• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.5
• /
• pp.1316-1332
• /
• 2012

Formation flying is an important technology that enables high cost-effective organization of outer space aircrafts. The ad-hoc wireless network based on direct-sequence ultra-wideband (DS-UWB) techniques is seen as an effective means of establishing wireless communication links between aircrafts. In this paper, based on the theory of matched filter and error bits correction, a hybrid detection algorithm is proposed for realizing multiuser detection (MUD) when the DS-UWB technique is used in the ad-hoc wireless network. The matched filter is used to generate a candidate code set which may contain several error bits. The error bits are then recognized and corrected by an novel error-bit corrector, which consists of two steps: code mapping and clustering. In the former step, based on the modified optimum MUD decision function, a novel mapping function is presented that maps the output candidate codes into a feature space for differentiating the right and wrong codes. In the latter step, the codes are clustered into the right and wrong sets by using the K-means clustering approach. Additionally, in order to prevent some right codes being wrongly classified, a sign judgment method is proposed that reduces the bit error rate (BER) of the system. Compared with the traditional detection approaches, e.g., matched filter, minimum mean square error (MMSE) and decorrelation receiver (DEC), the proposed algorithm can considerably improve the BER performance of the system because of its high probability of recognizing wrong codes. Simulation results show that the proposed algorithm can almost achieve the BER performance of the optimum MUD (OMD). Furthermore, compared with OMD, the proposed algorithm has lower computational complexity, and its BER performance is less sensitive to the number of users.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.7
• /
• pp.2246-2253
• /
• 2000

In this paper, we show that one can enhance the performance of blind segmentation of phoneme boundaries by adopting the knowledge of Korean syllabic structure and the regions of voiced/unvoiced sounds. eh proposed method consists of three processes : the process to extract candidate phoneme boundaries, the process to detect boundaries of voiced/unvoiced sounds, and the process to select final phoneme boundaries. The candidate phoneme boudaries are extracted by clustering method based on similarity between two adjacent clusters. The employed similarity measure in this a process is the ratio of the probability density of adjacent clusters. To detect he boundaries of voiced/unvoiced sounds, we first compute the power density spectrum of speech signal in 0∼400 Hz frequency band. Then the points where this paper density spectrum variation is greater than the threshold are chosen as the boundaries of voiced/unvoiced sounds. The final phoneme boundaries consist of all the candidate phoneme boundaries in voiced region and limited number of candidate phoneme boundaries in unvoiced region. The experimental result showed about 40% decrease of insertion rate compared to the blind segmentation method we adopted.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2371-2374
• /
• 2003

In transmitting compressed video bit-stream over Internet, packet loss causes error propagation in both spatial and temporal domain, which in turn leads to severe degradation in image qualify In this paper, a new approach for the recovery of lost or erroneous Motion Vector(MV)s by clustering the movements of neighboring blocks by their homogeneity is proposed. MVs of neighboring blocks are clustered according to ALA(Average Linkage Algorithm) clustering and a representative value for each cluster is determined to obtain the candidate MV set. By computing the distortion of the candidates, a MV with the minimum distortion is selected. Experimental results show that the proposed algorithm exhibits better performance in many cases than existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.7
• /
• pp.1377-1382
• /
• 2012

In this paper, we propose a novel recognition method that extract source data from encoded signal that are displayed on FND mounted on home appliances. First of all, it find a candidate FND region from sequential difference images taken by smartphone and extract segment image using clustering RGB value. After that, it normalize segment images to correct a slant error and recognize each segments using a relative distance. Experiments show the robustness of the recognition algorithm on smartphone.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.7
• /
• pp.1123-1128
• /
• 2008

The necessity of online dynamic security assessment is getting apparent under Electricity Market environments, as operation of power system is exposed to more various operating conditions. For on-line dynamic security assessment, fast transient stability analysis tool is required for contingency selection. The TEF(Transient Energy Function) method is a good candidate for this purpose. The clustering of critical generators is crucial for the precise and fast calculation of energy margin. In this paper, we propose a new method for fast decision of mode of instability by using stability indices and energy margin. The method is a new version of our previous paper.[1] Case studies are showing very promising results.

• Journal of KIISE:Software and Applications
• /
• v.36 no.9
• /
• pp.683-690
• /
• 2009

One of the major problems that prevent us from building a good recognition system for large-scale on-line Chinese character recognition using HMMs is increasing recognition time. In this paper, we propose a clustering method to solve recognition speed problem and an efficient distance measure between HMMs. From the experiments, we got about twice the recognition speed and 95.37% 10-candidate recognition accuracy, which is only 0.9% decrease, for 20,902 Chinese characters defined in Unicode CJK unified ideographs.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.591-593
• /
• 1995

Artificial neural network based pattern recognition method is one of the most probable candidate for on-line power system transient stability analysis. Especially, Kohonen layer is an adequate neural network for the purpose. Each node of Kehonen layer competes on the basis of which of them has its clustering center closest to an input vector. This paper discusses Kohonen's LVQ(Learning Victor Quantization) and points out a defection of the algorithm when applied to the transient stability analysis. Only the clustering centers located near the decision boundary of the stability region is needed for the stability criterion and the centers far from the decision boundary are redundant. This paper presents a new algorithm ratted boundary searching algorithm II which assigns only the points that are near the boundary in an input space to nodes or Kohonen layer as their clustering centers. This algorithm is demonstrated with satisfaction using 4-generator 6-bus sample power system.

• Proceedings of the IEEK Conference
• /
• 2000.11c
• /
• pp.137-140
• /
• 2000

In this paper, we propose as new algorithm for the selection of key frames in a given video. For the selected key frames to be well defined, the selected key frames need to spread out on the whole temporal domain of the given video and guaranteed not to be duplicate. For this purpose, we take the first frame of each shot of the video as the candidate key frame to represent the video. To reduce the overall processing time, we eliminate some candidate key frames which are visually indistinct in the histogram difference. The key frames are then selected using a clustering processing based on the singly linked hierarchical tree. To make the selected key frames be distributed evenly on the whole video, the deviation and time difference between the selected key frames are used. The simulation results demonstrate that our method provides the better performance compared with previous methods.

• Structural Engineering and Mechanics
• /
• v.82 no.3
• /
• pp.271-282
• /
• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Bulletin of the Korean Chemical Society
• /
• v.35 no.1
• /
• pp.253-256
• /
• 2014

Functionalization of zigzag graphene nanoribbon (ZGNR) segment containing 120 C atoms with pyridine (3NV-ZGNR) defects was investigated on the basis of density-functional theory (DFT) calculations, results show that edge-modified ZGNRs by Sc can adsorb multiple hydrogen molecules in a quasi-molecular fashion, thereby can be a potential candidate for hydrogen storage. The stability of Sc functionalization is dictated by a strong binding energy, suggesting a reduction of clustering of metal atoms over the metal-decorated ZGNR.