This study proposes a dynamic evaluation framework for diagnosing signal control adequacy using high-resolution Automated Traffic Signal Performance Measures (ATSPM) data. Traditional signal performance assessments have primarily relied on aggregated metrics, such as average delay and volume-to-capacity ratio, which are effective for evaluating overall operational efficiency but insufficient for capturing cycle-level control limitations and temporal variability. Although split failure-based measures, including the Split Failure Ratio (SFR), provide more direct insights into green time adequacy, most existing applications focus on the failure frequency within a fixed analysis period. To address this limitation, this study introduces a Dynamic Operational Strain (DOS) index that extends the split failure into a time-evolving state variable incorporating accumulation and recovery mechanisms. By modeling the recursive evolution of the operational strain, the proposed framework captures how often failures occur and how they persist or dissipate over time. Phase-level DOS measures are subsequently aggregated at the intersection level to derive a priority score reflecting structural control inadequacy. The framework is further applied to classify intersections using DOS–SFR quadrant analysis, enabling the identification of distinct operational patterns, such as persistent oversaturation, localized phase imbalance, intermittent strain accumulation, and stable control conditions. The results demonstrate that intersections with similar SFR values may exhibit substantially different temporal strain structures, highlighting the importance of a dynamic state-based evaluation. The proposed approach provides a diagnostic foundation for data-driven signal re-timing and future adaptive control strategies by shifting the signal performance assessment from static frequency-based measures to dynamic structural adequacy analysis.

목적 : 본 연구는 성장기 아동을 대상으로 주변부 디포커스 렌즈와 단초점 렌즈의 1년 추적 결과를 비교하여 근시 진행 억제 효과를 평가하고, 굴절력과 각막곡률 기반 예측 안축장을 통해 근시 진행의 구조적 원인을 분석하고자 하였다. 방법 : 본 연구는 만 6–12세 근시 아동을 대상으로 단초점 렌즈 착용군과 주변부 디포커스 렌즈 착용군으로 무 작위 배정하여 1년간 추적 관찰한 비교 연구이다. 굴절력(SE)과 평균 각막곡률(Ave K)은 자동굴절검사 및 자각적 굴절검사를 통해 측정하였으며, 안축장은 Kim 등이 제시한 예측 공식(AL = 24 × AveK / 7.8 − 0.4 × SE)을 사용하여 산출하였다. 군 내 변화는 대응표본 t-검정, 군 간 비교는 독립표본 t-검정을 실시하였으며, 유의수준은 p<0.05로 설정하였다. 결과 : 1년 추적 관찰 결과, 두 군 모두에서 등가구면굴절력(SE)은 유의하게 근시 방향으로 진행하였다(p< 0.001). 평균 각막곡률(Ave K)은 두 군 모두 유의한 변화가 없었다(p<0.05). 반면 안축장은 두 군 모두에서 유의 하게 증가하였으나(p<0.001), 단초점군의 증가 폭이 마이오군보다 더 크게 나타났다. 계산식 기반 예측 안축장 또 한 마이오군에서 유의하게 낮은 증가량을 보였다(p<0.01). 결론 : 본 연구는 성장기 아동에서 주변부 디포커스 렌즈가 단초점 렌즈에 비해 근시 진행과 안축장 증가를 유 의하게 억제함을 확인하였다. 근시 진행은 각막곡률의 변화보다는 안축장 성장에 기인하는 것으로 나타났으며, SE 와 Ave K를 활용한 예측 안축장 모델 또한 임상적 추적 지표로 활용 가능함을 시사한다. 이는 실제 임상 환경에서 근시 관리 전략 수립에 유용한 근거를 제공한다.

Odor emissions from sewer systems are a persistent environmental concern in urban areas, particularly in combined sewer systems where septic tanks are widely used. However, the contribution of septic tanks as sources of sewer odor has not been sufficiently quantified. This study investigated the characteristics of hydrogen sulfide (H2S) generation in septic tanks and evaluated its potential influence on sewer odor, as well as the effectiveness of odor mitigation technologies. Field investigations were conducted in combined sewer areas to measure aqueous H2S concentrations in septic tanks. The results showed that H2S concentrations in septic tanks were not significantly affected by septic tank capacity (ANOVA, p > 0.05), suggesting that tank size or user population is not a primary determinant of sulfide generation. In contrast, aqueous H2S exhibited a positive correlation with chemical oxygen demand (COD), indicating that organic matter availability plays an important role in sulfide production through microbial sulfate reduction processes. A significant relationship was observed between aqueous H2S in septic tanks and gaseous H2S measured at catch basins, demonstrating that sulfide derived from septic tanks can transfer to the sewer atmosphere and potentially impact human odor exposure in urban environments. In addition, the performance of odor control technologies applied to septic tanks was evaluated with aeration-based technologies found to significantly reduce H2S concentrations. These findings suggest that septic tanks can serve as important sources of sewer odor highlighting the need for effective management and proper operation of septic tank odor control systems in combined sewer areas.

본 논문은 펫 휴머니제이션 현상으로 펫푸드 시장이 질적으로 고도화되었음에도, 불구하고, 여전히 「사료관 리법」상 축산 생산 수단적 관점에 머물러 있는 현행 관 리 체계의 한계를 조명하고 실증적인 물성 표준화 방안 을 제시하고자 수행되었다. 우선 KS H 4897 및 법정 관 리 체계와의 비교·분석을 통해 펫푸드 물성 규격화의 제 도적 미비점을 규명하였다. 또한, 반려견 보호자 71명을 대상으로 시행한 설문조사 결과, 응답자의 97.0%가 제품 구매 시 물성을 주요하게 고려하나 54.0%는 제조사별 다 른 마케팅 용어로 인해 선택의 어려움을 겪고 있음을 확 인하였다. 특히 단단한 제형으로 인한 치아 및 잇몸 손상 경험(15.0%)과 습식 급여 중 사레 경험(46.0%) 등 실질적 인 급여 안전사고 실태를 통해 정량적 지표 도입의 당위 성을 확보하였다. 이를 바탕으로 반려견의 해부학적 구조 와 체급별 치악력 등 수의학적 근거를 반영하여 유동식 부터 고강직식까지 아우르는 5단계의 ‘반려견 Texture-Code’를 도출하였다. 본 논문은 영양 성분에 치 중되었던 기존 품질 관리 패러다임을 물리적 섭식 안전 분야로 확장하고, 향후 반려동물의 지위 변화를 반영한 선진적 사료 관리 체계 수립을 위한 기초 자료를 제공한 다는 점에서 학술적·제도적 의의를 지닌다.

영구자석 선형 전동기인 VCM(Voice coil motor)은 직접 구동 방식의 액츄에이터로 기어나 변속장치가 필요 없어 높은 정밀도 를 가지고 구조적인 특성상 기계적 마찰이 적어 소음이 발생하지 않는 장점을 가지고 있다. 아울러 회전운동을 직선 운동으로 변환하기 위한 별도의 장치가 필요하지 않고, 구동부가 가벼워 응답속도가 빠른 특징이 있다. 본 연구에서는 이러한 VCM을 다양한 산업 분야에 적용하기 위한 기초연구로 VCM의 속도제어를 위해 PSO(Pariticle swarm optimization) 기법을 적용하여 제어기의 유용성 평가를 위한 수 치 시뮬레이션을 수행하였다. 제어계는 전류와 속도 제어를 위한 이중 루프로 구성하였고, 각각의 제어 루프에는 PI 제어기를 적용하여 속도 목표치에 추종하는 출력값을 얻기 위한 제어기를 설계하였다. 제어기 파라미터 추정에는 PSO기법을 적용하였고, 제어기의 유용성 을 검증하기 위해 주파수 영역에서의 모델매칭기법을 적용한 제어 기법과의 제어 결과를 비교하였다. 두 가지 제어 기법은 MATLAB을 이용하여 수치 시뮬레이션을 수행했고, 제어 결과는 IAEU(Integral of absolute error units) 평가 지수를 이용하여 비교하였다. 수치 시뮬레 이션 결과 제안한 제어 기법의 유용성을 확인할 수 있었다.

도시 불투수 지역에서 발생하는 초기 강우 유출수는 고농도의 비점오염물질을 단기간에 하천으로 유입시켜 수질 악화를 초래한다. 본 연구에서는 우수받이 및 맨홀에 설치 가능한 다단 폴리프로필렌 섬유 여과장치를 개발하고, 실험실 및 현장자료를 기반으로 오염물질 제거 효율을 평가하였다. 섬유 여재 조합 실험을 통해 53 μm, 20 μm, 10 μm 구성의 3단 여과가 TSS, TN, TP 제거에 가장 효과적인 것으로 나타났다. 현장 초기유출수 분석 결과, 평균 제거 효율은 TSS 79.7%, TN 31.6%, TP 43.1%로 확인되었다. 또한 SWMM을 이용하여 학사마을 도시유역에 적용 시 25%, 50%, 100% 설치비율에 대해 TSS 47.3∼99.2%, TN 21.6∼49.4%, TP 23.6∼67.7%의 저감효과가 모의되었다. 본 연구의 결과는 공간 제약이 큰 도시지역에서 초기세척효과(first flush)를 제어하기 위한 실용적인 비점오염 관리기술로서 활용 가능성을 제시한다.

최근 인테리어 디자인 플랫폼들이 AR 및 3D 렌더링 기술을 빠르게 도입하고 있으나, 기 존 온라인 시뮬레이터와 AR 서비스가 제공하는 조명 미리보기 기능은 광학 효과가 반영되 지 않아 실제 공간의 분위기를 체감하는 데 한계가 있다. 본 논문에서는 이러한 현실감의 격차를 해소하기 위해 AI 기반 2D 실내 리라이팅 시스템을 제안한다. 제안한 시스템은 이 미지 전체의 스타일을 변환하는 대신 실제 조명 기구 위치에 광원을 적용하는 조명 기구 기반 리라이팅을 수행하며, 사용자 경험 향상을 위해 다중 색상 조명 변경을 지원한다. Latent-Intrinsics와 확산 모델을 기반으로 램프 객체에 특화되도록 U-Net의 Cross-attention을 미세 조정하고, 안정적인 색상 주입을 위해 학습된 ControlNet과 Color Adaptor를 적용하였다. 실험 결과, 기존 베이스라인 모델 대비 우수한 색상재현력과 RMSE와 LPIPS 성능이 약 2.5배, SSIM은 약 1.2배 향상되었다. 연구 결과는 AR 기반 인테리어 서비 스를 위한 정교한 조명 미리보기 기술로 활용되기를 기대한다.

이 논문에서는 강화학습 기반 제어기와 전통적인 제어기를 동일한 조건에서 비교함으로써 구조 진동 제어 문제에서 강화학습 제 어기의 성능 특성과 한계를 규명하는 것을 목적으로 한다. 가장 단순한 비선형 제어로서 단자유도 가변 강성 시스템을 대상으로 심층 결정적 정책 경사(DDPG) 기반의 강화 학습 제어기를 설계하고, bang-bang 제어 및 제한 최적 제어와의 성능 비교를 수행하였다. 자유 진동 및 El Centro 지진 가속도에 의한 강제 진동 조건에서 공칭 성능과 센서 잡음이 존재하는 경우의 강인 성능을 분석하였다. 그 결 과, 강화학습 제어기는 자유 진동 조건에서 우수한 강인 성능을 보였으나, 강제 진동 제어에서는 기존 제어기를 일관되게 상회하지는 못하였다. 이 연구는 동일한 보상 함수와 시스템 조건 하에서 강화학습 기반 진동 제어의 실질적 기여와 적용상의 한계를 기초적으로 제시하였다.

In this study, a particle shape control process was developed to fabricate flake-like SUS316L powders about 20 μm for application in semiconductor gas filters. The Flake powder was produced through a wet milling process using a Planetary Mill by varying the rotation speed, milling time, solvent, and polyvinylpyrrolidone (PVP) dispersant conditions. The fabricated powders were then characterized to evaluate their morphological and phase transformation behaviors. In the ethanol-based Planetary Milling process, as the rotation speed increased from 300, 400, 500 rpm, the powder morphology was observed to gradually change from spherical to flake-like due to the increase in milling energy. According to the XRD, as the rotation speed increased, a phase transformation from austenite to martensite occurred due to the increase in heat generation and collisions between the powder and balls. In addition, an increase in Full Width at Half Maximum (FWHM) was observed, indicating a decrease in crystallinity. Under different solvent and dispersant conditions, the addition of 5 wt% PVP to the deionized water (DI Water) solvent suppressed particle fracture and produced more uniform flake-like particles compared with the DI Water process without PVP. In addition, a smaller FWHM and reduced oxygen content were observed.

Background: Lumbar radiculopathy caused by disc herniation is frequently accompanied by pain, functional disability, and impairments in sensorimotor control, including reduced proprioception and altered motor control. Interventions that integrate neural and mechanical components may enhance rehabilitation outcomes beyond exercise alone. Objectives: To investigate the effects of manual therapy combined with neurodynamic exercise and motor control exercise (MTN) with motor control exercise alone (MCE) on lumbar proprioception, motor control, and functional disability in patients with lumbar radiculopathy. Design: Randomized, single-blind clinical trial. Methods: Thirty patients with lumbar radiculopathy due to L4–S1 disc herniation were randomly assigned to either the MTN group or the MCE group. Both groups participated in supervised interventions three times per week for six weeks. The MTN group received lumbar joint mobilization and slider-based neurodynamic mobilization integrated with motor control exercise, whereas the MCE group performed motor control exercise only. Lumbar proprioception was assessed using joint position error during trunk flexion and extension. Motor control was evaluated using pressure biofeedback–based abdominal drawing- in performance. Functional disability was assessed using the Korean version of the Oswestry Disability Index. Outcomes were measured at baseline and during follow-up. Results: Significant group-by-time effects were observed for lumbar joint position error, motor control outcomes, and functional disability. The MTN group demonstrated earlier and greater improvements across all outcome measures compared with the MCE group, whereas improvements in the MCE group were more gradual. Conclusion: Compared with motor control exercise alone, the addition of manual therapy and neurodynamic exercise resulted in superior improvements in lumbar proprioception, motor control, and functional disability. An integrated MTN approach may be an effective rehabilitation strategy for patients with lumbar radiculopathy.

Background: Weakness of the abdominal muscles reduces trunk control and impairs respiratory function in stroke patients. To strengthen the abdominal muscles, threshold expiratory muscle training and trunk FES can be used. Objectives: This study aimed to investigate whether a combined intervention of threshold expiratory muscle training and trunk FES is more effective in improving trunk control and respiratory function than threshold expiratory muscle training alone. Design: Randomized controlled trial. Methods: Thirty individuals with stroke were randomly assigned to either the experimental group (n=15) or the control group (n=15). The experimental group received threshold expiratory muscle training with trunk electrical stimulation, while the control group performed threshold expiratory muscle training only. Both groups underwent training three times per week for four weeks. Trunk control and respiratory function were assessed pre and post the intervention. Results: Both groups showed significant post-intervention improvement in respiratory function; however, the experimental group demonstrated a greater change than the control group. The control group showed significant improvement only in the total TIS score, whereas the experimental group showed significant improvement across all TIS subcomponents. Conclusion: Combining threshold expiratory muscle training with trunk FES is an effective approach for enhancing not only respiratory function but also trunk control. Synchronizing electrical stimulation with expiratory timing may increase efficiency and strengthen functional muscle contraction, suggesting meaningful clinical value.

Activated carbons with high micro-/meso-porosity derived from biomass are increasingly popular as sustainable materials. However, these carbons often struggle with low carbon content and limited structural stability. Here, we present Mongolian anthracite-based carbons synthesized via carbonization and chemical activation. Structural analysis shows that Act-MRA samples develop plate-like morphologies with reduced particle size and greater porosity as KOH content increases. The Act-MRA samples have a disordered carbon structure with small graphitic domains, even at higher KOH ratios without significant crystal defects. Notably, Act -MRA3 displays a large specific surface area and high pore volume, with welldeveloped micropores (7–20 Å) and mesopores (20–50 Å) that expand as KOH ratios rise. Electrochemical tests indicate that Act -MRA3 achieves high specific capacitance (220.6 F/g at 5 mV/s) and rate retention (~ 80% at 300 mV/s), owing to its optimized pore structure and enhanced ion transport. These findings underscore the importance of tailored pore structures and defect engineering in boosting activated carbon performance for energy storage.

Ambrosia trifida is an invasive annual plant species that creates dense stands, suppressing native vegetation in affected habitats. To assess its ecological impact and the short-term effectiveness of mechanical management, we conducted field removal experiments using cutting and uprooting methods. We examined plant community composition, species richness, and diversity before and after treatment. Mechanical removal significantly altered plant community structure, leading to increased emergence of native species and reduced dominance of A. trifida, while control plots showed minimal change. Treated plots also had substantially lower soil seed bank density, with most remaining seeds concentrated in the upper 0-5 cm layer, indicating that limiting annual seed input is crucial for suppressing population persistence. However, recovery responses varied by site: Mugunri experienced notable declines in A. trifida cover and a greater establishment of native species, whereas the CCZ site retained A. trifida as a sub-dominant and saw limited recruitment of native species. These differing outcomes suggest that site-specific environmental conditions, initial species pools, and residual seed bank size may affect vegetation recovery after invasive plant removal. While this study demonstrates that mechanical removal disrupts A. trifida dominance and encourages short-term vegetation recovery, its one-year duration limits our understanding of longterm successional pathways. Continued monitoring, repeated annual removal, and assessments across multiple sites are necessary to better understand the mechanisms driving post-removal recovery and to inform the development of effective restoration strategies.

This paper investigates the problem of ship course control in the presence of model uncertainties, external disturbances, and actuator saturation. A high-performance autopilot is developed based on a direct neural network adaptive dynamic surface control (DSC) framework integrated with deep reinforcement learning. To compensate for lumped uncertainties arising from unmodeled dynamics and disturbances, a radial basis function (RBF) neural network is employed to provide online approximation within the control design. Moreover, the actuator saturation constraint is explicitly incorporated into the controller, avoiding performance degradation commonly encountered in conventional DSC schemes.To alleviate the reliance on manual parameter tuning, the controller parameter adaptation is formulated as a continuous-action optimization problem and solved using a deep deterministic policy gradient (DDPG) algorithm. The DDPG agent learns an optimal tuning policy by maximizing a reward function that penalizes course tracking errors, excessive control variations, and energy consumption. Simulation results demonstrate that the proposed method achieves improved tracking accuracy, smoother control inputs, and enhanced robustness under complex operating conditions, thereby validating the effectiveness of the DDPG-based adaptive tuning strategy for autonomous ship navigation.

This paper investigates the problem of ship course control in the presence of model uncertainties, external disturbances, and actuator saturation. A high-performance autopilot is developed based on a direct neural network adaptive dynamic surface control (DSC) framework integrated with deep reinforcement learning. To compensate for lumped uncertainties arising from unmodeled dynamics and disturbances, a radial basis function (RBF) neural network is employed to provide online approximation within the control design. Moreover, the actuator saturation constraint is explicitly incorporated into the controller, avoiding performance degradation commonly encountered in conventional DSC schemes.To alleviate the reliance on manual parameter tuning, the controller parameter adaptation is formulated as a continuous-action optimization problem and solved using a deep deterministic policy gradient (DDPG) algorithm. The DDPG agent learns an optimal tuning policy by maximizing a reward function that penalizes course tracking errors, excessive control variations, and energy consumption. Simulation results demonstrate that the proposed method achieves improved tracking accuracy, smoother control inputs, and enhanced robustness under complex operating conditions, thereby validating the effectiveness of the DDPG-based adaptive tuning strategy for autonomous ship navigation.

Aiming at the control problem of nonlinear uncertain systems with asymmetric saturated actuators and u nknown external disturbances, a composite control method integrating dynamic surface control (DSC), ad aptive neural network estimation, and a nonlinear saturation compensation mechanism is proposed. In the scenarios of ship course and trajectory tracking, the system faces multiple challenges such as symmetric and asymmetric actuator saturation, as well as unknown external disturbances. Radial basis function (R BF) neural networks are utilized for online approximation of unknown nonlinear functions and external d isturbances. Combined with dynamic surface technology, the problem of "explosion of complexity" in tra ditional backstepping control is eliminated. A nonlinear function with inverse correlation to error gain is designed to dynamically adjust the control gain, balancing the requirements of tracking accuracy and sat uration suppression. Furthermore, a Gaussian error function is introduced to construct a continuously diff erentiable asymmetric saturation model. An auxiliary dynamic system is integrated to compensate for the saturation nonlinear effect, achieving smooth amplitude limitation of rudder angle commands. Comparati ve MATLAB simulation results demonstrate that the course tracking error is reduced by 1°, the fluctuati on amplitude of the rudder angle is decreased by approximately 50%, the number of rudder angle satura tion events is reduced by about 60%, and the error convergence time is shortened by roughly 30%. The proposed composite control method effectively addresses the issues of asymmetric saturation and externa l disturbances, significantly enhancing the accuracy and robustness of the ship course control system.

To address the issue of low heading tracking efficiency caused by nonlinear dynamic characteristics in ship heading motion, this paper proposes a neural network-based adaptive hyperbolic tangent control method for ship heading. By designing a second-order system robust controller, a saturated auxiliary design system is introduced into the regulator for direct internal compensation, enhancing the system's anti-interference capability under complex operating conditions. Meanwhile, hyperbolic tangent nonlinear modification is incorporated into the control strategy to optimize the output characteristics of control signals. The controller adopts a backstepping approach to design virtual control laws for trajectory tracking and utilizes the Radial Basis Function (RBF) of neural networks to approximate the uncertain parts of the ship model. The control algorithm is simulated and tested in the MATLAB environment, and its tracking effect is analyzed. Simulation results show that the control algorithm can ensure the stability of the closed-loop system under conditions of dynamic changes in system parameters, external disturbances, and uncertainties, and effectively solve the nonlinear problems in ship traffic control during trajectory tracking. The controller is designed concisely, meets the requirements of engineering practice, improves ship maneuverability, and has reference value for ship control.

This study proposes a Bayesian framework for sequential stock investment decision-making using daily high and low stock price data. The proposed methodology models stock behavior using the Beta distribution and constructs a prior Normal-Gamma distribution based on the derived mean and variance. Additional parameters are estimated from the observed stock price range to enhance the framework's adaptability. The methodology establishes two Bayesian control charts that simultaneously monitor investment performance (Expected Stock Performance Control Chart, ESCFCC) and volatility (Variability of Stock Performance Control Chart, VSCFCC). These control charts are periodically updated with observed data and iteratively revise the posterior probability distribution as new data becomes available. This updating procedure provides investors with timely and data-driven decision-making information. For empirical validation, four investment scenarios were analyzed based on Samsung Electronics stock data from January 4, 2010, to May 31, 2017. The results highlight the usability of a sequential Stock Cash Flow Control Chart (SCFCC) framework, which utilizes daily high and low stock price data to enable real-time evaluation of investment performance and risk. By integrating statistical quality control charts with Bayesian probabilistic models, the framework establishes a system for continuously updating investment information and dynamically monitoring performance throughout the investment period.

Against the backdrop of the rapid development of the global shipping industry and the deep advancement of “dual carbon” goals, energy transition, energy conservation, and emission reduction have become core issues in marine transportation. As a critical component of clean and renewable energy, the efficient development and utilization of wind energy are pivotal for achieving low-carbon shipping. Exhaust turbine sails, an innovative application of active suction control in marine aerodynamic propulsion, regulate boundary layer flow through active suction to enhance wind energy utilization efficiency, which has emerging as a research hotspot in the green transformation of modern shipping. This paper aims to synthesize research on exhaust turbine sails. First, based on fundamental fluid mechanics principles, it analyzes the impact of boundary layer separation on the aerodynamic characteristics of structural bodies. Second, through case studies, it summarizes flow control effects under different suction parameters. It further introduces combined blowing and suction control strategies to explore their influence on boundary layer management. Finally, it details the research progress of exhaust turbine sails, explaining their core principle: active suction control delays or prevents boundary layer separation, effectively suppressing vortex shedding, thereby significantly reducing ship navigation resistance and enhancing lift. The study reveals that the aerodynamic performance of exhaust turbine sails is jointly influenced by oncoming flow conditions, suction power, and structural parameters, necessitating multi-objective optimization to achieve energy efficiency balance. The paper concludes by addressing key challenges in their marine applications and envisioning future directions for integrating these sails with emerging technologies, providing practical implications for promoting the green and low-carbon transformation of the shipping industry.