Euclidean Distance Method Research Articles

Schedule Implementing the Modified Euclidean Distance Method in the Course Planning of the USN Kolaka Information Systems Curriculum

Scheduling is needed to anticipate the clash of lecture hours for lecturers in teaching. Some things that need to be considered when arranging a lecture schedule are lecturers, rooms, courses, hours, and days. In this study, the scheduling system uses the Euclidean Distance method by modifying the algorithm. The aim of this study is to prove the Euclidean distance method in the course planning system in universities. In general, to obtain data that will be used in the scheduling system, observations, interviews and bibliographic studies are carried out. From the research results, the lecture scheduling system using the Euclidean Distance method can be used in the scheduling system, especially lecture scheduling. This is proven, that the lecture scheduling system using the Euclidean Distance method does not have a schedule for lecturers who teach more than one course at the same time (collide).

Keanekaragaman dan Hubungan Kekerabatan Fenetik Spesies Anggota Famili Asteraceae di Jalur Pendakian Gunung Lawu Berdasarkan Karakter Morfologis

Asteraceae is one of the most diverse families in Angiosperms with an estimated of 23,600 species worldwide. One of the interesting place to explore the diversity of Asteraceae is Mount Lawu which has high potential for ecotourism. The newest track commonly used to climb to Mount Lawu is through Candi Cetho hiking track that was established in 2015. The study on the diversity of Asteraceae species on Candi Cetho hiking track has not been done before. The objective of this study was to explore the species diversity of Asteraceae and to determine their taxonomic relationship based on morphological characters. Plant samples were collected using explorative method along the hiking track, covering the altitude of 1,440 to 2,550 m above sea level. Identification of plant specimens based on morphological characters showed the existence of 13 species. Cluster analysis on 50 morphological characters was done using Euclidean distance and UPGMA method resulted in the recognition of two clusters. Based on the principal component analysis, the morphological characters which showed highest role in the grouping of Asteceae species into two clusters were the base of the upper leaf, flower color, and upper leaf attachment type.

A New Method of Ski Tracks Extraction Based on Laser Intensity Information

At present, the mainstream laser point cloud classification algorithms are mainly based on the geometric information of the target. Nevertheless, if there is occlusion between the targets, the classification effect will be negatively affected. Compared with the above methods, a new method of ski tracks extraction using laser intensity information based on target reflection is presented in this paper. The method can complete the downsampling of the point cloud datasets of ski tracks under the condition that the information of the target edge is complete. Then, the clustering and extraction of ski tracks are effectively accomplished based on the smoothing threshold and curvature between adjacent point clouds. The experimental results show that, different from the traditional methods, the composite classification method based on the intensity information proposed in this paper can effectively extract ski tracks from the complex background. By comparing the proposed method to the Euclidean distance method, the clustering segmentation method, and the RANSAC method, the average extraction accuracy is increased by 16.9%, while the over extraction rate is reduced by 8.4% and the under extraction rate is reduced by 8.6%, allowing us to accurately extract the ski track point cloud of a ski resort.

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis.

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward’s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance.

Rough IPFCM Clustering Algorithm and Its Application on Smart Phones with Euclidean Distance

New interval clustering technology for symbolic data analysis (SDA) on smart phones is shown to be beneficial for mobile computing devices for smart data analysis in this paper. A new interval clustering method that combined the rough set with interval possibilistic fuzzy C-means (IPFCM) algorithm under Euclidean distance is proposed and implemented on smart phones. Symbolic clustering algorithms (SCAs) have been widely used for pattern recognition, data mining, artificial intelligence, etc. In general, the SCA is unsupervised classification that is divided into groups according to symbolic data sets. However, the traditional interval fuzzy C-means (IFCM) clustering method still has noisy and data overlapping problems associated with these symbolic interval data. Hence, a new rough set with the interval possibilistic fuzzy C-means (RIPFCM) clustering algorithm with Euclidean distance was proposed to address the symbolic interval data (SID). That is, the proposed method can perform better than the traditional IFCM clustering algorithm for SID clustering in noisy environments and with data overlapping problems. The new RIPFCM algorithm under the Euclidean distance method was proposed to deal with SID on new applications in smart phones. Consequently, this method shows the expansion of the smart phone’s computing power and its future application in new SDA.

Multi-Fault Diagnosis of Lithium-Ion Battery Systems Based on Correlation Coefficient and Similarity Approaches

The continuous occurrence of lithium-ion battery system fires in recent years has made battery system fault diagnosis a current research hotspot. For a series connected battery pack, the current of each cell is the same. Although there are differences in parameters such as internal ohmic resistance, the relative change of parameters between cells is small. Therefore, the correlation coefficient of voltage signals between different cells can detect the faulty cell. Inspired by this, this paper proposes an improved Euclidean distance method and a cosine similarity method for online diagnosis of multi-fault in series connected battery packs, and compares them with the correlation coefficient method. The voltage sensor positions are arranged according to the interleaved voltage measurement design. The multi-fault involved in this study, including connection faults, sensor faults, internal short-circuit faults and external short-circuit faults, will lead to abnormal sensor readings at different positions, which in turn will cause changes in correlation coefficient, Euclidean distance and cosine similarity to achieve fault detection. Fault experiments were conducted to verify the feasibility of the three methods in a series connected battery pack.

Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering

Our aim was to explore whether changes in plasma essential AA (EAA) concentration ([EAA]p) or profile (defined here as the molar proportion of individual [EAA]p relative to the total [EAA]p) may serve as an indicator of the EAA status of a cow. We undertook a meta-analysis with the objectives to determine if different plasma EAA profiles exist among cows and to explore the association of [EAA]p or the profile of EAA with lactating cow performance and measures of N utilization. We hypothesized the existence of differences in [EAA]p and different plasma EAA profile for cows with greater milk output, feed efficiency, and greater N use efficiency (NUE; milk true protein-N:N intake) compared with cows with lower milk output, feed efficiency, and lower NUE. The data set included 22 feeding trials and 96 dietary treatments. First, a mixed-effect model analysis was used to predict [EAA]p in response to the categorical fixed effect of EAA, continuous fixed effect of National Research Council model-predicted metabolizable protein (MP) supply, continuous fixed effect of body weight, the fixed effect of EAA and MP supply interaction, the fixed effect of EAA and body weight interaction, and the random effect of study. Then, residuals of the model were standardized based on Z-score and clustered using the hierarchical method (Euclidean distance and Ward's minimum variance method) resulting in 2 clusters. Finally, a fixed-effect model was used to evaluate the significance with which clusters were associated with [EAA]p, cow performance, feed efficiency, and NUE. The total concentration of [EAA]p was lower (784 vs. 983 µM) and the concentration of each EAA was on average 22 µM lower for cows in cluster 1 compared with cluster 2 with the smallest and greatest difference found for Met (4 µM) and Val (59 µM), respectively. The percentage difference in [EAA]p was the smallest for Thr (-5.3%) and the greatest for Leu (-37.1%). There was no difference between clusters for Arg, His, and Met molar proportions; however, cows in cluster 1 had a lower molar proportion of Leu and a tendency for lower molar proportion of Val compared with cows in cluster 2. Additionally, cows in cluster 1 had greater molar proportions of Ile, Lys, and Thr and a tendency for greater molar proportion of Phe compared with cows in cluster 2. The fixed-effect model analysis indicated that cows in cluster 1 had higher milk energy output (+3.2 Mcal/d), true protein yield (+87 g/d) and fat yield (+236 g/d), feed efficiency (milk Mcal:dry matter intake; +8% unit), and a tendency for greater MP efficiency (Milk true protein/MP supply; +2.3% unit) than cows in cluster 2. These results suggested greater use of EAA by the mammary gland (as reflected by greater milk protein synthesis) and lower hepatic catabolism of AA (as reflected by a tendency to greater MP efficiency) in cows of cluster 1 compared with cluster 2. Our findings should be evaluated further, including whether the relative molar proportions of plasma EAA might serve as a holistic indicator of the EAA status of cows as related to their productivity, feed efficiency and N utilization.

Path‐finding algorithm as a dispersal assessment method for invasive species with human‐vectored long‐distance dispersal event

AbstractAimAn assessment method that can precisely represent human‐vectored long‐distance dispersals (HVLDD) is currently in need for the effective management of invasive species. Here, we focussed on HVLDD happening along roads and proposed a path‐finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study.LocationBusan Metropolitan City, Republic of Korea.MethodsA path‐finding algorithm, which calculates distances by considering the spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019, and their dispersal distances were calculated using the path‐finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017 and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm.ResultsThe potential dispersal ranges of the path‐finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length.Main conclusionsThe path‐finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path‐finding algorithm for the dispersal assessment of invasive species that move along road networks, and for the development of more powerful HVLDD prediction models.

Analysis of Genetic Diversity by D2-Statistics using Morpho-Physiological Traits of Late Sown Wheat (Triticum aestivum L.)

The sixty-bread wheat (Triticum aestivum L.) genotypes were distributed among the seven clusters obtained using D2 statistics Euclidean distance method on the basis of observations taken on various morphological and physiological traits namely days to heading, days to anthesis, days to physiological maturity, grain filling duration, plant height, number of effective tillers, thousand grain weight, grain yield, chlorophyll fluorescence and canopy temperature. Results obtained revealed that the cluster IV had maximum number of genotypes i.e.,17 genotypes followed by cluster I (13 genotypes), Cluster IV (10 genotypes), cluster VII, II, III and VI (9, 5, 3 and 3 respectively). The highest inter cluster genetic distance existed between cluster I and VI (6.559) and the average intra cluster distance between the genotypes of cluster VII (3.817) was maximum. Therefore, genotypes in these clusters may be used to produce the superior hybrids and transgressive segregants. For grain yield and effective tillers had highest mean value in cluster VI. Cluster III had highest mean value for 1000-grain weight and lowest values for plant height indicating genotypes were short. Traits such as days to anthesis followed by canopy temperature, days to maturity, grain filling duration, chlorophyll fluorescence and grain yield per meter contributing a total of 68.1 per cent to the total divergence.

Genetic Diversity Analysis among Genotypes of Bread Wheat (Triticum aestivum L.) for Yield and Its component Traits under Timely Sown Conditions

Genetic diversity analysis for yield and its component traits among 60 genotypes of bread wheat (Triticum aestivum L.) under timely sown conditions distributed into seven clusters via D2 statistics using Euclidean distance method which revealed that the cluster IV had maximum number of genotypes followed by cluster II, Cluster VI, cluster I, cluster V and, cluster III and VII. The highest inter-cluster genetic distance existed between cluster IV and VII and the average intra cluster distance between the genotypes of cluster V was maximum. Therefore, genotypes in these clusters may be used to produce the superior hybrids and transgressive segregants. For grain yield and effective tillers had highest mean value in cluster V and cluster VI. Cluster V and VII had highest mean value for 1000-grain weight and plant height respectively. Traits such as by plant height followed by canopy temperature, days to maturity, days to anthesis and grain filling duration contributing total of 50 per cent to the total divergence.

Development of crowd detection warning system based on deep convolutional neural network using CCTV

The 2019 corona virus (Covid-19) pandemic is a global problem for now. One way to deal with the spread of the corona virus is to maintain a distance of at least one meter and stay away from crowds. Therefore, a crowd detection warning system based on a deep convolutional neural network (deep CNN) was developed using CCTV. The development of this system was carried out using the NVIDIA Jetson Nano microcontroller as the computing hardware. Crowd object detection uses the OpenCV library, the YOLOv3-Tiny algorithm, and the euclidean distance method to calculate the distance between 'person' objects. Based on the tests carried out on function and performance, the results obtained that this crowd detection warning system can detect 'person' objects with an accuracy rate of 92.79. In addition, this system has also been able to detect several types of colors from objects so that warning messages can be given more specifically on the color of the clothes of the 'person' in the detected crowd.

Identification of Buni Fruit Image Using Euclidean Distance Method

Identification is an important part of image analysis because in this procedure the desired image/image will be analyzed for further processing to make it easier to analyze for further purposes, for example in image identification pattern recognition which is part of image analysis used to divide an image into several parts and take some of the desired objects. This study aims to identify buni fruit with Euclidean distance and extract shape and texture features. Extraction of shape features using boundary metrics and whimsy. This boundary is considered to be able to recognize objects based on their shape and can distinguish them from other objects. For identification expositions, Euclidean distance is used which serves to represent the level of similarity of two images that take into account the distance value of the Euclidean distance. From the results of the evaluation using a disarray network by calculating precision, review, and accuracy, in order to identify the image of the buni fruit object properly.

Penerapan Euclidean Distance untuk Deteksi Kemiripan Citra Berbasis Scale Invariant Feature Transform (SIFT)

The similarity of a pair of images can be measured using the distance measurement method from the feature extraction. The feature extraction method used in this research was the Scale Invariant Feature Transform (SIFT). This method is an extraction method that is invariant to changes in scale, rotation, translation and illumination. In this research, each keypoint of test image is matched for its level of similarity with the Euclidean distance method. The similarity of each keypoint of the tested image is matched by Euclidean distance and it will be claimed similar if it has the smallest distance. Next, the corresponding keypoint of the tested image gets recall test by varying parameters of transformation in size, rotation, color, and angle as well as different image. The research results demonstrated that the recall averages of dataset were 100 for similar image test, 95 for size and rotation changes tests, 98 for colour changes test, 97 for angle changes test, and 0 for image test with different objects. Based on the results, SIFT is suitable for detecting the similarity of image object shapes.

Euclidean Distance Modeling of Musi River in Controlling the Dengue Epidemic Transmission in Palembang City

BACKGROUND: Various attempts have been made to control the population of Aedes aegypti with the help of chemicals or by engineering Wolbachia pipentis, an obligate intracellular bacterium that is passed down through DENV and arbovirus infections to manipulate the monthly average reproductive yield. This study reviews the phenomenon of the river border area which is one of the habitats for the Aedes aegypti mosquito in the Musi River, Palembang City.
 AIM: The application of the euclidean distance method in this study was carried out to determine the environmental exposure of settlements along the river basin area.
 METHODS: The research methodology was carried out objectively related to data on dengue incidence in 2019. It was carried out by taking location coordinates through the application of geographic information systems and the use of satellite imagery for data acquisition of existing buildings. This stage is followed by bivariate statistical calculations using the application of WoE where the probability value of the measurement is described using the Area Under Curve. Processing and accumulation carried out with existing buildings will result in a calculation of the estimated size of the exposure area.
 RESULTS: The results obtained provide information, where the natural breaks jeanks value of 0.007-0.016 range results in 1465ha of heavily exposed building area. The value of the temporary bivariate statistical calculation will produce an AUC probability number of 0.44 which describes the relationship between the Musi river and the findings of dengue symptoms in the sub-districts around the Musi river border area, Palembang City. Swamp soil conditions are vulnerable to being a habitat where Aedes aegypti larvae are found.
 CONCLUSIONS: Based on the analysis that we obtained from the population of dengue incidence and the condition of the river basin area showed a significant structure with the distribution of dengue incidence, it is known that the presence of buildings on the river Musi banks has a greater risk of infectious diseases transmissions and natural disasters ranging from sanitation, hygiene, flooding to river erosion.

IMAGE PROCESSING USING K MEANS CLUSTERING AND EUCLIDEAN DISTANCE METHOD

- Image processing refers to the processing of images, which includes many different techniques that are used till we reach our goal. The processing output can either be in the form of an image, or a feature of the given image. This can be used for decision making and a more comprehensive image analysis. The Image compression process is a variation of data compression that is used on images without affecting their quality. Reduced size of the file makes sure that a lot of images can be put in a certain amount of memory / disk space. Processing also decreases the required time for the images to travel over the Internet or to be downloaded from different web pages. K - means clustering using Euclidean distance method includes vector quantization, which is originally a part of signal processing. It is very popular in cluster analysis for mining data. In this Project, we are using Image compression and segmentation algorithms using K – means clustering and Euclidean Distance method and many more algorithms used to process images.

Agro-Morphological Characterization and Intra-Varietal Diversity of Akabarechilli (Capsicum spp.) Landraces of Nepal

Capsicum (Solanaceae), comprising of sweet and hot chilli pepper, is a globally known spice crop. This genus is well known for its huge genetic diversity at intra- and inter-species level. Diversity among Akabarechilli landraces, a unique Capsicum from Nepal, has not been studied so far. The present study characterized thirty accessions of Akabarechilli using 26 qualitative and 21 quantitative agro-morphological markers at the experimental plots of National Genebank, Khumaltar, Nepal during the summer season of 2018/019. Using principal component analysis, the first- principal components with eigen-values more than 1 contributed 25% of the variability among accessions for quantitative traits, whereas the first principal components with eigen-values more than 1 contributed 22.2% of the variability among accessions for qualitative traits. These findings suggested a wide range of morphological variations among the tested accessions. Based on qualitative and quantitative traits, 30 accessions were grouped into 6 distinct clusters by Euclidian distance and average method. Accessions CO-11048 and CO-11050 under cluster-III were round shape fruit type and accessions CO-11044, CO- 11046 andCO-11047 under cluster-I were high fruiting with longer fruitbearing period and could be utilized for breeding purpose as these are the traits preferred by consumers or processors in Nepalese market. SAARC J. Agric., 19(2): 37-55 (2021)

Impacts of the choice of distance measurement method on estimates of access to point-based resources.

Lack of access to resources such as medical facilities and grocery stores is related to poor health outcomes and inequities, particularly in an environmental justice framework. There can be substantial differences in quantifying "access" to such resources, depending on the geospatial method used to generate distance estimates. We compared three methods for calculating distance to the nearest grocery store to illustrate differential access at the census block-group level in the Atlanta metropolitan area, including: Euclidean distance estimation, service areas incorporating roadways and other factors, and cost distance for every point on the map. We found notable differences in access across the three estimation techniques, implying a high potential for exposure misclassification by estimation method. There was a lack of nuanced exposure in the highest- and lowest-access areas using the Euclidean distance method. We found an Intraclass Correlation Coefficient (ICC) of 0.69 (0.65, 0.73), indicating moderate agreement between estimation methods. As compared with Euclidean distance, service areas and cost distance may represent a more meaningful characterization of "access" to resources. Each method has tradeoffs in computational resources required versus potential improvement in exposure classification. Careful consideration of the method used for determining "access" will reduce subsequent misclassifications.

Identification of the promising olive (Olea europaea L.) cultivars based on morphological and pomological characters.

Olive (Olea europaea L.) is an ancient tree and can tolerate drought very well. In the present study, morphological and pomological diversity of 24 olive cultivars (5–15 replications for each cultivar, 243 trees in total) was evaluated. There were significant differences among the cultivars studied based on the characters recorded. The CV was more than 20.00% in 46 of 50 characters measured. Leaf length ranged from 27.07 to 78.54 mm, and leaf width varied from 5.42 to 23.06 mm. Ripening date ranged from late‐August to early‐October. Fruit length ranged from 13.04 to 33.72 mm, fruit diameter varied from 10.24 to 23.71 mm, fruit weighted from 0.97 to 9.61 g, and the range of fruit flesh thickness was 1.63–7.65 mm. There was high variability in terms of fruit color, ranging from light green to black. Hierarchical cluster analysis (HCA) performed based on the mean of replications with Euclidean distance and Ward method grouped the cultivars into two major clusters. Differences in many of the morphological traits were observed across the cultivars. These sets of data were used to identify unique and desirable cultivars morphologically. The present research demonstrates that local olive cultivars have unique characteristics that differentiate them from imported cultivars. Thus, local cultivars provide novel genetic resources that should be conserved.

Identification of superior late-blooming apricot (Prunus armeniaca L.) genotypes among seedling-originated trees.

One of the major limiting factors in the intensive spread of apricot (Prunus armeniaca L.) in most of the countries is spring frost. Thus, the breeding efforts have concentrated on the use of late‐blooming genotypes as a means of frost avoidance. The aim of the present study was to identify late‐blooming genotypes with high fruit quality among seedling‐originated trees. Firstly, pre‐selections were done according to blooming time within 278 apricot seedling‐originated trees. Secondly, the late‐blooming selections were further evaluated according to their vegetative and fruit characteristics to determine superior types. Significant differences were observed among the late‐blooming genotypes in terms of the traits recorded. Fruit ground color was strongly variable, including white, yellow, yellow–green, light orange, orange, and dark orange. Fruit weight ranged from 27.37 to 33.99 g, fruit flesh thickness varied from 11.16 to 13.47 mm, and total soluble solids varied between 17.00% and 23.87%. Hierarchical cluster analysis (HCA) performed with Euclidean distance and Ward's method divided the genotypes into two main clusters based on morphological traits and in some cases, the genotypes belonging to an area were clustered into different clusters. All the 48 late‐blooming date genotypes selected could be useful as a parent to improve flowering season of cultivars. Also, among them, 10 genotypes were superior in terms of fruit quality‐related characters, such as fruit weight, fruit color, fruit taste, and TSS, and thus they can be singled out for cultivation.

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.