Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of Textural and Petrographic Attributes of several Cretaceous Sandstones, Southern Nigeria

Textural and petrographic attributes of sandstones of several Cretaceous formations of southern Nigeria sedimentary basin have been evaluated. Sandstones of the first sedimentary cycle (Albian-Santonian) confined mainly to the Abakaliki basin are feldspathic and those of the second sedimentary cycle (Campanian-Eocene) confined mainly to the Anambra-Afikpo basin are quartz arenites. Nearness of the Abakaliki basin to the source region of Cameroun basement complex was the governing factor in the survival and accumulation of feldspars in the sand of the first cycle. Although detritus of the second cycle was derived from the same source region, it underwent intense chemical weathering in a humid climate and during long transportation to the Anambra site of deposition, and thereby attained a high degree of mineralogical maturity. In both cases, duration of erosion played a critical role in the presence or absence of feldspar in sediments. Quartz arenites with low textural index are perhaps indicative of a miogeosynclinal deposition whereas texturally mature quartz arenites are characteristic of cratonic or stable platform. Fragmentation of large polycrystalline quartz grains into finer subindividuals in a dynamic environment is advanced as an adequate mechanism to account for the origin of fine and silt-size particles in a sedimentary system.
Validation of Photovoltaic Inverter Controllers Using Automated Tests and Hardware-in-the-Loop

INMETRO Ordinance No 140 establishes a set of requirements for connecting photovoltaic inverters to the electrical grid. Therefore, the firmware of these devices needs to be properly designed. As a result, it is important to test the inverter and its control systems to make sure they meet the regulations for grid connection. Therefore, this work proposes the implementation of controllers and grid connection functionalities for a photovoltaic inverter, which will be validated through automatic tests using hardware-in-the-loop.
Design of Automatic Identification System of Electrical Measuring Instrument Based on Artificial Intelligence Technology

With the rapid judgment of equipment running faults in substations, the previous manual inspection, meter reading and recording modes can no longer meet the requirements of substation management and production efficiency, and the automatic counting technology of instrument indication is directly related to the development process of substation automation and informatization. In order to improve the reliability of power grid transaction data, aiming at the problem that the current measurement error correction methods of power measuring instruments have poor measurement data error verification ability, resulting in a big difference between error correction results and ideal results, a measurement error correction method of power measuring instruments based on artificial intelligence technology is designed. In order to build a more intelligent substation, more and more inspection robots are used to detect instruments in substations. According to the EM-KF principle of artificial intelligence technology, combined with the types and characteristics of error data, the error correction of measurement data is completed. The calibration experiment link of electric power measuring instruments is constructed. Through the measurement of multiple environments and experimental conditions, it is determined that the data obtained after using the new error correction method is closer to the ideal data, and the use effect of this method is good.
Analysis on Simultaneous Localization and Mapping of Mobile Robot Based on Two Multi-sensor Fusion Algorithm

This paper studies the problem of simultaneous localization and autonomous mapping of mobile machines, based on multiple sensor fusion SLAM algorithms. This method can solve the problem that the feature information of the collected image is not accurate or lost when a single laser or vision sensor is used in simultaneous localization and mapping (SLAM) research of mobile robots, which leads to localization failure. First, combining LIO-SAM algorithm and LVI-SAM algorithm, the simulation and construction of large scene maze environment are carried out for the mobile robot equipped with Lidar and depth camera. Then through the experimental simulation, the map built by the two algorithms is compared and analyzed. The results show that the map built by LID-SAM algorithm is more accurate in the maze environment with fewer feature points, and the map building effect is obviously better than that of LVI-SAM algorithm.
Islanding partition method based on chaotic OPL-GWO algorithm under local grid fault

In order to further improve the efficiency and reliability of the local power grid in the process of fault recovery, the local power grid with high proportion of distributed generation is divided into islands based on the chaotic OPL-GWO algorithm. Firstly, the optimal-worst method is used to evaluate the load to obtain the load weight value, and the priority of important load restoration considering island partition under local power grid fault is determined. Secondly, the load priority is considered to determine the load level weight coefficient value, so as to construct the objective function model of island partition of local power grid with distributed generation. Then, in order to obtain better solution results of the objective function, chaotic opposition-based learning (OPL) is introduced to optimize the initial population of the grey wolf algorithm (GWO), so as to obtain better solution results. Finally, the modified IEEE69 node is taken as an example for simulation analysis. The simulation results show that the proposed method can accurately realize the optimal strategy of island partition under local grid fault, ensure the restoration of power supply for important loads, and verify the effectiveness and superiority of the method.
Forecasting of Energy Demand for Electric Vehicles using Machine Learning Techniques

The growing adoption of electric vehicle demands the forecasting of accurate energy requirement to improve the quality of scheduling and resource allocation, thereby reducing the charging costs and mitigating grid stress. This study presents a comprehensive analysis of machine-learning models for forecasting the per-hour energy demand of Electric Vehicle (EV) charging stations. In this research, three machine learning models are investigated: Prophet, Random Forest Regressor, and Light Gradient Boosting, utilizing the Adaptive Charging Network dataset (ACN) dataset for predictions. The primary objective is to assess the performance of these models in short-term load forecasting. The tests reveal that the Random Forest Regressor model outperforms the other models, demonstrating remarkable accuracy in the prediction of per-hour energy demand. The outcome of this study can be utilised to make informed decisions for efficient resource management and enhanced grid stability.
A Framework to Improve the Comparability and Reproducibility of Morphing Attack Detectors

Morphing Attack, i.e. the deception of Face Recognition Systems (FRS) through a face morphing process between the identity of two subjects with criminal intent, has recently emerged as a serious security threat. Due to its significance, recently several Morphing Attack Detection (MAD) systems, i.e. methods based on Artificial Intelligence able to automatically detect the presence of morphing, have been proposed in the literature. Unfortunately, developing, comparing, and reproducing these MAD algorithms is challenging, particularly for deep learning-based solutions, since they are usually evaluated on private datasets and the source code is not publicly released. Therefore, we observe the need for an open-source framework that aims to simplify the development of new MAD systems, in combination with their evaluation. Thus, in this paper, after a discussion about the current limits of existing studies on the MAD task, we examine the desired properties and features of this framework, with a particular focus on its modularity, usability, and effectiveness.
Mobile Robot Path Planning Based on Improved A* Algorithm

To solve the problems of A* algorithm, such as excessive number of redundant nodes, slow search time and sharp turn of path. a new approach is proposed: a five-neighborhood search A* algorithm that incorporates dynamic weighting in the heuristic function and utilizes second-order Bezier curve for path smoothing. Through the simulation environment built by PyCharm, it can be seen that the number of redundant nodes and the search time of the A* algorithm after dynamic weighting of five neighborhoods are reduced by 86.57% and 82.07% compared with the traditional eight-neighborhood search algorithm, and the cost remains the same in the precision of ten thousand bits. the path obtained after Bezier curve processing has no large angle turning points. After smooth optimization, the continuity of speed and acceleration can be maintained during the driving process, and the driving fluency can be improved.
A power line fault cause identification method based on CNN-BiLSTM and attention mechanism

To accurately identify various power line fault causes such as lightning strike, bird pest, and crane touch line, A power line fault cause identification method based on convolutional neural network-bidirectional long short-term memory network and attention mechanism (CNN-BiLSTM-SE) is proposed. Firstly, the measurement data after a fault occurs in the power line, i.e. the three-phase voltage, three-phase current, zero-sequence voltage, zero-sequence current and the calculated harmonic data, are used as fault characteristic data samples. Secondly, considering that the combination of convolutional neural network, bidirectional long short-term memory network and attention mechanism has advantages of processing complex non-stationary time series data and enhancing data correlation, a power line fault cause identification model based on CNN-BiLSTM-SE is constructed. Finally, the fault cause identification model based on CNN-BiLSTM-SE is trained and tested by using the power line fault measurement data samples. Results show that the average accuracy of the power line fault cause identification model proposed in this paper exceeds 99%, which verifies the effectiveness and accuracy of the proposed method.
Rolling and Day-Ahead Forecasting of Electricity Market Prices: Evaluating the Performance of DL Models with Bayesian Optimization

This paper investigates the application of deep learning (DL) algorithms for short-term electricity market price forecasting which is crucial for cost-effective operation of power systems. We propose four DL models that combine different architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This further involves a Bayesian Optimization (BO) method for hyperparameter tuning of the two best models to ensure optimal performance, as these models are sensitive to hyperparameters. Moreover, to evaluate the performance of the model, this paper also presents rolling forecasting using the LSTM model. Forecasting capability of the DL models is evaluated by using accuracy measures such as mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). Further, the DL models are also compared with our previous work. This paper utilizes the hourly data of prices and loads acquired from the PJM electricity market. Our findings indicate that CNN+LSTM and CNN+BiLSTM models outperform the other models for day-ahead forecasting, whereas BiLSTM and CNN+BiLSTM models show better performance for week-ahead price forecasting.
Challenges and Experiences of Iranian Developers with MLOps at Enterprise

Data is becoming more complex, and so are the approaches designed to process it. Enterprises have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. This research explores the challenges and experiences of Iranian developers in implementing the MLOps paradigm within enterprise settings. MLOps, or Machine Learning Operations, is a discipline focused on automating the continuous delivery of machine learning models. In this study, we review the most popular MLOps tools used by leading technology enterprises. Additionally, we present the results of a questionnaire answered by over 110 Iranian Machine Learning experts and Software Developers, shedding light on MLOps tools and the primary obstacles faced. The findings reveal that data quality problems, a lack of resources, and difficulties in model deployment are among the primary challenges faced by practitioners. Collaboration between ML, DevOps, Ops, and Science teams is seen as a pivotal challenge in implementing MLOps effectively.
Non-Linear Programming Based Optimal Power Flow (N-OPF) for Bulk Transmission Grid: Modeling and Comparative Study

Optimal power flow is generally formulated as a non-convex and non-linear programming (NLP) problem. Traditionally, the OPF problem is solved in transmission systems through approximations and/or relaxation techniques. Such methods lead to infeasible and inaccurate solutions. This paper provides a numerical analysis of the performance of different NLP OPF formulations, such as the Interior Point Method (IPM), Sequential Quadratic Programming (SQP), and Alternating Direction Method of Multipliers (ADMM) in terms of, feasibility, optimality, and scalability. The performances are evaluated using small (IEEE 9 bus and 14 bus), medium (IEEE 30 bus, 39 bus, and 57 bus), and large (IEEE 118 bus) test cases.
Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)

With the rise of driving automation, optical sensors like cameras and LiDAR are playing a crucial role in vehicle perception. However, these sensors face challenges from harsh environmental conditions, including extreme temperatures and weather effects like fog and precipitation, which degrade their performance due to particle scattering. Consequently, significant efforts are being made to understand, model, and mitigate these effects. In this work, we address the reverse research question and demonstrate that the precise present precipitation state in form of the particle size and velocity distribution can be predicted using degraded Automotive LiDAR measurements and spatio-temporal 3D convolutional neural networks. By that, this approach provides a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for weighted heterogeneous sensor data fusion and adaptive filtering during data pre-processing.
A Doubly Grounded Boost Seven Level PV Inverter Topology with Model Predictive Controller

Leakage currents reduction and elimination process is important task during the development of photovoltaic (PV) inverters and their associated control and modulation schemes. Compared to existing solutions, doubly grounded (DG) PV inverters represent vital solution for total cancellation of existing common mode voltages and hence their resulting leakage current components. This paper presents a new DG PV inverter topology with boosting capability of PV voltages, outputting seven level output voltage, having continuous current from PV side, and possessing reduced component count and elements. Moreover, a model predictive controller (MPC) is proposed for the proposed topology for controlling multiple objectives without the need for cascaded linear control loops, and with achieving fast tracking and response. The simulation results are provided in this paper with performance comparisons with existing DG PV inverter topologies in literature. The results also prove superior output and control characteristics of the proposed topology with reduced control complexity and reduced component counts.
Low-Contrast SSVEP Stimuli to Improve User Experience of Brain-Computer Interface Involving Virtual Reality

From gaming to robotic device control to interaction with virtual devices, Steady State Visually Evoked Potential-based Brain-Computer Interfaces (SSVEP-based BCIs) have proven their potential and relevance. Although neuropsychological field also benefits from this technology, most research has focused on developing SSVEP-based BCIs for communication and motor recovery purposes. Despite previous works, SSVEP-based BCI involving virtual reality (VR) still requires 2D high contrast flickers with uniform 3D environments which can be ergonom-ically challenging for users. To address this, we have developed a new SSVEP-based BCI paradigm involving VR that provides an immersive, engaging environment with more complex visual elements than previous works. Using this tool, we conducted a pilot study testing flicker frequency-modulated VEP on 3D models (represented as animals) with low amplitude depth and reproducing previous results. Our new setup improves portability, reduces cost and visual fatigue while keeping a promising performance. Our new paradigm opens the way to more complex and ecological paradigms for future applications combining VR, BCI, and SSVEP paradigm.
Multi-View Fusion Networks for Multimodal Fake News Detection

The proliferation of multimedia fake news misleads social opinion, damages social harmony, and seriously challenges the authority of news media. Current multimodal fake news detection methods focus on extracting better unimodal features using pre-trained models and developing complex networks for visual and textual feature fusion, mostly ignoring social contextual information. In the text, we present a novel Multi-View Fusion Network (MVFN) for fake news detection, which attempts to achieve fusion learning of heterogeneous multimodal features. The model not only explores cross-modal graph interactions between visual regions and sentence words, but also investigates the effect of social context on feature learning. In addition, we consider the alignment relationship of features in both perspectives for better feature fusion. Experimental results on Chinese and English datasets show that the proposed model outperforms current multimodal detection approaches.
Constraining coral sensitivity to climate and environmental change: an integrated experimental approach

1 CHAPTER 1 . 4 1.0 INTRODUCTION . 4 1.1 Coral Sensitivity and Adaptability . 6 1.2 Seawater Carbonate Chemistry and the Calcification of Marine Organisms . 10 1.2. 1 Coral Calcification . 12 1.3 Incorporation of trace elements in the coral skeleton . 16 1.4 Boron Isotope Systematics in Marine Carbonates . 20 1.5 Thesis outline . 24 CHAPTER 2 . 25 2.0 CORAL PROTO – FREE OCEAN CARBON ENRICHMENT SYSTEM (CP-FOCE) EXPERIMENTAL DESIGN . 25 PH HOMEOSTASIS DURING CORAL CALCIFICATION IN A FREE OCEAN CO2 ENRICHMENT (FOCE) EXPERIMENT, HERON ISLAND REEF FLAT, GREAT BARRIER REEF . 28 ABSTRACT . 29 SIGNIFICANCE STATEMENT . 29. 29 SIGNIFICANCE STATEMENT . 29 2.
Visual Place Recognition for Water Environment Based on Semantic and Sequential Constrains

Recognizing places in water environment poses unique challenges due to the scarcity of distinctive features, significant variations caused by weather conditions, and the presence of moving objects. Existing visual place recognition (VPR) methods designed for land-based scenes often yield suboptimal results when applied to water environment. To address these difficulties, this paper proposes a visual place recognition method that leverages semantic and sequential constraints. The model incorporates a semantic constraint algorithm that effectively mitigates the issue of invalid and interfering features by directing focus towards key features. Additionally, a descriptor matching algorithm is introduced, which utilizes sequential constraints and hierarchical matching of descriptors to better capture and adapt to temporal information. Through comprehensive training and testing on real-world image sequences of marine scenes, the proposed method outperforms other state-of-the-art VPR algorithms. It achieves substantial improvements of 0.09, 0.22, 0.32, and 0.15 in Recall@1 compared to the second-best models, demonstrating its superior performance in water scene recognition.
Timing and magnitude of early to middle Holocene warming in East Greenland inferred from chironomids

Much of Greenland experienced summers warmer than present in parts of the early to middle Holocene, during a precession‐driven positive anomaly in summer insolation. However, the magnitude of that warmth remains poorly known, and its timing and spatial pattern are uncertain. Here we describe the first quantitative Holocene palaeotemperature reconstruction from central East Greenland based upon insect (chironomid) assemblages preserved in lake sediments. We postulate that landscapes like our study site, characterized by minimal soil and vegetation development through the Holocene and thus little influenced by some important secondary gradients, are especially well suited to the use of chironomids to reconstruct Holocene temperatures. The inferred timing of warmth at our study site near Scoresby Sund agrees well with other nearby evidence, including glacial geological reconstructions and temperatures inferred from precipitation isotopes at Renland ice cap, supporting the use of chironomids to reconstruct temperatures at this site. We infer highest temperatures from c. 10 to 5.5 ka, followed by gradual cooling after 5.5 ka and progressively colder and less productive conditions after 3.5 ka. Models based upon two independent training sets yield similar inferred temperature trends, and suggest an average summer temperature anomaly from c. 10 to 5.5 ka of 3 to 4 °C relative to the preindustrial last millennium. The estimated overall rate of Neoglacial cooling averaged over the period from 5.5 to 0.5 ka was 0.6 to 0.8 °C per thousand years, more than twice the rate previously estimated for the Arctic as a whole. Given strong apparent spatial variability in Holocene climate around the Arctic, and the utility of palaeoclimate data for improving climate and ice‐sheet models, it should be a priority to further quantify past temperature changes around the margins of the Greenland Ice Sheet, where few quantitative reconstructions exist and future warming will affect global sea level.
Real-Time Location-to-Image Generative Adversarial Networks with Sparse Sensor Data

The prevailing machine vision-based system monitoring faces challenges in complex optical environments, such as insufficient lumination or over-exposed light, which results in a severe reduction in the accuracy of visual surveillance. To fill the gap, this work proposes a novel dual generative adversarial networks model for real-time location-to-image transformation from the sparse distributed positional sensors. The conditional generative adversarial network (cGANis applied to learn high-dimensional features from pre-generated images for enhanced accuracy. The cycleGAN, with improved structure loss and key point loss functions, further improves the generation accuracy. The effectiveness of the proposed method is verified by real monitoring experiments designed to collect a few training data for both sensor data and real images. Experimental results show that the proposed method outperforms the mainstream cGANs variants in terms of overall generation quality and detail edge sharpening.