• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.5 no.2
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• pp.193-199
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• 1995

This study was designed to investigate the difference of time weighted average(TWA) of noise levels and noise doses by the different operating parameter settings such as exchange rate, threshold level and criterion level for noise dosimeter in the field measurements of noise at industrial working environments. The time weighted averages of noise level and noise doses for noise working environments were determined by noise dosimeter on 80 workers employed at 20 industrial establishments of 8 industries. The results obtained were as follows: 1. The mean time weighted average(TWA) of the noise working environments by the operating parameter settings showed 93.4 dB(A) in 3 dB of exchange rate, 80 dB of threshold level and 90dB of criterion level 92.0 dB(A) in 3 dB-exchange rate, 90 dB-threshold level and 90 dB-criterion level, in 90.8 dB(A) in 5 dB of exchange rate, 80 dB of threshold level and 90 dB of criterion level, and 86.7 dB(A) in 5 dB of exchange rate, 90 dB of threshold level and 90dB of criterion level. 2. ln group of noise level less than 90 dB(A), mean TWAs of 80 dB of threshold level were significantly higher than that of 90 dB of threshold level in 3 dB and 5 dB of exchange rate. 3. The case exceeded threshold limit value of noise was 49(61.3 %) in 3dB, 80dB and 90 dB setting, 44(55.0 %) in 3 dB, 90 dB, 90 dB setting, 33(41.3 %) in 5 dB, 80dB, 90 dB setting and 26(32.5%) in 5 dB, 90 dB, 90 dB setting. Above considerations in mind, it is suggested that exchange rate and threshold level be specified in related laws and regulations in the evaluation of working environments noise.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.66-70
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• 2013

In this paper, Threshold setting method is proposed to improve detection probability for cooperative sensing. Even if cooperative users have all same false alarm rate, each user has different threshold due to pass ad-hoc channel. threshold level is related to detection probability. So, we select the highest threshold among cooperative users and then share threshold information for getting the high detection probability.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.74-85
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• 2010

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.73-77
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• 2010

Purpose: The nuclear medicine technology has been changed. The hard ware is developed so much. Also the soft ware performs a meritorious deed for the development of nuclear medicine technology. We could use the automated region of interest (ROI) instead of manual ROI. We want to know that what difference of quantitative analysis is there between automated ROI and manual ROI Materials and Methods: There are three experimental to make results. The first is what comparing the renal automated ROI and manual ROI. The second is that we compared three threshold ROI that size is difference each others with visible decision. The third is that we compared full, half, quarter automated background, and survey relative function. Results: Although the first has statistically not significant difference, the second and third have significant difference. Threshold, setting smaller threshold then renal outline or bigger, has statistically significant difference (p<0.01). The third is performed with the first experimental. Full background has significant difference, comparing each three type background (p<0.05). Conclusion: The results that there is not significant difference between automated ROI and manual ROI will increase objectivity and operator's convenience. We could know that smaller threshold then renal out line has significant difference in the second experimental. And the third experimental has results because of a increased background nearby live and spleen.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.161-171
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• 2014

When the Hough transform is applied to identify an instance of a given model, the output is typically a histogram of votes cast by a set of image features into a parameter space. The next step is to threshold the histogram of counts to hypothesize a given match. The question is "What is a reasonable choice of the threshold?" In a standard implementation of the Hough transform, the threshold is selected heuristically, e.g., some fraction of the highest cell count. Setting the threshold too low can give rise to a false alarm of a given shape(Type I error). On the other hand, setting the threshold too high can result in mis-detection of a given shape(Type II error). In this paper, we derive two conditional probability functions of cell counts in the accumulator array of the generalized Hough transform(GHough), that can be used to select a scientific threshold at the peak detection stage of the Ghough.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1420-1428
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• 2017

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• International Journal of Highway Engineering
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• v.17 no.1
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• pp.129-142
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• 2015

PURPOSES : The purpose of this study is to suggest a basis for setting appropriate safety goals specifically related to the threshold zone luminance in a vehicular traffic tunnel. METHODS : In the test, drivers were divided into two groups. One group consisted of all drivers (average drivers) group with an age ratio of drivers holding domestic driver's license and driver group by age to produce threshold zone luminance in the tunnel. The threshold zone luminance produced as a result was used to analyze how it affects the safety level of each driver group and provide a basis for setting an appropriate safety criterion that can be used to determine threshold zone luminance. We used test equipment, test conditions, and ananalysis of threshold zone luminance identical to that reported by ChoandJung(2014) but the values of adaptation luminance in our analys is were expanded to range from100 to $10,000cd/m^2$. RESULTS : Adaptation luminance and threshold zone luminance are found to be related by a quadratic function. The threshold zone luminance needed by older drivers to ensure a certain safety level is significantly higher than that for drivers of other age brackets when adaptation luminance increases. 56% of older drivers are at an increased risk of an accident at the same luminance for which the safety level of average drivers is 75%. The safety level that can be achieved for older drivers increases to above 60% when threshold zone luminance level is set with the goal of attaining a safety level of more than 85% for average drivers. The safety level that can be attained for average drivers is above 90% when the threshold zone luminance is high enough to ensure over 75% in the safety level of older drivers. Results of this study are applicable to highways and others whose designed speed is 100 km/h. CONCLUSIONS : Threshold zone luminance determined on the basis of drivers having average visual ability is of limited value as a performance standard for ensuring the safety of older drivers. Hence, safety level for older drivers should be considered separately from safety levels for drivers with an average ability to avoid risk. Upward adjustment of older drivers' safety level in the process of determining appropriate threshold zone luminance in a vehicular traffic tunnel may bring both tangible and intangible benefit as a result of reducing accidents. However, there is an associated dollar cost arising from installing and operating lights. As a result, the economic impact of these trade-offs should also be considered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1797-1805
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• 1993

An algorithm for the threshold decision from the maximum mismatching correlation value in a direct-sequence spread-spectrum system is presented. This algorithm is named the TDMMC(Threshold Decision from the Maximum Mismatching Correlation value). The purpose of the algorithm is to set the decision threshold in the system which will provide large probability of signal detection. Using this algorithm, the proper setting of the threshold for various SNRs is possible. An additional block called the Threshold Block is used to improve the system performance. The result from the computer simmulation has shown that appling the TDMMC to the noncoherent DS/SS-CSK system can achieve performance improvement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.733-744
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• 2012

Dynamic threshold public-key encryption provides dynamic setting of the group of all users, receivers and the threshold value. Over recent years, there are many studies on the construction of scheme, called ID-based dynamic threshold encryption, which combines the ID-based encryption with dynamic threshold encryption. In this paper, we analyze the ID-based dynamic threshold encryption proposed by Xing and Xu in 2011, and show that their scheme has a structural problem. We propose a conversion method from ID-based encryption which uses the bilinear map to ID-based dynamic threshold encryption. Additionally, we prove this converted scheme has CPA security under the full model.