In this study platform, electrocatalytic detection of the antibiotic chloramphenicol (CAP) in phosphate buffer (pH 7) was easily achieved using a carbon paste electrode modified with NiO nanoparticles (note NiO-CPE). The peak reduction potential of chloramphenicol on the modified electrode is at (− 0.60 V/NiO-CPE vs. Ag/AgCl), its electrochemical behavior is completely irreversible, and controlled by adsorption phenomena. An excellent electrocatalytic activity has been demonstrated by the modified elaborated electrode towards the NO2 attracting group on the side chain of chloramphenicol. The structure and chemical composition of the NiO-CPE sensor were analyzed by SEM, and the X-ray diffraction results indicated that nickel oxide microcrystals were formed on the carbon sheets. The electrochemical characteristics of the NiO-CPE sensor were examined by cyclic voltammetry and electrochemical impedance spectroscopy in comparison with the unmodified carbon. Since the DPV technique allows plotting the linearity curve between the electrocatalytic current intensity of the Chloramphenicol peak and their concentration, the proposed sensor showed a remarkable detection limit of 1.08 × 10– 8 mol/L M (S/N = 3) and a wide determination range from 100 to 0.1 μM for Chloramphenicol. The developed sensor was successfully applied in the detection of Chloramphenicol in real samples.

Our research team has developed a gamma ray detector which can be distributed over large area through air transport. Multiple detectors (9 devices per 1 set) are distributed to measure environmental radiation, and information such as the activity and location of the radiation source can be inferred using the measured data. Generally, radiation is usually measured by pointing the detector towards the radioactive sources for efficient measurement. However, the detector developed in this study is placed on the ground by dropping from the drone. Thus, it does not always face toward the radiation source. Also, since it is a remote measurement system, the user cannot know the angle information between the source and detector. Without the angle information, it is impossible to correct the measured value. The most problematic feature is when the backside of the detector (opposite of the scintillator) faces the radiation source. It was confirmed that the measurement value decreased by approximately 50% when the backside of the detector was facing towards the radiation source. To calibrate the measured value, we need the information that can indicate which part of the detector (front, side, back) faces the source. Therefore, in this study, we installed a small gamma sensor on the backside of the detector to find the direction of the detector. Since this sensor has different measurement specifications from the main sensor in terms of the area, type, efficiency and measurement method, the measured values between the two sensors are different. Therefore, we only extract approximate direction using the variation in the measured value ratio of the two sensors. In this study, to verify the applicability of the detector structure and measurement method, the ratio of measured values that change according to the direction of the source was investigated through MCNP simulation. The radioactive source was Cs-137, and the simulation was performed while moving in a semicircular shape with 15 degree steps from 0 degree to 180 degrees at a distance of 20 cm from the center point of the main sensor. Since the MCNP result indicates the probability of generating a pulse for one photon, this value was calculated based on 88.6 μCi to obtain an actual count. Through the ratio of the count values of the two sensors, it was determined whether the radioactive source was located in the front, side, or back of the probe.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

The high-level nuclear waste (HLW) repository is a 500-1,000 m deep underground structure to dispose high-level nuclear waste. The waste has a very long half-time and is exposed to a number of stresses, including high temperatures, high humidity, high pressure These stresses cause the structure to deteriorate and create cracks. Therefore, structural health monitoring with monitoring sensors is required for safety. However, sensors could also fail due to the stresses, especially high temperature. Given that the sensors are installed in the bentonite buffer and the backfill tunnel, it is impossible to replace them if they fail. That’s why it is necessary to assess the sensors’ durability under the repository’s environmental conditions before installing them. Accelerated life test (ALT) can be used to assess durability or life of the sensors, and it is important to obtain the same failure mode for reliability tests including ALT. Before conducting the test, the proper stress level must be designed first to get reliable data in a short time. After that, acceleration of life reduction with increasing temperature and temperature-life model should be determined with some statistical methods. In this study, a methodology for designing stress levels and predicting the life of the sensor were described.

Micro-electronic gas sensor devices were developed for the detection of carbon monoxide (CO), nitrogen oxides (NOx), ammonia (NH3), and formaldehyde (HCHO), as well as binary mixed-gas systems. Four gas sensing materials for different target gases, Pd-SnO2 for CO, In2O3 for NOx, Ru-WO3 for NH3, and SnO2-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were then fabricated using a micro sensor platform. The gas sensing behavior and sensor response to the gas mixture were examined for six mixed gas systems using the experimental data in MEMS gas sensor arrays in sole gases and their mixtures. The gas sensing behavior with the mixed gas system suggests that specific adsorption and selective activation of the adsorption sites might occur in gas mixtures, and allow selectivity for the adsorption of a particular gas. The careful pattern recognition of sensing data obtained by the sensor array made it possible to distinguish a gas species from a gas mixture and to measure its concentration.

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

목적 : 본 연구는 휠체어 사용자의 욕창 예방을 위한 센서 기반 방석에 대한 선행 문헌을 분석하여 센서의 위치, 개수, 적 용 대상자를 알아보고자 하였다. 연구방법 : PRISMA(Preferred Reporting Items for Systematic Review and Meta-Analysis) 연구방법을 통해 IEEE Xplore, PubMed, Embase 등 데이터베이스에서 2010년부터 2021년까지 게재된 논문을 선정기준을 근거로 논문을 선정 하였다. 최종적으로 총 9편의 문헌이 선정되었으며, 각 문헌을 분석하였다. 결과 : 연구 대상자의 특성은 척수 손상 환자에 관한 연구가 4편으로 가장 많았으며, 비장애인은 3편, 다발성 경화증 환자 는 1편으로 보고되었다. 대상자의 수는 비장애인의 경우 8명에서 32명으로, 장애인의 경우 1명에서 17명으로 조사되었 다. 센서의 개수는 3개에서 1,024개로 다양하게 보고되었다. 센서는 주로 방석에만 삽입된 연구들이 조사됐지만, 등판에 도 센서를 삽입한 연구가 2편이 있었다. 센서의 위치를 쉽게 파악할 수 있도록 시각화된 자료도 함께 제시하였다. 앉은 자세 또는 활동 수준을 분류하는 연구가 가장 많았으며, 사용된 기기들의 정확도는 높은 것으로 조사되었다. 결론 : 센서 기반 방석을 설계할 때 센서의 위치는 욕창이 주로 발생하는 부위를 고려해야 하며, 압력 측정 외에도 알람 시스템을 통해 욕창 위험성에 관한 정보를 제공해야 한다고 사료된다. 클라이언트의 특성을 고려한 요인들을 제품에 반 영해야 하며, 임상가의 전문적인 서비스도 함께 이루어져야 한다. 클라이언트 중심의 접근과 근거 기반의 접근은 센서 기 반의 방석 개발과 중재에서도 중요한 요소이다. 본 연구는 센서 기반의 방석 개발에 있어 기초자료로 활용될 수 있을 것 으로 사료된다.

A facile and efficient method was developed to prepare highly stretchable and conductive graphene conductors with wrinkled structures by the mechanical stretching and shrinking of elastomeric substrates, in which graphene inks were printed on a prestretched elastomeric substrate. Stretchable and exfoliated graphene inks were prepared by mixing graphite and Ecoflex in a shear-assisted fluid dynamics reactor. The resultant graphene conductor exhibited excellent stretchability at 150% strain and high electrical conductivity of 64 ± 1.2 S m− 1. The resistance of the conductor did not change in bent, twisted, and stretched states. The resistance did not change during 10,000 cycles of stretching/releasing, with a maximum strain of 150%. Based on the graphene conductor, a stretchable conductometric sensor with a two-electrode configuration was fabricated to measure impedance changes at different concentrations of electrolyte ions. This sensor exhibited a good and linear sensitivity curve (298.61 Ω mM− 1, R2 = 0.999) in bent and stretched states.

Low-cost particulate matter (PM) sensors based on the light scattering principle measure the concentration of particles by the change of scattering intensity after light is irradiated onto the particles. It has been reported that when the relative humidity is high, water vapor may cause the expansion of airborne particles and affect the accuracy of the light scattering method for PM measurement, but it has also been shown that the effect of humidity is not significant or even negligible. Therefore, to determine the effect of humidity on the Plantower PMS7003 light scattering sensor, in this study, a BAM1020 (Beta Attenuation Monitoring) was installed alongside to continuously monitor the ambient atmospheric PM concentration for approximately four weeks. The sensors collected data at 10-minute intervals, resulting in a 1-hour average for comparative analysis. To accurately measure humidity, the performance of the Arduino + DHT22 humidity sensor was also pre-evaluated using a series of saturated salt solutions. The humidity was grouped into five intervals and analyzed by visual analysis. The results confirmed that there was no significant correlation between PM2.5 differences and humidity, which were randomly and uniformly distributed around the mean. However, since in the very low and high concentration ranges based on the beta-ray monitor measurements, the difference between the light scattering sensor PM10 measurement and the reference value is much larger than the difference between the PM2.5 and the reference value., there is an additional need to investigate the appropriate correction method for dust season or PM10. The results show that the outcomes of the light scattering sensor are more influenced by particle size and concentration than by humidity.

최근 신체활동에 대해 인식하는 센서와 그 제품군에 대한 관심 및 수요가 증가하고 있다. 특히 유연하고 연신이 가능하 며 사용자의 생체신호를 감지할 수 있는 웨어러블 소재에 대한 개발이 주목받고 있다. 본 연구에서는 소수성 소재에 Micro Needle을 통해 미세 구멍을 형성한 후 SWCNT 분산용액에 대한 함침 효율을 향상시키는 실험을 수행하였다. 본 연구에서 는 구멍을 뚫지 않은 소재를 대조(control) 군으로 함침을 진행, 비교 분석하였다. 센서의 전기전도도를 평가하기 위해 Strain UTM (Universal Testing Machine, UTM, Dacell)과 저항을 측정하는 멀티미터(Keysight)를 이용해 센서를 인장했 을 때의 센서의 전기전도도를 측정하였다. 또한 센서의 내구성을 평가하기 위해 시료별로 500회 인장을 진행한 후에 센서 의 전기전도도를 평가하였다. 그 결과 Needling을 한 센서의 전기전도성이 Needling을 하지 않은 센서에 비해 최소 16배 이상 뛰어남을 알 수 있었다. 또한 센서의 초기 저항에 비해 게이지 팩터도 우수해 센서로서 좋은 성능을 확인할 수 있었다. 이를 통해 친수성 소재에 비해 물성이 뛰어나지만, 높은 표면장력 때문에 함침 효율이 좋지 않았던 소수성 소재의 함침 효율을 높여 신체의 움직임을 더 효과적으로 감지하고 내구성과 활용 가능성이 뛰어난 센서를 제작했다.

해수 중 존재하는 유해화학물질 검출을 목적으로 센서 시작품 제작하고 성능을 확인하였다. 센서 시작품은 검지부, 기구부, 구 동부로 구성하였다. 센서의 검지부는 ITO (Indium-Tin-Oxide) 금속산화물 나노입자 (metal oxide nanoparticle) 필름을 기판위에 인쇄하여 제작 하였고, 온도와 HNS 농도를 동시에 검출할 수 있도록 2개의 검출 부분을 갖도록 설계하였다. 센서의 기구부는 검지부와 구동부를 연결하 며, 검출에 영향을 줄 수 있는 화학적 반응을 막기 위해 테프론 재질을 이용하여 제작하였고, 특히 검지부의 착탈이 용이하도록 설계 하 였다. 구동부는 브릿지 회로와 아두이노 보드를 이용하여 전원 공급과 데이터 측정 및 디스플레이가 가능하도록 제작하였다. 시작품의 성 능에 대해서는 기존의 수질 센서를 참고한 성능 사양을 제시하고, 유기용제를 사용한 검지부와 시작품의 동작을 확인하여 응답 (ΔR), 검 출하한 (Limit of Detection), 응답시간 (response time), 오차 (error) 등을 평가하였다. 또한 해수 중 동작 특성을 파악하여 설계 사양이 구현되 었는지 확인하였다.

PURPOSES : An automated driving guidance framework was developed for automated vehicles based on cooperation between infrastructure and automated vehicles. The proposed automated driving guidance framework is assumed to function only when an automated vehicle encounters situations in which it cannot safely pass through without cooperation with the infrastructure. METHODS : A four-step concept of automated driving guidance levels was employed, and the decision criteria, such as moving object, event, and externality, were defined as the criteria for determining the automated driving guidance level. The judgment criteria of each stage and procedure for determining the autonomous driving guidance level were determined based on successive judgments, and the proposed automated driving guidance framework was designed based on an expert survey. The survey was aimed at experts with experience related to automated driving system research or technology development. RESULTS : The resulting framework shows the steps and criteria for determining whether automated driving guidance is required under a specific situation and what the guidance should be. CONCLUSIONS : The proposed automated driving guidance framework is designed to function only when an automated vehicle encounters situations in which it cannot safely pass through without cooperation with the infrastructure.