본 연구는 야생조류 생물음향 탐지와 현장 조사의 결과 비교를 통해 효과적인 야생조류 조사 방법을 제시하는데 목적이 있다. 연구대상지는 제주 동백동산과 1100고지 습지였다. 생물음향 탐지 기간은 2020년 12개월간이었다. 생물음향 탐지는 시간당 1분씩, .wav, 44,100Hz 포맷으로 생물음향 측정장비(Song meter SM4)를 각 연구대상지에 설치하여 녹음하였다. 현장 조사는 Banjade et al. (2019)의 ｢제주 동백동산과 1100고지 습지의 조류군집 장기간 동향｣ 결과를 인용하였다. 연구결 과는 다음과 같다. 첫 번째, 생물음향 탐지 기법을 통해 확인된 조류상은 동백동산에서 29과 46종, 1100고지 습지에서 16과 25종이었다. 두 번째, 울음 빈도를 통해 나타난 종 구성은 동백동산에서 직박구리(33.62%), 섬휘파람새(12.13%), 동박새(9.77%) 순으로 나타났고, 1100고지 습지에서 큰부리까마귀(27.34%), 섬휘파람새(19.43%), 직박구리(16.56%) 순으 로 나타났다. 세 번째, 동백동산에서 실시한 현장 조사의 경우 2009년 39종, 2012년 51종, 2015년 35종, 2018년 45종이 조사되었고, 생물음향 탐지를 통해 46종이 확인되었다. 1100고지 습지에서 실시한 현장 조사의 경우 2009년 37종, 2012년 42종, 2015년 34종, 2018년 38종이 조사되었고, 생물음향 탐지를 통해 25종이 확인되었다. 종합적으로 현장 조사를 통해 동백동산에서 확인된 78종 중 43.6%인 34종, 1100고지 습지에서 확인된 47종 중 38.3%인 18종이 생물음향 탐지로 확인되었 다. 네 번째, 현장 조사에서 확인되지 않았지만, 생물음향 탐지를 통해 확인된 종은 동백동산에서 9과 12종, 1100고지 습지에서 3과 7종으로 대표적으로 소쩍새, 솔부엉이와 같은 야행성 조류, 쇠유리새, 울새와 같은 나그네새, 도래하는 개체수가 상대적으 로 적은 황여새, 솔잣새와 같은 겨울철새가 있다. 다섯 번째, 현장 조사에서 확인된 종 중에서 생물음향 탐지를 통해 확인되지 않은 종은 동백동산에서 18과 48종, 1100고지 습지에서 14과 27종으로 대표적으로 백로과, 수리과, 솔딱새과가 있다. 본 연구는 국내에서 생물음향 장비 운용을 통한 조류 연구가 활성화되고 있는 가운데 현장 조사와 생물음향 탐지를 병행했을 때 효과적인 조사를 실시할 수 있다는 기초 자료를 작성했다는 점에서 의미가 있다.

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

PURPOSES : Traffic volume, an important basic data in the field of road traffic, is collected from traffic survey equipment installed at certain locations, which sometimes results in missing traffic volume data and abnormal detection. Therefore, this study presents various missing correction techniques using traffic characteristic analysis to obtain accurate traffic volume statistics. METHODS : The fundamental premise behind the development of a traffic volume correction and prediction model is to set the corrected data as the reference value, and the traffic volume correction and prediction process for the outliers and missing values in the raw data were performed based on the set values. RESULTS : The simulation results confirmed that the algorithm combining seasonal composition, quantile AD, and aggregation techniques showed a detection performance of more than 91% compared with actual values. CONCLUSIONS : Raw data collected due to difficulties faced by traffic survey equipment will result in missing traffic volume data and abnormal detection. If these abnormal data are used without appropriate corrections, it is difficult to accurately predict traffic demand. Therefore, it is necessary to improve the accuracy of demand prediction through characteristic analysis and the correction of missing data or outliers in the traffic data.

PURPOSES : This study analyzes the estimated traffic volumes on roads and railways based on econometrics. METHODS : The accuracy of traffic forecasting was analyzed based on the average difference between predicted and actual values. This study distinguishes itself from existing literature by conducting a comparative analysis categorized by project type. In this study, econometric analyses, including bias and efficiency evaluation, were conducted for 308 projects in Korea. RESULTS : We conducted econometric analysis by dividing the data into project types. This study examines the accuracy of estimates in South Korea's road and railway projects concerning various factors, including project types (mobility-focused or accessibility-focused), implementing agencies, and the performance of preliminary feasibility studies. Notably, it identifies a tendency for overestimation, particularly in railway projects and mobility-focused road projects, such as expressways and national highways, as well as in projects executed by local governments. The mean percentage error (MPE) for the analyzed projects was -46.62%, indicating a significant overestimation bias with resulting inefficiencies. However, our analysis revealed that road projects, particularly those accompanied by preliminary feasibility studies and implemented by the central government, exhibited reduced bias and improved efficiency. The presence or absence of preliminary feasibility studies significantly influenced estimation bias. Interestingly, even when preliminary feasibility studies are conducted, the choice of the implementing agency remains a crucial factor affecting estimation bias. In addition, railway projects continue to demonstrate a notable overestimation bias, warranting further attention. CONCLUSIONS : Considering bias, efficiency, and MPE is advisable when forecasting traffic.

PURPOSES : The purpose of this study is to derive dropout rates according to various international roughness index (IRI) specifications using ProVAL, develop a comparative methodology, and indirectly assess the level of road management in each country. METHODS : Based on a literature review, the IRI specifications for each country were collected, and the ProVAL analysis tool was used to compare and analyze dropout rates according to each specification. Thus, the dropout rate rankings for each country were calculated. Additionally, by analyzing the correlation between dropout rates according to each threshold, a model was created to convert the threshold between the most commonly used baseline distances of 100 m and 161 m. RESULTS : Dropout rates were derived according to the standards of each country and rankings were assigned. Comparing 51 standards, the IRI level of New Mexico appeared to be the highest, whereas the domestic specifications ranked 36th. A model was created to convert the threshold between the standard distances of 100 m and 161 m. CONCLUSIONS : This study objectively assessed the roughness standards in various countries using the dropout rate and IRI ranking specifications. The highest specification was found for the asphalt of New Mexico in the USA, with the domestic specification ranking 36th. A model that converts the thresholds between the most commonly used baseline distances of 100 m and 161 m was developed, with slight differences across sections. For a precise conversion, individual models may be required for each section.

담관 결석은 폐쇄성 황달, 담도염 및 췌장염 등 합병증을 일으킬 수 있으며, ERCP가 1차 치료로 권유된다. 그러나 ERCP 후 부작용의 발생이 적지 않은 만큼, 이를 최소화하기 위해서는 시술 전 담석의 크기 및 개수, 환자의 출혈성 경향 등 위험인자의 확인이 필요하며, 이에 따른 담석 제거를 위한 적절한 방법이 무엇인지에 이해가 필수적이다. 또한 풍선도관이나 바스켓과 같은 부속기구의 장, 단점 및 적응증 그리고 부작용 대처법에 대해서 충분한 숙지가 필요하다.

In this study, in order to develop a method to efficiently inject essential nutrients necessary for plant growth into wood chips, which are simply used as soil covering materials in the agriculture, landscaping and horticultural industries, the atmospheric pressure dipping method and the vacuum pressure impregnating method are used to improve the plant nutrients injectability and impregnation amount were comparatively analyzed. Nutrient ingredients and 8 major heavy metal contents of wood chips injected with nutrients were analyzed, and soil covering effects were examined by covering wood chips injected with nutrients on soil. Comparing the dipping method and the vacuum pressure impregnation method, it took about 48 hours or more to inject 1,500 g or more of the nutrient aqueous solution into 1 kg of wood chips in the dipping method, but the vacuum pressure impregnation method could be impregnated in about 5 minutes. Components of the impregnated nutrients were detected in proportion to the diluted concentration. As a result of covering the wood chips developed in this study on soil, they showed weakly acidic pH, and the heat insulation and moisturizing effects during the winter season were evaluated to be superior to those of uncovered soil. In the future, wood chips impregnated with nutrients are expected to contribute to the more efficient use of waste wood resources and the long-term supply of nutrients essential for plant growth, reducing excessive use of chemical fertilizers and reducing costs.

PURPOSES : Recently, corrosion and deterioration of highway facilities have been increasing owing to the excessive use of deicers. This study aimed to find an optimal snow removal method to develop countermeasures for the problem of excessive deicer use and improve the efficiency of snow removal. METHODS : Theoretical investigations and experiments related to deicing were conducted to determine the differences between deicing chemical types and states. Based on regional weather patterns, the entire country was categorized into four groups: warm and heavy snow, warm and light snow, cold and heavy snow, and cold and light snow, and matched with each regional office of the Korea Expressway Corporation. RESULTS : Optimal snow removal methods were proposed considering regional characteristics and deicing chemical types and states. CONCLUSIONS : Different deicer types were proposed according to the region type, such as using only salt and salt/calcium chloride in the warm and cold regions, respectively. Second, plowing was more effective at lower temperatures. Third, liquid deicer spraying could reduce the amount of deicer used. A liquid deicer is suitable for preliminary spraying because its quick-acting properties are superior to those of a solid deicer, although its lasting properties are inferior to those of the solid deicer.

기후변화 영향으로 이상고수온, 태풍, 홍수, 가뭄 등 재난 및 안전 관리기술은 지속적으로 고도화를 요구받고 있으며, 특히 해 수면 온도는 한반도 주변에서 발생되는 여름철 적조 발생과 동해안 냉수대 출현, 소멸 등에 영향을 신속하게 분석할 수 있는 중요한 인자 이다. 따라서, 본 연구에서는 해수면 온도 자료를 해양 이상현상 및 연구에 적극 활용되기 위해 통계적 방법과 딥러닝 알고리즘을 적용하 여 예측성능을 평가하였다. 예측에 사용된 해수면 수온자료는 흑산도 조위관측소의 2018년부터 2022년까지 자료이며, 기존 통계적 ARIMA 방법과 Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU)을 사용하였고, LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence(s2s) 구조에 Attention 기법을 추가한 Attention Long Short-Term Memory (LSTM)기법을 사용하여 예측 성능 평가를 진행하 였다. 평가 결과 Attention LSTM 모델이 타 모델과 비교하여 더 좋은 성능을 보였으며, Hyper parameter 튜닝을 통해 해수면 수온 성능을 개 선할 수 있었다.