• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.627-630
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• 2010

무선 센서 & 액터 네트워크(WSAN)와 같이 다수의 베이스 노드가 존재하거나 베이스 노드의 이동성이 큰 센서 네트워크에서 최소 Wiener수 신장 트리(MWST)기반 라우팅 방법은 최소 신장 트리(MST)기반 라우팅 방법에 비해 패킷 전송 거리가 짧고 전력 소모가 적다. 하지만 주어진 그래프로부터 최소 Wiener 수 신장 트리를 찾는 문제는 NP-hard 문제이고 최소 신장 트리에 비해 네트워크 수명이 짧은 단점이 있다. 본 논문은 이러한 문제를 해결하고자 Wiener 수 적응도, 네트워크 수명 적응도, 차수 적응도 등을 동시에 고려한 다목적 유전자 알고리즘을 설계하고 네트워크 전체 전력 소모를 크게 증가시키지 않으면서도 네트워크의 수명을 Wiener 수 적응도만을 사용했을 때 보다 연장시킴을 실험을 통해 보인다.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.91-100
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• 2003

In this paper we investigate the role that warfare played In the formation of the network of alliances between sites that are associated with the formation of the state in the Valley of Oaxaca, Mexico. A model of state formation proposed by Marcos and Flannery (1996) is used as the basis for an agent-based simulation model. Agents reside in sites and their actions are constrained by knowledge extracted from the Oaxaca Surface Archaeological Survey (Kowalewski 1989). The simulation is run with two different sets of constraint rules for the agents. The first set is based upon the raw data collected in the surface survey. This represents a total of 79 sites and constitutes a minimal level of warfare (raiding) in the Valley. The other site represents the generalization of these constraints to sites with similar locational characteristics. This set corresponds to 987 sites and represents a much more active role for warfare in the Valley. The rules were produced by a data mining technique, Decision Trees, guided by Genetic Algorithms. Simulations were run using the two different rule sets and compared with each other and the archaeological data for the Valley. The results strongly suggest that warfare was a necessary process in the aggregations of resources needed to support the emergence of the state in the Valley.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• Journal of Korea Trade
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• v.27 no.1
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• pp.119-138
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• 2023

Purpose - This research is to empirically explore the differences in apparel consumption among male and female teenagers and college students in Korea and China. By conducting a survey to understand customers' needs and behaviors, fashion businesses will be able to improve their customer satisfaction and avoid redundancy, inventory, and the waste of resources, effort and money. Design/methodology - The research design considers the consumption patterns of male and female high school and college students in Korea and China. To analyze the data, the study employs decision trees, a type of machine learning algorithm. A decision tree model was developed to examine the relationship between the explanatory and response variables, which can be either quantitative or qualitative in nature. Findings - The main findings of this study indicate that there are differences in shopping behavior among different customer segments. The results show that men have a simpler shopping behavior compared to women. Additionally, cultural factors and the difference in fashion needs between students and non-students have a significant impact on the shopping choices of Chinese and Korean individuals. Originality/value - Existing studies often assume that the shopping behavior of high school and university students is similar and that there are no significant differences in clothing purchases between men and women across countries. The results provide valuable insights into the unique shopping behavior of different customer segments, and can inform fashion businesses in their efforts to meet the needs of their customers.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.103-106
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• 2023

코로나 팬데믹 사태로 인해 업무환경이 재택근무를 하는 환경으로 바뀌고 악성코드의 변종 또한 빠르게 발전하고 있다. 악성코드를 분석하고 백신 프로그램을 만들면 새로운 변종 악성코드가 생기고 변종에 대한 백신프로그램이 만들어 질 때까지 변종된 악성코드는 사용자에게 위협이 된다. 본 연구에서는 머신러닝 알고리즘을 사용하여 악성파일 여부를 예측하는 방법을 제시하였다. 일반적인 악성코드의 구조를 갖는 Portable Executable 구조 파일을 파이썬의 LIEF 라이브러리를 사용하여 Certificate, Imports, Opcode 등 3가지 feature에 대해 정적분석을 하였다. 학습 데이터로는 정상파일 320개와 악성파일 530개를 사용하였다. Certificate는 hasSignature(디지털 서명정보), isValidcertificate(디지털 서명의 유효성), isNotExpired(인증서의 유효성)의 feature set을 사용하고, Imports는 Import Address Table의 function 빈도수를 비교하여 feature set을 구축하였다. Opcode는 tri-gram으로 추출하여 빈도수를 비교하여 feature set을 구축하였다. 테스트 데이터로는 정상파일 360개 악성파일 610개를 사용하였으며 Feature set을 사용하여 random forest, decision tree, bagging, adaboost 등 4가지 머신러닝 알고리즘을 대상으로 성능을 비교하였고, bagging 알고리즘에서 약 0.98의 정확도를 보였다.