Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Visual Perception for Mobile Robot Navigation: Methods, Limitation, and Applications

Successful robot navigation in unknown environments relies mostly on the surrounding information they perceive. This paper presents a review of various state-of-the-art vision-based methods that deal with the perception problems for mobile robot navigation and control in unknown environments. These methods are thoroughly discussed in a few groups, including visual Simultaneous Localization and Mapping (SLAM), bio-inspired vision, and machine learning, and in terms of their working principles, advantages and disadvantages for robot navigation, and current research trends. Since each group has different characteristics and challenges, the selection of an appropriate group should be made based on the purpose of the application. This review shows that the current research focus has been shifted toward lightweight perception solutions that could reduce computational time and improve accuracy, enabling real-time processing for robot navigation.
THE AEROELASTIC RESPONSE OF A TWO-DIMENSIONAL AIRFOIL WITH BILINEAR AND CUBIC STRUCTURAL NONLINEARITIES

Abstract A two-dimensional airfoil with either a bilinear or cubic structural nonlinearity in pitch, and subject to incompressible flow has been analysed; the aerodynamic forces on the airfoil are evaluated using Wagner's function. The resulting equations are either integrated numerically using a finite difference method to give time histories of the airfoil motion, or solved in a semi-analytical manner using a dual-input describing function technique. For both types of nonlinearity regions of limit cycle oscillation (LCO) are detected for velocities well below the divergent flutter boundary. Using the finite difference method it is shown that the existence of the LCOs is strongly dependent on the initial conditions of the airfoil. Although the describing function method cannot predict the effect of initial conditions, it does give reasonable predictions of the velocity at which LCOs commence, and good predictions of the magnitude of the LCOs—at least for those cases where the LCO motion is predominantly period-one. The existence of the LCOs is strongly dependent on the properties of the airfoil. In some cases, most notably those with small structural preloads, regions of chaotic motion are obtained, as suggested by power spectral densities, phase-plane plots and Poincare sections of the airfoil time histories; the existence of chaos was confirmed for the cubic nonlinearity via calculation of the Lyapunov exponents, one of which is positive. The fact that chaotic motion is obtained with both bilinear and cubic nonlinearities suggests that it is not the discontinuous nature of the stiffness, associated with the bilinear nonlinearity, which is responsible for producing this chaotic motion.
A Novel Ramp Metering Algorithm based on Deep Reinforcement Learning

In order to solve the problems of slow response and inaccurate control of the traditional ramp metering algorithm, we propose an intelligent ramp metering model based on deep reinforcement learning. By building a neural network and designing a reinforcement learning model based on the Double-Deep Q Network, the intelligent control of on-ramp traffic flow can be realized and the real-time of ramp metering is ensured. The reinforcement learning environment state space is designed based on key traffic flow parameters, and the incentive mechanism of the ramp metering system is established based on part of the traffic data to measure the value of the action. The JavaScript simulation platform is built, the update mode of the detectors is reset to real-time update, and new training rules are defined. The experimental results show that the proposed method can effectively improve the mainline service level. Compared with the case of no-control, the throughput of the system is improved by 52.67%, and the average travel time of the system is reduced by 58.21%, superior to ALINEA and another machine learning method. In addition, the ramp throughput is similar to the comparison method, and the average travel time of ramp vehicles is further shortened.
Finite-time Consensus Algorithm for Power Systems using Cucker-Smale Model

Consensus is one of the most actively studied topics within the realm of multi-agent systems. It generally aims to drive flocks to achieve a common group objective. In this study, a Cucker-Smale model for consensus algorithm has been implemented to resolve the issue of flocking for power systems. By using this model, consensus can be achieved by randomly chosing the initial positions and velocities of the birds with a minimum number of iterations. Moreover, at the end the simulation results for 7 and 25 birds have been presented to confirm the convergence of the agent's average velocities and directions.
Modeling the distribution of primes in computable biomolecules

Prime numbers are an important research area with application in computer security. The security of certain cryptographic schemes critical in information security lies in the difficulty of factoring large numbers into their prime factors. Determination of the distribution of prime numbers is an important unsolved problem in number theory. Biomolecules such as DNA have extended the domains of what is considered a standard computer model. This report investigated the distribution of prime numbers generated from viral DNA. The digits of Euler’s number and Pi were found to be encoded between Bacteriophage T4 Watson-Crick DNA segments comprised of prime numbers.
A branching process with a state dependent immigration component

Consider the well known Galton-Watson branching process (Harris (1963)) in which individuals reproduce independently of each other and have probability a j (j = 0, 1, · · ·) of giving rise to j individuals in the next generation. In recent years some attention has been given to the branching process in which there is an independent immigration component at each generation, b j (j = 0, 1, · · ·) being the probability of j immigrants entering each generation. For a review of this work see Seneta (1969), and Pakes (1971 a, b and c) for further results.
Model-free Voltage Control for Flexible Distribution Networks With Measurement Uncertainty

The high permeability access of distributed generations DGs may lead to voltage violations in flexible distribution networks (FDNs). The development of flexible interconnection equipment represented by soft open point (SOP) in FDN has provided a solution for voltage violation alleviation. However, the traditional control method is based on the physical model, which is difficult to adapt to the practical complex operating environment of FDN. Thus, it cannot guarantee optimal performance. Enabled by sufficient operational data from the real-time measurement and communication systems, the application of model-free voltage control methods is expert in adaptiveness. However, data quality problems such as data loss and data delay harm the control performance. Therefore, this paper proposes a model-free voltage control method of SOP with a data compensation mechanism. Firstly, the measurement data uploaded by FDNs is judged whether lost or not, and the data in the data vault is used to compensate for the missing data. Then the data model of FDNs with SOP is established by the model-free voltage control method, and the voltage control strategies of SOP are iteratively solved. Finally, the effectiveness of the proposed method is validated on the IEEE 33-node power system.
Research on Speed Loop PI Oscillation Detection Method

In order to detect whether the speed of a permanent magnet synchronous motor oscillates in real time, a speed loop PI oscillation detection method is proposed. The speed oscillation fault is judged by analyzing the feedback speed data and the observed speed data and the difference data set of the two. The observed speed data is obtained using a reduced-order state observer. Sliding average filtering is performed on the feedback speed data and the difference data to well suppress periodic disturbances and improve smoothness for high frequency oscillation systems. Simulations show the effectiveness of this speed oscillation detection method.
Deep Neural Network Based Automatic Detection and Classification of Lung Nodules from CT images

The main cause of mortality globally is cancer, with lung cancer being the most common among all cancer types. Radiologists use computer tomography (CT) scans to detect and monitor cancer in the body. Due to subjectivity, visual complexity, and wide variances among interpreters, human professionals ability to interpret images is relatively restricted. Consequently, early cancer detection using image processing techniques is possible. Image segmentation and the extraction of radiomic features from CT scans are the two main components of image processing. Deep learning is currently offering innovative, highly authentic solutions for medical imaging and is recognized as a crucial technique for upcoming applications in the health sector as a result of its success in other real-world applications. A a deep learning model along with image processing techniques is proposed for detecting lung cancer. SPIE-AAPM Lung CT Challenge dataset is used for this study. The proposed Deep Learning Model based Lung Cancer Detection (DLMLCD) achieves an accuracy of 99% on evaluation of the above dataset.
Conservative level controllable robust economic dispatch considering wind power

This paper discusses the form of uncertainty set commonly used in robust optimization and the control method of conservatism. Furthermore, the definition and linearization method of the value-at-risk index of wind power acceptance conditions are given, and the boundary between disturbance range and system risk acceptance ability is realized. Linear association. On this basis, a robust real-time dispatching model is constructed that takes into account the probabilistic characteristics of wind power, aiming at minimizing the operating cost and the risk cost of node wind power acceptance. make decisions. In the process of model solving, the risk cost function of the node receiving wind power is linearized, and at the same time, the robust peer-to-peer model is obtained by using the linear transformation process of robust optimization. Among them, the AGC unit adopts the affine mechanism to participate in the smoothing process of wind power disturbance, which improves the solution efficiency and practicability of the model.
Cloud ERP adoption in SMEs of New Zealand Retail Industry: exploring CSFs and risks

The adoption of cloud enterprise resource planning (ERP) systems highly benefit small and medium sized enterprises (SMEs) in competitive markets. These systems assist in reducing costs and enhancing efficiency and effectiveness of the company. ERP systems have developed from on-premise to cloud-based, which is based on cloud computing features to store data on online databases. However, there are no studies that have investigated the critical success factors (CSFs) and risk factors for adopting cloud ERP systems, particularly in New Zealand (NZ) retail SMEs. Based on decision makers' perspectives, this paper identifies the CSFs and risk factors that affect cloud ERP adoption by SMEs in NZ using a case from retail industry. Five in-depth interviews were conducted with cloud ERP decision makers to create a comprehensive picture of cloud ERP adoption. The findings reveal effective cloud ERP software management, business plan and vision, as well as adopters' sufficient education and training as the CSFs. Lack of awareness and knowledge, and in-house resistance are the risk factors for cloud ERP adoption. The findings deliver practical insights for decision-making teams that can assist companies to successfully adopt cloud ERP systems.
FL-CNN-LSTM: Indoor Air Quality Prediction Using Fuzzy Logic and CNN-LSTM Model

With generations spending about 90% of their time indoors, indoor environmental quality monitoring and prediction is a very important tool to protect the health of indoor people from indoor air pollution. This paper proposes a hybrid fuzzy logic and deep neural network method to predict indoor air pollution. And using PM2.5 pollutants as an example, we use the proposed model to train and predict the dataset collected by indoor sensors from November 2016 to March 2017 in Shanghai. In the procedure of prediction, we designed comparison experiments using LSTM (Long Short-Term Memory), CNN-LSTM, and our proposed FL-CNN-LSTM network based on the PyTorch framework. The results show that deep neural networks injected with fuzzy logic can provide both better predictive performance and interpretability for the intended application. This helps to provide control strategies for smart IoT devices to improve the quality of human life.
Experimental Modal Analysis

The technical report “Experimental modal analysis” is divided into four numbered sections, and a list of references is situated after the last section. Tables, equations and ﬁgures are indicated with consecutive numbers. Cited references are marked as e.g. Bendat and Piersol (1986), with author speciﬁcation and year of publication in the text. The work within this report has only been possible with the ﬁnancial support from the Energy Research Programme (ERP) 1 administered by the Danish Energy Author-ity. The project is associated with the ERP programme “Soil–Structure interaction of Foundations for Oﬀshore Wind Turbines”. The funding is sincerely acknowledged.
Prime cordial labeling of some special graph families

A bijection f from vertex set V of a graph G tof1;2;:::;jVjg is called a prime cordial labeling of G if each edge uv is assigned the label 1 if gcd(f(u);f(v)) = 1 and 0 if gcd(f(u);f(v)) > 1, where the number of edges labeled with 0 and the number of edges labeled with 1 differ by at most 1. In this paper we exhibit some new constructions on prime cordial graphs.
Non-Uniform Distributed Energy Storage Systems Allocation in Unbalanced Distribution Networks

The increase in the global energy demand has accelerated the consumption of fossil fuel resources, increased the cost of living, and exacerbated the world's environmental pollution. Therefore, renewable distributed generation (DG), mainly by wind and solar power generation, has developed rapidly in recent years due to its low power generation cost and pollution-free characteristics. However, considering the inherent randomness and intermittency of wind power output, the integration of large-scale renewable distributed generation not only increases the system's complexity but also affects the stable operation of the distribution network, including increased power loss and deteriorated power quality. Connecting the energy storage system (ESS) to the power grid is one of the effective ways to solve the above problems since it plays an active role in stabilizing grid voltage and frequency. In order to improve energy storage utilization efficiency and to reduce the power grid's investment and operation cost, the installation location and capacity optimization of energy storage must be considered. This paper proposes a methodology for the optimal allocation of ESS in the distribution systems to improve the system performance and minimize the investment cost of energy storage. An unbalanced IEEE-33 bus system is modeled with high renewable DGs penetration in DIgSILENT PowerFactory, as an example, to validate the effectiveness of the proposed method. The simulation results confirmed that the proposed method can effectively configure the energy storage with optimal capacity and location and also improve the unbalanced distribution networks' economic efficiency and power quality.
Economic analysis of energy storage power station applied to distribution network

Power grids are increasing the volume of renewable energy generation from unpredictable sources such as solar and wind. As a consequence, the problem of increasing peak-to-valley load difference and the difficulty of renewable energy consumption is becoming more and more obvious. Energy storage power stations are an effective means to solve such problems. With the development of energy storage technology and the decline of energy storage costs, the economic benefits of energy storage power station construction in the distribution network have become increasingly significant. On the basis of the economic benefits of traditional energy storage systems, this paper establishes a life-cycle cost model for energy storage power plants, and considers the benefits of energy storage power plants on the side of new energy power plants. Finally, through the example analysis, it is concluded that the energy storage power station on the distribution network side has economic benefits without government subsidies.
INERTIAL EFFECTS IN SUSPENSION AND POROUS-MEDIA FLOWS

▪ Abstract The current understanding of the average flow properties of packed beds and particle suspensions, in which inertia plays a significant role on the particle length scale, is examined. The features of inertial suspensions posing challenges to theoriticians include the nonlinear and unsteady nature of the governing equations, the inability to superimpose solutions, the prevalence of hydrodynamic instabilities, and the existence of particle-particle collisions. We discuss two special cases of inertial suspensions, for which detailed kinetic theories have been developed: (a) particles in a gas, and (b) spherical, high-Reynolds number bubbles in liquid. Subsequently, we review recent applications of computational fluid dynamics to simulate the motion in particle suspensions with both inertia and vorticity in the continueous phase. The synthesis of these analytical and numerical techniques is a promising approach to address the many challenges of modelling inertial suspensions.
Advanced TOGI Controller for Weak Grid Integrated Solar PV System

This work describes an unique Advanced Third Order Generalised Integrator (ATOGI) based control approach for a 2-stage three-phase photovoltaic (PV) system. The suggested ATOGI is implemented to retrieve fundamental elements from distorted grid voltages and nonlinear load current. It solves the integrator delay and inter-harmonic issues of conventional SOGI and also the DC-Offset Estimator has the ability to completely reject dc-offset. The control method also helps with eliminating harmonics, load balancing, and power factor correction. The voltage source converter (VSC), which connects the photovoltaic panel to the power grid, is controlled by the ATOGI controller, which generates reference grid currents. The described ATOGI control has the potential to successfully deal with power quality challenges caused by nonlinear loads. The performance of the system is examined in MATLAB/Simulink via simulations of Sag/Swell, fluctuating irradiance, and load changes. As a whole, this standard remains true to the IEEE-519 2014 specification.
Development Trend of Customer Side Energy Internet Communication Protocol

It is an inevitable trend for renewable energy to replace fossil energy. Energy Internet is an important way to realize the balanced consumption of high proportion of new energy. Due to the huge number of equipment, customer side has great potentialities of load regulation. The transmission and sharing of energy information especially depends on the ability of the customer side energy Internet communication protocol system. This paper discusses the data exchange requirements of energy Internet on the customer side, and analyses the technical characteristics of power communication protocol as IEC61850, IEC60870-5, IEC62056, DL/T645, DL/T698 and internet of things protocol as MQTT and CoAP, and then puts forward the design idea of customer side energy Internet communication protocol system, and finally looks forward to the improvement direction of customer side energy Internet communication protocol system, which provides a reference for the technical route of communication protocol for the construction of energy Internet.
State of Charge Estimation using Extended Kalman Filter with Temperature Compensation

State of charge (SoC) is a key factor for any battery management system. This paper presents a robust method for SoC estimation based on Extended Kalman Filter using a second order cell model. The proposed algorithm offers good performance even with large errors in the initial SoC. The algorithm is even capable of adapting to temperature changes as they have a considerable impact on SoC estimation. This is achieved by the extraction of model parameters for a wide range of temperatures. The developed method was tested using MATLAB simulation on various drive cycles and temperature ranges.
Formation Control and Sub-Swarm Generation of Multirotor UAVs

The research field of collective behaviors takes inspiration from natural self-organizing systems like honeybees, fish schools, social insects, bird flocks, and other social animals. These behaviors can be transformed into robots by replicating the same set of rules as found in the natural swarms. The deployment of aerial swarm robotics has become significant due to their multiple applications in surveillance, agriculture, and military. These applications lead to the development of aerial swarm robotic platforms to solve real-world problems. For example, a swarm of low-tech, cost-effective small UAVs can engage a high-tech target effectively. Similarly, a swarm of UAVs can perform surveillance and rescue missions in disastrous areas and build an emergency communication network through multiple UAV swarms. The critical part of swarm technology is multiple swarm intelligence algorithms that have been proposed and tested by various simulation platforms such as V-rep, Gazebo and MATLAB. Very few of them have been deployed on hardware to solve real scenarios that include formation control, navigation, pattern formation, and a few sub-swarm generation algorithms. Team connectivity is one of the essential aspects of a swarm network that emerges the collective intelligence of the group and ensures the readiness of the algorithm to deploy and solve real-world problems. Many researchers proposed the team connectivity algorithms in a single swarm network but still left the connectivity gap between the sub-swarms during a mission. This research work contributes toward designing a swarm intelligence algorithm to improve the connectivity techniques and team connectivity between the swarm agents and the sub-swarm groups.
Control Simulation of Proportional-Integral and Sliding Mode Control for Precision Seed Planters

The desired development and design of an electric control system (ECS) for a precision planter is one of the most crucial elements in evaluating the efficacy of applicators for plant production. The seeds would be sown using this approach. Years have been invested in the continuous development of a range of application methods and metering systems, each of which offers specific advantages for applying the required system with a high degree of precision. Researchers worldwide have been trying to make electric-driven seed meters (EDSM) for planters, but they haven't succeeded. This study looks into the current state of installing seed metering systems for precision planters. Proportional-integral (PI) and sliding mode control (SMC) designs of the control system have been implemented to improve the planting quality of the planters. We want to highlight the electrically driven control system (EDCS) using these designs. An overview of the noteworthy features and limitations of past research is given. We also discuss several future directions for future research, concentrating on the knowledge gaps in this area.