Survey control points constitute essential infrastructure for the establishment and management of national spatial information. However, conventional control point management methods suffer from several limitations, including inefficient maintenance, limited accessibility, and frequent data loss, resulting in reduced reliability and operational efficiency. To address these issues, this study proposes a QR code-based survey control point management system that enables rapid identification and real-time management of control point information through mobile devices connected to a centralized database. The proposed system consists of four major components: QR code-attached survey control points, a mobile application for field operations, a centralized cloud database, and a web-based management platform. By integrating these components, the system allows users to retrieve, update, and synchronize control point information efficiently in real time. Compared with conventional management methods, the proposed system improves field accessibility, enhances data accuracy and reliability, and increases maintenance efficiency while reducing operational costs. The proposed approach is expected to contribute to the digital transformation of survey control point management and provide a practical framework for the efficient management of national spatial information infrastructure.

When an Energy Storage System (ESS) is used as the primary power source rather than a supplementary one, reliability becomes paramount. In ESS-based power supply systems without alternative generation sources, the supply capability depends entirely on the ESS, and any failure can lead to severe power interruptions. To address this issue, research has shifted from centralized ESS configurations toward distributed ESS systems, focusing on enhancing ESS stability. In distributed ESS systems where ESS serves as the main source, a representative device employed to improve utilization and reliability is the Power Interface Unit (PIU). For stable PIU operation, it is essential to have devices that control and isolate power flow between ESS units. Consequently, the characteristics of the power controller significantly influence PIU performance, making the development of power converters suited to distributed ESS systems critical. This paper proposes a novel bidirectional buck converter topology for power exchange and isolation between ESS units. The proposed topology leverages existing bidirectional switches used for overcharge and overdischarge protection, offering economic advantages. Furthermore, the switching signal algorithm ensures that power flow is consistently directed from higher to lower voltage levels, regardless of switching states, making it well-suited for distributed ESS applications. Considering the small voltage differences typically present between ESS units, the paper also introduces a design method that accounts for the duty ratio operating range of the buck converter. The feasibility of the proposed approach was validated through Psim simulations, prototype implementation, and experimental testing with two ESS units.

This paper proposes a hybrid CNN-SVM based switch open fault diagnosis method for dual three-phase inverter systems applied to asymmetric six-phase permanent magnet synchronous motors (PMSMs). The proposed method utilizes phase current waveform data obtained under normal and fault conditions without requiring additional sensors or complex mathematical models. Current waveforms are converted into image-based datasets and analyzed using a hybrid CNN-SVM structure. A ResNet18-based CNN extracts fault-related features from the current signals, while an SVM classifier performs fault classification. Principal Component Analysis (PCA) is additionally applied to analyze the distribution characteristics of the extracted features. Experimental results demonstrate that the proposed method effectively distinguishes normal operation, upper switch open faults, lower switch open faults, and simultaneous upper/lower switch open faults. The classification accuracy reached 100% at a switching frequency of 20 kHz and 92.86% at 10 kHz, confirming that higher switching frequencies provide richer waveform information and improve fault classification performance.

This study proposes an unsupervised anomaly detection framework for worker physiological data and workspace sensor data in smart manufacturing environments where labeled abnormal data are limited. Worker physiological data were preprocessed and augmented using Gaussian Process Regressor (GPR), while workspace sensor data were modeled using original normal data based on physical safety thresholds. Isolation Forest, PCA reconstruction error, Autoencoder, and score-level ensemble models were compared, and supervised models were used as auxiliary validation. Experimental results showed that the PCA reconstruction error model achieved the best performance for worker physiological data, with an accuracy of 98.94% and an F1-score of 98.92%. For workspace sensor data, PCA also achieved the best performance, with an accuracy of 99.03% and an F1-score of 99.04%. These results indicate that PCA-based reconstruction error can effectively detect anomalies in both worker physiological and workspace sensor data under limited abnormal labels.

This study employed a single-case study design to examine changes in self-expression and self-esteem in an elementary school student participating in Object Relations Art Therapy. The participant was a second-grade boy in a regular elementary school in City A, referred for art therapy due to emotional difficulties and challenges in expressing his emotions and needs within family relationships. A total of 40 sessions, including pre- and post-assessments, were conducted at Center B from January to May 2023, twice weekly for 50 minutes each. Changes in self-expression and self-esteem were examined through pre- and post-assessments, drawing tests, and session-by-session observations. The results were as follows. First, positive changes were observed in the participant's self-expression, including more active and specific expression of his thoughts and emotions. Second, his self-esteem improved, particularly in social and school-related self-esteem. Third, the analysis of changes across sessions and therapeutic phases showed gradual improvements in both self-expression and self-esteem. These findings suggest that Object Relations Art Therapy may be associated with positive changes in self-expression and self-esteem in elementary school students and may have practical applicability in child counseling settings.

This study evaluated the flexural strength and permeability characteristics of eco-friendly pervious concrete blocks incorporating liquid cellulose pulp (LCP) and oyster shell (OS). To determine the optimum mixture, LCP was used as a replacement for styrene-butadiene rubber (SBR), and oyster shell was utilized as a partial replacement for fine aggregate at various replacement ratios. The experimental results showed that the flexural strength increased with increasing LCP replacement ratio, whereas the permeability coefficient exhibited a slight decrease. The mixture with 100% LCP replacement achieved the highest flexural strength of 5.10MPa while maintaining a permeability coefficient of 2.54mm/s. In addition, the permeability coefficient increased as the oyster shell replacement ratio increased. The mixture containing 25% oyster shell replacement exhibited the highest permeability coefficient of 2.70mm/s and a flexural strength of 4.60MPa. All mixtures satisfied the performance requirements specified in KS F 4419. Based on a comprehensive evaluation of flexural strength and permeability performance, the optimum mixture was determined to be the combination of 100% LCP replacement and 25% oyster shell substitution. The results suggest that liquid cellulose pulp can be applied as a viable alternative to conventional SBR and that oyster shell has the potential to be utilized as a recycled fine aggregate in pervious concrete blocks.

This study investigates the effects of geometric aspect ratio(L/D) changes on the mechanical behavior and failure mechanism transition of large-scale CFRP propulsion shafts under torsion. Multi-zone strain data (CH1~CH9) obtained from a verified full-size model were set as the baseline and compared with the pure torsion test results of a shortened model with an effective length reduced to approximately 35%(34.5%). The shortened-scale model exhibited initial bolt slip behavior at 1,000kNm due to joint clearance elimination, followed by non-linear stiffness reduction from delamination-induced acoustic emissions near 5,500kNm. The structure reached ultimate failure at a maximum torque of 6,577kNm(6.28˚ rotation angle). Cross-examination showed that shortening the length suppressed macro-shear buckling by theoretically improving the critical buckling margin by over 18%. However, because torsional energy concentrated rapidly at the bolted joints, the final failure mode shifted completely from a conventional helical buckling pattern to a local bearing failure mode characterized by composite crushing around the bolt holes. These results will serve as a reliable reference for the design of next-generation naval vessel shafting systems and propulsion modules for unmanned undersea vehicles requiring enhanced acoustic stealth.

There is a growing demand for high-precision micro-scale products; however, the processing equipment for such products has not yet been localized. To develop this equipment, research is required to ensure structural stability and to verify the maintenance of precision during actual machining processes. This study focuses on the structural analysis of machine for this purpose, and the machine under study applies a linear motor to the feed axis to achieve high precision. The research methodology consists of two main approaches. First, whereas previous studies assumed and applied cutting forces, this study identifies specific workpieces and selects realistic cutting conditions for machining them. Based on these conditions, the main cutting force is calculated. Second, since the linear motor and servo motor installed on the feed axis operate during cutting, the heat generated by the motors is estimated based on the actual motor specifications. As a result, it was possible to obtain the maximum deformation and maximum stress of the core structure, where external forces and heat sources are applied simultaneously as an actual cutting process, through this, structural stability was verified. However, since this was a national R&D project, the spindle head was excluded from the analysis due to task allocation.

This study investigates whether university research funding composition is directly associated with technology commercialization performance or whether such association is realized through downstream conversion processes, including patent registration and technology transfer contracts. Using the 2024 higher education disclosure data, this study constructs a university-level dataset by integrating indicators on research funding, patent registrations, transfer contracts, and licensing income. The final sample comprises 139 universities with complete observations for the principal variables. Licensing income is transformed using log(1+x), and the regression model includes total research funding, funding-source shares, patent registrations, transfer contracts, regional location, and public/special institutional status. Robust regression estimates with HC3 standard errors indicate that patent registrations and transfer contracts are more closely associated with licensing income than the shares of private, local, and international funding. Total research funding is also positively related to licensing income, whereas the independent effects of private and local funding shares are not statistically supported in the cross-sectional model. These findings suggest that funding acquisition and commercialization management are interrelated but analytically distinct processes. Universities should therefore manage not only the scale and composition of research funding but also the conversion routines through which research outputs are transformed into protected rights, transfer contracts, and licensing revenue.

In this study, flow analysis and structural analysis were conducted to investigate the explosion flow behavior and structural response that may occur inside a waste battery storage shelter for electric vehicles. In particular, the optimal design conditions were derived by comparing and analyzing the internal flow characteristics and structural safety according to the hole size and arrangement of perforated plates, the number and arrangement of buffer plates, and the thickness and height of each chamber in the battery shelter. The flow analysis results according to the number and arrangement of buffer plates showed that simply increasing the number of buffer plates narrowed the flow passage, resulting in local jet flow and velocity concentration phenomena. In contrast, when the thickness of the buffer plates was reduced and the flow channel geometry was adjusted, the maximum flow velocity decreased to approximately 5.23×10³ m/s, indicating that the concentration of internal velocity could be effectively suppressed. In addition, when the number of holes in the perforated plates was reduced by approximately 10%, the maximum flow velocity was lowered to about 7.49×10³ m/s, while the flow distribution became more uniform. Structural analysis according to changes in the hole arrangement of the perforated plates also showed that adjusting the number and arrangement of holes in chambers 2 and 3 reduced the maximum stress by more than 20%.

This study is a single-case art therapy study conducted to examine two research aims in a child who, after being diagnosed with dyslexia due to persistent learning difficulties, exhibited emotional and psychological difficulties such as anxiety and withdrawal. The first aim was to explore the process of emotional-psychological change across treatment sessions. The second aim was to examine the effects of the art therapy program on self-esteem. The participant was one fourth-grade boy living in City A. The program consisted of 14 sessions in total, including one pre-assessment session, twelve art therapy sessions, and one post-assessment session, and was implemented for 50 minutes per session over approximately nine weeks (from June 28 to August 31, 2019). Because peer conflict, withdrawal, and anxiety had intensified after the child entered the fourth grade, twice-weekly intensive sessions were initially applied to promote rapid emotional-psychological stability and to establish a therapeutic relationship. Subsequently, as emotional-psychological changes were observed and judged to have emerged, the frequency was adjusted to once a week to reduce the child's burden and to help maintain the therapeutic changes in a stable manner. In accordance with the two research questions, session-by-session observation records, verbatim transcripts, and artworks were qualitatively analyzed by stage (initial, middle 1, middle 2, and final) to trace the emotional-psychological change process, and pre- and post-test scores on the self-esteem scale were compared. Regarding Research Question 1, the participant's emotional-psychological state shifted from anxiety, withdrawal, and defensive responses in the initial stage to expanded emotional expression and reduced tension in the middle stage, and then to greater stability, self-expression, and recognition of personal strengths in the later stage. Regarding Research Question 2, the total self-esteem score increased from 87 to 105, and all subdomains-general, social, family, and academic self-esteem-improved. This study is meaningful in that it systematically describes both the emotional-psychological change process and changes in self-esteem, thereby offering foundational data for clinical practice with children with dyslexia.

This study evaluated the effects of a manual cardiopulmonary resuscitation (CPR) assist device (Biota-3) on fatigue, task difficulty, and workload during CPR. Twenty-five university students participated in a crossover experimental study. Fatigue (Borg RPE), task difficulty, and NASA-TLX workload were assessed. The fatigue change score was significantly higher during manual CPR (5.08±1.82) than during Biota-3-assisted CPR (3.72±2.15; p=.002). The total task difficulty score was significantly lower with Biota-3 (26.16±6.70 vs. 34.76±8.42; p<.001). Physical Demand, Effort, Frustration, and overall NASA-TLX workload were also significantly lower with Biota-3 (p<.05). These findings suggest that Biota-3 may help reduce perceived task difficulty and workload during CPR. Further studies incorporating objective CPR quality (QCPR) measurements are needed to confirm its effectiveness.

This study proposes a condition-responsive automatic collaboration architecture for intelligent command and control systems to address the operational challenges of Multi-Domain Operations (MDO) and Manned-Unmanned Teaming (MUM-T) in future battlefields. With the exponential growth of battlefield data and the increasing complexity of tactical decision-making, single AI models face significant limitations in processing heterogeneous information across multiple domains. To overcome these constraints, we develop a multi-agent collaboration framework comprising three distinct topologies-Sequential Pipeline, Parallel Ensemble, and Intelligent Routing-coupled with a Dynamic Re-Orchestration mechanism. This meta-controller-driven architecture enables real-time reconfiguration of AI workflows through hot-swapping of specialized agents based on confidence scores, latency thresholds, and environmental changes. Using Army TIGER 4.0 defensive operations as a case study, we map the proposed architecture to four distinct operational phases (deep area, security area, main defense area, and rear area operations), demonstrating adaptive AI collaboration patterns for each scenario. As a conceptual framework establishing the theoretical foundation for AI-enabled command and control, this research provides a structured approach to human-AI teaming while explicitly deferring empirical performance validation to future implementation and field testing. The findings contribute to the development of intelligent C2 systems for the Korean military.

This study investigated the surface characteristics of micro arc oxidized (MAO) Ti-6Al-4V and evaluated its potential applicability as a functional surface material for skin-contact rehabilitation assistive devices. Titanium specimens were surface-treated using MAO in an electrolyte containing KOH and K₃PO₄. Surface microstructure, oxide layer formation, and surface roughness characteristics were analyzed using scanning electron microscopy (SEM), atomic force microscopy (AFM), and surface roughness measurements. The results demonstrated that porous microstructures and uniform oxide layers were formed on the MAO-treated titanium surfaces. The average pore size ranged from approximately 1 to 5 ㎛, while the oxide layer thickness ranged from approximately 8 to 15 ㎛. Surface roughness analysis showed that the average roughness (Ra) value of the MAO-treated group increased significantly compared with the untreated group (p < 0.05). AFM analysis confirmed uniform roughness distribution and microscale protrusion structures on the MAO-treated surfaces. These findings suggest that MAO-treated titanium may serve as a candidate functional surface material for future skin-contact wearable rehabilitation devices.

This study investigated the effects of Micro Arc Oxidation (MAO) treatment on the surface microstructure and apparent tensile behavior of Ti-6Al-4V alloy. MAO treatment was performed using an alkaline electrolyte containing KOH and K3PO4 under a current density of 300 mA/cm², a frequency of 60 Hz, and a treatment time of 5 min. Surface characteristics were analyzed using scanning electron microscopy (SEM), a surface roughness tester, and atomic force microscopy (AFM), while apparent tensile behavior was evaluated using a universal testing machine according to ISO 22674. SEM observations revealed a porous oxide structure with micro-pores and discharge channels on the MAO-treated surface. The observed micro-pores were approximately 1-5 ㎛ in size and tended to be distributed throughout the observed surface area. The average surface roughness (Ra) increased significantly from 0.42 ± 0.03 ㎛ in the untreated group to 1.85 ± 0.12 ㎛ in the MAO-treated group (p<0.05). AFM analysis also revealed irregular micro-topographic features and local height variations on the MAO-treated surface. The apparent yield strength increased from 512.4 ± 18.5 MPa in the untreated group to 841.2 ± 22.7 MPa in the MAO-treated group (p<0.05), while elongation showed a slight increase from 9.8 ± 0.7% to 11.2 ± 0.5%, although the difference was not statistically significant (p>0.05). These findings indicate that MAO treatment induces distinct changes in the surface microstructure and roughness of Ti-6Al-4V alloy and that differences in apparent tensile behavior are observed between untreated and MAO-treated specimens. However, the observed difference in apparent yield strength should not be interpreted as a direct improvement in the intrinsic bulk mechanical properties of Ti-6Al-4V. The present results provide fundamental data for understanding the relationship between MAO-induced surface modifications and the apparent tensile behavior of Ti-6Al-4V alloy.

This study presents a simulation-driven optimal design methodology for a 48V Co-Axial powertrain IPMSM considering real vehicle driving conditions. A forward vehicle dynamics model integrating battery, inverter, motor, and longitudinal vehicle resistance was developed in MATLAB-Simulink to extract torque-speed operating point distributions under representative driving cycles. Frequency-based clustering of the extracted points enabled identification of the core operating region and definition of design specifications including maximum torque, base speed, and voltage limit constraints considering the usable fundamental voltage within the 48V system. Based on the initial 6-pole, 36-slot IPMSM model, seven structural parameters were defined to establish a parametric design framework. Using PIAnO with Bruce LHD sampling, 1000 candidate designs were generated and electromagnetically analyzed in JMAG to evaluate torque capability and voltage harmonic characteristics. Meta-modeling using PRG and MLP methods provided accurate surrogate models of the objective functions, enabling multi-objective optimization via MOGA. The optimized final model achieved a peak torque of 105.97 Nm at 1390 rpm and output power of approximately 15 kW while successfully reducing phase current from 380 Arms to 330 Arms as required by the application. Comparison between surrogate predictions and full FEM analysis confirmed model accuracy within acceptable error tolerances, demonstrating that linking vehicle-level operating analytics with electromagnetic optimization provides an effective approach for achieving performance-efficient low-voltage traction motors for electric mobility applications.

Recently, DC-based power conversion systems such as batteries and renewable energies have been growing in importance in the field of distributed power, and isolated bidirectional DC/DC converters are widely used to meet this demand. Among them, the DAB converter offers a smaller size, higher power density, and ZVS operation. However, the calculated and actual output power often differ due to errors between the design and actual values of the transformer inductance and the dead-time. Therefore, this paper analyzes the characteristics of the DAB converter and proposes a feed-forward control method that compensates for these errors in proportion to the output current, enabling stable voltage control with simple structure, excellent transient characteristics, and low dependence on the PI controller. The proposed scheme is verified by PSIM simulation and experiments. Simulation results show that the steady-state voltage error can reach up to -7.09% without compensation due to the dead-time effect. Experimentally, the proposed α compensation maintains the transient voltage variation within ±3% under load step changes.

This cross-sectional descriptive study investigated college students' attitudes toward expanded automated external defibrillator (AED) installation and improved accessibilityand identified associated factors. A total of 158 college students in Gyeongsangnam-do, Republic of Korea, completed an online questionnaire between July 11 and 13, 2026. Data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA with Scheffé post hoc tests, and multiple linear regression. The mean attitude score was 4.26 ± 0.54, indicating generally positive attitudes. Significant differences were observed according to academic major (t=2.25, p=.026), academic year (F=3.13, p=.027), previous CPR performance experience (t=2.19, p=.030), and previous AED use experience (t=-2.35, p=.020). In multiple regression analysis, religion (𝛽=-.295, p=.023) was the only statistically significant variable; however, the explanatory power of the model was low (Adjusted R²=.062). These findings suggest that college students generally support expanding AED installation and improving accessibility. Strategic AED placement, effective location guidance systems, and continuous first-aid education should be promoted. Future studies should include more diverse populations and examine additional psychological and social factors, such as first-aid knowledge, self-efficacy, and actual AED access experience.

Purpose: This study aimed to investigate the perception and acceptance of cardiopulmonary resuscitation (CPR) assistive devices among college students, examine differences according to general characteristics, and identify factors associated with their perception and acceptance. The findings are intended to provide baseline data for the development, education, and dissemination of CPR assistive devices. Methods: A cross-sectional descriptive study was conducted among 158 college students in Gyeongsangnam-do, Republic of Korea. Data were collected using an online self-administered questionnaire between July 11 and July 13, 2026. The collected data were analyzed using IBM SPSS Statistics 29.0. Descriptive statistics, independent t-tests, one-way analysis of variance (ANOVA) with Scheffé post hoc tests, and multiple regression analysis were performed. Results: The mean score for the perception and acceptance of CPR assistive devices was high (4.32±0.62), whereas the perceived burden of physical contact was relatively low (2.84±0.92). Significant differences in the perception and acceptance of CPR assistive devices were observed according to CPR education experience (t=2.14, p=.033) and automated external defibrillator (AED) use experience (t=-2.33, p=.021). However, the overall multiple regression model was not statistically significant (F=1.93, p=.069), and no independent factors associated with the perception and acceptance of CPR assistive devices were identified. Conclusion: College students demonstrated positive perceptions and high acceptance of CPR assistive devices and expressed strong support for their development and installation in public settings. These findings suggest that developing user-friendly CPR assistive devices capable of supporting accurate chest compressions and incorporating them into hands-on CPR training programs may facilitate greater bystander participation in CPR.

This study focused on the design of energy saving device, mainly an Eco-Ring Stator (ERS) with a pre-swirl stator suitable for the Suez max-class hull form. As the stern becomes more slender, a more compact device becomes effective by reducing its own drag. The successfully developed ring stator concept was applied to design for the present target ship of Suezmax tanker. The asymmetric pre-swirl stator was also designed for comparison with the ERS. The Star-CCM+ was used as a CFD tool for the optimum design of ESDs which was validated by comparison of the experiments and the computations regarding the resistance and the self-propulsion. Prior to the ERS design, an asymmetric stator was first designed and its efficiency gain resulted in a little over 3%. For the ERS design, the parameter study such as the ring diameter, the number of stator blades, the chord distribution according to stator radius, and the pitch angle (based on equivalent angle of attack concept) was conducted. As a result, the propulsion efficiency was improved when an asymmetric blade configuration featuring a chord length that is 5% of the propeller diameter and an equivalent angle of attack of -11° was applied. Finally, the efficiency improvement of 5.1% was achieved compared to the bare hull condition by CFD analysis which is larger than that (3.4%) of the previous results of the crude oil carrier case.

This study aimed to identify psychological factors related to Emergency Response Initiation (ERI) and CPR Behavioral Persistence (CBP) among healthcare-related college students. A structured self-report questionnaire was administered to 506 healthcare-related students from universities located in Gyeongsangnam-do, Korea. The collected data were analyzed using descriptive statistics, Pearson's correlation analysis, and multiple linear regression analysis. The results showed that behavioral control and situational attention were positively associated with both ERI and CBP. Self-efficacy was positively associated with ERI, while informational sufficiency was positively associated with CBP. In contrast, crisis stress response showed a significant negative association with ERI, but its relationship with CBP was not statistically significant. These findings suggest that CPR-related behaviors are not solely determined by knowledge or behavioral intention but are influenced by complex psychological factors, including behavioral control, situational attention, self-efficacy, and informational sufficiency. Therefore, CPR education for healthcare-related college students should be expanded beyond skill-based training to include strategies that enhance behavioral regulation, situational judgment, and stress coping abilities.

Conventional rule-based robot programming for welding requires time-consuming manual reprogramming whenever workpiece geometry changes. To address this limitation, this study investigates a learning-based welding control approach for a 6-DOF cooperative robot using 3D CAD position data. The overall control process is divided into Phase 1 (welding start point approach and posture alignment) and Phase 2 (weld line tracking). In Phase 1, Proximal Policy Optimization (PPO) is applied to guide the robot end-effector (TCP) to the start point P₁ within a 0.5 mm error while aligning the torch angle. However, using reinforcement learning alone for Phase 2 tracking resulted in trajectory deviation. To overcome this limitation, Behavior Cloning-Recurrent Neural Network (BC-RNN) and a line-local frame transformation are additionally introduced. Simulation evaluations show that the transition error remains under 0.5 mm, ensuring a stable initial state. Across three representative weld lines, the robot completes the paths to end point P₂ with mean cross-track errors of 1.26-2.86 mm. This study demonstrates through simulation that learning-based control enables welding without manual reprogramming.