• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.116-122
• /
• 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
• /
• v.31 no.2
• /
• pp.121-128
• /
• 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
• /
• v.32 no.1
• /
• pp.120-128
• /
• 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.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1400-1407
• /
• 2019

In this paper, we propose a support vector machine (SVM) based gas classifier that can support real-time self-learning. The modified sequential minimal optimization (MSMO) algorithm is employed to train the proposed SVM. By using a shared structure for learning and classification, the proposed SVM reduced the hardware area by 35% compared to the existing architecture. Our system was implemented with 3,337 CLB (configurable logic block) LUTs (look-up table) with Xilinx Zynq UltraScale+ FPGA (field programmable gate array) and verified that it can operate at the clock frequency of 108MHz.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.34 no.5
• /
• pp.46-55
• /
• 2006

In general, 3-dimensional point-mass equation has been widely used for the trajectory optimization of the fixed-wing aircraft and reentry vehicle. But it should be modified and represent target vehicle's own characteristics. For a lighter-than-air vehicle such as an airship, there exists different and peculiar flight characteristics compared with the aircraft. The first part of this paper is to derive the dynamic equation of motion for the mid-altitude unmanned airship and the second part is to obtain the optimal trajectories under the minimal time flight given constraints. The trajectory optimization problem is converted into the nonlinear programming problem using Sequential Quadratic Programming approach. Finally numerical solutions are presented in the last part of the paper.

• Nuclear Engineering and Technology
• /
• v.29 no.3
• /
• pp.211-217
• /
• 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
• /
• v.13 no.2
• /
• pp.40-50
• /
• 2000

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. The buckling load 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. Gradient structures have also shown an advantage in the weight reduction of composites compared with the conventional homogeneous structures.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04b
• /
• pp.592-594
• /
• 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.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 1999.11a
• /
• pp.68-72
• /
• 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
• /
• v.36 no.4
• /
• pp.686-689
• /
• 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.