• Geomechanics and Engineering
• /
• v.36 no.4
• /
• pp.381-390
• /
• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• Journal of Information Technology Services
• /
• v.23 no.2
• /
• pp.1-12
• /
• 2024

This study aims to find ways to increase the declining donation intention by using machine learning techniques. To this end, in order to predict factors that affect donations between the MZ generation and the senior generation, various machine learning algorithms, including logistic regression analysis, are applied to build a model to determine variables that affect donation intention, and provide statistical verification and evaluation indicators. In this study, differences in donation intention by generation were expected as a variable affecting donation intention, and the senior generation was expected to show a higher donation intention tendency than the younger generation. However, although the research results were not statistically significant, the younger generation showed a higher intention to donate, and these results are interpreted to mean that value consumption and ethical consumption, which are important to today's MZ generation, also influenced donations. However, there were differences between generations in the amount of donations, and higher donation amounts were confirmed among the senior generation (those in their 50s or older) than the younger generation. In addition, the results of the logistic regression analysis showed that previous donation experience had a positive effect on future donation intention, and the more motivation and importance of donation and various social participation activities online and offline, the more active one became in donating.

• Journal of Computational Design and Engineering
• /
• v.1 no.2
• /
• pp.128-139
• /
• 2014

This study investigates the effects of machining parameters as they relate to the quality characteristics of machined features. Two most important quality characteristics are set as the dimensional accuracy and the surface roughness. Before any newly acquired machine tool is put to use for production, it is important to test the machine in a systematic way to find out how different parameter settings affect machining quality. The empirical verification was made by conducting a Design of Experiment (DOE) with 3 levels and 3 factors on a state-of-the-art Cincinnati Hawk Arrow 750 Vertical Machining Center (VMC). Data analysis revealed that the significant factor was the Hardness of the material and the significant interaction effect was the Hardness + Feed for dimensional accuracy, while the significant factor was Speed for surface roughness. Since the equally important thing is the capability of the instruments from which the quality characteristics are being measured, a comparison was made between the VMC touch probe readings and the measurements from a Mi-tutoyo coordinate measuring machine (CMM) on bore diameters. A machine mounted touch probe has gained a wide acceptance in recent years, as it is more suitable for the modern manufacturing environment. The data vindicated that the VMC touch probe has the capability that is suitable for the production environment. The test results can be incorporated in the process plan to help maintain the machining quality in the subsequent runs.

• Journal of Computational Design and Engineering
• /
• v.2 no.4
• /
• pp.197-205
• /
• 2015

Bladed disk (BLISK) is a vital part in jet engines with a complicated shape which is exclusively machined on a five-axis machine and requires high accuracy of machining. Poor quality of tool orientation (e.g., false tool positioning and unsmooth tool orientation transition) during the five-axis machining may cause collision and machine vibration, which will debase the machining quality and in the worst case sabotage the BLISK. This paper presents a reference plane based algorithm to generate a set of smoothly aligned tool orientations along a tool path. The proposed method guarantees that no collision would occur anywhere along the tool path, and the overall smoothness is globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a BLISK model and the tool orientation generated is found to be stable, smooth, and well-formed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.263-268
• /
• 1999

The most important things to the tube the of the heat exchanger are the precision of t hole position and the quality of the drill face. Nowadays, 6 and 12 spindle multi-drilling machine controlled by CNC or used to drill holes of the tube sheet. The drilling of 12 axes can offer high speover three times as fast as the drilling of axis. However, the drilling of 12 axes h difficulty in controlling many motors to d spindles and assigning a corresponded numbe accurately to each axis. In the past, conventional method to inspect the code the drilling was machining holes on a thin plate previously which resulted in the productivity because it required a h production cost by machining and weldin time. In this thesis, there are two drilling codes different from CNC code. M code is used to control many motors and S code is used to assign a correspondent number for each axis. For increasing the productivity by removing process, this paper is intended to take simulation of the drill machining c including 6 and 12 axis on the persona computer.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.4
• /
• pp.455-461
• /
• 2010

Automated magnetic abrasive polishing which can be applied after machining of the mold on a machine tool without unloading is very effective for finishing a complicated injection mold surface. This study aims to realize one step polishing of free form surface with the same machine tool. For this purpose, magnetic flux density according to the change of curvature radii was simulated for selecting polishing conditions and experimental verification was performed with a complicated mold of aluminum alloy. As a result, it was seen by the simulation that the magnetic flux density at a gradual curvature of the mold was higher than at a steep curvature and the higher magnetic flux density produced the better surface roughness in the experimentation. The deviation for the surface roughness of the mold decreased on the whole and the uniform mold surface was obtained after the automated magnetic abrasive polishing.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.3
• /
• pp.13-19
• /
• 1999

In this paper, we suggests a WIP (Work In Process) of FMC (Flexible Manufacturing Cell) analysis methods based on the TPN (Time Petri Nets) unfolding. Unfolding of PN is a partial order-based method for the verification of concurrent system without the state space explosion. The aim of this work is to formulate the general cyclic state scheduling problem to minimize the WIP to satisfy economical constraints. The method is based on unfolding of the original net into the equivalent acyclic description.

• Journal of Information Processing Systems
• /
• v.11 no.3
• /
• pp.325-341
• /
• 2015

This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.192-197
• /
• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2529-2549
• /
• 2023

For effectively lowering down the risk of cyber threating, the zero-trust architecture (ZTA) has been gradually deployed to the fields of smart city, Internet of Things, and cloud computing. The main concept of ZTA is to maintain a distrustful attitude towards all devices, identities, and communication requests, which only offering the minimum access and validity. Unfortunately, adopting the most secure and complex multifactor authentication has brought enterprise and employee a troublesome and unfriendly burden. Thus, authors aim to incorporate machine learning technology to build an employee behavior analysis ZTA. The new framework is characterized by the ability of adjusting the difficulty of identity verification through the user behavioral patterns and the risk degree of the resource. In particular, three key factors, including one-time password, face feature, and authorization code, have been applied to design the adaptive multifactor continuous authentication system. Simulations have demonstrated that the new work can eliminate the necessity of maintaining a heavy authentication and ensure an employee-friendly experience.