• Journal of Information Technology Applications and Management
• /
• 제13권1호
• /
• pp.77-94
• /
• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• 한국통신학회논문지
• /
• 제28권4C호
• /
• pp.384-391
• /
• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• Journal of information and communication convergence engineering
• /
• 제20권2호
• /
• pp.96-102
• /
• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• 스마트미디어저널
• /
• 제11권2호
• /
• pp.39-52
• /
• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• 인터넷정보학회논문지
• /
• 제12권5호
• /
• pp.1-9
• /
• 2011

일반화된 PF 스케줄링을 사용하는 무선이동 네트워크는 스케줄링 변수를 조정하여 동적인 무선 자원 할당을 가능케 한다. 기존의 확률론적 접근 방법으로는 일반적인 환경에서 네트워크의 용량을 예측하는 데에는 한계가 있다. 더욱이 그 한계는 요구되는 네트워크 용량을 얻도록 하는 스케줄링 변수를 효율적으로 찾을 수 없게 한다. 본 논문은 결정론적 접근 방법을 사용하여 네트워크의 용량을 예측하는 알고리즘을 유도한다. 얻어진 용량 예측 알고리즘을 이용하여 요구되는 용량에 따른 스케줄링 변수 설정을 효과적으로 할 수 있는 용량 조정 알고리즘과 용량 교환 알고리즘 제안한다. IEEE 802.16m 시스템 기반 의 시스템 레벨 시뮬레이션을 통해 제안된 용량 예측 알고리즘과 용량 교환 알고리즘의 성능을 확인한다.

• Nuclear Engineering and Technology
• /
• 제53권4호
• /
• pp.1199-1209
• /
• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Smart Structures and Systems
• /
• 제26권6호
• /
• pp.753-763
• /
• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• 인터넷정보학회논문지
• /
• 제25권2호
• /
• pp.21-28
• /
• 2024

레이다는 자율주행 차에 있어 필수적인 센서 부품으로, 레이다가 활용되는 시장은 점차 커지고 있으며 제품 종류도 다양해지고 있다. 본 연구에서는 평가 공정에서부터 레이다의 불량 여부를 예측해 자율주행의 안정성과 효율성을 높일 수 있도록 성능 예측 모델을 구축하고 평가하였다. 레이더 공정 과정의 39607개 입력 데이터로 모델을 학습하였으며, 결과적으로 17개 모델을 스태킹 앙상블했을 때 Meta Ridge 모델이 가장 높은 학습률을 나타내는 것을 확인하였다. 이러한 연구 결과가 제품의 불량을 공정 단계에서 우선 예측해 수율을 극대화하고 불량으로 인한 제품 폐기 비용을 감축하는 데 도움이 될 것으로 기대 한다.

• Steel and Composite Structures
• /
• 제51권4호
• /
• pp.417-440
• /
• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

• Structural Engineering and Mechanics
• /
• 제90권2호
• /
• pp.189-208
• /
• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.