• Structural Engineering and Mechanics
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• 제86권5호
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• 한국음향학회지
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• 제28권7호
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• pp.674-679
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• 2009

본 논문은 골프 동영상에 포함된 오디오 정보로부터 검출된 이벤트 사운드 구간과 골프 선수이름이 포함된 음성구간을 결합하여 선수별 이벤트 구간을 검색하는 방식을 제안한다. 전체적인 시스템은 동영상으로부터 분할된 오디오 스트림으로부터 잡음제거, 오디오 구간분할, 음성 인식 등의 과정을 통한 자동색인 모듈과 사용자가 텍스트로 입력한 선수 이름을 발음열로 변환하고, 색인된 데이터베이스에서 질의된 선수 이름과 상응하는 음성구간과 연결되는 이벤트 구간을 찾아주는 검색 모듈로 구성된다. 선수이름 검색을 위해서 본 논문에서는 음소 기반, 단어 기반, 단어와 음소를 결합한 하이브리드 방식을 적용한 선수별 이벤트 구간 검색결과를 비교하였다.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• 한국정보통신학회논문지
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• 제20권9호
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• pp.1763-1770
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• 2016

본 논문에서는 넓은 출력 부하 전력에서도 동작하는 하이브리드 변환기를 제안했다. 스위치드 커패시터 변환기는 높은 부하 전력에서 효율이 낮고, 낮은 부하 전력에서는 효율이 높다. 반대로, 벅 변환기는 높은 부하 전력에서는 효율이 높고, 낮은 부하 전력에서는 효율이 낮다. 제안된 하이브리드 변환기는 스위치드 커패시터 변환기와 벅 변환기를 혼합했다. 낮은 부하 전력에서는 스위치드 커패시터 변환기를 동작시키고, 높은 부하에서는 벅 변환기를 동작시켜, 넓은 출력 부하 전력에서 전력 효율을 향상시켰다. 제안된 하이브리드 변환기는 $0.18{\mu}m$ CMOS 공정으로 구현되었다. 하이브리드 변환기의 출력 부하 전력 범위는 0.05~100mW 이며, 벅 변환기와 스위치드 커패시터 변환기에서 각각 93%와 77%의 최대 전력 효율을 가진다.

• 한국산학기술학회논문지
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• 제17권8호
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• pp.582-594
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• 2016

그 동안 하이브리드 버스에 대한 연구로 플러그인 하이브리드, 직렬형, 병렬형 하이브리드 등에 대한 연구가 많이 진행되어져 왔다. 하지만 연구가 진행된 대부분의 차량들은 대형 버스이며 현재 국내에는 중형저상버스에 대한 연구는 전무한 실정이다. 또한 중형저상버스의 하이브리드화에 대한 연구 역시 미미한 실정이다. 본 논문은 MATLAB을 이용한 시뮬레이션을 통해 디젤 중형저상버스의 연비 평가를 수행하였으며, 이를 하이브리드화하였을 경우에 대한 최적의 용량 조합과 기어비를 제시하고 내연기관 시뮬레이션 연비 결과와 비교 분석하였다. 하이브리드화를 위한 구조로 전륜과 후륜이 독립적으로 동력을 전달하는 병렬형 하이브리드 시스템을 선택하였다. 동력원 용량 설계를 위해 목표 성능을 만족하는 요구파워를 계산하여 적용 가능한 동력원 용량 영역을 설계하였다. 설계 영역을 만족하는 각 단품들의 용량은 스케일링하여 구성하였으며, 엔진과 모터에 대한 동력 전달계의 용량 설계 알고리즘을 제시하고 동적 계획법을 이용하여 최적화를 수행하였다. 최종적으로 본 연구를 통해 내연기관 차량인 중형저상버스를 하이브리드화하였을 경우에 대한 연비 향상률과 최적의 동력원 용량, 기어비를 제시하였다.

• 한국균학회지
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• 제39권2호
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• pp.99-104
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• 2011

표고버섯의 육종은 두 개의 다른 모균주에 의한 교배와 버섯생산에 좋은 형질을 지닌 교잡균주의 선발이 요구된다. 본 연구에서는 서로 다른 두 계통의 표고 모균주에서 유래한 단핵균주와 이들 단핵균주간의 교배로부터 만들어진 교잡균주에 대하여 발색반응 배지를 이용하여 세포외 분해효소의 분비능력 정도를 비교하여 생화학적 특성이 우수한 균주를 선발할 가능성에 대하여 조사하였다. 모균주로부터 유래한 단핵균주들 간에 ${\beta}$-glucosidase, avicelase, CM-cellulase, amylase, pectinase, xylanase, protease의 분비능력에 차이가 있음을 확인하였다. 교잡균주에 있어서도 단핵균주의 조합에 따라 효소 분비능력이 달라지는 것을 볼 수 있었다. 이에 따라 발색반응배지를 이용한 세포외효소 평가법이 표고의 육종과정 중에 생성되는 교잡균주들의 평가에 활용 할 수 있을 것으로 사료된다.

• Bulletin of the Korean Chemical Society
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• 제33권10호
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• pp.3233-3240
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• 2012

We demonstrated the combined applications of online protein digestion using trypsin immobilized enzyme reactor (IMER) and dual tandem mass spectrometry with collisionally activated dissociation (CAD) and electron transfer dissociation (ETD) for tryptic peptides eluted through the trypsin-IMER. For the trypsin-IMER, the organic and inorganic hybrid monolithic material was used. By employing the trypsin-IMER, the long digestion time could be saved with little or no sacrifice of the digestion efficiency, which was demonstrated for standard protein samples. For three model proteins (cytochrome c, carbonic anhydrase, and bovine serum albumin), the tryptic peptides digested by the IMER were analyzed using LC-MS/MS with the dual application of CAD and ETD. As previously shown by others, the dual application of CAD and ETD increased the sequence coverage in comparison with CAD application only. In particular, ETD was very useful for the analysis of highly-protontated peptide cations, e.g., ${\geq}3+$. The combination approach provided the advantages of both trypsin-IMER and CAD/ETD dual tandem mass spectrometry applications, which are rapid digestion (i.e., 10 min), good digestion efficiency, online coupling of trypsin-IMER and liquid chromatography, and high sequence coverage.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2012년도 춘계학술대회
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• pp.110-112
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• 2012

Education and training of technical staff for the new generation technological equipment has a vital importance for developing countries in order to maintain sustainable development and keep up with new generation technologies. Mechatronics the synergistic combination of mechanical, electronic and software engineering from an engineering perspective to serve the purposes of controlling advanced hybrid systems is a product of new generation technology which can be given as a sample to this phenomenon. Recent marine engines are combination of heavy industry with high technology. Nowadays, ships are built in full automation and equipped with computer controlled mechatronics systems. However, finding qualified officer and engineers who can operate, maintain, control and repair when it is required such full automatic systems with knowledge of a new generation system became a serious issue. Due to importance of this fact STCW Manila amendment has new training and certification requirements for electro-technical officers. In this paper, Mechatronics applications on ship are introduced, importance of it highlighted and a new syllabus is proposed for the training of marine engineers to be nurtured with mechatronics knowledge as it is required.

• 한국경영과학회지
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• 제22권3호
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.