Multilevel Research Articles

Analytical study on fire resistance performance of precast concrete hollow-core slabs for electric vehicle fire in parking structures

This study evaluated the effects of fires that occur in internal combustion engine vehicles (ICEVs) and battery electric vehicles (BEVs) on the structural safety of hollow-core slabs (HCS) installed in parking structures. A fire simulation of a prototype parking structure was performed using the time–heat release rate curves of ICEVs and BEVs derived from fire experiments, and fire behavior and temperature history were derived according to the fire curve. Additionally, nonlinear finite element analysis was conducted using a general-purpose finite element analysis program, and the fire resistance performance of HCS was evaluated according to the fire curve and slab load ratio. In the case of a fire in ICEVs, the performance criteria of ISO 834–1 were met even at a slab load ratio of 0.7. However, in the case of a fire in BEVs, the standard for insulation was not met as the temperature change of the upper surface of HCS exceeded 180 K, and the load-bearing capacity was also not satisfied as the limiting deflection reached a slab load ratio of 0.7.

Learning super-resolution and pyramidal convolution residual network for vehicle re-identification

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.

Vocs pollution and respiratory exposure in commercial and residential underground parking garages

The indoor air quality in Underground Parking Garages (UPGs) has deteriorated significantly, primarily due to the high concentrations of particulate matter and volatile organic compounds (VOCs) emitted by idling or low-speed motor vehicles. However, the compositional characteristics and respiratory exposure to VOCs in UPGs have not been quantitatively analyzed. To establish a method for investigating the respiratory exposure to VOCs among different populations in UPGs, a three-dimensional dynamic diffusion model of indoor pollutants was developed based on monitoring data from 116 components in various UPGs in a large city in northern China. The results indicated that air pollution in underground spaces poses significant health risks to workers and children. Furthermore, a comprehensive approach is essential for improving ventilation capacity and air quality in underground transportation spaces.

Three-Dimensional (3D) Flood Simulation Aids Informed Decision Making: A Case of a Two-Story Underground Parking Lot in Beijing

Flooding in underground spaces, such as subway stations, underground malls, and garages, has increased due to intensified rainfall, urbanization, and population growth. Traditional 2D simulations often overlook crucial vertical flow variations, especially in steep transitions like stairs and ramps. The current study aims to investigate the flood dynamics in large underground geometries by taking a parking lot in Beijing, China, as a study case. The model overcomes the limitations of previous simulations by adapting a full 3D mesh-based simulation with reasonable computational cost. Unlike earlier studies, this model employs a high temporal resolution transient inflow at the inlet to the underground space. Simulation scenarios consider different return periods (5, 20, and 100 years) and inlet water depths, providing an analysis of their impact on flood status in the underground structure. The model generates high spatial–temporal results, enabling precise detection of flood-prone locations, evacuation times, and suggested mitigation techniques. The results recommend evacuating from hazard areas before the 10th minute during extreme flood events. Additionally, the study estimates a 40% increase in flood hazards for scenarios with direct connections between levels. Overall, the study highlights the importance of 3D simulations for accurate risk assessment.

Intensive construction technology for urban underground parking shaft

Intensive construction technology for urban underground parking shaft

Smoke and Hot Gas Removal in Underground Parking Through Computational Fluid Dynamics: A State of the Art and Future Challenges

Smoke and Hot Gas Removal in Underground Parking Through Computational Fluid Dynamics: A State of the Art and Future Challenges

The problem of noise generated during the use of garage parking platforms in residential buildings

The problem of noise generated during the use of garage parking platforms in residential buildings

მიწისქვეშა ავტოსადგომების კვამლისაგან დაცვა ხანძრის დროს

The work examines current issues of fire, sanitary, hygienic and environmental safety of ventilation of underground parking lots. It has been established that, according to European standards, smoke removal systems with jet fans act much more efficiently than air ducts, quickly displacing smoke and exhaust gases towards the air intake grilles, from where and they come out. The design and parameters of the ventilation system are selected based on its ability to quickly and safely ensure the evacuation of people through smoke-free exits, as well as provide air parameters in the parking lot that meet the requirements of the design and installation of a ventilation unit in accordance with industry regulations of Georgia.

Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN

Brain tumors are the life killing and threatening disease which affects all age groups around the world. The timely detection and followed by the perspective treatments saves the human life. The tumor regions in brain are detected and segmented using UNET-CNN architecture in this paper. During training process of the proposed work, both Glioblastoma and Healthy brain Magnetic Resonance Imaging (MRI) is preprocessed and then multi level transform is applied on the preprocessed image. The features are further computed from the transformed coefficients and these features are trained by UNET-CNN architecture to obtain trained vectors. During testing process of the proposed work, the test brain MRI image is preprocessed and then decomposed coefficients are obtained by multi level transform. Features are computed from these decomposed coefficients and they are classified using UNET-CNN architecture with the trained vectors. The simulation results of the developed methodology are compared with similar studies on both BRATS 2017 and BRATS 2018 datasets

Exploring gaps in residential and parking location choice models for autonomous vehicles: a proposed evaluation framework

ABSTRACT The advent of autonomous vehicles (AVs) is poised to transform urban landscapes, notably by altering perceptions of travel time value, reshaping parking structures, and introducing additional travel demand through empty-cruising and self-parking capabilities. These changes are anticipated to influence both residential and parking location choices. This study delves into the complexities of these decisions by integrating insights from 28 modelling studies on residential and/or parking location choices in the context of AVs and by proposing an evaluation framework for future modelling efforts. A critical gap identified is the oversight of empty-cruising and parking costs in the context of residential relocation choice for owners of privately-owned AVs. Furthermore, no study has adopted a joint-choice modelling approach for residential and parking location choices. By offering guidelines for the incorporation of these elements, this study contributes to the development of a holistic residential and parking location choice model with AVs, thus providing a valuable roadmap for future research in urban planning and transportation economics.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

An Indoor-Outdoor Layered HD Map Construction for Unmanned Ground Vehicles

Abstract. High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs.

Evaluation of Çankırı and Iğdır salt caves within the scope of health and recreation tourism

With the increasing popularity of healing methods involving salt, the importance of salt mines has grown significantly over time. Today, as salt therapy (Speleotherapy) becomes more widespread, salt caves in many countries around the world have begun to transform into salt therapy centers. Salt caves in Spain (Cardona), Belarus (Soliharsk), Romania (Slanic, Salina Turda, and Praid), Poland (Wieliczka), Pakistan (Khewra), Germany (Berchtesgaden), Azerbaijan (Nakhchivan), and Turkey (Çankırı and Iğdır Tuzluca) are used for therapeutic purposes. Particularly, Çankırı Salt Cave and Iğdır Tuzluca Salt Cave are important resources in Turkey that can be evaluated within the scope of health tourism. In this study, a literature review, official website analysis, and on-site observations were conducted. In the literature review, previous research on the subject was examined. Through website analysis, the spatial units and recreational activities of the salt caves were identified. The spatial units and recreational activities of each salt cave were determined using the collected data. Subsequently, on-site observations were made by visiting the salt caves in Çankırı and Iğdır. During these observations, the spatial units within the salt caves were visited and photographed individually. Then, the data obtained from the literature, official website analyses, and on-site observations were combined. Finally, the characteristics of all the examined caves were tabulated, and based on the created table, the caves were evaluated. As a result of the evaluation, the Cardona Salt Mine in Spain stands out for its historical and cultural activities; the Nakhchivan in Azerbaijan, Soligorsk in Belarus, Slanic and Praid Salt Mines in Romania are treatment centers offering salt therapy services; the Salina Turda Salt Mine in Romania is an underground theme park; the Wieliczka Salt Mine in Poland, Khewra Salt Mine in Pakistan, and Berchtesgaden Salt Mine in Germany are primarily used for recreational purposes; while the developing Çankırı and Iğdır Tuzluca Salt Mines in Turkey are also noted for their recreational use.

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions

AbstractIn this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://go.vision.in.tum.de/4seasons.

Comparison of space vector and switching frequency optimal pulse width modulation for diode free F‐type multi level inverter

AbstractThis study presents a comprehensive examination of space vector pulse width modulation (SVPWM) and switching frequency optimal PWM (SFOPWM) for an F‐type multilevel inverter (FTMLI). SVPWM offers a simple, digital implementation for three‐phase, three‐level inverter structures, and carrier‐based SFOPWM allows for maximum use of switching frequency with a simple construction. This approach reveals the underlying relationship between the above two PWM techniques with various amplitude and frequency modulation indices to ensure the performance of FTMLI. A suitable comparative study was carried out to verify the PWM techniques based on the inverter output voltage, total harmonic distortion, switching stress, and filter size. The analytical conclusions are supported by the simulation results and their experimental validation.

Observation of dynamic position and morphological changes of temporomandibular joint discs under Angle's classification

Objective: To observe the dynamic changes of the temporomandibular joint (TMJ) disc in joint movement under different Angle's classification, providing reference for understanding joint functional movement and providing a basis for more accurate clinical imaging diagnosis. Methods: A total of 30 patients (13 males and 17 females) with temporomandibular disorders who were admitted to Beijing Friendship Hospital, Capital Medical University and General Hospital of the People's Liberation Army from January 2022 to April 2024 were enrolled. Thirty adults (13 males and 17 females) with different Angle's classification, with an average age of (34.4±8.5) years, were subjected to dynamic imaging of their TMJ from the closed position to the maximum opening position, and then to the closed position using MRI. The position and morphological changes of the articular discs were observed. Results: The results showed that volunteers with no displacement of the articular disc in class Ⅰ, Ⅱ, and Ⅲ relationships had different shapes of the articular disc during open and closed mouth movements. However, in the maximum opening position, the articular disc were all located directly below the maxillary nodules, and their shape is double concave. In terms of irreversible anterior displacement of the articular disc, in class Ⅰ Angle, the posterior zone of the disc contacts the anterior inclined plane of condyle from the maximum opening position back to the front of the closing position. In class Ⅱ, the posterior zone of the disc contacts the anterior inclined plane of condyle from the beginning of opening position to maximum opening position. In class Ⅲ, the posterior zone of the disc is always in contact with the anterior inclined plane of condyle throughout the entire movement process. And among them, the articular disc presents a forward displacement state at the closing position, its morphology undergoes folding phenomenon. When the openness is 2.5 cm, the articular disc moves up to a certain extent, and is closer to the anterior inclined plane of condyle, and its shape is also partially changed. When the openness is 4.3 cm, the shape of the articular disc, located between the anterior inclined plane of the joint node and the posterior inclined plane of the condyle, is typical double concave, which is sufficient to show that the articular disc is reversible when maximum opening position is reached. In terms of reversible anterior disc displacement, in class Ⅰ Angle, the posterior zone of the disc contact with the anterior inclined plane of condyle at the beginning of the opening position and the end of the closing position. In classⅡ Angle, the posterior zone of the articular disc is not in contact with the anterior inclined plane of condyle. In class Ⅲ Angle, the posterior zone of the articular disc contact with the anterior inclined plane of condyle at the end of the closing position. Conclusions: Multi level dynamic MR imaging data of the temporomandibular joint can dynamically observe the movement of the temporomandibular joint, intuitively and accurately display the position and shape of the articular disc during movement, and can serve as a useful supplement to static conventional MR imaging of the TMJ. The patient's TMJ needs to reach the maximum opening position in order to determine whether the joint disc displacement can be reversible or not.

Automated vehicles and the urban parking paradigm: Environmental implications and Citizen preference

This study examines parking decisions in a future populated by automated vehicles (AVs), focusing on their energy implications. Using a multinomial logit model, preferences among cruising, garage parking, sending the AV home, and on-street parking in Santander, Spain, were evaluated. Home sending emerged as the favourite (52%) for its convenience, while garage parking was chosen by 36%, valued for security and environmental benefits. Cruising was least popular (6%), deterred by environmental concerns. Integrating survey data with traffic simulations, the research uncovered that cruising, despite its low preference, accounted for 16% of energy consumption due to empty trips. Surprisingly, sending AVs home, despite being the most popular, led to significant energy use, although it maintained a good consumption ratio. Garage parking, especially when located on city outskirts, was also inefficient. The study highlights the urgent need for strategies to mitigate inefficient parking behaviours, thereby enhancing the sustainability of AV-driven mobility.

딥러닝 기반의 주차 층수 탐지 애플리케이션의 개발

In this study, we develop an application to detect the number of indoor parking floors using deep-learning techniques. Previously, floor-number predictions relied solely on simple atmospheric pressure data. In this study, we expand this solution to create applications for both Android and iPhone platforms. This ensures that user experience is optimized for the unique characteristics of mobile applications on each platform. Our approach renders parking-space navigation more efficient and intuitive, thus significantly enhancing user convenience. Ultimately, this development contributes positively to environmental, social, and governance fields.

Performance of Integrated High Voltage Gain DC-DC Converter and Diode Clamped Multi Level Inverter with Renewable Energy Source in Standalone Applications

Performance of Integrated High Voltage Gain DC-DC Converter and Diode Clamped Multi Level Inverter with Renewable Energy Source in Standalone Applications