Geum Bong Yu

Crop leaf area index (LAI) is one of the important indicators to evaluate crop growth and guide field management, and can be used to predict crop yield. Spectral and thermal information extracted by multispectral (MS) and thermal infrared (TIR) sensors mounted on an unmanned aerial vehicle (UAV) can be used for LAI estimation. Image texture is sensitive to the changes in crop surface grayscale or color characteristics, and can be combined with spectral and thermal information to estimate the LAI. But single texture metric has limitations in LAI estimation. Therefore, the purpose of this study is to construct new texture indices based on texture metrics extracted from MS and TIR images, and combined spectral and thermal information to enhance the estimation accuracy of maize LAI. Three replicates of maize experiments under different irrigation treatments were conducted in 2020. The MS and TIR sensors were mounted on a UAV to acquire maize canopy images during critical growth stages and acquire field LAI value of samples synchronously. The LAI estimation models were established using MS data, TIR data, as well as their combination. These models were constructed by Back Propagation Neural Network (BPNN), Partial least squares regression (PLSR), and Random Forest Regression (RFR). Finally, the performance of LAI estimation models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (rRMSE). Results shown that: (i) Among the eight kinds of texture metrics extracted from MS and TIR images, the texture metric mean (MEA) has the best performance. Compared with single texture metrics, texture indices constructed by different metrics has stronger correlation with LAI. (ii) Adding texture indices to estimation models significantly improved model accuracy, especially multispectral three-texture index(MS-TTI) has higher LAI estimation potential than thermal infrared three-texture index(TIR-TTI). (iii) Compared with the use of MS or TIR data alone, the estimation model constructed by combining MS data and TIR data have better performance. The best estimation model obtained by the RFR method (R2 = 0.862, RMSE = 0.246 and rRMSE = 10.20 %) further improved the LAI estimation of maize, with R2 increasing by 6.55 % and 14.48 %, respectively. In conclusion, the combination of MS and TIR data can effectively improve the estimation accuracy of maize LAI, and also provide a feasible method for monitoring crop growth.

Aiming at the problems of low extraction efficiency, high false detection rate, weak Raman signal and serious interference by fluorescence signal in the detection of microplastics in marine organisms, this paper establishes a set of rapid detection methods for microplastics in organisms, including confocal Raman spectroscopy, fluorescence imaging, differential Raman spectroscopy, and rapid identification of microplastics based on the ELM modeling assistance. Firstly, to address the problem of low extraction efficiency of microplastics, we explored and optimized the digestion method of tissues, which effectively improved the digestion effect of fish tissues and excluded the influence of tissues on microplastics detection. Aiming at the problems of high misdetection rate and low pre-screening efficiency of microplastics, fluorescence imaging technology is adopted to realize the visualization and detection of microplastics, which effectively improves the detection efficiency and precision of microplastics. Based on the confocal microscopy Raman spectroscopy detection system built independently in the laboratory, using 784/785 nm as the excitation light, the differential Raman spectroscopy technique effectively excludes the interference of fluorescence signals in the Raman spectra, and improves the signal-to-noise ratio of the Raman spectra, and the recovery rate of the Raman characteristic peaks in the differential Raman spectroscopy reaches 100 % compared to the traditional baseline correction method, which is 33.3 % higher than that of the baseline correction method. Finally, a microplastic identification model is constructed based on ELM to assist in realizing the rapid and accurate identification of microplastics. The more complete detection method of microplastics in marine organisms proposed in this paper can realize the rapid and nondestructive, efficient and accurate detection of microplastics in fish, which can help to further promote the development of marine microplastics monitoring technology.

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make $\mathbf{P}[T > t]$, the chance a job's response time exceeds $t$, decay as quickly as possible in the $t \to \infty$ limit. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail: $\mathbf{P}[T > t] \sim C e^{-\gamma t}$. FCFS achieves the optimal *decay rate* $\gamma$, but its *tail constant* $C$ is suboptimal. Only recently have policies that improve upon FCFS's tail constant been discovered. But it is unknown what the optimal tail constant is, let alone what policy might achieve it. In this paper, we derive a closed-form expression for the optimal tail constant $C$, and we introduce *$\gamma$-Boost*, a new policy that achieves this optimal tail constant. Roughly speaking, $\gamma$-Boost operates similarly to FCFS, but it pretends that small jobs arrive earlier than their true arrival times. This significantly reduces the response time of small jobs without unduly delaying large jobs, improving upon FCFS's tail constant by up to 50% with only moderate job size variability, with even larger improvements for higher variability. While these results are for systems with full job size information, we also introduce and analyze a version of $\gamma$-Boost that works in settings with partial job size information, showing it too achieves significant gains over FCFS. Finally, we show via simulation that $\gamma$-Boost has excellent practical performance.

Phase transition dynamics of Bi 0.5 Na 0.5 TiO 3 is analyzed using in situ Raman technique with varying temperature. Complex polarization properties are also revealed, such as ferroelectric origin and the thermal hysteresis behavior.

The patient-specific bolus fabricated by a mold-and-cast method using a 3D printer (3DP) and silicon rubber has been adopted in clinical practices. Manufacturing a mold using 3DP, however, can cause time delays due to failures during the 3D printing process. Thereby, we investigated an alternative method of the mold fabrication using computer numerical control (CNC) machine tools. Treatment plans were conducted concerning a keloid scar formed on the ear and nose. The bolus structures were determined in a treatment planning system (TPS), and the molds were fabricated using the same structure file but with 3DP and CNC independently. Boluses were then manufactured using each mold with silicone rubbers. We compared the geometrical difference between the boluses and the planned structure using computed tomography (CT) images of the boluses. In addition, dosimetric differences between the two measurements using each bolus and the differences between the measured and calculated dose from TPS were evaluated using an anthropomorphic head phantom. Geometrically, the CT images of the boluses fabricated by the 3DP mold and the CNC mold showed differences compared to the planned structure within 2.6 mm of Hausdorff distance. The relative dose difference between the measurements using either bolus was within 2.3%. In conclusion, the bolus made by the CNC mold benefits from a stable fabricating process, retaining the performance of the bolus made by the 3DP mold.

Autonomous mobile robots (AMRs) require SLAM technology for positioning and mapping. Their accuracy and real-time performance are the keys to ensuring that the robot can safely and accurately complete the driving task. The visual SLAM systems based on feature points have high accuracy and robustness but poor real-time performance. A lightweight Visual Odometry (VO) based on Lucas–Kanade (LK) optical flow tracking is proposed. Firstly, a robust key point matching relationship between adjacent images is established by using a uniform motion model and a pyramid-based sparse optical flow tracking algorithm. Then, the grid-based motion statistics algorithm and the random sampling consensus algorithm are used to eliminate the mismatched points in turn. Finally, the proposed algorithm and the ORB-SLAM3 front-end are compared in a dataset to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm effectively improves the real-time performance of the system while ensuring its accuracy and robustness.

Nowadays, E-mail communication has been abused for numerous illegitimate purposes such as E-mail spamming, terrorist attack, business fraud, etc. As a result, to analysis the rich personal information hidden in E-mail is significant for investigation and evidence collection. In this paper, an investigation and analysis system aiming to Email was presented, which supports a variety of data sources including the preserved Email client data files, databases as well as text files. The system firstly parses related data files, preprocess the data, and then, a key word search technique based on KMP algorithm was adopted to classify the E-mail collections into different categories. Afterwards, an association frequency mining based on statistics will be performed to discover the association features behind email accounts. To make the forensic results more readable, we will associate the E-mail accounts with personnel information table in reality. The final forensic results will be visualized using related layout techniques to make the information more illustrative and understandable.

Robot vision is an important aspect of computer science and artificial intelligence research. It mainly simulates human visual function through computer application technology, and analyzes and processes the information extracted from the image to achieve the actual detection and control effect. Firstly, this paper introduces the shape analysis method combining genetic algorithm and visual algorithm. Through the analysis of the results of the motion analysis experiment of the automatic mobile robot in linear motion, the actual theoretical speed is 0.199m/s, and the robot analyzes the turning motion of the mobile robot at a speed of 0.2m/s, and the actual turning speed is 0.2m/s, and the one-time optional turning is 360. Finally, this paper mainly analyzes and studies the feature recognition and classification methods of workpieces based on machine vision to help people better understand the classification and recognition technology of machine vision.

Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model’s evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing’s potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection.

We report an analysis of the {Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}{sup +}{pi}{sup -}{pi}{sup +}{pi}{sup -} decay in a data sample collected by the CDF II detector at the Fermilab Tevatron corresponding to 2.4 fb{sup -1} of integrated luminosity. We reconstruct the currently largest samples of the decay modes {Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}(2595){sup +}{pi}{sup -} (with {Lambda}{sub c}(2595){sup +} {yields} {Lambda}{sub c}{sup +}{pi}{sup +}{pi}{sup -}), {Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}(2625){sup +}{pi}{sup -} (with {Lambda}{sub c}(2625){sup +} {yields} {Lambda}{sub c}{sup +}{pi}{sup +}{pi}{sup -}), {Lambda}{sub b}{sup 0} {yields} {Sigma}{sub c}(2455){sup ++}{pi}{sup -}{pi}{sup -} (with {Sigma}{sub c}(2455){sup ++} {yields} {Lambda}{sub c}{sup +}{pi}{sup +}), and {Lambda}{sub b}{sup 0} {yields} {Sigma}{sub c}(2455)0{pi}{sup +}{pi}{sup -} (with {Sigma}{sub c}(2455)0 {yields} {Lambda}{sub c}{sup +}{pi}{sup -}) and measure the branching fractions relative to the {Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}{sup +}{pi}{sup -} branching fraction. We measure the ratio {Beta}({Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}{sup +}{pi}{sup -}{pi}{sup +}{pi}{sup -})/ {Beta}({Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}{sup +}{pi}{sup -})=3.04 {+-} 0.33(stat){sub -0.55}{sup +0.70}(syst) which is used to derive {Beta}({Lambda}{sub b}{sup 0} {yields} {Lambda}{sub c}{sup +}{pi}{sup -}{pi}{sup +}{pi}{sup -})=(26.8{sub -11.2}{sup +11.9}) x 10{sup -3}.

Results on the first search for a light charged Higgs boson H + decaying to c b in top quark pair events using the CMS detector at the LHC are presented.The total dataset corresponds to 19.7 fb -1 of proton-proton collisions at √ s = 8 TeV.In t t decays, if one top quark decays to H + b and the H + subsequently decays to c b, while other top quark decays leptonically ( t → W -b → l ν b)), the final state then consists of four jets (three b quark jets), one lepton (electron or muon), and missing energy: t t → (H + b)(W -b) → (c bb)(l ν b).The main observable used in the analysis is an invariant mass of two jets, one of which is identified as a b-quark jet.The dijet pair is selected from at least four jets in an event by a dedicated kinematic fitter.No signal for the presence of a charged Higgs boson is observed and upper limits are set at 95% confidence level on the branching ratio for t → H + b from 1.1-0.4% for the charged Higgs boson mass in the range 90-150 GeV in the assumption of branching ratio of B(H + → c b) = 100%.

In this dissertation we report on the first direct search for charged Higgs bosons in decays of top quarks in p$\bar{p}$ collisions at √s = 1.96 TeV. The search uses a data sample with an integrated luminosity of 2.2 fb-1 collected by the CDF II detector at Fermilab and looks for a resonance in the invariant mass distribution of two jets in the lepton+jets sample of t$\bar{t}$ candidates. We observe no evidence of charged Higgs bosons in top quark decays; hence 95% C.L. upper limits on the branching ratio are placed at β(t → H+b) < 0.1 to 0.3 for charged Higgs boson masses of 60 to 150 GeV/c2 assuming β(H+ → c$\bar{s}$) = 1.0 and β(t → Wb)+β(t → H+b) = 1.0. The upper limits on β(t → H+b) are also used as model independent limits on the decay branching ratio of top quarks to any charged scalar bosons beyond the standard model.

Geum Bong Yu, Chang Heon Choi, Jung-in Kim, Jin Dong Cho, Euntaek Yoon, Hyung Jin Choun, Jihye Choi, Soyeon Kim, Yongsik Kim, Do Hoon Oh, Hwajung Lee, Lee Yoo, Minsoo Chun. Progress in Medical Physics 2022;33:150-7. https://doi.org/10.14316/pmp.2022.33.4.150

The Pickering emulsion stabilized by cellulose nanocrystals prepared from sisal fiber (SCNCs) was used as a template to fabricate PMMA or curcumin loaded PMMA composite microspheres coated by SCNCs (PMMA@SCNC or Cur-loaded PMMA@SCNC CMs) via solvent evaporation.The core-shell structure of PMMA@SCNC CMs can protect the curcumin loaded and improve its UV light resistance and thermal property.Meanwhile, the Cur-loaded PMMA@SCNC CMs have good controlled release for curcumin.This work by