Distance Calculation Method Research Articles

Retraction Note: Rock stress and deformation characteristics based on SVM and sports high-intensity interval training

Since the 21st century, with the rapid development of China’s domestic economy and the continuous improvement of people’s living standards, the development and construction of urban infrastructure are also faster and faster, and the requirements for the stability of foundations such as buildings, roads and bridges, subway, and high-speed railway are also higher and higher. In the southwest of China, due to the influence of terrain conditions, there are a large number of soil rock mixtures with uneven particle size change and complex particle composition. Understanding the mechanical properties of soil rock mixture and effectively measuring the strength and deformation parameters of soil rock mixture are one of the most important research topics. At the same time, in order to identify the soil rock mixture, the conventional Gauss Euclidean distance calculation method cannot fully reflect the structure of soil rock mixture, so an improved Gauss kernel function multiclass support vector machine (MSVM) method is proposed. On the basis of Gaussian radial, Gaussian kernel function is used to replace Euclidean distance in ranging. The attitude kernel function of Gaussian kernel function is established according to ranging. Many types of support vector machines (SVM) are constructed by a binary tree method to complete classification. The experimental results show that, based on the improved Gaussian kernel function, this paper discusses the high-intensity interval training, so as to achieve a good recognition effect, whether it has a promoting effect on improving the physical quality of college boys, and designs the training scheme to carry out experimental intervention on college boys, so as to provide a new training theoretical reference for school physical education teaching. It also provides a training method for college boys to improve their physical fitness.

Arrangement Method of Vibration Dampers Considering Frequency Response Physical Characteristics and Stiffness of Conductor

In order to study the calculation method of installation distance of vibration dampers with higher precision, so as to maximize its anti-vibration performance on the conductor. The arrangement method of dampers based on the frequency response characteristics of conductors is proposed, the dynamic model of conductor considering stiffness is established. The model is solved by finite difference method, the natural frequency and vibration mode of conductor in case of Aeolian vibration are obtained, and the wavelength of conductor vibration is obtained according to its mode, so as to calculate the installation distance of vibration dampers. The calculation results of the new method are compared with those of the traditional method, which proves the advantage of the new method in calculation accuracy. The new method has a good effect on improving the calculation accuracy of the installation distance of the vibration dampers on the conductors, which is greatly influenced by its stiffness. At the same time, the frequency response of the conductor should be considered as much as possible in the calculation of the installation distance of the damper.

Energy‐Efficient Memristive Euclidean Distance Engine for Brain‐Inspired Competitive Learning

Inspired by competitive rules of the nature, competitive learning contributes to the specialization of the human brain and the general creativity of mankind. However, the construction of hardware competitive learning neural network still faces great challenges due to the lack of an accurate distance computation method and a self‐adaptive in situ training scheme. Herein, a fully memristive Euclidean distance (ED) engine based on analog multiply‐accumulate operation in a 32 × 32 TiN/TaO x /HfO x /TiN 1T1R array is demonstrated. The dual‐layer devices perform multilevel modulation under the target‐aware programming method with excellent read linearity in a dynamic range of 10–100 μS. The ED calculation is verified experimentally on a test board with an O(1) temporal complexity. Furthermore, in situ training and offline inference schemes for competitive learning, based on the ED engine, are developed and the simulated results show comparable success rates with those obtained by the CPU‐based software. Compared with a state‐of‐the‐art RTX6000 GPU (0.5 TOPS W−1), the energy efficiency of competitive learning models on ED engines can yield 100× improvements by utilizing optimized memristive devices.

RETRACTED ARTICLE:Rock stress and deformation characteristics based on SVM and sports high-intensity interval training

Since the 21st century, with the rapid development of China’s domestic economy and the continuous improvement of people’s living standards, the development and construction of urban infrastructure are also faster and faster, and the requirements for the stability of foundations such as buildings, roads and bridges, subway, and high-speed railway are also higher and higher. In the southwest of China, due to the influence of terrain conditions, there are a large number of soil rock mixtures with uneven particle size change and complex particle composition. Understanding the mechanical properties of soil rock mixture and effectively measuring the strength and deformation parameters of soil rock mixture are one of the most important research topics. At the same time, in order to identify the soil rock mixture, the conventional Gauss Euclidean distance calculation method cannot fully reflect the structure of soil rock mixture, so an improved Gauss kernel function multiclass support vector machine (MSVM) method is proposed. On the basis of Gaussian radial, Gaussian kernel function is used to replace Euclidean distance in ranging. The attitude kernel function of Gaussian kernel function is established according to ranging. Many types of support vector machines (SVM) are constructed by a binary tree method to complete classification. The experimental results show that, based on the improved Gaussian kernel function, this paper discusses the high-intensity interval training, so as to achieve a good recognition effect, whether it has a promoting effect on improving the physical quality of college boys, and designs the training scheme to carry out experimental intervention on college boys, so as to provide a new training theoretical reference for school physical education teaching. It also provides a training method for college boys to improve their physical fitness.

An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks

Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.

MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint Kashin-Beck disease

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare KCA of MapReduce (MR-KCA) and Improved MR-KCA (IMR-KCA). With the same data, the sum of squared errors of MR-KCA and IMR-KCA decreased with the increase in the number of center points. Compared with MR-KCA, the quality of IMR-KCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-KCA and IMR-KCA increased with the increase in the number of center points. Compared to MR-KCA, the total execution time of IMR-KCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-KCA could reduce the total execution time by 50%. Through experiments, IMR-KCA was proved to better present the diagnosis and treatment data of patients with knee joint KBD. The scalability rates of MR-KCA and IMR-KCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-KCA, IMR-KCA had significantly higher scalability. The IMR-KCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of KBD diagnosis and treatment.

Analysis and Prediction Of Pima Indian Diabetes Dataset Using SDKNN Classifier Technique

The newly proposed weighted k nearest neighbour is known as standard deviation K nearest neighbour(SDKNN) classifier technique. It is based on the principle of standard deviation. Standard deviation measures spreading of attribute about mean. Spreading of attribute plays a significant role to improve the classification accuracy of a dataset. Most of our distance calculation method between two points is determined by using euclidean distance process for finding nearest neighbour. Our proposed technique is based on a new distance calculation formula to find nearest neighbour in KNN. We apply here standard deviations of attributes as power for calculating distance between train dataset and test dataset. Distance calculation between two points in k nearest neighbour classifier is modified according to the standard deviation of attribute. In this paper, standard deviation of attributes are used. In first attempt, we have used standard deviation of attributes as power for calculating K Nearest Neighbour to improve classification accuracy and in second attempt, based on mean of standard deviation attributes, distance in K Nearest Neighbour is processed to further improve the classification accuracy. Our concept is implemented on Pima Indian Diabetes Dataset (PIDD). The analysis on Pima Indian Diabetes Dataset (PIDD) is carried out by splitting dataset in to 90% training data and 10% testing data. We have found that, in our proposed technique, average classification accuracy gives result 83.2%, a great improvement as compared to other conventional technique.

KINERJA METODE MAHALANOBIS DISTANCE YANG DIBENTUK DARI DUA UKURAN PUSAT DAN DUA DISPERSI MULTIVARIAT (UNTUK UKURAN SIMILARITAS KLASIFIKASI IMAGE)

Distance is a space that connects two points or two locations which can be calculated by length and time. Distance is used to measure the similarity of two objects (for example, an image object). An image can be considered to be similar to another image if the similarity size value is small. On the contrary, if the value of the similarity distance between the training object and the object being tested is large, the object can be said to be different or not. In this design, the image classification of Lakes, Forests and Settlements will be carried out by taking the Color feature using the Color Moment extraction method and the Texture feature using the GLCM (Gray Level Co-occurrence Matrix) extraction method and taking the method of calculating the distance between one data and another data that has High similiarity using the Mahalanobis Distance calculation method with two center sizes namely Mean and Median and three multivariate dispersions, namely the covariance matrix formed by the mean center value, the covariance matrix formed by the value of Median, and the covariance matrix formed by the value of Grand Median. From the research conducted, the performance results that can be considered for use are the Mahalanobis Distance with a median center size with a covariance matrix formed by the Median center value with an accuracy of 69.855% and a covariant matrix formed by the Grand Median center value with an accuracy of 69.565%. In this case the percentage is taken from testing images based on color characteristics using the Color Moment extraction method.

A Novel LOS Decision Technique Reflecting 3D DTED for Modeling an Electronic Warfare Environment

A new line-of-sight (LOS) decision algorithm applicable to simulation of electronic warfare (EW) is developed. For accurate simulation, the digital terrain elevation data (DTED) of the region to be analyzed must be reflected in the simulation, and millions of datasets are necessary in the EW environment. In order to obtain real-time results in such an environment, a technology that determines line-of-sight (LOS) quickly and accurately is very important. In this paper, a novel algorithm is introduced for determining LOS that can be applied in an EW environment with three-dimensional (3D) DTED. The proposed method shows superior performance as compared with the simplest point-to-point distance calculation method and it is also 50% faster than the conventional interpolation method. The DTED used in this paper is the data applied as level 0 for the Republic of Korea, and the decision of the LOS at approximately 1.8 million locations viewed by a reconnaissance plane flying 10 km above the ground is determined within 0.026 s.

Design and development of feasibility analysis software for live working

The work load of the traditional calculation method of safety distance check and circuit mechanics calculation is huge inefficiency, this paper presents a set of software of safety analysis and mechanics calculation for live working, the electrical and mechanical parameters of live working transmission lines are obtained, the feasibility of operation is analyzed, working tools are reasonably selected to improve the safety and efficiency of live working, it is of great significance to reduce the emergency response time, the electric safety distance check and the line mechanics calculation methods.

Safety Distance Analysis of 500kV Transmission Line Tower UAV Patrol Inspection

Aiming at the problem that UAV (Unmanned Aerial Vehicle) is widely used in transmission line inspections, and the inspection safety distance control is not clear enough. Based on the relevant domestic standards and regulations for UAV inspection operations, this letter establishes a 500kV cat head Tower UAV inspection operation electromagnetic field simulation finite element model. The model uses the field decomposition method to decompose the three-dimensional unbounded field into bounded sub-areas, and calculates and obtains the electric and magnetic field changes around the wire and when the UAV is at different positions on the wire. Combined with the average breakdown field strength of the bar-to-plate gap and the UAV electromagnetic compatibility limit, the safe distance calculation method for the UAV power inspection is comprehensively determined. The application of this safety distance calculation method can improve the reliability and safety of the transmission line UAV when conducting inspection operations, and is of great significance for ensuring the safety of lines and equipment.

FIXED VOTERS CLUSTERING TO DETERMINE THE LEVEL OF BEGINNER VOTERS USING DATA MINING TECHNIQUES

Data mining clustering technique is used to classify the level of beginner voters using the K-Means method. Fixed voter clusters are used for decision making for stakeholders regarding information on beginner voters in each district and sub-district. The error calculation method is used to measure the level of error value for each distance calculation used, the distance calculation method used ie Euclidean, Manhattan, and Minkowski Distance with the Means Square Error (MSE) approach to measure the level of the error value. The calculation results show that the lowest error occurs in the calculation of the Minkowski Distance model 3 cluster, where the error rate is 11%, while the highest error rate occurs in the calculation of the Manhattan Distance model 5 cluster, which is 38%.

Three-Dimensional Stopping Sight Distance Calculation Method under High Slope Restraint

Sight distance is an important indicator to ensure the safety of drivers, and is also an indispensable evaluation basis in highway safety engineering. In mountainous highways, high slopes and small radius often lead to poor visibility and traffic accidents. Through the combined calculation of horizontal and vertical sections, this paper comprehensively considers the specific sizes of roadside clearance, high slope, as well as the position and height of the driver’s view point and other factors, and it analyzes the limited visibility of the driver in the process of driving right turn. An effective and simplified calculation method based on design data for three dimensional (3D) stopping sight distance (S.S.D.) in high fill sections is proposed. Finally, the S.S.D. inspection of the actual highway, based on design speed and operating speed, is carried out, and the sight distance of the calculated point is judged by comparing the value with the normal value and the calculation result of the horizontal sightline offset. The results show that the method proposed in this paper is consistent with the sight distance results obtained by the horizontal sightline offset method, which indicates the calculation method is accurate and provides a technical reference for S.S.D. evaluation in highway safety engineering.

Positioning Method for a Radiation Source Using Radiation Spectroscopy Detectors

A system consisting of a gamma camera and a collimator is used to measure the location of a radiation source in a space where people use it or in places where people are crowded. Systems with collimators require considerable time to measure the location of the radiation source due to their low sensitivity, while can be a problem when minimizing damage through rapid tracking of radiation sources. As a solution to this low-sensitivity problem, a method of tracking the location of a radiation source by using several spectroscopy detectors for the detection of radiation has been developed. Become a radiation spectroscopy detector does not use a collimator, the sensitivity can be significantly improved compared to that of a gamma camera system with a collimator, which in turn enables the quick identification of the radiation source. The position measurement method is based on the principle that the amount of detected radiation is inversely proportional to the square of the distance. By using several radiation spectroscopy detectors, it was possible to derive the unknown equations for distance and the distance calculation method for each axis by using these equations. The radiation was measured using a Geant4 Application for Tomographic Emission (GATE) simulation, and the accuracy of the position measurement was evaluated using the distance the calculation method.

Research on Accurate House Price Analysis by Using GIS Technology and Transport Accessibility: A Case Study of Xi’an, China

Based on the symmetrical public transportation network data of Xi’an, China obtained by geographic information system (GIS) technology in 2019, three urban public transportation indexes of walking accessibility, bus accessibility, and metro accessibility were established, and a real estate price prediction model was built by using several machine learning algorithms to predict and analysis the housing price in Xi’an, China. Firstly, the symmetrical road network data and real estate property data of Xi’an were collected and preprocessed, secondly, the spatial syntax theory and distance calculation method were applied to establish three indexes of traffic accessibility; finally, taking the house property data and the calculated traffic accessibility indexes as the characteristic index, the real estate price prediction model of Xi’an was constructed by using the random forest algorithm (RF), lightweight gradient lift algorithm (LGBM), and gradient lifting regression tree algorithm (GBDT). The prediction accuracy of the final model is 89.2% and the root-mean-square error is 1761.84. The results show that the accessibility of bus and metro to some extent represent the convenience of public transportation in different areas of urban space. The higher the accessibility index is, the more convenient the traffic is. The real estate price model has high prediction accuracy and can reflect the real situation of urban real estate price. The importance of the three accessibility features to the real estate price prediction model are nearly more than 20%, which indicates that the accessibility of urban public transportation has an important impact on the change of urban real estate price, and the development of urban public transportation plays an important role in the real estate economy.

Energy stations and pipe network collaborative planning of integrated energy system based on load complementary characteristics

Abstract In view of the lack of effective energy station site optimization method in the existing integrated energy system (IES) planning, and the failure to consider the load characteristics in the division process of the energy supply area of energy stations, a collaborative planning method of energy stations and pipe networks considering the complementary load characteristics is proposed. Firstly, based on the analysis of the interaction between the station and network planning of IES, an alternated optimal method framework of energy station site, energy supply area division and pipe network layout are proposed. Secondly, based on the energy supply radius of the energy station, this paper proposes a method to determine the number of new energy stations, and based on the topological characteristics of the cellular networks, a method of determining the initial energy station site is proposed. Thirdly, considering the complementary load characteristics, the evaluation index and division method of energy supply area of integrated energy station are proposed. Fourthly, aiming at the problem of pipe network layout on the road, a pipe network optimization method based on Kruskal algorithm is proposed, and optimizes the direction of load access to the pipe network by the linear optimization model. Fifthly, the calculation method of equivalent energy distance of load and the optimization method of energy station site based on equivalent energy distance are proposed. Finally, the rationality and effectiveness of this method are verified by an example.

Parallel distance: A new paradigm of measurement for parallel driving

In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then, Markov chain &#x0028 MC &#x0029 is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.

Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods.

Monitoring Pneumatic Actuators\u2019 Behavior Using Real-World Data Set

Developing a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component’s all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward’s hierarchical clustering with multivariate Euclidean distance calculation and Kohonen self-organizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.

The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds

Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds.