• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.334-342
• /
• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Design & Manufacturing
• /
• v.17 no.2
• /
• pp.15-21
• /
• 2023

In this study, a diamond turning machine and a laser-assisted machining module were utilized for the complex combined cutting of aspheric shapes and fine patterns on the surface of high-hardness brittle material, silicon. The analysis of material's form accuracy and corrective machining was conducted based on key factors such as laser output, rotational speed, feed rate, and cutting depth to achieve form accuracy below 1 ㎛ and surface roughness below 0.1 ㎛. The cutting condition and corrective machining methods were investigated to achieve the desired form accuracy and surface roughness. The rotational speed of the spindle and the linear feed rate of the diamond turning machine were varied in five stages for the cutting condition test. Surface roughness and form accuracy were measured using both a contact surface profilometer and a non-contact surface profilometer. The experimental results revealed a tendency of improved surface roughness with increased rotational speed of the workpiece, and the best surface roughness and form accuracy were observed at a feed rate of 5 mm/min. Furthermore, based on the cutting condition experiments, corrective machining was performed. The experimental results demonstrated an improvement in form accuracy from 0.94 ㎛ to 0.31 ㎛ and a significant reduction in the average value of the surface roughness curve from 0.234 ㎛ to 0.061 ㎛. This research serves as a foundation for future studies focusing on the machinability in relation to laser output parameters.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.545-558
• /
• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.95-100
• /
• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.10a
• /
• pp.13-18
• /
• 1998

The assessment of machined surface is difficult because the freeform surface must be evaluated by surface fairness as well as dimensional accuracy. In this paper, the methodology of freeform surface generation using measured data on the machine tool is presented. The reliability of measured points data is obtained by measuring error compensation. The compensated data are formulated through Non-uniform G-spline surface modeling. In order to improve the surface fairness, the generated model si smoothened by parameterization The validity and usefulness of the proposed method are examined through computer simulation and experiments on the machine tool.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.333-337
• /
• 1996

The spindle directly affects parts accuracy during machining, or work piece in machine tools. In this paper a comprehensive study is performed combined mr performance of machine tool spindle. The developed methology has been practically applied to a spindle of machine tools.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.675-677
• /
• 1998

In this paper, a development of the control system for the milling machine which is used to cut teeth of the automibile keys is presented as an example of the industry-university cooperation. The machine is controlled by a computer and a PLC. The control of the servo motors are accomplished by the computer and mechanical sequences are by PLC. This machine is capable of cutting key teeth up to the accuracy of 10 microns.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.11
• /
• pp.958-962
• /
• 2003

We acquired data of injection molding machine in operation and stored the data in database. We acquired the data of injection molding machine for fault detection and diagnosis (FDD) continuously and estimated the fault results with a fuzzy algorithm. Many of FDD are applied to a huge system, nuclear power plant and a computer numerical control(CNC) machine for processing machinery. But, the research of FDD is rare in injection molding machine compare with computer numerical control machine. We appraise the accuracy of the FDD and the limit of the application to the injection molding machine. We construct the fault detection and diagnosis system based on fuzzy algorithm in the injection molding machine. Data of operating injection molding machine are acquired in order to improve the reliability of detection and diagnosis.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.5 s.176
• /
• pp.1343-1351
• /
• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.10 no.2
• /
• pp.102-109
• /
• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.