• Journal of Korea Society of Industrial Information Systems
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• v.29 no.5
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• pp.21-32
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• 2024

This paper proposes a smart safety system that combines low-cost CW(Continuous Wave) radar and IMU sensors to enhance blind spots that pose safety risks to workers in industrial manufacturing environments. The system employs a 24 GHz radar and a 6-axis IMU sensor to detect worker movements and utilizes a machine learning model to recognize worker situations in vibrating manufacturing sites. The ensemble boosting tree-based model achieved over 92.8% worker detection accuracy, demonstrating its effectiveness in improving safety in industrial settings.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.165-171
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• 2021

The current study investigated cyber learners' use and perceptions of online machine translation (MT) tools. The results show that learners use several MT tools frequently and extensively for various second language learning (L2) purposes according to their needs. The learners' overall perceptions of using MT for English learning were generally positive. The learners reported several advantages of machine translation: ease of use, helpful feedback, effective revision, and facilitation of self-directed learning. At the same time, a considerable number of learners were aware of MT's drawbacks, such as awkward sentences, inaccurate grammar, and inappropriate words, and thus held a negative or skeptical view on the quality and accuracy of MT. These findings have important pedagogical implications for using MT in the context of a cyber university. For successful integration of MT in English classes, teachers need to provide appropriate guidelines and training that will help learners use MT effectively.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.38-43
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• 2022

In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Coupled systems mechanics
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• v.1 no.2
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• pp.205-217
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• 2012

A smart beam made of magneto-electro-elastic (MEE) material having piezoelectric phase and piezomagnetic phase, shows the coupling between magnetic, electric, thermal and mechanical under thermal environment. Product properties such as pyroelectric and pyromagnetic are generated in this MEE material under thermal environment. Recently studies have been published on the product properties (pyroelectric and pyromagnetic) for magneto-electro-thermo-elastic smart composite. Hence, the magneto-electro-elastic beam with different volume fractions, investigated under uniform temperature rise is the main aim of this paper, to study the influence of product properties on clamped-free boundary condition, using finite element procedures. The finite element beam is modeled using eight node 3D brick element with five nodal degrees of freedom viz. displacements in the x, y and z directions and electric and magnetic potentials. It is found that a significant increase in electric potential observed at volume fraction of $BaTiO_3$, $v_f$ = 0.2 due to pyroelectric effect. In-contrast, the displacements and stresses are not much affected.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.19-26
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• 2014

The advent of smart phones brought adverse effect between devices recently. For example, adverse effects of info-communication with advent of computer. Also, hacking threat aiming cyber space that is getting more advanced is spreading in terms of range and danger, so that it reaches the level that the nation has to concern. In this circumstance, crimes involving info-technology is now problem in society. As internet technology advances, it enlarges the range of hacker's threat to not only smart phones, but ships, aircrafts, buildings, and cars. It could be seen as social threat of between human and human, between machine and machine, and between human and machine. This study discuss these problems.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.53-60
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• 2018

The ICT concept has been introduced to realize a highly productive smart factory and respond to the demand for small quantity and mass production between textile processes. ICT convergence monitoring system that can produce high productivity textile products by improving product development period, cost, quality and delivery time through ICT based production and optimization of manufacturing process is needed. In this paper, we propose and implement a system design that senses the amount of remaining sewing material using a non-contact sensor that can be mounted on a sewing machine and displays it on a display using IOT-based LATTE-PANDA board.

• Smart Media Journal
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• v.12 no.2
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• pp.36-41
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• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.