• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Ecology and Resilient Infrastructure
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• v.3 no.3
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• pp.207-211
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• 2016

This study focuses on the categorization of the phenomenon of vegetative recruitment on riparian channels, so called, the phenomenon from "white river" to "green river", and proposes for the corresponding research direction. According to the literature review and research outputs obtained from the authors' previous research performed in Korea within a limited scope, the necessary and sufficient conditions for the recruitment and retrogression of riparian vegetation may be the mechanical disturbance (riverbed tractive stress), soil moisture (groundwater level, topography, composition of riverbed material, precipitation etc.), period of submergence, extreme weather, and nutrient inflow. In this study, two categories, one for the reduction in spring flood due to the change in spring precipitation pattern in unregulated rivers and the other for the increase in nutrient inflow into streams, both of which were partially proved, have been added in the categorization of the vegetative recruitment and retrogression on the riparian channels. In order to scientifically investigate further the phenomenon of the riparian vegetative recruitment and retrogression and develop the working riparian vegetative models, it is necessary to conduct a systematic nationwide survey on the "white to green" rivers, establishment of the categorization of the vegetation recruitment and retrogression based on the proof of those hypotheses and detailed categorization, development of the working mathematical models for the dynamic riparian vegetative recruitment and retrogression, and adaptive management for the river changes.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.327-332
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• 2004

USB(Universal Serial Bus) is one of the most popular communication interfaces. When USB is used in an extended range, especially configurating In-home network by connecting multiple digital devices each other, USB interface uses the bandwidth in the way of TDM(Time Division Multiplexing) so that the bottleneck of bus bandwidth can be brought. In this paper, the more effective usage of bus bandwidth to overcome this situation is introduced. Basically, in order to realize the system for transferring realtime moving picture data among digital information devices, we analyze USB transfer types and Descriptors and introduce the method to upgrade detailed performance of Isochronous transfer that is one of USB transfer types. In the case that Configuration descriptor of a device has Interface descriptor that has two AlternateSetting, if Isochronous transfers are not processed smoothly due to excessive bus traffic, the application of the device changes AlternateSetting of the Interface descriptor and requires a new configuration by SetInterface() request. As a result of this adaptive configuration, the least data frame rate is guaranteed to a device that the sufficient bandwidth is not alloted. And if the bus traffic is normal, the algorithm to return to the original AlteranteSetting is introduced. this introduced method resolve the bottleneck of moving picture transfer that can occur in home network connected by multiple digital devices.

• Korean journal of applied entomology
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• v.42 no.4
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• pp.367-381
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• 2003

Dispersal polymorphism in insects Is a kind of adaptive strategy of the life history together with the diapause, consisting of the “long-winged or alate forms” of migratory phase and the “short-winged or apterous forms” of stationary phase. Dispersal polymorphism is a polymorphism related with the flight capability, and has three categories ; the wing polymorphisms, flight muscle polymorphisms, and flight behavior variations. Phase variation is another type of dispersal polymorphism varying in morphology, physiology and wing forms in response to the density of the population. The dispersal migration is a very adaptive trait that enables a species to keep pace with the changing mosaic of its habitat, but requires some costs. In general, wing reduction has a positive effect on the reproductive potential such as earlier reproduction and larger fecundity The dispersal polymorphism is a kind of optimization in the evolutionary strategies of the life history in insects; a trade-off between the advantages and disadvantages of migration. Wing polymorphism is a phenotypically plastic trait. Wing form changes with the environmental conditions even though the species is the same. Various environmental factors have an effect on the dispersal polymorphisms. Density dependent dispersal polymorphism plays an important role In population dynamics, but it is not a simple function of the density; the individuals of a population may be different in response to the density resulting different outcomes in the population biology, and the detailed information on the genotypic variation of the individuals in the population is the fundamental importance in the prediction of the population performances in a given environment. In conclusion, the studies on the dispersal polymorphisms are a complicated field in relation with both physiology and ecology, and studies on the ecological and quantitative genetics have indeed contributed to understanding of its important nature. But the final factors of evolution; the mechanisms of natural selections, might be revealed through the studies on the population biology.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9A
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• pp.1048-1057
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• 2004

WCDMA system is 3rd generation wireless mobile system specified by 3GPP. In WCDMA downlink, two power control schemes are operated. One is inner loop power control operated m every slot Another is outer loop power control based on one frame time. Base staion (BS) can estimate proper transmission power by these two power control schemes. However, because each MS's transmission power makes a severe effect on BS's performance, BS cannot give excessive transmission power to the speclfic user 3GPP defined Power Control Dynamic Range (PCDR) to guarantee proper BS's performance. In this paper, we propose Adaptive PCDR algorithm. By APCDR algorithm, Radio Network Controller (RNC) can estimate each MS's current state using received signal to interference ratio (SIR) APCDR algorithm changes MS's maximum code channel power based on frame. By proposed scheme, each MS can reduce wireless channel effect and endure outages in cell edge. Therefore, each MS can obtain better QoS. Simulation result indicate that APCDR algorithm show more attractive output than fixed PCDR algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1265-1275
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• 2002

In the network management system, there are two approaches; the centralized approach based on SNMP and the distributed approach based on mobile agent. Some information changes with time and the manager needs to monitor its value in real time. In such a case, the polling is generally used in SNMP because the manager can query agents periodically. However, the polling scheme needs both request and response messages for management information every time, which results in network traffic increase. In this paper, we suggest an adaptive network monitoring method to reduce the network traffic for SNMP-based network management. In the proposed strategy, each agent first decides its on monitoring period. Then, the manager collects them and approves each agent's period without modification or adjusts it based on the total traffic generated by monitoring messages. After receiving response message containing monitoring period from the manager, each agent sends management information periodically without the request of manager. To evaluate performance of the proposed method, we implemented it and compared the network traffic and monitoring quality of the proposed scheme with the general polling method.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.417-424
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• 2007

Mesh networks using WLAN technology have been paid attention as a key wireless access technology. However, many technical issues still exist for its successful deployment. One of those issues is the routing problem that addresses the path setup through a WLAN mesh network for the data exchanges between a station and a wired network. Since the characteristics of a WLAN mesh network can be very dynamic, the use of single routing protocol would not fit for all environments whether it is reactive or proactive. Therefore, it is required to develop an adaptive routing protocol that modifies itself according to the changes in the network parameters. As a logical first step for the development, an analytical model considering all the dynamic features of a WLAN mesh network is required to evaluate the performance of a reactive and a proactive routing scheme. In this paper, we propose an analytical model that makes us scrutinize the impact of the network and station parameters on the performance of each routing protocol. Our model includes the size of a mesh network, the density of stations, mobility of stations. and the duration of network topology change. We applied our model to the AODV that is a representative reactive routing protocol and DSDV that is a representative proactive routing protocol to analyze the tradeoff between AODV and DSDV in dynamic network environments. Our model is expected to help developing an adaptive routing protocol for a WLAN mesh network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.235-243
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• 2021

River surveying is conducted to acquire basic geographic data for river master plans and various river maintenance, and it is also used to predict changes after river maintenance construction. ABL (Airborne Bathymetric LiDAR) system is a cutting-edge surveying technology that can simultaneously observe the water surface and river bed using a green laser, and has many advantages in river surveying. In order to use the ABL data for river surveying, it is prerequisite step to segment and extract the water surface and river bed points from the original point cloud data. In this study, point cloud segmentation was performed by applying the ground filtering technique, ATIN (Adaptive Triangular Irregular Network) to the ABL data and then, the water surface and riverbed point clouds were extracted sequentially. In the Gokgyocheon river area, Chungcheongnam-do, the experiment was conducted with the dataset obtained using the Leica Chiroptera 4X sensor. As a result of the study, the overall classification accuracy for the water surface and riverbed was 88.8%, and the Kappa coefficient was 0.825, confirming that the ABL data can be effectively used for river surveying.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.61-73
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• 2021

Metaheuristic search methods have been developed to solve problems with a range of purpose functions in situations lacking information and time constraints. In this study, the Hybrid Vision Correction Algorithm (HVCA), which enhances the performance of the Vision Correction Algorithm (VCA), was developed. The HVCA has applied two methods to improve the performance of VCA. The first method changes the parameters required by the user for self-adaptive parameters. The second method, the CGS structure of the Exponential Bandwidth Harmony Search With a Centralized Global Search (EBHS-CGS), was added to the HVCA. The HVCA consists of two structures: CGS and VCA. To use the two structures, a method was applied to increase the probability of selecting the structure with the optimal value as it was performed. The optimization problem was applied to determine the performance of the HVCA, and the results were compared with Harmony Search (HS), Improved Harmony Search (IHS), and VCA. The HVCA improved the number of times to find the optimal value during 100 repetitions compared to HS, IHS, and VCA. Moreover, the HVCA reduced the Number of Function Evaluations (NFEs). Therefore, the performance of the HVCA has been improved.