Sejong University

In the digital age, Quick Response (QR) codes have become essential in sectors such as digital payments and ticketing, propelled by advancements in Internet of Things (IoT) and deep learning. Despite their growing use, there are significant challenges in the accurate extraction and verification of QR codes, particularly in dynamic environments. Traditional methods struggle with issues like variable lighting, complex backgrounds, and counterfeits, which degrade the performance of QR code extraction and verification processes. This paper introduces a comprehensive approach that refines QR code extraction using enhanced adaptive thresholding techniques and incorporates a deep learning framework specifically tailored for robust QR code verification. Our methodology integrates dynamic window size adjustment, statistical weighting, and post-thresholding refinement to ensure precise QR code extraction under varying conditions. The verification process employs the ShuffleNetV2 network to ensure high performance with significantly low processing times suitable for real-time applications. Additionally, our deep learning model is trained on a comprehensive dataset comprising 28,523 images across 24 distinct QR code pattern classes, including variations in lighting, noise, and backgrounds to simulate real-world conditions. Experimental results demonstrate that our proposed methodology outperforms competing techniques in both processing speed and recognition accuracy, achieving a processing time of 0.08 seconds and a validation accuracy of 99.99% in constrained scenarios. Our approach shows an exceptional ability to distinguish real QR codes from counterfeits and highlights the significance and efficacy of our method in addressing contemporary challenges.

This study addresses the persisting issue of restaurant reservation no‐shows by applying the availability heuristic principle to analyze booking behaviors. Three experiments identified cancellation policy features and factors influencing booking and no‐show behaviors. Findings confirmed that a cancellation barrier (i.e., strict policy and complex cancellation method) decreases no‐show intentions and negatively affects booking intentions. This research suggests that a lenient cancellation policy, an easy cancellation method, and an awareness campaign can encourage positive guest booking behavior and reduce no‐shows. Applying the availability heuristic principle to restaurant management, the current research enhances the understanding of psychological factors affecting guest decisions and identifies effective strategies to reduce no‐shows while supporting favorable booking behavior.

In this research, the three‐dimensional (3D) rotating /hybrid nanofluid (HNF) developed in Darcy–Forchheimer porous medium (DFPM) has been examined numerically across the stretching/shrinking surface in impact of the convective heat transfer and heat source/sink effect. Moreover, the impact of Forchheimer, suction, and MHD parameters on magnitudes of reduced skin friction and heat transfer were examined using the Tiwari‐Das model. The governing equations of the considered problem are transmuted into the appropriate structure of ordinary differential equations (ODEs) by employing the linear similarity variables. The resulting model of ODEs is numerically solved utilizing the “three‐stage Labatto III‐A technique” provided in the “MATLAB software's” bvp4c solver. In addition, to make evaluations, the current numerical results are compared with those from previous studies. Dual branches are created by the ODE governance system. With the aid of stability analysis, a single stable branch is identified. Reduced in both branches has been shown to increase the reduced heat transfer rate. In addition, the outcomes demonstrated that the number of branches is dependent on the parameter ranges for suction, porosity, and magnetic for a given value of the parameter for rotation. Moreover, temperature profile enhanced as increased the amount of heat source strength. Dual branches are obtained for specified assortments of the related parameters. Moreover, when the solid volume of copper was improved, the strength of reduced heat transfer intensified toward the Forchheimer parameter and declined in MHD and suction effects for both branches. Consequently, the findings demonstrate that branch duality occurs for , , and , but no fluid flow is conceivable when , , and . Future research on dual solutions in rotating HNFs may concentrate on improved fluid models, turbulent flows, biological applications, and experimental validation. These developments will help to build more efficient energy systems, biological heat transfer applications, and industrial thermal management solutions.

Students receiving Western instrumental music education develop cognitive skills, creativity, and academic achievement. This study examines inequalities in resource availability for Western instrumental music education between urban and rural schools and their impact on student outcomes. A mixed-methods approach was used, combining quantitative surveys from 410 students measuring access to music resources, participation in programs, and academic performance, along with semi-structured interviews with 67 music teachers to explore instructional challenges and perceived effects on student motivation. Quantitative data were analyzed using SPSS for descriptive and inferential statistics, while qualitative data underwent thematic analysis in NVivo. Findings revealed significant disparities in music education resources. Urban schools had more instruments, structured programs, and highly qualified teachers, whereas rural schools faced limited access to instruments, fewer music programs, and a shortage of specialized instructors in Western music traditions. These disparities influence student motivation, proficiency, and long-term academic success. This study’s novelty lies in its comprehensive integration of qualitative and quantitative methods, offering a holistic understanding of educational inequalities in music. The findings highlight the need for targeted policy interventions to bridge the urban-rural gap and ensure equitable access to music education for all students.

This study proposes a novel approach to improving radar-based precipitation nowcasting using a boosting algorithm to blend traditional physics-based extrapolation and data-driven deep learning (DL). Here, a semi-Lagrangian approach (PySTEPS) and a data-driven DL model (RainNet) are considered as two representative types of nowcasting models of precipitation. The LightGBM model is adopted as a boosting algorithm due to its efficiency and ability to correct for biases sequentially. The boosting model significantly outperforms both RainNet and PySTEPS with up to 90 min of lead time for critical success index (CSI), probability of detection (POD), false alarm ratio (FAR), root mean square error (RMSE), and fractions skill score (FSS) at the 0.1, 1, and 5 mm/h thresholds. The CSI at the 0.1 and 1 mm/h thresholds for the boosting model is approximately 10% higher than that of both the RainNet and PySTEPS models. In addition to high CSI, the boosting approach can also achieve superior results in POD and RMSE compared with the RainNet and PySTEPS models across various thresholds and lead times. The proposed modeling framework significantly outperforms the individual models in predicting rainfall intensity and spatial distribution, highlighting the potential of blending precipitation nowcasting models.

This study investigates the mechanisms and conditions through which coworker knowledge sharing influences employee creativity, grounded in the conservation of resources (COR) theory. Specifically, the study proposes that employee self‐efficacy functions as a mediating mechanism in the relationship between coworker knowledge sharing and employee creativity, while job demands serve as a boundary condition for this indirect effect. To test these hypotheses, two studies were conducted in South Korea. Study 1 analyzed data from 198 supervisor–employee dyads within a state‐owned enterprise, while Study 2 examined data from 241 dyads across six of the nation's largest private companies. Results indicate that coworker knowledge sharing significantly enhances employee creativity, with self‐efficacy fully mediating this relationship. Furthermore, the findings reveal that job demands moderate both the effect of coworker knowledge sharing on employee self‐efficacy and the indirect effect of coworker knowledge sharing on employee creativity via self‐efficacy. The study discusses implications for both theory and practice.

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360$^{\circ }$ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks; instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model.

This paper proposes a generalized method for designing tendon-driven serial-chained manipulators with an arbitrary number of tendon redundancy. First, a special class of tendon-driven structures is defined by introducing the controllable block triangular form (CBTF) of a null space matrix and its complementary CBTF of a structure matrix, satisfying physical constraints related to the minimal connection of tendons and to the placement of actuators. Then it is shown that any general design of tendon-driven serial manipulators can be reduced to the design of such a special class of tendon-driven structures. Two associated design problems are derived and solved. The first design problem is about finding a complementary CBTF structure matrix for a given CBTF null space matrix using algebraic relations, whereas the second one seeks the both matrices that optimize the wanted structural characteristics based on the result of the first design problem. Numerical design examples are provided to show the validity of the proposed method.

In this study, a novel tunable dopingless band-to-band tunneling mechanism based Leaky Integrate and Fire (LIF) neuron is proposed with a notable improvement in integration density and energy consumption. The forward transfer characteristics of Tunnel FET with sharp sub-threshold swing have been utilised to simulate the neural activity. The simulations performed using Atlas 2D software confirm that the proposed TFET can effectively replicate the spiking behavior of a biological neuron, eliminating the need for additional circuitry, in addition to offering tunable features. The proposed LIF neuron demonstrates significantly lower energy consumption, operating at just 144 aJ per spike. This energy efficiency is at least $10^6$ times lower than the single MOSFET-based neuron and $10^3$ times lower than TFET-based 1-transistor neurons reported in prior literature. This remarkable improvement is attributed to the underlying mechanism, which leverages tunneling and material engineering techniques. The proposed neuron has also been successfully investigated for the implementation of adaptable threshold logic functions (NOT, OR and AND). This offers a solution for the design of highly scalable and energy efficient threshold logic circuits for future neuromorphic computing systems. Lastly, we implement a multilayer SNN that confirms the image recognition ability of the proposed neuron with 92.1$\%$ accuracy.

Flat back syndrome is a condition characterized by the loss of lumbar lordosis due to weakened lumbar-supporting muscles, causing pain and inability maintaining the lumbar spine for extended periods. This condition leads patients to adopt incorrect postures in daily life, resulting in various inconveniences in activities of daily livings (ADLs). In this study, we aim to develop a soft wearable lumbar support robot that utilizes a tendon-driven system and the principle of three-point pressure to assist the lumbar function during daily activities. The developed robot includes a switch that enables the adjustment of the lumbar spine angle based on individual characteristics and specific situations. Performance evaluations demonstrated that the exosuit effectively assisted with trunk bending tasks, achieving an average range of motion from −2.13° to 37.87° in the unworn condition (without exo) and 0° to 23.75° when worn and activated (exo on), indicating improved flexibility. Electromyography (EMG) analysis revealed a significant reduction in activation of the thoracolumbar erector spinae (TES) and lumbar erector spinae (LES) during exo on conditions, with decreases of 9.95% and 10.36%, respectively, compared to the without exo condition. However, when the robot effectively assists lumbar extension and the spine is fully extended, lumbar flexion movements such as sitting induce tension in the wires, thereby restricting the user’s motion. Therefore, we developed a learning-based algorithm by attaching inertial measurement unit (IMU) sensors to both thighs to detect user intentions. The algorithm achieved validation and test accuracies of 94.12% and 92.31%, respectively, with high classification performance across most activities, though precision and recall were lower for dynamic transitional movements. This allows users to perform tasks involving extended and fixed lumbar positions more easily. We validated the algorithm by conducting verification assessments using test data and real-time evaluations of intention detection and motor control.

This study investigates the impact of sea spray parameterization on typhoon prediction in the Yellow and East China Seas (YECS) region. Using an air-sea-wave coupled model, we evaluate changes due to sea spray effects in the simulated intensity and structure of Typhoons Lingling (2019) and Maysak (2020). Enabling sea spray effect enhances surface turbulent heat fluxes considerably around the typhoon centers (74% increase for Lingling, 92% for Maysak), leading to a better representation of typhoon intensification phases. Analysis of thermodynamic processes reveals that sea spray-induced warming emerges before rapid intensification, with enhanced temperature and moisture profiles throughout the troposphere supporting stronger secondary circulation. As a result, key aspects of typhoon prediction exhibit significant improvements: root-mean-squared errors decreased by 63% in minimum central pressure and 60% for maximum wind speed in the case of Maysak. The results demonstrate that sea spray effects are strongly modulated by sub-surface ocean conditions, with a greater surface heat flux enhancement for Maysak that moved along warmer Kuroshio and Tsushima currents than for Lingling which passed over Yellow Sea Bottom Cold Water. Our findings demonstrate the significant potential to improve typhoon predictions in the YECS region by incorporating sea spray effects.

In this study, we investigated a color-tunable carboxyl-rich carbon inverse photonic ball (CIPB) ink, which was fabricated using a polymeric photonic ball (PB) as a template, with characteristic self-assembled opalline structures from monodisperse polystyrene (PS) microspheres. The PBs were prepared on a large scale via an optimized diffusive drying method using an aqueous dispersion of polystyrene microspheres. Via acid-catalyzed thermal dehydration of monosaccharides within the interstitial space of PB followed by template removal, iridescent CIPB, which is insoluble in water or organic solvents because the crosslinked structure is similar to a naturally occurring humin, was obtained. The use of PS microspheres of different sizes for the preparation of the respective PBs resulted in CIPBs with different structural colors. Optical characterization revealed that the individual CIPB particles exhibit specific colors on the basis of the angular dependence of the Bragg condition for each particle. The overall structural color of the CIPB ink was sensitively tuned by changing dispersing media with different indices of refraction. Spectroscopic analysis confirmed the presence of carboxyl groups within CIPB due to the light thermal condensation of sugar, and the osmotic swelling/deswelling of the charged CIPB at pH values above/below the pKa of the bound carboxylate drove the reversible pH-responsive changes in structural color, indicating the promising applicability of CIPB as a colorimetric chemical sensor.

Pervious concrete (PC) has been widely employed in parking areas, residential streets, walkways, etc. because it allows water to run through it at a rapid pace, thus reducing runoff from a site and allowing groundwater recharge. The major hindrance in utilizing PC for various applications is its lower compressive strength than conventional concrete. This work employed fillers such as chopped glass fibers (GFs), glass fiber mats, and graphene oxide (GO) to increase the compressive strength of PC without compromising its capability to drain water. The PC samples prepared by incorporating 0.375″ gravels exhibited maximum compressive strength (5.01 MPa) while the PC sample comprised of 6 mm sized chopped GF demonstrated ~1.7 times higher compressive strength than that prepared with GF mats. Furthermore, GO loading of 0.036 wt.% in PC improved the compressive strength by two times compared to the neat PC sample without altering the flow rate of the PC.

This study reports fabrication of Graphene-titanium dioxide (G-TiO₂) nanocomposites with varying graphene content (0%, 25%, 50%, and 75%) through sonication-assisted coprecipitation method. The synthesized materials were analyzed through FTIR, DRS, SEM, and XRD techniques, which confirmed the successful formation of an anatase-phase TiO₂ tetragonal structure with crystallite sizes ranging from 10 to 25 nm after calcination at 600 °C for 4 hours. The addition of graphene resulted in a significant reduction in the TiO₂ bandgap energy from 3.2 eV to 1.55 eV, enhancing the material's absorption in the visible light spectrum. The nanocomposite with 50% graphene loading exhibited the strong adsorption and highest photocatalytic efficiency, achieving 98.29% degradation of cationic methylene blue dye under visible light irradiation. This exceptional performance is attributed to the synergistic effects of graphene, including improved light absorption, enhanced charge carrier separation, and increased electron transfer efficiency. Kinetic analysis revealed that the photocatalytic degradation followed pseudo-first-order reaction kinetics. Furthermore, recycling tests demonstrated the structural stability and reusability (upto 5 cycle) of the nanocomposite, indicating its potential as an effective photocatalyst for environmental applications. The effectiveness of G-TiO₂ in mitigating industrial pollutants also underscores the significance of sonication assisted-coprecipitation approach.