• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.

• Steel and Composite Structures
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• v.45 no.6
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• pp.877-894
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• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.86-93
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• 2004

The DCT algorithm needs an efficient hardware architecture to compute inner product. The conventional design method, like ROM-based DA(Distributed Arithmetic), has large hardware complexity. Because of this reason, a CSHM(Computation Sharing Multiplication) was proposed for implementing inner product by Park. However, the Park's CSHM has inefficient hardware architecture in the precomputer and select units. Therefore it degrades the performance of the multiplier. In this paper, we presents the optimization design method for inner product using CSHM algorithm and applied it to implementation of 1-D DCT processor. The experimental results show that the proposed multiplier is more efficient than Park's when hardware architectures and logic synthesis results were compared. The designed 1-D DCT processor by using proposed design method is more high performance than typical methods.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.12
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• pp.1539-1547
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• 1999

캐리-세이브 가산기 (CSA)는 회로 설계 과정에서 빠른 연산 수행을 위해 가장 널리 이용되는 연산기 중의 하나이다. 그러나, 현재까지 산업체에서 CSA를 이용한 설계는 설계자의 경험에 따른 수작업에 의존하고 있고 그 결과 최적의 회로를 만들기 위해 매우 많은 시간과 노력이 소비되고 있다. 이에 따라 최근 CSA를 기초로 하는 회로 합성 자동화 기법에 대한 연구의 필요성이 대두되고 있는 상황에서, 본 논문은 연산 속도를 최적화하는 효율적인 CSA 할당 알고리즘을 제안한다. 우리는 CSA 할당 문제를 2단계로 접근한다: (1) 연산식의 멀티 비트 입력들만을 고려하여 최소 수행 속도 (optimal-delay)의 CSA 트리를 할당한다; (2) (1)에서 구한 CSA 트리의 수행 속도 증가가 최소화 (minimal increase of delay) 되는 방향으로 CSA들의 캐리 입력 포트들에 나머지 싱글 비트 입력들을 배정한다. 실제 실험에서 우리의 제안된 알고리즘을 적용하여 연산식들의 회로 속도를 회로 면적의 증가 없이 상당한 수준까지 줄일 수 있었다.Abstract Carry-save-adder (CSA) is one of the most widely used implementations for fast arithmetics in industry. However, optimizing arithmetic circuits using CSAs is mostly carried out by the designer manually based on his/her design experience, which is a very time-consuming and error-prone task. To overcome this limitation, in this paper we propose an effective synthesis algorithm for solving the problem of finding an allocation of CSAs with a minimal timing for an arithmetic expression. Specifically, we propose a two step approach: (1) allocating a delay-optimal CSA tree for the multi-bit inputs of the arithmetic expression and (2) determining the assignment of the single-bit inputs to carry inputs of the CSAs which leads to a minimal increase of delay of the CSA tree obtained in step (1). For a number of arithmetic expressions, we found that our approach is very effective, reducing the timing of the circuits significantly without increasing the circuit area.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.538-540
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• 2004

본 연구는 컴퓨터 연산을 위한 하드웨어 설계에서 고성능 연산에 사용되는 케리-세이브 가산기 (Carry-save adder) 합성에 관한 연구이다. 기존의 연구에서는, 연산 합성 문제와 합성된 연산의 배치 문제를 두개의 연속된 독립된 두개의 문제로 간주하고 풀었지만, 본 연구에서는 연산 합성 과정에서 연산 배치를 고려한 통합된 방법을 제시하여 전체적인 최적화된 결과를 얻었다. 연결선 상에서의 전력 소모나 지연시간이 점점 더 중요해지는 시스템-온-칩 (system-on-chip) 설계에서 본 연구의 통합적인 설계 방법은 매우 긴요하며 앞으로 효과적으로 이용될 수 있을 것이다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.341-344
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• 2007

본 논문에서는 계층적 공정 경쟁 개념을 병렬 유전자 알고리즘에 적용하여 계층적 공정 경쟁 기반 병렬유전자 알고리즘 (Hierarchical Fair Competition Genetic Algorithm: HFCGA)을 구현하였을 뿐만 아니라 실수코딩 유전자 알고리즘(Real-Coded Genetic Algorithm: RCGA)에서 좋은 성능을 갖는 산술교배(Arithmetic crossover), 수정된 단순교배(modified simple crossover) 그리고 UNDX(unimodal normal distribution crossover)등의 다양한 교배연산자들을 적용, 분석함으로써 개선된 병렬 유전자 알고리즘을 제안하였다. UNDX연산자는 다수의 부모(multiple parents)를 이용하여 부모들의 기하학적 중심(geometric center)에 근접하게 정규분포를 이루며 생성된다. 본 논문은 UNDX를 이용한 HFCGA모델을 구현하고 함수파라미터 최적화 문제에 많이 쓰이는 함수들에 적용시킴으로써 그 성능의 우수성을 증명 한다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.7
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• pp.503-512
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• 2001

The general DSP algorithm, like orthogonal transform or filter processing, needs efficient hardware architecture to compute inner product. The typical MAC architecture has high cost of silicon. Because of this reason, the distributed arithmetic without multiplier is widely used for implementing inner product. This paper presents the optimization to reduce required hardware in distributed arithmetic by using extraction method of adder sharing component. The optimization process uses Boltzmann-machine which is one of the neural network. This proposed method can solve problem that is increasing complexity depending on depth of inner product and compose optimal summation-network with the minimum FA and FF in a few time. The designed DCT by using Proposed method is more efficient than a ROM-based distributed arithmetic.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.51-57
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• 2014

In this paper, we introduce the adaptive threshold detection scheme based on an evolutionary arithmetic algorithm for the M-ary holographic data storage(HDS) system. The genetic algorithm is a particular class of evolutionary arithmetic based on the process of biological evolution, which is a very promising technique for optimization problem and estimation applications. In this study, to improve the detection performance that is degraded by the HDS channel environment and the pixel misalignment, the threshold value was assumed to be a population set of the evolutionary algorithm. The proposed method can find an appropriate population set of bit threshold, which minimizes bit error rate(BER) as increased generation. For performance evaluation, we consider severe misalignment effect in the 4-ary holographic data storage system. Furthermore, we measure the BER performance and compare the proposed methods with the conventional threshold detection scheme, which verifies the superiority of the proposed scheme.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.