CAD System Research Articles

Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.

Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy

This study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery between January 2020 and December 2022 was performed. Pre- and post-NAC mammograms were analyzed using conventional CAD and AI-CAD systems, with negative exams defined by the absence of marked abnormalities. Two radiologists reviewed mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI). Concordance rates between CAD and AI-CAD were calculated, and the diagnostic performance, including the area under the receiver operating characteristics curve (AUC), was assessed. The pre-NAC concordance rates were 90.9% for CAD and 97% for AI-CAD, while post-NAC rates were 88.6% for CAD and 89.4% for AI-CAD. The MRI had the highest diagnostic performance for pCR prediction, with AI-CAD performing comparably to other modalities. Univariate analysis identified significant predictors of pCR, including AI-CAD, mammography, ultrasound, MRI, histologic grade, ER, PR, HER2, and Ki-67. In multivariable analysis, negative MRI, histologic grade 3, and HER2 positivity remained significant predictors. In conclusion, this study demonstrates that AI-CAD in digital mammography shows the potential to examine the pCR of breast cancer patients following NAC.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Безплазовий метод конструкторської підготовки виробництва літаків авіації загального призначення

The article’s subject is the design preparation for producing light general aviation aircraft. This article aims to develop design solutions for a light cheap aircraft with low flight and ground costs, to substantiate the possibility and necessity of using modern CAD systems and new plazless methods to ensure the interchangeability of light aircraft components to reduce design and production costs. Objective: to analyze the global demand for light aircraft, to substantiate the possibility and feasibility of introducing independent (plazless) methods for linking technological equipment and aircraft design components, to develop a list of design work and expected results, to develop digital models (electronic drawings) of the aircraft as a whole and its components, to prove the effectiveness of such approaches. The research methods used are the analysis of scientific and technical documentation, numerical modeling of the aerodynamic flow around the aircraft, the stress-strain state of the units, and the design of the aircraft and its components considering the results of numerical modeling. The following results were obtained: a set of digital models (electronic drawings) of the whole aircraft, a fuselage, a wing, a horizontal and vertical tail, a chassis, digital models (electronic drawings) of the aircraft parts. The scientific and practical novelty of the obtained results is as follows: a set of aircraft design engineering documents developed based on statistical data analysis and numerical modeling will allow manufacturing, testing the aircraft, and obtaining a certificate of airworthiness, which, in turn, will make it possible to subsequently begin serial production of the aircraft and create full-fledged primary flight training classes designed to improve flight training activities for cadets of higher military educational institutions and create opportunities for training civilian pilots.

Molecular mechanism of proteolytic cleavage-dependent activation of CadC-mediated response to acid in E. coli

Colonizing in the gastrointestinal tract, Escherichia coli confronts diverse acidic challenges and evolves intricate acid resistance strategies for its survival. The lysine-mediated decarboxylation (Cad) system, featuring lysine decarboxylase CadA, lysine/cadaverine antiporter CadB, and transcriptional activator CadC, plays a crucial role in E. coli’s adaptation to moderate acidic stress. While the activation of the one-component system CadC and subsequent upregulation of cadBA operon in response to acid and lysine presence have been proposed, the molecular mechanisms governing the transition of CadC from an inactive to an active state remain elusive. Under neutral conditions, CadC is inhibited by forming a complex with lysine-specific permease LysP, stabilized in this inactive state by a disulfide bond. Our study unveils that, in an acidic environment, the disulfide bond in CadC is reduced by the disulfide bond isomerase DsbC, exposing R184 to periplasmic proteases, namely DegQ and DegP. Cleavage at R184 by DegQ and DegP generates an active N-terminal DNA-binding domain of CadC, which binds to the cadBA promoter, resulting in the upregulated transcription of the cadA and cadB genes. Upon activation, CadA decarboxylates lysine, producing cadaverine, subsequently transported extracellularly by CadB. We propose that accumulating cadaverine gradually binds to the CadC pH-sensing domain, preventing cleavage and activation of CadC as a feedback mechanism. The identification of DegP, DegQ, and DsbC completes a comprehensive roadmap for the activation and regulation of the Cad system in response to moderate acidic stress in E. coli.

Design of Gearings from Involute-Bevel Gears

The article presents formulas for determining the dimensions of involute-bevel gears and the gearings composed of them. It examines the most general cases of gearings formation, consisting of two involute-bevel gears with crossing axes (hyperboloid gearings), with intersecting axes (bevel gearings), and with parallel axes (cylindrical gearings). Gearings composed of involute-bevel gears have design, technological, and operational advantages compared to gearings composed of conventional cylindrical and bevel gears. The use of involute-bevel gearings with crossing axes allows for achieving the required contact character of the teeth from localized contact to linear contact, reducing sensitivity to manufacturing and assembly errors, and increasing the load capacity of the gearing compared to helical gearings from cylindrical gears. Bevel gearings with involute-bevel gears allow for the creation of gearings with any desired small shaft angles, which is difficult to achieve with conventional bevel gears. The use of internal meshing bevel gearings with modified tooth profiles in planetary reducers allows for gearings comparable to harmonic drives in terms of power and kinematic characteristics, but with a higher operational life. The use of involute-bevel gears in cylindrical gearings improves the smoothness of the mechanism and allows for adjusting the center distance and backlash in the meshing, making them suitable for backlash-free drives. By combining the taper angles of the gears and the inclination of the teeth, it is possible to create a one-way rotation mechanism - a freewheel mechanism. Modern CAD systems do not take into account the geometry features of gearings composed of involute-bevel gears, which limits their application in technology. The article proposes an algorithm for calculating the main geometric parameters of gearings composed of involute-bevel gears with arbitrary axis placement in space. Examples of geometric calculations of various types of gearings based on the proposed algorithm are provided.

ПОДХОД К ВЕКТОРИЗАЦИИ ЧЕРТЕЖЕЙ КОНСТРУКТОРСКОЙ ДОКУМЕНТАЦИИ НА БУМАЖНОМ НОСИТЕЛЕ

The work proposes a solution to the problem of vectorization and machine interpretation of drawings of design documentation on paper, which provides the ability to automate the transfer of images of products, parts and assembly units into CAD systems. A number of neural network architectures have been proposed for detecting and recognizing the main elements of a drawing (frame, title block, specification, views, projections and sections), inscriptions, dimension and extension lines, as well as primitives that directly describe the product. For hierarchical and interconnected vectorization, a mechanism for semantic segmentation of drawings based on a graph neural network is proposed. The results of the implementation of the main stages of solving the problem of vectorization of design drawings are presented.

Investigation of the quality of the frame surface when modeling the hull of a vessel depending on the type of frame lines in the Compass 3D CAD

The article provides a brief overview of the CAD systems used in the field of shipbuilding. The potential possibilities of using the domestic computer-aided design system COMPASS 3D in shipbuilding in the task of three-dimensional modeling of the theoretical surface of the hull are shown. The methods of modeling the surface of the ship's hull using the Compass 3D CAD in the form of a frame surface, in which the shaping sections are various types of vessel contours: frames, buttocks and lines. A method for checking the simulated frame surface using control sections is proposed. A comparative analysis of the quality of surfaces obtained by different methods of its formation has been carried out according to a number of proposed quantitative indicators characterizing the smoothness and accuracy of the surface. The influence of the introduction of intermediate sections into the frame set on the quality indicators of the frame surface is analyzed. The results of a quantitative comparative analysis of the quality of the frame surface depending on the type of frame lines are presented. It has been shown that an increase in the total number of forming lines in the frame does not necessarily lead to an increase in the quality of the simulated surface. The results of the work may be of practical interest in the tasks of modeling and evaluating the quality of frame surfaces for various purposes, set by a set of sections in various planes, and will also help in choosing an automated design system for shipbuilding enterprises.

Quantum computing intelligence algorithm for structural topology optimization

Structural topology optimization methods that are driven by the geometric features of components, such as the Moving Morphable Component (MMC), are widely explored due to their convenient interaction with design software. However, these methods exhibit significant sensitivity to the initial positioning of components, limiting their suitability for applications involving complex geometric boundaries. This study addresses these challenges by introducing a novel dual-layer optimization framework that employs a quantum computing-based intelligent optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). The potential drawbacks of the MMC method in engineering applications, particularly its sensitivity to initial conditions, are critically examined, leading to the proposal of a global-gradient hybrid framework for geometry feature-driven topology optimization. This proposed method demonstrates superior capability in optimizing material arrangements compared to the original MMC framework and enhances engineering applicability, making it more suitable for real-world applications. Through three representative examples, the limitations of the original MMC method are illustrated, and the advantages of the proposed dual-layer framework are highlighted. The results indicate that this method not only overcomes sensitivity issues but also stably identifies superior configurations, particularly for structures with complex geometric boundaries, providing models that facilitate interaction with CAD systems. This method offers a robust and precise approach for optimizing designs in various engineering fields.

A comparison between CAD system and building information modelling (BIM)

Abstract Building Information Modeling (BIM) has gained widespread recognition as a collaboration process in the construction industry, leading to increased investment by construction companies across all construction stages. in recent years, engineers and project managers have encountered various challenges. These challenges include communication gaps among stakeholders and decision-makers, frequent design changes during implementation resulting in repetitive work, increased project costs, delays, and compromised project quality. This research proposes a methodology that compares BIM technology with traditional CAD methods by applying both systems to the same project. It aims to highlight the problems associated with the traditional system and demonstrate how BIM resolves them. Clash detection, for instance, addresses implementation issues and reduces the need for rework. Studies have shown that BIM significantly reduces errors and omissions in documents by up to 64%, repetitive work by up to 43%, project costs and project duration. Considering these advantages, it is highly recommended to incorporate Building Information Modeling (BIM) technology in all construction projects.

Computer Augmented Reality Model-Based Interactive Teaching System for Chinese Line Drawing CAD

To achieve automatic target region detection, an interactive image segmentation technique has been always used as preferences. However, when it comes to the implementation of computer aided design, there is a problem with the structured method. The technique of stroke contour extraction based on boundary detection is used in this investigation. The high-frequency lifting filter improves the original image, and the grey scale and spatial distribution of the pixels are used to determine how the pixels are distributed on the pen track. The segmentation of mural lines and strokes is achieved well in a limited space. To ascertain each pixel's grey value distribution, a non-realistic rendering technique is suggested. Draw in accordance with the boundary tangential flow and each pixel's category. In conclusion, the two approaches can cover more application scenarios and yield good results in the extraction of creative materials from Chinese classical mural painting while maintaining the entire shape of the original strokes in the painting.

Computer aided diagnostic system with reduced electrode set for depression detection using spatio-temporal attention mechanism

Computer-aided diagnostic systems (CAD) have been extensively used in medical applications to identify neurological abnormalities. It enhances diagnostic accuracy, decreases misdiagnosis rates, and provides reliable decision support. The proposed manuscript has introduced an automated CAD system that uses EEG signals to detect depression. The proposed automated CAD system is designed to work with a reduced set of electrodes. This manuscript introduces a method for electrode ranking based on the Fisher score. Electrodes are selected based on the threshold criteria. EEG is spatio-temporal data. The spatial and temporal self-attention mechanism is used to extract spatial and temporal features in the spatial and temporal domain. The proposed automated CAD system reduces the requirements of electrodes by an order of magnitude without compromising the performance of the model. The proposed CAD system has a higher degree of automation. It reduces the cost of the scan, experimental time, and complexity of the system.

Internet of Healthcare Things Based Detection of EEG Epileptic Seizures: A Smart System

Background: A seizure is a sudden and uncontrolled electrical activity in the brain that can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It can be a symptom of an underlying neurological disorder or can occur without an apparent cause. Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication. Aim and Objective: A comprehensive analysis of the literature revealed that several CAD system designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively. CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection. Method: In this research article, the authors proposed the framework of the Internet of Healthcare Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and techniques to improve seizure detection's precision and dependability. Results: DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and logistic regression. The authors also considered another dataset for validation and received 96.83% accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter as a preprocessing technique. Conclusion: The IoHT framework offers a multi-modal, adaptive method of seizure detection that enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications for patient care and monitoring. This work shows how IoHT and machine learning can be combined to build a reliable, real-time seizure detection system. These developments, which make it possible for prompt interventions and individualized treatment plans, can significantly improve the quality of care for individuals with epilepsy.

On the correlation between pre-processing workflow and dimensional accuracy of 3D printed parts in high-precision Material Jetting

Material Jetting (MJ) is distinguished in the additive manufacturing field for its ability to create accurate multi-material functional objects with features in the range of few micrometers. Analyzing the impact of each step in MJ from virtual design to 3D printed object on the process response is essential for optimizing the technology’s capabilities. The current state-of-the-art reveals a gap in MJ regarding the fundamental understanding of the correlation between the pre-processing workflow and the geometrical and dimensional accuracy. This study aims to bridge this gap by examining factors such as utilization of different CAD software with diverse working principles, part-based versus assembly-based design, alternative 3D model formats (STL, OBJ, 3MF, AMF, STP), and slicing approaches (using open-source slicers). Two test specimens, each containing ten elements (either cylinders or hemispheres) with diameters ranging from 254 µm to 12.7 mm are examined. The results demonstrate the significant influence of the aforementioned factors on geometrical and dimensional accuracy, except for 3D model formats. Specifically, for large elements, the achievable accuracy depends on the tessellation approach of the CAD system, while for very small elements, the rasterization process is defining, and designing in assembly mode can further enhance accuracy. The handling of the 3D model by the slicer particularly affects geometrical accuracy. Experimental validation confirms the impact of the pre-processing workflow, though the data-related dimensional deviation for the base layer is masked by the wetting behavior of the dispensed droplets on the substrate.

MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification

Background:Monkeypox virus has quickly expanded throughout several nations, raising serious public health concerns. Lack of precautionary measures raises concerns about the possibility of global pandemic. As a result, early detection of monkeypox is essential for efficient diagnosis and treatment before broad community transmission. Artificial intelligence (AI) and machine learning (ML) have recently shown tremendous promise in early monkeypox diagnosis using image-based diagnoses methods. But, challenges such as low contrast lesions, overloaded datasets, high memory complexity, and redundant feature extraction still need to be addressed. Methods:This work proposes a coalesced CAD system for monkeypox classification using a deep learning framework. The proposed approach includes multiple stages: First, the given dataset images are pre-processed by a proposed fusion-based contrast enhancement method. Second, six pre-trained deep learning models i.e., Vision Transformers (ViT), Shifted Windows (Swin) Transformers, ResNet-50, ResNet-101, EfficientNetV2, and ConvNeXt-V2 are modified and transfer learning is used for training. Third, from all the deep learning networks, the deep feature vectors are acquired and integrated through the convolutional sparse image decomposition fusion strategy. Subsequently, an entropy-controlled firefly feature selection approach is applied to identify the most suitable features for classification. Lastly, the multi-class support vector machine is utilized to classify the input data based on the selected features. Results:The proposed algorithm utilized the MSLID dataset and obtained an accuracy of 98.64% which is better than other state-of-the-art works. Conclusions:Experimental outcomes reveals that the proposed monkeypox classification algorithm outperforms well-established existing algorithms in both visual and quantitative evaluations, resulting in improved accuracy.

Explicit design optimization of air rudders for maximizing stiffness and fundamental frequency

In aerospace engineering, there is a growing demand for lightweight design through topology optimization. This paper presents a novel design optimization method for stiffened air rudders, commonly used for aircraft attitude control, based on the Moving Morphable Component (MMC) method. The stiffeners within the irregular enclosed design domain are modeled as MMCs and discretized by shell elements, accurately capturing their geometry and evolution during optimization process using explicit parameters. In order to maximize the stiffness and fundamental frequency of the rudder structures, numerical analysis algorithms were developed with shape sensitivity analysis conducted. To comply with the manufacturing requirement, a minimum thickness is prescribed for the stiffeners, and the thickness and density penalty schemes are proposed to meet the minimum thickness constraint and prevent the occurrence of spurious modes respectively. The method’s effectiveness was demonstrated through optimization examples of a typical trapezoidal air rudder, illustrating the significance of stiffener’s distribution on design objectives. The explicit modeling characteristics allow for directly importing the optimization results into CAD systems, significantly enhancing the engineering applicability.

Digital Customization for Product Design and Manufacturing: A Case Study within the Furniture Industry

Computational design together with the digitization of most fabrication processes play an important role in many research areas. Digital tools such as 3D modeling and computational design have been increasingly used. Computational design combines traditional 3D product design together with programming a general-purpose CAD system in order to promote system integration. In essence, using CAD-based textual or visual programming languages a series of products can be designed with accuracy and take advantage of product customization and automation of downstream applications. The present paper aims at customizing furniture design based on automating both the design and the fabrication procedures. The customer is able to define a series of geometrical characteristics, i.e., width, length, internal dimensions, and various other properties. The outcome consists of automating a great deal of processes, i.e., 3D modeling and assembling, visualization, creating the bill of materials (BOM), producing assembly instructions for the user, drawings and prototyping files, weight estimation.

Isogeometric analysis of functionally graded panels using Bézier triangles

Isogeometric Analysis is a numerical method that integrates the concepts of geometric modeling and structural analysis. It approximates the displacement field using the same basis functions employed by CAD systems to describe the structure’s geometry. This work proposes an isogeometric formulation for analysis of functionally graded panels based on rational Bézier triangles, allowing the exact geometry representation and automatic discretization of topologically complex models. The formulation is applied to the free vibration and stability analysis of functionally graded plates and curved panels. Monotonic convergence under mesh refinement was observed in all examples. Furthermore, results show that curved functionally graded panels display a complex nonlinear behavior and can present bifurcation buckling before reaching the limit load.

A New Methodology, Based on a CAD System, for Evaluating Partial and Global Asymmetries in Deer Antlers from Hunting Quality Assessment Data

In the field of biology, the ’biomarkers’ of the different animal species are of great relevance, as well as the relationships between them and the quality of the habitat. A study was proposed, analysing the antlers of the Iberian deer, to measure the bilateral asymmetry of its branches. The antlers are characterised in a 3D CAD model of their axial structure obtained via photogrammetric restitution from two photographs. This method quantifies the global and partial asymmetries of the antler’s structural features. The research evaluated 48 deer antlers from hunting days and taxidermy workshops. To quantify asymmetries, the measurements required are the same as those considered to assess the hunting quality of antlers. Bilateral deviations are estimated for each structural trait and the whole antler. Preliminary results showed a convergence between the values of global asymmetry and hunting quality. The methodology proved to be fast, inexpensive, and easy to implement, which facilitates its use in future asymmetry studies of other biological elements of animal or plant origin. It is only necessary that the starting data, which is the bilateral axial structure of the analysed element, is characterised in a 3D model.

Implications of Generative Al and Machine Learning on Automotive Industry Development &amp; Reduction of Carbon Footprint: An Analysis of the U.S. Economy Perspective

The U.S. automotive industry is instrumental to the economy and contributes significantly to the American GDP, employment, and global trade. The principal objective of this research paper is to examine the implications of Generative AI and ML in advancing the automotive industry from the U.S. economic perspective. Generative AI is the latest frontier in artificial intelligence software development, where algorithmic generation can be achieved across various types of content: text, images, audio, and video. The generative AI in the Automotive market at the global level had witnessed boisterous growth and commanded a value of approximately $389.47 million by 2023. The analysis exposed that North American regions are dominating the market, which was attributed to high technological infrastructure, along with partnerships among automotive companies with research institutes and universities to foster AI innovations. Application analysis exposed that Advanced Driver Assistance Systems (ADAS) had the biggest market share, indicating a strong focus on developing and implementing Generative AI technologies to enhance driver safety and vehicle autonomy. Followed by, Connected Car Technologies, representing growing efforts towards implementing generative AI solutions that will improve vehicle connectivity, infotainment, and user experience. The impact of Generative AI and Machine Learning can be witnessed in terms of virtual prototyping, generative automotive designs, consolidation with the CAD system, supply chain optimization, Sensor Fusion and Perception Enhancement as well as automotive manufacturing process optimization.