Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization and Development of Marine Engineering Technology Based on Intelligent Control

Since the steam turbine unit is the main part of the whole propulsion system, its normal operation is directly related to the safety of the whole ship system. Therefore, it is necessary to introduce an optimization scheme into the design of the unit to improve the safety, reliability, operability and rational layout of the unit. This paper aims to study the optimization and development of hydraulic turbine engineering technology based on intelligent control. On the basis of analyzing the characteristics and working principle of the water turbine, an improved particle swarm optimization algorithm is proposed, and the differential evolution algorithm is introduced into the basic particle swarm optimization algorithm to obtain an improved particle swarm optimization algorithm. Subsequently, this paper establishes a mathematical model of a ship's steam turbine, and designs its high-pressure steam turbine and low-pressure steam turbine. Finally, the improved particle swarm optimization algorithm is used to optimize the design of the ship turbine under the specific structure and characteristic constraints, with the minimum weight as the objective function. The experimental results show that the weight of the turbine is significantly reduced after the optimization method is selected, which proves the effectiveness of the improved particle swarm optimization algorithm for the optimization of the turbine.
PerfMon: Measuring application-level performance in a large-scale campus wireless network

WiFi has become one of the most popular ways to access the Internet. However, in large-scale campus wireless networks, it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance. In this paper, we implement PerfMon, a network performance measurement and diagnosis system, which integrates collected multi-source datasets and analysis methods. Based on PerfMon, we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university (TWLAN), which is one of the largest campus wireless networks. Then we systematically study the application-level network performance. We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types. The performance is far from satisfactory in some cases. To diagnose these problems, we distinguish locations and device types, and further locate the most crucial factors that affect the performance. The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.
Deep reinforcement learning-based collaborative routing algorithm for clustered MANETs

Flexible adaptation to differentiated quality of service (QoS) is quite important for future 6G network with a variety of services. Mobile ad hoc networks (MANETs) are able to provide flexible communication services to users through self-configuration and rapid deployment. However, the dynamic wireless environment, the limited resources, and complex QoS requirements have presented great challenges for network routing problems. Motivated by the development of artificial intelligence, a deep reinforcement learning-based collaborative routing (DRLCR) algorithm is proposed. Both routing policy and subchannel allocation are considered jointly, aiming at minimizing the end-to-end (E2E) delay and improving the network capacity. After sufficient training by the cluster head node, the Q-network can be synchronized to each member node to select the next hop based on local observation. Moreover, we improve the performance of training by considering historical observations, which can improve the adaptability of routing policies to dynamic environments. Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion. In addition, the effectiveness of the routing policy in a dynamic environment is verified.
FlexPlan.jl - An open-source Julia tool for holistic transmission and distribution grid planning

In the ever complex world of the power systems, robust decision making in the context of investment planning becomes a difficult problem. In the decision making process, the costs and benefits of implementing complementary or com-peting technologies (AC versus DC or storage versus classical transmission expansion) need to be assessed. Additionally, the electrification of industrial demand and the integration of smart loads, such as heat pumps or electric vehicles, pave the way for demand flexibility provision on large scale. Considering that the majority of the demand flexibility is located in the distribution grid, there is a need for planning models and tools able to perform combined transmission and distribution grid planning. This paper introduces FlexPlan.jl, a Julia/JuMP-based open-source tool for holistic planning of transmission and distribution grids which includes a complete set of planning candidates for transmission and distribution networks and fully internalizes demand flexibility and storage usage. We use stochastic op-timisation, in order to find robust decisions with respect to different climate conditions and operating hours within given climate years. To keep the optimisation problem tractable, we introduce a novel decomposition between the transmission and distribution grid planning problems. We demonstrate that using the proposed approach a speed improvement of up to 100 times can be achieved for cases with a large number of distribution grids, with negligible increase of the objective function value and a good solution quality in terms of power flow.
A Fault Tolerant Multilevel Inverter for Off-grid Solar Photovoltaic Application

Multilevel inverters (MLIs) provide a number of advantages including, modularity, higher efficiency, lower dv/dt stress, and minimal filtering requirements. The number of switching devices and the complexity of control will increase with increasing voltage levels in the conventional MLI. As a result of the higher number of switching devices, there may be an increased chance of failure, which compromises the MLI’s reliability. The goal of current research trends is to increase the reliability of MLIs by using an optimal number of switching devices. It is important to ensure fault tolerance in many industrial applications where continuity of power supply is a key requirement. The fault tolerant (FT) operation of an MLI has, therefore, been the subject of considerable research. This paper proposes a new topology with FT capabilities for solar photovoltaic (PV) application that enhances the reliability of MLIs. The topology has the ability to generate 7-level, and (9-level and 11-level) in normal condition at the output using symmetric and asymmetric source configurations, respectively. Based on simulation results performed within the MATLAB/Simulink environment, the feasibility of the proposed FT MLI is demonstrated for different switch faults.
Smart Grid Peer-to-Peer Exchanging Energy System using Block Chain

Among the most effective methods Exchanging Energy Management Smart Grid (SG) enhances user involvement in energy production and it creates decentralized power sector systems with peer-to-peer technique (P2P). In Peer to peer, prosumers produce electricity on-site using Sustainable Energy sources. Next it is traded with customers in the surrounding area. Peer-to-peer makes it easier for people to interchange energy in the Transactive Energy Management system's regional micro-energy markets. This study suggests a block chain-based Decentralized and apparent Peer-to-peer Energy Trading (DA-P2PET) to solve the identified issues. Its target is to decrease grid energy generation and raising the gain for both consumer and prosumer by flexible price system. The DA-P2PET system conducts peer-to-peer energy trading using Smart Contracts built on the Ethereum block chain and the Interplanetary File System (IP. In the suggested DA-P2PET system, the Ethereum SCs are created to carry out P2P in real time. In comparison to existing methods, the DA-P2PET scheme is rated based on numerous criteria including profit creation, data transfer speed, networking access
High Step Down/Step Up Full Soft Switching Bidirectional DC-DC Converter Without Auxiliary Switches

In this paper, a new bidirectional DC-DC converter with full soft switching operation and high voltage gain is presented. The full soft switching is achieved by resonant elements and one-way switches. The diodes used in the proposed converter are not reverse parallel with switches and this helps to achieve the ZCS condition for switches. ZVS and ZCS operation are provided for the switches. Auxiliary switch is not used in the proposed converter and just one auxiliary inductor is used as an energy storage cell. This inductor transfers the energy to the output in boost mode and increases the output energy. Also, this causes to absorb the energy in buck mode and decreases the output energy. Therefore, the voltage gain of the proposed converter is increased more. The operation principles and structure analysis are presented. Finally, efficacy of proposed converter is validated via simulations performed by PSCAD software.
ZigBee Communication in WSN with Mesh Configured Routers and Hardware Chip

The latest developments in processor technologies and low-power radio communication technologies have produced low-power low-cost, multi-functional small sensor devices that can track alterations in the physical spectacles of their surroundings and respond accordingly. These sensing capabilities of such devices can be combined to produce an overall result when connected via wireless media. ZigBee (IEEE 802.15.4) is wireless communication technology that works in star, mesh, and cluster tree topology in a configured sensor network. It can function for many different monitoring and control applications for many years on low-cost batteries. The research article offers ZigBee mesh-configured node connectivity and low-power hardware chip design in Xilinx ISE 14.7. The software Modelsim 10.0 b is used to carry out the simulation and data transfer. The functional test cases have verified the 40-bit data communication for 16 nodes with a maximum frequency of 375.00 MHz. The design’s novelty is in its scalability, which allows it to be tailored to a greater number of routers depending on the situation.
Mental State Estimation System for Virtual Meetings with Auditory Modulation

We are developing a system to estimate and provide feedback on the mental state of individual participants with the aim of assisting videoconference facilitators/moderators as well as the participants themselves. In the present paper, we introduce a system that quantifies signs indicative of the engagement of individual participants in an online meeting by applying auditory modulation.
Analysis of a Metamaterial based Reflective Filter at Mid-IR frequencies

In this paper, we design and theoretically investigate a tunable and compact reflective filter in graphene based hyperbolic metamaterials. Using an effective medium theory and a transfer matrix approach, we calculate the reflectance characteristics at mid-infrared frequencies. Our theoretical analysis shows that by changing the chemical potential of the graphene, the reflectance spectra of the filter can be shifted to the shorter wavelengths. We also show that for a fixed chemical potential, the reflectance spectra of the filter can be further blue-shifted by increasing the number of graphene monolayers. Interestingly, we find that the reflectance is almost independent of the angle of incident and polarization (TE/TM) of the incoming waves. We believe that our proposed metamaterial based filter could be a potential candidate for developing tunable filters and switches.
Power conditioning from EVs using bidirectional chargers: A Review

Reactive power flow control is the most effective way to optimize operating conditions in transmission, distribution, and utility networks. Interest in electric mobility and plug-in electric vehicles (EVs) is growing. Chargers of Electric vehicles are highly nonlinear loads. The grid assets, Distribution transformers, which aren’t typically built for overloading, are negatively impacted by this. To lessen the effect of EV loads on the distribution system, a straightforward reactive power compensation mechanism is reviewed in this study. By offering auxiliary services like reactive power compensation, voltage management, and peak load shaving, bidirectional onboard battery chargers enable EVs to satisfy grid energy storage requirements. Bidirectional chargers are nowadays replacing unidirectional chargers and conventional chargers, “Nissan” and “Tesla” has already begun the use of bidirectional chargers in their EVs. This review includes the advantages of using bidirectional chargers and the impact of bidirectional chargers on battery components.
Optimal Multi-type Component Reassignment Design Under Internal Degradation and External Shocks

This paper investigates the multi-type component reassignment problem (MCRP) in consideration of internal degradation and external shocks. First, the hybrid shock arrival model with Wiener process distributed internal degeneration path is demonstrated. Then, we propose a unification method that converts the hybrid shock model into an equivalent single shock model by introducing the concept of statistical virtual shock. To dynamically maximize the system reliability, we construct the optimization model to determine the reassignment time and strategy. The case study verifies the power of reassignment as well as the efficiency of the proposed unification algorithm.
RTLB_Sched: Real Time Load Balancing Scheduler for CPU-GPU Heterogeneous Systems

Heterogeneous computing systems consisting of Central Processing Units CPUs and Graphical Processing Units GPUs are found everywhere, from mobile phones, laptops to cloud clusters, due to their low cost/performance ratio and their impressive peak computational performance. One of the main and most complex problems in these systems is the load imbalance. This imbalance is caused by the large computing power difference between its computing nodes. We present RTLB_Sched, a Real Time Load Balancing Scheduler that minimizes the load difference between the CPU and GPU nodes using machine learning. We define a new metric called load difference using execution time prediction. We describe the method and provide mathematical formulas to calculate this metric. Experiments of scheduling multiple OpenCL applications on a heterogeneous system show that RTLB _Sched outperforms the Device Suitability and Round Robin approaches in load balancing.
A CONTINUUM MECHANICAL SURROGATE MODEL FOR ATOMIC BEAM STRUCTURES

Starting from a fully atomistic system, we outline a general approach to obtain an approximate continuum surrogate model incorporating specific kinematic state variables. The continuum mechanical system is furnished with a hyperelastic material model. We then adapt the procedure to slender structures with beam-like character, such as Silicon nanowires or carbon nanotubes. The surrogate model can be described as a geometrically exact beam, which can be treated numerically using finite elements. Based on molecular dynamics simulations, we show how to obtain for a given atomistic beam system both a set of suitable deformed states as well as generalized stress and strain measures. Finally, we benchmark the obtained continuum model by assessing its accuracy for a beam coming into contact with an infinite Lennard-Jones wall.
Song Recommendation based on Voice Tone Analysis

Music suggestions based on elements including user listening history, music genre, etc. have become more popular in recent years. The user's emotional state is not considered by conventional music recommendation algorithms; therefore, they might not offer suggestions that are appropriate for their present mood. This study introduces a song recommendation system that uses artificial intelligence and machine learning to make individualized music suggestions based on the emotional state of the user. The system extracts feature with the help of MFCC to analyze the tone of the user. The suggested method incorporates deep learning models such as Artificial Neural Networks that provide better accuracy to train the model. The major challenge in creating such a system is to successfully determine the data for the recommendation process by accurately and consistently detecting the user's emotional state from speech. The proposed system offers new pathways for research in the fields of artificial intelligence and music recommendation and has the potential to alter the way people listen to music.
Bidirectional DISO DC-DC Converter Based on Lyapunov Control Strategy

This paper introduces a novel bidirectional dual-input single-output (BDISO) DC-DC converter to integrate an energy source and a battery to supply the desired load. The proposed BDISO converter can deliver power from input energy sources to the load simultaneously. Also, the proposed topology benefits from performing in buck, boost, Buck-boost, and bidirectional operating modes. Accordingly, Mathematical model of the proposed converter are described and analyzed as well. Furthermore, to obtain the closed-loop control of the suggested BDISO converter, the Lyapunov control strategy is applied, ensuring global stability and the advantage of fast dynamic response. Finally, the performance of the proposed converter is verified through experimental results obtained from manufacturing a prototype model of the BDISO converter.
A Novel Approach for PE Malware Detection using Random Forest Algorithm and Prevention

In light of the increase in malware activity spurred on by the quick development of technology, security has evolved to be a significant concern and now threatens the safety and security of both computer systems and stakeholders. One of the most urgent concerns is safeguarding the data from fraudulent attempts in order to preserve stakeholder security, notably that of end users. Malware is a collection of harmful software code, scripts, dynamic content, or intrusive software that is meant to damage legitimate computer programs, mobile, or web apps. This study has highlighted the portable and executable malware detection using machine learning approach. A study found that the novice users will not be able to differentiate the dangerous and good software. Here, the machine learning based Random Forest (RF) classification method is used by examining the components of Portable executable file. With this detection, an accuracy value of 99% is achieved through classification. Several techniques are available to identify the malware activity that make use of cutting-edge technologies like Machine Learning (ML).
A Simple Bio-Instrumentation Platform for Vital-Sign Estimation Using MagnetoPleythsmography

This paper presents a simple magnetics-based instrumentation system for the estimation of two vital parameters, namely Heart Rate and Respiration Rate. A sensor-head realising the concept of magnetic plethysmography has been designed to acquire the biosignal from the human arm (i.e. radial artery). Further, an efficient electronic system and signal processor stage is designed for accurate estimation of heart rate and breathing rate. The proposed electronics include novel stages, such as (1) an improved instrumentation amplifier (for reducing artefacts and bias effects) and (2) a precision envelope estimator (for respiratory rate estimation). The design methodology of the system has been explained in the paper. Hardware prototypes of the sensor-head using a giant magnetoresistance sensor, as well as the processing electronics, have been built and tested. Studies show that the system can estimate HR with an error of less than 2 beats per minute. The non-contact working of the developed sensor-head is also proved in experimentation. Basic feasibility study on RR estimation has also been carried out. The proposed sensor system can be suitably packaged into compact modules and used in portable health monitors, ambulatory services, etc.
Crop And Fertilizer Recommendation to Improve Crop Yield using Deep Learning

To fulfill the rising global food demand, agricultural production must be improved. Using fertilizers as efficiently as possible based on the needs of the various crops is one approach to this. To assist farmers to increase agricultural yields, deep learning algorithms may be used to produce suggestions for crops and fertilizers. A deep learning model is created using methods like CNNs, RNNs, or LSTM networks, and suggestions for crops and fertilizers are then made depending on the input data. The procedure entails gathering data on crop yields, soil properties, and fertilizer consumption. The model’s accuracy may then be tested to get better outcomes over time. The development of crop and fertilizer recommendations using deep learning can assist farmers in increasing crop yields, enhancing agricultural sustainability, and meeting the global demand for food.
Design of an In-Wheel Rotor Permanent Magnet Flux Switching Motor for Electric Bicycle

Electric bicycles have been catching on in the recent decade due to their lightweight and portability. Hence, different types of electric machines have been proposed to be used in this application. Flux switching machines have attracted a lot of attention as a new type of permanent magnet machines due to their inherent characteristic arise from airgap flux modulation that empowers this topology in low-speed applications. In this paper, a rotor permanent magnet flux switching machine for an electric bicycle is proposed. In this regard its design procedure and required modifications based on sensitivity analysis are described as well. Additionally, a 2D finite element method (FEM) simulation is employed to analyze the performance characteristics of the machine. Finally, it is shown that the proposed design of the machine exhibits desirable performance such as high torque density and low back EMF THD.
An Optimized H6-Type Single-Phase PV Inverter with Bi-Directional Quasi-Diode Clamping

Transformerless inverters (TLIs) have become increasingly popular in single-phase distributed photovoltaic power generation systems due to the superiorities of their high efficiency, slighter volume, moreover lower cost than the one with the transformer. However, in the absence of a transformer, high-frequency common-mode voltage (CMV) brings about hazardous ground leakage current flow. To reduce the leakage current, plenty of TLI structures have been published in the works, and among them, H5, H6, and Heric topologies are the three topology's very popular mainstays. The third has the highest efficiency but the worst CM behavior owning to the switches' junction capacitances. Consequently, the CM leakage current may exceed the recommended limit. In this paper, an optimized HERIC inverter with a quasi-diode clamp structure, namely OHERIC-BQDC topology, is suggested, in that a bi-directional clamping based upon MOSFETs, but like a diode clamping without dead time, is added on the basic HERIC inverter to eradicate the common-mode voltage fluctuation in freewheeling periods. A Matlab/Simulink pattern is constructed to investigate the feasibility of the suggested approach. In addition, PSIM's software package is used to confirm the effectiveness of the suggested HERIC-based inverter.