Development and Performance Evaluation of Multi-sensor Module for Use in Disaster Sites of Mobile Robot (조사로봇의 재난현장 활용을 위한 다중센서모듈 개발 및 성능평가에 관한 연구)
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- Korean Journal of Remote Sensing
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- v.38 no.6_3
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- pp.1827-1836
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- 2022
Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.
Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.
Lactic acid bacteria (LAB) are widespread in a variety of environments including fermented dairy products, gastroinstetinal and urogenital tracts of human and animals, plant, soil and water. Leuconostoc mesenteroides DB3 was detected by the strongest antibacterial activities among 24 Leuconostoc strains isolated from Camellia japonica flowers. Acid tolerance of L. mesenteroides DB3 existed up to pH 2.5, but the resistance did not show at pH 2.0, which relatively excellent acid resistance existed. Bile acid tolerance was very stable within the test range to 1.2%. L. mesenteroides DB3 exhibited the optimal growth at 30℃, and showed a slight slow growth when compared with L. mesenteroides KCTC3505, which reached a stationary phase at 18 hr. The pH was changed along with the growth curve, but was maintained above pH 3.98. L. mesenteroides DB3 had higher initial antibacterial activities when compared to L. mesenteroides KCTC3505, but it showed similar activities with the standard strain after the latter part of the logarithmic growth phase. Although lactic acid production in L. mesenteroides DB3 was induced by lower amount in the initial part to the standard strain, it was exhibited by similar amounts after the late logarithmic growth phase. Muicin adhesion of L. mesenteroides DB-3 maintained superior to L. mesenteroides KCTC3505. Both strains showed excellent emulsification ability for kerosene. In summary, we evaluate that L. mesenteroides DB-3 has a high potential for application as probiotics owing to its excellent antibacterial activity, acid resistance, bile acid resistance, and muicin adhesion.
Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.
As an aquatic ecotoxicity test method, a bioassay using the inhibition of sporualtion of the green macroalga, Ulva pertusa, has been developed. Optimal test conditions determined for photon irradiance, pH, salinity and temperature were
In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.
This study investigated the effectiveness of using pathogens and aqueous acids to reduce the Aspergillus ochraceus and Rhodotorula mucilaginosa contamination in livestock production environments. For this study, 1 mL of each bacterial suspension (107-108 spores/mL) was inoculated on a knife surface, dried at 37℃, and used under each treatment condition. First, to investigate the effect of organic acids, acetic, lactic, and citric acids were used. Subsequently, to select the appropriate concentration, they were prepared at concentrations of 0.5, 1, 2, 3, 4, and 5%, respectively. Accordingly, to further maximize the effect of organic acid treatment, we combined the treatment with ultraviolet light. The two strains showed a significant difference (P<0.05) compared to the initial strain, with a greater than 90% decrease in the concentrations of all organic acids. Consequently, acetic and lactic acids decreased by approximately 5 and 2 log colony forming unit (CFU)/cm2, respectively, when treated with ultraviolet light (360 mJ/cm2); however, citric acid decreased by less than 1 log CFU/cm2. However, when manufactured with 4% acetic acid, a severe malodor was emitted, making it difficult for workers to use it in a production environment. Accordingly, the optimal treatment conditions for organic acid and ultraviolet light for application were selected as follows: immersion in a 4% lactic acid solution for 1 minute and then, sterilization with ultraviolet light at 360 mJ/cm2. Finally, when a pork meat sample was cut with a knife that was finally washed with lactic acid and treated with ultraviolet light, the low level of inoculum transferred from the cleaned knife to the surface of the sample was not detected. In conclusion, using this established method can prevent cross-contamination of the surface of the meat during processing.
The experiment was conducted to determine the changes in seed productivity of Italian ryegrass (Lolium multiflorum Lam.) according to nitrogen fertilization levels in the southern region of Korea. Italian ryegrass (IRG) variety 'Green Call' was sown in the fall of 2021 in Jinju, Gyeongsangnam-do. The experiment consisted of three nitrogen fertilizer levels (100, 120, and 140 N kg/ha) with three replications using a randomized complete block design. Harvesting was done approximately 30 days after heading on May 18th. There was no difference in heading date among treatments, which occurred on April 18th. The longest IRG was observed in the 140 N kg/ha treatment, but there was no significant difference. No significant differences were observed in lodging, disease resistance, and cold tolerance among treatments, but lodging was severe in all treatments. The length of the spike averaged 44.95 cm, with no difference among treatments, and the number of seeds per spike was highest in the 120 N kg/ha treatment. Seed yield increased with increasing nitrogen fertilizer levels, averaging 3,707 kg/ha (as-fed basis). DM content of seed and straw averaged 76.95% and 62.19%, respectively, with no significant differences among treatments. The remaining straw after harvesting averaged 6,525 kg/ha on a dry matter basis, with the highest value observed in the 140 N kg/ha treatment. Overall, considering the results, the optimal nitrogen fertilizer application rate for seed production of Italian ryegrass in the southern region when sown in autumn was found to be 120 N kg/ha.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70