차량동역학 해석에서 스태빌라이저의 모델링

조병관(Byoung-Kwan Cho),송성재(Sung-Jae Song) Korean Society for Precision Engineering 1996 한국정밀공학회지 Vol.13 No.10

FT-NIR을 이용한 상추(Lactuca sativa L) 종자의 비파괴 선별 기술에 관한 연구

안치국,조병관,강점순,이강진,Ahn, Chi-Kook,Cho, Byoung-Kwan,Kang, Jum-Soon,Lee, Kang-Jin 충남대학교 농업과학연구소 2012 Korean Journal of Agricultural Science Vol.39 No.1

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

초분광 영상을 이용한 송이토마토의 비파괴 품질 예측

김대용,조병관,김영식,Kim, Dae-Yong,Cho, Byoung-Kwan,Kim, Young-Sik 충남대학교 농업과학연구소 2012 Korean Journal of Agricultural Science Vol.39 No.3

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

육류 신선도 판별을 위한 휴대용 전자코 시스템 설계 및 성능 평가 II - 돈육의 미생물 총균수 예측을 통한 전자코 시스템 성능 검증

김재곤,조병관,Kim, Jae-Gone,Cho, Byoung-Kwan 충남대학교 농업과학연구소 2011 Korean Journal of Agricultural Science Vol.38 No.4

The objective of this study was to predict total bacteria count of pork meats by using the portable electronic nose systems developed throughout two stages of the prototypes. Total bacteria counts were measured for pork meats stored at $4^{\circ}C$ for 21days and compared with the signals of the electronic nose systems. PLS(Partial least square), PCR (Principal component regression), MLR (Multiple linear regression) models were developed for the prediction of total bacteria count of pork meats. The coefficient of determination ($R_p{^2}$) and root mean square error of prediction (RMSEP) for the models were 0.789 and 0.784 log CFU/g with the 1st system for the pork loin, 0.796 and 0.597 log CFU/g with the 2nd system for the pork belly, and 0.661 and 0.576 log CFU/g with the 2nd system for the pork loin respectively. The results show that the developed electronic system has potential to predict total bacteria count of pork meats.

육류 신선도 판별을 위한 휴대용 전자코 시스템 설계 및 성능 평가

김재곤,조병관,Kim, Jae-Gone,Cho, Byoung-Kwan 충남대학교 농업과학연구소 2011 Korean Journal of Agricultural Science Vol.38 No.3

The aim of this study was to develop a portable electronic nose system for freshness measurement of meats, which could be an alterative of subjective measurements of human nose and time-consuming measurements of conventional gas chromatograph methods. The portable electronic system was o optimized by comparing the measurement sensitivity and hardware efficiency, such as power consumption and dimension reduction throughout two stages of the prototypes. The electronic nose systems were constructed using an array of four different metal oxide semiconductor sensors. Two different configurations of sensor array with dimension were designed and compared the performance respectively. The final prototype of the system showed much improved performance on saving power consumption and dimension reduction without decrease of measurement sensitivity of pork freshness. The results show the potential of constructing a portable electronic system for the measurement of meat quality with high sensitivity and energy efficiency.

광 반사방식을 이용한 감염 씨감자 비파괴 선별 기술 개발

김대용,조병관,이윤수,Kim, Dae-Yong,Cho, Byoung-Kwan,Lee, Youn-Su 충남대학교 농업과학연구소 2012 Korean Journal of Agricultural Science Vol.39 No.1

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

중형 트랙터용 원형베일러 성능평가

장동일,정선옥,조병관,조남홍,Chang, Dong-Il,Chung, Sun-Ok,Cho, Byoung-Kwan,Cho, Nam-Hong 충남대학교 농업과학연구소 2010 Korean Journal of Agricultural Science Vol.37 No.2

Bale is an operation of collecting livestock feed materials from field crop residue, and mechanization demand on the operation has been increased. Bailers imported from foreign countries such as Japan and European countries have been used, but those models showed improper performance in Korean situations. In recent years, a steel-roller type round baler attachable to medium size tractors(40 to 60 HP) for effective bale operation in Korea was developed. This study was conducted to evaluate field performance of the baler. For proper baling operation, engine speed was greater than 1,800rpm, average traction force and PTO torque were about 4kN and in a range of 380-671Nm, and maximum values were about 7kN and 3,000Nm, respectively. Performance evaluation tests for sudan grass, rice straw, and blue barley showed that field capacity was 0.59ha/h for blue barley and 0.99ha/h for sudan grass and rice straw. Bale weight, diameter, width, and bulk density were in ranges of 176.1~418.4kg, 1.07~1.12m, 1.02~1.04m, and 175.3~454.1kg/$m^3$. Noise sound level during the baling operation was 4dB greater than idle operation condition, which was considered to be ignorant.

휴대용 전자코를 이용한 계육의 냉장 중 신선도 평가

이훈수,정창호,김기복,조병관,Lee, Hoon-Soo,Chung, Chang-Ho,Kim, Ki-Bok,Cho, Byoung-Kwan 한국축산식품학회 2010 한국축산식품학회지 Vol.30 No.2

The purpose of this study was to evaluate the freshness of chicken meat during 19 d of storage at $4^{\circ}C$ using a portable electronic nose. The portable system consisted of six different metal oxide sensors and a moisture sensor. Determination of volatile compounds with gas chromatography-mass spectrometry, total bacterial count (TBC), and 2-thiobarbituric acid reactive substances (TBARS) monitored the quality change of the samples. These results were compared with the results measured by the electronic nose system. TBC and TBARS measurements could be separated into five groups and seven groups, respectively, among ten groups. According to principal component analysis and linear discriminant analysis with the signals of the portable electronic nose, the sample groups could be clearly separated into eight groups and nine groups, respectively, among ten groups. The portable electronic nose demonstrated potential for evaluating freshness of stored chicken.

노지 재배 고구마의 수분 스트레스 수준 평가를 위한 인공지능 기반 다중 영상 시스템

조수빈(Soo Been Cho),최지원(Ji Won Choi),조병관(Byoung-Kwan Cho),황운하(Woon-Ha Hwang),송대빈(Dae-Bin Song),김건우(Geonwoo Kim) 한국비파괴검사학회 2025 한국비파괴검사학회지 Vol.45 No.1

Recent abnormal weather conditions in South Korea, including unexpected droughts and floods, have adversely affected the yield and quality of field-grown sweet potatoes. This has highlighted the critical need for an effective system to evaluate water stress in crops. In response, in this study, an artificial intelligence-based multi-imaging system was developed for assessing water stress in field-grown sweet potatoes. The system incorporates RGB image preprocessing, background removal, and machine learning models, specifically Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The models achieved coefficients of determination of 0.80 for CNN and 0.86 for SVM. This system offers a reliable method for quantitatively evaluating water stress in sweet potatoes and managing irrigation. Furthermore, it holds potential for application in water stress assessment across various crops.

딥러닝을 이용한 솔껍질깍지벌레 탐지 알고리즘 개발

윤원섭 ( Wonsub Yun ),조병관 ( Byoung-kwan Cho ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.2

솔껍질깍지벌레(M. matsumuare)는 노린재목의 곤충으로 한국, 일본 및 중국 북동부 지역에 서식한다. 솔껍질깍지벌레는 암수의 생활경과가 달라지기 전인 후약충 시기에 소나무와 곰솔(해송)의 가지에 기생하며 흡즙 가해를 일으킨다. 약충의 피해를 입은 나무는 수관 하부의 잎부터 갈변하게 되는데 피해가 누적될 시 수관 전체가 갈변하여 고사하게 된다. 따라서 솔껍질깍지벌레의 개체수를 모니터링하여 적기에 방제하는 것이 중요한데, 현재 이 벌레의 개체수 파악은 페로몬트랩에 포획된 수컷 성충을 육안으로 개수하는 방식을 사용하고 있다. 그러나 인력에 의한 동정은 시간 및 노동력 소모가 많고, 장시간 작업 시 피로도가 급증하고 정확성이 떨어지는 단점이 있다. 본 연구에서는 이러한 기존의 수작업방식을 개선하기 위해 페로몬트랩에 포획된 솔껍질깍지벌레를 자동으로 동정할 수 있는 딥러닝 영상분석 기술을 개발하고자 하였다. 검출모델 개발을 위해 스마트폰으로 촬영된 RGB 광각 영상에 Faster R-CNN (Region Convolutional Nerual Network) 딥러닝 알고리즘을 적용하여 영상 내의 솔껍질깍지벌레 개체를 자동으로 인식하고 개수할 수 있는 방법을 개발하였다. 본 연구는 끈끈이트랩에 포집된 솔껍질깍지벌레의 개체수를 딥러닝 영상분석 기술을 활용하여 신속 정확하게 자동으로 파악할 수 있음을 보여주었다.