• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.04a
• /
• pp.359-364
• /
• 2004

A high-speed and intelligent CNC system has been developed by Turbotek Co., Ltd. This paper presents the study for commercialization of the developed CNC system. In order to acquire stability and reliability of the developed CNC system, its hardwares and softwares ate improved. The CNC main unit is revised to a compact box-type CNC controller. Moreover, the integrated CNC main unit that has built-in and expandable I/O modules is also developed. Remote monitoring, fault diagnosis End NURBS interpolation functions are realized on the CNC system as software modules. Through these efforts, the developed CNC system can be loaded on machine tools successfully.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.837-848
• /
• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.32-42
• /
• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.4
• /
• pp.1614-1622
• /
• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• Safety and Health at Work
• /
• v.14 no.1
• /
• pp.124-130
• /
• 2023

The increase in the number of fatal electric accidents over the years has escalated the demand for specialized forensic engineers to determine their relevant technical causes. Likewise, the complexities associated with identifying the causes of electrocution accidents have prompted the General Department of Forensic Science and Criminology at Dubai Police to adopt a new methodology to diagnose electrocution accidents, consisting of an approach that involves medico-legal examination, electrical diagnosis of the evidence, and trace evidence analysis. This paper will discuss the application of the adopted method in further detail by unfolding two case reports. The first report outlines a case in which a worker got electrocuted at a construction site while attempting to turn on a lamp. The second case report involves the death of a technician in a workshop after trying to disconnect a washing machine from its plug. The methodology was utilized during the investigation of both cases, which were attended by the appointed forensic engineers and showed promising results.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.24 no.1
• /
• pp.42-47
• /
• 2010

The pulse diagnosis is an important method in Oriental Medicine. Recently, there have been continuous attempts to replace the finger palpation by Oriental medical doctors (OMDs) by more objective tools based on machines, e.g., pulse analyzers. To improve the performance of the pulse analyzers, both the machine-appropriate interpretations for the pulse images appeared in the literature and the improvement in the repeatability and reproducibility of the measurement sensors are to be developed. As an attempt towards the transformation of the pulse images in terms of machine-appropriate language, in this work, we suggest an upgraded algorithm for the solid/deficient pulses, which are the two representative pulse images informing us how strong the pulse pressure is. It has been argued that one could determine the solid/deficient pulses by the maximum pulse pressure from pulse analyzers. However, by a clinical test, we found that the maximum pulse pressure alone is not sufficient to determine the solid/deficient pulses. In addition to the maximum pulse pressure, the mean pulse pressure averaged over for five different hold-down pressures(3-D MAC) is needed to improve the agreement with the OMD's decision for the solid/deficient pulse. We found that, among the data diagnosed with having either the solid pulse or deficient pulse by OMDs, the novel algorithm showed 86.0% diagnosis rate and 81.6% concordance rate.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.37 no.6
• /
• pp.551-558
• /
• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.150-155
• /
• 2024

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By pre-processing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.

• Advances in nano research
• /
• v.15 no.5
• /
• pp.451-466
• /
• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.126-136
• /
• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.