• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.6
• /
• pp.443-449
• /
• 2015

This paper considers the physical layer security problem for a two-way vehicle-to-vehicle network, where the two source vehicles can only exchange information through an untrusted relay vehicle. The relay vehicle helps the two-way transmission but also acts as a potential eavesdropper. Each vehicle has a random velocity. By exploiting the random carrier frequency offsets (CFOs) caused by random motions, a secure double-differential two-way relay scheme is proposed. While achieving successful two-way transmission for the source vehicles, the proposed scheme guarantees a high decoding error floor at the untrusted relay vehicle. Average symbol error rate (SER) performance for the source vehicles and the untrusted relay vehicle is analyzed. Simulation results are provided to verify the proposed scheme.

• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.4
• /
• pp.216-225
• /
• 2013

This paper discusses the issues regarding an abnormal V-shaped error confronting algebraic-based deconvolution process. Deconvolution was applied to an analysis of the effects of the Random Telegraph Noise (RTN) and Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. This paper proposes a technique to suppress the problematic phenomena in the algebraic-based RDF/RTN deconvolution process. The proposed technique can reduce its relative errors by $10^{10}$ to $10^{16}$ fold, which is a sufficient reduction for avoiding the abnormal ringing errors in the RTN deconvolution process. The proposed algebraic-based analyses allowed the following: (1) detection of the truncating point of the TD-MV distributions by the screening test, and (2) predicting the MV-shift-amount by the assisted circuit schemes needed to avoid the out of specs after shipment.

• Communications for Statistical Applications and Methods
• /
• v.26 no.4
• /
• pp.371-383
• /
• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.1
• /
• pp.85-90
• /
• 2022

In this paper, an asynchronous nonvolatile memory module using a self-diagnosis function was designed. For the system to work, a lot of data must be input/output, and memory that can be stored is required. The volatile memory is fast, but data is erased without power, and the nonvolatile memory is slow, but data can be stored semi-permanently without power. The non-volatile static random-access memory is designed to solve these memory problems. However, the non-volatile static random-access memory is weak external noise or electrical shock, data can be some error. To solve these data errors, self-diagnosis algorithms were applied to non-volatile static random-access memory using error correction code, cyclic redundancy check 32 and data check sum to increase the reliability and accuracy of data retention. In addition, the possibility of application to an asynchronous non-volatile storage system requiring reliability was suggested.

• Journal of the Korean Society of Mechanical Technology
• /
• v.13 no.3
• /
• pp.7-16
• /
• 2011

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot's posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot's posture and payload change. That's why the moments $M_x$, $M_y$ and $M_z$ working on every joint of a robot vary with robot's posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot's posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot's posture and payload through neural network learning.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.350-358
• /
• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• ETRI Journal
• /
• v.32 no.6
• /
• pp.965-968
• /
• 2010

In this letter, we derive the distribution functions of five ratios involving two correlated Gaussian random variables by using the rotation of Cartesian coordinates. The results can be used in evaluating the various probability performances of wireless communications systems.

• East Asian mathematical journal
• /
• v.31 no.3
• /
• pp.379-382
• /
• 2015

The purpose of this note is to study the estimation of errors of the implicit Mann iterative process with random errors.

• Journal of KIISE:Software and Applications
• /
• v.37 no.9
• /
• pp.706-711
• /
• 2010

In the applications of a human speech interface, reducing the error rate in recognition is the one of the main research issues. Many previous studies attempted to correct errors using post-processing, which is dependent on a manually constructed corpus and correction patterns. We propose an automatically learnable post-processing method that is independent of the characteristics of both the domain and the speech recognizer. We divide the entire post-processing task into two steps: error detection and error correction. We consider the error detection step as a classification problem for which we apply the conditional random fields (CRFs) classifier. Furthermore, we apply transformation-based learning (TBL) to the error correction step. Our experimental results indicate that the proposed method corrects a speech recognizer's insertion, deletion, and substitution errors by 25.85%, 3.57%, and 7.42%, respectively.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.27-36
• /
• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.