• 한국기계가공학회지
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• 제21권3호
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• pp.77-85
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• 2022

In this study, to diagnose the grinding state of a micro drill bit, a sensor attachment location was selected through random vibration analysis of the grinding unit of the micro drill-bit grinding system. In addition, the vibration data generated during the drill bit grinding were collected from the grinding unit for the grinding wheels under the steady and worn conditions, and data feature extraction and dimension reduction were performed. The wear of the micro-drill-bit grinding wheel was diagnosed by applying KNN, a machine-learning algorithm. The classification model showed excellent performance, with an accuracy of 99.2%. The precision, recall and f1-score were higher than 99% in both the steady and wear conditions.

• Geomechanics and Engineering
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• 제31권3호
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 하계학술대회 논문집 Vol.7
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• pp.282-283
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• 2006

본 연구에서는 분말입자의 미립화에 따른 $(K,Na)NbO_3$(KNN) 세라믹스의 소결밀도와 압전특성을 평가하였다. 먼저 입자를 미립화 시키기 위해 planetary milling machine을 이용하였으며, 소결밀도 및 압전 특성을 측정하였다. Ball milling을 24~72시간동안 한 결과 particle size는 730~490nm 정도였다. Milling 시간이 증가할수록 입자크기는 감소하였고, 소결밀도는 particle size가 작을수록 증가하였고, $4.50g/cm^3$으로 가장 높은 밀도를 나타냈다. 또한 소결 밀도가 증가함에 따라 기계적 품질 계수(Qm) 역시 증가한 반면, particle size가 작아짐에 따라 전기기계 결합계수(Kp)는 감소하는 경황을 보였다.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.63-74
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• 2017

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 춘계학술발표대회
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• 한국지진공학회논문집
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• 제28권2호
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권2호
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.343-364
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• 2021

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.