• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.10
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• pp.932-941
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• 2004

The objective of this study is to detect the multi-fault of HVAC system using a new pattern classification technique. To classify the effect of single-fault in determining the pattern, supply air temperature, OA-damper, supply fan, and air flowrate were chosen as experimental parameters. The combination of supply temperature, flow rate, supply fan and OA-damper were chosen as multi-fault conditions. Three kinds of patterns were introduced in the analysis of multi-fault problem. To solve multi-fault problem, the new pattern classification technique using residual ratio analysis was introduced to detect the multi-fault as well as single-fault. The residual ratio could diagnose single-fault or multi-fault into several patterns.

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.181-181
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• 2004

• Resources Recycling
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• v.10 no.6
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• pp.29-34
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• 2001

Fly ash powders were refined and separated into fine and coarse size by multi-air classification, and each particle was used for synthesizing mullite and zeolite. Mullite was prepared by sintering the mixture of fine fly ash with mean size of 6.5 $\mu$m and $A1_2$$O_3$powder at above $1450^{\circ}C$. Zeolite was synthesized through hydrothermal reaction with coarse fly ash mean size of $56.3\mu$m in 3.5 M NaOH solution at $120^{\circ}C$. The whole range of particle size can be recycled through size classification into fine and coarse fractions, which are used for syntheses of inorganic materials.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.228-233
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• 2006

This paper introduces design method for air pump system using bond graph and genetic programming to maximize outflow subject to a constraint specifying maximum power consumption. The air pump system is a mixed domain system which includes electromagnetic, mechanical and pneumatic elements. Therefore an appropriate approach for a better system for synthesis is required. Bond graphs are domain independent, allow free composition, and are efficient for classification and analysis of models. Genetic programming is well recognized as a powerful tool for open-ended search. The combination of these two powerful methods, BG/GP, was tested for redesign of air pump system.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1065-1071
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• 2023

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.323-326
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• 2005

This paper introduces design method for air pump system using bond graph and genetic programming to maximize outflow subject to a constraint specifying maximum power consumption. The air pump system is a mixed domain system which includes electromagnetic, mechanical and pneumaticelements. Therefore an appropriate approach for a better system for synthesis is required. Bond graphs are domain independent, allow free composition, and are efficient for classification and analysis of models, Genetic programming is well recognized as a powerful tool for open-ended search. The combination of these two powerful methods for evolution of multi-domain system, BG/GP, was tested for redesign of air pump system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.4
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• pp.404-413
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• 2015

The target classification for ballistic target(BT) is one of the most critical issues of ballistic defence mode(BDM) in multi-function radar(MFR). Radar responds to the target according to the result of classifying BT and air breathing target(ABT) on BDM. Since the efficiency and accuracy of the classification is closely related to the capacity of the response to the ballistic missile offense, effective and accurate classification scheme is necessary. Generally, JEM(Jet Engine Modulation), HRR(High Range Resolution) and ISAR(Inverse Synthetic Array Radar) image are used for a target classification, which require specific radar waveform, data base and algorithms. In this paper, the classification method that is applicable to a MFR system in a real environment without specific waveform is proposed. The proposed classifier adopts kinematic data as a feature vector to save radar resources at the radar time and hardware point of view and is implemented by fuzzy logic of which simple implementation makes it possible to apply to the real environment. The performance of the proposed method is verified through measured data of the aircraft and simulated data of the ballistic missile.

• Korean journal of aerospace and environmental medicine
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• v.30 no.3
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• pp.113-129
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• 2020

With the operation of the KC-330 MRTT (Multi Role Tanker Transport), which had been fielded in 2019, the ROKAF (Republic of Korea Air Force) has given fighter pilots a new mission of air refueling. As a result, fighter pilots are more likely to be exposed to risks they have never faced before, and it is necessary to look at the risk factors associated with human factors in air refueling missions. Therefore, in this study, an analysis using the HFACS (Human Factors Analysis and Classification System) model was performed for fighter pilots with air refueling qualifications. This study tried to prevent hazard in advance by discriminating the risk factors according to the human factors related to the fighter pilot during the air refueling mission.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.459-464
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• 2009

This study has been conducted on how to determine the multi-evaporator vapor compression cycle system is charged correctly by using sensor readings which are used to control system. In this paper, the characteristics of the multi-evaporator were presented and sensor values were classified using fuzzy clustering. finally classification logic and it's performance were discussed by applying commercial VRF system.