• Structural Engineering and Mechanics
• /
• v.78 no.3
• /
• pp.269-280
• /
• 2021

Reinforced concrete (RC) structural walls with L-shaped sections are commonly used in RC buildings. The walls are often expected to sustain biaxial load and Unsymmetrical bending in an earthquake event. However, there currently exists limited experimental evidence regarding their seismic behaviour in these lateral loading directions. This paper makes experimental and numerical investigations to these walls behaviours. Experimental evidences are presented for four L-shaped wall specimens which were tested under simulated seismic load from different lateral directions. The results highlighted some distinct behaviour of L-shaped walls sustaining Unsymmetrical bending relating to their seismic performance. First, due to the Unsymmetrical bending, out-of-plane reaction forces occur for these walls, which contribute to accumulation of the out-of-plane deformations of the wall, especially when out-of-plane stiffness of the section is reduced by horizontal cracks in the cyclic load. Secondly, cracking was found to affect shear centre of the specimens loaded in the Unsymmetrical bending direction. The shear centre of these specimens distinctly differs in the flange in the positive and negative loading direction. Cracking of the flange also causes significant warping in the bottom part of the wall, which eventually lead to out-of-plane buckling failure.

• Korean Journal of Metals and Materials
• /
• v.47 no.12
• /
• pp.873-880
• /
• 2009

Water-vapor permeation through metallic barriers deposited on polymer substrates has been an important technological issue because the performance of the barrier is critical to the reliability of flexible organic devices. For the development of long-lifetime flexible organic devices, two different sets of samples were designed and demonstrated from the viewpoint of the water-vapor transmission rate (WVTR). Aluminum (Al) and polyethylene terephthalate (PET) were chosen for the barrier layer and the polymer substrate, respectively. Two stacking structures, a single-layer (Al/PET) structure and a double-layer (Al/PET/Al) structure, were used for the WVTR measurement. For the single-layer structure, the WVTR decreases as the thickness of the barrier layer increases. Compared to the single-layer sample, the double-layer sample showed superior WVTR performance (by nearly three times) when the total thickness of the Al barrier was greater than 100 nm.

• Safety and Health at Work
• /
• v.12 no.1
• /
• pp.1-9
• /
• 2021

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.331-340
• /
• 2021

The Internet of Things (IoT) is a rapidly growing physical network that depends on objects, vehicles, sensors, and smart devices. IoT has recently become an important research topic as it autonomously acquires, integrates, communicates, and shares data directly across each other. The centralized architecture of IoT makes it complex to concurrently access control them and presents a new set of technological limitations when trying to manage them globally. This paper proposes a new decentralized access control architecture to manage IoT devices using blockchain, that proposes a solution to concurrency management problems and enhances resource locking to reduce the transaction conflict and avoids deadlock problems. In addition, the proposed algorithm improves performance using a fully distributed access control system for IoT based on blockchain technology. Finally, a performance comparison is provided between the proposed solution and the existing access management solutions in IoT. Deadlock detection is evaluated with the latency of requesting in order to examine various configurations of our solution for increasing scalability. The main goal of the proposed solution is concurrency problem avoidance in decentralized access control management for IoT devices.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.286-299
• /
• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• ETRI Journal
• /
• v.44 no.4
• /
• pp.613-623
• /
• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.458-464
• /
• 2022

Hazard recognition is considered as one of the pre-requisites for effective hazard management and injury prevention. However, in complex and changing environments, construction workers are often unable to identify all possible hazards that can occur in the jobsite. Therefore, identification of factors that impact hazard recognition in the work environment is necessary to reduce safety incidents as well as to develop strategies that can improve worker's hazard recognition performance. This study identified factors/problems that impact worker's hazard recognition abilities and suggested some potential technologies that can mitigate such problems. Literature reviews of journal articles and published reports related to hazard recognition studies were conducted to identify the factors. The study found out that the major factor responsible for affecting worker's hazard recognition abilities were human-related. Industry factors, Organizational factors and Physical factors of the site were the other factors identified from the study that impact worker's hazard recognition performances. The findings from the study can help site personnel recognize areas where effective measures can be directed towards worksite safety of workers while working in complex construction environments.

• Journal of the Korean Society of Industry Convergence
• /
• v.27 no.3
• /
• pp.693-699
• /
• 2024

The main cause of scattering dust generated by transportation equipment such as automobiles was largely due to exhaust gas from internal combustion engines in the past, but it was generally recognized that non-exhaust causes such as abrasion of the tires or brake pads were low. Accordingly, scattering dust generated by exhaust gas has consistently existed in many studies, such as technological progress and related regulations, but research on non-exhaust is relatively insignificant, and the need for research on scattering dust generated by non-exhaust is emerging. In this study, a dust collector that can be easily mounted on a caliper to collect scattering dust generated by pad wear during the brake operation of an automobile was manufactured. In this study, we developed a dust collector that is easy to mount on calipers to collect scattering dust caused by pad wear during brake operation of automobiles. According to the installation of the manufactured dust collector, the performance of scattering dust by brake operation and the temperature change characteristics of calipers according to the structure of the dust collector were evaluated.

• Journal of Information Technology Applications and Management
• /
• v.30 no.5
• /
• pp.41-57
• /
• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• Nuclear Engineering and Technology
• /
• v.56 no.7
• /
• pp.2641-2649
• /
• 2024

Effective management of slightly used nuclear fuel (SUNF) is crucial for both technical and public acceptance reasons. SUNF management, radiotoxicity risk, and associated financial investment and technological capabilities are major concerns in nuclear power production. Reducing the volume of SUNF can simplify its management, and one possible solution is utilizing small modular reactors (SMR) and advanced fuel designs like those with thorium. This research focuses on studying the neutronic performance and radionuclide inventory of three different thorium fuel configurations. The mass of fissile material in thorium-based fuel significantly impacts Kinf, burn-up, and neutron energy spectrum. Compared to uranium, thorium as a fuel produces far fewer transuranic elements and less long-lived fission products (LLFPs) at the end of the core cycle (EOC). However, certain fission product elements produced from thorium-based fuel exhibit higher radioactivity at the beginning of the core cycle (BOC). Physical separation of thorium and uranium in the fuel block, like seed-and-blanket units (SBU) and duplex fuel designs, generate less radioactive waste with lower radioactivity and longer cycle lengths than homogeneous or mixed thorium-uranium fuel. Furthermore, the SBU and duplex feel designs exhibit comparable neutron spectra, leading to negligible differences in SUNF production between the two.