• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.145-154
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• 2017

It is aimed to examine the effect of literary therapy program on the improvement of friendship among elementary school students using various media and activities of literature. After the literature therapy program was conducted for elementary students, post-test was conducted. The results of the study are as follows. First, there was no significant difference between the experimental group and the comparative group in the post-test immediately after the end of the program, but in the follow-up test performed 5 weeks after the counseling, the average of the peer relations of the experimental group was significantly higher than that of the comparative group. Second, in the post-test, there was no significant difference between the experimental group and the comparative group in the "intimacy" sub-area of the peer relationship, but in the follow- up test, the "intimacy" sub-region average of the experimental group was significantly higher than the comparative group. Third, the level of 'interest' among the peer relationship sub-domains of the experimental group showed a significant difference in the follow-up test. As a result, literature therapy had no immediate effect on the improvement of friendship among adolescents, but it was effective after a long time.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.201-210
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• 2018

WorldJob+, being operated by The Human Resources Development Service of Korea, provides a recruitment support services to overseas companies wanting to hire talented Korean applicants and interns, and support the entire course from overseas advancement information check to enrollment, interview, and learning for young job-seekers. More than 300,000 young people have registered in WorldJob+, an overseas united information network, for job placement. To innovate WorldJob+'s services for young job-seekers, Splunk, a powerful platform for analyzing machine data, was introduced to collate and view system log files collected from its website. Leveraging Splunk's built-in data visualization and analytical features, WorldJob+ has built custom tools to gain insight into the operation of the recruitment supporting service system and to increase its integrity. Use cases include descriptive and predictive analytics for matching up services to allow employers and job seekers to be matched based on their respective needs and profiles, and connect jobseekers with the best recruiters and employers on the market, helping job seekers secure the best jobs fast. This paper will cover the numerous ways WorldJob+ has leveraged Splunk to improve its recruitment supporting services.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.145-156
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• 2018

Unlike the past, when distributors distributed games through retail stores, they are now selling digital content, which is based on online distribution channels. This study analyzes the effects of eWOM (electronic Word of Mouth) on sales volume of game sold on Steam, an online digital content distribution channel. Recently, data mining techniques based on Big Data have been studied. In this study, emotion index of eWOM is derived by emotional analysis which is a text mining technique that can analyze the emotion of each review among factors of eWOM. Emotional analysis utilizes Naive Bayes and SVM classifier and calculates the emotion index through the SVM classifier with high accuracy. Regression analysis is performed on the dependent variable, sales variation, using the emotion index, the number of reviews of each game, the size of eWOM, and the user score of each game, which is a rating of eWOM. Regression analysis revealed that the size of the independent variable eWOM and the emotion index of the eWOM were influential on the dependent variable, sales variation. This study suggests the factors of eWOM that affect the sales volume when Korean game companies enter overseas markets based on steam.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.115-121
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• 2013

This paper related with the DSE-MMA (Dithered Sign-Error MMA) that is the simplification of computational arithmetic number in blind equalization algorithm in order to compensates the intersymbol interference which occurs the passing the nonlinear communication channel in the presence of the band limit and phase distortion. The SE-MMA algorithm has a merit of H/W implementation for the possible to reduction of computational arithmetic number using the 1 bit quantizer in stead of multiplication in the updating the equalizer tap weight. But it degradates the overall blind equalization algorithm performance by the information loss at the quantization process compare to the MMA. The DSE-MMA which implements the dithered signed-error concepts by using the dither signal before qualtization are added to MMA, then the improved SNR performance which represents the roburstness of equalization algorithm are obtained. It has a concurrently compensation capability of the amplitude and phase distortion due to intersymbol interference like as the SE-MMA and MMA algorithm. The paper uses the equalizer output signal, residual isi, MD, MSE learning curve and SER curve for the performance index of blind equalization algorithm, and the computer simulation were performed in order to compare the SE-MMA and DSE-MMA applying the same performance index. As a result of simulation, the DSE-MMA can improving the roburstness and the value of every performance index after steady state than the SE-MMA, and confirmed that the DSE-MMA has slow convergence speed which meaning the reaching the seady state from initial state of adaptive equalization filter.