Gas sensors play a crucial role in monitoring harmful gas concentrations and air quality in real-time, ensuring safety and protecting health in both environmental and industrial settings. Additionally, they are essential in various applications for energy efficiency and environmental protection. As the demand for hydrogen refueling stations and hydrogen fuel cell vehicles increases with the growth of the hydrogen economy, accurate gas concentration measurement technology is increasingly necessary given hydrogen's wide explosion range. To ensure safety and efficiency, gas sensors must accurately detect a wide range of gas concentrations in real-world environments. This study presents two types of gas sensors with high sensitivity, stability, low cost, fast response time, and compact design. These sensors, based on volume and pressure analysis principles, can measure gas filling amounts, solubility, diffusivity, and the leakage of hydrogen, helium, nitrogen, and argon gases in high-density polyethylene charged under high-pressure conditions. Performance evaluation shows that the two sensors have a stability of 0.2 %, a resolution of 0.12 wt･ppm, and can measure gas concentrations ranging from 0.1 wt･ppm to 1400 wt･ ppm within one second. Moreover, the sensitivity, resolution, and measurement range of the sensors are adjustable. Measurements obtained from these sensors of gas filling amounts and the diffusivity of four gases showed consistent results within uncertainty limits. This system, capable of real-time gas detection and characterization, is applicable to hydrogen infrastructure facilities and is expected to contribute to the establishment of a safe hydrogen society in the future.

본 연구는 손 재활을 위한 탐색적 고찰의 일환으로, 자수 기반 스트레인 센서를 단층과 복층 구조로 설계하여 각 구조에서의 접촉 면적 변화와 센싱 성능의 차이를 비교⋅분석함으로써 손가락 동작 센싱에 적합한 센서 구조 설계 방향을 제시하고자 하였다. 1차 실험에서는 다양한 스티치 밀도와 층 구성으로 제작된 센서를 3D 프린팅 관절 모형 에 올린 후 1 Hz 주기의 신전–이완 동작을 반복 적용하여, 생성된 신호의 peak-to-peak 전압(mVp-p)을 측정하였다. 수집된 신호는 형상 분석과 비모수 통계 검정을 통해 정량적으로 분석하였다. 2차 실험에서는 1차 실험 결과를 바탕 으로 복층 구조 센서를 선정하고, 접촉 점 수와 스티치 밀도를 기준으로 네 가지 조합의 센서를 장갑 형태로 제작하 였다. 그리고 스마트 장갑을 착용한 피험자의 엄지와 검지에 대해 굽힘–폄 동작을 기준으로, 센싱 신호의 안정성과 품질을 형상적 특성과 정량 지표를 통해 분석하였다. 실험 결과, 1차에서는 복층-고밀도 구조 센서가 단층-저밀도 구조에 비해 유의하게 높은 신호 크기를 나타냈다. 2차 실험에서도 복층-고밀도 구조가 상대적으로 더 우수한 신호 품질을 보이는 것으로 확인되었다. 결론적으로, 1차 실험에서는 센서의 구조적 설계가 신호 세기에 직접적인 영향을 미친다는 점을 입증하였고, 2차 실험에서는 실제 사용 환경에서도 자수 구조적 변수에 따라 신호 품질이 달라짐을 확인하였다. 이는 자수형 센서 설계 시 구조적 설계 의 중요성을 시사하며, 웨어러블 손 재활 장치 개발에 기초 자료로 활용될 수 있을 것이다.

Gas sensors are crucial devices in various fields including industrial safety, environmental monitoring, gas infrastructure and medical diagnosis. These sensors measure specific gases in different environments, guaranteeing operational safety and efficiency through precise on-site measurements. Designed for high sensitivity, stability and reliability, gas sensors must also be cost-effective, quickly responsive and compact. To address these diverse requirements, we have developed two types of gas sensors based on the volumetric and the manometric method. These sensors operate by measuring the gas volume and the pressure changes, respectively, of the emitted gas. These sensors are capable of determining gas transport parameters such as gas uptake, solubility and diffusion coefficient for gas-charged polymers in high pressure environment. The sensors provide rapid responses within one second and can measure gas concentrations ranging from 0.01 wt ppm to 1500 wt ppm with adjustable sensitivity and measurement ranges. Performance evaluations demonstrate the sensors' reliability, adaptability to varying measurement ranges and stability under temperature and pressure fluctuations. As a result, this sensor system facilitates the real time detection and analysis of gas transport properties in pure gases including H₂, He, N₂, O₂ and Ar, making it suitable for pure gas sensing.

Wireless sensor systems are primarily used for monitoring natural environments or industrial automation. The physical environment where these systems are installed is often unstable, making it difficult to replenish sensor energy immediately. Complex and harsh conditions can impact the network's structure, affecting monitoring performance. Wireless sensor systems consist of hundreds of sensors that collect data from hazardous environments and transmit information to a central system. However, due to the system's physical structure, information delays or losses may occur. This paper proposes a distance-based tree structure to address these issues in wireless sensor systems, and experimental results confirm its superior performance.

PURPOSES : This study aimed to compare the object detection performance based on various analysis methods using point-cloud data collected from LiDAR sensors with the goal of contributing to safer road environments. The findings of this study provide essential information that enables automated vehicles to accurately perceive their surroundings and effectively avoid potential hazards. Furthermore, they serve as a foundation for LiDAR sensor application to traffic monitoring, thereby enabling the collection and analysis of real-time traffic data in road environments. METHODS : Object detection was performed using models based on different point-cloud processing methods using the KITTI dataset, which consists of real-world driving environment data. The models included PointPillars for the voxel-based approach, PartA2-Net for the point-based approach, and PV-RCNN for the point+voxel-based approach. The performance of each model was compared using the mean average precision (mAP) metric. RESULTS : While all models exhibited a strong performance, PV-RCNN achieved the highest performance across easy, moderate, and hard difficulty levels. PV-RCNN outperformed the other models in bounding box (Bbox), bird’s eye view (BEV), and 3D object detection tasks. These results highlight PV-RCNN's ability to maintain a high performance across diverse driving environments by combining the efficiency of the voxel-based method with the precision of the point-based method. These findings provide foundational insights not only for automated vehicles but also for traffic detection, enabling the accurate detection of various objects in complex road environments. In urban settings, models such as PV-RCNN may be more suitable, whereas in situations requiring real-time processing efficiency, the voxelbased PointPillars model could be advantageous. These findings offer important insights into the model that is best suited for specific scenarios. CONCLUSIONS : The findings of this study aid enhance the safety and reliability of automated driving systems by enabling vehicles to perceive their surroundings accurately and avoid potential hazards at an early stage. Furthermore, the use of LiDAR sensors for traffic monitoring is expected to optimize traffic flow by collecting and analyzing real-time traffic data from road environments.

With the growth of silicon-based semiconductor sensors in the global sensor market, advancements in body motion detection for wearable devices and sustainable health monitoring have accelerated. This has led to a significant attention on various sensors with excellent flexibility and stretchability, such as PDMS, in numerous applications. In this study to adjust the sensitivity of conventional conductive pressure sensors, a porous sponge structure was initially created using a sugar template method. The polymer was prepared with four different ratios (5:1, 10:1, 20:1, 30:1) to achieve varying flexibilities. To ensure conductivity, the sponge was coated using a dip-coating method with a 3wt% CNT solution. The conductive sponges with various ratios were tested for sensitivity, demonstrating characteristics suitable for a wide range of pressure sensing applications.

본 연구에서는 탄소나노튜브(CNT) 패치 센서를 기반으로 하여 구조물의 이상 거동을 감지하고 대 응할 수 있도록 하는 첨단 스마트 모니터링 시스템을 제안한다. 복합소재로 제작되는 CNT 센서는 유 연한 특성을 갖게 되어 다양한 형태의 구조물 표면에 적용할 수 있으며, 이를 통해 충격이나 피로 등 에 의해 발생되는 균열과 같은 비정상적인 거동을 감지할 수 있다. CNT 센서를 통해 수집한 데이터 는 IoT 시스템을 통해 실시간으로 분석되어 구조물의 거동 상태를 확인하고 건전성을 모니터링 할 수 있게 한다. 이 시스템의 성능 검증 및 사용성 검토를 위해 미국 소재 교량에서 실증 테스트를 하였으 며, 테스트 결과 CNT 센서를 이용한 구조물 거동 감지 시스템을 통해 구조물의 이상 거동을 효과적 으로 감지하고 모니터링하여 구조물에서 발생 될 수 있는 잠재적 문제를 사전에 예방할 수 있음을 확 인하였다. 이와 같은 기술은 추후 다양한 분야에서 적극적으로 활용될 수 있을 것으로 기대된다.

항만 내 선박과 부두의 사고를 예방하기 위하여 통항 및 접안 안전성 평가를 통하여 안전한 부두가 건설되어 관리하고 있으나, 선 박의 접안 및 계류 과정에서 선박이 부두에 충돌하거나 로프로 인한 인명사고의 발생 등 예측할 수 없는 사고들이 종종 발생한다. 자동계류장 치는 선박의 신속하고 안전한 계류를 위한 자동화된 시스템으로 로봇 매니퓰레이터와 흡착 패드로 구성된 탈/부착 메커니즘을 가지고 있다. 본 논문은 자동계류장치의 흡착 패드의 위치 및 속도제어에 필요한 선체와의 변위 및 속도 측정 시스템을 다룬다. 자동계류장치에 적합한 측 정 시스템을 설계하기 위하여, 본 논문은 우선 센서의 성능 및 실외 환경적 특성 분석을 수행한다. 다음으로 이러한 분석 결과를 토대로 실외 부두환경에서 설치되는 자동계류장치에 적합한 변위 및 속도 측정시스템의 구성 및 설계 방법에 대해 기술한다. 또한 센서의 측정상태 감지 및 속도 추정을 위한 알고리즘을 제시한다. 제안된 방법은 다양한 속도 구간에서의 변위 및 속도 측정 실험을 통해 그 유용성을 검증한다.

In this study, the performances of H2S, NH3, and HCl sensors for real-time monitoring in small emission facilities (4, 5 grades in Korea) were evaluated at high concentration conditions of those gases. And the proper approach for the collection of reliable measurement data by sensors was suggested through finding out the effect on sensor performances according to changes in temperature and humidity (relative humidity, RH) settings. In addition, an assessment on sensor data correction considering the effects produced by environmental settings was conducted. The effects were tested in four different conditions of temperature and humidity. The sensor performances (reproducibility, precision, lower detection limit (LDL), and linearity) were good for all three sensors. The intercept (ADC0) values for all three sensors were good for the changes of temperature and humidity conditions. The variation in the slope value of the NH3 sensor showed the highest value, and this was followed by the HCl, H2S sensors. The results of this study can be helpful for data collection by enabling the more reliable and precise measurements of concentrations measured by sensors.

Owing to the great demand for portable and wearable chemical sensors, the development of all-solid-state potentiometric ion sensors is highly desirable considering their simplicity and stability. However, most ion sensors are challenged by the penetration of water and gas molecules into ion-selective membranes, causing unstable and undesirable sensing performances. In this study, a hydrophobic ionic liquid-modified graphene (Gr) sheet was prepared using a fluid dynamics-induced exfoliation and functionalization process. The high hydrophobicity and electrical double-layer capacitance of Gr make it a potential solid-state ion-to-electron transducer for the development of potentiometric sodium-ion ( Na+) sensors. The as-prepared Na+ sensors effectively prevented the formation of the water layer and penetration of gas species, resulting in stable and high sensing performances. The Na+ sensors showed a Nernstian sensitivity of 58.11 mV/[Na+] with a low relative standard deviation (0.46), fast response time (5.1 s), good selectivity (K < 10− 4), and good durability. Furthermore, the Na+ sensor demonstrated its feasibility in practical applications by measuring accurate and reliable ion concentrations of artificial human sweat and tear samples, comparable to a commercial ion meter.

This study monitored temperature using electronic sensors and developed a prediction model for compost maturity. The experiment used swine manure in a mechanical composting facility equipped with a screw-type agitator, and the composting process was conducted for 60 d during the summer season in South Korea. Four electronic temperature sensors were installed on the inner wall between the compost piles on Days 7, 14, 21, and 28 for daily temperature monitoring. Compost samples were collected daily for 60 d, and compost maturity was analyzed using the Solvita method. Multiple comparisons, correlations, and modeling were performed using the stat package in R software. The average compost pile temperatures was 39.1±3.9, 36.4±4.3, 31.3±4.5, and 35.4±8.1 on days 7, 14, 21, and 28, respectively, after composting. The average compost maturity according to the composting date was 3.61±0.60, 4.13±0.59, 4.26±0.47, and 4.32 ±0.56 on days 7, 14, 21, and 28, respectively. A significant negative correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the temperature of all compost piles (p<0.05), where the correlation coefficients were -0.329, -0.382, -0.507, and -0.634, respectively. A significant positive correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the maturity of the compost (p<0.05), where the correlation coefficients were 0.410, 0.550, 0.727, and 0.840, respectively. The model for predicting the maturation of the 14 d average compost pile according to the compost composting period and the average temperature for 14 d was y=0.026 x d – 0.021 x mt.x_14 d (mean temperature for 14 d) + 4.336 (R2=0.7612, p<0.001). This study can be considered a basic reference for predicting compost maturity by the proposed model using electronic temperature sensors.

In this paper, we proposed and tested an indoor obstacle recognition and avoidance algorithm using vision and ultrasonic sensors for effective operation of drone with low-power. In this paper, the indoor flight of a drone is mainly composed of two algorithms. First, for the indoor flight of the drone, the vanishing point and the center point of the image were extracted through Hough transform of the input image of the vision sensor. The drone moves along the extracted vanishing point. Second, we set an area of interest so that the drone can avoid obstacles. The area of interest is a space where the drone can fly after recognizing an obstacle at a distance from the ultrasonic sensor. When an obstacle is recognized in the drone's area of ​​interest, the drone performs an obstacle avoidance action. To verify the algorithm proposed in this paper, a simple obstacle was installed in an indoor environment and the drone was flown. From the experimental results, the proposed algorithm confirmed the indoor flight and obstacle avoidance behavior of the drone according to the vanishing point.

수처리 후 직접 해양으로 배출하는 산업시설 등에서 Hazardous and Noxious Substance (HNS) 농도 변화를 연속 자동 측정하기 위 한 센서의 기본적 성능으로 상온에서도 ppb 수준의 검출이 가능한 센서가 필요하다고 판단하여 기존의 센서의 감도를 높이기 위한 방법 을 제안하였다. 우선 나노입자 박막에 전도성 탄소계 첨가물을 이용하여 필름의 전도도를 높이는 방법과 촉매 금속을 이용하여 표면에서 의 이온 흡착도를 높이는 방법에 대해서 각각 연구하였다. 전도성 개선을 위해서 ITO 나노입자를 활용한 필름에 carbon black을 첨가물로 선택하여, 첨가물 함유량에 따른 센서의 성능변화를 관찰하였다. 그 결과 CB 함량 5 wt% 정도에서 전도성 증가에 의한 저항과 응답시간 의 변화를 관찰할 수 있었고, 유기용제를 대상으로 한 실험에서 검출하한은 250 ppb 정도까지 낮아지는 것을 확인하였다. 또한 액체 중 이 온 흡착도를 높이기 위하여 센서 표면에 촉매로 Au를 스퍼터로 제작한 표면 촉매층을 형성한 시료를 이용한 실험에서 센서의 응답은 20% 이상 증가하고 평균 검출하한은 61 ppm까지 낮아지는 것을 확인하였다. 이 결과로부터 금속산화물 나노입자를 활용한 화학저항형 센서가 상온에서도 수십 ppb 정도의 HNS를 검출할 수 있다는 것을 확인하였다.