• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.515-519
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• 2003

This paper proposes a new image sequence stabilization (ISS) scheme based on filtering of absolute frame positions. The proposed ISS scheme removes undesired motion effects in real-time, while preserving desired gross camera displacements. The well-known finite impulse response (FIR) filter is adopted for filtering. The proposed ISS scheme provides a filtered position and velocity with fine inherent properties. It is demonstrated that the filtered position is not affected by the constant velocity. It is also shown that the filtered velocity is separated from the position. Via numerical simulations, the performance of the proposed scheme is shown to be superior to the existing Kalman filtering scheme.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.50-58
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• 2007

We design a filtering management scheme with synchronization function under a realtime RFID middleware system for larger-scale data processing. The application interface(AI) is to support a various access protocol, HTTP, XML, JMS, and SOAP for the RFID applications. Generally, the synchronization problem is occurred in multiple accessing of clients for single filtering file. In this paper, we implement a filtering management scheme supporting the synchronization using the filter management process, and then demonstrate the RFID middleware filtering scheme.

• ETRI Journal
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• v.28 no.4
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• pp.509-512
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• 2006

In a real environment, sound recordings are commonly distorted by channel and background noise, and the performance of audio identification is mainly degraded by them. Recently, Philips introduced a robust and efficient audio fingerprinting scheme applying a differential (high-pass filtering) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the robustness of the audio fingerprinting scheme is still important in a real environment. In this letter, we introduce alternatives to the frequency-temporal filtering combination for an extension method of Philips' audio fingerprinting scheme to achieve robustness to channel and background noise under the conditions of a real situation. Our experimental results show that the proposed filtering combination improves noise robustness in audio identification.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.159-162
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• 2008

Generally, sensor nodes can be easily compromised and seized by an adversary because sensor nodes are hostile environments after dissemination. An adversary may be various security attacks into the networks using compromised node. False data injection attack using compromised node, it may not only cause false alarms, but also the depletion of the severe amount of energy waste. Dynamic en-route scheme for Filtering False Data Injection (DEF) can detect and drop such forged report during the forwarding process. In this scheme, each forwarding nodes verify reports using a regular probability. In this paper, we propose verification probability adjustment scheme of forwarding nodes though a fuzzy rule-base system for the Dynamic en-route filtering scheme for Filtering False Data Injection in sensor networks. Verification probability determination of forwarding nodes use false traffic rate and distance form source to base station.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.101-109
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• 2018

In this paper, we propose an adaptive filtering scheme of connection requests for the defense of malicious energy consumption attacks against wireless computing devices with limited energy budget. The energy consumption attack tries to consume the battery energy of a wireless device with repeated connection requests and shut down the wireless device by exhausting its energy budget. The proposed scheme blocks a connection request of the energy consumption attack in the middle, if the same connection request is repeated and its request result is failed continuously. In order to avoid the blocking of innocuous mistakes of normal users, the scheme gives another chance to allow connection request after a fixed blocking time. The scheme changes the blocking time adaptively by comparing the message arriving ate during non-blocking period and that during blocking period. Evaluation shows that the proposed defense scheme saves up to 94% energy consumption compared to the non-defense case.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.504-512
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• 2016

In this paper, we propose a personalized event recommendation scheme using user's activity analysis and collaborative filtering in social network environments. The proposed scheme predicts un-evaluated attribute values through analysis of user activities, relationships, and collaborative filtering. The proposed scheme also incorporates a user's recent preferences by considering the recent history for the user or context-aware information to precisely grasp the user's preferences. As a result, the proposed scheme can recommend events to users with a high possibility to participate in new events, preventing indiscriminate recommendations. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.53-63
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• 2010

Recently, various monitoring systems are implemented actively by using wireless sensor networks(WSN). When implementing WSN-based monitoring system, there are three important issues to consider. At First, we need to consider a sensor node failure detection method to support the ongoing monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient data filtering method to reduce energy consumption. At Last, a reducing processing overhead method is necessary. The existing Kalman filtering scheme has good performance on data filtering, but it causes too much processing overhead to estimate sensed data. To solve these problems, we, in this paper, propose a new data filtering scheme based on data statical analysis. First, the proposed scheme periodically aggregates node survival massages to support a node failure detection. Secondly, to reduce energy consumption, it sends the sample data with a node survival massage and do data filtering based on those messages. Finally, it analyzes the sample data to estimate filtering range in a server. As a result, each sensor node can use only simple compare operation for filtering data. In addition, we show from our performance analysis that the proposed scheme outperforms the Kalman filtering scheme in terms of the number of sending messages.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.55-62
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• 2009

Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.

• MALSORI
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• no.60
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• pp.181-190
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• 2006

This paper describes a speech enhancement scheme that leads to significant improvements in recognition performance when used in the ASR front-end. The proposed approach is based on adaptive comb filtering and an MMSE-related parameter estimator. While adaptive comb filtering reduces noise components remarkably, it is rarely effective in reducing non-stationary noises. Furthermore, due to the uniformly distributed frequency response of the comb-filter, it can cause serious distortion to clean speech signals. This paper proposes an improved comb-filter that adjusts its spectral magnitude to the original speech, based on the speech absence probability and the gain modification function. In addition, we introduce the modified comb filtering-based speech enhancement scheme for ASR in mobile environments. Evaluation experiments carried out using the Aurora 2 database demonstrate that the proposed method outperforms conventional adaptive comb filtering techniques in both clean and noisy environments.