Handle Complex Scenarios Research Articles

Vehicle Target Detection Using the Improved YOLOv5s Algorithm

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure’s convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%.

Face Recognition in the Modern Era: A Survey of Techniques, Challenges, and Innovations

This review paper talks about the development of face recognition, ranging from traditional methods to the latest methods of deep learning (Hasan et al., 2021; Sáez-Trigueros et al., 2018). The early methods were based on separate characteristics like SIFT and LBP (Balaban, 2015) that could not handle complex scenarios. The use of statistical subspace methods helped to improve face representation. Deep learning, however, has transformed the domain, where systems like DeepFace attain nearly human-like performance by taking advantage of vast and various datasets (Taigman et al., 2014). Nonetheless, the bias, fairness, and privacy issues that remain unsolved have led to the ongoing research to make the face recognition systems more robust and ethically acceptable. Keywords--- Face recognition; Illuminations; partial occlusion; pose invariance

Research on the Wild Mushroom Recognition Method Based on Transformer and the Multi-Scale Feature Fusion Compact Bilinear Neural Network

Wild mushrooms are popular for their taste and nutritional value; however, non-experts often struggle to distinguish between toxic and non-toxic species when foraging in the wild, potentially leading to poisoning incidents. To address this issue, this study proposes a compact bilinear neural network method based on Transformer and multi-scale feature fusion. The method utilizes a dual-stream structure that integrates multiple feature extractors, enhancing the comprehensiveness of image information capture. Additionally, bottleneck attention and efficient multi-scale attention modules are embedded to effectively capture multi-scale features while maintaining low computational costs. By employing a compact bilinear pooling module, the model achieves high-order feature interactions, reducing the number of parameters without compromising performance. Experimental results demonstrate that the proposed method achieves an accuracy of 98.03%, outperforming existing comparative methods. This proves the superior recognition performance of the model, making it more reliable in distinguishing wild mushrooms while capturing key information from multiple dimensions, enabling it to better handle complex scenarios. Furthermore, the development of public-facing identification tools based on this method could help reduce the risk of poisoning incidents. Building on these findings, the study suggests strengthening the research and development of digital agricultural technologies, promoting the application of intelligent recognition technologies in agriculture, and providing technical support for agricultural production and resource management through digital platforms. This would provide a theoretical foundation for the innovation of digital agriculture and promote its sustainable development.

Physiological and performance metrics during a cardiopulmonary real-time feedback simulation to estimate cognitive load

Multitasking is crucial for First Responders (FRs) in emergency scenarios, enabling them to prioritize and treat victims efficiently. However, research on multitasking and its impact on rescue operations are limited. This study explores the relationship between multitasking, working memory, and the performance of chest compressions during cardiopulmonary resuscitation (CPR). In this experiment, eighteen first-year residents participated in a CPR maneuver using a real-time feedback simulator to learn chest compressions. Different additional secondary tasks were developed and accomplished concurrently with the chest compressions. Heart rate, respiration rate, galvanic skin response, body temperature, eye gaze movements and chest compression performance data were collected. The findings of this study indicated that multitasking impacted chest compression quality for all secondary tasks, showing significance (p-value < 0.05) for the frequency of the chest compressions which worsened in all cases. Additionally, vital signs such as heart rate, respiration rate, and eye gaze speed were also affected during multitasking. Nevertheless, this change on vital signs was different depending on the type of secondary task accomplished. Therefore, as a conclusion, performing multiple tasks during chest compressions affects performance. Understanding cognitive load and its impact on vital signs can aid in training FRs to handle complex scenarios efficiently.

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

Vehicle Detection in Complex Scenes based on YOLOV5

The purpose of the algorithm is to effectively detect vehicles in different conditions, especially in scenes recorded by vehicle recorders. The proposed method is designed to handle complex scenarios with high accuracy and efficiency. Through the use of YOLOV5, the algorithm is able to identify and locate vehicles despite occlusions, density, and low lighting. The intermediate hidden layer's activation function is achieved by using the Leaky ReLU, and data enhancement strategies are utilized to enhance detection performance. By recognizing 100 images of traffic vehicles in complex scenes, the validation results show that the recognition rate of this recognition method is 78.87%, 89.11% and 91.88% for occluded vehicles, crowded vehicles and independent vehicles, respectively. Furthermore, the recognition speed reached 0.012 s/a, which was reduced by 0.005s compared to the original time. All the results demonstrate that the proposed algorithm has a high recognition rate and real-time speed for the complex scenes, indicating that the convolutional neural network has a promising future in the vehicle detection of complex application scenes.

Advancing human action recognition: A hybrid approach using attention-based LSTM and 3D CNN

In this paper, we propose a novel approach to video action recognition that integrates a modified and optimized 3D Convolutional Neural Network, a Long Short-Term Memory network, and attention mechanisms. This synergy enhances the overall performance, offering an advantage over existing methods in managing the intricacies of real-world scenarios. The uniqueness of our approach lies in its capacity to capture both spatial and temporal information from video sequences and the incorporation of an attention mechanism that selectively emphasizes key areas within the sequences, thereby enhancing recognition accuracy. The model is particularly tailored to handle complex scenarios, such as those with multiple actors or objects, or instances of occlusion. It effectively addresses the subjectivity and variability inherent in action annotations within datasets. We also apply an array of preprocessing techniques to further optimize model performance. Through rigorous experimental evaluations on benchmark datasets, namely UCF101 and HMDB51, we demonstrate that our proposed approach significantly outperforms existing state-of-the-art methods in action recognition. These results underscore the potential of our approach for further advancements in video action recognition research.

Selection of suitable biomass conservation process techniques: a versatile approach to normal wiggly interval-valued hesitant fuzzy set using multi-criteria decision making

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and CO_2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables.

Past, Present, and Future Applications of Ultradeep Directional Resistivity Measurements: A Case History From the Norwegian Continental Shelf

With the introduction of ultradeep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools toward the beginning of the last decade, the oil and gas industry went from real-time mapping of formation boundaries a few meters from the wellbore to tens of meters away. This innovation allowed early identification of resistivity boundaries and promoted proactive geosteering, allowing for optimization of the wellbore position. Additionally, boundaries and secondary targets that may never be intersected are mapped, allowing for improved well planning for sidetracks, multilaterals, and future wells. Modern tool design and inversion algorithms allow mapping the reservoir in 3D and exploring the sensitivity of these tools to the electromagnetic field ahead of the measure point for look-ahead resistivity. Improvements in the technology over the past decade have changed the way wellbores are planned, drilled, and completed, and reservoir models are updated. This paper presents a case study summarizing the advances in UDAR measurements and inversions over the last decade. The case study presents the whole workflow from prejob planning, service design, and execution of one-dimensional (1D) and three-dimensional (3D) inversion in addition to the future potential of look ahead in horizontal wells. Prewell simulations provide a guide to expected real-time tool responses in highly heterogeneous formations. This identifies how far from the wellbore 1D inversions can map major boundaries above and below the well. A fault was expected toward the toe of the well, and UDAR was used as a safeguard to avoid exiting the reservoir. Standard 1D inversion approaches are too simplistic in this complex geologic setting. Thus, 3D inversion around the wellbore and ahead of the transmitter is also explored to demonstrate the improvements this understanding can bring regarding geostopping toward the fault and reservoir understanding in general. Successful geosteering requires personnel trained to handle complex scenarios. Geosteering training simulators (GTS) could be efficient tools for training to interpret inversions where the “truth” is known from realistic 3D model scenarios. The team can learn how to best exploit UDAR technology and inversion results within its limits and not extend the interpretation beyond acceptable uncertainty levels. It will also be addressed how the understanding of inversion uncertainty could be updated in real time in the future. The continued future success of UDAR technology and 1D to 3D inversion results for look-ahead and look-around applications will depend heavily on uncertainty management of the inversions to avoid wrong decisions and potentially reduced well economy.

SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis

Mining the distribution of features and sorting items by combined attributes are two common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these two tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above two tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via two practical cases.

Phase field lattice Boltzmann model for air-water two phase flows

Two phase flows occur in different forms with liquid and gas in general, among which, the interaction of the flow of air and water is a common scenario. However, modeling the two phase flow still remains a challenge due to the large density ratio between them and different space-time scales involved in the flow regimes. In the present work, the lattice Boltzmann (LB) model capable of handling large density ratio (1000) and high Reynolds number (104) simultaneously is proposed. The present model consists of two sets of LB equations, one for the flow field in terms of normalized pressure-velocity formulation and the other for the solution of the conservative Allen-Cahn equation to capture the interface. The numerical tests such as stationary drop, bubble coalescence, and capillary wave decay have been performed, and the results exhibit excellent mass conservation property. The capability of the present model to handle complex scenarios has been tested through test cases, for example, rise of an air bubble, splash of a water droplet on a wet bed, and Rayleigh-Taylor instability. In all test cases, the simulation results agree well with the available reference data. Finally, as an application of the present model, the breaking of a deep water wave with high Reynolds number (104) is simulated. The plunging breaker with wave overturning and the generation of secondary jet and splashes are well described by the present LB model. The evolution of wave energy dissipation during and after breaking is in agreement with the reference data.

Machine Learning Based Automatic Diagnosis in Mobile Communication Networks

The self-healing function in self-organizing networks can not only detect the presence of fault conditions but also diagnose the root causes in a fully autonomous fashion. In this paper, we propose a machine learning based diagnosis algorithm that uses network condition indicators such as key performance indicators and performance management counters for network condition diagnosis. The proposed algorithm judiciously combines the classical supervised softmax neural network (SNN) and support vector machine (SVM), and therefore can be efficiently implemented by off-the-shelf tools while achieving promising diagnosis performance. In particular, the proposed algorithm combines the features extracted from SNN and SVM, and is able to robustly diagnose fault conditions with different levels of severity. Besides, the proposed algorithm can also handle complex scenarios where there is more than one fault condition present at the same time. Considering that data with labels of multiple faults are not available in general, we further propose a simple retraining procedure which allows the proposed algorithm to perform multi-fault diagnosis even when the training data are only single labeled. Simulation results demonstrate that the proposed algorithms provide desired diagnosis performance in both single-fault and multi-fault scenarios and outperform the traditional scoring based methods.

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Over Camera Network

Object detection and tracking are two fundamental tasks in multicamera surveillance. This paper proposes a framework for achieving these tasks in a nonoverlapping multiple camera network. A new object detection algorithm using mean shift (MS) segmentation is introduced, and occluded objects are further separated with the help of depth information derived from stereo vision. The detected objects are then tracked by a new object tracking algorithm using a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM). It employs a Gaussian mixture (GM) representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional Kalman filters (KFs) using GM. When coupled with an improved MS tracker, a new BKF-SGM with improved MS algorithm with more robust tracking performance is obtained. Furthermore, a nontraining-based object recognition algorithm is employed to support object tracking over nonoverlapping network. Experimental results show that: 1) the proposed object detection algorithm yields improved segmentation results over conventional object detection methods and 2) the proposed tracking algorithm can successfully handle complex scenarios with good performance and low arithmetic complexity. Moreover, the performance of both nontraining- and training-based object recognition algorithms can be improved using our detection and tracking results as input.

Novel Heuristic UTD Coefficients for the Characterization of Radio Channels

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents novel heuristic coefficients for the uniform theory of diffraction (UTD), suited to analyze the scattering by lossy conducting wedges in an efficient and reciprocal manner. For its reciprocal properties, the coefficients are appropriate to handle complex scenarios with arbitrary transmitter and receiver positions. The new coefficients take advantage of two previously proposed heuristic coefficients, combining both in a novel approach. The diffraction by arbitrary conducting wedges is investigated and results are compared with those given by accurate Maliuzhinets and method-of-moments analyses. The proposed technique is then applied to characterize the scattering through a typical urban scenario in order to demonstrate its applicability. </para>