• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.1-9
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• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2232-2240
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• 2015

Recently in the field of Big Data, there is a trend of collecting and reprocessing the existing data such as products having high interest of customers and past purchase details to be utilized for the analysis of transaction propensity of users(product recommendations, sales forecasts, etc). Studies related to the propensity of previous users has limitations on its range of subjects and investigation timing and difficult to make predictions on detailed products with lack of real-time thus there exists difficult disadvantages of introducing appropriate and quick sales strategy against the trend. This paper utilizes the machine learning algorithm application to analyze the transaction propensity of users. As a result of applying the machine learning algorithm, it has demonstrated that various indicators which can be deduced by detailed product were able to be extracted.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.291-296
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• 2024

This study analyzed various machine learning models that predict employment outcomes after vocational training using pre-assessment data of disabled vocational trainees. The study selected and utilized the most appropriate machine learning models based on a data set containing various personal characteristics, including trainees' gender, age, and type of disability. Through this analysis, the goal is to improve the employment rate and job satisfaction of disabled trainees using only pre-assessment data. As a result, it presents a universal approach that can be applied not only to people with disabilities, but also to vocational trainees from a variety of backgrounds. This is expected to make an important contribution to the development and implementation of tailored vocational training programs, ultimately helping to achieve better employment outcomes and job satisfaction.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.73-77
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• 1997

This paper describes the analysis of measuring error of on the machine measuring(OMM) system which can directly measure the three dimensional machined free surface dimension using scanning probe on milling machine. 21 inch TV shadow mask mould was measured using PTP(point to point)measurement algorithm at pallet clamped and unclamped state on OMM system, and using coordinate measuring machine(CMM) one after another. The OMM system was evaluated probe error, stylus contact error, center shift error, repeatability and so on. Consequencely, the conclusion derived that elastic displacement of pallet had effect on measuring error mainly, and pallet design and setup method would be important.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.133-136
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• 2003

In these days, the reliability analysis and prediction are applied for many industrial products and many products require guaranteeing the quality and efficiency of their products. In this study reliability prediction for core units of machine tools has been performed in order to improve and analyze its reliability. ATC(Automatic Tool Changer) and interface Card of PC-NC that are core component of the machine tools were chosen as the target of the reliability prediction. A reliability analysis tool was used to obtain the reliability data(failure rate database) for reliability prediction. It is expected that the results of reliability prediction be applied to improve and evaluate its reliability. Failure rate, MTBF (Mean Time Between Failure) and reliability for core units of machine tools were evaluated and analyzed in this study.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of Information Science Theory and Practice
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• v.1 no.1
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• pp.10-26
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• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.135-142
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• 2024

Recently, along with digital transformation, technologies such as cloud computing, big data, and artificial intelligence have been actively introduced. In a situation where these technological changes are progressing rapidly, it is often difficult to manage processes efficiently using existing simple workflow management methods. Companies providing current cloud services are adopting virtualization technologies, including virtual machines (VMs) and containers, in their distributed system infrastructure for automated application deployment. Accordingly, this paper proposes a process-based orchestration system for integrated execution of corporate process-oriented workloads by integrating the potential of big data and machine learning technologies. This system consists of four layers as components for performing workload processes. Additionally, a common information model is applied to the data to efficiently integrate and manage the various formats and uses of data generated during the process creation stage. Moreover, a standard metadata protocol is introduced to ensure smooth exchange between data. This proposed system utilizes various types of data storage to store process data, metadata, and analysis models. This enables flexible management and efficient processing of data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.321-328
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• 2019

A military shelter for communication and information is necessary to optimize the integrated combat ability of weapon systems in the network centric warfare. Therefore, the military shelter is required for EMI shielding performance. This study examines the stable measurement points for EMI shielding effectiveness of a military shelter for communication and information. The measurement points were found by analyzing the EMI shielding effectiveness measurement data with data mining technique and process capability analysis. First, a support vector machine was used to separate the measurement point that has stable EMI shielding effectiveness according to set condition. Second, this process was conducted with process capability analysis. Finally, the results of data mining technique were compared with those of process capability analysis. As a result, 24 measurement points with stable EMI shielding effectiveness were found.

• Asia pacific journal of information systems
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• v.10 no.1
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• pp.19-35
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• 2000

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.