• The Journal of the Acoustical Society of Korea
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• 제18권5호
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• pp.21-27
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• 1999

In this paper, we propose an improved MLP-based English pronunciation dictionary generator to apply to the variable vocabulary word recognizer. The variable vocabulary word recognizer can process any words specified in Korean word lexicon dynamically determined according to the current recognition task. To extend the ability of the system to task for English words, it is necessary to build a pronunciation dictionary generator to be able to process words not included in a predefined lexicon, such as proper nouns. In order to build the English pronunciation dictionary generator, we use context-dependent grapheme-to-phoneme multi-layer perceptron(MLP) architecture for each grapheme. To train each MLP, it is necessary to obtain grapheme-to-phoneme training data from general pronunciation dictionary. To automate the process, we use dynamic programming(DP) algorithm with some distance metrics. For training and testing the grapheme-to-phoneme MLPs, we use general English pronunciation dictionary with about 110 thousand words. With 26 MLPs each having 30 to 50 hidden nodes and the exception grapheme lexicon, we obtained the word accuracy of 72.8% for the 110 thousand words superior to rule-based method showing the word accuracy of 24.0%.

• Journal of the Korea Convergence Society
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• 제9권5호
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of IKEEE
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• 제18권4호
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• pp.593-602
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• 2014

One of the purposes of an artificial neural netowrk(ANNet) is to implement the largest number of functions as possible with the smallest number of nodes and layers. This paper presents a CoreNet which has a multi-leveled input value and a multi-leveled output value with a 2-layered ANNet, which is the basic structure of an ANNet. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}={\frac{1}{2}}p(p-1)q^2-{\frac{1}{2}}(p-2)(3p-1)q+(p-1)(p-2)$. I've applied this CoreNet into the simulation model 1(5)-1(6), which has 5 levels of an input and 6 levels of an output with no hidden layers. The simulation result of this model gives, the maximum 219 convergences for the number of implementable functions using the cot(${\sqrt{x}}$) input leveling method. I have also shown that, the 27 functions are implementable by the calculation of weight values(w, ${\theta}$) with the multi-threshold lines in the weight space, which are diverged in the simulation results. Therefore the 246 functions are implementable in the 1(5)-1(6) model, and this coincides with the value from the above eqution $a_{5,6}(=246)$. I also show the implementable function numbering method in the weight space.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제40권8호
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• pp.1631-1636
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• 2015

With the advance of wireless communication technology in terrestrial area, underwater communication is also evolving very fast from a simple point-to-point transmission to an elaborate networked communications. Underwater acoustic channel has quite different features comparing with the terrestrial radio channel in terms of propagation delay, Doppler shift, multipath, and path loss. Thus, existing technologies developed for terrestrial communication might not work properly in underwater channel. Especially medium access control (MAC) protocols which highly depend on propagation phenomenon should be newly designed for underwater network. CSMA/CA has drawn lots of attention as a candidate of underwater MAC protocol, since it is able to resolve a packet collision and the hidden node problem. However, a received signal could be degraded by the interferences from the nodes locating outside the receiver's propagation radius. In this paper, we study the effects of interference on the CSMA/CA based underwater network. We derived the SNR with the interference using the sonar equation and analyzed the degradation of the RTS/CTS effects. These results are compared with the terrestrial results to understand the differences. Finally we summarized the design considerations in CSMA/CA based underwater network.

• The Journal of the Korea Contents Association
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• 제14권9호
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• pp.529-537
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• 2014

To establish a foundation to objectively interpret Chosun history, we construct people network of the Chosun dynasty. The network shows scale free network properties as if most social networks do. The people network is composed of 1,379 nodes and 3,874 links and its diameter is 14. To analysis of the network dynamics, whole network that is composed of 27 king networks were constructed by adding the first king, Taejo network to the second king, Jeongjong network and then continuously adding the next king networks. Interestingly, betweenness and closeness centralities were gradually decreased but stress centrality was drastically increased. These results indicate that information flow is gradually slowing and hub node position is more centrally oriented as growing the network. To elucidate key persons from the network, k-core and MCODE algorithms that can extract core or module structures from whole network were employed. It is a possible to obtain new insight and hidden information by analyzing network dynamics. Due to lack of the dynamic interacting data, there is a limit for network dynamic research. In spite of using concise data, this research provides us a possibility that annals of the Chosun dynasty are very useful historical data for analyzing network dynamics.

• Journal of KIISE:Information Networking
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• 제33권1호
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• pp.16-27
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• 2006

The wireless transmission medium inherently broadcasts a signal to all neighbor nodes in the transmission range. Existing asynchronous MAC protocols do not provide a concrete solution for reliable broadcast in link layer. This mainly comes from that an omni-directional broadcasting causes to reduce the network performance due to the explosive collisions and contentions. This paper proposes a reliable broadcast protocol in link taller based on directional antennas, named MDB(MAC protocol for Directional Broadcasting). This protocol makes use of DAST(Directional Antennas Statement Table) information and D-MACA(Directional Multiple Access and Collision Avoidance) scheme through 4-way handshake to resolve the many collision problem wit]1 omni-directional antenna. To analyze its performance, MDB protocol is compared with IEEE 802.11 DCF protocol [9] and the protocol 2 of reference [3], in terms of the success rate of broadcast and the collision rate. As a result of performance analysis through simulation, it was confirmed that the collision rate of the MDB protocol is lower than those of IEEE 802.11 and the protocol 2 of reference [3], and that the completion rate of broadcast of MDB protocol is higher than those of IEEE 802.11 and the protocol 2 of reference [3].

• KIPS Transactions on Computer and Communication Systems
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• 제2권12호
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• pp.533-540
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• 2013

Anonymity is the one of main reasons for substantial improvement of Internet. It encourages various users to express their opinion freely and helps Internet based distributed systems vitalize. But, anonymity can cause unexpected threats because personal information of an online user is hidden. Especially, distributed systems are threatened by Sybil attack, where one malicious user creates and manages multiple fake online identities. To prevent Sybil attack, the traditional solutions include increasing the complexity of identity generation and mapping online identities to real-world identities. But, even though the high complexity of identity generation increases the generation cost of Sybil identities, eventually they are generated and there is no further way to suppress their activity. Also, the mapping between online identities and real identities may cause high possibility of losing anonymity. Recently, some methods using online social network to prevent Sybil attack are researched. In this paper, a new method is proposed for extracting a user's system-wide Sybil-resistant trust value by using the properties embedded in online social network graphs. The proposed method can be categorized into 3 types based on sampling and decision strategies. By using graphs sampled from Facebook, the performance of the 3 types of the proposed method is evaluated. Moreover, the impact of Sybil attack on nodes with different characteristics is evaluated in order to understand the behavior of Sybil attack.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2019년도 학술발표회
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Journal of Bio-Environment Control
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• 제31권1호
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• pp.43-51
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• 2022

This study was conducted to investigate the effect of ventilation at high temperature on the control of powdery mildew, silverleaf whitefly two-spotted spider mite occurred at Korean melon cultivation greenhouse, and on leaf rolling and flowering of the plant in summer season. 'Alchanggul' grafted onto 'Hidden Power' rootstock was planted on soil bed with the distance of 40 cm. Three ventilation temperatures of 45℃, 40℃, and 35℃ as set points were compared. Ventilation treatment was done by control of side window operation from 18th June to 13th July when silverleaf whitefly, mite, and powdery mildew were occurred in all greenhouses. The temperature inside greenhouse was increased up to the set temperature point on sunny days and maintained for about 9 hours with high relative humidity at 45℃ condition. The differences of day maximum air temperature and day minimum RH were the highest at 45℃ treatment. After 11 days of treatments, the damage by powdery mildew and two-spotted spider mite was almost recovered at 45℃ treatment but not at 40 and 35℃. The population of silverleaf whitefly and two-spotted spider mite were significantly decreased at 45℃ treatment at 14 days after treatment, while powdery mildew symptom was not significantly decreased. Leaf rolling was observed at high temperature but not severe at 45℃ treatment. After 26 days of treatments, female flowers did not bloom at all at 45℃ treatment, and the number of male flowers was 1.2 among 15 nodes of newly grown shoots. As the result, it indicates that ventilation at the high temperature of 45℃ for about 2 to 3 weeks can be an applicable method to control above mentioned pests and disease, and to recover the vegetative growth of Korean melon by reducing flowering of the plant.

• Journal of Intelligence and Information Systems
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• 제19권2호
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.