Evaluation Of Algorithms Research Articles

Layer porosity in powder-bed fusion prediction using regression machine learning models and time-series features

Additive manufacturing (AM) using laser powder-bed fusion (L-PBF) has become a common industrial process for high-end component production. The uptake of the process has been accelerated through the broad acceptance of the L-PBF process toward achieving high-quality parts with complex geometry. However, the L-PBF process faces challenges from the process’s sensitivity to the process build parameters, which, when incorrectly set, can cause defects such as porosity, which in turn have a detrimental effect on the produced part properties. On the other hand, the AM processing equipment generates a vast amount of data captured through in situ sensors such as pyrometers and imaging cameras. Having such an abundance of process data facilitates the employment of advanced machine learning (ML) tools to understand and extract patterns and information about the underlying AM process and gain “predictive control.” Driven by this idea, we aimed to employ ML tools over pyrometer time-series data from an L-PBF process to predict the porosity percentage of layers of an AM-built part. Sensor data are naturally modeled by time series; however, most ML algorithms work with tabular data (i.e., one single vector describes a feature). In the work presented here, feature engineering tools were used to transform the time-series data into informative features. These features were fed into the tabular ML algorithms for evaluation, broadening the selection of ML algorithms available in the literature. It was hypothesized that the time-series summary features would capture the interaction of melt-pool temperature with resulting porosity, from which the resulting models could better predict porosity occurrence. The dataset contains layer porosity values in the range of 0.00175 – 7.160%, to which we divide the data into “low” and “high” porous layers using a splitting threshold value of 1%. From evaluating these algorithms, it was concluded that classifying “low” versus “high” porosity layers is relatively easier than predicting the layer’s porosity percentage.

A framework to automatically detect near-falls using a wearable inertial measurement cluster

Accurate and automatic assessments of body segment kinematics via wearable sensors are essential to provide new insights into the complex interactions between active lifestyle and fall risk in various populations. To remotely assess near-falls due to balance disturbances in daily life, current approaches primarily rely on biased questionnaires, while contemporary data-driven research focuses on preliminary fall-related scenarios. Here, we worked on an automated framework based on accurate trunk kinematics, enabling the detection of near-fall scenarios during locomotion. Using a wearable inertial measurement cluster in conjunction with evaluation algorithms focusing on trunk angular acceleration, the proposed sensor-framework approach revealed accurate distinguishment of balance disturbances related to trips and slips, thereby minimising false detections during activities of daily living. An important factor contributing to the framework’s high sensitivity and specificity for automatic detection of near-falls was the consideration of the individual’s gait characteristics. Therefore, the sensor-framework presents an opportunity to substantially impact remote fall risk assessment in healthy and pathological conditions outside the laboratory.

Analysis of Immersive Virtual Reality Tourism Resources Combined with Fuzzy Comprehensive Evaluation Algorithm

Analysis of Immersive Virtual Reality Tourism Resources Combined with Fuzzy Comprehensive Evaluation Algorithm

Machine learning evaluation of a hypertension screening program in a university workforce over five years

The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous opportunities as a unique, easily accessible and practical avenue for early diagnosis and treatment of hypertension among the workforce class. The evaluation of such a Workplace Screening Strategy can give insight into its possible effects. Innovative machine learning approaches like k-means clustering are underutilized for such assessments. We set out to use this technology to analyze the results of our university’s yearly health checkup of the employees for hypertension. An anonymized dataset including the demographics and blood pressure monitoring information gathered from workers in various departments/units of a learning organization. The overall amount of samples or data values is 1, 723, and the supplied dataset includes six attributes, such as year group (2018, 2019, 2021, 2022), Department/Unit (academic and non-academic), and gender (male and female), with the intended output being the blood pressure status (low, normal, and high). The dataset was analyzed using machine learning approaches. In this longitudinal study, it was discovered that the average age for the workforce is 42. Similarly, it was revealed that hypertension was common among employees over the age of 40, regardless of gender or occupational type (academic or nonacademic). The data also found that there was a consistent drop in the prevalence of hypertension from 2018 to 2022. According to the study findings, the use of machine learning algorithms for periodic evaluations of workplace health status monitoring initiatives (particularly for hypertension) is feasible, realistic, and sustainable in diagnosing and controlling hypertension among those in the workforce.

The Evaluation Method of the Power Supply Capability of an Active Distribution Network Considering Demand Response

The accurate quantification of power supply capability (PSC) is crucial for the planning and operation of active distribution networks. To address the issue of PSC quantification, this paper presents a PSC evaluation method of active distribution networks while considering demand response (DR). Firstly, an incentive-based DR model is introduced, followed by the development of a PSC evaluation model that incorporates DR. This model completely describes the PSC of active distribution networks while considering N-1 security constraints. Secondly, a PSC evaluation algorithm based on uniform state-space sampling is presented, enabling the quantification of the complete PSC of active distribution networks with DR. Then, the influence of the load reduction coefficient in DR on the PSC is studied. It is found that the maximum PSC increases with the load reduction coefficient initially and then stabilizes. Furthermore, measures such as appropriately increasing the load reduction coefficient and expanding the bottleneck component capacity are proposed to enhance the PSC. Finally, the effectiveness of the proposed PSC evaluation method is verified by two active distribution networks, CASE1 and CASE2. The proposed method visualizes the complete PSC of active distribution networks considering DR as a curve and quantifies it as an interval value. For CASE1, the complete PSC is quantified within the range of [4.0, 8.4] MVA, and for CASE2, it is quantified within the range of [17.0, 33.0] MVA. The proposed measures effectively enhance the PSC, facilitating the efficient, safe, and low-carbon operation of smart distribution networks.

Bioinspired Algorithm for Performance Evaluation of Biopolymerized Expansive Subgrade Soil Blended with Industrial Waste Additive

Bioinspired Algorithm for Performance Evaluation of Biopolymerized Expansive Subgrade Soil Blended with Industrial Waste Additive

A back-propagation neural network optimized by genetic algorithm for rock joint roughness evaluation

A back-propagation neural network optimized by genetic algorithm for rock joint roughness evaluation

A Fast flatness deviation evaluation algorithm for point cloud data

This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for high-speed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed methods can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.

FLOOD HAZARD ESTIMATION AND EVALUATION IN LAGOS STATE USING MACHINE LEARNING TECHNIQUES

Floods threaten human life, infrastructure, and economic stability in Lagos State, Nigeria. Accurate flood hazard estimation is crucial for effective flood risk management and mitigation. This study employs machine learning techniques to estimate flood hazards in Lagos State. A dataset comprising historical flood events, meteorological factors (rainfall, temperature, humidity), topographical features (elevation, slope, land cover), and socioeconomic variables (population density, urbanization) is compiled. Feature selection and engineering techniques are applied to optimize model performance. Flooding is the most frequent and destructive natural catastrophe that may happen anywhere in the globe. The frequency and severity of flooding events have increased worldwide in recent years due to climate change and human activity. Flooding has caused widespread death and devastation of property, farms, and vegetation in several emerging Nigeria, including Lagos State, and has forced the relocation of many more. Flooding has been Lagos State’s most common natural disaster during the last decade. Modern machine learning methods have shown great promise for improving flood Estimation and Evaluation. The optimum machine learning algorithm for flood Estimation and Evaluation is debated. To reduce the harm caused by floods, finding better ways to anticipate their occurrence is crucial. This paper initially applied 4 machine learning algorithms (Support Vector Machine SVM, Classification and Regression Trees CART, K-Nearest Neighbors KNN, and 4. Generalized Linear Model Network GLMNET) on the default dataset. The results reveal fair accuracy (over 60%) and kappa values (< 0.4). The same ML algorithms were again applied to the transformed dataset using the Boxcox transformation technique; the accuracy and kappa values improved but not significantly. Finally, Models for predicting floods were implemented using 3 different ensemble algorithms: Bagged CART (Bootstrap Aggregating BAG), Random Forest (RF), and Stochastic Gradient Boosting ( Gradient Boosting Machine GBM). Compared to the other three models, the performance of RF (Area Under the Curve AUC = 0.93) and BAG (AUC = 0.92) indicated superior accuracy.

Visual Edge Feature Detection and Guidance under 3D Interference: A Case Study on Deep Groove Edge Features for Manufacturing Robots with 3D Vision Sensors

For manufacturing robots equipped with 3D vision sensors, the presence of environmental interference significantly impedes the precision of edge extraction. Existing edge feature extraction methods often enhance adaptability to interference at the expense of final extraction precision. This paper introduces a novel 3D visual edge detection method that ensures greater precision while maintaining adaptability, capable of addressing various forms of interference in real manufacturing scenarios. To address the challenge, data-driven and traditional visual approaches are integrated. Deep groove edge feature extraction and guidance tasks are used as a case study. R-CNN and improved OTSU algorithm with adaptive threshold are combined to identify groove features. Subsequently, a scale adaptive average slope sliding window algorithm is devised to extract groove edge points, along with a corresponding continuity evaluation algorithm. Real data is used to validate the performance of the proposed method. The experiment results show that the average error in processing interfered data is 0.29mm, with an average maximum error of 0.54mm, exhibiting superior overall performance and precision compared to traditional and data-driven methods.

Motion intensity evaluation algorithm under fuzzy observer

Abstract. With the progress of society and the improvement of people's living standards, people's requirements for physical fitness are also increasing. Especially for middle-aged and young men, they often need to work at their desks for a long time in their daily work and life, and their physical fitness is relatively poor, making them prone to various diseases. Therefore, this article focuses on the study of motion intensity evaluation algorithms based on fuzzy observers. By classifying human motion intensity and establishing corresponding fuzzy observers, the difference between the output of the fuzzy observer and the set value is used to determine whether the human motion intensity is too high or too low. The research results indicate that the fuzzy observer has a good performance in evaluating exercise intensity, with an evaluation accuracy of up to 94%.

Analysis and Research of Printmaking Teaching Effect Based on Virtual Generation Technology and Neural Network Evaluation Algorithm

Traditional printmaking teaching is often limited by factors such as materials, equipment, and space, making it difficult for every student to fully practice and experience the entire process of printmaking creation. By introducing virtual generation technology, this study aims to create an immersive learning environment that allows students to intuitively observe, operate, and learn printmaking in a virtual environment, thereby stimulating their learning interest and motivation. In this system, students can create and learn prints through virtual reality technology, and at the same time, a neural network evaluation algorithm can evaluate and feedback on students’ works in real time. The combination of virtual generation technology and neural network evaluation algorithms can effectively improve the effect of printmaking teaching. First, virtual reality technology provides students with an immersive learning environment that enables them to more intuitively understand the creative process of printmaking. Second, the neural network evaluation algorithm can evaluate students’ works in real time, provide personalized feedback, and help students better master printmaking skills. The combination of virtual generation technology and neural network evaluation algorithms cannot only improve students’ learning interests but also improve teachers’ teaching efficiency. By using virtual reality technology, teachers can better demonstrate the process of printmaking, while neural network evaluation algorithms can help teachers understand students’ learning more accurately, so as to achieve more targeted teaching.

A knowledge empowered graph learning feature selection method based on variation propagation effect representation and analysis for human-centric manufacturing systems

As the construction goals of manufacturing systems shift from Industry 4.0 to Industry 5.0, the development goals of manufacturing systems have also shifted from technology driven to human-centric. Feature Selection (FS) of manufacturing systems plays an important role in helping human control and upgrade manufacturing system quality. However, due to the propagation of variations in manufacturing systems, traditional FS methods cannot accurately select the key features that have an important disturbance to quality indicator. In this paper, a process knowledge-enabled FS method is proposed to obtain key features of quality indictor (QI) that would cause bias of prediction model accuracy. Firstly, a process representation learning algorithm embedded in the process knowledge graph is proposed, which represents the manufacturing variation propagation process during the manufacturing process. Secondly, a feature importance evaluation algorithm based on disturbance intensity judgment rules is proposed to evaluate the importance of process features. Finally, the top k important features are selected as key features based on feature importance ranking. Experimental results demonstrate the proposed method outperforms state-of-art and classic methods in four evaluated indicators. Moreover, the four indicators of MSE, MAE, RMSE, and R 2 have respectively increased by 18%, 16%, 18%, and 33% with 7 features compared to the cutting-edge method.

Speckle characteristic based auto-focusing approach for diffuse object in digital holography

Multi-wavelength digital holography extends the axial measurement range to the order of several tens of microns or more and makes the inspection of optical-rough objects and objects with steep slopes possible. As the optical-rough surface would superimpose speckle field on hologram, the existing focus evaluation criteria frequently fail in indicating correct reconstruction distance. This paper proposes a focus evaluation algorithm for focus detection of diffused surfaces. The average size of the speckle is used as the index and the average size of speckle is evaluated through morphological image processing. Two roughness specimens and a gear tooth are used as test pieces to validate the feasibility of the proposed method. Compared with the existing image focus evaluation method, the proposed morphological average size of speckle approach shows good robustness and sensitivity to various kinds of diffusing surface of mechanical parts.

Research on prediction and evaluation algorithm of sports athletes performance based on neural network.

The Ultimate Fighting Championship (UFC) stands as a prominent global platform for professional mixed martial arts, captivating audiences worldwide. With its continuous growth and globalization efforts, UFC events have garnered significant attention and achieved commendable results. However, as the scale of development expands, the operational demands on UFC events intensify. At its core, UFC thrives on the exceptional performances of its athletes, which serve as the primary allure for audiences. This study aims to enhance the allure of UFC matches and cultivate exceptional athletes by predicting athlete performance on the field. To achieve this, a recurrent neural network prediction model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed. The model seeks to leverage athlete portraits and characteristics for performance prediction. The proposed methodology involves constructing athlete portraits and analyzing athlete characteristics to develop the prediction model. The BiLSTM-based recurrent neural network is utilized for its ability to capture temporal dependencies in sequential data. The model's performance is assessed through experimental analysis. Experimental results demonstrate that the athlete performance prediction model achieved an overall accuracy of 0.7524. Comparative analysis reveals that the proposed BiLSTM model outperforms traditional methods such as Linear Regression and Multilayer Perceptron (MLP), showcasing superior prediction accuracy. This study introduces a novel approach to predicting athlete performance in UFC matches using a BiLSTM-based recurrent neural network. By leveraging athlete portraits and characteristics, the proposed model offers improved accuracy compared to classical methods. Enhancing the predictive capabilities in UFC not only enriches the viewing experience but also contributes to the development of exceptional athletes in the sport.

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing & Evaluation (NDT & E) approach to monitor the health of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP) sample. The proposed approach models the observed data covariance into combination of temporal signal covariance and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by the presence of heterogeneity in the experimental data obtained from GFRP sample.This novel method is based on the coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect detection with the proposed technique.

Enhanced GDM Algorithms for College Mathematics Teaching Quality Evaluation Under Interval-Valued Intuitionistic Fuzzy Sets

The college mathematics teaching quality evaluation is the multiple-attribute group decision-making (MAGDM). Currently, the Exponential TODIM (ExpTODIM) and MABAC was executed to put forward MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are executed for portraying fuzzy data during the college mathematics teaching quality evaluation. In this study, the interval-valued intuitionistic fuzzy number ExpTODIM-MABAC (IVIFN-ExpTODIM-MABAC) approach is put forward the MAGDM with IVIFSs. At last, numerical example for college mathematics teaching quality evaluation is executed to verify the IVIFN-ExpTODIM-MABAC. The major contributions are executed: (1) the ExpTODIM-MABAC based was extended to IVIFSs along with Entropy model; (2) the entropy is employed to execute the weight under IVIFSs. (3) the IVIFN-ExpTODIM-MABAC approach is put forward MAGDM with IVIFSs; (4) numerical example for college mathematics teaching quality evaluation and comparative analysis is executed to proof the IVIFN-ExpTODIM-MABAC approach.

Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry.

Integration of digital transformation and the concept of sustainable development in small and medium-sized businesses

The purpose of the study is to develop a concept that allows integrating sustaina-ble development and digital transformation in small and medium-sized businesses using an evaluation algorithm for determining the potential and risks for the sub-ject. The study uses such approaches as the rating method, methods of economic and statistical analysis, forecasting based on retrospective data, system and struc-tural method, causal analysis, expert evaluation method, matrix of potential as-sessment, as well as analysis and synthesis. As a result of the conducted research, the need for the introduction of digital transformation in small and medium-sized businesses was proved. Analysis of the results of ratings on indicators of sustaina-ble development and digital transformation shows a fairly high position of the Re-public of Kazakhstan on a number of indicators, which shows the high potential of the country in the context of further implementation and development of the con-cepts under study. An important incentive for small and medium-sized businesses in the context of the integration of digital transformation and sustainable devel-opment is the expansion of long-term lending opportunities for such entities in the Republic of Kazakhstan. The concept of integration of sustainable development and digital transformation in small and medium-sized businesses was established using an algorithm for assessing the potential of such entities and risk factors. The results obtained can find practical application and be useful for the management of small and medium-sized businesses in the process of implementing digital trans-formation and aspects of sustainable development.

A Large Model-Derived Algorithm for Complex Organoids with Internal Morphogenesis and Digital Marker Derivation.

Automated segmentation and evaluation algorithms have been demonstrated to enhance the simplicity and translational utility of organoid technology. However, there is a pressing need for the development of complex organoids that possess epithelium environmental elements, dense regional cell aggregation, and intraorganoid morphologies. Nevertheless, there has been limited progress, including both the construction of data sets and the development of algorithms, in the use of user-friendly microscopy to address such complex organoids. In this study, a data set of bright-field and living cell fluorescence images in paired forms and with temporal variance was constructed using droplet-engineered lung organoids. Additionally, a large model-based algorithm was developed. Both the organoid contours and intraorganoid morphologies were included in the data set, and their physical parameters were included and screened to form multiplex digital markers for organoid evaluation. The algorithm has been demonstrated to outperform existing methods and is therefore suitable for the evaluation of complex organoids. It is expected that the algorithm will facilitate the successful demonstration of AI in organoid evaluation and decision-making regarding their status.