• Journal of Intelligence and Information Systems
• /
• 제24권2호
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The Journal of the Acoustical Society of Korea
• /
• 제19권3호
• /
• pp.35-44
• /
• 2000

In this paper, we propose a new trajectory model for characterizing segmental features and their interaction based upon a general framework of hidden Markov models. Each segment, a sequence of vectors, is represented by a trajectory of observed sequences. This trajectory is obtained by applying a new design matrix which includes transitional information on contiguous frames, and is characterized as a polynomial regression function. To apply the trajectory to the segmental HMM, the frame features are replaced with the trajectory of a given segment. We also propose the likelihood of a given segment and the estimation of trajectory parameters. The obervation probability of a given segment is represented as the relation between the segment likelihood and the estimation error of the trajectories. The estimation error of a trajectory is considered as the weight of the likelihood of a given segment in a state. This weight represents the probability of how well the corresponding trajectory characterize the segment. The proposed model can be regarded as a generalization of a conventional HMM and a parametric trajectory model. The experimental results are reported on the TIMIT corpus and performance is show to improve significantly over that of the conventional HMM.

• The Korean Journal of Applied Statistics
• /
• 제28권5호
• /
• pp.977-990
• /
• 2015

Food delivery services are well developed in the Republic of Korea, The increase of one person households and the success of app applications influence delivery services these days. We consider a prediction model for the food delivery service based on weather and dates to predict the number of food delivery services in 2014 using various data mining techniques. We use linear regression, random forest, gradient boosting, support vector machines, neural networks, and logistic regression to find the best prediction model. There are four categories of food delivery services and we consider two methods. For the first method, we estimate the total number of delivery services and the posterior probabilities of each delivery service. For the second method, we use different models for each category and combine them to estimate the total number of delivery services. The neural network and linear regression model perform best in the first method, this is followed by the neural network which is the best for the second method. The result shows that we can estimate the number of deliveries accurately based on dates and weather information.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• 제9권8호
• /
• pp.783-790
• /
• 2019

Cognitive science attempts to explain human intelligence on the basis of success of artificial neural network, which is called connectionism. The neural network, e.g., deep learning, seemingly promises connectionism to go beyond what it is. But those(Fodor & Pylyshyn, Fodor, & McLaughlin) who advocate classical computationalism, or symbolism claim that connectionism must fail since it cannot represent the relation between human thoughts and human language. The neural network lacks systematicity, so any output of neural network is at best association or accidental combination of data plugged in input units. In this paper, I first introduce structure of artificial neural network and what connectionism amounts to. Second, I shed light on the problem of systematicity the classical computationalists pose for the connectionists. Third, I briefly introduce how those who advocate connectionism respond to the criticism while noticing Smolensky's theory of vector product. Finally, I examine the debate of computationalism and connectionism on systematicity, and show how the problem of systematicity contributes to the development of connectionism and computationalism both.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• 제49권1호
• /
• pp.1-12
• /
• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• 제16권11호
• /
• pp.1123-1130
• /
• 2005

In this paper, we propose an improved side information processing scheme which is important in the sub-block phase weighting(SPW) method for the peak-to-average power ratio(PAPR) reduction. SPW method is to divide the input OFDM subchannels into several subblocks and to multiply phase weighting with each subblocks, properly for the reduction of the peak power. SPW method is similar to the conventional PTS method when the number of sub-carriers, signal modulation format and the number of subblocks are the same. However, unlike the conventional PTS(Partial Transmit Sequence) and SLM(Selected Mapping) method using many stages of IFFT(Inverse Fast Fourier Transform), SPW method only needs one IFFT. Although PAPR can be reduced by SPW method, complex computation burden still remains. In this paper the flipping algorithm and the full iteration algorithm are used f3r the phase control method. Through the computer simulation, we analyze and discuss the properties and the performance of the suggested method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• 제18권10호
• /
• pp.1172-1180
• /
• 2007

OFDM system has very good high spectral efficiency and the robustness to the frequency-selective fading. Because of the high PAPR, OFDM signals can be distorted in nonlinear HPA(High Power Amplifier). So, to overcome the nonlinear distortion, it is very important to reduce the IMD value. With respect to the BER performance, IMD reduction method is better than the PAPR reduction method. However, IMD reduction method has much more system complexity because of the additional FFT processor in transmitter. In this paper, we study the OFDM communication system based on the IMD reduction method using SPW method. A new IMD reduction method is proposed to reduce the computational complexity. SPW method is to divide the input OFDM data into several sub-blocks and to multiply phase weighting values with each sub-blocks for the reduction of PAPR or IMD. Unlike the conventional method, the system size and computational complexity can be reduced.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• 제49권4호
• /
• pp.23-30
• /
• 2012

This paper proposes an improved steganalysis method to detect the existence of secret message. Firstly, we magnify the small stego noise by multiplying the speckle noise to a given image and then we estimate the denoised image by using the soft thresholding method. Because the noises are not perfectly eliminated, some noises exist in the estimated cover image. If the given image is the cover image, then the remained noise will be very small, but if it is the stego image, the remained noise will be relatively large. The parent-child relationship in the wavelet domain will be slighty broken in the stego image. From this characteristic, we extract the joint statistical moments from the difference image between the given image and the denoised image. Additionally, four statistical moments are extracted from the denoised image for the proposed steganalysis method. All extracted features are used as the input of MLP(multilayer perceptron) classifier. Experimental results show that the proposed scheme outperforms previous methods in terms of detection rates and accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• 제37권1A호
• /
• pp.51-56
• /
• 2012

In order to reduce the complexity of HEVC, we propose a fast mode decision algorithm in depth videos. Since almost CU mode is decided as SKIP mode in depth-continuity regions, we design the algorithm using the property of depth videos. If cost of SKIP is smaller than the multiplication between the threshold for EarlySKIP and average cost of SKIP, EarlySKIP is performed. Otherwise, we calculate Inter $2N{\times}2N$. Then, if motion vector of Inter $2N{\times}2N$ is 0 and variance of CU is smaller than threshold for inter, we skip Inter $2N{\times}N$, Inter $N{\times}2N$. Experimental results show that our proposed algorithm reduces the encoding time from 39% to 82% with negligible PSNR loss and bitrate increase.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• 제35권4A호
• /
• pp.360-370
• /
• 2010

In this paper, a distributed beamforming problem is considered in an amplify-and-forward (AF) wireless relay network consist of multiple source-destination pairs and relaying nodes. To exploit degree of freedom of the number of beamformers, in the first step, we proposed that the sources transmit their signals through orthogonal channels. During the second step, the relays transmit their received signals multiplied by complex weights to amplify and compensate for phase changes introduced by the backward channels through one common channel. The optimal beamforming vectors are obtained through minimization of the total relay transmit power while the signal-to-interference-plus-noise ratios (SINRs) at the destinations are above certain thresholds to meet a quality of services (QoSs) level. In the numerical example, it is shown that the proposed scheme needs less transmit power for moderate network data rates than other schemes, such as space division multiplexing or time-division multiplexing scheme.