• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.21 no.9
• /
• pp.98-104
• /
• 2022

Because a robot actuator is directly affected by the external force of the robot and accounts for the largest portion of the robot system weight, developing an optimized actuator suitable for each characteristic of the robot system is essential. Although there have been many developments and studies related to robot actuators in various industrial fields, lightweight and compact actuator designs that can control force are still lacking. In this study, a novel actuator module was developed, and its performance was verified experimentally. The structure and control of various robot systems can be optimized by utilizing the proposed actuator. It can be used for various tasks by sensing external force and through feedback control.

• Structural Engineering and Mechanics
• /
• v.81 no.6
• /
• pp.665-675
• /
• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Nuclear Engineering and Technology
• /
• v.53 no.7
• /
• pp.2334-2340
• /
• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of Information Science Theory and Practice
• /
• v.9 no.1
• /
• pp.1-23
• /
• 2021

This study introduces novel research using Practice Context Models supported by Knowledge Networks and Percolation Theory with the aim to contribute to knowledge management in Proof-of-Concept (PoC) activities. The authors envision this proposal as a potential instrument to identify network structures based on a percolation (propagation) threshold and to analyze the importance of nodes (e.g., practitioners, practices, competencies, movements, and scenarios) during the percolation of knowledge in PoC activities. After thirty months immersed in the natural PoC habitat, acting as observers and practitioners, and supported by an ethnographic exercise and a designer-research mindset, the authors identified the production of meaning in PoC activities occurring in a hermeneutic circle characterized by the presence of several knowledge networks; thus, discovering the 'natural knowledge' in PoC as a spectrum of cognitive development spread throughout its network, as each node could produce and disseminate certain knowledge that flows and influences other nodes. Therefore, this research presents the use of Practice Context Models 'connected' to Knowledge Networks and Percolation Theory as a potential and feasible proposal to be built using the attribution of values (weights) to the nodes (e.g., practitioners, practices, competencies, movements, scenarios, and also knowledge) in the context of PoC with the aim to allow the players (e.g., PoC practitioners) to have more flexibility in building alliances with other players (new nodes); that is, focusing on those nodes with higher value (focus on quality) in collaboration networks, i.e., alliances (connections) with the aim to contribute to knowledge management in the context of PoC.

• The Korean Journal of Mycology
• /
• v.49 no.4
• /
• pp.455-467
• /
• 2021

Macrofungi are valuable resources as novel drug candidates, new biomaterials, and edible materials. Recently, genetic approaches pertaining to macrofungi have been continuously growing for their identification, molecular breeding, and genetic engineering. However, purification and amplification of fungal DNA is challenging because of the rigid cell wall and presence of PCR inhibitory metabolites. Here, we established a direct PCR method to provide a rapid and efficient method for PCR-grade macrofungal DNA preparation applicable to both conventional PCR and real-time PCR. We first optimized the procedure of lysis and PCR using the mycelia of Lentinula edodes, one of the most widely consumed macrofungal species. Lysates prepared by neutralizing with (NH₄)₂SO₄ after heating the mycelia in a mixture of TE buffer and KOH at 65℃ for 10 min showed successful amplification in both conventional and real-time PCR. Moreover, the addition of bovine serum albumin to the PCR mixture enhanced the amplification in conventional PCR. Using this method, we successfully amplified not only internal transcribed spacer fragments but also low-copy genes ranging in length from 500 to 3,000 bp. Next, we applied this method to 62 different species (54 genera) of macrofungi, including edible mushrooms, such as Pleurotus ostreatus, and medicinal mushrooms such as Cordyceps militaris. It was found that our method is widely applicable to both ascomycetes and basidiomycetes. We expect that our method will contribute to accelerating PCR-based approaches, such as molecular identification, DNA marker typing, gene cloning, and transformant screening, in macrofungal studies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.3
• /
• pp.205-210
• /
• 2022

A novel OLED pixel circuit is proposed in this paper that uses only n-type thin-film transistors(TFTs) to improve the luminance non-uniformity of the AMOLED display caused by the threshold voltage variation of an OLED driving TFT. The proposed OLED pixel circuit is composed of 6 n-channel TFTs and 2 capacitors. The operation of the proposed OLED pixel circuit consists of the capacitor initializing period, threshold voltage sensing period of an OLED·driving TFT, image data voltage writing period, and OLED·emitting period. As a result of SmartSpice simulation, when the threshold voltage of·OLED·driving TFT varies from 1.2 V to 1.8 V, the proposed OLED pixel circuit has a maximum current error of 5.18 % at IOLED = 1 nA. And, when the OLED cathode voltage rises by 0.1 V, the proposed OLED pixel circuit has very little change in the OLED current compared to the conventional OLED pixel circuit. Therefore, the proposed pixel circuit exhibits superior compensation characteristics for the threshold voltage variation of an OLED driving TFT and the rise of the OLED cathode voltage compared to the conventional OLED pixel circuit.

• Journal of Ginseng Research
• /
• v.46 no.3
• /
• pp.357-366
• /
• 2022

Background: Withania somnifera (Solanaceae), generally known as Indian ginseng, is a medicinal plant that is used in Ayurvedic practice for promoting health and longevity. This study aims to identify the bioactive metabolites from Indian ginseng and elucidate their structures. Methods: Withanolides were purified by chromatographic techniques, including HPLC coupled with LC/MS. Chemical structures of isolated withanolides were clarified by analyzing the spectroscopic data from 1D and 2D NMR, and HR-ESIMS experiment. Absolute configurations of the withanolides were established by the application of NMR chemical shifts and ECD calculations. Anti-adipogenic activities of isolates were evaluated using 3T3-L1 preadipocytes with Oil Red O staining and quantitative real-time PCR (qPCR). Results: Phytochemical examination of the roots of Indian ginseng afforded to the isolation of six withanolides (1-6), including three novel withanolides, withasilolides GeI (1-3). All the six compounds inhibited adipogenesis and suppressed the enlargement of lipid droplets, compared to those of the control. Additionally, the mRNA expression levels of Fabp4 and Adipsin, the adipocyte markers decreased noticeably following treatment with 25 µM of 1-6. The active compounds (1-6) also promoted lipid metabolism by upregulating the expression of the lipolytic genes HSL and ATGL and downregulating the expression of the lipogenic gene SREBP1. Conclusion: The results of our experimental studies suggest that the withasilolides identified herein have anti-adipogenic potential and can be considered for the development of therapeutic strategies against adipogenesis in obesity. Our study also provides a mechanistic rationale for using Indian ginseng as a potential therapeutic agent against obesity and related metabolic diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3836-3854
• /
• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of the Korean Electrochemical Society
• /
• v.25 no.4
• /
• pp.162-173
• /
• 2022

The development of new materials is an essential key for unraveling the environmental and energy problems all over the world. Among the various application materials in this area, crystalline and two-dimensional carbon materials have been studied from points of view such as electrical conductivity, chemical stability, and surface engineering due to the assembly of honeycomb and sp/sp² hybridization structure. Novel two-dimensional materials, including graphene and graphyne, have been continuously reported for several decades to develop in renewable energy fields. Also, various pristine/engineered two-dimensional carbon allotropes have been researched to combine metal nanoparticles in the form of a sphere, cubic, and so on. The renewable energy performance to apply for these materials is drastically increased. In this review, we introduce the research points of the 2D carbon allotrope materials, graphene and graphyne, and applications to improve the performance of renewable energy applications.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.2
• /
• pp.101-106
• /
• 2022

Recently, virtual human has been widely used in various fields such as education, training, information guide. In addition, it is expected to be applied to services that interact with remote users in metaverse. In this paper, we propose a novel method to make a virtual human' interaction to perceive the user's surroundings. We use the editing authoring tool to apply user's interaction for providing the virtual human's response. The virtual human can recognize users' situations based on fuzzy, present optimal response to users. With our interaction method by context awareness to address our paper, the virtual human can provide interaction suitable for the surrounding environment based on automatic adaptation.