• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.50-54
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• 2014

This study presents a method for heart rate detection using oscillation wave signal and tries to enhance the performance of peak detection. For this objective, the method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• Journal of Veterinary Clinics
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• v.16 no.1
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• pp.128-137
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• 1999

Plasma progesterone (P$_4$) concentrations were measured for confirming the estrus observation and for the early pregnancy diagnosis in 130 cows of small farmers. Ultrasonographic examinations were performed from day 30 after artificial insemination to establish the characteristic ultrasonographic appearances of gestational structures in each pregnant stages. Of the 130 cows inseminated, 111 cows (85.4%) were an ovulatory estrus, 12 cows (9.2%) were an unovulatory estrus, and 7 cows (5.4%) were the error of estrus detection, respectively. The accuracy for early pregnancy diagnosis in 111 ovulatory estrus cows achieved when the discriminatory concentration at day 21 after artificial insemination was placed at 3.0 ng-/ml in plasma, was 86.7 % for positive diagnosis and 100% for negative diagnosis, respectively. Pregnancy diagnosis by ultrasonography were performed to evaluate gestational structures from day 30 after artificial insemination in 83 cows. Pregnant cows were 72 of 83 cows. The characteristic ultrasonography of gestational structures in each gestational stages was as follows. The embryo proper was observed within anechoic fetal fluid between 28 and 40 days after insemination, and amnion and embryonic heartbeat was also detected in this period. Between days 41 and 50, embryo proper was detected as an discriminated from head and body, and forelimb buds and hindlimb buds were also observed in this period. Between days 51 and 60, an embryo proper was clearly discriminated from head and body, and fetal movement, forelimb buds and hindlimb buds were observed in this period. Between days 61 and 70, fetus was completely developed, and fetal skeleton, organs and cotyledon were observed. After day 71, each organs of fetus were rapidly developed and a fetus was partially observed in screen because fetus was too big and larger, These results indicate that plasma P$_4$ determination at days 0,6 and 21 after artificial insemination can be utilized for confirming the estrus observation and for early pregnancy diagnosis. Also, ultrasonography was reliable method for early pregnancy diagnosis at day 30 after artificial insemination.

• Journal of the Korea Society for Simulation
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• v.22 no.3
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• pp.35-41
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• 2013

This paper applies an active period based bottleneck detection method to flow shop manufacturing system with limited buffer size. Manufacturing systems are constrained by one or more bottlenecks which degrades the system throughput. Conventional bottleneck detection methods include the waiting time or queue length of production stations and their utilization. Due to the random events such as production time of items, machine failure and repair times, the systems may change over time, and subsequently bottlenecks shift from one station to another station. Active period of working station may cause other stations to wait for productions. Information when and where active periods occur helps to find bottlenecks in production systems. Based on these informations, we predict bottlenecks in applying AweSim simulation language. We compare the simulation results of conventional methods with those obtained from duration of active period method, and duration ratio method of both sole and shift bottleneck periods. Even though simulation results are from simple flow shop model, they are quite promising for predicting bottlenecks of production stations. We hope this study aids in decision making regarding the improving system production yield and allocation of available resources of system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.287-294
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• 2016

APT attack aimed at the interruption of information and communication facilities and important information leakage of companies. it performs an attack using zero-day vulnerabilities, social engineering base on collected information, such as IT infra, business environment, information of employee, for a long period of time. Fragmentary response to cyber threats such as malware signature detection methods can not respond to sophisticated cyber-attacks, such as APT attacks. In this paper, we propose a cyber intrusion detection model for countermeasure of APT attack by utilizing heterogeneous system log into big-data. And it also utilizes that merging pattern-based detection methods and abnormality detection method.

• Journal of Microbiology and Biotechnology
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• v.17 no.11
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• pp.1885-1889
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• 2007

The tissue blot immunobinding assay (TBIA) is widely used for the detection and localization of plant viruses in various plant tissues. The basic experimental procedures of TBIA sampling and blotting were simplified using commercially available micropipette tips. This method was termed the ring-blot immunobinding assay (R-BIA), as the blot on the membrane forms a ring shape. The detection efficacy of R-BIA was tested for two chili pepper viruses, pepper mild mottle tobamovirus (PMMoV) and pepper mottle potyvirus (PepMoV), following the optimized serological procedures of TBIA (length of the incubation period and BSA concentration, and primary and secondary antibodies). Sensitivity of the R-BIA was about 1 ng/ml of purified PMMoV in pepper leaf sap from a healthy pepper plant. R-BIA also showed high specificity in the detection of PMMoV and PepMoV. Moreover, the modified sampling and blotting procedures were simpler and more reliable than other TBIA methods (such as whole-leaf blotting and crushed-leaf blotting), suggesting that the R-BIA may be used for medium- to large-scale detection of plant viruses in laboratories with minimal facilities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1253-1258
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• 2013

This Paper deals with detection and defense methods for underwater weapons because there are so many dangers of underwater weapons not only in the war period but also in the peace time. Underwater mines are the representative strategic arms. The sensors and target detection methods, threat elimination method of mines included in this paper. Among the various sensors of mine, we use the magnetometor for target detection method in the simulation and execute the analysis of magnetic field of detected target ships. It will be also provided that effectiveness of target detection, sweeping method of mine, tactics of mine planning and mine sweeping and so on.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.421.3-422
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• 2002

Carvedilol is a nonselective $\beta$-adrenoblocking agent with vasodilating activities. The pharmacokinetics and pharmacodynamics of carvedilol were studied in healthy volunteers following single oral administration. After oral administration of carvedilol 25mg. blood samples were collected for a period of 30 hours. Plasma concentrations of carvedilol were determined by HPLC with spectrofluorometric detection. The effects of carvedilol on systolic and diastolic blood pressure (BP) and heart rate (HR) were measured during the same period. (omitted)

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.77-83
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• 2004

In this paper, the Classification of Energy Labeling has been proposed. Energy parameters of input signal which are extracted from each phoneme are labelled. And groups of labelling according to detected energies of input signals are detected. Next. DTW processes in a selected group of labeling. This leads to DTW processing faster than a previous algorithm. In this Method, because an accurate detection of parameters is necessary on the assumption in steps of a detection of speeching duration and a detection of energy parameters, variable windows which are decided by pitch period are used. A pitch period is detected firstly : next window scale is decided between 200 frames and 300 frames. The proposed method makes it possible to cancel an influence of windows and reduces the computational complexity by $25\%$.