Miracle-dz/UniFAR-FTUs huggingface dataset:
Steady State and Transient State Analysis of Buck-Boost Converter with Genetic Algorithm Optimized PID Controller

DC-DC converters are used in applications such as renewable energy electronic systems, dc motor drives, charge controller for battery, etc. In the proposed method, the Proportional-Integral-Derivative (PID) controller is applied to a Buck-boost converter operating in Continuous Conduction Mode (CCM). Since machine learning is becoming increasingly important in all fields of engineering and technology, and Genetic Algorithms (GAs) have a wide range of applications in machine learning and optimization problems, the proposed method employs a GA for the optimization of DC-DC converter control parameters. The GA is used in determining the optimized parameters for PID controller and Integral of Absolute Error (IAE) is considered as the object function to minimize the estimated error. The optimization of the PID controller is performed using MATLAB Simulink software. In the simulations, the output performance parameters such as rise time, overshoot, settling time, steady-state error are estimated and overshoot of output voltage for various source and load transient conditions are analyzed to determine the performance of the proposed system. The effectiveness of the controller has been tested in both Buck and Boost mode of operations through simulation experiments. The developed GA optimized PID has shown good performance both in steady-state and transient conditions.
Short-term Electricity Price Forecasting Using Interpretable Hybrid Machine Learning Models

In this paper, a combination of single and hybrid Machine learning (ML) models were proposed to forecast the electricity price one day ahead for the Nord Pool spot electricity market. The proposed models were evaluated based on performance metrics, such as Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). Further, a model interpretation by employing SHapley Additive exPlanations (SHAP) framework to show the impact of each feature in the forecasting output. Based on the SHAP, the lag electricity price EP(t-1) impacts the forecast result most, followed by EP(t-2) and time stamp, respectively. Finally, the results show that hybrid models performed better than single ones, where the LR-CatBoost model surpassed other models and attained 7.94 and 10.49, which are the lowest values of MAE and RMSE respectively. Moreover, the kNN and SVM models performed poorly, achieving the highest RMSE values of 12.88 and 12.39, respectively.
Finding Top‐k Answers in Node Proximity Search Using Distribution State Transition Graph

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large‐scale, mutable datasets and user queries, top‐k query processing has gained significant interest. This paper presents a novel method to find top‐k answers in a node proximity search based on the well‐known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.
A Review on Wireless Charging Methods, Coil Design, Applications, Optimization Techniques and Challenges of Electric Vehicle

This article provides an up-to-date overview of wireless charging technologies, coupler pad design, standards, and optimization strategies. First, the key qualities of an electric vehicle (EV) are explained. Wireless charging approaches and the kind of charging system that depends on vehicle location during charging have been described in detail. The many varieties of coupler pad designs are presented using a graphic and a table. The topics of application and standards are covered. Following that, some of the most often utilized optimization approaches for EV charging station sizing and location are examined. A comparative analysis of the optimization of various optimization techniques has been outlined in tabular form. The paper gave researchers and scholars a thorough understanding of the fundamentals of WPT and its mechanism of operation, with a focus on its explicit application in EVs.
Research on Indoor Mobile Robot Navigation System Technology

In order to enable mobile robots to build maps and path planning in indoor environments, we applied LIDAR and vision fusion methods for map building experiments through a combined indoor mobile robot navigation system, used visual SLAM technology and path planning technology and built a hardware and software platform, configured relevant function packages under ROS operating system, and conducted map building and path planning experiments in a real indoor static environment. The experimental results show that the indoor mobile robot has completed the localization, map building, and subsequent path planning and navigation of the indoor environment, and at the same time, the effectiveness and feasibility of the indoor mobile robot navigation system has been verified by estimating the camera pose based on static point features and static linear features.
Indoor Visual Odometry Algorithm Based on Adaptive Feature Fusion

An indoor scene has both texture-less regions and dense repeated texture regions. When the VO algorithm based on image feature points is adopted to conduct positioning and mapping, the positioning accuracy will be low. Even the tracking will be missed due to problems such as insufficient effective feature points and too many mismatches. In this paper, we proposed a visual odometry algorithm based on adaptive feature fusion to solve the problems. The algorithm extracts both line and point features and matches them precisely using GMS (Grid-based Motion Statistics) algorithm and RatioTest algorithm, respectively. Then the two features are fused as per adaptive thresholds. The algorithm in this paper is adopted in the visual odometry section of ORB-SLAM2, and the result obtained from 18 sets of TUM data set sequences with different structures, and texture features are compared with ORB-SLAM2. The experimental result shows that in the dense repeated texture regions, the absolute trajectory error of ORB-SLAM2 is decreased by 8.87%. In the texture-less regions, the absolute trajectory error is reduced by an average of 9.96%. The positioning accuracy and robustness are effectively improved thanks to the algorithm.
Based On Improved Yolov5 Mine Infrared Target Detection Algorithm

The environment under mine is dimly complex and has low visibility. The infrared detection equipment has a better target detection effect than visible light equipment in this environment. Therefore, the monitoring of personnel and vehicles by infrared thermal imaging monitoring equipment can accurately determine the vehicle-to-person position and generate an alarm through the edge end and embedded equipment, which can guarantee the safety of personnel. This paper addresses the problem of low detection accuracy in IR thermography, proposing to improve the detection of targets by the yolov5s network by adding a se-attention mechanism to the head of the neck network, improving the feature extraction capability of the network. Aiming at an embedded platform with limited resources, the lightweight convolution gsconv is introduced to replace the original convolution, being able to maintain accuracy while reducing the computational effort of the model. And to achieve mine car and human detection on the embedded side ends, the Yolov5s-gconv-se presented in this paper, with a map lift of 3.2% versus the original yolov5s and a frame rate of 2 frames per second, provides better accuracy for target detection.
Robust Time Series Forecasting via Time Differencing and Stacking

Future price prediction of stocks is a challenging task for researchers. Artificial intelligence and enhanced computing powers have shown to be successful in forecasting price of the stock. With the help of this study, we propose machine learning strategies that can be incorporated with stock market systems to help users get the stock's closing price the next day. In this study, the performance of the most popular and widely applied linear based models are compared like - Linear Regression, Polynomial, SGDRegression and Support Vector Machine, Regularized Models like - Lasso, Ridge and Elastic-Net Regression, and Robust Models like - TheilSen and RANSACRegressor on daily stock price data fetched from Zerodha with technical analysis as additional features and pre-processed with time differencing. We propose a novel architecture to robustly forecast time series data by stacking SGDRegressor, Polynomial Regressor and Support Vector Machine with meta model as Support Vector Machine with RBF kernel.
The design of optional two-part tariff for electricity transmission and distribution service based on the load factor

Power consumers don’t pay network access tariff in China currently, and proportion of capacity cost for transmission and distribution service recovered by fixed charges is relatively low in the existing two-part tariff, which both might lead to difficulty to recycle the cost. This paper uses the Bary curve to explain the relationship of load factor and coincident factor and its application in the allocation of capacity cost for users whose load factors are quite different. Based on the clustering of users by K-means algorithm according to their load factors, the parameter of Bary curve is estimated, and the allocation ratios of capacity cost between fixed charges and running charges are determined by drawing tangent lines to the curve for each user group. According to the allocation ratios, two-part tariff combinations for different load factors users can be obtained. Taking 10KV users as an example, an empirical analysis shows the rationality and feasibility of the model. The design of optional two-part tariff combinations can improve the allocation of capacity cost, and the users can achieve economic efficiency and fairness by matching their load factor, thereby elevate the coefficient of utilization of electricity equipment and cut down or put off the investment for power grid.
Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Bearings are important because they play a critical role in the operation of many machines and devices by reducing friction and enabling smooth motion between two moving parts. They are commonly used in rotating machinery, such as electric motors, turbines, pumps, and gearboxes, where they support and guide the rotating shafts. The primary cause of failure in wind turbines and induction motors is bearing faults, resulting in significant downtime and economic losses. Detecting these faults accurately and promptly is crucial to preventing unexpected shutdowns. Most of the existing methods for bearing fault detection rely on envelope analysis, which involves filtering and demodulating the raw signal, and Fourier analysis of the envelope signal to identify defect frequencies. Recent research has focused on improving the visibility of these frequencies by selecting an optimal band for filtering, using sub-band analysis and spectral kurtosis, which is a complex process and can be time-consuming. This paper presents a new approach to envelope signal analysis using texture analysis of the envelope signals that are projected as 2D grayscale images where each fault generates a unique texture. These textures are encoded using the local binary pattern operator and then used for fault classification. The method is tested on a publicly available seeded fault dataset.
A 99.22% Energy-saving and 86.91% Area-reduction Capacitor Switching Scheme for SAR ADCs

This paper proposes an energy-saving and area-reduction capacitor switching scheme for successive approximation analog-to-digital converters (SAR ADCs). The switching scheme combines top-plate sampling technology, a novel switching method and the MSB capacitor splitting technology to eliminate the power consumption of the first two conversion cycles. In addition, there is no energy consumption during the reset phase. This switching scheme reduces the area of the DAC capacitor array through C-2C technology. Compared with the traditional switching scheme, the proposed scheme achieves an area reduction of 86.91%. For the first N-1 conversion cycles of an N-bit SAR ADC, the positive and negative capacitor arrays of the proposed switching scheme switch symmetrically. The common mode voltage remains constant. LSB conversion cycle, positive and negative capacitor arrays are switched unilaterally. The 10-bit SAR ADC of the proposed switching scheme is modeled and simulated by MATLAB. The results show that 99.22% switching energy saving is achieved compared with the traditional switching scheme.
Detecting Privacy Policies Violations Using Natural Language Inference (NLI)

Data privacy laws like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) provide guidelines for collecting personal information from individuals and processing it. These frameworks also require service providers to inform their customers on how clients' data is gathered, used, protected, and shared with other parties. A privacy policy is a legal document used by service providers to inform users about how their personal information is collected, stored, and shared with other parties. It is expected that the privacy policies adhere to the data privacy regulations. However, it has been observed that some policies may deviate from the practices recommended by data protection regulations. Detecting instances where a policy may violate a certain regulation is quite challenging because the privacy policy text is long and complex, and there are numerous regulations. To address this problem, we have designed an approach to automatically detect whether a policy violates the articles of GDPR. This paper demonstrates how we have used Natural Language Inference (NLI) tasks to compare privacy content against the GDPR to detect privacy policies' text in violation of GDPR. We provide two designs using the Stanford Natural Language Inference (SNLI) and the Multi-Genre Natural Language Inference (MultiNLI) datasets. The results from both designs are promising as our approach detected the deviations with 76% accuracy
Computation Of The NTUA/LTT CompressorAnnular-cascade Flow Using TheMulti-dimensional Upwind Residual DistributionMethod

This paper extends to a rotating co-ordinate system the three-dimensionalsymmetric form, in space and time, of the Euler equations, previously reported by the authors, in an inertial frame of reference. This form of the Euler equations is discretized in space using the multi-dimensionalupwindresidualdistributionmethod. The integration of the cell residual and the evaluation of the distribution matrices is performed using the Roe’s parameter vector and a mean weighted square radius. An implicit time step is adopted to reduce the computational time required to achieve the steady-state solution. Numerical results are presented for the flow through the NTUA/LTT annular-compressor cascade in a rotating frame of reference.
Detection of low, small, and slow targets in the air based on improved YOLOv5

At present, the drone industry is very mature and has been widely used in many fields. While it brings us convenience, it also brings many hidden dangers. Since the target of the UAV is relatively small, the target detection effect of simply using YOLOv5 is poor. Based on this situation, a low-altitude UAV detection algorithm based on improved YOLOv5 is proposed. Targeted improvements have been made to YOLOv5's Backbone, Neck, and Loss. Experiments show that the new method achieves an accuracy of 0.918 and a mAP@. 5:. 95 of 0.529, which is significantly improved compared with the original.
An Approach for Prediction of Epileptic Seizures from EEG Signals through Analysis of Slow Components

An accurate and compact epileptic seizure prediction system would be significant to intractable patients. This paper researches on the forecasting of epileptic seizures based on scalp electroencephalography (EEG) recordings. The EEG signals can be regarded as a time series generated by a dynamic process. Considering the dynamic variation of the process, this paper puts forwards a novel approach employing slowness analysis that derives some latent variables. A Discriminative Slow Component Analysis algorithm is introduced to derive predictive features from EEG signals. Moreover, discrete wavelet transform is used to form epileptiform sub-waves that bring more predictive information. Finally, a cost-sensitive linear support vector machine is adopted to classify the preictal and interictal signals. Evaluated on a public EEG dataset, i.e., the CHB-MIT scalp EEG database, the proposed approach exhibited acceptably satisfactory performance.
Analytical Modeling of Three-Winding Tunable Common-Mode Choke

The common-mode (CM) noise is generally addressed using a CM choke due to its simple design and reliability. However, the performance of the CM choke degrades and the CM impedance starts decreasing after the resonant frequency. Hence, tuning the CM impedance of the CM choke is widely adapted by inserting an auxiliary winding on the CM choke core to alter the resonant frequency. Thus, in this paper, the analytical approach to estimate the CM impedance of the tunable CM choke with known external circuit parameters connected to the auxiliary winding terminals and vice-versa is presented. In addition, the estimated CM impedance profiles and the auxiliary winding parameters are simulated in MATLAB/Simulink and are validated through experimentation.
Overview of Power System Planning under Uncertainty

With the gradual increase of the proportion of renewable energy generation dominated by wind power and photovoltaic power and the change of load characteristics, the source-load uncertainty of power system will be one of the new power system characteristics with high proportion of new energy. Therefore, at this stage, the uncertainty method should be mainly used for power system planning. For the planning of power system under uncertain conditions, the uncertain variables that affect the planning of power system are first introduced, including new energy output and load forecasting, new factors of power system, investment cost, policy and environment. Then, the application of stochastic programming method in power system uncertainty planning is introduced, including the general scenario model of stochastic programming and the stochastic programming model of power system with opportunity constraints, and the probability prediction of uncertain variables in stochastic programming is introduced. Secondly, the application of power system robust optimization method in power system uncertain planning is introduced, including classical robust optimization method and distributed robust optimization method, and the interval prediction of uncertain variables in classical robust optimization method is introduced. Stochastic programming and classical robust optimization should study how to better describe the information of uncertain variables in the future. At present, the distributed robust optimization method should first solve the planning adaptability problem of high-proportion new energy power system. Finally, the future research on uncertain planning of power system is prospected.
Integrated Big Data Analytics Conceptual Framework for Information Sharing across the fast-moving consumer goods industry

Industrial revolution 4.0. (I.R.4.0) has augmented the number of cyber-physical systems (CSP) that generate. , store and process data at an unprecedented rate hence. , requiring big data (BD) and big data analytics (BDA) technologies. The FMCG consist of complex and dynamic entities. , wherein. , physical goods and information exchange occur and generate massive amounts of data. , classified as big data (BD). , which is siloed. , fragmented. , etc. and requires BD technologies to be processed. The absence of information integration and data defusion deprives the FMCG SC of opportunities to achieve and maintain balanced demand and supply. , reduce inventory holding costs and improve logistic efficiency. The utilization of technologies such as Hadoop. , etc. to process and integrate the massive amounts of BD provides streamlined information processes. , reduced operational costs. , information integration and fusion throughout the diverse SC to achieve accurate. , timely and reliable data/information was presented in this paper using a BDA conceptual framework.
Pulse Frequency Modulated Three-Phase Interleaved Boost-Integrated LC Series Resonant Converter

This paper proposes a pulse frequency modulated three-phase interleaved boost integrated LC series resonant converter, which consists of the boost-integrated unit and the LC series resonant unit. By changing the switching frequency, the output voltage can be controlled as a constant value under different input voltages. In a wide switching frequency range, the transformer design is dependent on the fixed LC series resonant frequency rather than the variable switching frequency. Moreover, the zero voltage switching of all switches can be guaranteed. The working characteristics of the proposed converter are presented in detail and well verified by the simulation results.
Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments.
ANISOTROPIC GAUSSIAN FILTERING FOR INFRARED IMAGE

To achieve much better noise suppression with minimum edge blurring, an adaptive anisotropic gaussian filtering algorithm was proposed. In this algorthm, the scale of filter’s long axes was determined by Intensity-Dependent-Spread model in each pixel, and the ratio between filter’s long axes and short axes was determined by the degree of local smoothness, then the orientation of the filter’s long axes was also calculated according to the gray gradient orientation in the position. The experimental result shows that the algorithm has good capability in noise suppression and edge-preserving.