• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4476-4490
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• 2017

The aim of image annotation is to determine labels that can accurately describe the semantic information of images. Many approaches have been proposed to automate the image annotation task while achieving good performance. However, in most cases, the semantic similarities of images are ignored. Towards this end, we propose a novel Visual-Semantic Nearest Neighbor (VS-KNN) method by collectively exploring visual and semantic similarities for image annotation. First, for each label, visual nearest neighbors of a given test image are constructed from training images associated with this label. Second, each neighboring subset is determined by mining the semantic similarity and the visual similarity. Finally, the relevance between the images and labels is determined based on maximum a posteriori estimation. Extensive experiments were conducted using three widely used image datasets. The experimental results show the effectiveness of the proposed method in comparison with state-of-the-arts methods.

• 한국표면공학회지
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• 제50권4호
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• pp.277-281
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• 2017

Ferroelectric ceramics are broadly used for various industrial applications. In this research, the lead-free ferroelectric ceramics of $(K_{0.5}Na_{0.5})NbO_3$ was fabricated by using the solid state synthesis. The $(K_{0.5}Na_{0.5})NbO_3$ pellets were sintered at 1200, 1150 and $1100^{\circ}C$ for 4 hours in air atmosphere. Field-emission scanning electron microscopy (FE-SEM) characterization of the sintered KNN ceramics revealed surface morphology and grain size. And we used the X-ray diffraction (XRD) for measuring the sample crystal phase. Temperature dependence of the dielectric constant was measured by using an LCR meter. The sintered at $1150^{\circ}C$ for 4 hours sample has a highest dielectric constant 6011 at Curie temperature ($T_C$) and dense structure with $2.33{\mu}m$ grain size.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권4호
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• pp.405-414
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• 2016

IC-implementations provide high performance for solving the high computational cost of pattern matching but have relative low flexibility for satisfying different applications. In this paper, we report an associative memory architecture for k nearest neighbor (KNN) search, which is one of the most basic algorithms in pattern matching. The designed architecture features reconfigurable vector-component parallelism enabled by programmable switching circuits between vector components, and a dedicated majority vote circuit. In addition, the main time-consuming part of KNN is solved by a clock mapping concept based weighted frequency dividers that drastically reduce the in principle exponential increase of the worst-case search-clock number with the bit width of vector components to only a linear increase. A test chip in 180 nm CMOS technology, which has 32 rows, 8 parallel 8-bit vector-components in each row, consumes altogether in peak 61.4 mW and only 11.9 mW for nearest squared Euclidean distance search (at 45.58 MHz and 1.8 V).

• Geomechanics and Engineering
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• 제25권1호
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• Geomechanics and Engineering
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• 제29권3호
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.257-268
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• 2022

When there is a missing value in the raw data, if ignore the missing values and proceed with the analysis, the accuracy decrease due to the decrease in the number of sample. The method of imputation and analyzing patterns and significant values can compensate for the problem of lower analysis quality and analysis accuracy as a result of bias rather than simply removing missing values. In this study, we proposed to study irregular data patterns and missing processing methods of data using machine learning techniques for the study of correction of missing values. we would like to propose a plan to replace the missing with data from a similar past point in time by finding the situation at the time when the missing data occurred. Unlike previous studies, data correction techniques present new algorithms using DNN and KNN-MLE techniques. As a result of the performance evaluation, the ANAE measurement value compared to the existing missing section correction algorithm confirmed a performance improvement of about 0.041 to 0.321.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 춘계학술대회
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• pp.72-74
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• 2022

해상에 설치된 항로표지 부표의 발달로 다양한 자료가 수집된다. 그러나 원시 관측자료는 기계 결함 및 기상환경에 따라 결측과 이상치를 포함한 오류로 인하여 곧바로 사용되기 어렵다. 따라서 본 연구에서는 항로표지에서 수집된 미흡한 기상 관측 자료를 기계학습이 가능하도록 누락된 시각의 자료를 추가하여 선형 보간을 실시했다. 이후 XGBoost기법과 KNN-regressor을 이용하여, 오류가 발생한 시점의 자료를 보간하는 기법을 연구하고자 한다.