• Smart Structures and Systems
• /
• v.11 no.1
• /
• pp.53-67
• /
• 2013

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2518-2533
• /
• 2020

In wireless sensor networks, adversaries may physically capture sensor nodes on the fields, and use them to launch false positive attacks (FPAs). FPAs could be conducted by injecting forged or old sensing reports, which would represent non-existent events on the fields, with the goal of disorientating the base stations and/or reducing the limited energy resources of sensor nodes on the fields. Researchers have proposed various mitigation methods against FPAs, including the statistical en-route filtering scheme (SEF). Most of these methods are based on key pre-distribution schemes and can efficiently filter injected false reports out at relay nodes through the verification of in-transit reports using the pre-distributed keys. However, their filtering power may decrease as time goes by since adversaries would attempt to capture additional nodes as many as possible. In this paper, we propose an adaptive key distribution method that could maintain the security power of SEF in WSNs under such circumstances. The proposed method makes, if necessary, BS update or re-distribute keys, which are used to endorse and verify reports, with the consideration of the filtering power and energy efficiency. Our experimental results show that the proposed method is more effective, compared to SEF, against FPAs in terms of security level and energy saving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.10a
• /
• pp.453-456
• /
• 2003

Spatial database systems achieve filtering steps with MBR(Minimum founding Rectangle) for efficient query processing, and then carry out refinement steps for candidate objects. While most operations require fast execution of filtering, it is necessary to increase the filtering rates and reduce the number of refinement steps in the low computing powered devices. The compact representation method is also needed in the mobile devices with low storage capacity. The paper proposes various approximation methods for efficient spatial operations in the multiplatform environments. This paper also designs a compression technique for MBR, which occupies almost 80% of index data in the two dimensional case. We also analyze the advantages and drawbacks of each method in terms of space utilization, filtering efficiency and speed.

• Journal of Applied Biological Chemistry
• /
• v.64 no.1
• /
• pp.1-4
• /
• 2021

Ambient air pollution, in particular, particulate matter (PM) pollution imposes serious health concerns such as hospitalization and premature deaths, worldwide. While commercial breathing masks are in use for protection against this hazardous issue, yet their efficiency in filtering PM was not up to the par, besides several other discomforts such as poor breathability due to reduced air flow, sweat production etc. In this study, commercial face mask coated with β-glucan, a high molecular weight polymer is tested for its efficacy in filtering PM. Quantification of PM before and after filtration and microscopic observation (using scanning electron microscopy (SEM)) of the fabric used in filtering the dust pollutants (generated from wood chips and cigarette) showed that β-glucan coated fabric were significantly efficient in capturing PM (size of 10 and 2.5 ㎛ in diameter) than that of the untreated control fabric, wherein the former had filtration efficacy with fold increase of 11.6 and 2.6 towards capturing PM2.5 and PM10 respectively than the latter. Thus, β-glucan coated fabric was found to be effective in filtering PM.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.339-345
• /
• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.433-444
• /
• 2020

This study proposes a method to improve the sleep stage and efficiency estimation of sleep apnea patients using a UWB (Ultra-Wideband) radar. Motion and respiration extracted from the radar signal were used. Respiratory signal disturbances by motion artifacts and irregular respiration patterns of sleep apnea patients are compensated for in the preprocessing stage. Preprocessing calculates the standard deviation of the respiration signal for a shift window of 15 seconds to estimate thresholds for compensation and applies it to the breathing signal. The method for estimating the sleep stage is based on the difference in amplitude of two kinds of smoothed respirations signals. In smoothing, the window size is set to 10 seconds and 34 seconds, respectively. The estimated feature was processed by the k-nearest neighbor classifier and the feature filtering model to discriminate between the sleep periods of the rapid eye movement (REM) and non-rapid eye movement (NREM). The feature filtering model reflects the characteristics of the REM sleep that occur continuously and the characteristics that mainly occur in the latter part of this stage. The sleep efficiency is estimated by using the sleep onset time and motion events. Sleep onset time uses estimated features from the gradient changes of the breathing signal. A motion event was applied based on the estimated energy change in the UWB signal. Sleep efficiency and sleep stage accuracy were assessed with polysomnography. The average sleep efficiency and sleep stage accuracy were estimated respectively to be about 96.3% and 88.8% in 18 sleep apnea subjects.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.2
• /
• pp.113-119
• /
• 2016

Wireless sensor networks are vulnerable to an adversary due to scarce resources and wireless communication. An adversary can compromise a sensor node and launch a variety of attacks such as false report injection attacks. This attack may cause monetary damage resulting in energy drain by forwarding the false reports and false alarms at the base station. In order to address this problem, a number of en-route filtering schemes has been proposed. Notably, a dynamic en-route filtering scheme can save energy by filtering of the false report. In the key dissemination phase of the existing scheme, the nodes closer to the source node may not have matching keys to detect the false report. Therefore, continuous attacks may result in unnecessary energy wastage. In this paper, we propose a key re-distribution scheme to solve this issue. The proposed scheme early detects the false report injection attacks using initially assigned secret keys in the phase of the key pre-distribution. The experimental results demonstrate the validity of our scheme with energy efficiency of up to 26.63% and filtering capacity up to 15.92% as compared to the existing scheme.

• Journal of Wetlands Research
• /
• v.3 no.1
• /
• pp.75-89
• /
• 2001

Constructed wetlands were typically cost less to build and operate, and require less energy than standard mechanical treatment technology but they have similar performance to centralized wastewater treatment plants. Therefore, they were constructed especially many in rural areas, where are small villages but not industries. Accordingly, plantless column tests were performed to investigate the possibility on using zeolite as a filter medium of constructed wetland for the wastewater treatment. $COD_{cr}$ removal efficiency was 94.63% at hydraulic load $314L/m^2{\cdot}d$ and filtering hight 100cm filled with a zeolite mixture. This zeolite mixture consisted of 1 : 1 by volume of a zeolite in the diameter range of 0.5 to 1mm to a zeolite in the diameter range of 1 to 3mm. According, hydraulic load $314L/m^2{\cdot}d$ was considered as optimal. Three zeolite mixture were used to determine the optimal mixing ratio by volume of a zeolite(A) in the diameter range of 0.5 to 1mm to a zeolite(B) in the diameter range of 1 to 3mm diameter. 1 : 3, 1 : 1 and only B in A to B by volume were tested at hydraulic load $314L/m^2{\cdot}d$ and filtering hight 100cm. $COD_{cr}$ removal efficiency was more than 89% at mixing ratios of 1 : 3 and 1 : 1 in A to B. Removal efficiency was lower at the column filled with only B. Removal efficiency was better at filter medium filled with mixing ratio 1 : 1 in A to B than with the other mixing ratios. Thus, it was found that the mixture of mixing ratio 1 : 1 in A to B was appropriate for filter medium of constructed wetland. Removal efficiency was higher in down-flow than in up-flow, and $COD_{cr}$ and BOD were removed best in 20cm filter height near feeding area.

• Journal of information and communication convergence engineering
• /
• v.1 no.1
• /
• pp.1-5
• /
• 2003

This paper discusses the spectral efficiency and performance of asynchronous direct sequency spread spectrum multiple access systems strict bandwidth limitation by Nyquist filtering. The signal to noise plus interference ratio(SNIR) at the output from the correlation receiver is derived analytically taking the cross correlation characteristics of spreading sequences into account, and also an approximated SNIR of a simple form is presented for the systems employing Gold sequences. Based on the analyzed result of SNIR, bit error rate performance and spectral efficiency are also estimated. and at last, we analyzed improvement rate using RS, convolution as a method for improving functions.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.9
• /
• pp.101-108
• /
• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.