• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.6
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• pp.546-550
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• 2013

We propose a novel negative differential resistance (NDR) device with ultra-high peak-to-valley current ratio (PVCR) by combining pn junction diode with depletion mode nanowire (NW) transistor, which suppress the valley current with transistor off-leakage level. Band-to-band tunneling (BTBT) Esaki diode with degenerately doped pn junction can provide multiple switching behavior having multi-peak and valley currents. These multiple NDR characteristics can be controlled by doping concentration of tunnel diode and threshold voltage of NW transistor. By designing our NDR device, PVCR can be over $10^4$ at low operation voltage of 0.5 V in a single peak and valley current.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.

• Journal of Preventive Medicine and Public Health
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• v.31 no.2 s.61
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• pp.285-292
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• 1998

To investigate the effect of smoking on the hearing threshold, 1,887 adult male workers who visited health care center during the period 1 January 1996 - 31 July 1997 were selected. Air-conduction hearing threshold level, diastolic blood pressure(DBP), total cholesterol, fasting blood sugar(FBS), hematocrit and obesity were measured. The data on age, occupation, and smoking were collected. Air-conduction hearing threshold in smoker was significantly higher than non-smoker in categories of 250, 500, 1000 and 4000 Hz(p<0.05). Mean values of PTA-low, PTA-mid, and PTA-high in smoker were also significantly higher than non-smoker(p<0.05). In multiple regression analysis, smoking is likely to play a significant role after controlling age, occupation, DBP, FBS, total cholesterol, hematocrit, and obesity(p<0.05). The hearing threshold was significantly increased with increasing age(p<0.05), the manufacturing worker may have higher hearing threshold(p<0.05). The higher hematocrit and the more obese, the higher hearing threshold(p<0.05). However, DBP, total cholesterol and FBS were not significantly related with hearing threshold level. In conclusion, smoking was significantly related with the hearing threshold level in adult men.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1047-1062
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• 2018

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for $5^{th}$ generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.

• The Korean Journal of Physiology and Pharmacology
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• v.11 no.5
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• pp.221-226
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• 2007

Melittin-induced tonic pain model is characterized by local inflammation, edema, spontaneous flinchings, and sustained mechanical hypersensitivity. These nociceptive responses are mediated through selective activation of capsaicin-sensitive primary afferent fibers by melittin. The present study was undertaken to elucidate the role of peripheral 5-hydroxytryptamine(5-HT) receptors in the melittin-induced nociceptive responses. Changes in mechanical threshold, flinching behaviors and paw thickness were measured in rat intraplantarly injected with melittin($40{\mu}g/paw$) alone or treated together with melittin and 5-HT receptor antagonists. WAY-100635($100{\mu}g\;&\;200{\mu}g/paw$), isamoltane hemifumarate($100{\mu}g\;&\;200{\mu}g/paw$), methysergide maleate($60{\mu}g,\;120{\mu}g\;&\;200{\mu}g/paw$) and ICS-205,930($100{\mu}g\;&\;200{\mu}g/paw$) were intraplantarly injected 20 min before melittin injection. All 5-HT receptor antagonists tested in this experiment significantly attenuated the ability of melittin to reduce mechanical threshold and to induce flinching behaviors. 5-HT receptor antagonists, except ICS-205,930, had mild inhibitory effect on melittin-induced edema. These experimental findings suggest that multiple peripheral 5-HT receptors are involved in the melittin-induced nociceptive responses.

• Journal of the Korean institute of surface engineering
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• v.48 no.3
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• pp.105-109
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• 2015

We studied emission characteristics of blue fluorescent multi-tandem OLEDs using $Al/MoO_x$ as charge generation layer(CGL). Threshold voltage for 2, 3, 4, and 5 units tandem OLEDs was 8, 11, 14 and 18 V, respectively. The threshold voltage in multi-tandem OLEDs was lower than multiple of 4 V for the single OLED. Maximum current efficiency and maximum quantum efficiency of single OLED were 7.6 cd/A and 5.5%. Maximum current efficiency for 2, 3, 4, and 5 units tandem OLEDs was 22.6, 31.4, 41.2, and 46.6 cd/A, respectively. Maximum quantum efficiency for 2, 3, 4, and 5 units tandem OLEDs was 11.8, 15.8, 21.8, and 25.6%, respectively. The maximum current efficiency and maximum quantum efficiency in multi-tandem OLEDs were higher than multiple of those for the single OLED. The intensity for 508 nm peak was changed and the peak wavelength was red shift by increase of tandem unit in electroluminescent emission spectra. These phenomena can be caused by micro-cavity effect with increasing of organic layer thickness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.1
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• pp.29-34
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• 2004

We present the first demonstration of waveguide-type depleted optical thyristor laser diode with InGaAs/InGaAsP multiple quantum well structure. The measured switching voltage and current are 4.63 V and 10uA respectively. The holding voltage and current are respectively 0.59 V, 20uA. The lasing threshold current at the temperature of $25^{\circ}C$ and $10^{\circ}C$ are 111 mAA and 72.5 mA, respectively. The lasing wavelength is centered at 1.561um at a bias current equal to 1.41 times threshold.