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The seismic design according to the DBD(displacement based design) procedure is initiated by specifying a target displacement, while the elastic response acceleration is determined in the first instant to obtain the design force in the current FBD(force based design) procedure. The main difference in between FBD and DBD can be stated that the strength and stiffness are not the design parameters but the outcomes at the end in DBD procedure. The advantage of DBD over FBD is that it only requires the linear static analysis when formulating the design procedure but it can show the yield displacement, ultimate displacement, ductility and design yield moments. This study proposes a seismic design procedure for MDOF steel structure, utilizing this advantage of DBD. This study shows that the ultimate displacement of the structures can be estimated successfully by DBD procedure and target displacement and the story height are the only initial design parameters. It also reveals that drift ratio is the key parameter in DBD procedure affecting ductility, the yield displacement, damping and fundamental period for the structure.
The the FSB concept is modified and new type of hybrid energy dissipation device, the Active Friction Slip Braces (AFSB), is described. The FSB is by far improved in the AFSB by inclusion of an active clamping mechanism on the friction interface. The results of Single-Degree-of Freedom(SDOF) structure have indicated that the action of dissipating vibrational energy in the AFSB impacts on the response at later cycles by keeping the drift amplitudes at much lower levels, revealing overshooting problem due to its early slippage, and the problem has been taken cared by modifying the algorithm successfully shown in the previous publication in December 2016. It has also shown that providing predetermined constant incremental strengths to the building by AFSB members with the proposed algorithm for a SDOF structure has improved the responses by reducing drift amplitudes and base shear under small and medium amplitude ground accelerations. In this study, the algorithm is tested on the Multiple-Degree-of-Freedom(MDOF) structure to verify the effectiveness of AFSB supplemented by the algorithm. A six story steel building which is the prototype building for part of the US-Japan Corporative Research Program is selected as an example building. The building is designed and modeled as an FSB and ASFB building, and subjected to a series of ground motions. The response envelopes of both buildings are obtained and compared to evaluate the design implication of AFSB applications.
Biomass contains cellulose, xylan and lignin in a complex interwoven structure that hinders enzymatic hydrolysis of the cellulose. To separate these components in yellow poplar biomass, we sequentially pretreated with dilute sulfuric acid and enzymatically-generated peracetic acid. In the first step, the dilute acid with microwave heating (140oC, 5 min) hydrolyzed 90% of xylan. The xylose yield in hydrolysate after dilute acid pretreatment was 83.1%. In the second step, peracetic acid (60oC, 6 h) removed up to 80% of lignin. This sequential pretreatment fractionated biomass into xylan and lignin, leaving a solid residue enriched in cellulose (~80%). The sequential pretreatment enhanced enzymatic digestibility of the cellulase by removal of the other components in biomass. The glucose yield after enzymatic hydrolysis was 90.5% at a low cellulase loading (5 FPU/g of glucan), which is 1.6 and 18 times higher than for dilute acid-pretreated biomass and raw biomass, respectively. This novel sequential pretreatment with dilute acid and peracetic acid efficiently separates the three major components of yellow poplar biomass, and reduces the amount of cellulase needed.
메모리 기반 추론 기법은 단순히 학습패턴이나 대표패턴의 형태로 메모리에 저장하며 테스트 패턴과의 거리 계산을 통하여 분류한다. 이 기법의 가장 큰 문제점은 학습 패턴 전체를 메모리에 저장하거나 학습 패턴들을 대표 패턴으로 대체하는 방법을 사용함으로 다른 기계학습 방법에 비하여 많은 메모리 공간을 필요로 하며, 저장되는 학습패턴이 증가할수록 분류에 필요한 시간도 많이 소요된다는 단점을 갖는다. 본 논문은 효율적인 메모리 사용과 분류 성능의 향상을 위한 EAS 기법을 제안하였다. 즉, 학습패턴에 대해 분할공간을 생성한 후 생성된 각 분할공간을 MDL기법과 PM기법으로 평가하였다. 그리고 평가 결과 가장 우수한 분할공간만을 취하여 대표패턴으로 삼고 나머지는 다시 분할하여 평가를 반복하는 기법이다. UCI Machine Learning Repository에서 벤치마크 데이터를 발췌한 실험 자료를 사용하여 제안한 기법의 성능과 메모리 사용량에 있어 우수함을 입증하였다. The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.
중첩형 일반화 사례 (NGE, Nested Generalized Exemplar) 기법은 거리 기반 분류를 최적 일치 규칙으로 사용하며, 노이즈에 대한 내구력을 증가시켜 주는 동시에 모델 크기를 감소시키는 장점이 있다. NGE 학습 중 생성된 교차(cross)나 중첩(overlap) 현상은 분류성능을 저해하는 요인으로 작용한다. 따라서 본 논문은 NGE 학습 중 생성된 교차나 중첩 현상이 발생한 초월 평면에대해 상호정보가 가장 큰 구간을 분리하여, 새로운 초월평면을 구성하게 하여, 분류성능 향상시키고 초월평면의 개수를 감소시키는 기법인 DHGen(Dominant Hyperrectangle Generation) 알고리즘을 제안하였다. 제안한 DHGen은 분류성능면에서 kNN과 유사하고 NGE이론으로 구현한 EACH보다 우수함을 UCI Machine Learning Repository에서 벤치마크데이터를 발췌한 실험자료로 입증하였다. NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.
The performance of a multi-head, computerized combination scaling system to automatically identify a group of agricultural products having a total weight within the target range has been optimized to reduce the package cycle time of the merchandise. First, the structure of the scale was modified to enable faster measurement by enhancing the dynamic stability during the process. Second, the high frequency noise in the measured signal was eliminated by a high frequency filter to provide more accurate weight data. Finally, the algorithm to identify a group of products with a total weight within the target range was modified to enable a user to select an optimal number of scales. According to the experimental verifications, this modified system reduced the package cycle time significantly and also was accurate in measuring the total weight of the selected products.