Miracle-dz/UniFAR-FTUs huggingface dataset:
It Is About Weather: Explainable Machine Learning for Traffic Accident Understanding

Road traffic accidents cause injuries, claim lives, and disrupt economic activities. It is among the key problems for intelligent transportation and smart cities. Cities, especially the mega ones, must strive for reducing accidents for public safety and sustainable growth, and the first task is to understand accidents. In this paper, we build up such understanding with the emerging explainable machine learning (ML) technique. We prepare a huge dataset with over two million accident records and use it to deliver ML models for accident modelling. Given ML models of high fidelity for mapping accident features and conditions to the accident severity, we apply several explainable ML techniques to explain the models and understand accidents. We first consider coarse granularity to capture the overall feature importance. Then we consider fine granularity methods including partial dependence plot and Shapley additive explanations. The former shows that feature impact varies at different feature values and uses a plot to reflect the impact changes. The latter puts attention to individual accident and quantifies the feature impact for the specific accident. Our core observation is that traffic accidents are often about weather, followed by location and road type. Extensive experimental study is performed to support our discussion and justify our conclusion. The deliverable of this paper offers an advanced way of understanding traffic accidents accurately in a quantitative manner and has great potential to be used for intelligent transportation and smart city applications.
Development and Validation of a BCI-Enabled Boccia Ramp for Sport Participation

We present a brain-computer interface (BCI) system designed to enable individuals with severe motor disabilities to play Boccia, a Paralympic sport. Boccia is a precision sport in which the objective is to get a ball as close as possible to a target. In its most adapted form, Boccia allows for the use of a ramp to assist the user. The proposed system consists of a BCI-enabled ramp that can be controlled by the user's brain signals using a visual control paradigm (i.e., P300, or SSVEP). We developed a software interface using custom tools in Unity and Python for the front-end and back-end, respectively. To validate the software, we tested the system with five subjects who performed six pipelines (three with P300 and three with SSVEP) to simulate real-world use. Each pipeline consisted of 10 guided selections in the software. The classifiers used Riemannian geometry and shrinkage linear discriminant analysis (sLDA) for P300 and canonical correlation analysis (CCA) for SSVEP. The results showed that the P300 $(93\pm 3\ \%,\ \text{mean} \pm \text{SEM})$ paradigm had higher classification accuracy than the SSVEP $(27\pm 0.02\%, \text{mean} \pm \text{SEM})$ paradigm. Additionally, we designed and built a 3D CAD model and a hardware prototype of the ramp. The hardware prototype uses linear actuators to change the incline of the ramp and the height of the ball. Stepper motors allow for the rotation of the ramp and the release mechanism of the ball. Recommendations on improvements to the hardware and software components are made for future prototypes. The presented system opens new possibilities for sports applications that can improve the quality of life of people with severe motor disabilities.
MAJPC: A Multi-Agent Reinforcement Learning-Based Joint P2P Energy and Carbon Trading Mechanism for Intelligent Buildings

Multi-energy system (MES) has garnered a great deal of attention at the district building level owing to its advantages of unlocking the system flexibility using the coupling across multiple energy sectors. However, the flexibility of MES may not be fully exploited if it is not being used properly. Peer-to-peer (P2P) energy trading that allows intelligent buildings as an individual to engage in flexible energy trading with each other, is regarded as an attractive and productive mechanism to benefit the economic perspectives. On the other hand, introducing emissions trading schemes (ETS) to decarbonize building energy management can additionally enhance the utilization of such highly flexible MES. To approach the above two perspectives simultaneously, this paper raises a Joint P2P energy and Carbon emissions trading mechanism for a building community. In this setup, each individual building is treated as an agent and then models the trading mechanism as a Multi-Agent reinforcement learning paradigm, named as MAJPC. Specifically, an abstracted critic network is introduced by involving the extra information of community net energy and carbon emission during the centralized training process. In experiments, we also compare MAJPC with the other two baseline mechanisms, i.e., multi-agent peer-to-grid energy trading (MAP2G) and multi-agent peer-to-peer energy trading(MAP2P). Experimental results obtained in real-world situations show that MAJPC outperforms MAP2G and MAP2P in realizing economic and environmental benefits, agnostic to the RL algorithm types such as A2C, DDPG and PPO.
Novel Automatic Clustering Algorithm Based on Proximity Graphs for DNA Microarray Data

DNA microarray (gene microarray or DNA chip) can provide a vast resource of medical diagnostic and disease understanding. The clustering of DNA microarray data has become more and more important in the filed of data mining. Sometimes, the understanding of the correct behavior of genes is hard because of the large amount of data. Especially the research on high-dimensional data cluster boundary detection has not yet entered a mature stage. To obtain better clustering results and clear cluster boundary, we proposed a genetic clustering algorithm based on the shortest path, referred to as NAP3M (Novel Automatic Multi-step Maxmin and Merging algorithm based on Proximity-graphs) clustering algorithm. The experimental result shows that our NAP3M is effective and promising.
A Smote-Based Churn Prediction System Using Machine Learning Techniques

Customer churn is the occurrence in which consumers stop utilizing a company's products or services. It is critical for businesses since it has a direct influence on revenue and long-term viability. Customer churn detection is crucial in the telecommunications industry, as it helps maintain existing customers by identifying factors like competitor offers and network faults. This loss significantly impacts customer lifetime value, future revenue, and service length. Companies use predictive models to forecast client attrition, reducing the industry's profitability. This paper proposes the use of machine learning to anticipate customer turnover in the telecom industry. It involves exploring datasets, dealing with missing data, and producing new features. Features are chosen and categorical variables are encoded. A pipeline employing SMOTE and RandomUnder Sampler is used to address the issue of unbalanced classes. Several algorithms' ROC curves, including Logistic Regression, KNN, Decision Trees, Random Forest, XGBoost, and LGBM, are presented after they have been trained and assessed using metrics. This study proposed the use of LGBM with SMOTE as it gives highest accuracy of 82.92%.
Beyond Within-Subject Performance: A Multi-Dataset Study of Fine-Tuning in the EEG Domain

There is a critical demand for BCI systems that can swiftly adapt to a new user and at the same time function with any user. We propose a fine-tuning approach for neural networks that serves a dual purpose; first, to minimize calibration times through requiring considerably less data - up to one-sixth - from the target subject than training from scratch, and second, to alleviate cases of user illiteracy by providing a substantial performance boost of over 11% in absolute accuracy from the features learned from other subjects. Ultimately, our adaptation method surpasses standard within-subject performance by a large margin in all subjects. We present ablation studies across three datasets, in which we demonstrate that fine-tuning outperforms other adaptation methods for BCI systems and that what matters most is the quantity of pre-training subjects, rather than their BCI-ability, achieving over 8% absolute increase in classification accuracy when scaling up the order of magnitude. Finally, we compare our approach to the state-of-the-art in EEG-based motor imagery and find it comparable, if not superior, to methods employing far more complex neural networks, obtaining 82.60% and 85.64% within-subject accuracy in the four-class BCIC IV-2a and binary MMI datasets respectively.
Assessment of Forecasting-Aided State Estimation Under Measurement Errors and Topology Changes

The level of penetration and uncertainties that come from distributed energy resources (DERs) and advanced controllable loads complicate obtaining the real-time states of power systems. Power system operations require an accurate state estimation to ensure efficient monitoring and controlling of the system behavior. One of the promising algorithms that can enhance the accuracy of distribution system state estimation is Forecasting-Aided State Estimation (FASE). However, the robustness of FASE needs to be evaluated and assessed against measurement errors, the presence of Photovoltaics systems (PV), and topological changes. This study was conducted on an IEEE 33 bus test system. FASE shows that this algorithm performs well against measurement errors; however, it suffers against inaccurate topology estimation and in the presence of PV systems. Incorporating topology error detection and PV system output forecast is suggested to enhance the performance of FASE.
Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep Learning Approaches

This article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elaborate their respective performance. Both methods leverage neural networks to facilitate navigation. The first approach employs a deep convolutional neural network to identify suitable areas for forward movement, while the second method utilizes the drone’s front camera to capture images, which are then processed by a generative network to generate corresponding depth images. Importantly, both approaches rely solely on an RGB camera, resulting in cost savings for the robot and reduced energy consumption. The training data used for classifying collision-free and appropriate paths were gathered from simulated environments closely resembling real indoor settings. The proposed model calculates the probability associated with each segment of the input image, enabling the selection of the most viable area for drone flight. Incorporating generative networks as a depth estimation module enhances flight accuracy, making navigation in uncharted environments more straightforward. The accuracy of depth estimation was evaluated using standard criteria, achieving an accuracy rate of $84 \%$. This result underscores the effectiveness of the proposed method.
Underwater Wireless Optical Communication: Channel Modeling of Multiple Scattering in Presence of Chlorophyll Concentration Variations

The communication qualities of underwater wireless optical communication systems (UWOC) are severely limited by attenuations caused by absorption and scattering of the seawater. Hence, analyzing the underwater wireless optical channel induced by absorption and scattering is of great practical value for the design and deployment of underwater wireless optical communication systems. Currently, the research on underwater scattering channels typically considers horizontal links, where the absorption and scattering effects in the seawater can been set to constant parameters. However, as for the vertical link, the chlorophyll concentration varies with the depth of seawater, which results in variations of absorption and scattering effects with the increment of link lengths. Consequently, this paper proposes a chlorophyll-induced seawater channel model and analyzes the absorption and scattering effects in the seawater using the Monte Carlo method. The simulation results show that as the increment in the seawater depth will increase the peak of channel impulse response (CIR) and probability density function (PDF), resulting in lower bit error rates (BER) for the UWOC system. Under the same depth and link distance conditions, the performance of the vertical downlink is slightly better than that of the horizontal optical link.
Arc Welding Process Monitoring Using Neural Networks and Audio Signal Analysis

Summary: This paper investigates the potential of airborne sound analysis in the human hearing range for automatic defect classification in the arc welding process. We propose a novel sensor setup using micro-phones and perform several recording sessions under different process conditions. The proposed quality monitoring method using convolutional neural networks achieves 80.5% accuracy in detecting deviations in the arc welding process. This confirms the suitability of airborne analysis and leaves room for improvement in future work.
A Novel and Adaptive Evaluation Mechanism for Deep Learning Models in Medical Imaging and Disease Recognition

Recent advancements in deep learning (DL) and machine learning (ML) algorithms have led to their extensive use in various fields, including healthcare, finance, image processing, etc. However, selecting the most appropriate evaluation metrics for these algorithms remains a challenge. In this paper, we propose a novel evaluation metrics mechanism that takes into account the problem domain and the specific application area. The proposed mechanism involves randomly assigning weights to each metric for each dimension, applying a correlation operation to measure the degree to which these variables are related, and repeating this process for all stages. The resulting matrix is then used to calculate the final Performance Measurement Matrix (PMM) vector that reflects the most optimal measurement metrics. Our proposed mechanism provides a systematic and objective approach to selecting evaluation metrics for DL and ML algorithms, and can be applied to a wide range of applications. We demonstrate the effectiveness of our mechanism using a case study on medical image processing applications.
Comparison of Control Structures for Fault Current Injection and Energy Balancing in MMC-HVDC

To comply with recent grid code requirements and maintain the internal energy balance of modular multilevel converter (MMC), a careful selection of fault current injection (FCI) methods and control structure design for FCI and energy balancing are required. Although FCI methods applied to voltage-source-converter (VSC) can also be used for MMC, how to integrate them with the total energy balancing remains unclear, especially for flexible positive and negative sequence control (FPNSC). Besides, the participation of AC-side power in total energy balancing causes an interaction between FCI and energy balancing, requiring a control structure that can mitigate it. This paper compares the effect of two FCI methods on grid code compliance, analyses three control structures for integrating FCI with total energy balancing, and discusses the impact of AC-side power-sharing ratios. It was found that FPNSC has better performance on the remaining active power after AC-side faults occur. Inserting the output of total energy balancing after the FCI current calculation can reduce the interaction between FCI and energy balancing control loops, while selecting AC-side power-sharing ratio carefully is essential to avoid overcurrent in this structure.
Multi-Hybrid STL-LSTM-SDE-MA Model Optimized with IWOA for Solar PV-Power Forecasting

The solar power forecasting has its importance in renewable energy future. In fact, there are infinite challenges behind accurate forecasting of solar PV power. This state-of-the-art research work proposes a hybrid model brings in the key strengths of Seasonal-Trend decomposition layer (STL), Long Short-Term Memory (LSTM), Stochastic Differential Equation (SDE), and Moving Average (MA) methodologies. So as to capture the complex patterns and dynamics of solar PV-power data. To add more concentration on accuracy forecasting the model is optimized using the Improved Whales Optimization Algorithm (IWOA). IWOA is an advanced optimization algorithm inspired by the social behaviour of humpback whales, known for their collaborative hunting strategies. The IWOA brings in improved accuracy to the model by enhancing search capabilities and convergence speed. The proposed hybrid STL-LSTM-SDE-MA model, optimized with IWOA, is evaluated using real time solar power data. The model's superiority in forecasting accuracy and robustness is proved by Comparative analyses with other forecasting approaches and conventional models. This optimized hybrid model represents a cutting-edge technological solution for solar PV-power forecasting and making it a valuable asset in shaping a greener and more resilient future.
An Oscillator with Inductively Coupled Resonators for Readout of Stretchable Resistive Strain Sensor

This paper proposes a wireless and stretchable sensor which uses an oscillator with an inductively coupled resonator. By delving into the malleable nature of these cutting-edge electronics, we determine the most effective circuit parameters to optimize signal behavior for specific applications. The findings showed that the simulated frequency shift sensitivity to variations in sensor resistance in a simple NIC based oscillator circuit could be accurately modeled. The simulation was then confirmed by measurement with a reader circuit inductively coupled to a stretchable sensor. Further resistive strain experiments revealed a frequency shift of 132 Hz per 1% of strain in the sensor resistor, which is potentially high enough to measure and detect any physical phenomena that can induce strain on a human body, even down to the extremely small artery deformations caused by a pulse wave from the heart.
NUMERICAL MODELING OF POLAR LOWS OVER THE BARENTS SEA: IMPACT OF WRF PARAMETRIZATIONS ON THE QUALITY OF FORECAST.

Polar lows are generally characterized by severe weather in the form of strong winds, showers and occasionally heavy snow, which have sometimes resulted in the loss of life, especially at sea. Numerical simulations with mesoscale atmospheric models is a good alternative to investigate polar low phenomenon, because they produce temporally and spatially regular-spaced fields of atmospheric variables with high resolution. To describe the evolution of atmospheric processes the Advanced Weather Research and Forecasting (WRFARW) model was used. The principal objectives of this study were 1) the understanding of mesoscale WRF model and adapting the model for the Barents Sea region; 2) to conduct numerical experiments using WRF model with different Planetary Boundary Layer parameterization (PBLs) schemes and investigate the impact of each scheme on the quality of forecast; and 3) the investigation of the capability of WRF model to successfully simulate evolution of polar lows. The impact on the quality of forecast was investigated. The results of the study, obtained by numerical modeling of polar mesoscale low over the Barents Sea. One polar low, near Spitsbergen, from 24 of March to 26 of March 2014 were targeted. The results of numerical experiments showed that each of Planetary Boundary Layer parameterization scheme isn't successful for simulation of polar low.
Heuristic Navigation Model Based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations

Efficient power inspection is crucial for maintaining a stable power system. During an inspection, unmanned aerial vehicles (UAVs) usually need to be recharged due to the wide geographical range of inspection and the limited battery capacity of UAVs. This limitation makes the problem more challenging that requires not only optimizing the task execution order, but also taking the chargings of UAVs into consideration. In order to address this complex problem, this work first formulates the UAV power inspection planning problem with charging stations. After that, we propose a new heuristic navigation model, in which UAVs can follow a heuristic rule to decide where to go next based on both its own information and task-related information. To obtain the heuristic rule, we design a set of features to describe the status of the UAVs and task completion. Then a genetic programming (GP) algorithm is introduced to evolve and get the heuristic rule. Finally, by applying heuristic navigation rule, the UAV navigation model can automatically prioritize task and charging order, and generate UAV flight routes that satisfy all constraints. The experiment results show that our method significantly outperforms the state-of-the-art algorithms.
Research on Virtual Network Embedding Model and Algorithm Based on Graph Attention Network and Multi-Layer Perceptron

Currently, 5G networks are widely used, and 6G network research has attracted much attention, but the development of hardware networks is slow, and network virtualization techniques need to be studied to achieve resource sharing, in which the virtual network embedding problem is the key challenge. In this paper, a virtual network embedding algorithm model with graph attention mechanism and multi-layer perceptron is proposed based on graph attention mechanism and multi-layer perceptron, which is used to determine the weights between nodes by introducing the attention mechanism to measure the relationship between nodes, and through simulation experiments, it can be verified that the models and algorithms proposed in this paper effectively reduce the degree of resource fragmentation, and at the same time improve the acceptance rate of virtual network requests.
Enhanced Dynamic Performance with MPC Controlled SMES-based Virtual Inertia Control

Renewable energy sources (RES) are gaining increasing importance due to the limited availability of fossil fuels. RES are interfaced to the grid through power electronic converters (PEC). As RES penetration grows, the power system’s inertia decreases due to the absence of rotating mass. This necessitates using advanced technologies to achieve and maintain frequency stability in power systems. This study proposes Superconducting Magnetic Energy Storage (SMES) based Virtual Inertia Control (VIC) in conjunction with a Model Predictive Control (MPC) controller. Leveraging the benefits of SMES technology, the VIC system proves to be highly effective in stabilizing system frequency. A comprehensive mathematical model is developed to facilitate the implementation of the proposed control scheme in the simulation platform. The results demonstrate that the proposed control scheme outperforms others in reducing the frequency deviation of a power system heavily integrated with RES. Moreover, the control scheme proves its stability and effectiveness when subjected to load demand and RES perturbation. The proposed approach continually minimizes frequency deviations during disturbances by combining the SMES-based VIC with the MPC controller, ensuring a stable and resilient power system. This study highlights the potential of SMES technology and advanced control techniques to address the challenges associated with increasing RES integration, paving the way for a more sustainable and efficient power grid.