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Lifetime Prediction of Silicone and Direct Ink Writing-Based Soft Sensors Under Cyclic Strain
Kyeongtaek Kim,Joonbum Bae 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.10 No.2
The softness and stretchability of soft sensors has generated much interest with respect to applying soft sensors for human activity monitoring and proprioception of soft robots. However, most of the research in this area has focused on electrical stability, despite the importance of mechanical failure, thus limiting practical application. In this study, the lifetime of silicone-based soft sensors was examined under accelerated cyclic strain conditions, to construct lifetime prediction models of crack nucleation and growth considering the failure properties of the sensor’s silicone elastomer. To establish the models, an accelerated life test was conducted, in which the lifetime was estimated according to a Weibull distribution under accelerated cyclic strain conditions. Specifically, a lifetime prediction model using the crack growth approach (CGA) was constructed by experimentally measuring the energy release rate (tearing energy) of the silicone elastomer due to crack propagation. Compared to the inverse power law-based model, the CGA-based model showed about 90% improvement in lifetime prediction accuracy in the strain ranges from 150 to 270% with root mean square error 456 and 4592 cycles, respectively, thus indicating that tearing energy is an important parameter for sensor lifetime prediction. The proposed model is expected to be useful for predicting the lifetime of soft sensors under various strain operating conditions.
A New F-Measure based Clustering Evaluation Measure
Kyeongtaek Kim 한국산업경영시스템학회 2012 한국산업경영시스템학회 학술대회 Vol.2012 No.추계
F-Measure is one of the external validity indexes for evaluating clustering results and has been widely used.Though it has clear advantage over other widely usedexternal measures such as Purity and Entropy, FMeasure has inherently been less sensitive than other validity indexes in some cases. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we define a new validity index based on F-Measure, called Fn-Measure and show that it can detect the difference in the cases that original F-Measure cannot detect the difference in clustering results.
김경택(Kyeongtaek Kim) 한국산업경영시스템학회 2011 한국산업경영시스템학회지 Vol.34 No.2
Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However, there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable precision rough set model has been proposed. In this paper, we propose a variable precision rough set model for interval values in which classification errors are allowed in determining if two intervals are same. To build the model, we define equivalence class, upper approximation, lower approximation, and boundary region. Then, we check if each of 11 characteristics on approximation that works in Pawlak’s rough set model is valid for the proposed model or not.
두 점과 분할 카디날리티가 주어진 퍼지 균등화조건을 갖는 퍼지분할
김경택(Kyeongtaek Kim)․,김종수(Chong Su Kim)․,강성열(Sungyeol Kang) 한국산업경영시스템학회 2008 한국산업경영시스템학회지 Vol.31 No.4
Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.
클러스터 평가 외부기준 척도 F<sub>n</sub>-Measure
김경태(Kyeongtaek Kim) 한국산업경영시스템학회 2012 한국산업경영시스템학회지 Vol.35 No.4
F-Measure is one of the external measures for evaluating the validity of clustering results. Though it has clear advantages over other widely used external measures such as Purity and Entropy, F-Measure has inherently been less sensitive than other validity measures. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we present F<sub>n</sub>-Measure, an external cluster evaluation measure based on F-Measure. F<sub>n</sub>-Measure is so sensitive that it can detect their difference in the cases that F-Measure cannot detect the difference in clustering results. We compare F<sub>n</sub>-Measure to F-Measure for a few clustering results and show which measure draws better result based upon homogeneity and completeness.
An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem
Jung Hun Kim(김정훈),Kyeongtaek Kim(김경택),Bong-Wan Choi(최봉완),Jae Joon Suh(서재준) 한국산업경영시스템학회 2017 한국산업경영시스템학회지 Vol.40 No.4
Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.
김지원(Ji Won Kim),이유민(You Min Lee),한상헌(Shawn Han),김경택(Kyeongtaek Kim) 한국산업경영시스템학회 2021 한국산업경영시스템학회지 Vol.44 No.3
The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.
Operational modal analysis of reinforced concrete bridges using autoregressive model
Kyeongtaek Park,Sehwan Kim,Marco Torbol 국제구조공학회 2016 Smart Structures and Systems, An International Jou Vol.17 No.6
This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg\'s algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.