Layout Complexity Research Articles

Beyond the empirical pillar design method: The strain criterion and the pillar load inversion concepts

Pillar design is a crucial aspect of underground mining engineering, as it directly impacts the safety, stability, and overall effectiveness of mining operations. In this paper we present a method to calculate pillar stability based on the strain criterion and pillar load inversion concept, which complements the empirical pillar strength formulae. Those formulae do not account for certain parameters that can affect the stability of the pillars, e.g. weak layers, changes in stress orientations due to mining, orebody dip, mining layout complexity, and the influence of regional stabilizing pillars. The approach presented here is not intended to replace the empirical method; rather, we suggest using it as the initial step in the pillar design process. This should be followed by iterative numerical modelling to study pillar behaviour, define pillar stability, and optimize the pillar design by considering site-specific and representative rock mass properties and criteria.

Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.

Lightweight Multilevel Feature-Fusion Network for Built-Up Area Mapping from Gaofen-2 Satellite Images

The timely, accurate acquisition of geographic spatial information such as the location, scope, and distribution of built-up areas is of great importance for urban planning, management, and decision-making. Due to the diversity of target features and the complexity of spatial layouts, the large-scale mapping of urban built-up areas using high-resolution (HR) satellite imagery still faces considerable challenges. To address this issue, this study adopted a block-based processing strategy and constructed a lightweight multilevel feature-fusion (FF) convolutional neural network for the feature representation and discrimination of built-up areas in HR images. The proposed network consists of three feature extraction modules composed of lightweight convolutions to extract features at different levels, which are further fused sequentially through two attention-based FF modules. Furthermore, to improve the problem of incorrect discrimination and severe jagged boundaries caused by block-based processing, a majority voting method based on a grid offset is adopted to achieve a refined extraction of built-up areas. The effectiveness of this method is evaluated using Gaofen-2 satellite image data covering Shenzhen, China. Compared with several state-of-the-art algorithms for detecting built-up areas, the proposed method achieves a higher detection accuracy and preserves better shape integrity and boundary smoothness in the extracted results.

La reconnaissance de l'écriture pour les manuscrits documentaires du Moyen Âge

Handwritten Text Recognition (HTR) techniques aim to accurately recognize sequences of characters in input manuscript images by training artificial intelligence models to capture historical writing features. Efficient HTR models can transform digitized manuscript collections into indexed and quotable corpora, providing valuable research insight for various historical inquiries. However, several challenges must be addressed, including the scarcity of relevant training corpora, the consequential variability introduced by different scribal hands and writing scripts, and the complexity of page layouts. This paper presents two models and one cross-model approach for automatic transcription of Latin and French medieval documentary manuscripts, particularly charters and registers, written between the 12th and 15th centuries and classified into two major writing scripts: Textualis (from the late-11th to 13th century) and Cursiva (from the 13th to the 15th century). The architecture of the models is based on a Convolutional Recurrent Neural Network (CRNN) coupled with a Connectionist Temporal Classification (CTC) loss. The training and evaluation of the models, involving 120k lines of text and almost 1M tokens, were conducted using three available ground-truth corpora : The e-NDP corpus, the Alcar-HOME database and the Himanis project. This paper describes the training architecture and corpora used, while discussing the main training challenges, results, and potential applications of HTR techniques on medieval documentary manuscripts.

Domino index: A rapid quantification tool for the domino effect in chemical plants

The severity of industrial accidents involving domino effects is widely acknowledged in chemical and process industries. The interdependence of installations and complexity of layouts pose significant challenges for the rapid quantitative assessment of domino effects in large chemical plants. In this study, a set of domino indices was introduced to measure the extent to which a given installation triggered and propagated domino effects, as well as to assess the overall domino effect in a specified area. An accelerated algorithm for domino accident modelling was developed based on Monte Carlo simulations to calculate the domino index. This algorithm can simulate all potential domino accident propagation pathways and the failure frequencies of installations. Two case studies, derived for a hypothetical chemical plant and actual oil-storage facilities, were examined to evaluate the applicability of the method. Furthermore, the method was validated using conditional probability calculations and vertex metrics. The results demonstrated that the proposed domino index is a useful tool for rapidly quantifying domino effects and that it can assist in identifying critical installations, designing plant layouts, and screening hazardous areas. The method and indices can provide guidance for the prevention of severe domino accidents.

Fully inkjet-printed dual-mode sensor for simultaneous pressure and temperature sensing with high decoupling

Pressure-temperature dual-mode sensors have been attracting much attention for their various potential applications in areas such as human–computer interaction and health monitoring. However, it is still a challenge to construct an all-in-one dual-mode sensor on a flexible substrate through a simple process. Herein, a flexible dual-mode sensor based on the pressure-capacitance sensitivity of porous polydimethylsiloxane (PDMS) and the temperature-resistance sensitivity of reduced graphene oxide (rGO) film has been fabricated. The dual-mode sensor not only owns an excellent pressure sensing capabilities with ultra-wide detection range (0–1000 kPa), a low-pressure detection limit (2.5 Pa), a fast response time (40 ms) and a high cyclic stability (10,000 cycles), but also can accurately distinguish a temperature change of 0.2 °C. In addition, theoretical formulas are successfully developed to explain the causes of signal crosstalk. Effective decoupling is achieved according to the mechanistic analysis, and the detected capacitance and resistance signals are converted into the actual pressure and temperature. Furthermore, a dual-mode sensor array is fabricated by fully inkjet printing method that incorporate the structure of the dual-mode sensor unit and the circuit design of shared electrodes to reduce the complexity of circuit layout for multi-position detection. Besides, a pressure-temperature alarm system is constructed using this dual-mode sensor to monitor different environmental changes. Based on its superior sensing performance and decoupling capabilities, the sensor has great potential in multifunctional synchronous sensing scenarios.

Store layout design and consumer response: a behavioural and EEG study

ABSTRACT A novel approach was used to evaluate the impact of retail store layouts on consumer responses, by triangulating subjective feedback, behavioural data and electroencephalogram (EEG) data in the context of a virtual environment. Participants (n = 45) were assigned to one of three store layout conditions. While there were no significant differences in participants subjective/conscious evaluations of the layout complexity, they demonstrated reduced product-recall scores, and greater cognitive workload in visual/spatial-processing brain regions, in store environments with more complex layouts. This study explores the correlation between perceived visual complexity and perceived store attractiveness and examines how pleasure and processing fluency mediate this relationship. The results demonstrate a strong association between these factors, with processing fluency playing a more significant role in mediating the effect (78%) compared to pleasure (22%). Overall, this study provides valuable insights into the psychological factors that influence consumer perceptions of store environments. The findings demonstrate the value of an EEG/VR-based approach in studying human behaviour during the building design process for identifying neural mechanisms and responses that self-reported feedback may obscure. The result of this study has implications for building designers and retailers, suggesting that simpler store layouts might be more effective in terms of product recall and reduced cognitive workload.

Superstructure optimization of subsea processing layouts

As the oil and gas industry expands the use of subsea processing, the complexity of subsea layouts increases, making manual design processes cumbersome and suboptimal. Here we propose a method to support subsea field design where optimization is performed on a model of the subsea system, to maximize the net present value of the project. The proposed mixed-integer nonlinear (MINLP) model is solved to compute a global optimum design considering constraints in production, equipment duties and cost, and reliability and maintenance aspects. The subsea layout, equipment capacity, oil and gas production rates, and system pressures are optimized. The method was applied on a synthetic field based on the Goliat field in the Barents Sea. The method successfully finds the best designs, while the second and third-best layouts give general insights for subsea processing layout optimization.

SRRV: A Novel Document Object Detector Based on Spatial-Related Relation and Vision

Document object detection is a challenging task due to layout complexity and object diversity. Most of existing methods mainly focus on vision information, neglecting representative inherent spatial-related relationship between document objects. To capture structural information and contextual dependencies among document objects, we propose a novel framework combining spatial-related relation and vision (SRRV) for document object detection. SRRV consists of three parts: vision feature extraction network, relation feature aggregation network and result refinement network. Vision feature extraction network enhances information propagation of hierarchical feature pyramid by adopting feature augmentation paths. Then, relation feature aggregation network combines graph construction module and graph learning module. Specifically, graph construction module calculates spatial awareness information from geometric attributes to encode relationship, while graph learning module stacks Graph Convolutional Network (GCN) layers to aggregate relation information propagated among objects at global scale. Both the vision and relation features are fed into result refinement network for feature fusion and relational reasoning. Experiments on the PubLayNet, POD and Article Regions demonstrate that spatial relation information significantly improve the performance with better accuracy and precise bounding box prediction.

Importance of Nanomedicines for cancer therapy - Review Article

Due to the restrictions of present day most cancers treatments, nanotechnology has been used to expand extra powerful and more secure most cancers treatments. The improvement of nanomedicines has made excellent development in overcoming a number of the issues related to conventional most cancers treatments, including negative drug solubility, negative concentrated on, and drug resistance. As a result of nanoparticle modulation, the pharmacokinetics of the drug may be improved, resulting in higher concentrated on and less facet effects. In addition, nanoparticles can bind to ligands that mainly target most cancers cells. Furthermore, techniques to result in drug launch in tumors via way of means of leveraging tumor traits have proven that they beautify the effectiveness of centered drug delivery. Despite a few medical success, maximum nanomedicines do now no longer attain the clinic. Clinical translation is hampered via way of means of many factors, consisting of layout complexity, incomplete expertise of organic mechanisms, and excessive production demands. Clinical translation can be progressed via means of integrating information from exceptional disciplines which includes chemistry and tumor pathophysiology. Understanding how nanoparticle change influences organic structures is crucial for growing greater powerful nanomedicines. This evaluation summarizes the important thing achievements of nanomedicines, consisting of excessive molecular weight nanoparticles to enhance drug shipping and release, and techniques to conquer drug resistance.

Analysis of the Vulnerability of Pilgrims in Imam Reza's Shrine during the Emergency Evacuation Due to the Crisis using Fuzzy Logic and Interpolation Method

INTRODUCTION: The need to carry out the emergency evacuation of people from disaster-affected areas and their transfer to safe areas, in the shortest possible time and with the least number of injured, has made the issue of urban emergency evacuation a complex one in the real world. The complexity of the spatial layout of the courtyards and the presence of a large population within the Shrine, among which vulnerable groups also have a significant share, increase the probability of the population's vulnerability to natural and human disasters. Based on this, the current research was conducted to investigate the vulnerability of the pilgrims of Imam Reza's Shrine during the emergency evacuation caused by the crisis and what measures could reduce the vulnerability. METHODS: The present applied study was conducted based on a descriptive-analytical method. The required data were collected through documentary-library and field studies methods. The gathered data were analyzed using the Analytic Hierarchy Process model, fuzzy logic, and the Inverse Distance Weighting interpolation method in Arc GIS and Expert Choice software. FINDINGS: The findings of this study indicated that based on the presented integrated model, which included 7 variables of the length of the route, the number of nodes along the route, the number of nodes in the surrounding space of the settlement, fixed and passing population density, the ratio of the population to the width of the exit door, and distance from buildings, the highest level of a possible vulnerability in the Shrine was related to Rozeh Monavvareh, Goharshad Courtyard, Sheikh Bahaie Sanctuary, the western part of the Great Prophet Courtyard, and Bab-Al-Javad in descending order. The north and northeast parts of the Shrine had a low level of vulnerability for people's exit. CONCLUSION: The results of this research showed that the presented model could be used as a suitable model in other religious places. In addition, some measures can be adopted to reduce the vulnerability of pilgrims, including increasing the number and width of the doors, removing obstacles in the place of the doors, and paying attention to the type of activities assigned to different parts of the Shrine

Tablext: A combined neural network and heuristic based table extractor

A significant portion of the data available today is found within tables. Therefore, it is necessary to use automated table extraction to obtain thorough results when data-mining. Today’s popular state-of-the-art methods for table extraction struggle to adequately extract tables with machine-readable text and structural data. To make matters worse, many tables do not have machine-readable data, such as tables saved as images, making most extraction methods completely ineffective. In order to address these issues, a novel, open-source, general format table extractor tool, Tablext, is proposed. This tool uses a combination of computer vision techniques and machine learning methods to efficiently and effectively identify and extract data from tables. Tablext begins by using a custom Convolutional Neural Network (CNN) to identify and separate all potential tables. The identification process is optimized by combining the custom CNN with the YOLO object detection network. Then, the high-level structure of each table is identified with computer vision methods. This high-level, structural meta-data is used by another CNN to identify exact cell locations. As a final step, Optical Characters Recognition (OCR) is performed on every individual cell to extract their content without needing machine-readable text. This multi-stage algorithm allows for the neural networks to focus on completing complex tasks, while letting image processing methods efficiently complete the simpler ones. This leads to the proposed approach to be general-purpose enough to handle a large batch of tables regardless of their internal encodings or their layout complexity. Additionally, it becomes accurate enough to outperform competing state-of-the-art table extractors on the ICDAR 2013 table dataset.

Wave interaction and energy absorption from arrays of complex-shaped point absorbers

Water wave interactions with arrays of wave energy converters are numerically investigated based on the interaction theory. The converter is a heaving point absorber that can harness the ocean wave energy through up-and-down movements. A semi-analytical hybrid method is developed that combines the boundary element method and the interaction theory. The developed numerical method is verified against theoretical solutions for arrays of truncated vertical circular cylinders. Three different array layouts are studied in detail. It is found that trapped waves exist at critical wave numbers just below the cutoff values, and the peak load on the middle device increases with the number of devices in head waves. With the increase in the complexity of the array layout, significant wave force enhancement is observed, leading to a broader range of magnitude and stronger variations over the frequency band in beam waves. Moreover, variations of the q-factor show that there are some remarkable “bright spot” regions, indicating that the wave energy absorption there is locally optimized against wave conditions. By arranging the layout in a more randomized way, the optimal conditions for maximized power output can be hard to achieve, but the maximum power output can increase to a higher level.

Optimization of gas detectors placement in complex industrial layouts based on CFD simulations

The optimal placement of hazardous gas detectors plays a relevant role in the prevention and mitigation of critical scenarios among chemical and process facilities. However, the operative performance of gas sensors networks is affected by the complexity of process facilities layout and sensors’ reliability. Moreover, economic constraints may bias the selection of the optimal number of sensors. The present work deals with the development of a methodology to support the selection and positioning of hazardous gas detectors in complex industrial layouts. The methodology exploited preliminary computational fluid dynamics (CFD) simulations to quantify the extension of hazardous gas clouds. A comparison among gas dispersion studies based on CFD and integral models was also carried out to demonstrate the limitation of adopting simplified analyses on the evaluation of gas dispersion hazards. The optimization methodology was then developed based on a twofold algorithm. The first algorithm aimed at determining the optimal sensors positions, seeking for the locations that maximize the detection frequency for a given number of sensors. The second algorithm adopted a multicriteria decision analysis based on the reference point method to find the optimal economic number of sensors and, thus, the most cost-effective strategy. A case study located in a real gasoline storage plant was analysed. The results demonstrated the advantage of adopting a sound quantitative evaluation of the number of detectors integrating economic and risk-based evaluations.

An accelerated modulus-based matrix splitting iteration method for mixed-size cell circuits legalization

Mixed-size cell circuits dominate in advanced technology node designs, with attendant increases in layout complexity. The introduction of multi-row-height cells requires additional constraints, such as power line alignment, to be considered in the legalization stage, in addition to eliminating overlap while maintaining the results of the global placement stage as much as possible. In this paper, a three-stage legalization methodology is presented. The first is cell preprocessing, namely cell diffusion based on the network flow algorithm. Then the legalization problem is modeled as a quadratic programming problem, which is converted into a linear complementarity problem and solved by an accelerated modulus-based matrix splitting iteration method. Finally, the illegal cells are reshaped. Experimental results reveal that our method achieves 11.8% and 0.4% reduction in total displacement, respectively, over existing two state-of-the-art works, and is 1.407x faster than the modulus-based matrix splitting iteration method. In particular, the proposed approach provides a reference for solving large-scale quadratic programming problems.

Graph-Model-Based Generative Layout Optimization for Heterogeneous SiC Multichip Power Modules With Reduced and Balanced Parasitic Inductance

Multichip silicon carbide power modules with integrated snubbers are promising for large-capacity converters with high speed and cost efficiency. In the design stage, the parasitic inductance of module layout generally attracts the primary efforts of designers because of its severe impact on dynamic performance, e.g., voltage overshoot and transient current imbalance. However, due to the complexity of the heterogeneous layout, the solution space and development efficiency are always limited by the traditional manual design approach. Thus, this article proposes a generative method to optimize the layout autonomously. First, a graph model is built to describe heterogeneous layouts with all interconnectivity and design constraints retained. Based on the model, integer programming is introduced to generate layout templates with variable geometric topologies. Then, coupled with a self-developed discrete extractor, the Pareto-front is obtained by genetic algorithm, providing a tradeoff boundary for loop inductance and branch mismatch. The proposed method is systematic, flexible, and requires few designers’ expertise. A 1200 V/240 A half-bridge module is designed and fabricated to validate its capability. The experimental results show that the loop inductance of 5.59 nH is achieved, and less than 5% of the transient current imbalance is realized under 6.2 A/ns turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> in the rated.

Toward Higher Integration Density in Femtosecond-Laser-Written Programmable Photonic Circuits.

Programmability in femtosecond-laser-written integrated circuits is commonly achieved with the implementation of thermal phase shifters. Recent work has shown how such phase shifters display significantly reduced power dissipation and thermal crosstalk with the implementation of thermal isolation structures. However, the aforementioned phase shifter technology is based on a single gold film, which poses severe limitations on integration density and circuit complexity due to intrinsic geometrical constraints. To increase the compactness, we propose two improvements to this technology. Firstly, we fabricated thermal phase shifters with a photolithography process based on two different metal films, namely (1) chromium for microheaters and (2) copper for contact pads and interconnections. Secondly, we developed a novel curved isolation trench design that, along with a state-of-the-art curvature radius, allows for a significant reduction in the optical length of integrated circuits. As a result, curved Cr-Cu phase shifters provide a compact footprint with low parasitic series resistance and no significant increase in power dissipation (∼38 mW) and thermal crosstalk (∼20%). These results pave the way toward the fabrication of femtosecond-laser-written photonic circuits with a steep increase in terms of layout complexity.

An Assessment Algorithm for Indoor Evacuation Model

The public buildings increased significantly with the economy's growth and the population's advancement. The complexity of the indoor layout and the involvement of many people cause the indoor evacuation wayfinding to the nearest exit to be more challenging during emergencies such as fire. In order to overcome the problem, each building is compulsory to follow the standard evacuation preparedness required by Uniform Building By-Law (UBBL). Researchers have also developed evacuation models to help evacuees evacuate safely during the evacuation from a building. However, building owners do not know which evacuation model is suitable for implementing the chosen high-rise building. Two problems were identified in choosing a suitable evacuation model during the decision-making process. First, many developed evacuation models focus on studying different features of evacuation behavior and evacuation time. Second, the validation and comparison of the evacuation model is the missing process before applying the suitable evacuation model. Both validation and comparison procedures were made independently without any standard assessment that encapsulates the critical incident features during the indoor evacuation and virtual spatial elements. Therefore, this research proposed an indoor evacuation assessment algorithm to solve the problem. The assessment algorithm refers to the elements developed in our previous study. We determined attributes, executed simulations, and evaluated the cluster performance using the developed framework. The outcome can help the building owners assess which suitable existing evacuation model is the best to implement at the chosen building.

Dynamic/static mechanical stimulation double responses and self-powered “green” electronic skin based on electrode potential difference

• Self-powered electronic skin based on electrode potential difference can respond to dynamic/static mechanical stimulation. • The device can be prepared by simple, efficient and low-cost screen printing. • The use of water-soluble and biodegradable materials enables devices to be environmentally friendly during manufacturing and disposal. Low-power or self-powered electronic skin (e-skin) is considered an important research area, as it reduces the requirements for stable energy sources in electronic systems. However, most reported self-powered e-skins can only detect dynamic mechanical stimuli, but cannot detect ubiquitous static mechanical stimuli, resulting in a significant amount of information loss. In this paper, we report a self-powered e-skin based on electrode potential difference that can detect dynamic/static mechanical stimuli with sensitivity and response time of 399 mV/kPa and 85 ms, respectively. In this device, the mechanical stimulus is converted into a voltage signal by regulating the impedance of the electrode/electrolyte interface. In the process of preparing the device, a “green” electrode ink was prepared using a water-soluble, biodegradable polymer as the binder of the active material. Owing to the printability of the ink, a patterned electrode can be formed on a flexible substrate using low-cost screen-printing technology. Finally, we designed a co-planar shared electrode structure, which effectively reduced the complexity of the circuit layout. This new device has been successfully used for information acquisition of objects and detection of physiological signals, and we believe it has significant implications for the future development of e-skins.

A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustness