Lighting Conditions Research Articles

NeRF-based Localization and Meshing with Wearable Laser Scanning System: A Case Study in Underground Environment

Abstract. Due to the subterranean scene’s poor lighting conditions and variability of the environment, real-time localizing and meshing in complex underground scenes present a challenging task, with high-potential applications in mining and tunnel protection. In this work, we propose a method that combines SLAM and NeRF for mesh reconstruction in the underground environment with a wearable device. First, a LiDAR-Inertial odometry is used for pose estimation. The resulting poses and sequential laser frames are synchronized to generate a novel data structure, scan-block, which is crucial for improving efficiency and ensuring local precision. This structure is designed to enhance efficiency and ensure local precision. Finally, the generated scan-blocks are passed to the backend NeRF for real-time mesh reconstruction. The experiments are carried out in a complex underground space. The experimental results proved that this method achieved good results. The demo video can be found at https://youtu.be/_y1vbaW06EM?si= hFnAl12u-ffU933h.

LLD-GAN: An end-to-end network for low-light image demosaicking

Demosaicking of low and ultra-low light images has wide applications in the fields of consumer electronics, security, and industrial machine vision. Denoising is a challenge in the demosaicking process. This study introduces a comprehensive end-to-end low-light demosaicking framework called LLD-GAN (Low Light Demosaicking Generative Adversarial Network), which greatly reduces the computational complexity. Our architecture employs a Wasserstein GAN framework enhanced by a gradient penalty mechanism. We have redesigned the generator based on the UNet++ network as well as its corresponding discriminator, which makes the model learning more efficient. In addition, we propose a new loss metric grounded in the principles of perceptual loss to obtain images with better visual quality. The contribution of Wasserstein GAN with gradient penalty and perceptual loss function was proved to be beneficial by our ablation experiments. For RGB images, we tested the proposed model under a wide range of low light levels, from 1/30 to 1/150 of normal light level, for 16-bit images with added noise. For actual low-light raw sensor images, the model was evaluated under three distinct lighting conditions: 1/100, 1/250, and 1/300 of normal exposure. The qualitative and quantitative comparison against advanced techniques demonstrates the validity and superiority of the LLD-GAN as a unified denoising-demosaicking tool.

Axes-aligned non-linear optimized PnP algorithm

Pose estimation is an important component of many real-world computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object’s appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, the movement of objects is limited, e.g. due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equations of the perspective-n-point problem to three variables instead of six. By using the simplified equations, our algorithm is more robust against detection errors with limited point correspondences. In this article, we study three scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot. Here, a stationary camera is detecting the vehicle to assist the driver. The second scenario describes the perspective of a moving camera detecting objects in its environment. This scenario is common for driver assistance systems, autonomous cars or mobile robots. Third, we describe a camera observing objects from a birds-eye view, which occurs in industrial applications. In all three scenarios, observed objects can only move in the ground plane and rotate around the vertical axis. Hence, three degrees of freedom are sufficient to estimate the pose. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm is able to achieve 50% better accuracy, while being equally fast.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

Usability and feasibility of an online intervention for older adults to support changes to routines and the home ('Light, activity and sleep in my daily life')

BackgroundIndoor lighting, exposure to outdoor daylight, physical activity and sleep interact to influence functioning, mood and cicadian rhythm. Older adults (≥ 65 years), who often spend more time at home, are less physically active and experience more sleep problems, could benefit from strategies to support behavioural change and self-managed modifications in the home. The study’s primary objective was to assess the usability and feasibility of the ‘Light, activity and sleep in my daily life’ intervention, delivered as a web-based course.MethodsThis 9-week intervention was delivered in a municipality in Sweden (55.70° N). Participants were eight healthy women (age 71–84), community-living in one-person households. We recruited through municipal staff and posters at senior citizen meeting points. Both qualitative and quantitative data were collected before and after the intervention. The outcome measures were intervention usability (ease of use, usefulness) and study feasibility (e.g., recruitment procedure, online engagement). Measures also included changes to routines and self-managed home adjustments to determine whether the participants applied what they had learnt.ResultsAll participants completed the intervention. Time logged in varied between 25 min and 3 h (M = 1 h 50 min) per week. Seven participants’ system usability scores were between 90 and 100 (‘Excellent’) out of 100. When interviewed, participants reported overall high satisfaction with what they had learnt. Six participants were particularly satisfied with the modules targeting light. Seven participants made changes to their lighting or darkness conditions, such as replaced bulbs with either 3-step dimming or higher colour temperature LEDs (samples were included in the intervention test kit). One suggestion to improve the online delivery was to enable participants to add text comments to the weekly evaluation form.ConclusionsThe web-based intervention was feasible to deliver but time for recruitment should be extended and advertisement in the local newspaper should be considered. Participants’ computer proficiency and access to the internet at home will be critical in a future study with a larger sample. Only minor changes to the online content of the intervention are needed based on participants’ feedback. The intervention will be possible to evaluate in a future pilot study.

PDC-YOLO: A Network for Pig Detection under Complex Conditions for Counting Purposes

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Effects of Light Conditions on Tunnel Construction Workers’ Quality of Life and Work Productivity

Higher lighting intensity promotes workers’ productivity but few studies focus on the problems caused by lower lighting intensities at tunnel construction sites without natural light. The purpose of this study is to explore the relationship between light intensity and workers’ sleep quality, alertness, vitality, and work productivity through a theoretical structural equation model based on the literature review. Data were collected through a questionnaire survey, and 5792 valid responses were adopted for the analysis. First, the results showed that greater lighting intensity promoted workers’ productivity directly and indirectly through three mediators: sleep quality, alertness, and vitality. Then, the whole sample was classified into four groups: high intensity/high comfort, moderate intensity/moderate comfort, moderate intensity/low comfort, and low intensity/low comfort. The clustered results showed that the lighting conditions of tunnel construction sites were generally poor and that lighting comfort promoted workers’ productivity to some extent. Besides, the influence of lighting intensity on productivity declined with improved lighting conditions while the impact of lighting intensity on workers’ physiological and psychological status showed differing trends as lighting conditions varied. However, the relationships between workers’ physiological and psychological status and productivity remained stable regardless of the lighting conditions. The findings could provide a reference for developing corresponding measures to promote workers’ productivity.

Rapid monitoring of structural deformation based on unsupervised segmentation model

This paper presents a novel approach for rapid structural deformation monitoring using an enhanced segmentation model, SAM-DM (Segment Anything Model-Deformation Monitoring). The method enhances the SAM model by integrating an innovative point prompt generation technique, image fusion, and connected domain analysis, enabling structural deformation monitoring with just a single manual click on the target area in the initial video frame. Specifically, this manual click generates coordinate information for the target area, serving as a point prompt for the SAM-DM model. This process results in the creation of a high-quality binary mask, which is then fused with the original image to isolate only the target area. Subsequently, connected domain analysis is employed to automatically process the mask, extracting the pixel centroid coordinates of the target area, which are then used as prompts for segmenting subsequent frames. This approach allows continuous tracking of changes in the target area's coordinates, thereby facilitating rapid structural deformation monitoring. Experimental results demonstrate that this method can accurately monitor deformation across all target points and adapt to varying lighting conditions and noise levels. Applied to monitor the bending deformation of reinforced concrete beams and the in-plane deformation of concrete slabs, the method achieves results closely aligned with actual measurements, yielding relative errors of 0.95 % and 0.48 %, respectively. Additionally, in monitoring the vibrational deformation of steel frames, the primary frequency of the curve precisely matches measured values, confirming that the SAM-DM-based method delivers both high accuracy and strong robustness.

Three-Dimensional Planimetry Assessment of Dental Plaque-Covered Area Reduction after Rinsing with 0.2% Sodium Hypochlorite Solution as Part of a Guided Biofilm Therapy® Protocol-Pilot Longitudinal Study.

Background/Objectives: Less often employed as a rinsing solution for controlling oral biofilms, NaOCL was used in oral rinses at various concentrations in steps 1 and 4 of periodontal therapy. The aim of this study was to quantitatively evaluate the biofilm-disruptive properties of a 0.2% NaOCl solution in standardized oral rinses using dedicated plaque-disclosing agents and 3D scanning methods in patients undergoing the regular Guided Biofilm Therapy® protocol. Methods: Eight patients with at least 20 teeth present evenly distributed between the two arches were included. After 24 h of refraining from oral hygiene, dental arches were stained with a disclosing agent, the subjects rinsed for 20 s, clinical photographs and 3D scans were performed, subjects rinsed again for 20 s, photographs and 3D scans were performed again, and then the GBT® protocol was resumed as usual. Data representing areas covered with dental plaque were acquired using the "Medit Scan for Clinics" software and then underwent a post-processing and rendering process. The outcome variable was the percent reduction in the plaque-covered areas. Results: For the upper jaw, the estimated mean percent reduction in the biofilm-covered area was 39.65%, while for the mandible, it was 38.26%. The analysis of individual photographs revealed changes in the plaque-covered areas and reductions in the color intensity of the residual plaque-covered areas under identical lighting conditions. Conclusions: When analyzed using 3D intraoral scanning, the 0.2% NaOCl rinsing solution seems to be a clinically efficient disruptor/dissolvent of the oral biofilm, both when integrated into modern protocols of periodontal therapy like GBT® and for home self-care.

Investigating the effects of indoor lighting on measures of brain health in older adults: protocol for a cross-over randomized controlled trial

BackgroundThe worldwide number of adults aged 60 years and older is expected to double from 1 billion in 2019 to 2.1 billion by 2050. As the population lives longer, the rising incidence of chronic diseases, cognitive disorders, and behavioral health issues threaten older adults’ health span. Exercising, getting sufficient sleep, and staying mentally and socially active can improve quality of life, increase independence, and potentially lower the risk for Alzheimer’s disease or other dementias. Nonpharmacological approaches might help promote such behaviors. Indoor lighting may impact sleep quality, physical activity, and cognitive function. Dynamically changing indoor lighting brightness and color throughout the day has positive effects on sleep, cognitive function, and physical activity of its occupants. The aim of this study is to investigate how different indoor lighting conditions affect such health measures to promote healthier aging.MethodsThis protocol is a randomized, cross-over, single-site trial followed by an exploratory third intervention. Up to 70 older adults in independent living residences at a senior living facility will be recruited. During this 16-week study, participants will experience three lighting conditions. Two cohorts will first experience a static and a dynamic lighting condition in a cluster-randomized cross-over design. The static condition lighting will have fixed brightness and color to match lighting typically provided in the facility. For the dynamic condition, brightness and color will change throughout the day with increased brightness in the morning. After the cross-over, both cohorts will experience another dynamic lighting condition with increased morning brightness to determine if there is a saturation effect between light exposure and health-related measures. Light intake, sleep quality, and physical activity will be measured using wearable devices. Sleep, cognitive function, mood, and social engagement will be assessed using surveys and cognitive assessments.DiscussionWe hypothesize participants will have better sleep quality and greater physical activity during the dynamic lighting compared to the static lighting condition. Additionally, we hypothesize there is a maximal threshold at which health-outcomes improve based on light exposure. Study findings may identify optimal indoor lighting solutions to promote healthy aging for older adults.Trial registrationClinicalTrials.gov Identifier: NCT05978934.

A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force.

Hierarchical Event-RGB Interaction Network for single-eye expression recognition

The Single-eye Expression Recognition task stands as a crucial vision task, aimed at decoding human emotional states through careful examination of the eye region. Nevertheless, traditional cameras face challenges in detecting and capturing relevant biological information, especially under demanding lighting conditions such as dim environments, high exposure scenarios, or when other radiation sources are present. In this regard, we use a new type of sensor data that can resist extreme lighting conditions, namely event camera data, to improve the performance of single-eye expression recognition. To this end, we propose a novel Hierarchical Event-RGB Interaction Network (HI-Net), to fully integrate RGB and event data to overcome the extreme lighting challenges faced by the single-eye expression recognition task. The HI-Net contains two novel designs: Event-RGB Semantic Interaction Mechanism (ER-SIM) and Hierarchical Semantics Modeling (HSM) Scheme. The former aims to achieve interaction between Event and RGB modality semantics, while the latter aims to obtain high-quality modality semantic representations. In the ER-SIM, we employ an effective cross-attention mechanism to facilitate information fusion, to adaptively integrate and complement multi-scale Event and RGB semantics to cope with extreme lighting conditions. In HSM Scheme, we first explore multi-scale contextual semantics for the event modality and the RGB modality respectively. Then, we perform a semantics interaction strategy for these multi-scale contextual semantics, to enhance each modality's semantic representation. Extensive experiments demonstrate that our HI-Net significantly outperforms many state-of-the-art methods on the single-eye expression recognition task, especially under degraded lighting conditions.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

A multi-source domain feature adaptation network for potato disease recognition in field environment.

Accurate identification of potato diseases is crucial for reducing yield losses. To address the issue of low recognition accuracy caused by the mismatch between target domain and source domain due to insufficient samples, the effectiveness of Multi-Source Unsupervised Domain Adaptation (MUDA) method in disease identification is explored. A Multi-Source Domain Feature Adaptation Network (MDFAN) is proposed, employing a two-stage alignment strategy. This method first aligns the distribution of each source-target domain pair within multiple specific feature spaces. In this process, multi-representation extraction and subdomain alignment techniques are utilized to further improve alignment performance. Secondly, classifier outputs are aligned by leveraging decision boundaries within specific domains. Taking into account variations in lighting during image acquisition, a dataset comprising field potato disease images with five distinct disease types is created, followed by comprehensive transfer experiments. In the corresponding transfer tasks, MDFAN achieves an average classification accuracy of 92.11% with two source domains and 93.02% with three source domains, outperforming all other methods. These results not only demonstrate the effectiveness of MUDA but also highlight the robustness of MDFAN to changes in lighting conditions.

Underwater image restoration via attenuated incident optical model and background segmentation

Underwater images typically exhibit low quality due to complex imaging environments, which impede the development of the Space-Air-Ground-Sea Integrated Network (SAGSIN). Existing physical models often ignore the light absorption and attenuation properties of water, making them incapable of resolving details and resulting in low contrast. To address this issue, we propose the attenuated incident optical model and combine it with a background segmentation technique for underwater image restoration. Specifically, we first utilize the features to distinguish the foreground region of the image from the background region. Subsequently, we introduce a background light layer to improve the underwater imaging model and account for the effects of non-uniform incident light. Afterward, we employ a new maximum reflection prior in the estimation of the background light layer to achieve restoration of the foreground region. Meanwhile, the contrast of the background region is enhanced by stretching the saturation and brightness components. Extensive experiments conducted on four underwater image datasets, using both classical and state-of-the-art (SOTA) algorithms, demonstrate that our method not only successfully restores textures and details but is also beneficial for processing images under non-uniform lighting conditions.

Obesity alters circadian and behavioral responses to constant light in male mice

Exposure to artificial light during the night is known to promote disruption to the biological clock, which can lead to impaired mood and metabolism. Metabolic hormone secretion is modulated by the circadian pacemaker and recent research has shown that hormones such as insulin and leptin can also directly affect behavioral outcomes and the circadian clock. In turn, obesity itself is known to modulate the circadian rhythm and alter emotionality. This study investigated the behavioral and metabolic effects of constant light exposure in two models of obesity – a leptin null mutant (OB) and diet-induced obesity via high-fat diet. For both experiments, mice were placed into either a standard Light:Dark cycle (LD) or constant light (LL) and their circadian locomotor rhythms were continuously monitored. After 10 weeks of exposure to their respective lighting conditions, all mice were subjected to an open field assay to assess their explorative behaviors. Their metabolic hormone levels and inflammation levels were also measured. Behaviorally, exposure to constant light led to increased period lengthening and open field activity in the lean mice compared to both obesity models. Metabolically, LL led to increased cytokine levels and poorer metabolic outcomes in both lean and obese mice, sometimes exacerbating the metabolic issues in the obese mice, independent of weight gain. This study illustrates that LL can produce altered behavioral and physiological outcomes, even in lean mice. These results also indicate that obesity induced by different reasons can lead to shortened circadian rhythmicity and exploratory activity when exposed to chronic light.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Promoting Lusi Island Tourism Through an Augmented Reality Application

Background: The integration of augmented reality (AR) in tourism offers innovative enhancements to visitor experiences. Specific Background: This study aims to develop a research framework for AR-based tourism, focusing on effective tools for user interaction with 3D models. Knowledge Gap: There is a lack of systematic evaluation methods for assessing AR applications in tourism. Aims: The objective is to design and test an AR application that accurately displays 3D models to engage visitors. Results: Analysis shows that all tested 3D objects render correctly with specific markers, and functionality is confirmed across various menus. Tracking capabilities demonstrate effective visibility across multiple distances and lighting conditions, though limitations exist in low-light settings. Novelty: This research presents a structured approach to AR tourism development, highlighting the importance of system performance analysis. Implications: Findings enhance the understanding of AR's role in tourism and stress the need for rigorous testing in application design, informing future innovations in the field. Highlights : Effective tools enhance visitor interaction with AR applications. Rigorous testing ensures accurate rendering of 3D objects. Performance evaluation identifies limitations in varying conditions. Keywords: Augmented Reality, Tourism, 3D Models, System Analysis, User Engagement

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

Modeling the Dynamic Response of a Light-Driven Liquid Crystal Elastomer Fiber/Baffle/Spring-Coupled System

Abstract A self-oscillating system can harness energy from the external environment to sustain its continuous motion, making it highly suitable for applications in soft robotics, military industry, energy, and other fields. This article introduces a system composed of a light-responsive liquid crystal elastomer fiber, a baffle, and a spring, capable of achieving self-oscillation under stable lighting conditions. Considering the established dynamic model of light-responsive liquid crystal elastomers (LCEs), a nonlinear dynamic model is introduced and used as the basis for exploring its dynamic characteristics. Numerical computations reveal that the coupled system exhibits two distinct motion states: self-oscillation and static states. The behavior of the system is sustained through the interaction of light energy and damping dissipation. Furthermore, a detailed investigation is conducted on the key system parameters affecting the frequency and amplitude of self-oscillation. In contrast to the complexity of current self-oscillating systems, this particular self-oscillating system features simplicity in structure, ease of manufacture, and strong adaptability. These advantages are expected to provide broader design possibilities for micro-machines and mechanical production processes.