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.

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.

본 논문에서는 다목적 구조물인 다중연결 해양부유체를 대상으로 변형 기반 모드 차수축소법을 적용하고 차수축소모델의 구조응 답 예측 성능을 향상시키기 위해 유전 알고리즘 기반의 센서 배치 최적화를 수행하였다. 다중연결 해양부유체의 차수축소모델 생성 에 필요한 변형 기반 모드 데이터를 얻기 위해 다양한 규칙파랑하중조건에 대한 유체-구조 연성 수치해석을 수행하고 변형 기반 모드 의 직교성, 자기상관계수를 이용하여 주요 변형 기반 모드를 선정하였다. 다중연결 해양부유체의 경우 차수축소모델의 구조응답 예 측 성능이 계측 및 예측 구조응답 위치에 따라 민감하기 때문에 유전 알고리즘 기반의 최적화를 수행하여 최적의 센서 배치를 도출하 였다. 최적화 결과, 모든 센서 배치 조합에 대한 차수축소모델 생성 및 예측 성능 평가 대비 약 8배의 계산 비용을 절감하였으며, 예측 성능 평가 지표인 평균 제곱근 오차가 초기 센서 배치보다 84% 감소하였다. 또한, 다중연결 해양부유체 모형시험 결과를 이용하여 불 규칙파랑하중에 대한 최적화된 센서 배치의 차수축소모델의 구조응답 예측 성능을 평가 및 검증하였다.

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NOx), and two odor gases, ammonia (NH3) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and hybridized SnO2-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.

Sensors for monitoring human body movements have gained much attention in the recent times especially in the health-care sector as these devices offer real-time monitoring of vital physiological signs, enabling health-care professionals to evaluate health conditions and provide remote feedback. In this work, we have fabricated carbon-nanotube (CNT)/ polydimethylsiloxane (PDMS) composite sensor through simple dispersion and freezing method for monitoring flexion movements in humans. Sensors with different CNT loadings, namely 0.1 wt %, 0.5 wt %, and 1 wt % were fabricated and analyzed to find the best performing sensor. Several characterizations like Raman, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), thermogravimetric analysis (TGA), tensile strength measurements, and piezoresistive studies were carried out to study the features of the sensors. Among the fabricated sensors, the one with the loading concentration of 0.5 wt% is found to be most sensitive for flexion applications with higher gauge factor of 533 at 60% strain level, response time of ~ 140 ms and lower hysteresis loss. The feasibility of the sensor for monitoring flexion like finger bending, wrist bending, elbow bending, and knee bending is also analyzed making it ideal for use in sports for athletes, physicians, and trainers to investigate physical performance and well-being.

Heavy metal ions pollution has become of worldwide critical concern, thus, it is particularly important to monitor it in the environment and food for ensuring human health. In this study, p-phenylenediamine and 2-mercaptothiazoline were used to prepare nitrogen (N) and sulfur (S) co-doped carbon dots (N/SCDs) for fluorescent and colorimetric detection of Cu2+. The fabricated N/SCDs with bright green fluorescence showed excellent optical characteristics and favorable water solubility. In an aqueous system, a significant fluorescence quenching of N/SCDs at 512 nm is obtained in the presence of Cu2+. It also caused a significant colorimetric response with the color of prepared N/SCDs solution changed from colorless to yellow. Under optimal conditions, the analytical results showed that the linear range spanning from 5 to 400 μM, with a detection limit of 0.215 μM in fluorescence and 0.225 μM in colorimetric detection. In addition, N/SCDs displayed high selectivity toward Cu2+. No obvious interference was observed over other metal ions. Furthermore, we have also used N/SCDs to monitor Cu2+ in tap and lake water. The recovery of Cu2+ ranged between 89.6% and 113.1%. Exhibiting remarkable sensitivity and selectivity, the designed sensor offers a promising detection method for Cu2+ detection in the real sample.

Bortezomib (BTZ) and dasatinib (DA) are two substantial anti-cancer agents with side effects on the human body. In this research, we fabricated a novel electrochemical sensor modified by CuFe2O4/ SmVO4 nanocomposite and 1-ethyl-3-methylimidazolium chloride (1E3MC) as an ionic liquid (IL) ( CuFe2O4/SmVO4/IL/CPE) for coinciding investigation of BTZ and DA for the first time. The CuFe2O4/ SmVO4 synthesized were determined and certified through field-emission scanning electron microscopy (FE-SEM), energy diffraction X-ray (EDX), and X-ray diffraction (XRD). The capability of the sensor was investigated by different electrochemical techniques such as cyclic voltammetry (CV), chronoamperometry (CHA), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). The attained data showed that the oxidation signal of bortezomib and dasatinib promoted as an innovative electrochemical sensor. After optimization of the conditions using this sensor at pH 7.0, the oxidation signal of bortezomib and dasatinib showed to be linear with drug concentrations in the range of 0.09–90 μM and 100–500 μM with a detection limit of 5.4 nM and 7.0 μM, respectively, using differential pulse voltammetry method. The values of D and electro-transfer coefficient (α) achieved 2.5 × 10− 5 cm2 s− 1 and 0.99, respectively. The proposed electrochemical sensor exhibited acceptable selectivity and sensitivity for simultaneous detection of bortezomib and dasatinib in pharmaceutical and biological samples.