• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1A
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• pp.55-61
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• 2005

Mobile communication systems are required not only to support high-data-rate transmissions in favorable channel conditions but also to be able to tolerate hostile environments possibly encountered by cellular communications. This paper compares CDMA(Code Division Multiple Access), OFDM(Orthogonal Frequency Division Multiplexing), and MC-CDMA(Multi-Carrier CDMA)with inaccurate channel estimates and low SNR environments. The equations and simulation results show that the performance losses in CDMA systems due to imprecise channel estimates are not considerable while frequency-hopping of OFDM systems can result in more than l0dB SNR losses. Also, this paper show that frequency-spreading of MC-CDMA can be very helpful for channel compensation performances than frequency-hopping or time-spreading of OFDM.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.595-612
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• 2011

Most organizations use performance appraisal system to evaluate the effectiveness and efficiency of their employees. In evaluating staff performance, performance appraisal usually involves awarding numerical values or linguistic labels to employees performance. These values and labels are used to represent each staff achievement by reasoning incorporated in the arithmetical or statistical methods. However, the staff performance appraisal may involve judgments which are based on imprecise data especially when a person (the superior) tries to interpret another person's (his/her subordinate) performance. Thus, the scores awarded by the appraiser are only approximations. From fuzzy logic perspective, the performance of the appraisee involves the measurement of his/her ability, competence and skills, which are actually fuzzy concepts that can be captured in fuzzy terms. Accordingly, fuzzy approach can be used to handle these imprecision and uncertainty information. Therefore, the performance appraisal system can be examined using Fuzzy Logic Approach, which is carried out in the study. The study utilized a Cascaded fuzzy inference system to generate the performance qualities of some University non-teaching staff that are based on specific performance appraisal criteria.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1431-1440
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• 2024

The study aimed to develop a laser-based distance meter (LDM) to improve water surface identification for clinical MeV electron beam dosimetry, as inaccurate water surface determination can lead to imprecise positioning of ionization chambers (ICs). The LDM consisted of a laser ranging sensor, a signal processing microcontroller, and a tablet PC for data acquisition. I₅₀ (the water depth at which ionization current drops to 50 % of its maximum) measurements of electron beams were performed using six different types of ICs and compared to other water surface identification methods. The LDM demonstrated reproducible I₅₀ measurements with a level of 0.01 cm for all six ICs. The uncertainty of water depth was evaluated at 0.008 cm with the LDM. The LDM also exposed discrepancies between I₅₀ measurements using different ICs, which was partially reduced by applying an optimum shift of IC's point of measurement (POM) or effective point of measurement (EPOM). However, residual discrepancies due to the energy dependency of the cylindrical chamber's EPOM caused remained. The LDM offers straightforward and efficient means for precision water surface identification, minimizing reliance on individual operator skills.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6563-6568
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• 2013

Background: Tobacco use and quit attempts are two key indicators of the Global Adult Tobacco Survey (GATS) that assess quit attempts among current as well as former tobacco users. The relevant data have inherent policy implications for tobacco cessation programme evaluation. This study aimed to review the concepts of quit attempt assessment and quantifying invalid responses considering GATS-India data. Materials and Methods: GATS assessment of tobacco use and quit attempts were examined in the current literature. Two categories of invalid responses were identified by stratified analysis of the duration of last quit attempt among current users and duration of abstinence among former users. Category A included absolute invalid responses when time-frame of assessment of current tobacco use and less than former tobacco use were violated. Category B included responses that violated the unit of measurement of time. Results: Current daily use, current less than daily use and former use in GATS were imprecisely defined with overlapping of time-frame of assessment. Overall responses of 3,102 current smokers, 4,036 current smokeless users, 1,904 former smokers and 1,343 former smokeless users were analyzed to quantify invalid responses. Analysis indicated overall 21.2% (category A: 7.32%; category B: 17.7%) and 22.7% (category A: 8.05%; category B: 18.1%) invalid responses among current smokers and smokeless users respectively regarding their duration of last quit attempt. Similarly overall 6.62% (category A: 4.7%; category B: 2.3%) and 10.6% (category A: 8.6%; category B: 3.5%) invalid responses were identified among former smokers and smokeless users respectively regarding their duration of abstinence. Conclusions: High invalid responses for a single assessment are due to the imprecise definition of current use, former use and quit attempt; and failure to utilize opportunity of direct data entry interface use during the survey to validate responses instantly. Redefining tobacco use and quit attempts considering an appropriate timeframe would reduce invalid responses.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.24-30
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• 2016

The accuracy of small and low-cost CCD cameras is insufficient to provide data for precisely tracking unmanned aerial vehicles (UAVs). This study shows how a quad rotor UAV can hover on a human targeted tracking object by using data from a CCD camera rather than imprecise GPS data. To realize this, quadcopter UAVs need to recognize their position and posture in known environments as well as unknown environments. Moreover, it is necessary for their localization to occur naturally. It is desirable for UAVs to estimate their position by solving uncertainty for quadcopter UAV hovering, as this is one of the most important problems. In this paper, we describe a method for determining the altitude of a quadcopter UAV using image information of a moving object like a walking human. This method combines the observed position from GPS sensors and the estimated position from images captured by a fixed camera to localize a UAV. Using the a priori known path of a quadcopter UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated quadcopter UAV's altitude. Since the equations are based on the geometric constraint equation, measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the quadcopter UAV. The Kalman filter scheme is applied for this method. Its performance is verified by a computer simulation and experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.115-131
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• 2008

Small mobile devices have become commonplace in our everyday life, from cellular phones to PDAs. Discovering dense regions for the mobile devices is one of the problems of grate practical importance. It can be used in monitoring movement of vehicles, concentration of troops, etc. In this paper, we propose a novel algorithm on continuously clustering a large set of mobile objects. We assume that a mobile object reports its position only if it is too far away from the expected position and thus the location data received may be imprecise. To compute the location of each individual object could be costly especially when the number of objects is large. To reduce the complexity of the computation, we want to first cluster objects that are in proximity into a group and treat the members in a group indistinguishable. Each individual object will be examined only when the inaccuracy causes ambiguity in the final results. We conduct extensive experiments on various data sets and analyze the sensitivity and scalability of our algorithms.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.3
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• pp.34-43
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• 2021

Wake turbulence generated by the lead aircraft has a significant impact on the following aircraft and it is has been considered a key factor to consider whenin determining the longitudinal separation between the aircraft. ICAO classifies aircraft into four wake turbulence categories based on the maximum takeoff weight and provides the longitudinal separation minima for each category. Due to richer measured data and better understanding of physical processes, it is raised that classifying aircraft with only four wake turbulence grades is imprecise and leads to over-separation in many instances. In this regards, much research on a new method of classifying Wake Turbulence Category(Re-CAT) has been done by EURO-CONTROL, FAA, and ICAO. The main purpose of this study is to conduct a comparative analysis of the existing wake turbulence separation standards with Re-CAT in terms of departure capacity and the resulting benefits of Re-CAT using the data from the Incheon International Airport. The results show that EUROCONTROL and new ICAO standards have the greater effect on reducing wake turbulence separation, compared to the FAA RE-CAT standards. It is also concluded that Re-CAT presents different results of wake turbulence separation depending on the flight characteristics of each airport.