Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmented Reinforcement Learning with Efficient Social-Based Motion Prediction for Autonomous Decision-Making

This paper presents an approach that improves the efficiency and generalization capabilities of Reinforcement Learning-based autonomous vehicles operating in urban driving scenarios. The proposed method introduces an Efficient Social-based Motion Prediction module, which predicts the future positions of vehicles within the scenario. These predictions serve as input to a Reinforcement Learning-based Decision-Making module, responsible for executing high-level actions. The Proximal Policy Optimization algorithm is employed to develop our approach. We conduct experiments in an unsignalized T-intersection scenario using the SMARTS framework, comparing our approach with and without the proposed state representation, as well as against various baseline methods. Through this study, we demonstrate that our approach achieves performance improvements, particularly in scenarios involving high velocities. Our code and qualitative results are available at https://github.com/Cram3r95/argo2goalmp.
New Induction Motor Fault Detection Method at No-Load Level Employed to Start Diagnosis of Anomalies

Among the techniques widely employed to analyse the induction motor health condition is the motor current signal analysis, which effectively extracts the characteristic frequencies related to fault at a high load levels. However, this technique failed to detect faults at low and no-load levels. Moreover, when using an inverter to feed the induction motor, it is challenging to detect faults even at high load levels. In this context, the present work aims to propose a new fault detection method, which helps to determine the health state of this engine at no-load condition through a scatter plot of positive current sequence in terms of zero sequence after making Fortescue transformation. Then, Artificial Neuron Network (ANN) uses as an input only one statistical feature to the identifier and classifier of different induction motor conditions. The practical results are interesting, promising and effective to apply in real industrial sectors.
Axle Surface Defect Detection Based on YOLOv8

In the field of high-speed trains, axles are vital components. Their life and conditions are directly related to the safety of train operations, so it is necessary to carry out regular maintenance on them. This paper presents a method for detecting axle surface defects using the YOLOv8 deep learning algorithm. The method solves the common problem of manual involvement in accurately locating axle surface defects in high-speed trains, and it overcomes traditional algorithms' limitations regarding recognition accuracy and efficiency. The proposed method promotes automation in axle surface defect detection. Through 200 epochs of training with an axle surface defect dataset, the concluding mAP50 (mean Average Precision at Intersection over Union threshold of 0.5) reached 89.94%. Further, the mAP50:95 (mAP at a higher IoU threshold of 0.95) hit 50.37%. The mAP50 improved from 61.78% to 89.94% compared to model validation on the Coco dataset. These results imply that the model effectively detects axle surface defects, indicating its potential to meet rigorous industrial precision detection standards.
Underwater Thruster Stripe Detection based on Principal Component Analysis and Deep learning

This article proposed a stripe detection method of underwater thruster image based on principal component analysis (PCA) and deep learning. Firstly, the principal component analysis method is used to denoise underwater thruster image, and the denoised image is taken as the input of the trimmed convolutional neural network similar to VGGNet. Then, the output value is obtained after network training by using the depth learning method, and the stripe are obtained. The experimental results have shown that this method can effectively extract underwater thruster stripe features, and the stripe detection results are clear and coherent.This method can be used to detect faults in underwater thruster.
Garment collar recognition based on improved Mask RCNN network

As one of the essential components of clothing, the collar is a factor that people pay close attention to when selecting clothes, and it also affects the overall visual effect of clothes, so the classification and recognition of clothing collar styles has important practical value. In this paper, we use Mask RCNN, a widely used target detection algorithm in the field of deep learning, to classify and recognize clothing collar styles on the clothing collar style dataset we participated in the production of, and improve the network on this basis. Firstly, data enhancement is performed on the clothing collar styles dataset, which alleviates the overfitting problem to a certain extent, and also improves the accuracy and robustness of the model; ResNet50, the original feature extraction network, is replaced with ResNeXt101, which improves the ability of the network to extract features; FPN, the feature fusion network, is replaced with PAFPN, which alleviates the problem of ineffectiveness of detecting small targets; and the improvement of the In addition, the non-maximum suppression method (NMS) used in filtering candidate frames is improved to enhance the accuracy of target detection without affecting the computing speed. The experimental results show that compared with the primitive Mask RCNN, the improved algorithm has greater improvement in the tasks of clothing collar recognition and segmentation.
Development of Heading Lock and Inverse Kinematics for an Unequal Degree Holonomic Transporter Robot

Internet of Things (IoT) technology is the optimal method for transporting products in a cyber-physical system (CPS) to accomplish Industry 4.0 solutions for the moving of mechatronic components in manufacturing facilities. Hence, the transporter robot communicates with all other manufacturing process tools in order to accept and execute any tasks. The omnidirectional mobile vehicle has proven that it performs better in crowded environments and confined aisles. The use of omnidirectional wheels provides simplifications compared to previous mobile robot architectures, which makes the proposed architecture more effective for mobility. Transport area restrictions in the manufacturing line require the development of a reliable and efficient robot transporter. To accomplish space efficiency, we propose a holonomic omnidirectional wheeled transporter robot platform with an unequal offset degree. The DC brushless motor position is adjusted to ensure the transporter robot is able to move in balance. The effectiveness was tested through experiments using a transporter robot platform prototype that generates a minimum error in heading and orientation about 2.2%.
Anomaly Transformer for Vibration Data Analysis: Case Study on CWRU Dataset

Time series anomaly detection has a wide range of applications in various fields of society, one of which is the detection of anomalies in vibration data. Abnormal vibration data often indicates the existence of potential disasters or abnormal operation of equipment, so timely detection and identification is important for the prevention of earthquakes and improvement of industrial production efficiency. In this paper, an unsupervised time series anomaly detection algorithm, Anomaly Transformer, is used to detect anomalies in CWRU vibration datasets.Anomaly Transformer leverages the powerful characterization capabilities of the Transformer network and an improved anomaly attention mechanism to capture anomalies in the time series. Experimental results show that Anomaly Transformer exhibits excellent performance in the CWRU vibration dataset, achieving high precision and recall. In addition, Anomaly Transformer demonstrated a low false alarm rate, indicating its high accuracy in recognizing real anomalies. Future research can further explore the application of Anomaly Transformer in other domains with deeper optimization and extensions.
CalibTower: Automatic Camera-Lidar-GNSS/INS Calibration Based on Low-Cost Infrastructure

We propose an automatic infrastructure-based calibration approach for vehicular sensor systems. The approach is flexible to calibrate the extrinsics between multiple cameras, multiple lidars and a GNSS/INS. It supports sensors of various field of views (FOVs) and does not rely on overlapping FOV. The calibration is executed for all sensors in a unified single framework and does not require pairwise calibration between sensors. This approach is convenient to calibrate vehicular sensor systems of large sizes, e.g., autonomous driving trucks. The proposed approach novelly designs a low-cost calibration target object, namely CalibTower, which composes multiple highly reflective fiducial tags. A vehicular sensor system moves around CalibTower to collect calibration data. Our approach first stitches the pointcloud map of the scene from lidar data sequence and calibrates the extrinsics between lidars and GNSS/INS. We then propose a novel approach to accurately detect the fiducial tags from the stitched pointcloud. With the corresponding tags detected in both pointcloud and images, the extrinsics of cameras are then calibrated. Our experiments show this approach practically effective when calibrating complex vehicular sensor systems with various setups.
A ScPSO based Multi-Objective Approach for Optimal Placement and Sizing of multiple DSTATCOMs in Unbalanced Distribution Network

Optimal DSTATCOM placement and sizing are crucial for power loss reduction, voltage profile improvement, balancing voltage, and economic burden alleviation on utility. Practical distribution networks are often unbalanced and finding optimal solutions with integrated devices requires an efficient algorithm. The standard PSO and ABC algorithms are slow and often stuck in local optima. This paper proposes a multi-objective Scout based PSO (MO-ScPSO) approach that integrates Scout phase of ABC into PSO. The approach is applied to achieve optimal DSTATCOM placement and sizing with the objective of reducing total real power loss, enhancing voltage profiles and mitigate voltage unbalance in the network and thus improving total annual cost savings. Simulations are conducted on the 25 – bus and IEEE 37 – bus test systems, demonstrating the proposed approach’s robustness and fast convergence rate.
Should Smart Homes Be Afraid of Evil Maids? : Identifying Vulnerabilities in IoT Device Firmware

The Internet of Things (IoT) revolution has transformed everyday consumer objects into interconnected, intelligent devices. Due to historically weak security designs, these devices are susceptible to compromises with far-reaching consequences. This paper aims to evaluate the current state of IoT device firmware security and assess the effectiveness of existing methods in safeguarding sensitive data. We conducted a manual analysis of 16 IoT devices, revealing a range of firmware management techniques, each varying in their effectiveness against an evil maid attack scenario. Out of the 16 devices, only 2 showed evidence of encrypting data at rest, despite 10 having crypto-enabled hardware. Additionally, 9 out of 16 devices possessed secure boot-enabled hardware, but 4 of them did not properly utilize or implement secure boot. Consequently, 13 devices were identified as vulnerable to sensitive data compromise, and 11 were at risk of firmware modification attacks. To address these critical security gaps, this study proposes a method for analyzing data-at-rest encryption and secure boot status in IoT devices. In this paper we shed light on the prevailing security shortcomings and provide practical analysis techniques to foster improved IoT device security and user data protection.
Comparative Study betwenn P&O, IncCond and MSC algorithmes for MPPT Control of PV systeme

PV energy is a valuable source, however devices must be operated at its MPP to maximum efficiency. The complexity of this task arises from the nonlinearity exhibited by the current-voltage characteristics of photovoltaic systems, which is impacted by factors such as temperature and radiance conditions.The aim of this investigation is to assess the efficacy of a SMC-based MPPT technique for a boost-type DC/DC converter. The focus is on assessing the ability of this technique to maintain stability and optimal performance in the face of rapidly changing sun radiances. The resilience of the controller is evaluated by comparing it with the P&O approach and IncCond method. This evaluation is conducted using Matlab/Simulink to model the PV-MPPT system.
Spatial-Temporal Graph Discriminant AutoEncoder for Traffic Congestion Forecasting

Traffic congestion is a growing issue in modern cities, with significant negative impacts on the environment, the economy, and people's daily lives. Accurately predicting congestion is crucial for effective road control and route planning, making it an essential component of intelligent transportation systems. In this paper, we propose a novel algorithm, the Spatial-Temporal Graph Discriminant Autoencoder (STGDAE), for improving congestion prediction. STGDAE combines graph convolution layers and recurrent neural networks to extract spatial and temporal features from traffic data efficiently. We introduce a distance loss term to improve the autoencoder's feature extraction effectiveness and utilize labels to retain more useful information for congestion prediction. Our extensive experiments on two real-world datasets demonstrate that STGDAE outperforms state-of-the-art methods, achieving an improvement of 0.1 in F1 score on the PeMSD8 dataset. The proposed algorithm has promising potential for improving traffic management in real-world scenarios, such as reducing travel times and fuel consumption and enhancing road safety.
Digital Twin-based Aerial Mobile Edge Computing System for Next Generation 6G Networks

The rapid advancement of communication technology has driven up increasing demand for next generation sixth-generation (6G) networks to meet ultra-low latency and high connectivity specifications. In this context, unmanned aerial vehicle (UAV)-based mobile edge computing (MEC) has recently emerged as an attractive prospect by providing low latency with faster tasks offloading at the edge of the network. On the other hand, the incorporation of the digital twin (DT) concept into the aerial MEC paradigm can enable intelligence, adaptability, and efficiency, making it a potent and innovative solution for the forthcoming ultra-dense heterogeneous 6G networks. Therefore, to provide high network performance to real-time and latency-sensitive applications in 6G systems, this paper offers an innovative DT-based aerial MEC architecture that employs UAVs as aerial base stations complemented by MEC features. Moreover, an adaptive task offloading (ATO) algorithm is also developed which can provide dynamic resource allocation and intelligent decision-making ability. Additionally, simulation-based mathematical assessments further demonstrate the proposed model’s superior performance compared with conventional UAV-based MEC, highlighting its potential in shaping the future of 6G networks with enhanced scalability and higher efficiency.
Radio Channel Predicted Model of Deployed Cellular Network in Algiers City

The study of the radio environment characteristics is necessary to enhance the precision of the cellular network mobile station localization. In this work, we use a linear regression technique based on the ordinary least squares approach to illustrate a radio channel model customized for the cellular network radio environment of Algiers, Algeria. We use the Log-normal, K-factor, and Okumura-Hata models. Real RSSI measurements were made on a deployed cellular network with the aid of special software made for this purpose. The generated findings can be verified by computing the square root of the mean square error between the real data, the radio measurements, and the predicted data created from the used model. The new model could improve the efficiency of the cellular network radio systems deployed in Algiers.
Maximum Consensus based Localization and Protection Level Estimation using Synthetic LiDAR Range Images

A core task of autonomous vehicles is the ego-localization. On the one hand, it needs to be accurate and robust towards outliers, and on the other hand, it needs to provide a high integrity. For an accurate localization, usually the likelihood as the most common objective function is maximized with respect to the sensor measurements. More precisely, based on the assumption of normally distributed random variables, usually a least squares minimization is conducted, e. g. in a recursive manner in a Kalman Filter. However, this approach severely lacks robustness since least squares is inherently sensitive to outliers, which, using LiDAR data, arise in a high proportion due to changing environments and dynamic traffic participants. Therefore, more robust loss functions have been introduced with the outlier count, also referred to as maximum consensus optimization, as the most robust one since the estimation result is independent of the outlier magnitude. Whereas localization itself has been investigated thoroughly, localization integrity, which describes the ability of a system to correctly and timely warn the user when specific error limits are exceeded, has received comparatively few attention in the domain of autonomous vehicles. In this paper, on the one hand, we propose a robust localization approach based on the maximum consensus criterion to ensure a high reliability even in challenging environments, and on the other hand, we propose an estimation of a grid-based probability distribution as a first step towards a new integrity framework for LiDAR based localization. Both outcomes are based on the same pipeline using GPU computed range images for the expected measurements. The introduced localization and probability estimation are tested for structured, semi-structured, and unstructured environments using overall around 2,500 epochs from two different LiDARs.
Performance of Hybrid Stacking and Bagging Methods Based on Machine Learning Algorithms in the Classification of Dengue Fever Incidence Rate

Dengue Hemorrhagic Fever (DHF) is a disease that occurs every year. Bandung City is one of the regions in Indonesia with a high incidence rate of dengue fever cases which continues to increase from 2017 to 2021 at 49.66%. Therefore, this study aims to combine the machine learning algorithms, such as Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest with the Hybrid Stacking and Bagging method to improve accuracy in the number of DHF cases dataset from the 2017 to 2021 in Bandung City. The results show that the accuracy using Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest algorithms are 85.50%, 61.30%, 77.40%, and 80.60%, respectively. The accuracy increases after applying hybrid approach. The bagging method gets 88.70% accuracy, by using stacking method, the accuracy increased to 90.30%. The stacking method gets precision, recall, and f1-score of 90.62%, 90.94%, and 90.65%, respectively. From the results obtained it is evident that the hybrid approach by combining machine learning algorithms can improve classification accuracy results. The contribution of this research is to help the government in predicting and classifying the incidence rate of dengue fever to reduce the rate of cases in each region.
Human-like Guidance with Gaze Estimation and Classification-based Text Generation

Car navigation systems are widely used and essential for driving assistance. However, drivers often struggle to understand voice guidance from these systems, leading to the need for constant map-checking on a monitor, which can be dangerous. In contrast, human guidance utilizing visible objects is clearer to drivers. Human-like Guidance is a task that realizes such human-like navigation on a system. In this paper, we propose a novel method for Our approach involves defining human-like navigation templates and selecting appropriate sentences for each object in an intersection scene. We also construct a model to estimate the driver's gaze and use this information to choose a reference object for navigation, resulting in a system that provides clear guidance to the driver. Furthermore, we provide a gaze information dataset called the Driving Gaze Dataset to build a driver gaze estimation model. Through experiments using the CARLA automated driving simulator, we demonstrate the feasibility of generating navigation instructions that drivers can intuitively understand. In addition, we confirmed that our method is able to generate navigation quickly. This research is expected to mitigate risky driving caused by navigation systems while driving.
Analysis of Serious Games for Nutrition Using NLP Techniques

Serious games, which are games that are designed not just for entertainment, are commonly used to teach important concepts such as a new language, or other specific subjects such as healthy nutrition. Serious games can be a fun and interactive way to educate people about healthy eating habits which can go a long way to address health challenges such as obesity. These games are typically available to users on the app store such as the Google Play Store where users can also write reviews based on their experience playing the game. The reviews are a rich source of information and feedback for game developers and other stakeholders. Analyzing the sentiments and emotions expressed in such reviews can help developers gain insights into how players perceive a game’s effectiveness in achieving its goals and aims. This can aid the development of future releases of the game where issues are fixed and improvements made. To contribute to research in this area, ten games are analyzed for sentiments and emotions using Natural Language Processing techniques. Our results show that all but one of the games have more positive sentiments than negative sentiments. In addition, some of the popular emotions we identified include admiration, approval, amusement, and love. To support game designers and other stakeholders in gaining useful insights from our results, we developed a tool in the form of an interactive web page where users can view the sentiments and emotions expressed in the reviews of each game including the raw reviews. This can help game designers and other stakeholders explore reviews of users in one place.
Harnessing the Power of GPT-3 and LSTM for Natural Language Processing in Agricultural Product News: Focus on Soybeans

In recent years, there have been remarkable advancements in deep learning and natural language processing (NLP) techniques, leading to significant improvements in extracting valuable insights from textual data. However, applying these techniques effectively to domain-specific content, such as agricultural news, presents unique challenges. In this study, we investigate the utilization of GPT-3 (Generative Pre-trained Transformer 3)’s impressive language understanding capabilities in conjunction with LSTM (Long Short-Term Memory) networks, which are well-known for their ability to capture sequential dependencies. Our objective is to enhance the processing and analysis of soybean-related news headlines. Through our experimental analysis, we demonstrate that the fusion of GPT-3 and LSTM models results in a substantial improvement in the accuracy and effectiveness of sentiment analysis specifically in the context of agricultural product news. This research contributes to the expansion of knowledge and paves the way for advancements in the field of emotional analysis within the context of soybean-related news.