• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.163-163
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• 2018

미래 수문 분석을 위한 기후변화 연구는 전 세계적으로 많이 수행되어 왔다. 하지만 불확실성 요소로 인해 연구 결과를 활용하는데 있어 여전히 한계가 있다. 따라서 장기적 측면의 기후변화에 대한 연구와 함께 단기간의 엘리뇨, 라니냐와 같은 자연적 기후시스템의 변동에 대한 연구도 현재 진행되고 있다. 본 연구에서는 IRI 연구소에서 매월 전지구 관측자료로 4-7개월 예측을 수행한 GCM 모형 자료를 활용하여 강우 발생을 예측하였다. 한국의 금강유역을 대상유역으로 하였으며, 계절에 따른 강우 변동성을 고려하기 위해 비동질성 은닉 마코프 모형(Nonhomogeneous Hidden Markov Model, NHMM)을 이용하여 일 강우를 모의하였다. 본 연구 결과는 강우 모의를 통한 자연 재난에 대한 예측의 정확도를 향상시키는 새로운 방법론을 제시하는데 활용될 수 있을 것으로 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.313-319
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• 1999

We devise the 3 models of Multilayered Recurrent Prediction Neural Network(MLRPNN), which are obtained by modifying the Multilayered Perceptron(MLP) with 4 layers. We experimentally study the speech recognition performance of 3 models by a comparative method, according to the variation of the prediction order, the number of neurons in two hidden layers, initial values of connecting weights and transfer function, respectively. By the experiment, the recognition performance of each MLRPNN is better than that of MLP. At the model that returns the output of the upper hidden layer to the lower hidden layer, the recognition performance shows the best value. All MLRPNNs, which have 10 or 15 neurons in the upper and lower hidden layer and is predicted by 3rd or 4th order, show the improved speech recognition rate. On learning, these MLRPNNs have a better recognition rate when we set the initial weights between -0.5 and 0.5, and use the unipolar sigmoid transfer function in the lower hidden layer.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.9-17
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• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.786-795
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• 2002

In this paper, we present a histogram and moment-based vidoe scencd change detection technique using hierarchical Hidden Markov Models(HMMs). The proposed method extracts histograms from a low-frequency subband and moments of edge components from high-frequency subbands of wavelet transformed images. Then each HMM is trained by using histogram difference and directional moment difference, respectively, extracted from manually labeled video. The video segmentation process consists of two steps. A histogram-based HMM is first used to segment the input video sequence into three categories: shot, cut, gradual scene changes. In the second stage, a moment-based HMM is used to further segment the gradual changes into a fade and a dissolve. The experimental results show that the proposed technique is more effective in partitioning video frames than the previous threshold-based methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.35-45
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• 2004

In explosively increasing internet environments, information security is one of the most important consideration. Nowadays, various security solutions are used as such problems countermeasure; IDS, Firewall and VPN. However, basically internet has much vulnerability of protocol itself. Specially, it is possible to establish a covert channel using TCP/IP header fields such as identification, sequence number, acknowledge number, timestamp and so on. In this Paper, we focus cm the covert channels using identification field of IP header and the sequence number field of TCP header. To detect such covert channels, we used Support Vector Machine which has excellent performance in pattern classification problems. Our experiments showed that proposed method could discern the abnormal cases(including covert channels) from normal TCP/IP traffic using Support Vector Machine.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.151-162
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• 2021

Steganography is an advanced technique that hides secret messages by transforming them into subtle noise and spreading them within multimedia files such as images, video and audio. This technology has been exploited in a variety of espionage and cyber attacks. SNS messenger is an attractive SNS Service platform for sending and receiving multimedia files, which is the main medium of steganography. In this study, we proposed two noble steganography communication techniques that guarantee the complete reception rate through the use of thumbnail images in the SNS messenger environment. In addition, the feasibility was verified through implementation and testing of the proposed techniques in a real environment using KakaoTalk, a representative SNS messenger in south korea. By proposing new steganography methods in this study, we re-evaluate the risk of the steganography methods and promoted follow-up studies on the corresponding defense techniques.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.435-446
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• 2014

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966~2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.411-416
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• 2007

The General security method of wireless network provides a confidentiality of communication contents based on the cryptographic stability against a malicious host. However, this method exposes the logical and physical addresses of both sender and receiver, so transmission volume and identification of both may be exposed although concealing that content. Covered address scheme that this paper proposes generates an address to which knapsack problem using super increasing sequence is applied, and replaces the addresses of sender and receiver with addresses from super increasing sequence. Also, proposed method changes frequently secret addresses, so a malicious user cannot watch a target system or try to attack the specific host. Proposed method also changes continuously a host address that attacker takes aim at. Accordingly, an attacker who tries to use DDoS attack cannot decide the specific target system.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.156-163
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• 2012

Recently, Information Hiding Technology are becoming increasingly demanding in the field of international security, military and medical image This paper proposes data hiding technique utilizing parity checker for gray level image. many researches have been adopted LSB substitution and XOR operation in the field of steganography for the low complexity, high embedding capacity and high image quality. But, LSB substitution methods are not secure through it's naive mechanism even though it achieves high embedding capacity. Proposed method replaces LSB of each pixel with XOR(between the parity check bit of other 7 MSBs and 1 Secret bit) within one pixel. As a result, stego-image(that is, steganogram) doesn't result in high image degradation. Eavesdropper couldn't easily detect the message embedding. This approach is applying the concept of symmetric-key encryption protocol onto steganography. Furthermore, 1bit of symmetric-key is generated by the self-reference of each pixel. Proposed method provide more 25% embedding rate against existing XOR operation-based methods and show the effect of the reversal rate of LSB about 2% improvement.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.