Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Inter-Dataset Generalisability of Autoencoders for Network Intrusion Detection

With the improving sophistication of computer network intrusions and the rising rate of number of novel attacks, the research focus in network intrusion detection has shifted to unsupervised and semi-supervised methods that have better zero-day detection ability and their ability to generalize across different network environments. Recently published datasets that have the same flow features but different network environments and attacks have eased the research on generalisability and improved method comparison abilities. This paper aims to continue the strive towards generalisability by examining the performance of, primarily autoencoder, and PCA, in an inter-dataset network intrusion detection tasks as these methods have not yet been evaluated across different network environments. The results indicate that while the performance of the traditionally used methods does not fully transfer on different network environments, they do perform better than a random classifier.
Machine Learning Approach for Multi Particle Tracking in Complex Plasmas

Detecting micron-sized particles is an essential task for the analysis of complex plasmas, because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches [1], [2]. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for fast and precise segmentation of images that was developed at the Computer Science Department of the University of Freiburg [3]. We compare the neural network with a conventional method we used so far in terms of accuracy in analyzing artificial data. We also apply the network to data of the PK-4 experiment. The experimental data were generated under laboratory conditions.
Artificial Neural Network (ANN) With Chan-Vese(CV) Algorithm-Based Plant Disease Detection And Classification

A country’s economy is influenced by a wide range of factors, with the agriculture, service, and food processing sectors. In our country, agriculture is one of the most significant economic sectors. The agriculture sector, however, has several difficulties, such as the different climatic conditions in other regions of our country that give rise to various infectious diseases in different plant species, resulting in a significant drop in crop productivity and income generation. Early and accurate plant disease diagnosis is crucial in improving the foundation for harvest formation. The traditional method of plant disease detection through eye observation is unsuccessful since it necessitates knowledge. Machine learning (ML) techniques are being utilized to identify and categorize plant ailments to address this issue. In this work, plant diseases can be classified using machine learning methods. These involve classifier training, classification, and stages like dataset construction, image loading, pre-processing, segmentation, and feature extraction. An artificial neural network is initially used to distinguish and find leaves in a challenging environment. The Chan-Vese (CV) technique states that symptoms can be found in images that have been segmented depending on the outcomes of the ANN algorithm. A transfer learning model trained on a dataset of ill leaves on an essential background is then given the segmented leaves. The model is also checked for rust, black rot, and bacterial plaque conditions. The findings demonstrate that the methodology is more accurate—98.22%—than the previous approach, minimizing the adverse effects of illness on agricultural productivity and promoting long-term agrarian growth. Utilizing data from PlantVillage, the model is trained and validated. The dataset includes images of plant diseases afflicting pepper, tomato, and potato plants. Fifteen different plant disease categories are used to train and evaluate the classifier. The results of the trials show that the proposed ANNCVA deep learning model can categorize the numerous varieties of plant leaves accurately and effectively.
Tree Boosting Methods Comparison for Landslide Susceptibility Maps, Case Study: Kejajar, Wonosobo

Landslides in Indonesia occur almost every year and cause large material losses. Early prevention by creating a landslide susceptibility map is one way to anticipate losses due to landslides. The search for the best method for predicting landslides using machine learning with several tree boosting methods has been carried out, but the comparison between the tree boosting methods is unknown. This study aims to compare the tree boosting methods in their use for creating landslide susceptibility maps. The case study used in this research is Kejajar District, Wonosobo. There are 25 data features used to determine landslide. The landslide data in this study is 84 polygons. The tree boosting methods used include XGBoost, LGBM, Adaboost and Catboost. Hyperparameter tuning and k-fold cross validation were used to get the best model. The results of the comparison show that LGBM is the best method with accuracy, recall, f1 score, and ROC AUC values of 0.9903, 0.9360, 0.9154, and 0.9648 respectively. It indicates that the boosting method using LGBM can provide good results for creating a landslide susceptibility map.
Simulation Study on the Post Arc Energy Characteristics of Double Break Vacuum Circuit Breaker

Vacuum circuit breakers (VCBs) have been widely used in medium and low voltage distribution system due to their superior arc extinguishing ability and environmental protection characteristics. However, in the application to higher voltage grade, the long gap vacuum arc has a long arcing time and the residual energy of post-arc is large, which is easy to cause restrike and lead to breaking failure. VCB with double breaks can, not only, greatly improves the ability to tolerate TRV, but also has characteristics in arc energy control. A long gap arc is divided into short gap arcs, which can reduce the residual energy density of post-arc, and is favorable to the recovery of vacuum dielectric, and improve the breaking success rate of the circuit breaker. In order to study the influence of arc energy control on the breaking performance of the switch, and to analyze the distribution characteristics of the residual energy between the double breaks, PIC-MCC method is used to simulate the energy distribution during the development of the post-arc sheath. The arc gap distance of single break VCB is set, and the basic data of residual energy density of single break/long vacuum gap is obtained. The single break long gap is divided into two short gaps in series, in condition of the other initial data remains unchanged. Particle simulation is carried out under different TRV parameters to analyze and compare the difference of particle energy between single break and double breaks, and to explore the energy distribution characteristics of double breaks under different TRV rate and different TRV distribution. The difference of energy density under different simulation conditions is obtained through simulation, which verifies the significance of using double breaks to disperse the residual energy of arc, and explores the test conditions for optimal energy distribution between double breaks. The research shows that the double break is beneficial to improve the breaking capacity and service life of VCBs under high voltage and high current conditions.
A New Wave in CyberSecurity: Democratising the Future Networking Landscape

Internet use has grown ubiquitous worldwide. Online crime has grown alongside Internet activity. Cybersecurity has evolved rapidly to keep up with cyberspace’s rapid changes. Cyber security refers to a nation’s or company’s online protections. The term “cyber security” was barely known by the general public two decades ago. Cybersecurity is an issue that impacts not just individuals but also businesses and governments. Everything is now digital, and cybernetics makes use of tools from the cloud, mobile devices, and the Internet of Things. The issues of confidentiality, safety, and restitution all arise when discussing cyber attacks. To protect networks, computers, programmes, and data from being attacked, damaged, or accessed without permission, cyber security has been developed. With an eye on the future of next-generation networks, this article provides a high-level review of several ANN and DL techniques utilized for Cybersecurity and CyberSecurity Capacity Building(CCB) strategies.
M-Class PMU for General Purpose Embedded Controllers in NI Veristand Environment

This paper presents the design, implementation and validation of a M-Class PMU on a general purpose embedded controller, concurrently used for inverter and grid control tasks, deployed over a unified real-time control environment. The rising number of embedded controllers in active distribution grid opens an opportunity for shared computation and instrumentation resources. Unified real-time control environments can efficiently coordinate multiple embedded controllers, IOs and models from different toolchain. The PMU in this paper, is implemented within the NI Veristand real-time control environment at CoSES laboratory in TU Munich. It uses the existing instrumentation and controller setup for inverter and prosumer control in an active distribution grid laboratory. The PMU passes all M-class device accuracy tests set by the IEC/IEEE 60255-118-1 standard. The output is compliant to the IEEE-SA C37.118. 2 messaging standard and connected to an open source PDC software. The proposed solution demonstrates the possibility of merging different grid control and operation functionalities within the unified Veristand RT control environment.
Underwater Terrain Map Building Based on Depth Image Using Multi-Beam Sonar Sensor

In order to perform work in an underwater environment, it is important to monitor the situation by mapping. However, the use of sensors in the underwater environment is more limited than in ground environments. Sonar sensors are mainly used for mapping work in underwater environments. The underwater environment data collected by the multi-beam sonar sensor is mostly composed of flat terrain data, which is difficult to extract robust features, and thus has challenging conditions in mapping research. To solve this challenge, we propose a method of building an underwater terrain map using a 3D point cloud collected from a multi-beam sonar sensor. The proposed method uses the HardNet-based image matching method to register the depth images and build a global underwater terrain map. Experiments were conducted based on point cloud-based underwater terrain data, and the high accuracy of registration was confirmed.
BDFRG based WECS with Partial-Scale Converter: Efficiency, Cost and Volume Comparison of SiC-based and IGBT-based Converter Solution

Power electronics is considered the enabling technology in the most renewable energy systems. Nowadays it is gaining increasing attention, as the renewable energy sources are increasingly being installed. Of particular interest are the wideband-gap devices, as they offer important benefits when considering the efficiency increase and volume reduction. Consequently, they can be viably adopted in the renewable energy sources, particularly when considering the green goals imposed by many countries. In this paper, the 1.5 MW brushless doubly-fed reluctance generator (BDFRG) based Wind Energy Conversion System (WECS) with bidirectional partial-scale frequency converter composed of two back-to-back converters is considered. The total and fair com-parison has been conducted. Both efficiency and volume as well as the cost have been analysed for the comparison, considering Si- and SiC-based converters. The fair comparison is considering the modules of the same power ratings. The thermal analysis has been implemented in PLECS, together with the converter control. The realistic libraries have been included for different devices, obtained from the manufacturers' experimental data. The results support the fact that in the case of SiC-based converter, reduced volume can be achieved, but also reduced cost when looking to the system as a whole.
Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging

Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.
Optimizing Transmission Power for Uplink Data in Cell-Free Wireless Body Area Networks

Wireless body area networks (WBANs) have recently become a topic of interest due to their large number of applications and the rapid development of individual gadgets and devices for medical purposes. The model in this study, called a "cell-free WBANs" scheme, is developed on the basis of a cell-free multiple-input multiple-output (MIMO) system where a lot of access points (APs) serve a number of sensors simultaneously. A new system model where the sensors are distributed around the body and communicate directly with APs rather than through a coordinator is proposed. The interference due to simultaneous signal transmission from multiple sensors is taken into consideration, and the transmission power control algorithm for uplink data is developed to enhance the spectrum efficiency of the system. According to simulation results, in all considered scenarios, the proposed system’s performance is considerably improved in comparison with the small-cell model.
Graphic Model Features of Distribution Network Electrical Engineering Based on Machine Learning Algorithm

With the rapid development of modern society, the power load is also growing rapidly. Whether in terms of power supply quality or power supply service, users have various demands for power. As the direct service object of electric energy, power users’ outage situation and power consumption feeling will largely reflect the power supply problem in the substation area. It is of great significance to improve the quality of power supply and the satisfaction of power customers if we can combine the demands of users with the operation of distribution transformers and explore the power laws therein. The feature of electrical engineering graphic model of distribution network based on machine learning algorithm is a software that helps to create and manage distribution network with real-time data. It provides graphic models for electrical engineering students to learn different types of networks. The software can be used to design, analyze and simulate various distribution networks, such as bus, star topology, ring topology, etc. It also provides the operation of these networks under different operating conditions, such as voltage drop caused by resistance and capacitance values.
EEG Classification Using Modified KNN Algorithm

Automatic classification of EEG signals is required for the early identification of various disorders. In this research paper, statistical denoising and a modified KNN algorithm are coupled to create a smart method for classifying the EEG signal in order to accurately detect disorders. The EEG data are denoised statistically to produce a clearer signal, and the modified KNN algorithm is then used to classify the appropriate disease features with the aid of a classification block. The suggested algorithm’s detection accuracy is compared to the detection accuracy of other existing algorithms, demonstrating the algorithm’s effectiveness.
Smart Traffic Detection and Control using Machine Learning Techniques

In the modern era, with the rise in vehicles and population, many urban cities have faced traffic problems like traffic congestion, which causes travel delays. The main reason for the delay is that current traffic systems use fixed signal timers at the traffic signal intersection. These unnecessary delays at the traffic signals lead to excessive fuel consumption and increased pollution. This paper uses vehicle detection methods through surveillance cameras and machine learning to predict traffic based on historical data in a region. Finally, optimizing traffic by utilizing the predicted data and proposed methodology reduces the average wait time of travelers.
The Modeling of Uncertainty in Planning of Smart Transmission Grid based on Artificial Intelligence

Power grid planning is an important part of power grid topology, but there are many uncertainties in the original manual planning method. This paper proposes an artificial intelligence method to analyze the intelligent planning of transmission network and improve the research of power grid topology, optimize factors such as subjective planning uncertainty and objective planning uncertainty. The modeling topology data is then comprehensively evaluated. Finally, continuous monitoring is used for intelligent planning of the transmission grid and the final planning results are output.The artificial intelligence method can accurately carry out the intelligent planning of the transmission network and remove the intelligent planning of the transmission grid There are a lot of uncertainties. Therefore, artificial intelligence methods can meet the requirements of intelligent planning of transmission grids and are suitable for transmission network planning Continuous development.
Exploring Technical Challenges for Thermal Sensation Estimation in Mobile Robot Driven Personalized Thermal Control

The personalized thermal control studies estimate users' thermal sensations based on wearable or vision-based approaches to control the temperature, humidity, and ventilation in response to individuals' different thermal preferences. However, there is a limitation that both conventional wearable-based and vision-based methods are practical for real-world deployment due to additional equipment, and fixed field of view. This paper discusses the mobile robot-driven human state estimation for personalized thermal control as one of the solutions to overcome these limitations. We investigate the technical issue and its possible solution in designing mobile robot-driven estimation and performed a simulation-based preliminary evaluation to evaluate the effectiveness of a mobile robot on human state estimation. Evaluation results show that the mobile robot can help in the accurate estimation of the user's thermal sensation with only vision data. Finally, we present future directions to further increase the accuracy to achieve wearable-level.
Heron: Scalable State Machine Replication on Shared Memory

The paper introduces Heron, a state machine replication system that delivers scalable throughput and microsecond latency. Heron achieves scalability through partitioning (sharding) and microsecond latency through a careful design that leverages one-sided RDMA primitives. Heron significantly improves the throughput and latency of applications when compared to message passing-based replicated systems. But it really shines when executing multi-partition requests, where objects in multiple partitions are accessed in a request, the Achilles heel of most partitioned systems. We implemented Heron and evaluated its performance extensively. Our experiments show that Heron reduces the latency of coordinating linearizable executions to the level of microseconds and improves the performance of executing complex workloads by one order of magnitude in comparison to state-of-the-art S-SMR systems.
A New Continuous Time Offset and Gain Calibration Strategy for Open-loop Residue Amplifiers of ADC

A continuous-time offset cancellation and gain calibration strategy is proposed for open-loop residue amplifiers of pipeline ADC's. The strategy detects gain and offset error using a reliable technique and saves digital amounts of the signals which are resulted from the calibration loop. This allows data conversion to proceed without any interruption. The structure is shared between several RA stages in ADC, decreasing power consumption and area occupation. The strategy does not require extra circuits like replica residue amplifier or wide-bits digital processor for offset and gain-error correction. Utilizing a unit gain and offset calibration structure for the whole RA in the main ADC results in a significant power consumption reduction. The simulations show that the input-referred offset is reduced to around 30 $\mu \mathrm{V}$. Also, the gain calibration loop provides a gain of 8 with an error deviation less than 0.001. Proposed structure has been simulated using HSPICE software in 0.18 μm standard CMOS technology and results show that in 14-bit and 16-bit 200 MS/s Pipeline ADC, there are a 23dB improvement in SNR and 3.9 bit in ENOB.
Energy Optimization of a Base Station using Q-learning Algorithm

A sleep strategy with several sleep mode (SM) levels for energy-efficient 5G base stations (BS) is proposed to reduce energy consumption. Energy consumption and Quality of Service (QoS) management are paired as a result of awakening sleeping BSs. Advanced Sleep Modes (ASMs) gradually deactivate BS components to save energy. Based on component transition times (deactivation and activation), different SM levels could be considered. This research proposes a Q-learning based approach for ASMs to present an optimal tradeoff between energy consumption and QoS.
Convolutional Neural Network Approach for Steady-State Somatosensory Evoked Potential-Based Robotic Exoskeleton Control

Steady-state somatosensory evoked potentials (SSSEP)-based brain-computer interface (BCI) has been developed to control real-life assisting robots, such as an exoskeleton for body-disabled patients. Although the SSSEP-BCI system has been advanced by various research groups, improving the accuracy is required for applying stable control of the appliance. Moreover, the SSSEP-BCI study on body-disabled patients is still required to confirm applicability. In this study, the convolutional neural network (CNN) was applied to the SSSEP-BCI and compared with the regularized linear discriminant analysis (RLDA) as the widely used method. For extracting the spatial feature, the common spatial pattern (CSP) was used in both methods, and the efficiency of the CNN was validated via the SSSEP data from five healthy subjects and two spinal cord injury (SCI) patients. As a result of 9-fold cross-validation, the CSP-CNN method showed significantly higher accuracy than CSP-RLDA by 7.8% (Healthy subject) and 7.2% (SCI patients). We can therefore conjecture that CNN can improve the performance of SSSEP-BCI.