Safe Area Research Articles

Local Water Inrush Risk Assessment Method Based on Moving Window and Its Application in the Liangshuijing Mining Area

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk.

Hydro-mechanical deep drawing of conical components: Wrinkling behavior and process enhancement

This study explores the hydro-mechanical deep drawing process of a conical part through finite element simulations using ABAQUS software. The central focus is on investigating the occurrence of wrinkling during the forming process. Given the limitations in generalizing previous studies on wrinkling, there is a critical necessity to comprehensively comprehend and mitigate wrinkling behavior through an investigation into the interplay of various parameters. In response to the recognized challenge of minimizing wrinkles, an experimental setup with a hydro-mechanical deep drawing die was resented, aiming to validate and refine the simulation results. Additionally, a comprehensive study is conducted regarding the effect of fluid pressure, chamber pressure, pre-bulge height, friction coefficients, and sheet thickness on wrinkling behavior. The findings reveal that, at a pre-bulge pressure of 40 bar and a pre-bulge height of 2 mm, the minimum wrinkling height is observed to be merely 0.02 mm — the lowest recorded value. Notably, an increasing drawing ratio demands a 50% higher minimum chamber pressure to prevent wrinkling in the flange. Furthermore, a larger distance between the sheet and the die reduces the safe working range by approximately 57%, impacting the production of a wrinkle-free part. Conversely, elevating the pre-bulge pressure results in a 50% expansion in the safe working area for producing a wrinkle-free part. By explicitly stating the challenge of minimizing wrinkles, this study aims to contribute valuable insights into optimizing the hydro-mechanical deep drawing process for enhanced efficiency and improved product quality.

A novel 4H–SiC IGBT with double gate PMOS for improving the switch controllability and FBSOA

In this work, a 4H–SiC insulated-gate bipolar transistor (IGBT) with double gate PMOS (DGPMOS) is proposed. In the on-state, DGPMOS IGBT can form a hole barrier, thus reducing the on-state voltage (VON). During the turn-on and turn-off transients, DGPMOS IGBT can form the extra hole extraction paths. This helps to reduce the turn-off loss (Eoff) and the gate displacement current (IG_dis). The simulation results show that compared with GS IGBT, the VON of DGPMOS IGBT is decreased by 58.04% under the same Eoff. Compared with PMOS IGBT, DGPMOS IGBT achieves better controllability in turn-on dIC/dt and peak turn-on current (Imax). At the same turn-on loss (Eon), the maximum reverse recovery dVKA/dt is lowered by 26.67%, and the electromagnetic interference (EMI) noise is suppressed. In addition, because DGPMOS IGBT has a small saturation current density (Jsat), its forward bias safe operating area (FBSOA) is greatly improved.

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.

Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees

Identifying safe areas is a key point to guarantee trust for systems that are based on Deep Neural Networks (DNNs). To this end, we introduce the AllDNN-Verification problem: given a safety property and a DNN, enumerate the set of all the regions of the property input domain which are safe, i.e., where the property does hold. Due to the #P-hardness of the problem, we propose an efficient approximation method called ε-ProVe. Our approach exploits a controllable underestimation of the output reachable sets obtained via statistical prediction of tolerance limits, and can provide a tight —with provable probabilistic guarantees— lower estimate of the safe areas. Our empirical evaluation on different standard benchmarks shows the scalability and effectiveness of our method, offering valuable insights for this new type of verification of DNNs.

Human disturbance and habitat structure drive eurasian otter habitat selection in heavily anthropized river basins

AbstractAssessing habitat selection is essential to protecting threatened species but also to understand what factors influence species that, although globally not currently in decline, act as flagships of their ecosystems and remain highly vulnerable to human impacts, such as the Eurasian otter. This paper examines otter habitat selection at the river reach scale in two heavily anthropized river basins. Both river basins encompass a wide spectrum of human pressures and biogeographic units, which offers an excellent opportunity to assess otter responses to anthropogenic activities in different scenarios. Through two modelling approaches (structure-agnostic way and a priori hypothesized habitat factors) we demonstrate that otters currently inhabiting these human-dominated landscapes show a trade-off between a preference for highly productive areas and for well-structured and safe areas. We suggest that habitat simplification and human disturbance, which were of minor relevance to the dramatic decline of otter populations in the 20th century, are emerging as potential threats in the context of worldwide increasing land use intensification. Furthermore, we found that otter habitat requirements were remarkably more stringent for breeding site selection than for occurrence, particularly concerning variables related to human disturbance. The results of this work provide tools for integrating ecological criteria oriented to effective otter conservation into river management in human-dominated landscapes, as well as serving as methodological support for lowland river restorations. Our results suggest that long-term otter conservation in anthropized rivers will depend on ensuring the availability of habitat patches that maintain sufficient structural complexity away from intensely outdoor recreational activities.

Nutritional Composition and Antinutritional Factors of Five Wild Edible Fruits Grown in the Mekdela District, South of Wollo, Ethiopia.

This study was carried out to determine the mineral content and nutritional properties of five wild fruits Rhus vulgaris, Rosa abyssinica, Rhus natalensis, Euclea racemosa, and Ficus sur. The proximate composition parameters (moisture, ash, crude fiber, crude fat, and crude protein) and antinutritional factors were evaluated using methods of the Association of Official Analytical Chemists and elemental analysis using the atomic absorption spectroscopy technique. Among the five wild edible fruit species, Rhus vulgaris had the highest carbohydrate content (83.3 ± 0.28 g/100 g) and a high total energy (344.5 ± 2.21 kcal/100 g). Euclea racemosa was found to have the maximum ash content (12.8 ± 0.37 g/100 g), protein content (3.22 ± 0.01 g/100 g), and moisture (16.24 ± 0.003 g/100 g), respectively. Rhus natalensis showed the highest fiber content (9.54 ± 0.003 g/100 g). Mineral analysis showed that local wild fruits contained a considerable amount of minerals. The calcium concentration ranged from 99.51 mg/100 g in Euclea racemosa to 160.12 mg/100 g in Ficus sur. Potassium concentration varied from 54.34 mg/100 g in Euclea racemosa to 234 mg/100 g in Rhus vulgaris. Iron ranges from 21.4 mg/100 g in Rosa abyssinica to 41 mg/100 g in Rhus natalensis, and zinc ranges from 2.3 mg/100 g in Rhus vulgaris to 4.2 mg/100 g in Ficus sur. A high saponin content (2.12 mg/100 g) and a low tannin content (0.23 mg/100 g) were obtained in Rosa abyssinica. The phytate content (1.52 mg/100 g) and the oxalate content (0.9 mg/100 g) were high in Rhus natalensis. In conclusion, the present study shows that wild fruits can be used as food supplementation in food in a safe area.

An integrated approach for sewage diversion: Case of Huayuan mine, Hunan Province, China

Environment protection is a core priority of many governments in this century. Most environmental problems have diverse causes: emission of greenhouse gases from fossil fuels, resource depletion, or intense mining activities such as the Huayuan manganese mine. The positioning of mining factories and water treatment stations impacts the surrounding groundwater reservoir. As the mine expands, the environmental impact also increases and the previous plan based on monitoring wastewater leakage has become inappropriate. Therefore, to solve this issue, a new study is required to understand the lateral resistivity distribution underground and to define a new station location for water treatment and divert the sewage to that station. In this study, the audio-frequency magnetotelluric method was used. Surveys of two long lines that cross the mining area to its boundaries were carried out. Data was robustly processed and inverted. Based on the inverted models in addition to geological information, drilling inspections, and solid waste distributions map, the integrated interpretation proposed two sites on the top of impermeable layers which constitute a buffer point between the unsafe (high concentration of pollutants) and the safe zones in the northwestern part of the mine. From the resistivity distribution combined with the water quality analysis, a relationship between fault structures reveals an interconnected conductive zone in the southeastern part. Being, the main channels for water circulating underground, these conductive zones delineate the main groundwater reservoir with a clastic aquifer layer. However, close to factories, water from faults contains solid wastes thereby making the groundwater in that zone non-potable, unlike the safety zone located in the northwestern part. To conclude, this workflow could become a field guide to improve the environment of mines and the deployment of hydrogeological drilling in a safe area.

Analysis of the Impact of Environmental Conditions on the Reliability in 5G PCB Assemblies

The designs of integrated systems are increasingly compact while the requirements of application, especially in the automotive sector, remain demanding. One main challenge is posed by the environmental conditions. In the present study, their impact on mmWave 5G PCB assemblies is evaluated with respect to both mechanical and highfrequency properties. To that end, material samples were stored at three temperatures for various durations in order to accelerate the ageing effects. Afterwards, the samples were tested by use of mechanical, dielectric and high-frequency test methods. Thermo-oxidative ageing processes were observed and lead to significant changes in mechanical and dielectric properties, with simultaneous changes of sample color. The combined approach of mechanical and high-frequency analysis allows for three innovative advantages: First, a correlation of the degradation in performance with other parameters, e.g. sample change of color, which is much easier to detect. Second, the definition of a Safe Area of Operation and a methodology to estimate the behaviour of a mmWave 5G PCB assembly over its lifetime. Finally, the study opens the pathway to a design for robustness of mmWave communication system in automotive applications.

Leveraging social capital for educational resilience in conflict: The impact of community behaviors on educational access in pre-Taliban ruling Afghanistan

This study investigates the role of communities in fostering resilience within the education sector amid conflict. By emphasizing the importance of collective community behaviors fostered by the cultivation of interpersonal connections in social capital, the study estimates the impact of community behaviors on the access to primary and lower secondary education in Afghanistan. Using the Geographic Information System (GIS) for battles in Afghanistan, the study conducted hotspot analysis and identified intensified conflict areas (hotspots), base areas, and relatively safe areas (coldspots) across Afghanistan as of 2014. Authors then estimated the determinants of community behaviors for access to education in each area with multi-level logit analysis using individual and community levels' information in the Afghanistan Living Conditions Survey (ALCS) 2014 collected from 20,700 households. The findings indicate that among the four community behaviors, gender-related behaviors within the community notably enhanced girls' access to education in hotspot areas. Similarly, security-related atmosphere facilitated improved access to education for both girls and boys, irrespective of the conflict intensity, and community behaviors against child labor also improved boys' access to education. However, classical educational assistance program was effective only in relatively safe areas. These findings illuminate the role of community social capital in fortifying the educational sector's resilience, providing a foundation for the development of effective education policies and support mechanisms in conflict situation.

Real-time safe validation of autonomous landing in populated areas: from virtual environments to Robot-In-The-Loop

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful integration of UAVs in populated environments. Nonetheless, validating autonomous landing in real scenarios is a challenging task with a high risk of injuring people. In this work, we propose a framework for safe real-time and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments and physics-based simulation. The proposed evaluation pipeline includes the use of Unreal graphics engine coupled with AirSim for realistic drone simulation to evaluate landing strategies. Then, Software-/Hardware-In-The-Loop can be used to test beforehand the performance of the algorithms. The final validation stage consists in a Robot-In-The-Loop evaluation strategy where a real drone must perform autonomous landing maneuvers in real-time, with an avatar drone in a virtual environment mimicking its behavior, while the detection algorithms run in the virtual environment (virtual reality to the robot). This method determines the safe landing areas based on computer vision and convolutional neural networks to avoid colliding with people in static and dynamic scenarios. To test the robustness of the algorithms in adversary conditions, different urban-like environments were implemented, including moving agents and different weather conditions. We also propose different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works on this challenging task, and analyze them through several randomized iterations. The proposed approach allowed us to safely validate the autonomous landing strategies, providing an evaluation pipeline, and a benchmark for comparison. An extensive evaluation showed a 99% success rate in static scenarios and 87% in dynamic cases, demonstrating that the use of autonomous landing algorithms considerably prevents accidents involving humans, facilitating the integration of drones in human-populated spaces, which may help to unleash the full potential of drones in urban environments. Besides, this type of development helps to increase the safety of drone operations, which would advance drone flight regulations and allow their use in closer proximity to humans.

Research on Analysis and Suppression Methods of the Bearing Current for Electric Vehicle Motor Driven by SiC Inverter

The silicon carbide (SiC) inverter brings great advantages to the motor drive systems of new energy vehicles; however, severe challenges to the bearings also happen. The high dc bus voltage and switching frequency of SiC inverter can increase the discharge frequency and energy when the bearing grease film collapses. As a result, the bearing suffers severe electric corrosion, and the service life of the motor drive system can be shortened. In this paper, the characteristics of common-mode voltage and bearing voltage are analyzed, firstly under space vector pulse width modulation (SVPWM). After that, the common-mode equivalent circuit model of the motor drive system is established. The frequency characteristics of bearing voltage are revealed, and the safe working area is determined. Then, the frequency characteristics of bearing voltage and current are verified based on IGBT and SiC inverters in experiments. After that, by designing a common-mode filter, the bearing voltage and current are significantly attenuated. Furthermore, the active zero state PWM (AZSPWM) is adopted to reduce the common-mode voltage from the inverter. At the same time, combined with the common-mode filter, the bearing voltage and current are further reduced. The experimental results show that the switching frequency has a decisive effect on the amplitude of bearing voltage and current. The bearing voltage can be attenuated to around half of the reference bearing voltage by using the common-mode filter and AZSPWM strategy, respectively. The combination of the common-mode filter and AZSPWM strategy can reduce the bearing voltage to around one-fourth of the reference bearing voltage, which can effectively reduce the breakdown time and discharge energy of the grease oil film.

MODELING OF THE EXTERNAL MAGNETIC FIELD OF ELECTRIC MACHINES

Powerful electric machines – such as electric generators, electric motors generate a magnetic field of great tension. These fields negatively effect on personnel and can to violate the stability of electronic equipment. To determine safe areas for workers, laying communication cables and placing sensitive electronic equipment, it is advisable to model the propagation of magnetic fields of electrical machines. This will make it possible to rationalize the placing of electrical equipment at design stages. The most common high-power electrical machines are four-pole electrical machines with a dipole-quadrupole structure of the external magnetic field. The purpose of the research is a development of models of the propagation of the external magnetic field generated by alternating current electric machines. Results of the research: it is substantiated that to simulate the propagation of the magnetic field of electric machines, it is advisable to use the Gauss equation for the magnetic scalar potential. This will make it possible to take into account the required number of spatial harmonics of the magnetic field to ensure an acceptable calculation error. An electric machine is considered in a spherical approximation. Calculations were carried out in spherical coordinates. The distances were determined in relative radii of the electric machine - the ratio of the radius of the machine to the definition of field strength. The calculations were made for two planes of spherical coordinates and a three-dimensional image was obtained. As a result of the simulation, it is possible to determine the magnetic field strength of a four-pole electric machine at selected distances and directions around the electric machine with the required accuracy. It has been established that there are points of zero external magnetic field strength around electrical machines. Verification of the simulation results was carried out using the method of full-scale measurements of the magnetic field strength around a real four-pole machine. The measurement results showed acceptable agreement with the calculated data. Conclusions: the chosen approach and the results of modeling the propagation of the external magnetic field of electrical machines can be used to design the placement of electrical equipment, taking into account the requirements for electromagnetic safety and electromagnetic compatibility of technical equipment.

A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal tradeoffs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study.

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

Behavioural-based risk of the Built Environment: Key Performance Indicators for Sudden-Onset Disaster in urban open spaces

The assessment of risk in the Built Environment (BE) involves understanding the inseparable relationship between the physical space and its users. In emergency management, particularly during Sudden-Onset Disasters (SUODs), effective evacuation is crucial for individuals’ resilience in BE. Key Performance Indicators (KPIs) offer a quantitative approach to risk assessment, facilitating a systematic evaluation of various risks within BE. The research focuses on seismic events and terrorist acts as relevant SUODs and proposes KPIs derived from literature-based indicators to assess the impact of construction and morphological features of Built Environment Typologies (BETs) and user-related factors on urban open space risk (behaviors, exposure, vulnerability). These KPIs are then applied to established BETs, that are archetypes from real-world configurations of urban open spaces, and tested through seismic and terrorist risk scenarios along with emergency simulations. Results indicate significant variations among BETs, emphasizing the importance of factors, such as geometry (static KPIs) and evacuation behaviour (dynamic KPIs) in determining risk levels. Evacuation route length and complexity emerge as critical in larger BETs, while insufficient safe areas pose challenges in smaller ones. This KPI-based approach is recognized as a crucial step toward establishing a comprehensive metric for risk assessment in BE open spaces, with potential real-world applications to quantify the efficacy of risk mitigation measures. The results demonstrate the KPIs' potential in quantifying disaster risks and user behaviours, representing a crucial step towards an overarching metric for BE-risk assessment during SUODs in open spaces.

A case report of iridoschisis and cataract: a challenging diagnosis.

To report the cataract surgery of a 79-year-old female patient with iridoschisis and senile nuclear cataracts. Ophthalmologic examination of the 79-year-old female patient who came to our clinic with complaints of decreased vision in the right eye was observed to have a grade-3 senile nuclear cataract and iridoschisis in quadrants 2-3, 4-7, and 10-11 of the iris in the right eye and a grade-2 senile nuclear cataract and iridoschisis in quadrants 5-7 in the left eye to a lesser extent compared to the right eye. The iris fibrils were not connected to the corneal endothelium. A safe surgical area was created by administering the viscoelastic material several times during cataract surgery. Although iridoschisis has a low incidence rate, it is important to also consider comorbid ocular pathologies when treating iridoschisis patients. Since cataract surgery for these patients is more specialized than for uncomplicated cases, the necessary surgical planning must be paid due diligence.

An design of robotic arm control algorithm for explosive disposal robots

Automatic explosive disposal robot have great potential to replace manual Explosive ordnance disposal(EOD) robots. In order to face the practical application scenarios of complex transformations, it is of great practical significance to study a fully automatic and autonomous EOD robot that can transfer dangerous goods to a safe area. In this paper, a mechanical arm algorithm based on the inverse kinematics of robot is presented. The relative position of the bomb is input by the electromagnetic induction device to guide the mechanical arm to the corresponding position. After obtaining the bomb, the deviation angle of the mechanical arm is input in real time by the gyroscope to achieve the relative stability of the object transportation. The experimental results indicate that the explosive disposal robot has certain practical significance.

KDP-Net: An Efficient Semantic Segmentation Network for Emergency Landing of Unmanned Aerial Vehicles

As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.

Calculation Method of Flange Bolt Preload Based on Finite Element

Flange bolt connection is one of the most important connection modes in bolt connection at present. In this paper, the finite element model of the bolt between the shell and the head flange is established to analyze the influence of different initial preload on the bolt stress state under external load. The results show that according to the bolt stress state, the range of initial preload is divided into the influence area of the external load, the influence area of the resultant force and the influence area of the initial preload. In the influence area of external load, the bending moment exerted on the bolt dominates the stress state of the bolt, and the stress state of the bolt is poor, which is not the safe value area of the initial preload；in the influence area of the initial preload, initial preloads dominates the stress state of the bolt, the average stress of the bolt section is larger, and the bolt safety is smaller, which is not the safe value area of initial preloads; in the influence area of the resultant force, the bolt has better stress state and higher safety coefficient, which is the safety value area of initial preload. The initial preload corresponding to the boundary point between the influence area of the resultant force and the influence area of the external load and the influence area of the external load is the limit of the safe value range of the bolt preload.