• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.329-344
• /
• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.3A
• /
• pp.341-347
• /
• 2008

Among coating systems used for steel bridge coatings on highway such as red lead-pigmented alkyd, chlorinated rubber, waterborne inorganic zinc, inorganic zinc/epoxy/urethane and inorganic zinc/epoxy/fluororesin, evaluation of deterioration degree and prediction of lifetime through regression analysis were carried out for coating systems widely used and grossly degraded. For evaluation of deterioration degree, 75 bridges on highway were selected, and evaluations were carried out according to point offering method regulated by Guideline of maintenance coating for steel bridges used in Korea Expressway Corporation. Lifetime prediction results showed 13.0~13.3 years for the whole nation, 11.8 years for urban and industrial region in the metropolitan area, 13.2 years for rural region except the metropolitan area, 13.5~13.7 years for chlorinated rubber coating systems, and 12.86 years for red lead-pigmented alkyd systems. For prediction of the rest life of coating, we tried to execute parallel translations of standard deterioration curve to current life and deterioration degree for both x and y axes, and it was thought that parallel translation for x axis corresponded to deterioration aspects in actual environment. Maximum and minimum equations were derived from standard deterioration equation by adding and subtracting error values deduced in regression analysis to/from each coefficient in order to establish maintenance coating criteria for overall steel bridges on highway. Whole domain was divided into 8 parts in order to predict the rest life of coating and optimum time of maintenance coating, and maintenance coating criteria for each 8 domains were presented.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1031-1040
• /
• 2023

Propagation characteristics in line-of-sight(LOS) paths in 3, 6, 10, and 17 GHz frequency bands were measured and analyzed in two different indoor corridors: second floors of Buildings D2 and E2. The measurement was designed to measure when the receiving antenna moved at 0.5 m intervals from 3 m to 30 m, while the transmission antenna was fixed. The analysis of the two indoor corridors was compared by applying basic transmission loss, root mean square (RMS) delay spread, and K-factor. For basic transmission loss, the loss coefficient of the floating intercept path loss model was higher in the indoor corridor of Building E2 than in that of Building D2. Similarly, the RMS delay spread in the time domain was greater in the indoor corridor of Building E2. However, the indoor corridor of Building D2 exhibited higher K-factor in the 3, 6, and 17 GHz bands with lower wave propagation in the 10 GHz band. Despite the 2 indoor corridors being identical, the propagation characteristics varied due to different internal structures and materials. The results provide measurement data for ITU-R Recommendations regarding various indoor environments.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Plant Biotechnology
• /
• v.43 no.3
• /
• pp.359-366
• /
• 2016

Tradescantia is a perennial plant in the family of Commelinaceae. It is known to be sensitive to radiation. In this study, Tradescantia BNL 4430 was irradiated with gamma radiation at doses of 50 to 1,000 mGy in a phytotron equipped with a $^{60}Co$ radiation source at Korea Atomic Energy Research Institute, Korea. At 13 days after irradiation, we extracted RNA from irradiated floral tissues for RNA-seq. Transcriptome assembly produced a total of 77, 326 unique transcripts. In plantlets exposed to 50, 250, 500, and 1000 mGy, the numbers of up-regulated genes with more than 2-fold of expression compared that in the control were 116, 222, 246, and 308, respectively. Most of the up-regulated genes induced by 50 mGy were heat shock proteins (HSPs) such as HSP 70, indicating that protein misfolding, aggregation, and translocation might have occurred during radiation stress. Similarly, highly up-regulated transcripts of the IQ-domain 6 were induced by 250 mGy, KAR-UP oxidoreductase 1 was induced by 500 mGy, and zinc transporter 1 precursor was induced by 1000 mGy. Reverse transcriptase (RT) PCR and quantitative real time PCR (qRT-PCR) further validated the increased mRNA expression levels of selected genes, consistent with DEG analysis results. However, 2.3 to 97- fold higher expression activities were induced by different doses of radiation based on qRT-PCR results. Results on the transcriptome of Tradescantia in response to radiation might provide unique identifiers to develop in situ monitoring kit for measuring radiation exposure around radiation facilities.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.133-148
• /
• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Asia pacific journal of information systems
• /
• v.24 no.2
• /
• pp.233-253
• /
• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.35 no.1
• /
• pp.10-24
• /
• 2017

The research takes note of the formation process and settling period of Gwandong-Palkyung(關東八景, Eight Sites of Eastern Korea), the representative palgyeong(prominent eight sites) and jipgyeong(集景, landscape collection of scenic beauty), and investigates the time of formation regarding the palkyung and jipgeyong of Gwandong's scenic beauty through the analysis and interpretation of bibliographic data, and reference data. The result of the study is as follows. As the first document that records the terminology of "Gwandong-Palkyung" is "Daphongeunggil(答洪應吉)" of Yi, Hwang(李滉), Gwandong-Palkyung is inferred to be settled within the recognition of the people even before the 16th century. The geographic analysis result including "Sinjeung Donggukyeojiseungram(新增東國輿地勝覽)", Gwandong-Palkyung expanded as Gwandong-Sipkyung in early to middle of the 16th century. The first confirmed landscape collection regarding Gwandong-Palkyung in this study is confirmed in Shin Zup(申楫)'s "Yeonggwandong-Palkyung(詠關東八景)", thus, the terminology of Gwandong-Palkyung existed before 16th century at the latest. The settlement time of current "Palkyung" collection is estimated to be early 17th century at the latest. Poetries regarding Gwandong-Palkyung, and the frequency on the appearance of Gwandong scenic beauties are analyzed as making clear of the concentrated phenomenon on the sceneries of Gwandong-Palkyung. On the other hand, the collection of Gwandong-Palkyung in the domain of arts is confirmed initially in the ${\ll}$Gwandongpalkyungdobyeong(關東八景圖屛)${\gg}$ of Heo, Pil(許泌). Gwandong-Palkyung, expressed as the actual scene landscape painting shows similar tendencies of the conditions in the jipgyeong from the poetry, but the appearance rate of the painting subject was more prominent in visual solidarity and cohesion due to the reflection of the importance on icon(圖像) of the art works produced with particular meaning in the case of fixed ideal system. From late Joseon to modern times, ${\ll}$palpokbyeongpung(八幅屛風)${\gg}$ of various forms of folk painting is a corroborative evidence notifying that the cultural phenomenon of Gwandong-Palkyung has entered the universal period of embrace. Also, the 13 scenic beauties of Gangwon-Do appearing in the games of Namseungdo and Myeongseungyuramdo include Gwandong-Palkyung, which confirms the settlement of Gwandog-Palkyung even within the culture of games in late Joseon. Such results demonstrate the existence of awareness regarding Gwandong-Palkyung from the first half of the 15th century, which is presumed to have completely settled in the 17th century through the continuous development of formative process in the 16th century. Ultimately, Gwandong-Palkyung is the concrete formation of regional scenic beauties that individually gained its reputations as scenery from the Koryo Dynasty to late 17th century. Gwandong-Palkyung of the scenic beauty of Gwandong is a unique cultural scenery of the region that have germinated and formed through the process of cutting and polishing of long time to collect the best eight of scenic beauty from the many participation of sightseeing culture.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.39-53
• /
• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.93-108
• /
• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.