• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2A
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• pp.147-156
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• 2004

This paper presents an implementation and performance analysis of an access channel decoder which exploits a diversity gain due to the independent magnitude of received signals energy at each of antenna elements of a smart antenna BTS (Base-station Transceiver Subsystem) operating in CDMA2000 1X signal environment. Proposed access channel decoder consists of a searcher supporting 4 fingers, Walsh demodulator, and demodulator controller. They have been implemented with 5 of 1 million-gate FPGA's (Field Programmable Gate Array) Altera's APEX EP20K1000EBC652 and TMS320C6203 DSP (digital signal processing). The objective of the proposed access channel decoders is to enhance the data retrieval at co]1-site during the access period, for which the optimal weight vector of the smart antenna BTS is not available. Through experimental tests, we confirmed that the proposed access channel decoder exploitng the diversity technique outperforms the conventional one, which is based on a single antenna channel, in terms of detection probability of access probe, access channel failure probability, and $E_{b/}$ $N_{o}$ in Walsh demodulator.r.r.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.463-470
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• 2009

In this paper, we propose the advanced fire-flame detection algorithm using camera image for better performance than previous sensors-based systems which is limited on small area. Also, previous works using camera image were depend on a lot of heuristic thresholds or required an additional computation time. To solve these problems, we use statistical values and divide image into blocks to reduce the processing time. First, from the captured image, candidate flame regions are detected by a background model and fire colored models of the fire-flame. After the probability models are formed using the change of luminance, wavelet transform and the change of motion on time axis, they are used for membership function of fuzzy logic. Finally, the result function is made by the defuzzification, and the probability value of fire-flame is estimated. The proposed system has shown better performance when it compared to Toreyin's method which perform well among existing algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• Parasites, Hosts and Diseases
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• v.39 no.2
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• pp.185-192
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• 2001

To evaluate the factors that determine the transmission level of vivax malaria using vectorial capacity, entomological surveys were conducted from .lune to August, 2000. From 6 nights of human-bait collection in Paju, the human biting rate (ma) was counted as 87.5 bites/man/night. The parity of Anopheles sinensis from human baiting collections fluctuated from 41% to 71% (average 48.8%) of which the rate gradually increased as time passed on: 35.2% in Jun. ; 55.0% in July; 66.2% in Aug. From this proportion of parous, we could estimate the probability of daily survival rate of An. sinensis to be 0.79 assumed with 3 days gonotrophic cycle and the expectancy of infective life through 11 days could be defined as 0.073. Blood meal analysis was performed using ELISA to determine the blood meal source. Only 0.8% of blood meals were from human hosts. We could conclude that An. sinensis is highly zoophilic (cow 61.8%) Malaria is highly unstable (stability index < 0.5) in this area. From these data, vectorial capacity VC) was determined to be 0.081. In spite of a high human biting rate (ma), malaria transmission potential is very low due to a low human blood index. Therefore, we could conclude that malaria transmission by An. sinensis is resulted by high population density, not by high transmission potential. For this reason, we need more effort to decrease vector population and vector-human contact to eradicate malaria in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1589-1596
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• 2010

In this paper, we propose the method that reduce computational complexity for intermode decision using CBP(coded block pattern) and coded information of colocated-MB(macro block). Proposed method classifies MB into best-CBP and normal-CBP according to the characteristics of CBP. On best-CBP, it eliminates the computation for $8{\times}8$ mode on intermode decision process because the probability for SKIP mode and M-Type mode is 96.3% statistically. On normal-CBP, it selectively eliminates the amount of computation for bit-rate distortion cost, because it uses coded information of colocated-MB and motion vector cost in deciding SKIP mode and M-Type mode. The simulation results show that the proposed method reduces total coding time to 58.44% in average, and is effective in reducing computational burden in videos with little motion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.964-969
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• 2008

This paper proposes a HMM-based recognition method using DMSVQ(Dynamic Multi-Section Vector Quantization) codebook by DMS(Dynamic Multi-Section) model and fuzzy concept, as a study for speaker- independent speech recognition. In this proposed recognition method, training data are divided into several dynamic section and multi-observation sequences which are given proper probabilities by fuzzy rule according to order of short distance from DMSVQ codebook per each section are obtained. Thereafter, the HMM using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. Other experiments to compare with the results of recognition experiments using proposed method are implemented as a data by the various conventional recognition methods under the equivalent environment. Through the experiment results, it is proved that the proposed method in this study is superior to the conventional recognition methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.715-723
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• 2015

In this paper, we propose a context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm in Mobile Ad Hoc Networks. The existing algorithm in MANET, it has a vulnerability that the dynamic network topology and the absence of network expandability of mobility of nodes. The proposed algorithm performs cluster formation using a range of direction and threshold of velocity for the base-station, we calculate the exchange of the cluster head node probability using the direction and velocity for maintaining cluster formation. The DDV algorithm forms a cluster based on the cluster head node. As a result of simulation, our scheme could maintain the proper number of cluster and cluster members regardless of topology changes.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.183-190
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• 2017

This paper proposes an audio event detection method using Deep Neural Networks (DNN). The proposed method applies Feed Forward Neural Network (FFNN) to generate output probabilities of twenty audio events for each frame. Mel scale filter bank (FBANK) features are extracted from each frame, and its five consecutive frames are combined as one vector which is the input feature of the FFNN. The output layer of FFNN produces audio event probabilities for each input feature vector. More than five consecutive frames of which event probability exceeds threshold are detected as an audio event. An audio event continues until the event is detected within one second. The proposed method achieves as 71.8% accuracy for 20 classes of the UrbanSound8K and the BBC Sound FX dataset.