Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of V2G, V2H and FCR to Electricity Costs of Households with Varying Primary Heating Sources

Beyond their primary function, electric vehicles (EVs) can support power grid stability and provide EV owners both savings and extra income through different demand response programmes and bidirectional charging. This research compares the financial benefits of implicit demand response, vehicle-to-grid (V2G), vehicle-to-home (V2H) and frequency containment reserve (FCR) in detached houses with varying primary heating sources. The approach involves mixed-integer linear programming (MILP) and the use of real-world data. The findings suggest that the combination of V2H and V2G offers the largest financial benefit, with yearly electricity bill savings reaching 10 % for a typical household. Largest percentual savings were reached by oil heated households, with largest monetary benefit gained by direct electric heated households.
An Adaptive Semi-Active Suspension System for Design of Vehicle Stability Control System

In this paper, an adaptive semi-active suspension system by adjusting air springs is proposed to design of vehicle stability control system, which can achieve handling stability. In order to develop the adaptive control algorithm, the CarSim dynamic model is acquired, and a reinforcement learning scheme using the deep deterministic policy gradient (DDPG) control strategy is used to decide the best amount of air spring pressure. This method is able to adjust the yaw activity to avoid understeer and oversteer issues as well as increasing the vehicle stability. In summary, the simulation results show that the handling stability is improved by 25.78% under high road curvatures.
LSTM Model and Data Segmentation Collaboration A Deep Learning Strategy for Traffic Flow Anomaly Detection Using SUMO Simulated Data

To address the escalating challenge of traffic congestion and the associated difficulties in obtaining specific congestion data sets, this paper explores the use of deep learning strategies to determine traffic flow conditions using traffic flow data generated by urban traffic simulation software (SUMO). This paper describes an approach that combines LSTM and data segmentation to convert SUMO data into a general model format. This conversion is compatible with LSTM-based models originally trained on SUMO traffic flows. The paper confirms through rigorous quantitative analyses that the use of SUMO-generated traffic flows in the field of traffic flow prediction has the advantages of data purity, ease of collection and ubiquity over the use of real traffic flows and verifies that there can be the same correspondence mapping between the real-world dataset and the SUMO dataset. We have also investigated the robustness and generality of the model, demonstrating its ability to adapt to different traffic scenarios, and its ability to identify anomalous traffic flows by 89.5% under experimental conditions, which is 8.1% higher than the model based on the NGSIM dataset (81.4%). This work highlights the promise of using simulation software to generate datasets, combined with deep learning approaches, to provide practical and scalable solutions to support data-driven traffic management decisions. In general, this paper propose the use of simulation software with data segmentation mapping to provide a solution for the research and analysis of regional traffic in circumstances where unavailability and realistic pristine datasets are not obtainable.
Multi-Scale Classroom Behavior Recognition Study Based on CA-YOLOv7

The identification and study of classroom behaviors is very important for evaluating the quality of teachers' teaching as well as students' engagement in class. Compared to elementary, middle and high school classrooms, in a university lecture setting, teachers have more teaching tools, students carry electronic devices and have more diverse body movements. Therefore, we constructed a dataset of dense crowds from real classroom videos. Meanwhile, for classroom behavior recognition in a dense classroom and multi-scale student group environment, general deep learning target detection models cannot well effectively incorporate global information such as the overall human profile. In this paper, based on YOLOv7 network, the CA (Coordinate Attention) attention mechanism module is added to improve the detection ability in the case of occlusion as well as the absence of the human body picture through the position information and the channel attention mechanism. The experimental results show that the model achieves a mean average precision (mAP) value of 71.5% on the self-built RUSCB-dataset classroom behavior dataset, which is a 1.3% improvement over the baseline model, and the inference time is 56ms, proving that the model enhances detection accuracy without compromising inference speed. This enables effective detection of students' classroom behaviors in real classroom environments.
Early Detection of Alzheimer's Disease Based on Deep Learning Approach Using Brain MRI

Alzheimer's disease is one kind of neurological condition for which people gradually decrease their mental capacity and memory. The number of Alzheimer's patients nowadays is rising quickly, which is quite concerning for the advancement of humanity. The scenario is much more terrible because this illness has no cure. A professional neurologist needs to examine medical imaging techniques including PET scans, CT scans, and MRIs for diagnosing an Alzheimer's patient. This procedure is time- and resource-intensive. Many people hesitate to treat Alzheimer's disease because of this instability. The potential for early detection of Alzheimer's disease might be achieved by the use of Magnetic Resonance Imaging (MRI). However, the MRI dataset has an issue in class imbalance problem. To address the problems in the dataset, a data balancing technique called SMOTETomek is used and then a lightweight CNN model is developed that uses MRI images as input and determines a patient's stage. In terms of recall, precision, accuracy, ROC curve, and f1 score, the performance of the CNN model has been compared with a few transfer learning algorithms. Therefore, using the proposed model, each Alzheimer's patient may determine what stage of the disease they are now in and take further actions to lower the mortality rate of Alzheimer's patients. The model under consideration demonstrates superior performance when compared to previous models, with an accuracy rate of 97.50%.
Hate Speech Detection in Indonesian Song Lyric Using BERT

This study was conducted with the aim of detecting hate speech in song lyrics. The background of this study stems from the numerous negative impacts caused by hate speech. We focus on detecting hate speech in song lyrics because music is a popular form of art that can have significant impacts on people’s lives. There have been many previous studies that attempted hate speech detection using various methods. However, one issue still persists, especially when dealing with slang or uncommon sentence structures. According to the previous works, deep learning algorithms, such as using pre-trained BERT model for sentiment analysis and R-CNN as a classifier, have shown promise in addressing these issues. Hence, we decided to utilize the BERT pre-trained model for sentiment analysis and R-CNN as the classifier in their study. The dataset used in this research was collected from the Twitter dataset on Kaggle and also utilized the Spotify API. The study resulted in an precision score of 100% a recall score of 37% and an F1-score of 54% for the hate speech detection. Our result suggests that more relevant data, such as datasets consisting of poetry or sentences with implicit meanings, will be beneficial to enhance hate speech detection in song lyrics.
Steel Surface Defect Detection Method Based on Improved YOLOv4-tiny

As small-scale targets on steel surfaces are prone to miss detection and the current mainstream target detection algorithms have the problems of large number of parameters and computational complexity, an improved YOLOv4-tiny method for steel surface defect detection is proposed. The CARAFE lightweight universal upsampling operator is used instead of upsampling to improve the feature fusion capability; the residual feature enhancement structure ASPP_ASF+ is introduced to capture multi-scale contextual information by parallel sampling with the null convolution of different expansion coefficients, and then the feature weighted fusion is realized by the ASF+ component to improve the detection accuracy of small targets; the Mish activation function is used to increase the nonlinear expression and enhance the network generalization capability. In the NEU_DET dataset, the improved YOLOv4-tiny network model improves the mAP by 6.17% compared with the original model; in the VOC2007 dataset, the mAP improves by 3.37%. The experimental results show that the improved algorithm can effectively improve the detection ability of the model and is generalizable.
Deep Learning-Based Real-Time Hand Landmark Recognition with MediaPipe for R12 Robot Control

In the evolving world of robotics, the way humans and robots interact plays a crucial role in the overall system performance and user experience. Traditionally, robotic control for accomplishing tasks have been dominated by programming languages like ROBOFORTH or touchpad interfaces. However, these methods often create barriers to user interaction. This study explores the potential of hand gesture recognition, specifically focusing on the deep-learning MediaPipe library’s real-time hand-landmark detection capabilities. We present a system that combines MediaPipes abilities with controlling the ST Robotics R12 robot, allowing real-time hand gestures to be translated into robotic drawing actions and tasks. This integration offers a natural and interactive communication medium that could revolutionize how humans carry out tasks with robots. By leveraging MediaPipes computer vision and deep learning techniques, we successfully controlled the R12 robot through gesture-based interactions. The system effectively translated hand gestures into robotic drawing functions and specific tasks such as object relocation. However, future improvements should concentrate on enhancing precision, reevaluating reliance on ROBOFORTH and exploring MATLAB Simulink’s potential for advanced gesture-based control algorithms.
Towards Explainable Road Navigation Systems

Road navigation systems are important systems for pedestrians, drivers, and autonomous vehicles. Routes provided by such systems can be unintuitive, and may not contribute to an improvement of users' mental models of maps and traffic. Automatically-generated explanations have the potential to solve these problems. Towards this goal, in this paper we propose algorithms for the generation of explanations for routes, based on properties of the road networks and traffic. We use a combination of inverse optimization and diverse shortest path algorithms to provide optimal explanations to questions of the type “why is path A fastest, rather than path B (which the user provides)? ”, and “why does the fastest path not go through waypoint W (which the user provides)? ”. The explanations reveal properties of the map–such as speed limits, congestion and road closure–that are not compatible with users' expectations, and the knowledge of which would make users prefer the system's path. We demonstrate the explanation algorithms on real map and traffic data, and conduct an evaluation of the properties of the algorithms.
Long-term localization with map compression based on solar information

In this paper we address visual based localization in outdoor environments where the appearance changes dra-matically. Such environmental changes result in a substantial transformation of the visual information of the scene, producing a significant impact on the visual based localization performance. Hence, these changes can lead to major difficulties when associating data between the current image and the landmarks in the map. One solution for this problem is to keep adding landmarks to the map in order to cover various environmental conditions. However, this solution leads to a continued growth of the map, which in turn, will result in a costly and resource-intensive localization. In this paper we present a map management approach in which we exploit information related to the suns position to compare resemblance between the traversals in the map and maintain a diverse map that incorporates a minimum amount of data and ensures a reliable localization in different environmental conditions. We evaluated our approach on a dataset that incorporates more than 100 sequences with different environmental conditions and we compared the obtained results with a state of the art approach.
A Real-Time Rain Detector Using An Electroda Probe On A Tipping Bucket Rain Gauge

A tropical country such as Indonesia experiences higher rainfall than other countries. Indonesia's BMKG, which monitors meteorology, climate, air quality, and geophysics, measures rainfall. They are currently being placed at certain points in Indonesia using manual tipping buckets. The problem with this tool is the inability to send data in real time. Our research involves creating a microcontroller-based rain detection tool that is connected to a long-distance communication system (LORA). As a result, the sensor data will automatically be sent to the server. Test results show that this tool can detect rainfall with 99 percent accuracy, which is almost as good as existing tools, and can transmit rainfall data over almost a kilometer distance in 699 ms.
Unsupervised Traffic Sign Classification Relying on Explanatory Visible Factors

Intelligent behavior of autonomous vehicles must rely on the understanding of explanatory factors of the environment in order to operate safely. To implement such intelligence, machine learning models such as CNNs are used to identify objects such as traffic signs. However, the process behind arriving at a certain result is hard to understand. Explainable artificial intelligence has the potential to overcome this limitation by unveiling explanatory factors learned by models and hence, increase the reliability of recognition systems. Recent progress in explainable artificial intelligence motivates research in various fields, and their application must become a core part of intelligent transport systems. We present in this paper an explainable and unsupervised methodology for traffic sign classification. The proposed pipeline combines methods for explainable feature extraction as well as out-of-distribution detection, which were previously applied in anomaly detection and evolutionary biology. This pipeline learns explanatory factors of a traffic sign class, and models a classification function without knowing other classes. Our method is evaluated using the GTSRB as well as the Tsinghua-Tencent-100k dataset and compared to a deep learning counterpart, namely GANs. The results show that the presented methodology is feasible to classify traffic sign images, is explainable and outperforms deep learning-based models.
Hybrid MPC and Spline-Based Controller for Lane Change Maneuvers in Autonomous Vehicles

This paper presents a novel approach for collision detection and overtaking maneuver. The proposed technique is considered hybrid as it combines the smoothness of spline curves for nominal trajectories and the capabilities of Model Predictive Control (MPC) to respond to unexpected obstacles on the lane. To detect collisions, a temporal spline parameterization procedure is introduced. In the case of a possible collision, two decoupled longitudinal and lateral MPC controllers adjust the vehicle's path without modifying the nominal trajectory, using linear models to ensure short computation times. The complete control system has been implemented on Robot Operating System (ROS) using the hyper-realistic simulator CARLA, with successful results in overtaking scenarios.
Real-Time Backstepping Control Based MRAS Speed Estimation of an Induction Machine

This work presents a real-time evaluation of a sensorless MRAS-backstepping control applied to an induction process (IM). To run the system in safe mode, a backstepping method is first implemented. It guarantees decoupled torque and flux management of the IM without the need for an additional regulator by removing the proportional/integral controller (PI) and decreasing the number of gains that are highly reliant on the temperature-dependent IM parameters. The backstepping control is designed to attain the required speed and flux, and it is ideally suited for regulating an uncertain system that may be affected by external disturbances. Its formulation is based on the application of Lyapunov functions to assure the overall stability of the system. The second section presents a model reference adaptive system (MRAS) to estimate rotor speed and eliminate the sensor, the system's weakest link. Utilizing dSPACE 1104 cards, the suggested control procedure is evaluated. The achieved experimental results indicate that the entire system runs smoothly, with the proposed MRAS-backstepping providing high performance, the system successfully tracking the speed profile under tolerable load, and the system maintaining its dynamic behavior at different speed levels.
Memory-Enhanced Sequential Variational Transformer for Inferring Neural Population Dynamics

As deep learning continues to expand its applications in neuroscience, studies on the dynamics of neural populations such as LFADS seek to infer the latent factors from the high-dimensional sequential features. However, these methods neglect the effects of the historical states on the current states of neurons. Therefore, we propose an innovative memory enhanced sequential variational transformer (MESVT), which combines the current states of neural population features with the historical states through the cross-attention mechanism. Furthermore, we validate the performance of MESVT on two collected single-cell-level recording datasets. Utilizing MESVT, we analyze the potential dynamic encoding mechanism of neural populations in the VISP (Ventrolateral Intraparietal Sulcus) for visual stimuli.
Investigation of the Impact of Various Stator Slot Configurations on Permanent Magnet Synchronous Motors

Permanent magnet synchronous motors (PMSMs) are widely used for various industrial applications such as robotics, industrial drives, solar water pumping. Thus, a lot of research on the various performance of PMSM motors are ongoing. Some of the research includes rotor configuration, torque ripple, control techniques of PMSM and their magnetic design. The stator slot configuration has significant impact on the performance parameters of PMSM motors. Most importantly the torque profile and stator winding currents are highly influenced by the structure of stator slot. There are few research works are available in the exiting literatures which discuss about the impact of stator slot orientation on motor performances. In this paper, the impact of various stator slot configurations has been investigated on PMSM motors in terms of torque ripple profile, solid loss and stator winding current along with its total harmonic distortion (THD). Using the Ansys maxwell software package, a couple of 2D and 3D numerical models are designed and investigated by changing the stator slot geometry of PMSM motors, and the resultant torques and stator currents are recorded. This analysis could be a promising solution to the industrial engineers while selecting PMSM motors based on suitable stator slot geometry for specific industrial applications.
Monitoring Greenhouse Room Temperature with Dragino LoRaWAN Gateway DLOS8N

Internet of Things (IoT) integration enables the precise control of environmental conditions needed for crop cultivation in greenhouses. The primary focus of this study is on IoT applications for greenhouse automation. Special emphasis is placed on Long Range Wide Area Networks (LoRaWAN) and Low Power Wide Area Networks (LPWAN). LoRaWAN is the recommended technology because of its advantages, which include low power consumption, a large coverage area, and affordability. In this study, an IoT device network is established inside the greenhouse using LoRaWAN as the underlying technology. The information produced by these gadgets, like temperature readings, is effectively transferred from the greenhouse to a central gateway, where it is subsequently forwarded to the Antares server and made available via an easy-to-use web interface. The physical technology of LoRa ensures seamless communication within the LoRaWAN network, increasing the effectiveness of greenhouse monitoring. To ascertain how feasible this system is, extensive testing was conducted at various distances. The results demonstrated that between 50 and 100 meters, LoRa operated satisfactorily, maintaining a Signal-to-Noise Ratio (SNR) of greater than 11.875 dB. However, issues started to arise at the 500-meter range, where LoRa performance started to drop below 4.3 dB SNR. These findings offer a helpful benchmark for assessing subsequent efforts to expand the application and efficacy of LoRa in greenhouse settings. The ongoing search for optimal IoT solutions in agriculture could lead to the advancement of a technologically advanced and sustainable cultivation approach.
A Money Laundering Structure Detection Method Based on Incremental Transaction Analysis

Money laundering is the process of legitimizing dirty money through complex transactions, posing a serious threat to a country’s financial stability and national security. Nowadays, with the prevalence of organized money laundering, launderers prefer to use intricate multi-hop laundering chains to transfer dirty money. Moreover, they engage in normal financial activities to disrupt detection by auditors. In response to these trends and challenges, we propose a novel unsupervised money laundering structure detection framework for the anti-money laundering field, called structure incremental expansion (SIE). Our framework consists of two main modules: 1) Initialization of suspicious structures, which adopts a control-limit based method to identify suspicious accounts exhibiting anomalous transaction behavior. These accounts will serve as the starting points for suspicious structure expansion. 2) Dynamic structure expansion, where we design three dynamic membership functions according to the Financial Action Task Force’s definitions of the three stages of money laundering evolution. Newly-added incremental transactions in the network will be assigned to appropriate expanding suspicious structures. We conduct extensive experiments on simulated and public financial networks. SIE exhibits desirable performance and scalability. We also provide a detailed case study, visualizing a complete money laundering structure detection process, demonstrating our method’s strong interpretability.
Research on Crack Size Identification Method Based on Digital Image Processing Technology

Cracks is one of the common hazards in offshore structures, and continuous crack propagation can cause serious consequences such as steel structure fracture. The steel structure adopts a regular inspection method to detect structural cracks. Once cracks are found, emergency repair and maintenance work is carried out to ensure the safety of the structure. One of the keys of crack visual inspection is to accurately extract crack size from image. Based on digital image processing technology, the camera calibration, image processing and crack feature extraction were achieved by OpenCV and Skimage open source algorithm package, and then the crack pixel size was extracted. The crack pixel size is converted to the actual size by pixel calibration. The crack length inspection error is 4.8%, and the maximum width inspection error is 13.7%. The results show that the crack identification method has high crack inspection accuracy,which is worth spreading and applying.