• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제1권1호
• /
• pp.19-31
• /
• 2007

RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water content of the object absorbs radio waves at high frequencies, typically approximately 900 MHz, resulting in unstable tag signal power. Currently, finding the best conditions for factors influencing the tag detection requires very time consuming work at deployment. Thus, a quick and simple RFID tag detection scheme is needed to improve the current time consuming trial-and-error experimental method. This paper proposes a back-propagation learning-based RFID tag detection prediction scheme, which is intelligent and has the advantages of ease of use and time/cost savings. The results of simulation with the proposed scheme demonstrate a high prediction accuracy for tag detection on a water content, which is comparable with the current method in terms of time/cost savings.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1352-1355
• /
• 1996

The control of diamond turning is usually achieved through a laser-interferometer feedback of slide position. If the tool post is rigid and the material removal process is relatively static, then such a non-collocated position feedback control scheme may surface. However, as the accuracy requirement gets tighter and desired surface contours become more complex, the need for a direct tool-tip sensing becomes inevitable. The physical constraints of the machining process prohibit any reasonable implementation of a tool-tip motion measurement. It is proposed that the measured force normal to the face of the workpiece can be filtered through an appropriate admittance transfer function to result in the estimated depth of cut. This can be compared to the desired depth of cut to generate the adjustment control action in addition to position feedback control. In this work, the design methodology on the admittance model-based control with a conventional controller is presented. The recursive least-squares algorithm with forgetting factor is proposed to identify the parameters and update the cutting process in real time. The normal cutting forces are measured to identify the cutting dynamics in the real diamond turning process using the precision dynamometer. Based on the parameter estimation of cutting dynamics and the admittance model-based nanodynamic control scheme, simulation results are shown.

• 한국소성가공학회:학술대회논문집
• /
• 한국소성가공학회 2007년도 추계학술대회 논문집
• /
• pp.173-176
• /
• 2007

The characteristics of the tool system give many effects into the costs and qualities for the finished components. This study proposes a new method for manufacturing of high manufacturing productivity, production process reduction and low cost through back pressure forming. The Lock-up hub is manufactured through many processes, such as upsetting($1^{st}$ Forming), piercing, direct extrusion($2^{nd}$ Forming), final sizing process($3^{rd}$ Forming). In this study, process design for closed-die forging of a Lock-up hub used for a component of automobile transmission was made using three-dimensional finite element simulations, and the strain distributions and velocity distributions are investigated through the post processor. The rigid-plastic finite-element method for back pressure forging has been used in order to reduce development time and die cost. Using the FEM simulation, we found the optimum value of back pressure. The prototypes of Lock-up hub parts were forged into the net-shape. In the experiment, lead precision of tooth are measured by the CCMM(Contact Coordinate Measuring Machine). The dimensional accuracy of forged part was improved up to the 40% when back press was applied.

• 대한전기학회논문지:전력기술부문A
• /
• 제52권8호
• /
• pp.471-480
• /
• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2005년도 춘계학술대회 논문집
• /
• pp.389-394
• /
• 2005

Microfactory is effective method for machining micro size component. Electro-chemical machining can be more suitable to a microfactory than other machining methods in terms of maintaining high accuracy. Surface profile of EDM Machined component is predicted by micro EDM simulation using superpositioning spark crater. Planar motor and micro pump are developed to construct microfactory system.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2005년도 춘계학술대회 논문집
• /
• pp.462-467
• /
• 2005

This paper presents virtual machining system in order to realize turning process in virtual space. A reliable virtual turning process simulation was developed based on the surface shaping system which is capable of considering geometric model, thermal error model, and vibration model. Accuracy of surface shape resulting from proposed machining simulator was verified experimentally. This paper also developed the watchdog agent that continuously assessed, diagnosed, and predicted performance of products and machines in machining. The Watchdog agent extracted feature signal using time-frequency analysis among various signals from multi-sensor and evaluated machining condition using performance confidence value.

• 한국멀티미디어학회논문지
• /
• 제22권5호
• /
• pp.558-572
• /
• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• International Journal of Computer Science & Network Security
• /
• 제21권1호
• /
• pp.97-106
• /
• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• ETRI Journal
• /
• 제43권6호
• /
• pp.991-1003
• /
• 2021

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.

• Geomechanics and Engineering
• /
• 제34권5호
• /
• pp.507-527
• /
• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.