• Title/Summary/Keyword: optimizations

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Performance Analysis on Intelligent Reflecting Surface Transmission for NOMA Towards 6G Systems

  • Chung, Kyuhyuk
    • International Journal of Advanced Culture Technology
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    • v.10 no.2
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    • pp.220-224
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    • 2022
  • The efficiencies of rates and energy in the fifth generation (5G) wireless channels can be improved via intelligent reflecting surface (IRS) transmissions, towards the sixth generation (6G) mobile communications. While previous works have considered mainly optimizations of IRS transmissions, we propose a performance analysis on the total power in terms of the number of reflecting devices for IRS transmissions in non-orthogonal multiple access (NOMA) networks. First, we derive an analytical expression of the total power gain factor in terms of the number of reflecting devices for the cell-edge user in IRS-NOMA systems. Then we evaluate how many reflecting devices we need to obtain a total power gain in dB. Moreover, we also demonstrate numerically the signal-to-noise ratio (SNR) gain of the IRS-NOMA system over the conventional NOMA system based on the achievable data rate.

Burnable poison optimized on a long-life, annular HTGR core

  • Sambuu, Odmaa;Terbish, Jamiyansuren
    • Nuclear Engineering and Technology
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    • v.54 no.8
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    • pp.3106-3116
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    • 2022
  • The present work presents analysis results of the core design optimizations for an annular, prismatic High Temperature Gas-cooled Reactor (HTGR) with passive decay-heat removal features. Its thermal power is 100 MWt and the operating temperature is 850 ℃ (1123 K). The neutronic calculations are done for the core with heterogeneous distribution of fuel and burnable poison particles (BPPs) to flatten the reactivity swing and power peaking factor (PPF) during the reactor operation as well as for control rod (CR) insertion into the core to restrain a small excess reactivity less than 1$. The next step of the study is done for evaluation of core reactivity coefficient of temperature.

Simulating the performance of the reinforced concrete beam using artificial intelligence

  • Yong Cao;Ruizhe Qiu;Wei Qi
    • Advances in concrete construction
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    • v.15 no.4
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    • pp.269-286
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    • 2023
  • In the present study, we aim to utilize the numerical solution frequency results of functionally graded beam under thermal and dynamic loadings to train and test an artificial neural network. In this regard, shear deformable functionally-graded beam structure is considered for obtaining the natural frequency in different conditions of boundary and material grading indices. In this regard, both analytical and numerical solutions based on Navier's approach and differential quadrature method are presented to obtain effects of different parameters on the natural frequency of the structure. Further, the numerical results are utilized to train an artificial neural network (ANN) using AdaGrad optimization algorithm. Finally, the results of the ANN and other solution procedure are presented and comprehensive parametric study is presented to observe effects of geometrical, material and boundary conditions of the free oscillation frequency of the functionally graded beam structure.

SEQUENTIAL MINIMAL OPTIMIZATION WITH RANDOM FOREST ALGORITHM (SMORF) USING TWITTER CLASSIFICATION TECHNIQUES

  • J.Uma;K.Prabha
    • International Journal of Computer Science & Network Security
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    • v.23 no.4
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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.

Aerodynamic shape optimization of a high-rise rectangular building with wings

  • Paul, Rajdip;Dalui, Sujit Kumar
    • Wind and Structures
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    • v.34 no.3
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    • pp.259-274
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    • 2022
  • The present paper is focused on analyzing a set of Computational Fluid Dynamics (CFD) simulation data on reducing orthogonal peak base moment coefficients on a high-rise rectangular building with wings. The study adopts an aerodynamic optimization procedure (AOP) composed of CFD, artificial neural network (ANN), and genetic algorithm (G.A.). A parametric study is primarily accomplished by altering the wing positions with 3D transient CFD analysis using k - ε turbulence models. The CFD technique is validated by taking up a wind tunnel test. The required design parameters are obtained at each design point and used for training ANN. The trained ANN models are used as surrogates to conduct optimization studies using G.A. Two single-objective optimizations are performed to minimize the peak base moment coefficients in the individual directions. An additional multiobjective optimization is implemented with the motivation of diminishing the two orthogonal peak base moments concurrently. Pareto-optimal solutions specifying the preferred building shapes are offered.

Parallel Implementation of Scrypt: A Study on GPU Acceleration for Password-Based Key Derivation Function

  • SeongJun Choi;DongCheon Kim;Seog Chung Seo
    • Journal of information and communication convergence engineering
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    • v.22 no.2
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    • pp.98-108
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    • 2024
  • Scrypt is a password-based key derivation function proposed by Colin Percival in 2009 that has a memory-hard structure. Scrypt has been intentionally designed with a memory-intensive structure to make password cracking using ASICs, GPUs, and similar hardware more difficult. However, in this study, we thoroughly analyzed the operation of Scrypt and proposed strategies to maximize computational parallelism in GPU environments. Through these optimizations, we achieved an outstanding performance improvement of 8284.4% compared with traditional CPU-based Scrypt computations. Moreover, the GPU-optimized implementation presented in this paper outperforms the simple GPU-based Scrypt processing by a significant margin, providing a performance improvement of 204.84% in the RTX3090. These results demonstrate the effectiveness of our proposed approach in harnessing the computational power of GPUs and achieving remarkable performance gains in Scrypt calculations. Our proposed implementation is the first GPU implementation of Scrypt, demonstrating the ability to efficiently crack Scrypt.

A concise overview of principal support vector machines and its generalization

  • Jungmin Shin;Seung Jun Shin
    • Communications for Statistical Applications and Methods
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    • v.31 no.2
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    • pp.235-246
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    • 2024
  • In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.

Compiler triggered C level error check (컴파일러에 의한 C레벨 에러 체크)

  • Zheng, Zhiwen;Youn, Jong-Hee M.;Lee, Jong-Won;Paek, Yun-Heung
    • The KIPS Transactions:PartA
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    • v.18A no.3
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    • pp.109-114
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    • 2011
  • We describe a technique for automatically proving compiler optimizations sound, meaning that their transformations are always semantics-preserving. As is well known, IR (Intermediate Representation) optimization is an important step in a compiler backend. But unfortunately, it is difficult to detect and debug the IR optimization errors for compiler developers. So, we introduce a C level error check system for detecting the correctness of these IR transformation techniques. In our system, we first create an IR-to-C converter to translate IR to C code before and after each compiler optimization phase, respectively, since our technique is based on the Memory Comparison-based Clone(MeCC) detector which is a tool of detecting semantic equivalency in C level. MeCC accepts only C codes as its input and it uses a path-sensitive semantic-based static analyzer to estimate the memory states at exit point of each procedure, and compares memory states to determine whether the procedures are equal or not. But MeCC cannot guarantee two semantic-equivalency codes always have 100% similarity or two codes with different semantics does not get the result of 100% similarity. To increase the reliability of the results, we describe a technique which comprises how to generate C codes in IR-to-C transformation phase and how to send the optimization information to MeCC to avoid the occurrence of these unexpected problems. Our methodology is illustrated by three familiar optimizations, dead code elimination, instruction scheduling and common sub-expression elimination and our experimental results show that the C level error check system is highly reliable.

Design of X-band Broadband Twist Reflector Using Hybrid Particle Swarm Optimization (Hybrid Particle Swarm Optimization 기법을 적용한 X-대역 광대역 편파 변환기 설계)

  • Hwang, Keum-Cheol
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.20 no.4
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    • pp.390-395
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    • 2009
  • Design and optimization of a broadband meander-line twist reflector was performed for X-band application. Based on the equivalent transmission line model, the polarization twist performance was evaluated. Genetic analysis, particles swarm, and hybrid swarm optimizations were employed to obtain the optimized geometrical parameters. The optimized design exhibits low cross-polarization level below - 25 dB between 8.45 and 11.38 GHz. The polarization twist loss was below 0.2 dB. Comparison between computed and simulated results was also discussed.

Hydrodynamic optimization of twin-skeg LNG ships by CFD and model testing

  • Kim, Keunjae;Tillig, Fabian;Bathfield, Nicolas;Liljenberg, Hans
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.6 no.2
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    • pp.392-405
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    • 2014
  • SSPA experiences a growing interest in twin skeg ships as one attractive green ship solution. The twin skeg concept is well proven with obvious advantages for the design of ships with full hull forms, restricted draft or highly loaded propellers. SSPA has conducted extensive hull optimizations studies of LNG ships of different size based on an extensive hull data base with over 7,000 models tested, including over 400 twin skeg hull forms. Main hull dimensions and different hull concepts such as twin skeg and single screw were of main interest in the studies. In the present paper, one twin skeg and one single screw 170 K LNG ship were designed for optimally selected main dimension parameters. The twin skeg hull was further optimized and evaluated using SHIPFLOW FRIENDSHIP design package by performing parameter variation in order to modify the shape and positions of the skegs. The finally optimized models were then built and tested in order to confirm the lower power demand of twin skeg designed compaed with the signle screw design. This paper is a full description of one of the design developments of a LNG twin skeg hull, from early dimensional parameter study, through design optimization phase towards the confirmation by model tests.