Background: Preparatory workforce often lacks pre-employment physical capacity assessments, leading to early occupational injuries. Trunk muscle endurance imbalances may affect lower-limb biomechanics through the kinetic chain. Objectives: To investigate the associations between trunk muscle endurance and static and dynamic balance abilities in the preparatory workforce (firstyear university students preparing to enter industrial workplaces) using sensor- based plantar pressure analysis. Design: Cross-sectional correlational study. Methods: Twenty-two first-year university students participated. Trunk muscle endurance was assessed using McGill trunk endurance tests, static balance was evaluated with the Romberg test using a sensor platform, and dynamic balance was analyzed through plantar pressure assessment during gait. Spearman's rank correlation analysis was performed. Results: The trunk lateral endurance ratios (right/extension and left/extension) demonstrated strong correlations with mediolateral plantar load distribution during gait (r=.616-.711, P<.05). Trunk flexion/extension ratio showed significant correlations with contralateral foot load distribution (r=±.557, P<.01). Correlations with dynamic plantar pressure variables ranged from r=.505 to .711 (moderate to strong), which were numerically stronger than correlations with static balance variables (r=.441-.442, moderate). Conclusion: Lateral trunk muscle endurance is essential for maintaining postural stability and may serve as a preliminary screening tool for the preparatory workforce during the pre-employment transition period. Dynamic gait analysis provides objective data for evaluating balance capacity in young adults transitioning to industrial employment. However, generalizability is limited by the small sample size and single-institution design.

This study quantitatively evaluated the real-world performance of an IoTbased, context-aware mobile air purification system. Additionally, this system is proposed as a practical alternative to conventional stationary purifiers, overcoming their spatial limitations. To analyze concentration variations, removal efficiency, and air cleaning ratio (ACR) for PM2.5, PM10, and HCHO, three scenarios were tested: S1 (natural ventilation), S2 (stationary purifier), and S3 (IoT-based mobile air purification system). The mobile system (S3) achieved a 1.6-fold higher removal efficiency for PM2.5 compared with the stationary purifier (S2) and reduced the ACR to below 0.4 within 30 minutes after high-concentration events. In contrast, stationary purifiers required approximately 333 minutes to reach background levels (17.11 μg/m3), revealing about a 10-fold difference in cleaning speed. Monte Carlo simulations confirmed the consistent superiority of S3 for both particulate and gaseous pollutants, with HCHO concentrations 36.7% lower (90th percentile) than under S2. According to the health risk assessment, the asthma hospitalization rate decreased by over 40%, the HQ for PM2.5 decreased from 1.1 (S1) to 0.64 (S3), and the ECR for HCHO was 0.62 times that of S2. These findings highlight that spatial responsiveness and mobility, along with filter capacity, are key determinants of air purification performance. In conclusion, the mobile air purifier effectively overcomes the structural constraints of stationary devices and establishes a new paradigm for realtime, adaptive indoor air quality management that helps safeguard occupant health.

본 연구에서는 도시 배수관망에서 제한된 센서 자원으로 침수 대응 성능을 향상시키기 위한 NSGA-II 기반 수위 센서 최적 배치 프레임워크를 제안하였다. 연구 대상지의 검⋅보정된 EPA-SWMM 모형을 기반으로, 실제 종관기상관측소의 강우 자료로부터 33개의 집중호우 시나리오를 구성하여 노드별 수위비(Depth Ratio)를 산정하고, 관망의 수리적 이질성과 침수 취약성을 정량적으로 분석하였다. 비관측 지점의 단기 수위를 예측하기 위해 Ridge 회귀모형을 적용하였으며, 예측 성능은 선행예측시간(Δt = 5–30분)에 대해 sMAPE와 Accuracy로 평가하였다. NSGA-II를 통해 532개 후보 노드를 탐색 공간으로 설정하여 센서 수(k=1–5)와 위치 조합을 동시에 최적화한 결과, 센서 수가 1개에서 3개로 증가할 때 예측 정확도가 크게 향상되었고, k ≥ 4에서는 정보 포화로 인해 개선 폭이 제한적임이 확인되었다. 또한 도출된 최적 조합은 다양한 Δt 조건에서도 sMAPE ≤ 0.35, Accuracy ≥ 0.998 수준의 안정된 성능을 유지하여 시간적 강건성을 보였다. 본 연구는 센서의 개수와 위치를 동시에 고려하는 정량적 배치 기준을 제시함으로써, 도시 배수관망에서 저비용⋅고효율의 모니터링 체계 구축, 조기 침수 감지 인프라 설계, 및 실시간 도시침수 대응 체계 고도화에 실질적 기초자료를 제공할 수 있을 것으로 기대된다.

This work focuses on the development of an innovative detection platform utilizing a novel ternary composite of transition metal dichalcogenide ruthenium disulfide ( RuS2), tungsten trioxide ( WO3) and multi-walled carbon nanotubes ( RuS2/ WO3/MWCNT) for the purpose of detecting hazardous pollutant catechol. An augmented current response for catechol was acquired by the synergetic effect of ternary composite. The unique combination of these materials enhances the sensor’s electrochemical performance due to the excellent catalytic activity of RuS2, redox properties of WO3 and the high surface area and electrical conductivity provided by MWCNTs. Morphological and structural characterizations were done using different characterization methods. The increased electroactive surface area and fast electron transfer rate resulted by the adaptation of the working electrode leads to the development of a sensitive and selective sensor. The RuS2/ WO3/MWCNT modified electrode exhibited remarkable sensitivity towards catechol determination with a wide linear detection range of 1.0–1028.0 μM and a modest low detection limit of 0.61 μM. The sensor demonstrated consistent performance in assessing the reproducibility and repeatability trials. The fabricated sensor gave reliable results and satisfactory recovery range when application on real-time sample analysis.

In the pursuit of achieving in-situ real-time detection of methanol production rate during the photocatalytic reduction of CO2, we developed a methanol sensor using a copolymer-coated fiber Bragg gratings. The theoretical model of methanol measurement by sensor was established. The effect of methanol-selective sensitive material and its thickness on the performance of the sensor were investigated. Humidity and temperature interference to sensor measurements was compensated. Furthermore, TiO2 photocatalyst was prepared and the photocatalytic reactor was constructed. The methanol production rate in the photocatalytic CO2 reduction process was monitored by the prepared sensor in-situ. The results highlight that the fiber Bragg grating methanol sensor with 600 nm-thick poly(N-isopropylacrylamide)/polymethyl-methacrylate coating showed a high sensitivity, lower limit of detection, fast response and recovery speed, and high selectivity. The methanol generation rate of TiO2 photocatalytic reduction of CO2 measured by gas chromatograph and prepared fiber Bragg grating methanol sensor was 1.42 and 1.53 μmol/g-cat·h, respectively, the error of the two detection methods was 7.86%. This highlights the efficacy of the developed fiber Bragg grating methanol sensor for real-time in-situ detection of the methanol production rate during the photocatalytic reduction of CO2.

In this study, we developed electrochemical sensors based on the composite of hydroxylated multiwalled carbon nanotubes (MWCNT-OH) and graphene for paraoxon-ethyl detection as pesticide residues in agricultural products. Chemical treatment was employed to produce MWCNT-OH from pristine MWCNT and its composite with graphene was subsequently characterized using FTIR, Raman spectroscopy, FESEM-EDX, TEM, and XPS techniques. The MWCNT-OH/graphene composite was employed as an electrode modifier on the glassy carbon electrode (GCE) surface, and its electroanalytical performances were studied using differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. It was revealed the optimum composition ratio between MWCNT-OH and graphene was 2:8, for paraoxon-ethyl detection at pH 7. This could be attributed to the enhanced electrocatalytic activity in the MWCNT-OH/graphene composite which displayed a linear range of paraoxon-ethyl concentration as 0.1–100 μM with a lower detection limit of 10 nM and a good sensitivity of 1.60 μA μM cm− 2. In addition, the proposed sensor shows good reproducibility, stability, and selectivity in the presence of 10 different interfering compounds including other pesticides. Ultimately, this proposed sensor was tested to determine the paraoxon-ethyl concentrations in green apples and cabbage as samples of agricultural products. The obtained concentrations of paraoxon-ethyl from this proposed sensor show no significant difference with standard spectrophotometric techniques suggesting this sensing platform might be further developed as a rapid detection of pesticide residue in agricultural products.

Ensuring operational safety and reliability in Unmanned Aerial Vehicles (UAVs) necessitates advanced onboard fault detection. This paper presents a novel, mobility-aware multi-sensor health monitoring framework, uniquely fusing visual (camera) and vibration (IMU) data for enhanced near real-time inference of rotor and structural faults. Our approach is tailored for resource-constrained flight controllers (e.g., Pixhawk) without auxiliary hardware, utilizing standard flight logs. Validated on a 40 kg-class UAV with induced rotor damage (10% blade loss) over 100+ minutes of flight, the system demonstrated strong performance: a Multi-Layer Perceptron (MLP) achieved an RMSE of 0.1414 and R² of 0.92 for rotor imbalance, while a Convolutional Neural Network (CNN) detected visual anomalies. Significantly, incorporating UAV mobility context reduced false positives by over 30%. This work demonstrates a practical pathway to deploying sophisticated, lightweight diagnostic models on standard UAV hardware, supporting real-time onboard fault inference and paving the way for more autonomous and resilient health-aware aerial systems.

Many recent research efforts have focused on developing high-performance wearable health monitoring systems. This work presents a mechanically stretchable and skin-mountable sensor system based on a conductive polymer composite-based elastic printed circuit board (EPCB) in which a resistive-type composite strain sensor is monolithically integrated. The composite-based EPCB is simply prepared by patterning a silver nanowire (AgNW)/dragon skin (AgNW/DS) composite film in a programmable manner using a direct cut patterning technique. The proposed sensor system was successfully fabricated by directly mounting various components (e.g., microcontroller, circuit elements, light emitting device chips, temperature sensor, Bluetooth module) on the prepared AgNW/DS-based EPCB. The fabricated sensor system was found to be highly stretchable and rollable enough to maintain tight adhesion to the wrist region without significant physical deterioration, even when the wrist was in motion. The wireless sensor system attached to the wrist part enabled us to monitor the wrist motion and surrounding temperature in real time, opening the possible application as a wearable health monitoring platform.

This study evaluated the field applicability of a real-time odor monitoring system combined with ozone water spraying technology to effectively control odors generated in livestock manure recycling facilities. Research was conducted at a Natural Circulation Agriculture Center located in N City, where concentrations of ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOCs) were measured in real time. Based on real-time data, ozone water was sprayed to assess the odor reduction rate, and the impact on surrounding areas was predicted through odor dispersion modeling. The results showed that the ammonia concentration measured at the upper section of the liquid aeration tank before ozone water spraying was 8.02 ppm, exceeding the emission limit of 1 ppm. VOCs were also found to have significantly contributed to odor generation. However, after spraying ozone water at a rate of 7 L/min and maintaining a concentration of 2.5 mg/L, ammonia was reduced by approximately 50%, and VOCs were reduced by about 98%, demonstrating a strong odor-reducing effect. Odor dispersion modeling using the CALPUFF modeling system simulated the range of odor dispersion before and after ozone water spraying. The results indicated that after ozone water spraying, the ammonia concentration at the facility boundary met the emission limit, effectively suppressing odor dispersion. In particular, the ozone water spraying system linked with the real-time sensor enabled automated odor control based on real-time data, demonstrating its potential for resolving odor complaints and ensuring compliance with environmental regulations.

본 연구는 습도센서에서 Zn-MOF (금속-유기구조)의 개발과 응용에 대해 다루며, 친환경적 합성과 우수한 전기적 특성을 보고한다. 그린 화학의 원리를 이용하여 제작된 Zn-MOF를 유연한 폴리에 틸렌테레프탈레이트 기판 상에 형성된 깍지낀 구조의 전극과 통합하였다. 상대습도가 10%부터 90%까지 증가할 때, 전기적 특성은 42.49 pF에서 370 nF까지 정전용량의 급격한 상승(약 939,322%)을 나타냈다. 또한, 임피던스는 47 MΩ에서 0.072 MΩ까지 약 99.81% 감소하였다. 제작된 습도센서는 반응시간 5초, 복구시간 약 0.7에서 0.9초로 동적으로 반응하였다. 이러한 결과는 Zn-MOF가 고도로 민감하고 반응성이 뛰어난 습도 모니터링할 수 있는 가능성과, 특히 다양한 환경 조건에서 센서의 정전용량성 반응성을 강조 하고자 한다.

Gait analysis can objectively assess abnormal walking, and some walking parameters can help recognize the disease. Existing commercial systems are either too expensive and require attachments to the body or have limitations in detecting abnormal gait. A vision system has been proposed to address this. However it had limitations where the accuracy was inferior in some parameters such as gait phase, step length and width, etc. Therefore we developed a Tactile sensor-based treadmill to detect gait phase, step length, and width. A pilot test was performed and analyzed through an infrared marker-based motion capture system to compare the accuracy of the proposed system. The measured spatiotemporal gait parameters were analyzed through mean and standard deviation and compared to the baseline system. As a result of the experiments, it was confirmed that higher step width performance was achieved compared to previous studies. Future studies will validate the system with many participants and conduct clinical studies on gait recognition through abnormal gait analysis.

The pressure sensor had been widely used to effectively monitor the flow status of the water distribution system for ensuring the reliable water supply to urban residents for providing the prompt response to potential issues such as burst and leakage. This study aims to present a method for evaluating the performance of pressure sensors in an existing water distribution system using transient data from a field pipeline system. The water distribution system in Y District, D Metropolitan City, was selected for this research. The pressure data was collected using low-accuracy pressure sensors, capturing two types of data: daily data with 1Hz and high-frequency recording data (200 Hz) according to specific transient events. The analysis of these data was grounded in the information theory, introducing entropy as a measure of the information content within the signal. This method makes it possible to evaluate the performance of pressure sensors, including identifying the most sensitive point from daily data and determining the possible errors in data collected from designated pressure sensors.

최근 결빙으로 인한 교통사고가 빈번히 발생하고 있으며, 도로순찰시 육안 인식이 어려운 도로살얼음 검지를 위해 다양한 방식의 검지센서가 도입되고 있다. 본 연구에서는 국내외 상용화되어 있는 차량부착식 노면상태 검지센서에 대한 현장 검증을 통해 국내 도 로조건에의 적용 가능성을 검토하였다. 차량부착식 검지센서의 성능을 평가하기 위해 한국건설기술연구원의 연천SOC실증연구센터 내 의 도로기상재현 실험시설에 결빙(Ice), 습윤(Wet), 건조(Dry) 등 3가지의 노면상태가 육안으로 명확히 구분이 가능하도록 도로환경을 구현하였으며, 센서종류별로 차량에 부착하여 다양한 도로상태를 측정하였다. 평가결과 노면상태 측정결과의 정확도는 높은 것으로 나 타났으나, 그 외의 측정항목의 정확도는 상당한 차이가 발생하기도 하였다. 향후 다양한 도로환경 조건에서 추가적인 시험을 통해 차 량부착식 노면상태 검지센서의 현장적용을 기반자료로 활용할 수 있을 것으로 판단된다.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

We introduce the technology required todevelop a bracket process for installing and verifying FRT bumper sensors for passenger cars. Establish and demonstrate process automation through actual design and manufaturing. We conduct quality inspection of the production process using artificial intelligence and develop technology to automatically detect good and defective products and increase the reliability of the process

본 논문에서는 스테레오 비전 센서를 이용한 프리팹 강구조물(PSS: Prefabricated Steel Structures)의 조립부 형상 품질 평가 기법을 소개한다. 스테레오 비전 센서를 통해 모형의 조립부 영상과 포인트 클라우드 데이터를 수집하였으며, 퍼지 기반 엣지 검출, 허프 변 환 기반 원형의 볼트 홀 검출 등의 영상처리 알고리즘을 적용하여 조립부 영역의 볼트홀을 검출하였다. 영상 내 추출된 볼트홀 외곽선 위 세 점의 위치 정보에 대응되는 3차원 실세계 위치 정보를 깊이 영상으로부터 획득하였으며, 이를 기반으로 각 볼트홀의 3차원 중심 위치를 계산하였다. 통계적 기법 중 하나인 주성분 분석 알고리즘(PCA: Principal component analysis) 알고리즘을 적용함으로써 3차 원 위치 정보를 대표하는 최적의 좌표축을 계산하였다. 이를 통해 센서의 설치 방향 및 위치에 따라 센서와 부재 간 평행이 아니더라도 안정적으로 볼트홀 간의 거리를 계측하도록 하였다. 각 볼트홀의 2차원 위치 정보를 기반으로 볼트홀의 순서를 정렬하였으며, 정렬된 볼트홀의 위치 정보를 바탕으로 인접한 볼트홀 간의 각 축의 거리 정보를 계산하여 조립부 볼트홀 위치 중심의 형상 품질을 분석하였 다. 측정된 볼트홀 간의 거리 정보는 실제 도면의 거리 정보와의 절대오차와 상대오차를 계산하여 성능 비교를 진행하였으며, 중앙값 기준 1mm 내의 절대오차와 4% 이내의 상대오차의 계측 성능을 확인하였다.