Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of the Generalization Capabilities of Machine Learning Models for Vine Line Detection

Precision agriculture can optimize the production of agricultural crops by analyzing aerial images with varying resolutions and acquired from different sources. It is widely accepted that machine learning (ML) model, especially deep neural networks (DNN), are very efficient for image segmentation. DNNs have been used to segment complex texture and planting structures, such as vine lines, due to their variations in shape, color and orientation. However existing DNNs reach their limits to segment aerial images with varying resolution and multiple instance of vine lines crossing a entire image. In this paper, we present an improvement of the generalization capabilities of ML models to segment vine lines in satellite images. An approach from a previous works that combine neural networks and other classifiers allow us to improve the classification and generalize the models that increase the f-score by 17%.
Re-synchronization of Multi-Power Islands during Blackstart with Enhanced Grid Resiliency

Restoration requires re-connection and re-synchronization of power islands and produces high transient current which leads to cascaded tripping of circuit breaker and decreasing the grid resiliency. This can be mitigated by reduction in inrush currents at the time of cold load pick up which is most vital during blackstart specially with high renewable energy penetration. This paper proposes a bottom-to-top approach for restoration and re-synchronization of a large distribution network consisting of small power islands. The proposed approach is tested on a modified IEEE 33-bus system with four renewable distributed generators. The inrush current is found decreasing by ≈49% during restoration with proposed technique which adheres to IEEE-1547 standard of re-connection, contributing to the increase in resiliency.
Speech Stammer Detection by Spectral Features Based Artificial Neural Network

In many applications of computer-assisted speech analysis, the detection of stammer from speech signals is essential. For training the stammer detection model, a strong feature is required to distinguish the speech signal as a stammer or non-stammer. The proposed work employs Mel Frequency Cepstral Coefficients (MFCC), Delta Delta MFCC (D2MFCC), Pitch, Spectral Flux, and Spectral Centroid to extract the dominant features from speech. These features are utilized to train a Multilayer Perceptron (MLP) Neural Network (NN) using a Bayesian Regularization (BR). When compare to NN training, classifiers, and the state of the art, this technique gives superior accuracy and training. Experiments have also been conducted to confirm the primacy of fused spectral features over other standard features used in literature and it has been demonstrated that the proposed features when combined with MLP-BR provide better accuracy to the level of 99.2% over the collected speech dataset.
Neural Network Based Self-Tuning PID Controller

Ziegler and Nichols developed the well-known Ziegler-Nichols approach to modify PID controller coefficients in order to find conventional PID controller parameters that are adequate. This tuning approach is straightforward and provides constant coefficient values, resulting in a PID controller with poor adaptability to model parameter variations and changing operating circumstances. Therefore, in this study, a self-tuning PID controller based on Neural Networks (NN) is proposed. The combination of a neural network and PID control compensates for the simplicity of this method of control with artificial intelligence, hence making it robust to dramatic parameter changes. The suggested adaptive PID controller is evaluated in the MATLAB/Simulink environment using sinusoidal and step signals as the desired outputs. In this paper, neural network PID controller is used for disturbance rejection. The artificial neural network enables parameter adjustments for the PID controller to be made in a dynamic manner. At the end of this work, it can be concluded that neural network based PID controller has a robust response with time-delay distribution.
Research on the Optimal Setting Scheme of Gateway Metering Point of Power Plant

The setting of gateway metering points for power generation enterprises is a research hotspot in recent years. The process of my country’s electricity spot market has entered the fast lane. In order to achieve accurate trade settlement for power generation enterprises under different settlement cycles, there is undoubtedly a higher requirement for the setting of gateway metering point. At present, when there are complex situations such as crossing power in the on-grid and off-grid electricity measurement methods of power generation enterprises, the self-consumption electricity cannot be accurately measured. This paper proposes an optimal setting scheme for gateway metering point, and innovatively designs a logic judgment device that can accurately distinguish off-grid electricity and self-consumption electricity. It is proved by an example that this scheme can solve the problem of inaccuracy of power plants in complex operation modes. It is feasible to measure the problem of on-grid and off-grid electricity.
Citrus Fruit and Leaf Disease Detection Using DenseNet

Citrus plants are high in Vitamin $C$, which has several health benefits and is used as a raw material in a wide range of agro-industrial processes. The ripple effects of citrus fruit diseases are recognized to have a considerable impact on citrus fruit production. This is why it is critical to build a detection method for these diseases. Deep learning techniques have made it possible to execute a variety of tasks linked to the recognition of leaf and citrus diseases. In this paper we have proposed DenseNet-121 model, which tries to compare healthy leaves and fruits with those infected with citrus diseases like black spot, greening, scab, and canker. This model may extract many attributes from its various layers. The results showed that the model performs well on a variety of criteria. In 50 epochs and five classes, we achieved 96% accuracy reducing degradation and vanishing gradient problems using the ImageNet pre-trained DenseNet-121 model.
Generative Adversarial Network based Chest Disease Detection and Binary Mask Generation

The infectious disease tuberculosis (TB) continues to pose a serious risk to global health specially in developing world. There are over 10 million new cases of tuberculosis each year. Machine and deep learning models are trained to recognize specific pixels inside a medical image for the purposes of classification and disease progression tracking, however the decision-making mechanism of these models is hidden from the user. In this context, Explainable artificial intelligence (XAI) refers to strategies that allow humans to understand the results of AI algorithms. Recently, a variety of XAI methods for classification and generative have been proposed; however, these methods only use a subset of most discriminative power characteristics, resulting in false positives. This article proposes CycleGAN-based multi-functional generative adversarial networks to efficiently solve these challenges. Proposed model is trained in weakly supervised context to identify and visualize the disease effects and finally generate binary mask in data augmentation context. The model takes a Chest radiography (CXR) as input, creates a change map showing the disease's effect at a specific spot, and then uses this map to create a binary mask of original image. Results on publicly available TB dataset, TBX11K, confirm that the proposed model produces highly accurate result.
Operational Risk for Integrated Power and Gas Systems Considering Varying Hydrogen Concentrations With High Penetration of Wind

We establish an operational risk assessment approach for integrated power and gas systems (IPGS) considering varying hydrogen concentrations with high penetration of wind. First, the operational availability of wind generation is formulated considering the stochastic process of wind is developed. Then, an operational risk mitigation scheme (ORMS) is devised to minimize both load shedding and gas security violation. The varying gas composition on the physical properties of IPGS is characterized to fully reflect the impact of alternative gas on the IPGS operation. Moreover, several risk indices, improved from traditional gas security indices, are proposed to evaluate gas security under various uncertainties. An analytical risk evaluation method is used for better computation efficiency. Finally, a test IPGS is utilized to demonstrate the proposed approach. We can find that the proposed operational risk evaluation approach is able to evaluate the weak spot of the IPGS when distributed injections of hydrogen are considered, and the trend of the risk evolution can also be obtained during the operation.
Influence of finite ground conductivity on the propagation of VLF radio waves

Calc ul a ti ons of the mod a l c ha rac te ri s ti cs of th e ea rth -ionosph e re waveguide a re prese nt ed fo r a wid e ra nge of ground condu c tiviti es . Tt is s hown lha t the a tt e nu a ti o n ra te of th e do min a nt modes, as a fun c ti o n of groun d co ndu c ti vit y, has a max imum va lue whi c h d e pe nds o n freque ncy a nd va ri ous waveguid e para me te rs. One of the importa nt fi ndings is that frequ e nc ies of the o rd e r of 10 kc/s may be Im>re adve rse ly a ffec ted b y ve rylow cu nd ucti vit y th a n freq ue nc ies of the o rd e r of 30 kc/s .
Forecasting of Ship Power Load based on EMD-GA-LSTM Algorithm

This paper analyzed the characteristics of ship power load forecasting. Aiming at the shortcomings of traditional load forecasting, such as low accuracy and poor adaptability to working conditions, an improved EMD-GA-LSTM power load estimation algorithm was proposed. Through the combination of data preprocessing module, self-learning module and error feedback module, the advantages of different algorithms can be brought into play. The numerical results showed that the EMDGA-LSTM algorithm can improve the effect and forecasting accuracy of ship power load model forecasting.
Combination of Diesel Generator Set with Solar PV-Powered Battery Proposed EV Battery Exchange Charging System

In this study, a EV charging station powered by the grid (CS), a diesel generating set with an energy storage system, a solar PV (Photovoltaic) array, and battery are used to enable continuous charging in connected to the grid, attached to a DG set, and islanded modes. With the help of a BES and solar photovoltaic array, the charging station is primarily used for charging EV batteries. In the event that the storage battery runs out or the solar array does not generate electricity, smart use of the charging station can utilize a DG (Diesel Generator) unit or the grid to provide electricity. The DG sets are designed so that they always operate at 80–85 percent loading in order to optimize fuel economy. As an additional benefit, the charging station utilizes a storage battery to keep the generator running at a constant frequency and voltage rather than relying on an external speed governor for this purpose. In addition, it ensures that any power used for to raise and lower girds and DG sets has a power factor of one (UPF) even when under nonlinear loads. Syncing the PCC (Point of Common Coupling) voltage to the grid/generator voltage is also necessary to maintain continuous charging. The global environment is contaminated by the transportation industry's large carbon emissions. Electric vehicle (EV) adoption has a significant possibility to cut back on carbon emissions. A Battery Swapping Station (BSS) is an effective way to power electric vehicles while decreasing the time it takes to charge them at battery charging stations (BCS). For completing a lengthy interstate excursion, Swapping Technology offers the perfect option.
A two-stage differential evolution for constrained multi-objective optimization problems

The constrained multi-objective optimization problems (CMOPs) are always hard to handle when the constraints are too stringent. In this paper, a two-stage differential evolution algorithm (TSCMODE) is proposed to solve CMOPs, and non-dominated sorting is used to select offspring individuals. Firstly, in the early stage of evolution, a efficient constraint handing technique is proposed to generate more feasible solutions, and differential mutation is used to generate high quality offspring individuals. Then, at a later stage in evolution, the feasibility conditions are used to divide the population into upper and lower sub-populations, the good genes from the upper sub-population are passed to the lower offspring individuals by differential evolution. Besides, the objective values of the lower sub-population are adjusted self-adaptively to avoid the potential infeasibility being discarded. Finally, the proposed algorithm is executed on some recent benchmark functions and compared with four state-of-the art constrained multi-objective evolutionary algorithms. The experimental results show that TSCMODE can solve the CMOPs as well as.
$\beta$ Link: A Pattern Recognition Approach for Identifying Superior Event-to-Sink Links in a Spatiotemporal Correlated Sensor Network

In large-scale wireless sensor networks (WSNs) deployment, sensed data for dissemination are highly spatial-temporal correlated within close geographical proximity. The sink is not interested in individual's sensor data but accurate information of the physical phenomenon. Pattern recognition has been proven to be a successful technique in identifying WSN links with superior performance for data transmission among spatial-temporal correlated links. This paper proposes βLink a data-driven design framework, which exploits a pattern recognition technique to identify this superior performance route for data transmission (Beta link). The technique provides an insight into; a metric that measures the degree of temporal link correlation using a Two State Markov Chain approach, and pattern classification using Support Vector Machine that also models spatiotemporal link correlation. The scheme could reduce the number of links to be utilized for data dissemination to 21 %, a significant reduction to the overall network's transmission cost. Therefore, the results to be presented are encouraging as it clearly indicates the potential of Pattern Recognition approach in identifying Beta links.
Deep Learning Based Phonocardiogram Signals Analysis for Cardiovascular Abnormalities Detection

Cardiovascular diseases are among the vital causes of mortality worldwide which need early detection with the use of auscultation examination. Heart diseases could be diagnosed in a convenient way of heartbeat sound analysis. Manual auscultation is time-consuming and problematic to differentiate heart sounds related to different kinds of heart abnormalities. Also, as it requires an expert in the field, it becomes costly and quite prone to human error. Due to all these issues, there is a high demand for an automatic diagnostic system, an alternative way to human examination. This research focuses on developing Artificial Intelligence (AI) based system of the latest computational algorithms for detecting heart abnormalities from heart sounds. Heart sounds are classified as to be normal or abnormal from Phonocardiogram (PCG) signals. One of the recent techniques introduced in deep learning algorithms for audio classification is 1-Dimensional Convolutional Neural Network (1D-CNN). This research work includes a 1D-CNN as a classification algorithm. A widely used publicly available dataset of heart sounds from PhysioNet/CinC (2016) challenge is utilized. The method acquires accuracy, sensitivity, specificity, F1 score, and precision of 95.45%, 97.44%, 93.6%, 95.45%, and 95.54% respectively. The proposed approach uses a less-complicated customized 1D-CNN algorithm, outshining most of the previous competitive methods by securing high performance that makes it appropriate for diagnosing heart diseases from PCG data.
Capturing Request Execution Path for Understanding Service Behavior and Detecting Anomalies Without Code Instrumentation

With the increasing scale and complexity of cloud platforms and big-data analytics platforms, it is becoming more and more challenging to understand and diagnose the processing of a service request across multi-layer software stacks of such platforms. One way that helps to deal with this problem is to accurately capture the complete end-to-end execution path of service requests among all involved components. This paper presents REPTrace, a generic methodology for capturing such execution paths in a transparent fashion. Moreover, this paper demonstrates the effectiveness of REPTrace by presenting how REPTrace can be leveraged for knowledge extraction and anomaly detection on the platforms’ request processing. Our experimental results show that, REPTrace enables capturing a holistic view of the request processing across multiple layers of the platforms (which is missing in official documentation) and discovering important undocumented features of the platforms. Fault injection experiments show execution anomalies are detected with 93% precision and 96% recall with aid of REPTrace.
Reduced Switching Frequency Operation of Parallel Connected Two-level Inverters with Isolated DC-links for STATCOM Application

Parallel operation of two-level inverters with isolated dc-links are of profound importance for STATCOM operation in high voltage applications where the importance of cost, current sharing, modularity, effective multi-level voltage generation and consequently minimised ripple content of the STATCOM injected current are prevalent. Conventional approach using phase shifted carriers satisfies the criterion of effective multilevel voltage generation but suffers from the achievable current controller bandwidth with low switching frequency operation of the inverter. The proposed control method enables switching of individual inverters at a very low frequency while the total STATCOM current injected in to the grid is controlled at reasonably high bandwidth. Hence, the current ripple is minimised considerably. Also, due to isolated dc-links, the flow of zero sequence currents among the inverters is absent thus further reducing current ratings of individual inverters. The proposed control method is verified in simulation and experiment in a laboratory prototype and the results are presented.
Wayside Hyper-loop System using Double Sided Linear Induction Motor

Linear Induction motors are becoming more and more popular day by day. High speed Electromagnetic propulsion is the demand of many upcoming industry projects viz. the Hyper-loop transport system, factory operation and Defence establishment for moving large mass rapidly. The short secondary long primary Double Sided Linear Induction Motor (DSLIM), with conventional single layer or double layer distributed winding when operating at extremely high acceleration results in oscillating thrust which restricts the movement of the mover. A case study has been reported for wayside Hyper-loop system with German Silver mover gives satisfactory performance for moving a heavy mass at high speed using DSLIM. Here slot by slot double layer three phase winding has been used.
A Comparative Analyze of FinFET and Bulk MOSFET SRAM Design

FinFET technology has become the most popular topic in submicron SoC and VSLI design in recent years, attributable to its numerous advantages, including excellent scalability and low power consumption. This paper aims to provide a comparative study on the performance of FinFET SRAM and bulk MOSFET SRAM. Five architectures (6T, 7T, 8T, 9T, 10T) of conventional CMOS SRAM are investigated in this paper to understand the existing constraints on bulk MOSFETs in nanoscale integration. The options for replacing the bulk MOSFETs were evaluated, including DG SOI (Double Gate Silicon on Insulator) MOSFET, and FinFET. Then this paper elaborated on the benefits of incorporating FinFET into 6 - 9T SRAM configurations and explored the possibility of SRAM topology with the independent gate (IG) FinFET, while the potential drawbacks of FinFET technology are also discussed at the end. With the scaling down of transistors and increasingly demanding lower power devices, FinFET proved its irreplaceable significance in the future SRAM design.
Location planning of electric vehicle charging stations based on a multi-objective three-level optimization model

The construction of electric vehicle (EV) charging stations (CSs) plays an important role in promoting the adoption of EVs. This paper mainly solves EVs' charging station location planning problem (CSLP). The CSLP involves various optimization objectives, such as waiting time, driving distance, station construction costs, profits, etc. Currently, most papers select one or two optimization objectives to model and optimize. We formulate the CSLP problem as a multi-objective three-level optimization problem that simultaneously optimizes user waiting time, annual investor profit, user driving distance, and expense. It involves three sub-problems: user-directed decision level (lower level), charging piles number decision level (middle level), and CS location decision level (upper level). The upper-level optimization problem is optimized by using a decomposition-based evolutionary algorithm and the other-level optimization problems are optimized by a greedy algorithm. Simulation results demonstrated that this approach can provide effective solutions to the location of CS quickly and effectively.
An improved PCA-K-means algorithm for ancient Chinese glass classification

The classification of ancient glass is of great significance for understanding cultural exchange. However, a large number of chemical compositions of ancient glass, the small variation of its chemical compositions, and the difficulty of obtaining data make it difficult for most classification methods to achieve great results when classifying glass. The PCA-K-means algorithm has been proven to perform well when classifying small-scale data with high-dimensional. In this paper, we propose a weighted Euclidean distance and a composite distance evaluation function to improve the PCA-K-means algorithm further after fully considering the characteristics of the data. We use the novel PCA-K-means algorithm to classify a batch of ancient Chinese glassware compared to traditional K-means and PCA-K-means. The results show that our improved model is better than the other two models in classifying small-scale glass samples.
Reduced Order Modelling of Higher Order Electric Vehicle System by Ensuring Stability

This research article presents the reduction of higher order electric vehicle systems (HOEVS) utilizing Routh-Padé approximation along with higher order systems (HOS's) and its reduce order model's (ROM's) time moments (TMs) and Markov parameters (MPs) matching. The denominator of the reduced order EV model (ROEVM) is ascertained with the help of the Routh table. The Routh approximation method is exploited to ensure the stability of desired ROEVM. The matching of TMs and MPs determines the unknown numerator coefficients of ROEVM. The fifth-order HOEVS is reduced to first and second-order ROEVMs. The compatibility of obtained ROEVMs with respect to HOEVS is proved with the help of step, impulse, Bode and Nyquist responses. The comparative analysis of ROEVMs is also incorporated with the help of time domain specifications and error indices presented in tabular form.