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Prof. Ji Min Kim
Seoul National University of Science and Technology

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0 Built Environment
0 Environmental Economics
0 Ergonomics
0 Sustainable Development
0 IoT

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Journal article
Published: 26 March 2021 in Measurement
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A convolutional neural network (CNN) is a deep learning algorithm, which can be utilized in various engineering fields due to its superior prediction and classification performance. In recent years, CNN that is known to be outstanding to handle large volumes of data, it is has been in the spotlight to solve the problems of sensor defects and data loss, which may have resulted from the limitations of the current structural health monitoring (SHM) techniques. However, although the architecture of CNN should be constructed differently depending on the characteristics of each problem, there is no rational nor reasonable method for the construction of the architecture. In this regard, this study seeks to propose a technique for constructing an optimal architecture for the effective utilization of CNN in recovery and estimation of measured data dealt in the field of SHM. In the proposed technique, the number of kernels, the kernel size, and the subsampling size are set as the decision variables, among the variables that determine the CNN architecture. To prevent CNN from being trained with bias toward specific datasets, root mean square errors are calculated for each of the training datasets and validation datasets, and set as objective functions, respectively. Then these two objective functions are minimized at the same time. In this case, non-dominated sorting genetic algorithm-II, a multi-objective optimization algorithm, is introduced to minimize these two objective functions. The proposed technique is verified by a numerical study on beam-like structures and an experimental study on reinforced concrete structures. These two studies explore the optimal CNN architecture, which estimates the dynamic strain of the structure, and evaluates the performance of the explored architecture.

ACS Style

Byung Kwan Oh; Jimin Kim. Optimal architecture of a convolutional neural network to estimate structural responses for safety evaluation of the structures. Measurement 2021, 177, 109313 .

AMA Style

Byung Kwan Oh, Jimin Kim. Optimal architecture of a convolutional neural network to estimate structural responses for safety evaluation of the structures. Measurement. 2021; 177 ():109313.

Chicago/Turabian Style

Byung Kwan Oh; Jimin Kim. 2021. "Optimal architecture of a convolutional neural network to estimate structural responses for safety evaluation of the structures." Measurement 177, no. : 109313.

Journal article
Published: 08 February 2021 in IEEE Access
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To ensure the safety of structures, structural health monitoring (SHM) techniques that use cutting-edge sensing technologies have been developed. However, in the process of long-term structural health monitoring, sensor defects and data loss commonly occur, which pose limitations in the current SHM technique. To recover lost data and predict structural responses, convolutional neural networks (CNNs) have been used in SHM, but no obvious technique or rule for configuring CNN architecture with optimal performance has been presented yet. This study proposes a method for searching for the optimal CNN architecture capable of predicting the structural response of structures to evaluate their long-term safety. In this method, multi-objective optimization, considering both prediction performance and CNN training efficiency, is presented as a strategy. The optimization method using the two objective functions is applied to the structural response estimation, and the characteristics of the derived solutions are examined. Furthermore, the solutions derived using the two objective functions are classified into two solution groups that are biased to each objective function, and a strategy for minimizing the discrepancy between the two solution groups is additionally presented based on their trade-off relationship. The architecture characteristics, estimation performance, and training efficiency of the solutions derived by setting the discrepancy as the third objective function are investigated. The CNN derived by the proposed method with the third objective function reduced 40.35% of computational cost compared with that derived with two objective functions while they showed similar accuracies for the response estimation.

ACS Style

Byung Kwan Oh; Jimin Kim. Multi-Objective Optimization Method to Search for the Optimal Convolutional Neural Network Architecture for Long-Term Structural Health Monitoring. IEEE Access 2021, 9, 44738 -44750.

AMA Style

Byung Kwan Oh, Jimin Kim. Multi-Objective Optimization Method to Search for the Optimal Convolutional Neural Network Architecture for Long-Term Structural Health Monitoring. IEEE Access. 2021; 9 (99):44738-44750.

Chicago/Turabian Style

Byung Kwan Oh; Jimin Kim. 2021. "Multi-Objective Optimization Method to Search for the Optimal Convolutional Neural Network Architecture for Long-Term Structural Health Monitoring." IEEE Access 9, no. 99: 44738-44750.

Journal article
Published: 26 October 2020 in Renewable and Sustainable Energy Reviews
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To reduce CO2 emissions in the building sector, South Korea uses an operational rating system, an indicator for evaluating CO2 emission performance. To conduct a reasonable operational rating, it is necessary to develop a rational and reliable CO2 emission (CE) benchmark for buildings. The conventional CE benchmarks, however, have limitations accounting for regional differences of multi-family housing complexes (MFHCs). Thus, a separate CE benchmark is required for each region for improving the rationale and reliability of the conventional CE benchmarks. To solve this problem, a data-driven approach for establishing a CE benchmark using data mining techniques was applied in this study. Data on a total of 1,212 MFHCs were established, and a total of 11 CE benchmarks (central region: 7; southern region: 4) for MFHCs were established based on the decision tree. The developed CE benchmarks were then validated using statistical methods (Mann-Whitney test, Kruskal-Wallis test, etc.). Compared to the average operational rating based on conventional CE benchmarks, the average operational rating based on the newly developed CE benchmarks decreased by 1.85% in the central region, and increased by 5.19% in the southern region, respectively. This means that the unreliability and irrationality of the conventional operational rating system (ORS) can be solved by the established ORS. The established ORS, based on the newly developed CE benchmarks, can help policymakers select and manage MFHCs with poor CE performance.

ACS Style

Kwangbok Jeong; Taehoon Hong; Jimin Kim; Jaewook Lee. A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques. Renewable and Sustainable Energy Reviews 2020, 138, 110497 .

AMA Style

Kwangbok Jeong, Taehoon Hong, Jimin Kim, Jaewook Lee. A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques. Renewable and Sustainable Energy Reviews. 2020; 138 ():110497.

Chicago/Turabian Style

Kwangbok Jeong; Taehoon Hong; Jimin Kim; Jaewook Lee. 2020. "A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques." Renewable and Sustainable Energy Reviews 138, no. : 110497.

Journal article
Published: 26 August 2020 in Building and Environment
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This study analyzed the psychophysiological effect of the indoor thermal condition on the college students' learning performance. Towards this end, an experiment was conducted on 20 subjects in a climate chamber. Five thermal conditions (PMV -2, −1, 0, 1, and 2) were created using the climate chamber. The indoor environment quality was monitored. At the same time, to analyze learning performance, the subjects were made to perform four cognitive tests to evaluate attention, perceptual, working memory, and executive ability. Furthermore, the subjects' psychophysiological responses, such as their mental workload, mental stress, alertness, and mental fatigue, were measured using an electroencephalogram. Meanwhile, in this study, the statistical significance of the various factors was investigated using one-way repeated-measures ANOVA. It was found through the analysis that there is a significant negative relationship between alertness and working memory ability in the warm condition, whereas there are significant negative relationships between executive ability on one hand and mental workload, alertness, and mental fatigue on the other in the cool condition. Highest learning performance was at a 25.7 °C indoor temperature. When the indoor temperature decreased to 17 °C, the students' learning performance decreased by about 9.9%, and when the indoor temperature increased to 33 °C, the it decreased by about 7.0%. Therefore, this study confirmed that the indoor thermal condition does not directly influence the students’ learning performance, but it activates the psychophysiological responses of the students, thus increasing their task load.

ACS Style

Hakpyeong Kim; Taehoon Hong; Jimin Kim; Seungkeun Yeom. A psychophysiological effect of indoor thermal condition on college students’ learning performance through EEG measurement. Building and Environment 2020, 184, 107223 .

AMA Style

Hakpyeong Kim, Taehoon Hong, Jimin Kim, Seungkeun Yeom. A psychophysiological effect of indoor thermal condition on college students’ learning performance through EEG measurement. Building and Environment. 2020; 184 ():107223.

Chicago/Turabian Style

Hakpyeong Kim; Taehoon Hong; Jimin Kim; Seungkeun Yeom. 2020. "A psychophysiological effect of indoor thermal condition on college students’ learning performance through EEG measurement." Building and Environment 184, no. : 107223.

Journal article
Published: 14 December 2019 in Building and Environment
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As people spend more time indoors, it is important to identify the relationship between indoor environmental quality (IEQ) and building occupants’ health. This study aims to analyze building occupants' psycho-physiological response to the indoor climate and CO2 concentration changes. While the indoor climate and CO2 concentration change, the study measured verbal scales (Indoor air quality (IAQ) satisfaction, thermal comfort vote (TCV), thermal satisfaction (TS), thermal sensation vote (TSV) and thermal preference (TP)) from 22 healthy subjects as psychological responses and monitored blood pressure (BP) at the seated state as the physiological response. The results are as follows: (i) IAQ satisfaction, TCV and TS had a negative correlation between −0.558 and −0.789 with BP; and (ii) if IAQ satisfaction, TCV and TS were below the neutral level, the systolic BP of some subjects was shown to exceed 140 mmHg, the hypertension warning state. The study is differentiated from previous studies as follows: (i) the verbal scale of the previously-used psychological responses was validated from the perspective of the psychophysiological approach (TCV ≈ TS≠TSV); and (ii) for states with a high CO2 concentration, even if the operative temperature decreases from warm to neutral, the TCV and TS increase and then BP decreases as opposed to in previous studies. Namely, the unhealthier the IEQ condition is, the more the BP increased regardless of the change in the operative temperature. Through the results of this study, it is possible to implement a healthy indoor environment in both a psychological and physiological approach during the building operation phase.

ACS Style

Jimin Kim; Taehoon Hong; Minjin Kong; Kwangbok Jeong. Building occupants' psycho-physiological response to indoor climate and CO2 concentration changes in office buildings. Building and Environment 2019, 169, 106596 .

AMA Style

Jimin Kim, Taehoon Hong, Minjin Kong, Kwangbok Jeong. Building occupants' psycho-physiological response to indoor climate and CO2 concentration changes in office buildings. Building and Environment. 2019; 169 ():106596.

Chicago/Turabian Style

Jimin Kim; Taehoon Hong; Minjin Kong; Kwangbok Jeong. 2019. "Building occupants' psycho-physiological response to indoor climate and CO2 concentration changes in office buildings." Building and Environment 169, no. : 106596.