• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.11 s.353
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• pp.118-127
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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 omnidirectional broadcasting causes to reduce the network performance due to the explosive collisions and contentions. This paper proposes a directional broadcast protocol by using neighborhood information in the link layer based o,1 directional antennas, named MNDB (MAC protocol with Neighborhood for reliable Directional Broadcast). This protocol makes use of neighborhood information and DMACA (Directional Multiple Access and Collision Avoidance) scheme through 4-way handshake to support a reliable directional broadcast. To analyze its performance, MNDB protocol si compared with $RMDB^{[1]}$, the protocol 2 of reference [3], and IEEE 802.11 $protocol^{[9]}$, in terms of the number of collisions, the number of dropped packets, the number of redundant packets, and broadcast delay.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.313-320
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• 2008

The dynamic clustering technique has some problems regarding energy consumption. In the cluster configuration aspect the cluster structure must be modified every time the head nodes are re-selected resulting in high energy consumption. Also, there is excessive energy consumption when a cluster head node receives identical data from adjacent cluster sources nodes. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects duster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. Furthermore, the issue of redundant data occurring at the cluster head node is dealt with by broadcasting metadata of the initially received data to prevent reception by a sensor node with identical data. A simulation experiment was performed to verify the validity of the proposed approach. The results of the simulation experiments were compared with the performances of two of the must widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 29.3% and 21.2% more efficient than LEACH and HEED, respectively.

• Journal of Korean Society of Steel Construction
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• v.22 no.1
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• pp.13-20
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• 2010

Seismic risk assessment of railway bridges is an important issue for a transportation network, because loss of functionality of railway bridges could result in severe disruption of the railway line, as no redundant routing systems generally exist. Although many studies have been conducted by numerous researchers regarding fragility analyses of bridge structure, little or no studies have been done for fragility analyses of a class of bridge structures considering their geometric variability. This study performs a fragility analysis for Track-on Steel-Plate-Girder (TOSPG) railway bridges in Korea considering their span variability. Seismic fragility curves are developed for a series of bridges with different spans varying from 2 to 15. At last, the fragility curves for the whole TOSPG bridges in Korea are also developed using the total probability theorem. This study is expected to effectively contribute to the seismic risk assessment of railway lines, where a number of bridges are present.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.3
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• pp.48-58
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• 2009

Geo-graphical routing protocols as GPSR are known to be very suitable and useful for vehicular ad-hoc networks. However, a corresponding node can include some stale neighbor nodes being out of a transmission range, and the stale nodes are pone to get a high priority to be a next relay node in the greedy mode. In addition, some useful redundant information can be eliminated during planarization in the recovery mode. This paper deals with a new recovery mode, the Greedy Border Superiority Routing(GBSR), along with an Adaptive Neighbor list Management(ANM) scheme. Each node can easily treat stale nodes on its neighbor list by means of comparing previous and current Position of a neighbor. When a node meets the local maximum, it makes use of a border superior graph to recover from it. This approach improve the packet delivery ratio while it decreases the time to recover from the local maximum. We evaluate the performance of the proposed methods using a network simulator. The results shown that the proposed protocol reveals much better performance than GPSR protocol. Please Put the of paper here.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.9-20
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• 2015

Nowadays, Smart Wallet technology trend that is able to save users' consuming costs and also retain users' redundant behaviors such as Single-tapping, One-way communication, Integrated ID, has been issued in recent Mobile Industrial Fields. As one of Smart Wallet functions, Integrated ID is proposed for users' convenience, handiness, and immediate responses. It is designed for the effective management of users' IDs which are easy to be forgot because of its unusual structures. To be detail, instead of user, Integrated ID system can certificate users identification from various online sites (where user resisted) authorization requests via one-clicking, not putting identification data in each sites. So, this technology would be helpful much to a certain user who has lots ID and its Password in multiple Online shopping companies by establishing integrated ID. However, although Integrated ID has lots advantages to be used, most Mobile Service Companies has hesitated to apply Integrated ID service in their shopping systems because this technology requires them sharing their users' data. They have worried that this service would be not helpful to gain their profits. Furthermore, Users who join in multiple shopping companies and use Integrated ID services also are difficult to decide which company they have to save their points in before payment because this system could not show any financial benefit analysis data to their users. As following facts, via this paper majorly we propose the advanced Integrated ID system which concern shopping point management. Basically, this system has a strong security payment service and secure network services like other mobile Shopping systems. Additionally, this system is able to service (or to support) shopping -point -saving guide for customers' financial benefits and conveniences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.35-40
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• 2011

In this paper, we compare and analyze data aggregation methods based on event area in wireless sensor networks. Data aggregation methods consist of two methods: the direct transmission method and the aggregation node method. The direct aggregation method has some problems that are data redundancy and increasing network traffic as all nodes transmit own data to neighbor nodes regardless of same data. On the other hand the aggregation node method which aggregate neighbor's data can prevent the data redundancy and reduce the data. This method is based on location of nodes. This means that the aggregation node can be selected the nearest node from a sink or the centered node of event area. So, we describe the benefits of data aggregation methods that make up for the weak points of direct data dissemination of sensor nodes. We measure energy consumption of the existing ways on data aggregation selection by increasing event area. To achieve this, we calculated the distance between an event node and the aggregation node and the distance between the aggregation node and a sink node. And we defined the equations for distance. Using these equations with energy model for sensor networks, we could find the energy consumption of each method.

• Archives of design research
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• v.17 no.3
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• pp.167-178
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• 2004

The purpose of this treatise is to show merits and method of establishing Lotte department store design division Online Documents Communication System through illustration of examples of intranet in which internet environment convenient to use for its openness is applied for establishing Design Online Documents Communication System for fundamentals of organization. In this connection merits and effect attainable from establishing Design Outline Documents Communication System of the enterprise as found were as follows: Firstly, it brought about reduction in workload of staffs through sharing various existing resources. It reduced redundant works and enables speedy handling of works. Secondly, it was possible to exchange viewpoints and share information by pertinent parties. Thirdly, by expediting information exchange and communication among persons in charge it was possible to improve work efficiency. Fourthly, it was possible to build and operate such system at relatively low cost on the basis of web browser. Without using any other significant instrument or equipment but by linking it to business network and using existing computer system operation was possible. Fifthly, by common sharing of work exclusive to design room through on-line it was possible to improve professionalism and convenience in data preservation. Through this treatise and survey and study on process for establishing intranet it was possible to find that there were sharing work, improving work efficiency, reducing workload, saving cost and expediting communication to a significant degree.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.