used the Owens Valley Millimeter Array to conduct a sensitive search for CO (1→0) emission in the radio-loud quasar 3C 48. An emission feature is detected (4 σ) at the redshift of the narrow optical emission lines (z=0.3695). The width of this feature is ∼250 km s -1 (FWHM) similar to that expected for a galactic disk, and the integrated CO line flux implies an H 2 mass of 7×10 10 M ○. (assuming the same CO to H 2 conversion factor found for giant molecular clouds in the Milky Way)
A Wide Intermediate and Output DC Link Based Efficient Modulation Technique to Design PEV Charger Using Single Phase Vienna Rectifier

For EV charging applications, single phase Vienna rectifier operated using classical modulation approach has problems with significant variations in efficiency and input current ripples. Additionally, for the efficient operation of DC-DC stage, the design of an electric vehicle (EV) charger employing an LLC resonant converter necessitates active management of the intermediate DC link with wide operation voltage range. To address this problem, this paper presents the design of single-phase portable EV charger using Vienna rectifier and full bridge LLC converter (FB-LLC). Here, rectifier stage is controlled through an efficient modulation technique (EMT), which provides half switching losses, current ripples and wide regulation of rectified voltage over real time variation in the battery terminal voltage. The LLC stage is controlled with optimum efficiency point frequency modulation technique to provide high efficiency operation over complete battery terminal voltage range. For better power factor correction (PFC) under distorted grid voltage scenarios, a frequency fixed (FF) second order generalized integrator (SOGI) is utilized for grid angle generation. This technique provides healthy estimation of grid angle under the presence of DC offset, harmonics, frequency, and phase drift in grid voltage. With the integration of grid voltage angle estimation algorithm in the control of single-phase Vienna rectifier, this converter possesses better PFC performance under both weak and strong grid conditions. A 2 kW EV charger with a battery pack terminal voltage range from 240–550 V, is designed and simulated to validate the performance of the designed system.
Fault Prediction Method Based on Improved Bidirectional Long Short-Term Memory Combined with Sample Entropy for Battery

Fast and accurate prediction of battery failures is essential to improve the safety and reliability of electric vehicles (EVs). In this paper, a fault prediction method based on improved bidirectional long short-term memory (IBiLSTM) combined with sample entropy (SampEn) is proposed. The parameters of the BiLSTM are optimized using the whale optimization algorithm (WOA), and attention mechanism is used to assign weights to the prediction values of the BiLSTM to obtain the optimal prediction values. The SampEn can detect the abnormal degree of the sequence containing the predicted values in order to achieve fault prediction. Finally, the accuracy and practicality of the proposed fault prediction method are verified by algorithmic principles and experimental studies.
Control of Dual $3-\phi$ VSC for Solar PV-BES Microgrid with Seamless Transition Operation

This paper presents a solar photovoltaic (PV)-battery energy storage (BES) microgrid with grid connection and disconnection capabilities. Whenever, the grid is not present; the system automatically shifts from grid-connected mode (GCM) to islanded mode (IM). Moreover, after recovering the grid, the system shifts to GCM. Here, a $l_{0}$ -norm penalized shrinkage linear least mean square ($l_{0}-\text{SH}$ -lms) adaptive control is used to generate the VSC triggering pulses, which improves the power quality. The bidirectional converter (BDC) is used for controlling the charging/discharging of the battery, which keeps the voltage source converter (VSC) DC-link maintained. The system behavior is analyzed through the simulated performance using MATLAB/Simulink.
ERBFV-Net: An Emotion Recognizer Based on Face Video for MAHNOB-HCI

Emotion recognition has attracted the attention of the research community, due to its wide application in different fields, such as medicine and autonomous driving. At present, most of the emotion recognition experiments are carried out on the datasets that actors actively express emotions, but in the passive aroused emotion experiments, the effect of these methods is not ideal, and the expressions cannot be recognized well. This paper proposes an emotion recognition network ERBFV-Net composed of Action Units and MKRBlock (Multi Kernel Res-Block). For the facial key point AUs, this method uses OpenFace to extract feature on the video, and pass the signal through the AUBTS Model (AUs-Based Time Sequential Model). For the video itself, the rectified face image is directly used, and the frame image in the video is sent to the multilayer network. Finally, the two signals are fused and classified. ERBFV-Net achieved an accuracy of 64.74% in the evaluation of the emotional arousal experimental dataset MAHNOB-HCI, classifying nine emotions. The results show that these two channels of information carry relevant information for detecting the user's aroused emotion, and their combination can improve the final detection accuracy.
Social Network Power System Alarm Application

Millions of people share their thoughts and experiences instantaneously through social media. This could be a good source of information for power system operators to glimpse what is going on with their equipment before something bad really happens to the system such as a wildfire near power lines. Gaining a few minutes of head start could save a tremendous amount of time and effort if it could help prevent a fault or even reduce the impact of a fault in the system. This paper proposes a real-time warning system using the information gathered from Twitter to detect a possible threat to the grid. The collected tweets are processed by a machine learning algorithm to determine the possibility of threats to the system in their message. The training data for machine learning consists of six thousand tweets. The confidence of the machine learning is calculated based on the distance of the potential threat, the frequency of similar tweets being posted in a short period of time, likes, retweets, and some other factors. The tweets with a high possibility of threat are displayed on a website to give a warning so the system operators can react to them as early as possible. The proposed system is evaluated with real-time fake tweets to show its validation.
Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.
WLAN Throughput Prediction Using Deep Learning with Throughput, RSS, and COR

Throughput prediction of wireless LAN (WLAN) is important technology for effective use of frequency spectrum. In conventional throughput prediction methods, the future throughput is predicted by learning variations of throughput and some related information such as Received Signal Strength (RSS). On the other hand, the WLAN throughput is highly affected by Channel Occupancy Ratio (COR) due to carrier sense multiple access with collision avoidance. Therefore, this paper proposes simultaneous learning of throughput, RSS, and COR to learn the latent cause of the throughput variation. We compare the prediction accuracy of several prediction models, and it is confirmed that the accuracy is improved by the proposed simultaneous learning regardless of the network structure.
Embedded Impedance Measurement for DC Microgrid Towards PHM and Control

Emrging DC grids, including microgrids, incorpo-rate multiple conversion units for distributed energy resources (DERs such as photovoltaics, wind, and batteries) connected to different loads (passive and active). Consequently, future DC grids' resilience and real-time health must be continually assessed. This paper presents an embedded Prognostic Health Monitoring (PHM) and control approach associated with the grid power conversion units, which enables progressive crosstalk between the converters at multiple nodes to evaluate the grid's health dynamically. This research advances the use of digital tools to provide accurate online grid health monitoring assisted by machine learning techniques, implemented in situ on tools such as Field-Programmable Gate Arrays (FPGAs). By measuring the grid impedance (magnitude and phase shift) at the terminals of the power converters at the grid's various nodes, the trained machine learning model helps assess the grid's health index and identify the potential fault-prone zones. A small perturbation sig-nal is injected through specific converters over a wide frequency range (i.e., 0.5 to 10 kHz) to determine the impedance and phase-shift characteristics. This paper uses Matlab/Simulink tools to simulate a small grid network to collect impedance-related data under different conditions for machine learning training. The associated results validate the benefits of the proposed concepts.
Vision of IoT, 5G and 6G Data Processing: Applications in Climate Change Mitigation

The Earth is becoming increasingly hot as we experience more record-breaking heat waves, ice melting, and an increase in average temperatures. The repercussions of global climate change are becoming increasingly evident, which led to climate change being named the “greatest global health threat of the twenty-first century. ” As a result, the international community established an ambitious plan for going green, promoting human development, and preserving the biosphere by adopting the Paris Agreement and the Sustainable Development Goals. As a result, international cooperation efforts have been increasingly put forward. This paper presents ways in which the Internet of Things could benefit the current efforts toward climate change mitigation endeavors. We discuss state-of-the-art projects that have been established worldwide to use recent advancements in 5G/6G technologies and IoT to monitor, model, and combat the impacts of global climate change on agriculture, water resources depletion, and coastal erosion.
Research and Design of Wireless Charging System for Inspection Robot

Intelligent inspection robots are widely used in the industrial field, and wireless charging technology provides a new solution for safe and reliable charging of inspection robots. Firstly, the basic principle of wireless charging system for inspection robots is analyzed, and the equivalent circuit model of magnetically coupling wireless power transmission is established using mutual inductance theory. Secondly, the system hardware structure of the robot wireless charging vehicle and the control process of wireless charging are designed, and the control program is written to realize the automatic wireless charging of the inspection robot vehicle.
A Multi-Feature Base Multi-Adaptation Lidar Odometry

To address the problems that the current feature-based lidar odometry algorithms have difficulties in achieving high accuracy based on sparse features and adapting to different kinds of lidar at the same time, a multi-adaptation lidar odometry algorithm based on multi-features (MFMA-LOAM) is proposed. In addition to the traditional edge points and plane points, intensity points and ground points are extracted as supplementary features. Among them, the intensity point features that meet the requirements are extracted based on lidar intensity reflection information. The ground feature extraction method is improved by using the estimated normal vector to achieve simultaneous adaptation of multiple types of lidar. Further, in order to ensure acceptable speed and improve the alignment accuracy at the same time, a two-stage alignment method is applied to complete the positional calculation, in which feature weights are added to improve the alignment accuracy and categorical nearest neighbor search is used to speed up the alignment. Finally, experimental validation is carried out using the KITTI dataset and the self-collected dataset respectively; the experimental results show that the MFMA-LOAM algorithm with satisfactory real-time performance is more accurate than related classical feature-based algorithms and can be adapted to both solid-state lidar and mechanical lidar at the same time.
Multi-Agent based Cloud Energy Storage Framework for Residential Community

Energy storage is substantially admitted as an immense potential for distributed energy sources in the smart grid and load balancing. It is an enabling aid to the adaptation of renewable energy resources by small-scale residential users. However, the generated power from these sources is irregular/intermittent in nature. This affects the stability of smart grids. Therefore, energy storage is a new technology for coordinating and managing their fusing. The acceptance of distributed energy storage (DES) is still a challenge, including control effectiveness, installation cost, integration of information communication technology (ICT), and enactment. A Multiagent System (MAS) is a new solution of meeting the demands of distributed computing, communication, and data integration for smart grid applications. This study recommends a new distributed multi-agent-based architecture of storage in the community, i.e., cloud energy storage (CES), providing energy storage service to users at a significantly lower cost. To study autonomous energy management in residential communities, the proposed MAS platform and architecture are designed on the Stateflow platform. The platform provides an environment to model the decision logic and controls. In the suggested platform, Simulink's advantages are combined with Stateflow's decision-making and reactive features.
Micromagnetic analysis of the effect of intergranular exchange on thermal stability in magnetic recording. II. Slightly oriented longitudinal media

For pt.I see ibid., vol.38, no.2, p.1405-16 (Mar. 2002). We describe micromagnetic simulations to examine the effect of intergranular exchange on thermal stability of dibit transitions in slightly oriented longitudinal media. We found that, by increasing the intergranular exchange and the anisotropy orientation ratio, Ku is reduced to maintain the fixed M/sub r/t, the writing coercivity, and the average grain planar area. Within the drive working temperatures (T/spl les/350 K), to increase signal-to-noise ratio (SNR) at 10/sup 9/ s, both the intergranular exchange and the anisotropy orientation ratio must be reduced. However, if both K/sub u/ and M/sub r/t are fixed, for a constant circumferential loop closure field, a larger SNR is always exhibited at a higher OR(M/sub r/) initially and at 10/sup 9/ s. For both cases, if the intergranular exchange cannot be neglected, SNR always slightly increases with increasing OR(M/sub r/).
Research on the Speed and Accuracy of Full Port Scanning

As the most basic means of network attack, port scanning is the most commonly used attack tool in penetration testing. Full-port scanning of the system can identify potential threats to the system in advance, however, existing full-port scanning methods need to scan the full amount of ports, which is relatively time-consuming. In this paper, we propose a sampling-based full-port scanning method that can minimize the scanning cost while maintaining the scanning accuracy. We use three port scanning tools to perform port scanning in a controlled environment to validate our method, and quantitatively analyze the scanning accuracy and scanning speed of different scanners in different scenarios. Besides, by analyzing the experimental results, we give a set of solutions for the use of scanners in different environments.
Distributional Characteristics: Just a Few More Moments

The ability of statistical models to accurately characterize distributional characteristics such as skewness and kurtosis can impact the results of statistical analysis. This article compares the feasible skewness–kurtosis spaces for two generalizations of the lognormal, the inverse hyperbolic sine (IHS) and g-and-h probability density functions (pdf’s), each of which can accommodate a wide variety of distributional characteristics. For h ≥ 0, the boundary of the skewness–kurtosis spaces for g-and-h and IHS coincides with that of a generalized (three-parameter) lognormal (LN*) distribution. The increased skewness–kurtosis flexibility of the g-and-h distribution, for h < 0, relative to the IHS is obtained by introducing vertical asymptotes, compact support, and possibly U-shaped pdf’s. This increased coverage, however, may not be helpful if the data are unimodal. Empirical daily, weekly, and monthly stock return data are used to compare the descriptive ability of the IHS, g-and-h, and LN* distributions.
Comments on a derivation and application of the ‘maximum entropy production’ principle

We show that (1) an error invalidates the derivation (Dewar 2005 J. Phys. A: Math. Gen. 38 L371) of the maximum entropy production (MaxEP) principle for systems far from equilibrium, for which the constitutive relations are nonlinear; and (2) the claim (Dewar 2003 J. Phys. A: Math. Gen. 36 631) that the phenomenon of ‘self-organized criticality’ is a consequence of MaxEP for slowly driven systems is unjustified.
Automated Hyperparameter Optimization in Machine Learning for Stock Prediction

Stock prediction is the key area of focus in financial analysis. The growing amount of data and readily available machine learning algorithms has surged the amount of research in this field. This research in particular, involved in stock prediction of NEPSE using machine learning algorithms such as Linear Regression and LSTM. The research also studied traditional financial models such as ARIMA and GARCH. The analysis involved in manual and automated hyperparameter optimization via Optuna framework for single and stacked LSTM models. Initially, traditional financial models performed better than manually optimized LSTM variants. But the automated hyperparameter tuning approach significantly lowered the error loss and the single LSTM model best predicted the stock price with 7.21 RMSE score. https://github.com/SudipBishwakarma/MSc-Dissertation-2021.
Internet of Things Security and Forensics: Concern and Challenges for Inspecting Cyber Attacks

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.
Multi-Objective Design Optimization of SiC-Based Electric Vehicle Drive-train Inverter

Silicon Carbide (SiC) MOSFETs are used in state-of-the-art drive-train inverter designs for electrical vehicle (EV). Due to a lack of comprehensive multi-objective inverter design framework, such inverters designed through trials would result in sub-optimal designs. The optimal inverter design framework proposed in this paper, involves the objectives of maximizing efficiency $(\eta)$ and power density $(\rho)$. An analytical approach based algorithm is used to simulate the total power loss and volume parameters required to calculate $\eta$ and $\rho$ respectively, and iterated over two loops: (i) across a range of switching frequency $(\gamma_{sw})$ to plot the $\eta-p$ pareto curve and find the optimal designs of one SiC device category, (ii) across different SiC device categories (having various blocking voltages, $V_{b}$) to plot all $\eta-\rho$ pareto curves in different categories of inverter design, and finally store as optimal design datasets. The datasets will be analyzed to identify the globally $\eta-p$ optimal inverter design with superior $\eta-\rho$ performance and corresponding $V_{b}$ and operating $f_{sw}$. The global-optimal designs for both 300 kW and 80 kW drive-train inverters involve utilizing the 1200 V half-bridge module at $f_{sw}$ of 40 kHz and 35 kHz respectively.