• Journal of the Korean Wood Science and Technology
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• 제51권3호
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• pp.207-221
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• 2023

The increasing demand for clean energy requires considerable effort to find alternative energy sources, such as briquettes. This research aims to develop a charcoal briquette with added pine resin (API) that has excellent combustion speed and distinctive aroma. Briquettes are composed of charcoal, pine resin (concentration: 0%-30%), and starch (up to 7%). They are produced in several stages, including coconut shell pyrolysis in conventional combustion, to obtain charcoal for the briquette precursor. Briquette compaction is conducted by mixing and densifying the charcoal, pine resin, and starch using a hydraulic press for 3 min. The hydraulic press has a total surface area and diameter of 57.7 cm² and 3.5 cm, respectively. The briquettes are dried at different temperatures, reaching 70℃ for 24 h. The study results show that the briquettes have a thickness and diameter of up to 2 and 3.5 cm, respectively; moisture of 2.18%-2.62%; ash of 11.61%-13.98%; volatile matter of 27.15%-51.74%; and fixed carbon content of 40.24%-59.46%. The compressive strength of the briquettes is 186-540 kg/cm². Their calorific value is 5,338-6,120 kcal/kg, combusting at a high speed of 0.15-0.40 s. The methoxy naphthalene, phenol, benzopyrrole, and lauryl alcohol; ocimene, valencene, and cembrene are found in the API. The API briquette has several chemical compounds, such as musk ambrette, ocimene, sabinene, limonene, 1-(p-cumenyl) adamantane, butane, and propanal, which improve aroma, drug application, and fuel production. Accordingly, API briquettes have considerable potential as an alternative energy source and a health improvement product.

• Radiation Oncology Journal
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• 제37권2호
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• pp.117-126
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• 2019

Purpose: The purpose of this study was to characterize and evaluate the clinical significance of volume changes of soft tissue sarcomas during radiation therapy (RT), prior to definitive surgical resection. Materials and Methods: Patients with extremity or pelvis soft tissue sarcomas treated at our institution from 2013 to 2016 with RT prior to resection were identified retrospectively. Tumor volumes were measured using cone-beam computed tomography obtained daily during RT. Linear regression evaluated the linearity of volume changes. Kruskal-Wallis tests, Mann-Whitney U tests, and linear regression evaluated predictors of volume change. Logistic and Cox regression evaluated volume change as a predictor of resection margin status, histologic treatment response, and tumor recurrence. Results: Thirty-three patients were evaluated. Twenty-nine tumors were high grade. Prior to RT, median tumor volume was 189 mL (range, 7.2 to 4,885 mL). Sixteen tumors demonstrated significant linear volume changes during RT. Of these, 5 tumors increased and 11 decreased in volume. Myxoid liposarcoma (n = 5, 15%) predicted decreasing tumor volume (p = 0.0002). Sequential chemoradiation (n = 4, 12%) predicted increasing tumor volume (p = 0.008) and corresponded to longer times from diagnosis to RT (p = 0.01). Resection margins were positive in three cases. Five patients experienced local recurrence, and 7 experienced distant recurrence, at median 8.9 and 6.9 months post-resection, respectively. Volume changes did not predict resection margin status, local recurrence, or distant recurrence. Conclusion: Volume changes of pelvis and extremity soft tissue sarcomas followed linear trends during RT. Volume changes reflected histologic subtype and treatment characteristics but did not predict margin status or recurrence after resection.

• Animal Bioscience
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• 제36권6호
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• pp.899-907
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• 2023

Objective: The better understanding of laying pattern of birds is crucial for developing breed-specific proper breeding scheme and management. Methods: Daily egg production until 50 wk of age of six chicken breeds including one layer (White Leghorn, WL), three dual-purpose (Rhode Island Red, RIR; Columbian Plymouth Rock, CR; and Barred Plymouth Rock, BR), one synthetic dwarf (DY), and one indigenous (Beijing-You Chicken, BYC) were used to characterize their clutch traits and egg production. The age at first egg, egg number, average and maximum clutch length, pause length, and number of clutches and pauses were calculated accordingly. Results: The egg number and average clutch length in WL, RIR, CR, and BR were higher than those in DY and BYC (p<0.01). The numbers of clutches and pauses, and pause length in WL, RIR, CR, and BR were lower than those in DY and BYC (p<0.01). The coefficient variations of clutch length in WL, RIR, CR, and BR (57.66%, 66.49%, 64.22%, and 55.35%, respectively) were higher than DY (41.84%) and BYC (36.29%), while the coefficient variations of egg number in WL, RIR, CR, and BR (9.10%, 9.97%, 10.82%, and 9.92%) were lower than DY (15.84%) and BYC (16.85%). The clutch length was positively correlated with egg number (r = 0.51 to 0.66; p<0.01), but not correlated with age at first egg in all breeds. Conclusion: The six breeds showed significant different clutch and egg production traits. Due to the selection history, the high and median productive layer breeds had higher clutch length than those of the less productive indigenous BYC. The clutch length is a proper selection criterion for further progress in egg production. The age at first egg, which is independent of clutch traits, is especially encouraged to be improved by selection in the BYC breed.

• Geomechanics and Engineering
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• 제35권2호
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• 지능정보연구
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• 제23권2호
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• pp.71-88
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• 2017

언어모델은 순차적으로 입력된 자료를 바탕으로 다음에 나올 단어나 문자를 예측하는 모델로 언어처리나 음성인식 분야에 활용된다. 최근 딥러닝 알고리즘이 발전되면서 입력 개체 간의 의존성을 효과적으로 반영할 수 있는 순환신경망 모델과 이를 발전시킨 Long short-term memory(LSTM) 모델이 언어모델에 사용되고 있다. 이러한 모형에 자료를 입력하기 위해서는 문장을 단어 혹은 형태소로 분해하는 과정을 거친 후 단어 레벨 혹은 형태소 레벨의 모형을 사용하는 것이 일반적이다. 하지만 이러한 모형은 텍스트가 포함하는 단어나 형태소의 수가 일반적으로 매우 많기 때문에 사전 크기가 커지게 되고 이에 따라 모형의 복잡도가 증가하는 문제가 있고 사전에 포함된 어휘 외에는 생성이 불가능하다는 등의 단점이 있다. 특히 한국어와 같이 형태소 활용이 다양한 언어의 경우 형태소 분석기를 통한 분해과정에서 오류가 더해질 수 있다. 이를 보완하기 위해 본 논문에서는 문장을 자음과 모음으로 이루어진 음소 단위로 분해한 뒤 입력 데이터로 사용하는 음소 레벨의 LSTM 언어모델을 제안한다. 본 논문에서는 LSTM layer를 3개 또는 4개 포함하는 모형을 사용한다. 모형의 최적화를 위해 Stochastic Gradient 알고리즘과 이를 개선시킨 다양한 알고리즘을 사용하고 그 성능을 비교한다. 구약성경 텍스트를 사용하여 실험을 진행하였고 모든 실험은 Theano를 기반으로 하는 Keras 패키지를 사용하여 수행되었다. 모형의 정량적 비교를 위해 validation loss와 test set에 대한 perplexity를 계산하였다. 그 결과 Stochastic Gradient 알고리즘이 상대적으로 큰 validation loss와 perplexity를 나타냈고 나머지 최적화 알고리즘들은 유사한 값들을 보이며 비슷한 수준의 모형 복잡도를 나타냈다. Layer 4개인 모형이 3개인 모형에 비해 학습시간이 평균적으로 69% 정도 길게 소요되었으나 정량지표는 크게 개선되지 않거나 특정 조건에서는 오히려 악화되는 것으로 나타났다. 하지만 layer 4개를 사용한 모형이 3개를 사용한 모형에 비해 완성도가 높은 문장을 생성했다. 본 논문에서 고려한 어떤 시뮬레이션 조건에서도 한글에서 사용되지 않는 문자조합이 생성되지 않았고 명사와 조사의 조합이나 동사의 활용, 주어 동사의 결합 면에서 상당히 완성도 높은 문장이 발생되었다. 본 연구결과는 현재 대두되고 있는 인공지능 시스템의 기초가 되는 언어처리나 음성인식 분야에서 한국어 처리를 위해 다양하게 활용될 수 있을 것으로 기대된다.