• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• ETRI Journal
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• 제31권2호
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• pp.121-128
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• 2009

In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for structured SVM problems. FSMO is conceptually simple, easy to implement, and faster than the standard support vector machine (SVM) training algorithms for structured SVM problems. Because FSMO uses the fact that the formulation of structured SVM has no bias (that is, the threshold b is fixed at zero), FSMO breaks down the quadratic programming (QP) problems of structured SVM into a series of smallest QP problems, each involving only one variable. By involving only one variable, FSMO is advantageous in that each QP sub-problem does not need subset selection. For the various test sets, FSMO is as accurate as an existing structured SVM implementation (SVM-Struct) but is much faster on large data sets. The training time of FSMO empirically scales between O(n) and O($n^{1.2}$), while SVM-Struct scales between O($n^{1.5}$) and O($n^{1.8}$).

• ETRI Journal
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• 제32권1호
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• pp.120-128
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• 2010

In this paper, we describe a modified fixed-threshold sequential minimal optimization (FSMO) for 1-slack structural support vector machine (SVM) problems. Because the modified FSMO uses the fact that the formulation of 1-slack structural SVMs has no bias, it breaks down the quadratic programming (QP) problems of 1-slack structural SVMs into a series of smallest QP problems, each involving only one variable. For various test sets, the modified FSMO is as accurate as existing structural SVM implementations (n-slack and 1-slack SVM-struct) but is faster on large data sets.

• 전기전자학회논문지
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• 제23권4호
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• pp.1400-1407
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• 2019

본 논문은 실시간 자가학습과 분류 기능을 모두 지원하는 support vector machine (SVM) 기반 가스 분류기의 하드웨어 구조 설계 및 구현 결과를 제시한다. 제안된 가스 분류기는 학습 알고리즘으로 modified sequential minimal optimization(MSMO)을 사용하였고, 학습과 분류 기능을 공유구조를 사용하여 설계함으로써 기존 논문 대비 하드웨어 면적을 35% 감소시켰다. 설계된 가스 분류기는 Xilinx Zynq UltraScale+ FPGA를 사용하여 구현 및 검증되었고, 108MHz의 동작 주파수에서 3,337개의 CLB LUTs로 구현 가능함을 확인하였다.

• 한국항공우주학회지
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• 제34권5호
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• pp.46-55
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• 2006

비행체의 궤적 최적화를 위해서는 비행체의 특성을 반영한 3차원 운동방정식이 유도되어야 하며, 비행선과 같이 공기보다 가벼운 비행체의 경우는 일반 고정익 항공기와는 다른 특성들을 반영하여야 한다. 본 연구에서는 중고도 무인비행선의 궤적 최적화를 위해 비행선의 운동방정식을 유도하고, 이를 이용한 최소시간 문제를 다루었다. 최적 궤적을 얻기 위하여 최적 궤적 문제를 제어입력 파라미터화를 이용한 비선형 프로그래밍 문제로 변환한 후 연속 2차 계획법을 이용하여 궤적을 산출하였으며, 이에 대한 수치결과를 나타내었다.

• Nuclear Engineering and Technology
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• 제29권3호
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• pp.211-217
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• 1997

Adverse effects caused by the surveillance test for the components of nuclear power plant involve plant transients, unnecessary wear, burden on licensee's time, and the radiation exposure to personnel along with the characteristics of each component. The optimization methodology of STI and AOT has been developed and applied to AFWPs of a reference plant. The approach proposed in this paper consist of the resole in minimal mean unavailability of the two-out-of-four system with adverse effects are analytically calculated for the example system. The surveillance testing strategy are given by the sequential test, the staggered test and the train staggered test which is a mined test scheme. In the system level, the sensitivity analyses for the STI and AOT, are performed for the measure of the system unavailability of the top event in the fault tree developed for the example system. This methodology may contribute to establishing the basis for the risk-based regulations.

• Composites Research
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• 제13권2호
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• pp.40-50
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• 2000

종횡비가 다른 적층복합재료에 경사형 구조를 도입하고, 이것이 일방향으로의 하중을 받을 때의 좌굴특성을 최대화하기 위해서 복합재료의 각 층에서의 섬유부피분율과 두께를 변수로 sequential linear programming method를 이용하여 최적화 하였다. 이로부터 좌굴특성을 최대화 할 수 있는 최적구조를 제안하였다. 적층복합재료는 종횡비의 영향이 커서 종횡비가 1보다 작은 경우는 최외각층의 섬유부피분율을 최대화하는 방향으로 최적화가 이루어졌으나 종횡비가 2인 경우는 각층에서의 섬유부피분율과 두께비가 어느 정도 균형을 이루는 형태로 최적화가 이루어 졌다. 경사형 구조는 전통적인 균일구조의 복합재료에 비해서 섬유부피와 복합재료의 무게 절감에 큰 효과를 가지는 것으로 확인되었다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (B)
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• pp.592-594
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• 2004

본 논문에서는 SVM의 효과적인 학습 알고리즘인 SMO(Sequential Minimal Optimization)를 하드웨어적으로 설계하고 구현하는 방법을 제시한다. SVM은 Vapnik에 의한 제안된 기계학습 방법으로 음성인식, 문자인식, BT, 보안 등 다양한 응용분야에서 기존의 신경망보다 우수한 성능을 나타내었다. 그러나 SVM은 계산량이 많아 연산속도가 느려지는 단점을 가진다. 이를 개선하기 위해 본 논문에서는 SVM의 학습 알고리즘인 SMO의 핵심인 지수함수와 실수 연산기를 VHDL로 설계하고 Mentor의 ModelSim을 이용하여 시뮬레이션하고 Synopsys의 Design Analyzer를 이용하여 합성하였다. 구현된 칩은 시뮬레이션 결과 약 50MHz의 속도로 동작하며, 이는 소프트웨어적으로 구현된 SMO보다 약 10～20배 빠른 성능을 나타내었다.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 1999년도 추계학술발표대회 논문집
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• pp.68-72
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• 1999

In an effort to construct a structure under the design principle of minimal use of materials for maximum performances, a discrete gradient structure has been introduced in laminate composite systems. Using a sequential linear programming method, the gradient structure of composites to maximize the buckling load was optimized in terms of fiber volume fraction and thickness of each layer. Theoretical optimization results were then verified with experimental ones. The buckling load of laminate composite showed maximum value with the outmost [$0^{\circ}$] layer concentrated by almost all the fibers when the ratio of length to width(aspect ratio) was less than 1.0. But when the aspect ratio was 2.0, the optimum was determined in a structure where the thickness and fiber volume fraction were well balanced in each layer. From the optimization of gradient structure, the optimal fiber volume fraction and thickness of each layer were proposed. Experimental results agreed well with the theoretical ones. Gradient structures have also shown an advantage in the weight reduction of composites compared with the conventional homogeneous structures.

• ETRI Journal
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• 제36권4호
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• pp.686-689
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• 2014

This paper studies energy-efficiency (EE) power allocation for cognitive radio MIMO-OFDM systems. Our aim is to minimize energy efficiency, measured by "Joule per bit" metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non-convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy-efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.