Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Autoencoder and Isolation Forest in Network Anomaly Detection

Anomaly detection is essential to spot cyber-attacks within networks. Unsupervised anomaly detection methods are becoming more popular due to difficult and expensive process of labeling network data as well as their superior ability to detect unknown attacks when compared with supervised or signature-based solutions. In this paper, we use an LSTM-based Autoencoder (RAE) anomaly detection model trained in a fully unsupervised environment, with optimizations for minimal memory usage. Secondly, we compare RAE with an Isolation Forest model by analysing their results. RAE attempts to capture the profile of the data by dimensionality reduction and the use of LSTM layers enables it to leverage the data from previous requests. Reconstruction error is calculated to decide about the abnormality. We train models on a dataset of requests towards a webserver in an unsupervised fashion. Before training, significant feature engineering is done to process multiple categorical attributes. The training process of RAE is optimized for minimum memory usage. We evaluated the results based on our analysis of the data as well as their statistical features. A manual analysis revealed differing focuses between numerical and categorical attributes. Isolation Forest disregards most categorical attributes and emphasizes numerical values. RAE on the other hand detects missing features more effectively but largely disregards numerical attributes. As such, RAE might have a higher probability of detecting a zero-day attack when compared to Isolation Forest.
Transformer-based CSI Feedback with Hybrid Learnable Non-Uniform Quantization for Massive MIMO Systems

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) needs to be acquired via CSI feedback to reap the potential benefits of massive MIMO. However, the large-scale antenna array enlarges the dimension of the CSI matrix to be fed back and thus leads to unaffordable CSI feedback overhead. In addition, the quantization and dequantization processes in CSI feedback unavoidably introduce non-neglectable quantization errors, which greatly restrict the performance of CSI feedback. To this end, in this paper, we propose a Transformer-based CSI feedback method with a hybrid learnable non-uniform quantization method to eliminate quantization errors and improve CSI feedback accuracy with reduced feedback overhead. Experimental results on a public dataset demonstrate that the proposed Transformer-based CSI feedback method can achieve higher CSI feedback accuracy with the help of the hybrid learnable non-uniform quantization method.
Vehicle Offline Localization Based on Computer Vision: an Approach Based on Image Matching Retrieval Algorithms and Implementation

Driver assistant systems have approved their essential role in increasing safety in the driving environment. One of their main features is the ability to provide information about location of the vehicle so that the driver can understand how and where to go. Moreover, automatically report the emergency in case of accidents to accelerate the rescue process. Unfortunately, poor Internet connection in many places and the inaccurate GPS information on traveling roads, in particular, make the localization process even harder. In this paper, we propose an offline localization system that can estimate the location of the vehicle in real-time using edge devices. Our proposed system consists of three main components; the first component is the image matching part to estimate the rotation and transition between two images; the second component is the dynamic objects (vehicles, pedestrians, and etc.) segmentation to filter out the matches from them; and finally, an image retrieval system to auto-correct the location using predefined coordinates of famous landmarks.
Connected Vehicles Travel Time Prediction in a Scenario with Adaptive Traffic Light Control

The paper is devoted to the short-term travel time prediction of individual connected vehicles in a regulated road network with adaptive control of traffic lights. The estimation of the total travel time combines both the travel time along road network links, obtained by a neural network model, and the waiting time at regulated intersections. At the first stage, it is proposed to use the model based on a neural network to estimate the travel time along the road links of the transportation network. At the second stage, the phase of the traffic light is predicted using the adaptive control method. Finally, the waiting time at the intersection is calculated based on the predicted arrival time of the vehicle at the intersection and the duration of the traffic light phase. Experimental results in a microscopic simulation environment allow us to conclude that the proposed approach outperforms baseline methods in terms of the mean absolute error criterion.
Simulation and Optimal Design of Closing Buffer System of the High Voltage Vacuum Circuit Breaker

With the proposed dual carbon target, vacuum circuit breakers have attracted much attention due to their pollution-free and maintenance-free properties. Currently, vacuum circuit breakers are being developed for high voltage and high current. As the breaking capacity increases, so does the volume of contacts, and the vacuum circuit breaker faces the more severe problem of closing bounce. To address this issue, an electromagnetic repulsive buffer structure was proposed in this paper, which can reduce the closing speed of the moving contact and the bouncing time. First, several factors affecting the closing bounce were analyzed theoretically and through simulations. It is shown that the bouncing time can be greatly reduced by reducing the closing speed and the mass of the moving contact. In this way, an electromagnetic repulsive buffer structure was designed. Through simulations, it was found to be effective in reducing the bouncing time. Finally, simulations were performed to explore the effect of different capacitance C0, capacitive pre-charge voltage U0 and buffer stroke l0 on the buffer effect. Optimized buffer mechanism parameters were obtained, and the bouncing time can be reduced to 0 ms.
Case Studies of Harmonic Distortion in Power Transformers Due to Geomagnetically Induced Current Flows

Geomagnetically induced currents (GICs) flow through grounding circuits into power systems, when triggered by geomagnetic disturbances. Power transformers (with grounding connections) are one of the common paths for GIC flows into power systems. The flow of GICs through a power transformer can create adverse impacts on its operation, including disruptions in the reactive power flow and significant harmonic distortions in primary and secondary side currents. This paper presents case studies for analyzing the harmonic distortion in primary and secondary side currents due to various levels of GIC flows in power transformers. Presented case studies are conducted on two laboratory power transformers with different core types, power ratings, voltage ratings, and winding configurations. Each of these power transformers is tested for various levels of GIC flows with its loading being varied from no-load to rated power. Results of conducted case studies conclude that the dc component, $2^{\mathrm{n}\mathrm{d}}$ harmonic, and $3^{\mathrm{r}\mathrm{d}}$ harmonic are dominant in primary and secondary sides currents at all levels of GIC flows. Other harmonic components ($5^{\mathrm{t}\mathrm{h}}, 6^{\mathrm{t}\mathrm{h}}$, and $7^{\mathrm{t}\mathrm{h}}$) can be observed with lower magnitudes than the dc component, $2^{\mathrm{n}\mathrm{d}}$ harmonic, and $3^{\mathrm{r}\mathrm{d}}$ harmonic.
Performance Tests on Temperature-Press Composite MEMS Sensor for Transformer Oil

Power transformer is a crucial piece of electrical equipment in power grid. In the digitalization process of power grid, rapid and effective monitoring technologies for power transformers are always a research hotspot. Optical sensor is passive and highly resistant to anti-interference, which is very suitable for high voltage and high electromagnetic environment. In this paper, the performances of a temperature-press composite optical sensor made by Micro-Electro-Mechanical System (MEMS) technology are tested for monitoring transformer oil status. The test results show that the composite optical sensor presents a satisfied measurement linearity, and the temperature-press composite MEMS sensor has long-term stability.
Safety and Security Requirements in AAS Integration: Use Case Demonstration

The Digital Twin (DT) paradigm has received attention for its potential in diverse industrial sectors like manufacturing, automotive, healthcare, electric grid, and transportation. The Asset Administration Shell (AAS) as an instantiation of the DT paradigm is proposed by Plattform Industrie 4.0, aiming to exchange asset-related data and services from when the asset is produced to its disposal in an interoperable way involving the key stakeholders. In Industrial Control Systems (ICS), AAS integration can bring about new safety and security concerns. Although there are standards covering safety and security separately, no finalised standard supports safety and security in a combined way. The increase in safety and security concerns because of AAS integration and recent cyber attacks that showed security and safety are interconnected, encourage us to explore one of the earliest activities in system development, requirement specification. Therefore, this paper investigates the impact of AAS integration into a use case on safety and security requirements specification according to IEC 62443 and 61511. The results highlight the interconnection of safety and security requirements in the proposed use case due to AAS integration and illustrate security requirements that potentially can affect safety.
Unit Commitment Model Considering Wind Power Uncertainties for Wind Power Bundled with Thermal Power Transmitted by DC Transmission Line

A large-scale integration of wind power brings new challenges to the optimal operation of power system. In this paper, according to the historical data of wind farms, two methods of interval prediction and scenario analysis are used to describe uncertainties in wind power output in the future. In different description modes of wind power prediction information, the corresponding day-ahead unit commitment models considering DC power adjustment constraints are established. The analyses of numerical examples show that the proposed wind power prediction method can accurately reflect the real information of wind power to a large extent, and the optimized decisions can be obtained using the established unit commitment models based on different prediction information. In the end, the influences of unit cost of wind power curtailment and unit cost of DC power regulation on the optimization result are analyzed and compared, which offer suggestions for the formulation of the day-ahead plan for the wind power bundled with thermal power transmitted by DC.
Synchronization in electrical circuits with memristive elements

This thesis presents and analyzes the dynamics of electrical circuits with memristive components. Memristive devices add a new dimension for modelling innovative circuits and systems in a wide range of applications, such as neural networks, storage devices and neuromorphic circuitsin which the memristive devices can act as artificial synapses. The main property of memristive devices is the non-volatile memory, which makes memristors ideal as potential building blocks for neuromorphic computer architectures. Neuromorphic computing is a new technology inspired by the human brain, with great potential to enable information processing at very low cost, using electronic devices that imitate the electrical behaviour of biological neural networks. First, we study synchronization in networks consisting of memristors and capacitors. Subsequently, electrical circuit models of (biological) neurons coupled via memristors are considered. Accordingly, the system of differential equations that simulate the well-known FitzHugh-Nagumo (FHN) neuron model is adopted, in order to derive a sufficient condition on the coupling coefficient to guarantee synchronization of FHN neuronmemristor network, by using Lyapunov stability theory. Finally, simulation results illustrate the synchronization phenomena of the proposed electrical circuits.
PID Based Path Following Algorithm for the MiddleAUV

The baseline path following algorithm based on PID controller for horizontal movement of the MiddleAUV is constructed. The MiddleAUV is small autonomous underwater vehicle designed to study the control algorithms. This article discusses the one case of PID controllers design and results of numerical simulations. The path is constructed using waypoints. The movement of the AUV between waypoints is accomplished by controlling the distance and yaw between the current position of the AUV and the waypoint. When the AUV approaches a waypoint, a special unit generates a new waypoint and the AUV continues moving. The main difficulty was configuring the controllers for the non-linear version of the AUV model. To solve this problem, we tuned the PID controller for the linear model and chose the best one for the nonlinear model. Numerical experiments were performed on the nonlinear mathematical model, which showed that the resulting PID controllers could be used as the basis for the robot's motion along the waypoints.
Heuristic reasoning strategy for automated sensor placement

A heuristic reasoning strategy for automating the placement of sensors in the design of multi-station convergent closerange photogrammetric networks is presented. Optical inspection of three-dimensional (30) objects requires acquisition of a configuration of multiple, spatially separated images about the object. The placement of these sensor stations is subject to a complex and interrelated set of constraints and measurement considerations and, therefore, heuristic solutions are necessary. This strategy exploits the idea of generic multi-station sensor configurations which comprise an ideal number and geometry of sensor stations for a class of measurement problems. A representation, termed the "constraint sphere," is introduced which accommodates a reasoning scheme in which these ideal station poses can be efficiently modified to suit the current object geometry, workspace restrictions, and measurement criteria. Examples demonstrate the strategy's potential. This approach is the first to deal with the mensuration considerations in multi-station sensor placement. vision applications is often conducted in a trial-and-error
Integration of spectral channels in the classification of coniferous and deciduous vegetation from satellite images

The results of the study of the classification procedure of coniferous and deciduous, as well as coniferous and mixed (in equal percentages) vegetation according to Landsat–7 images for different seasons of the year are presented. Spectral channels with a spatial resolution of 30 meters were used. Vegetation classification was carried out by brightness characteristics using the nearest neighbor method. The reference brightness of objects (simple standards) was determined by their mathematical expectations in each spectral channel for each season. Additionally, aggregated standards are formed by combining the brightness of plant objects in spectral channels. It is shown that when separating coniferous and deciduous objects, the probability of correct selection of coniferous can reach 1.0000 when using simple standards. At the same time, the probability of correct allocation of deciduous does not exceed 0.9697. The use of aggregated standards makes it possible to increase this probability to 0.9899. When classifying coniferous and mixed vegetation, the effectiveness of aggregated standards turned out to be lower than that of simple ones selected by spectral channels and shooting seasons. The obtained results suggest the continuation of research when dividing plant objects into a larger number of classes.
Optimized Protocol for Image Transmission over IoT

In day-to-day population increase needs communication stronger for fast and accurate results. Communication means transmission of data in terms of audio, text or image and videos transmissions. For text communication or audio communication most of the medium are suitable and long standing. But video transmission or image transmission needs higher bandwidth of data. Hence providing guided transmission media is very difficult task with higher bandwidth and more expensive also. In this regard wireless communication is most preferred mode of data transmission. Enabling image transmission in wireless transmission with low data error rate preferred to switch over the IOT devices communication. The way of data transmission defined as image transmission protocol are identified and verified. Out of these available protocols one of the most optimized protocol discussed in this paper. The transmission of images and videos is one of the many multimedia-based applications that make up a wireless sensor network (WSN). Applications, multimedia sensor nodes should optimize communication quality while reducing energy consumption. Lack of proper processing architecture and communication mechanisms to handle huge volume data has been the main barrier to sending images over WSN. Information security is becoming more crucial in data transmission as electronic data interchange advances quickly. Given how frequently photographs are used in industrial processes, it is crucial to safeguard sensitive image data from unwanted access. We offer an overview of transmission structures developed by diverse researchers for safe and effective picture transmission in this work.
Simulation and Comparison of RF Performance of the Novel Inverted F SIW Antenna with Triangular Split Rings SIW Antenna for X Band Applications

Aim: To evaluate the RF performance of X band application using an inverted F SIW(Substrate Integrated Waveguide) antenna and triangular split rings SIW antenna on 50 Ω characteristics impedance at 10 GHz. Materials and Methods: The effectiveness of two new antenna designs, the inverted F SIW antenna and the triangular split ring SIW antenna, was assessed in regards to their radio frequency (RF) performance. This included analysis of return loss, voltage standing wave ratio (VSWR), and gain. The antennas were tested on a 50 Ω impedance with specific values for alpha (0.05) and beta (0.2), and a pre-test power of 80%. The total sample size is 54. Result: In comparison to a triangular split rings SIW antenna (return loss: -13.4897 dB, VSWR:1.5368, gain:2.75 dBi, bandwidth:28.1 MHz), an inverted F SIW antenna performs substantially better (return loss: -24.4889 dB, VSWR: 1.126, gain: 4.76 dBi, bandwidth: 89.4 MHz). Inverted F and triangular split ring SIW antennas both have optimized dimensions of 19.8 mm x 16.8 mm. Significant RF performance of p<0.05 is obtained. Conclusion: The RF performance of the inverted F SIW antenna appears to be higher than that of the triangular split rings SIW antenna.
Demographic and Environmental Stochasticity Concepts and Definitions

SUMMARY General definitions of demographic and environmental variances as well as demographic covariance are given from first principles. The sum of the environmental variance and the demographic covariance is the covariance betweeen two individuals' contributions to the population growth within a year and corresponds to the coefficient of x2 in the expression for the variance of the change in population size. Hence, this coefficient may actually be negative. The demographic variance is the variance among individuals. It is shown that the definitions are consistent with use of these terms in models with additive effects and in diffusion approximations to population processes. The connection to classical birth and death processes, in which the environmental variance and demographic covariance is always zero, is discussed. The concepts are illustrated by some stochastic simulations.
Plant Disease Detection Using Ensemble Learning

Ensemble learning is a machine learning method that combines the predictions of multiple models in order to make a more accurate prediction. In this study, a plant disease detection system was developed using ensemble learning, with six base models Inception V3, MobileNet, MobileNetV2, VGG16, GoogleNet, and ResNet50 trained on a dataset of 87,000 images of healthy and diseased plant leaves from 38 different classes. The base models achieved accuracies ranging from 72.7% to 97.2% on the plant disease dataset. To improve the accuracy of the system, both soft and hard voting classifiers were applied to the base models. Soft voting involves weighting the predictions of each base model according to their accuracy and taking the class with the highest weighted average as the final prediction. Hard voting consists of simply counting the number of votes cast for each class, and the class with the most votes is selected as the final prediction. Both soft and hard voting significantly improved the accuracy of the system, with the soft voting ensemble achieving an accuracy of 97.8% and the hard voting ensemble achieving an accuracy of 98.3%. The results of this study demonstrate the effectiveness of using ensemble learning for plant disease detection and the potential for such a system to assist in the accurate and efficient identification of plant diseases.
Cooperative Application of Vehicular Traffic Rerouting Method and Adaptive Traffic Signal Control Method

Nowadays, due to the growth in the number of vehicles, the problem of effective traffic control remains relevant. Popular approaches to solve this problem are the use of traffic signal control methods and the use of vehicular rerouting methods. The paper considers the problem of the cooperative application of vehicular traffic rerouting method and the method of adaptive control of traffic signals for effective traffic control. A presented traffic rerouting method takes into account the state of adaptively controlled traffic lights. The results of experimental studies of the developed method in the SUMO simulation environment are presented.
Anomaly Detection for Vertical Plant Wall System using Novel Support Vector Machine in comparison with Linear Regression for improving accuracy

Aim: The purpose of the study and the goal is to create a plant wall system that can regulate the temperature and humidity of an interior environment by utilising a novel support vector machine in contrast to linear regression. Materials and the Methods: In order to identify the alterations in the indoor climate that are caused by vertical plant wall systems, a Machine Learning method for the regulation of indoor changes has been presented and developed. Results: In this method, Linear Regression and Innovative Support Vector Machine are compared with one another. After running the results of the calculation through the G Power (80%) calculator, the number 534 was decided upon as the appropriate size for the sample. 1068 different samples were used in total during this investigation. According to the findings, the Novel Support Vector Machine had the highest accuracy in forecasting changes inside of a building (68.42%), while the Linear Regression model had the lowest mean error (52.46%), and the value that was considered significant was 0.543. It illustrates that there is no statistically significant difference between the groups presented here. Conclusion: When it comes to accuracy in indoor control, the cutting-edge Support Vector Machine technique outperforms the more traditional Linear Regression method.