Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological implications of the correlation of avian footprints with wing characteristics: a mathematical approach

The characteristics of avian wings that evolved for flying appear to show a distinct relationship to the shape of the pes and walking abilities as reflected in footprints. Wing area, wing span and body weight data of modern birds were collected and analysed in order to quantify the possible correlation, which was previously only inferred from empirical data. Discriminant analysis demonstrated that avian wings can be divided into three habitat groups, in a similar way to footprints. Multiple regression analyses revealed that the avian wing loading and aspect ratio were correlated with the parameters of footprint shape and can be expressed by a simple equation. The results may reflect the adaptation of avian locomotion to habitat. The relationships between wing area and wing span, and between wing area and footprint area, which are apparent in modern avians, were derived and used to estimate wing area and wing span from the footprints of extinct Cretaceous avian taxa. The estimated values of body weight, wing span and wing area suggest that the trackmakers of Archaeornithipus meijidei, Hwangsanipes choughi and Yacoraitichnus avis had bodies similar to herons (or cranes), large sandpipers (or small sea birds) and medium‐sized gull‐like birds, respectively.
Refined Feature Fusion-Based Transformer Network for Hyperspectral and LiDAR classification

Multimodal remote sensing images describe the same surface scene from different viewpoints, and can gain more reliable information about the earth's surface. As a result, fusion classification of multi-modal remote sensing images has gradually become a hot area of research in recent years. There is a large body of work on devising better fusion algorithms. However, if the features of single-modal data sources cannot be thoroughly explored, the classification performance of the network will be significantly degraded. In this paper, a refined feature fusion-based Transformer network(RFFT) for hyperspectral and LiDAR classification is proposed, which effectively compensates for the aforementioned shortcomings. In particular, a shallow specific feature extraction network(SSFE) is designed. It can use a more appropriate feature extractor to obtain the shallow feature of the individual sources. A refined spectral Tokenizer module (RSTM) is then proposed to finely explore richer unique spectral information through grouping and parallel perception. Finally, a spectral intergroup-spatial perception fusion(SISP) is built to capture global multimodal feature dependencies and perform a complete fusion. Experiments results on the public Houston2013 and Trento datasets show that the proposed RFFT performs well.
Design and Construction of Photovoltaic Preventive Maintenance Robotic for Roof-Top PLTS Installation

The decrease in irradiance level can be caused by dust and animal droppings adhering to the surface of the solar panel module. If the solar panel module is not cleaned regularly, environmental dust will accumulate on the surface of the solar panel module. Because of this dust, solar panels cannot work optimally. Solar panels will work optimally at a temperature of 25°C. The greater the temperature of the solar panel will have an impact on the power generated by the solar panel. Based on some of the existing reviews, in this Final Project a PLC-based automatic cleaning tool is designed with a cleaning method using pressurized water spray to reduce the temperature of solar panels and clean adhering dirt. The system that was initiated is to use 2 control modes using a PLC to be able to operate the system and use water as a cleaner. The highest power of the motor in this system is 4.44 Watt. With a speed of 30 RPM, the cleaning kit in 1x cleaning loop takes 2.24 minutes. The flow rate used is 14.33 m/s and the pressure is 46 PSI. With this flow rate, this technology is able to clean dust for 2.24 minutes or 1x loop, clean sawdust for 2.24 minutes or 1x loop, clean animal waste (rats) for 4.48 minutes or 2x loops and clean oil /oil for 8.96 minutes or 4x loops. The results of the visual test using the quantitative method showed that 53.6% of the assessors chose the cleaning result, which was very clean. And as many as 46.4% of assessors chose the cleaning result which was quite clean. Nobody chooses unclean. Thus, this cleaning technology is effective in cleaning dust and dirt attached to solar panels. The results of the power output test showed that solar panels using cleaning technology (array 1) increased the output power. The temperature of the array 1 solar panel is also lower than that of the array 2 solar panel.
Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation

Deep Learning methods for visual tasks have seen significant improvements in accuracy and resilience. To enhance their performance, many approaches now leverage multiple modalities or train models for various tasks concurrently. Datasets encompassing rich annotations and multiple modalities enable training and assessing a broader spectrum of methods and facilitate the development of more intricate models. However, a dilemma arises concerning dataset diversity and the richness of annotations. Datasets that support multiple tasks tend to be constrained to specific domains, while datasets encompassing diverse scenarios often concentrate on a single task with a single annotation type. Synthetic data offers a potential resolution to this challenge, combining the advantages of both approaches. However, current synthetic datasets are also subject to a similar trade-off, either having various annotations and modalities but limited domains (e.g., traffic scenarios) or extensive data variation with limited support for different tasks. This paper introduces a novel annotation approach for generating synthetic datasets by expanding upon the Blender Annotation Tool. The enhanced approach can automatically produce ground-truth data of segmentation masks, depth maps, surface normals, and optical flow in synthetic 3D scenes within Blender after minimal manual setup. Utilizing this extended annotation method, we have created the initial subset of a synthetic benchmark dataset known as the Synthetic Multimodal Video Benchmark. This illustrates that synthetic datasets can be generated with substantial diversity and rich annotations by harnessing freely available 3D scenes from a wide array of domains on the internet.
Travel-Activity Patterns Identification Using Graph Edit Distance

Understanding travel-activity behaviors is important for transport planning, particularly for the provisions of new mobility services. Exploratory analyses can be helpful in getting initial insights into these behaviors. This study presents an application of Graph Edit Distance (GED), which has yet to be explored in a transportation context, for clustering weekly Travel-Activity Patterns (TAPs). We used a dataset of 20,480 respondents in an online survey in Japan in 2021 for evaluating the approach. TAPs were represented as graph objects and a GED matrix was computed with the optimal edit cost structure obtained using a robustness index (RI). The K-medoids algorithm identified six clusters, including two clusters of car drivers, two clusters of public transport, one cluster of active travel modes, and one cluster of multiple travel modes. These clusters were visualized by a two-dimensional space produced by Discriminant Correspondence Analysis (DICA), with car access, occupation status, and age identified as the most important variables for discriminating them. Overall, the usefulness of GED (and the follow up DICA) in clustering TAPs was illustrated, and some directions for further uses of GED in studying travel-activity behaviors were discussed.
Continuous Trajectory Tracking Across Sensors at Intersection using Dynamic Target Set within the Overlapping Area

Across-sensor tracking is an important technology to realize global perception in the field of next-generation intelligent transportation. However, identifying targets within an overlapping area and merging trajectories from different sensors remain challenging. Previous methods require high labor costs to manually identify an overlapping area and neglect the problem of noise and occlusion when data present inconsistency. To address these issues, this paper proposes a novel across-sensor tracking method at intersection. The proposed method adaptively identifies the target set for the overlapping area based on the Gaussian product of target states from two sensors and fuses trajectories based on Arithmetic Average algorithm. Additionally, the paper improves the birth model of the Poisson Multi-Bernoulli Mixture filter in order to handle complex intersection situations. Extensive simulation and real-world experiments evaluate the method's effectiveness, and its superior performance compared to previous methods.
Crude Oil Price Forecasting: A Comparative Analysis of ARIMA, GRU, and LSTM Models

Since 2020, the world hasn’t been able to recover fully from the economic crisis that is caused by the pandemic and the ongoing war between Russia and Ukraine. Crude oil is one of the key ingredients that shapes the world’s economy as of today. This research paper is aimed to predict uncertainty and volatility of crude oil’s prices with time series approaches. To predict crude oil prices, we need to implement Machine Learning and Deep Learning models such as RNN models (LSTM, Bi-LSTM, GRU, and Multi-LSTM) and ARIMA models. To improve the accuracy of the models, we need to preprocess certain data. Picking a variable to be processed is also necessary, in this case we used opening price and date to determine crude oil prices going forward. Data scaling is necessary to maintain numeric data and Ad fuller Test is a must to acknowledge data stationarity. After preprocessing and fitting the data into models, we can see that LSTM and ARIMA stood out within other models, LSTM has an RMSE score of 6.62 and MAE of 4.96 whilst ARIMA has an RMSE score of 6.5 and MAE of 5.03. All the models used are good in predicting crude oil prices, because the R2-Score is ideal, not overfitting and underfitting. This approach on predicting crude oil prices will certainly be impactful in the global economy and avoiding panic bull and bear markets of the crude oil price.
Improved YOLOv5s for Object Detection

The utilization of deep learning technology for object detection offers the notable benefit of attaining high levels of accuracy. The level of accuracy attained is contingent upon the duration of processing time required by the deep learning technology. The algorithm for object detection is commonly referred to as You Only Look Once (YOLO). This study presents a novel object detection algorithm utilizing the Yolov5 framework, coupled with a substantial dataset. The improvement of YOLOv5 commenced with the incorporation of the Spatial Pyramid Pooling Fast module (SPPF) into the yolov5s neck network to extract multiple scale image features. Then, the depth of the backbone SPPF module has been raised from 1024 to 2048 to improve accuracy. The modified YOLOV5 model was utilized for object detection, resulting in the extraction of object information categorized into seven distinct classes. The achieved results include a mean average precision (mAP) of 0.821 at an intersection over union (IoU) threshold of 0.5, and a mAP of 0.386 within the IoU range of 0.5 to 0.95, with a precision value of 0.761. Additionally, a recall value of 0.769 was obtained. The application of our research framework has demonstrated its effectiveness in enhancing the performance of object detection algorithms.
Robot Crowd Navigation Based on Spatio-Temporal Interaction Graphs and Danger Zones

One of the main challenges in mobile robotics is achieving safe and efficient navigation in crowded environments. Previous work in robot crowd navigation has primarily focused on all pedestrians and assumed that the dynamics of all agents are known in simulation scenarios. However, in partially observable real-world crowd environments, the performance of existing methods deteriorates rapidly and may even result in the Frozen Robot Problem. To address these challenges, we propose an attention mechanism based on spatio-temporal interaction graphs to capture cooperative interactions between the robot and other agents for navigation decision-making in partially observable environments. To encourage the robot to stay away from potential freeze areas, we construct a danger zone based on pedestrian motion characteristics, which defines the constrained motion space for the robot. We train our network using model-free deep reinforcement learning without any expert supervision. Experimental results demonstrate that our model outperforms state-of-the-art methods in challenging scenarios of partially observable crowd navigation.
Data Collection in Automotive: A Deep Analysis of Carmakers' Mobile App Privacy Policies

In recent years, big data generated while driving have transformed road vehicles and facilitated the development of autonomous cars and personalized services. However, these developments have raised concerns about the privacy of user data. In this work, we conduct a semi-automatic data-centric analysis of the privacy policies of sixteen mobile apps from different carmakers to identify the personal data collected by automotive companies, how they manage and protect user privacy, and the privacy risks posed by new vehicle features. Firstly, from the privacy policies, we retrieve information on how car makers deal with data management and, secondly, starting from the European privacy regulation GDPR, we create a framework to categorize the collected data in automotive. Then, we analyze the findings to single out possible common patterns among carmakers in data collection. Our results reveal that carmakers collect a relevant amount of users and vehicle information, but some privacy policies do not provide sufficient information on data management. Hence, our work with its framework can serve also as a foundation for future research on driver privacy and emphasize the need for clear and comprehensive privacy policies.
SafeSens - Uncertainty Quantification of Complex Perception Systems

Safety testing and validation of complex autonomous systems requires a comprehensive and reliable analysis of performance and uncertainty. Especially uncertainty quantification plays a vital part in perception systems operating in open context environments that are neither foreseeable nor deterministic. Therefore, safety assurance based on field tests or corner cases alone is not a feasible option as effort and potential risks are high. Simulations offer a way out. They allow, for example, simulation of potentially hazardous situations, without any real danger, by systematically computing a variety of different (input) parameters quickly. In order to do so, simulations need accurate models to represent the complex system and in particular include uncertainty as inherent property to accurately reflect the interdependence between system components and the environment. We present an approach to creating perception architectures via suitable meta-models to enable a holistic safety analysis to quantify the uncertainties within the system. The models include aleatoric or epistemic uncertainty, dependent on the nature of the approximated component. A showcase of the proposed method highlights, how validation under uncertainty can be used for a camera-based object detection.
A Situation Deduction Method for Rail Transit Network Based on Dynamic Bayesian Network with Gaussian Mixture Model

In the past work, we proposed a transport capacity risk prediction method based on linear Gaussian Bayesian network. Aiming at the problem that the global capacity risk index is single and the existing methods are difficult to deal with dynamic and nonlinear data, this paper proposes a method of transportation safety situation deduction for rail transit network based on dynamic Bayesian network and Gaussian mixture model, combined with the situation awareness theory. The hierarchical transport capacity risk is used as the core to construct the network safety situation index system. According to the network topology and the interaction between the transport capacity risk and the single point risk, the dynamic Bayesian network is constructed. The EM algorithm is used to estimate the parameters of the Gaussian mixture model to realize the Bayesian network parameter learning. This method is verified by the example of Chongqing rail transit network, and the prediction performance outperforms other comparison methods.
Rethinking Trajectory Prediction in Real-World Applications: An Online Task-Free Continual Learning Perspective

Trajectory prediction is essential in improving the safety of automated vehicles (AVs), However, most learning-based models only aim to improve the trajectory prediction accuracy and are tested offline in evaluations. When additional data come from a new environment, the offline models need to be re-trained with both the new and old data to avoid catastrophic forgetting of previously learned knowledge. Moreover, all data from a new environment is assumed to be available simultaneously, conflicting with the online data collection of AVs in the real world. Considering these problems, this paper rethinks the research orientation of trajectory prediction. First, a novel learning paradigm named online task-free continual learning (OTFCL) is proposed, highlighting new goals, including learning online data from new environments efficiently and avoiding catastrophic forgetting without re-training. Then, according to the goals of OTFCL, a testing methodology is designed for a comprehensive evaluation of trajectory prediction. Finally, a state-of-the-art model is evaluated in experiments by applying the proposed testing methodology based on the INTERACTION dataset. Experimental results reveal limitations of the state-of-the-art model in real-world applications, and potential solutions based on OTFCL to overcome these limitations are also discussed.
Continental-NTU Delivery Robot Dataset for Perception and Navigation on Footpath

Compared to roadways with structured characteristics, sidewalk scenarios in urban environments are often more complex and variable, and are also easily encountered with special and unknown challenges. The Continental-NTU Delivery Robotic Platform dataset was constructed to provide a class of multimodal sensor information sets that can be used for sidewalk navigation in unstructured environments, and to provide validation support for navigation and decision-making algorithms of last-mile delivery robots. The sensor information in the released dataset not only covers 3D LiDAR, camera, and inertial measurement unit data for sidewalk navigation, but also provides ground truth odometry and mesh data for verification and computer simulation, respectively. This dataset was collected in real-time by the prototype food delivery robot platform developed by Continental and organized in JSON file format. In addition, eight routes that can be used for food delivery tasks were selected for recording in the campus environment of Nanyang Technological University. In this paper, the composition of this dataset is described in detail. Furthermore, a variety of existing SLAM algorithms are used on the basis of this dataset for validating reliability and providing benchmarks. The website for access: https://ntu-conti-a2.github. io/Continental-NTU-Dataset.
Behavioral Intention to Use Audit Software using DeLone-McLean and Technology Acceptance Model

In the industrial era 4.0, the work of auditors has become increasingly complex. Auditors can no longer rely solely on manual work methods, but must have switched to working using audit software. However, there are still many auditors who do not use software or use software, but their use is still very limited to basic functions. This is what made us organize a study to examine the factors that determine the auditor’s intention to use audit software. We use a combination of two theories, namely DeLone-McLean and the Technology Acceptance Model to predict these factors. The object of our research is auditors who work in the Jakarta Greater Area. We use statistical analysis of structural equation modeling partial least squares (SEM PLS). The results of our study state that information quality, system quality, perceived usefulness and perceived ease of use have a significant effect on the auditor’s intention to use audit software. On the other hand, service quality has no significant effect. (Abstract)
Velocity Planning for Multi-Vehicle Systems via Distributed Optimization

This work presents a distributed velocity planning strategy for multi-vehicle cooperation along pre-defined paths. Specifically, we consider a class of tasks where multiple vehicles must navigate given paths with conflict zones (e.g., merging and crossing) as fast as possible without any inner collisions. Given the paths, the biggest challenge for velocity planning is to create collision avoidance constraints without complete spatio-temporal information. To overcome the challenge, a scheme is proposed to project collision-related information from coordinate space to 1-D space with geometry-based safety guarantees. To enhance the ability to deal with medium-and large-scale problems, the alternating direction method of multipliers (ADMM) is introduced. Unlike classic ideas of applying distributed optimization, we formulate ADMM in a semi-centralized, semi-parallel fashion instead of a fully distributed fashion. In this way, a trade-off between overall performance and computational efficiency can be achieved. We evaluate our distributed planning strategy through simulations in multiple cases11Video of the results is available at https://youtu.be/GR6BwFTLErw. The results demonstrate that our method not only plans collision-free paths but also balances between overall performance and computational load.
Thermal Modeling and Analysis of Transverse-Radial Flux Magnetic Gear

The importance of thermal analysis for permanent magnet (PM) magnetic gears is emphasized, as it plays a crucial role in understanding the temperature distribution and its impact on the performance and reliability of the system. This paper presents a comprehensive thermal modeling and analysis of transverse-radial flux magnetic gears (TRFMGs). The study focuses on calculating the losses of the magnetic gear and designing a thermal circuit to model the thermal problem and analyze heat transfer paths in all regions. The losses are derived using finite element analysis (FEM), providing crucial inputs for the subsequent thermal analysis. To accurately model the thermal behavior, a thermal circuit is designed to represent the heat transfer paths within the TRFMG system. The circuit incorporates resistances, capacitances, and thermal conductance to capture the thermal interactions between the various components. Through the conducted thermal analysis, a detailed temperature distribution and heat dissipation is obtained, providing valuable insights for optimizing the design and operation of TRFMGs.