• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.63-70
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• 2003

Real-time information on vehicle queue at intersections is essential for optimal traffic signal control, which is substantial part of Intelligent Transport Systems (ITS). Computer vision is also potentially an important element in the foundation of integrated traffic surveillance and control systems. The objective of this research is to propose a method for detecting an exact queue lengths at signalized intersections using image processing techniques and a neural network model Fuzzy ARTMAP, which is a supervised and self-organizing system and claimed to be more powerful than many expert systems, genetic algorithms. and other neural network models like Backpropagation, is used for recognizing different patterns that come from complicated real scenes of a car park. The experiments have been done with the traffic scene images at intersections and the results show that the method proposed in the paper could be efficient for the noise, shadow, partial occlusion and perspective problems which are inevitable in the real world images.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.341-359
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• 2019

To improve research productivity, it is important to acquire up-to-date information quickly. To this end, researchers seek information resources through various channels and methods and utilize them in their research and development processes. The Korea Institute of Science and Technology Information (KISTI) has developed a platform for integrated scientific and scientific information service called ScienceOn to provide specialized information, infrastructure resources, industry and technology analysis resources in one place to support the research and information ecosystem. Through this process, accessibility and usability are enhanced through the connection and convergence of various information and services. In this study, we look at recent R&D trends in scientific technology academic information integration services and recommended packaging services that can be utilized in batches according to the purpose of use. We look forward to improving national R&D productivity by strengthening the linkage and convergence of scientific and technological information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.374-380
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• 2017

The Internet of Things (IoT) refers to intelligent technologies and services that connect all things to the internet so they can interactively communicate with people, other things, and other systems. The development of the IoT environment accompanies advances in network protocols applicable to more lightweight and intelligent sensors, and lightweight and diverse environments. The development of those elemental technologies is promoting the rapid progress in smart car environments that provide safety features and user convenience. These developments in smart car services will bring a positive effect, but can also lead to a catastrophe for a person's life if security issues with the services are not resolved. Although smart cars have various features with different types of communications functions to control the vehicles under the existing platforms, insecure features and functions may bring various security threats, such as bypassing authentication, malfunctions through illegitimate control of the vehicle via data forgery, and leaking of private information. In this paper, we look at types of smart car services in the IoT, deriving the security threats from smart car services based on various scenarios, suggesting countermeasures against them, and we finally propose a safe smart car application plan.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.11-23
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• 2014

Wireless Mesh Network(WMN) has drawn much attention due to easy deployment and good scalability. Recently, major power utilities have been focusing on R&D to apply WMN technology in Smart Grid Network. Smart Grid is an intelligent electrical power network that can maximize energy efficiency through bidirectional communication between utility providers and customers with ICT(Information Communication Technology). It is necessary to guarantee QoS of some important data in Smart Grid system such as real-time data delivery. In this paper, we suggest QoS enhancement method for WMN based Smart Grid system using IEEE 802.11s. We analyze Smart Grid Application characteristics and apply IEEE 802.11s WMN scheme for Smart Grid in domestic power communication system. Performance evaluation is progressed using NS-2 simulator implementing IEEE 802.11s. The simulation results show that the QoS enhancement scheme can guarantee stable bandwidth irrespective of traffic condition due to IEEE 802.11s reservation mechanism.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.41-47
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• 2017

With the introduction of new technologies, ground weapons have led to the development of artificial intelligence and the attention of major developed countries. In this study, CFD was performed through the BLU-103 model to obtain aerodynamic data for aircraft that are subjected to rotational motion. To simulate the steady-state of a rotating body, the body was fixed and the principle of rotating the body by rotating the surrounding air was used. In order to examine the aerodynamic feasibility of the rotating aircraft, the analysis was carried out at intervals of $30^{\circ}$ angle from $0^{\circ}$ to $90^{\circ}$ for the simple shape and the side slip angle. It was confirmed that the drag coefficient for the simple model satisfies the quantitative results of 1.0 ~ 1.2 through CD presented in "Drag Book". The aerodynamic data was constructed by applying the valid input verified through the simple type analysis conditions to the actual shape, and the tendency was analyzed. The analysis confirmed that CX, CZ and CY increase not only in the simple model but also in the rotation of the actual model. Especially, the influence of CZ was judged to have contributed to the flight.