The sampling bag is used as a storage container for odor gas samples. It is known that the substances recovery rate of odor bags decreases during storage time, and the degree of recovery varies depending on the characteristics of the gas sample and the material of the bag. This study investigated the recovery rate of VFA (ACA, PPA, BTA, VLA) in PEA bags during storage time. In addition, a model was developed to estimate the recovery rate of each substance as a function of time. Standard gas (ACA, PPA, BTA, VLA mixed) recovery rate was used for the model development. The concentration of the compound in the bag was measured by SIFT-MS at intervals of 1 to 2 hours. The recovery rate according to the storage time was calculated as the ratio to the initial concentration. The recovery rate of each substance according to the storage period (12h, 24h, 36h, 48h) was ACA (66.2%, 62.8%, 55.6%, 52.0%), PPA (77.6%, 72.1%, 63.0%, 58.1%, 86.6%), BTA (86.6%, 81.3%, 71.6%, 66.9%), VLA (94.8%, 89.0%, 76.6%, 71.7%). The recovery rate continued to decrease over the course of 48 hours of storage time. ACA, PPA, and BTA showed the greatest decrease within the initial 12 hours, which is form of exponential decrease. Therefore, we considered a 1~3 degree polynomial regression model and a 1~2 degree exponential decay model. Each developed model was evaluated by r², RMSE, MAPE, AIC, and then a model for each substance was selected. Selected models were tested with recovery rate data from swine farm odor samples. Only the ACA model exhibited a good performance (r² = 0.76).

In this research we investigate motion controller performance for mobile robots according to changes in the control loop sampling time. As a result, we suggest a proper range of the sample time, which can minimize final posture errors while improving tracking capability of the controller. For controller implementation into real mobile robots, we use a smooth and continuous motion controller, which can respect robot’s path curvature limitation. We examine motion control performance in experimental tests while changing the control loop sampling time. Toward this goal, we compare and analyze experimental results using two different mobile robot platforms; one with real-time control and powerful hardware capability and the other with non-real-time control and limited hardware capability.

To find out the affection of the sampling techniques to the result of a faunistic study, we surveyed the insect fauna of the Chungbuk National University (four different sites) for a year, from spring to fall. For each site, four different collecting methods: light trap, net sweeping, pitfall trap, and window trap, were applied and the collecting was done every other week for a total of 16 times. A total of 14 orders and 672 species were collected. 501 species were collected by the light trap, which covers about 75% of the total number of species, turn out to be the most effective, while other methods could only cover 18% or less. On average, only about 30% of the species collected at a given time of collecting were re-collected at the next collecting, which means about 70% of the species collected from the first collecting remains not collected in the next collecting if you collect insects every other week. The result suggests that, in addition to applying diverse collecting methods, frequent sampling, or narrow sample window, is another very important factor for a good representation of species diversity in an insect faunistic study.

The purposes of this study were to: a) investigate the percentage distribution and the time spent of dietetic activities and b) estimate dietitian's staffing needs in employee foodservices. In 6 employee foodservices, the dietetic activities were analyzed by work sampling methodology. The results of this study were as follows: 1. The percentage distributions of dietetic activities, delay and non-dietetic activities were 79.06, 20.39, and 1.55%, respectively; 2. The major activities of dietitians in employee foodservice were production management 21.00%, purchasing management 16.73%, record keepig 14.40%, and menu management 6.30%, 3. The total labor time per week was 3,310 min (55.16 hr) and specially the time spent on 13 dietetic activities was 2,626 min (43.77 hr). 4. The time spent per week on major activities of dietitians such as production management, purchasing management record keeping, and menu management were 693.93, 554.83, 483.99, and 205.22 min, respectively.