scholarly journals Klasifikasi Tingkat Kekakuan Dinding Beton Terhadap Getaran Dengan Metode K-Nearest Neighbor

2020 ◽  
Vol 10 (2) ◽  
pp. 189
Author(s):  
Mochammad Shidqi Taufiqurrahman ◽  
Lukman Awaludin
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Earthquake Intensity ◽  
Ground Acceleration ◽  
Low Level ◽  
Drift Ratio ◽  
Wall Stiffness ◽  
High Degree ◽  
The Impact

The low level of wall stiffness can cause damage to buildings during large-scale earthquakes. There are many systems for measuring the level of stiffness in buildings, but they have not yet reached the classification stage. Therefore, a system that can classify stiffness is needed to determine the impact of vibrations on the wall to minimize the losses incurred.This study creates a system that can classify the level of wall stiffness using the K-Nearest Neighbor (KNN) method into several categories (safe, vulnerable, dangerous, and destroyed). The data taken at the acquisition stage are ground acceleration, inclination angle, displacement, drift ratio, and peak value. The KNN input is a peak ground acceleration value, which causes a drift ratio of 1%. The resulting output is a category of wall stiffness based on the Earthquake Intensity Scale by BMKG.Functionally, the system designed can classify wall stiffness with non-linear data input using the K-Nearest Neighbor (KNN) method. The success rate of KNN reaches a value of 100%. Based on the PGA drift ratio reading, it is assumed that the wall can withstand the maximum vibration with a PGA drift ratio value of 0.34 g without causing damage to the wall even though it has a low level of stiffness. Testing on the walls has a less high degree of precision. That may be due to factors other than PGA. That can affect the drift ratio on the walls, which have not been considered in this study.

The academic profession in Russia’s two capitals: The impact of 20 years of transition

2017 ◽  
Vol 16 (5) ◽  
pp. 626-644 ◽  
Author(s):  
Elizaveta Sivak ◽  
Maria Yudkevich
Keyword(s):  
Large Scale ◽  
Academic Career ◽  
Academic Profession ◽  
University Faculty ◽  
Key Characteristics ◽  
Personal Strain ◽  
High Degree ◽  
The Impact ◽  
Russian Universities

This paper studies the dynamics of key characteristics of the academic profession in Russia based on the analysis of university faculty in the two largest cities in Russia – Moscow and St Petersburg. We use data on Russian university faculty from two large-scale comparative studies of the academic profession (‘The Carnegie Study’ carried out in 1992 in 14 countries, including Russia, and ‘The Changing Academic Profession Study’, 2007–2012, with 19 participating countries and which Russia joined in 2012) to look at how faculty’s characteristics and attitudes toward different aspects of their academic life changed over 20 years (1992–2011) such as faculty’s views on reasons to leave or to stay at a university, on university’s management and the role of faculty in decision making. Using the example of universities in the two largest Russian cities, we demonstrate that the high degree of overall centralization of governance in Russian universities barely changed in 20 years. Our paper provides comparisons of teaching/research preferences and views on statements concerning personal strain associated with work, academic career perspectives, etc., not only in Russian universities between the years 1992 and 2012, but also in Russia and other ‘Changing Academic Profession’ countries.

scholarly journals A CloudSat–CALIPSO View of Cloud and Precipitation Properties across Cold Fronts over the Global Oceans

2015 ◽  
Vol 28 (17) ◽  
pp. 6743-6762 ◽  
Author(s):  
Catherine M. Naud ◽  
Derek J. Posselt ◽  
Susan C. van den Heever
Keyword(s):  
Large Scale ◽  
Cold Front ◽  
Conveyor Belt ◽  
Low Level ◽  
Scale Parameters ◽  
Convective Clouds ◽  
Cold Fronts ◽  
Surface Front ◽  
High Level ◽  
The Impact

Abstract The distribution of cloud and precipitation properties across oceanic extratropical cyclone cold fronts is examined using four years of combined CloudSat radar and CALIPSO lidar retrievals. The global annual mean cloud and precipitation distributions show that low-level clouds are ubiquitous in the postfrontal zone while higher-level cloud frequency and precipitation peak in the warm sector along the surface front. Increases in temperature and moisture within the cold front region are associated with larger high-level but lower mid-/low-level cloud frequencies and precipitation decreases in the cold sector. This behavior seems to be related to a shift from stratiform to convective clouds and precipitation. Stronger ascent in the warm conveyor belt tends to enhance cloudiness and precipitation across the cold front. A strong temperature contrast between the warm and cold sectors also encourages greater post-cold-frontal cloud occurrence. While the seasonal contrasts in environmental temperature, moisture, and ascent strength are enough to explain most of the variations in cloud and precipitation across cold fronts in both hemispheres, they do not fully explain the differences between Northern and Southern Hemisphere cold fronts. These differences are better explained when the impact of the contrast in temperature across the cold front is also considered. In addition, these large-scale parameters do not explain the relatively large frequency in springtime postfrontal precipitation.

Region-Based Graph Learning towards Large Scale Image Annotation

2012 ◽  
pp. 244-260
Author(s):  
Bao Bing-Kun ◽  
Yan Shuicheng
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Label Propagation ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph ◽  
Modeling Data

Graph-based learning provides a useful approach for modeling data in image annotation problems. In this chapter, the authors introduce how to construct a region-based graph to annotate large scale multi-label images. It has been well recognized that analysis in semantic region level may greatly improve image annotation performance compared to that in whole image level. However, the region level approach increases the data scale to several orders of magnitude and lays down new challenges to most existing algorithms. To this end, each image is firstly encoded as a Bag-of-Regions based on multiple image segmentations. And then, all image regions are constructed into a large k-nearest-neighbor graph with efficient Locality Sensitive Hashing (LSH) method. At last, a sparse and region-aware image-based graph is fed into the multi-label extension of the Entropic graph regularized semi-supervised learning algorithm (Subramanya & Bilmes, 2009). In combination they naturally yield the capability in handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets well validate the effectiveness and efficiency of the framework for region-aware and scalable multi-label propagation.

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

Optimized Cost Model for k-NN Queries in R*-Trees over Random Distribution

2011 ◽  
Vol 403-408 ◽  
pp. 3315-3321
Author(s):  
Sirisala Nageswara Rao
Keyword(s):  
Nearest Neighbor ◽  
Cost Model ◽  
Neighbor Join ◽  
Multidimensional Data ◽  
Analysis Model ◽  
K Nearest Neighbor ◽  
Storage And Retrieval ◽  
Key Issues ◽  
Improved Performance ◽  
The Impact

Efficient storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. Today’s research has turned towards the development of powerful analytical method to predict the performance of such indexing structures such as for varies categories of queries such as range, nearest neighbor, join, etc .This paper focuses on performance of R*-tree for k nearest neighbor (kNN) queries. While general approaches are available in literature that works better for larger k over uniform data, few have explored the impact of small values of k. This paper proposes improved performance analysis model for kNN query for small k over random data. The results are tabulated and compared with existing models, the proposed model out performs the existing models in a significant way for small k

scholarly journals Impact of a pulsed xenon disinfection system on hospital onset Clostridioides difficile infections in 48 hospitals over a 5-year period

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarah Simmons ◽  
Grady Wier ◽  
Antonio Pedraza ◽  
Mark Stibich
Keyword(s):  
Nearest Neighbor ◽  
Negative Binomial ◽  
Learning Algorithm ◽  
Compliance Rate ◽  
Negative Binomial Regression ◽  
Inverse Association ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Clostridioides Difficile ◽  
The Impact

Abstract Background The role of the environment in hospital acquired infections is well established. We examined the impact on the infection rate for hospital onset Clostridioides difficile (HO-CDI) of an environmental hygiene intervention in 48 hospitals over a 5 year period using a pulsed xenon ultraviolet (PX-UV) disinfection system. Methods Utilization data was collected directly from the automated PX-UV system and uploaded in real time to a database. HO-CDI data was provided by each facility. Data was analyzed at the unit level to determine compliance to disinfection protocols. Final data set included 5 years of data aggregated to the facility level, resulting in a dataset of 48 hospitals and a date range of January 2015–December 2019. Negative binomial regression was used with an offset on patient days to convert infection count data and assess HO-CDI rates vs. intervention compliance rate, total successful disinfection cycles, and total rooms disinfected. The K-Nearest Neighbor (KNN) machine learning algorithm was used to compare intervention compliance and total intervention cycles to presence of infection. Results All regression models depict a statistically significant inverse association between the intervention and HO-CDI rates. The KNN model predicts the presence of infection (or whether an infection will be present or not) with greater than 98% accuracy when considering both intervention compliance and total intervention cycles. Conclusions The findings of this study indicate a strong inverse relationship between the utilization of the pulsed xenon intervention and HO-CDI rates.

scholarly journals K-Nearest Neighbor Classification for Detection of The Effect of Game Addiction on Cognitive Activity in The Late Adolescent Phase based on Brainwave Signals

2019 ◽  
Vol 1 (2) ◽  
pp. 46-62
Author(s):  
Ahmad Azhari ◽  
Ajie Kurnia Saputra Swara
Keyword(s):  
Cognitive Abilities ◽  
Nearest Neighbor ◽  
Cognitive Activity ◽  
World Health ◽  
Gaming Disorder ◽  
Negative Consequences ◽  
K Nearest Neighbor ◽  
Late Adolescent ◽  
Game Addiction ◽  
The Impact

World Health Organization (WHO) has determined that Gaming disorder is included in the International Classification of Diseases (ICD-11). The behavior of playing digital games included in the Gaming disorder category is characterized by impaired control of the game, increasing the priority given to the game more than other activities insofar as the game takes precedence over other daily interests and activities, and the continuation or improvement of the game despite negative consequences. The influence of video games on children's development has always been a polemic because in adolescence not only adopts cognitive abilities in learning activities, but also various strategies related to managing activities in learning, playing and socializing to improve cognitive abilities. Therefore, this research was conducted to analyze the cognitive activity of late teens in learning and playing games based on brainwave signals and to find out the impact of games on cognitive activity in adolescents. Prediction of the effect of the game on cognitive activity will be done by applying Fast Fourier Transform for feature extraction and K-Nearest Neighbor for classification. The results of the expert assessment showed the percentage of respondents with superior cognitive category but game addiction was 63.3% and respondents with cognitive categorization were average but were addicted by 36.6%. The percentage of accuracy produced by the system shows 80% in games and cognitive by using k values of 1, 6, and 7. The correlation test results show a percentage of 0.089, so it is concluded that there is no influence of the game on cognitive activity in late adolescents.

scholarly journals On the Orographically Generated Low-Level Easterly Jet and Severe Downslope Storms of March 2006 over the Tacheng Basin of Northwest China

2018 ◽  
Vol 146 (6) ◽  
pp. 1667-1683 ◽  
Author(s):  
Guangxing Zhang ◽  
Da-Lin Zhang ◽  
Shufang Sun
Keyword(s):  
Large Scale ◽  
Inversion Layer ◽  
Dust Storms ◽  
Frozen Soil ◽  
Gradient Force ◽  
Mountain Range ◽  
Pressure Gradient Force ◽  
Low Level ◽  
Downslope Winds ◽  
The Impact

A high-latitude low-level easterly jet (LLEJ) and downslope winds, causing severe dust storms over the Tacheng basin of northwestern China in March 2006 when the dust source regions were previously covered by snow with frozen soil, are studied in order to understand the associated meteorological conditions and the impact of complex topography on the generation of the LLEJ. Observational analyses show the development of a large-scale, geostrophically balanced, easterly flow associated with a northeastern high pressure and a southeastern low pressure system, accompanied by a westward-moving cold front with an intense inversion layer near the altitudes of mountain ridges. A high-resolution model simulation shows the generation of an LLEJ of near-typhoon strength, which peaked at about 500 m above the ground, as well as downslope windstorms with marked wave breakings and subsidence warming in the leeside surface layer, as the large-scale cold easterly flow moves through a constricting saddle pass and across a higher mountain ridge followed by a lower parallel ridge, respectively. The two different airstreams are merged to form an intense LLEJ of cold air, driven mostly by zonal pressure gradient force, and then the LLEJ moves along a zonally oriented mountain range to the north. Results indicate the importance of the lower ridge in enhancing the downslope winds associated with the higher ridge and the importance of the saddle pass in generating the LLEJ. We conclude that the intense downslope winds account for melting snow, warming and drying soils, and raising dust into the air that is then transported by the LLEJ, generated mostly through the saddle pass, into the far west of the basin.

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