• Journal of Science Education
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• v.32 no.1
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• pp.33-46
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• 2008

The purpose of this study was to know whether or not any wrongful description or simple errors were in photosynthesis unit of Biology II textbook under 7th national curriculum and if so, to know whether or not high school teachers recognized and corrected properly the mistakes. The mistakes in photosynthesis unit of text books were determined by the comparison with several reference books and through examination by three plant physiologists in 8 different Biology II textbooks. After the mistakes were analysed, the survey using contents of textbook containing the mistakes was conducted on high school teachers teaching Biology II. As a result, 48 mistakes were determined in 13 subjects. As many as four mistakes were found even in one subject in a certain textbook and a same mistake was found repeatedly in several textbooks. The survey result showed that the teachers who pointed exactly the mistakes out corrected properly, however, the percentage of these ones out of 35 teachers replied to survey was less than 50%. The ratios of correction out of total number of responses were high in question #6 (43%), #4-3 (40%), and #1-2 (32%) which were containing a simple mistake in graph, a wrong word and a wrong picture, respectively. But, no one pointed out and made correction in question #5-1 and #5-2 which were containing Z scheme of light reaction without the legend of vertical axis that should be explained as electron energy or standard reduction potential. The result indicates the possibility that the mistakes in photosynthesis unit of Biology II textbook can be corrected and teached properly by teachers may be low. In order to reduce the possibility that students may have misconceptions about photosynthesis, the list of print's errors should be provided to the teachers and/or the training program and/or workshop for in-service high school biology teachers was recommended.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.194-200
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• 2009

Purpose: This aim of this study was to evaluate changes in gastric volume and organ position as a result of delayed gastric emptying after a subtotal gastrectomy performed as part of the treatment of stomach cancer. Materials and Methods: The medical records of 32 patients who underwent concurrent chemoradiotherapy after a subtotal gastrectomy from March 2005 to December 2008 were reviewed. Of these, 5 patients that had more than 50 cc of residual gastric food detected at computed tomography (CT) simulation, were retrospectively enrolled in this study. Gastric volume and organ location was measured from CT images obtained before radiotherapy, twice weekly. In addition, authors evaluated the change of radiation dose distribution to planning the target volume and normal organ in a constant radiation therapy plan regardless of gastric volume variation. Results: A variation in the gastric volume was observed during the radiotherapy period (64.2~340.8 cc; mean, 188.2 cc). According to the change in gastric volume, the location of the left kidney was shifted up to 0.7 - 2.2 cm (mean, 1.2 cm) in the z-axis. Under-dose to planning target volume (V43, 79.5${\pm}$10.4%) and over-dose to left kidney (V20, 34.1${\pm}$12.1%; Mean dose, 23.5${\pm}$8.3 Gy) was expected, given that gastric volume change due to delayed gastric emptying wasn't taken into account. Conclusion: This study has shown that a great change in gastric volume and left kidney location may occur during the radiation therapy period following a subtotal gastrectomy, as a result of delayed gastric emptying. Detection of patients who experienced delayed gastric emptying and the application of gastric volume variation to radiation therapy planning will be very important.

• Journal of the Korean Geophysical Society
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• v.3 no.4
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• pp.269-280
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• 2000

Rail vibration in city zone is becoming a serious environmental problem. In order to make a reduction plan for rail vibration, the research was conducted in which many experiments to measure actual rail vibration along the railroad through the central Deajeon area. A digital vibration level meter was used to measure rail vibration. Vibration levels of Z-axis were measured at every second for the duration of the train passing. The measuring station was placed at every 5m for the distance of 55m. A total of 353 different sets of vibration level were obtained. The signals were processed to get $L_{10}$ value and analyzed in terms of distance, train velocity, and number of trains. As a result, it has been found that rail vibration exceed the allowable vibraton limit of 60 dB, at the point of 25 m far from the railroad center, which is regulated by the las of vibration and noise. Train velocity was found to affect a little for vibration level within the zone. It was also found that a trench installed along a railroad could reduce vibration level up to approximately 10 percent. A model test was conducted to investigate the influence of the location and size of trench, on the transfer of vibration. A heavy steel ball was used to generate vibrations. On the basis obtained from this study, it could be concluded that the application of distance-attenuation and the installment of a trench along railroad could be applied as a reduction plan for rail vibration. Because limitions might exist to depend on the effect of distance attenuation, trenchs excavated along a railroad might be suggested as the most efficient solution to reduce railroad vibration.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.7-13
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• 2023

Purpose: In this study, we evaluated the effect of using a customized bolus on dose delivery in the treatment plan when cervical cancer protruded out of the body along with the uterus and evaluated reproducibility in patient set-up. Materials & Methods: The treatment plan used the Eclipse Treatment Planning System (Version 15.5.0, Varian, USA) and the treatment machine was VitalBeam (Varian Medical Systems, USA). The radiotherapy technique used 6 MV energy in the AP/PA direction with 3D-CRT. The prescribed dose is 1.8 Gy/fx and the total dose is 50.4 Gy/28 fx. Semiflex TM31010 (PTW, Germany) was used as the ion chamber, and the dose distribution was analyzed and evaluated by comparing the planned and measured dose according to each position movement and the tumor center dose. The first measurement was performed at the center by applying a customized bolus to the phantom, and the measurement was performed while moving in the range of -2 cm to +2 cm in the X, Y, and Z directions from the center assuming a positional error. It was measured at intervals of 0.5 cm, the Y-axis direction was measured up to ±3 cm, and the situation in which Bolus was set-up incorrectly was also measured. The measured doses were compared based on doses corrected to CT Hounsfield Unit (HU) 240 of silicon instead of the phantom's air cavity. Result: The treatment dose distribution was uniform when the customized bolus was used, and there was no significant difference between the prescribed dose and the actual measured value even when positional errors occurred. It was confirmed that the existing sheet-type bolus is difficult to compensate for irregularly shaped tumors protruding outside the body, but customized Bolus is found to be useful in delivering treatment doses uniformly.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.