Euclidean Distance Method Research Articles

Construction of Evaluation System for English Translation Teaching in Colleges and Universities under Cross-cultural Perspective

Abstract The construction of the evaluation system of English translation teaching in colleges and universities under the cross-cultural perspective to improve the quality of translation education. The focus is on integrating the LMBP neural network algorithm and quantitative judgment indexes to provide an adequate evaluation model for English translation teaching. The study adopts the neural network algorithm based on LMBP, combined with the quantitative processing methods of “AHP+DEA” and “Entropy Assignment Method+Euclidean Distance Method”, to systematically construct and empirically analyze the evaluation indexes of teaching. It is found that teaching construction, application and effect are the core indicators of evaluation. Regarding teaching construction, classroom content integrity (correlation coefficient 0.428) and learning resources integrity (correlation coefficient 0.439) are the key factors. Teaching application analysis showed that hybrid teaching positively correlated with student achievement (r=0.569). Conducting effectiveness analysis showed that student discussion and in-class performance were significantly and positively correlated with teaching evaluation (p<0.005). These results prove the effectiveness and usefulness of the constructed evaluation system.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Lost item identification model development using similarity prediction method with CNN ResNet algorithm

<p><strong>Background:</strong> Incidents of personal belongings being lost often occur due to our negligence as human beings or criminal acts such as theft. The methods used to address such situations are still manual and ineffective. The manual process of reporting lost items requires significant time and effort. Additionally, matching the information of lost items with the found ones becomes increasingly difficult, and finding the original owners can be time-consuming. <strong>Objectives and Methods:</strong> This research aims to develop an approach that aids the community in the management of lost items by incorporating a process of item identification. It proposes the creation of an iOS-based prototype model that implements image comparison and string matching. The ResNet-50 architecture extracts features from images, and the Euclidean Distance method measures similarity between these features. Natural language processing used for text pre-processing and employs the cosine similarity metric to assess textual similarity in item descriptions. <strong>Result and Conclusion:</strong> By combining Euclidean distance and cosine similarity values, the model predicts similar lost item reports. Image comparison provides an accuracy result of 29.96% correctness, while string matching with 97.92% correctness. Thorough testing and validation confirm the model’s success across different reports.</p>

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Novel framework for assessing economic viability and environmental impacts: Use of waste products in soil stabilization

This study developed a framework that evaluated the carbon emission and cost-effectiveness of fly ash, rice husk ash, and face masks as additives for soil stabilization. The research scope of this study included modification of soil using varying dosages of additives, quantification of improvement in soil properties, pavement design under different traffic conditions, and estimation of cost and emission. In this process, a total of 30 pavement sections were considered, and Euclidean distance method was employed to develop a combined ranking of the pavement alternatives based on their resource consumption and environmental impacts. The study findings showed that Rice Husk Ash and Fly Ash offered lower costs of construction and carbon footprint than the control pavement sections under all traffic conditions. Additionally, face mask was found to be suitable under low traffic conditions at minimum content. It is envisaged that the framework developed in this study would provide important insights to the policymakers, engineers, practitioners, road agencies, and other stakeholders in assessing various pavement alternatives that ensure the economic and sustainable development of transportation infrastructure.

Analisis Perbandingan Pengukuran Jarak Algoritma K-Nearest Neighbor Dengan Menggunakan Data Breast Cancer Dan Data Heart Disease


 
 
 
 Breast Cancer is a cancerous condition that appears in the breast area. This type of cancer is often experienced by women with a characteristic feature of Breast Cancer, namely the appearance of unusual lumps in the breast area. Heart or Heart Disease is a type of Non-Communicable Disease (PTM): which results in a fairly high mortality rate. Heart Disease is caused by several risk factors including smoking, an unhealthy lifestyle, high cholesterol, hypertension, and diabetes.
 Based on these facts, an appropriate algorithm is needed to classify Breast Caner and Heart Disease as an effort to prevent an increase in mortality rates due to Breast Cancer and Heart Disease. And the algorithm that will be used is the K-Nearest Neighbor algorithm with 3 distance measurement methods, namely Euclidean distance, Manhattan distance, and Minkowsky distance .
 
 
 
 
 From the stages that have been carried out, the final results of the Euclidean distance method obtained an Accuracy value of 80.88% Breast Cancer data at K = 11, and 78.69% heart Disease data at K = 11. The Manhattan distance method obtained an Accuracy value of 89.71% of Breast Cancer data on K=11, and 78.69% of Heart Disease data on K=20.The Minkowsky distance method obtained an Accuracy value of 98.53% of Breast Cancer data on K=11, and 79.41% of Heart Disease data on K=11. This shows that the Minkowsky distance method works more optimally than the Euclidean distance and Manhattan distance methods.

MaOEA/D with adaptive external population guided weight vector adjustment

In order to make multiobjective evolutionary algorithm based on decomposition (MOEA/D) have better performance in dealing with many-objective optimization problems (MaOPs) with different Pareto fronts (PFs), this paper proposes a MOEA/D with adaptive external population guided weight vector adjustment (MaOEA/D-AEW). The algorithm modifies the MOEA/D method of constructing mating pools by calculating the utility function value of individuals in the population to produce high-quality offspring. For the maintenance of non-dominated solutions in the external population (EP), solutions exceeding the capacity of the EP are removed by the method of minimum Euclidean distance combined with the aggregation function value to ensure the convergence and diversity of non-dominated solutions in the EP. For the addition of weight vectors, a new sparsity evaluating method (SL+) is proposed to determine the sparsity of weight vectors by multiplying parallel distances between individuals. For the deletion of the weight vector, the minimum Euclidean distance combined with the aggregation function value is used to delete the weight vector corresponding to invalid or crowded individuals in the population. Comparing MaOEA/D-AEW with six state-of-the-art algorithms on regular PF and irregular PF test problems respectively, simulation results show that the proposed algorithm can adapt well to MaOPs with different PFs and significantly outperforms other comparative algorithms in terms of convergence and diversity of solution sets.

An Approach towards Measurement of Color Shifting in Misregistration Print Defect using Euclidean and Manhattan Distance Metrics

Misregistration print defect occurs during offset printing affects the visual appearance of printed image. Registration refers to the proper superimposition of colors whereas improper alignment or shifting of colors is resulted as blurred image. Generally registration marks is used in printed sheet to detect this kind of print problem manually. In the presented study a computer vision technique is applied to detect and quantify the problem. Euclidean distance and Manhattan distance measurement method is applied for quantification of color shifting. Therefore this presented study is a novel approach in printing industry which can be a substitute of the usual human perception based method.

Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara

Understanding patterns among foreign tourists is an urgent matter. These patterns can become knowledge that helps in making better decisions because they are data-driven. The pattern to be elaborated on is regarding the clustering of visits by foreign tourists to tourist destinations in Jakarta. Data mining is an approach that extracts knowledge patterns from a dataset. K-Means is one of the data mining algorithms used for clustering data, where data is grouped based on similarity in features and attributes. This study compares the Euclidean Distance, Manhattan Distance, and Haversine Distance methods to obtain more representative data clusters for the datasets. The datasets in this study are not normally distributed due to outlier data; hence, the DBSCAN algorithm is used for improvement without removing or cutting the data, as it can result in a significant amount of missing values that could affect information that does not align with empirical facts. In this study, 5 clusters were created based on elbow calculation results. The K-Means cluster testing in Euclidean distance yielded a Silhouette Score of 0.36, Inertia of 0.86, and Davies-Bouldin Index of 2.39. The Manhattan method resulted in a Silhouette Score of 0.65, Inertia of 1.46, and Davies-Bouldin Index of 0.47. Meanwhile, applying the Haversine method resulted in a Silhouette Score of 0.36, Inertia of 0.03, and a value of 2.39 for the Davies-Bouldin Index.

Modified non-local means: A novel denoising approach to process gravity field data

Abstract Gravity measurement is a basic geophysical method for non-destructive exploration of mineral resources and hydrocarbons and also for geological studies. The quality of gravity data measured is constantly degraded by a low signal-to-noise ratio. Noise attenuation thus plays a vital role in processing and interpreting gravity field data. The non-local means (NLM) filtering was first and successfully introduced to attenuate randomly distributed noise situated in seismic records in geophysical community. However, less attention has been drawn to apply NLM to denoise potential field data, since the success of NLM is guaranteed by carefully tuned parameters and large computational costs. Here we propose the modified NLM (MNLM), based on unweighted Euclidean distance and integral image method, to denoise gravity datasets. The unweighted Euclidean distance is used to reduce the uncertainty and complexity of tuning the control parameters involved, and the integral image strategy is applied to avoid the enormous computational cost of the NLM. Since these filtering properties are desirable to mitigate noise in gravity datasets, we test and evaluate the MNLM filter on noisy synthetic gravity models with uniform and normal distributions and on real data from the Mariana trench and Slovakia, and compare it to other standard and representative denoising filters. The results on synthetic and field datasets confirm the high speed of this modified algorithm and show that, it removes noise most effectively while clearly preserving and not blurring structural details with less tuned parameters. The MNLM filter can therefore be considered as a promising and novel algorithm for denoising gravity data.

Image cascade matching method based on an improved random sampling consistency algorithm

In the process of image matching, image quality will have a certain impact on the extraction of image features, directly affecting the matching accuracy of subsequent images. Therefore, an image cascade matching method based on improved random sampling consistency algorithm is proposed. This method first needs to improve the quality of the matched target image through preprocessing results such as image denoising and enhancement, and prepare for feature point extraction during subsequent image cascade matching; Then extract the feature points of the target to be matched in the image, and use the improved random sampling consistency method to quickly estimate and simplify the registration model based on the extraction results. Use similar feature triangles to perform model pre testing, and use the maximum Euclidean distance method to improve the accuracy of image cascade registration, achieving image cascade matching. The experimental results show that the proposed method can achieve image enhancement while removing image noise, improve the quality of the image, comprehensively extract the feature points to be matched in the image, and achieve accurate matching of image feature points.

Fuzzy graph convolutional network for hyperspectral image classification

—Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral image classification for its excellent feature representation and convolution on arbitrarily structured non-Euclidean data. However, most state-of-the-art methods build a graph utilize the distance measure, which makes it challenging to fully characterize the complex relationship of hyperspectral remote sensing data. Moreover, the hyperspectral image usually has uncertainty introduced by the problems of the spectral variability and noise interference. This article uses fuzzy theory to optimize the GCN and thus solve the uncertainty problem in hyperspectral images, and presents a novel fuzzy graph convolutional network (F-GCN) for hyperspectral image classification. By calculating the fuzzy similarity of samples, a robust graph is first built rather than using the traditional Euclidean distance method, which allows a better representation of the complex relationship between hyperspectral remote sensing data. Furthermore, the proposed network introduces fuzzy layers into the model to cope with the ambiguity of the hyperspectral image. Finally, the classification results for three real-world hyperspectral data sets to show its feasibility and effectiveness in hyperspectral image classification.

New pilot protection method based on the current fault component waveform similarity

AbstractIn order to improve the ability of the modular multi‐level converter to quickly clear and isolate faults in the multi‐terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi‐terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid Dynamic Time Warping (DTW) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. Compared with other similarity methods, this method combines Euclidean DTW distance and entropy method to process the signal, realizes the fault pole identification, and can still operate correctly under the interference of 500 Ω transition resistance and 20 dB noise ratio. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.

Development of composite drought indices for the coastal areas of southeastern China: A case study of Jinjiang and Jiulongjiang River basins

Given the complexity of coastal drought events and their unique impacts on coastal ecosystems, it is vital to develop drought indices that can reflect both freshwater availability and impacts on ecological resources to better assess drought conditions over coastal regions. Existing drought indices developed for upland areas may not be suitable to capture the timing and severity of drought in the coastal regions. To explore coastal drought issue more comprehensively, this paper constructed the Coastal Composite Drought Indices (CCDIs) based on the entropy-weighted Euclidean distance method by freely combining six variables: precipitation (P), evaporation (E), runoff (R), salinity (S), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the Jinjiang River Basin (JJRB) and the Jiulong River Basin (JLRB). The CCDIs were later compared to the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) to select the Optimized Drought Indices (ODIs). The results demonstrated that the Precipitation Evaporation Runoff Composite Index (PER) is more sensitive to capturing short-term coastal drought events compared to other CCDIs. PER performed better than SPI and PDSI in detecting the onset of drought development and reflecting drought persistence. The salinity exhibited mixed responses to long-term drought events in different river basins depending on the size of each coastal system and its characteristics, thus raising the issue of regional applicability. Through a series of cross-comparisons and validations, the Runoff NDVI LST Composite Index (RNL) and the Runoff Salinity NDVI Composite Index (RSN) were chosen to monitor long-term droughts in the JJRB and JLRB, respectively. Overall, all three indices have the ability to capture the dynamics and trends of the variables and provide better information on the occurrence, persistence, and end of droughts in coastal areas.

Type of Refugee Accommodation and Health of Residents: A Cross-Sectional, Population-Based Cluster Analysis in South-West Germany.

Objectives: Few studies have assessed whether refugees' health is associated with accommodation characteristics. We aimed to devise a typology of refugee accommodation based on variables on the accommodation and its physical context before assessing its association with health in multivariate analyses. Methods: We performed a cluster analysis based on a hierarchal, agglomerative clustering algorithm using Euclidean Distance and Ward's method. We analysed accommodation clusters based on number of inhabitants, degree of housing deterioration, urbanity of location (urban/rural distinction), and remoteness (walking distance to shops, medical or administrative services). In total, we analysed health and accommodation data of 412 refugees and asylum seekers from 58 different accommodation facilities in the federal state of Baden-Württemberg in the south-west of Germany. Results: Accommodations with a moderate occupation, lowest levels of deterioration, and a central urban location showed the best health outcomes in terms of subjective general health status, depression, and generalized anxiety disorder (GAD). Associations were strongest for GAD and weakest for depression. Conclusion: Our findings inform policymakers on layout and location of refugee collective accommodation centres.

Design and application of a fault diagnosis system for machine tool angle heads based on Boolean matrix filtering and an optimized BP neural network

In this paper, a fault diagnosis method based on Boolean matrix filtering and optimizing backpropagation (BP) neural networks is proposed for angle heads of computer numerical control (CNC) machine tools. The matrix filtering is first carried out with the fault case database and the fault cause symptom Boolean matrix according to the fault types and characteristics of the machine tool angle head. On the basis of the combination of multiple fault causes obtained from the initial filtering, the Euclidean distance method is used to narrow the results of fault cause filtering. The BP neural network model with weight vector is established and optimized to perform an accurate diagnosis. Finally, the fault diagnosis and management system of the angle head of a CNC machine tool, integrating with the Boolean matrix filtering method, Euclidean distance method, and BP neural network model, is developed and implemented with the Python language and QT development framework.

Prediction of Rockburst Propensity Based on Intuitionistic Fuzzy Set—Multisource Combined Weights—Improved Attribute Measurement Model

A rockburst is a geological disaster that occurs in resource development or engineering construction. In order to reduce the harm caused by rockburst, this paper proposes a prediction study of rockburst propensity based on the intuitionistic fuzzy set-multisource combined weights-improved attribute measurement model. From the perspective of rock mechanics, the uniaxial compressive strength σc, tensile stress σt, shear stress σθ, compression/tension ratio σc/σt, shear/compression ratio σθ/σc, and elastic deformation coefficient Wet were selected as the indicators for predicting the propensity of rockburst, and the corresponding attribute classification set was established. Constructing a model framework based on an intuitionistic fuzzy set–improved attribute measurement includes transforming the vagueness of rockburst indicators with an intuitionistic fuzzy set and controlling the uncertainty in the results of the attribute measurements, as well as improving the accuracy of the model using the Euclidean distance method to improve the attribute identification method. To further transform the vagueness of rockburst indicators, the multisource system for combined weights of rockburst propensity indicators was constructed using the minimum entropy combined weighting method, the game theory combined weighting method, and the multiplicative synthetic normalization combined weighting method integrated with intuitionistic fuzzy sets, and the single-valued data of the indicators were changed into intervalized data on the basis of subjective weights based on the analytic hierarchy process and objective weights, further based on the coefficient of variation method. Choosing 30 groups of typical rockburst cases, the indicator weights and propensity prediction results were calculated and analyzed through this paper’s model. Firstly, comparing the prediction results of this paper’s model with the results of the other three single-combination weighting models for attribute measurement, the accuracy of the prediction results of this paper’s model is 86.7%, which is higher than that of the other model results that were the least in addition to the number of uncertain cases, indicating that the uncertainty of attribute measurement has been effectively dealt with; secondly, the rationality of the multiple sources system for combined weights is verified, and the vagueness of the indicators is controlled.

Webcam-Based Physical Distancing Prototype to Calculate Real-Time Distance by Applying Euclidean Distance

The Covid-19 virus has spread rapidly in Indonesia in recent years, significantly increasing the number of victims in the community. However, there is a lack of warnings for violators of physical distancing. This study aims to create a system as a solution to overcome the problems of implementing physical distancing protocols. Image capture media in the form of cameras were used to monitor human objects in crowded situations by tracking the movement of objects at regular physical distances. In this study, computer vision, image processing, and pattern recognition were used to construct the distance measurement between humans. Using a webcam by applying the Euclidean Distance and Haar Cascade methods, this system is intended to determine the distance between human-made objects. The results demonstrated that a digital image system employing the Haar Cascade and Eucludien Distance methods could make it easier to control the gathering point in a given area. Researcher has developed a software usage assistance feature for use with this application. Based on the findings of the study, the accuracy of this system was evaluated using 100 trials, with a succes rate of 100% since the system resulted correct notification in all trials. In addition, this digital image system is anticipated to assist developers and the general public in controlling gathering locations that do not adhere to established health protocols, allowing violations of physical separation to recognize their errors when captured on camera immediately.

Two-way referral cooperative hospital selection with uncertain information: A two-sided matching decision-making approach

To better rationalize the allocation of medical resources and improve the efficiency of medical resource utilization, researchers have focused on the problem of two-way referral cooperation among hospitals. The selection of an appropriate referral hospital constitutes a crucial aspect of a two-way referral system, which affects the quality of patient treatment and the financial outcome of hospital operations. Within this context, this study regards the collaborative referral hospital selection problem as a two-sided matching decision-making process. Given the hesitance and bounded rationality of decision-makers, a novel two-sided matching framework is developed that integrates hesitant fuzzy linguistic term sets and regret theory to more accurately reflect real-world conditions. Specifically, evaluation information is converted to hesitant fuzzy linguistic term sets via context-free grammar, and new utility functions are introduced to calculate the utility value, regret utility, and rejoice utility through the hesitant degree and score function. Furthermore, an optimization model for calculating the criteria weight is established based on the Euclidean distance and maximization deviation method. Ultimately, the matching satisfaction degree is determined, and a biobjective programming model that maximizes the overall matching satisfaction degree is formulated and solved. The results from experiments and analysis indicate that the proposed framework produces optimal and stable matching solutions, thereby providing a useful reference for hospitals seeking satisfactory referral partners. Moreover, this model could be extended to address large-scale two-sided matching problems or to refine the referral hospital selection problem by considering individual patient needs.