Coastal protection structures such as submerged and detached breakwaters are increasingly being installed to prevent coastal erosion, scouring, and subsidence, which pose serious threats to the stability of these structures. In this study, we used airborne light detction and ranging (LiDAR) bathymetry (ALB) to detect precise topographic changes around artificial underwater structures. Digital surface models with a 1 m grid resolution were obtained for the Won-Pyeong Beach area from ALB data acquired in November 2020 and July 2024. We analyzed the topographical changes (erosion/deposition patterns) and bathymetric contour shifts around preexisting submerged and newly installed detached breakwaters and groins. ALB has been validated as an effective tool for acquiring precise bathymetric data in shallow waters inaccessible to vessels, with ALB data serving as a foundational baseline for assessing structural conditions, such as scouring and subsidence, and managing coastlines.
Industrial manufacturing processes involve various types of electrical load profile, which can generally be categorized into continuous-operation loads and start-stop operation loads. These load profiles are often highly fluctuating rather than stable, forcing factories to contract demand capacities significantly higher than their average power consumption, thereby increasing basic electricity charges. By installing a small-capacity battery energy storage system at the equipment front end and utilizing its fast charge-discharge characteristics to discharge during heavy-load periods and charge during light-load periods, the total power demand of the supply line can be effectively smoothed, resulting in a substantial reduction in contracted demand capacity. In this study, a sensor-based power monitoring system consisting of current sensors, power transducers, and a programmable logic controller is used to acquire high-resolution electrical load data from industrial machines. The measured sensing data were used as the input for short-term load forecasting and battery energy storage control. We focus on industrial machinery exhibiting pronounced periodic start-stop operating characteristics, in which short-term actions on the order of seconds or minutes frequently generate peak power demands that compel users to increase contracted capacity. Time-series forecasting models, including the autoregressive, autoregressive integrated moving average, and seasonal autoregressive integrated moving average models, are first applied to predict the load behavior of such equipment. On the basis of the forecasting results, a smoothing-baseline-based load smoothing control strategy is proposed to regulate the charge-discharge operation of the energy storage system, thereby stabilizing power demand and enabling an economic benefit analysis. The results verify the feasibility of applying energy storage systems to smooth the power demand fluctuations of industrial machinery and to effectively reduce basic electricity charges.
For carbon capture and utilization, real-time monitoring technologies are employed in syngas production through efficient CO2 capture and conversion, achieving a synergistic relationship between chemical process control and sensor performance. An alkaline absorption system was developed to capture CO2 (14500-14800 ppm), which was subsequently converted into syngas using waste platinum (Pt) catalysts via microwave induction. Integrated sensing systems, including nondispersive infrared sensors and electrochemical probes, were employed to quantify CO2 capture kinetics and evaluate process stability. Increasing the NaOH concentration from 1 to 7% extended the CO2 breakthrough time from 138 to 1782 s. High-resolution sensors enabled the identification of ionic conductivity signatures, where the 3.6-fold difference in equivalent conductivity between OH- and CO32- facilitated the predictive monitoring of solvent exhaustion. Microwave-assisted dry reforming (450 W) using 6 g of waste Pt catalyst and 0.8 g of micronsized iron powder achieved average H2 and CO yields of 18.8 and 25.1%, respectively. The results demonstrate that integrated sensor networks are essential for managing the inherent variability of CO2 absorption, thereby enabling an automated, sensor-driven circular economy.
In response to the increasing demand for noninvasive physiological monitoring in dementia care, particularly with horticultural therapy, in this study, we evaluated the feasibility of Eulerian video magnification (EVM) for contact-free heart rate estimation. Utilizing dorsal hand imagery from older adults with dementia, we compared two region-of-interest (ROI) selection strategies against electrocardiography (ECG) benchmarks. Empirical results indicated that the large-area ROI strategy outperformed the small-area alternative, yielding a lower mean absolute error of 9.00 bpm and a stronger correlation trend with ECG data (rho = 0.516, p = 0.059). This performance advantage is primarily due to the spatial averaging effect, which effectively mitigates noise from skin heterogeneity, specular reflections, and microtremors characteristic of this demographic. Overall, integrating EVM with dorsal hand imaging demonstrates preliminary feasibility for clinical monitoring. Future research should prioritize expanding cohort sizes and validating signal robustness within dynamic horticultural environments to ensure practical therapeutic utility.
With the increasing demand for intelligent and sensor-driven systems, traditional monolithic agent architectures face critical limitations in scalability, adaptability, and seamless integration with heterogeneous sensing resources. The objective of this study is to develop a scalable intelligent task management framework capable of coordinating multiple sensing and reasoning components in dynamic environments. To address these challenges, we propose a hierarchical multi-agent architecture consisting of a Task Dispatching Layer, Domain Agent Layer, and Modular Tool Layer. Inter-agent collaboration is standardized through a custom Agent-to-Agent protocol, while sensing and tool functionalities are modularized through a Modular Communication Protocol. In addition, a dynamic negotiation mechanism is incorporated to support conflict resolution and multi-objective task optimization. A prototype system was implemented and evaluated using sensor-driven task scenarios. Experimental results demonstrate up to a 50% improvement in task success rate and significant gains in operational efficiency compared with conventional single-agent architectures. Despite these promising results, challenges remain in large-scale deployment, dynamic sensor reliability, and adaptive learning from long-term interactions. Future work will focus on integrating machine-learning-based negotiation strategies, adaptive sensor management mechanisms, and intelligent resource allocation to further enhance system robustness, scalability, and real-time decision-making capabilities.
