• Journal of Home Health Care Nursing
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• v.28 no.2
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• pp.164-177
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• 2021

Purpose: The purpose of this study was to develop effective management indicators for improving efficiencies of visiting nursing centers. Method: This was a methodological research study to develop the key performance indicators based on balanced score cards for long-term care visiting nursing centers. The main methods used in this study were literature review, focus group interview, and content validity index. The data analysis was used frequency, percentage, mean, and standard deviation. Results: The common vision of the long-term care visiting nursing centers was identified as "The healthy visiting nursing center to serve high quality cares." Eight action strategies and 15 key performance indicators to achieve this vision were developed. Conclusion: Based on the results of this study, we suggest that the developed balanced score cards will be used as an effective managerial guideline to improve performances of long-term care visiting nursing centers.

• Journal of Korean Dental Hygiene Science
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• v.6 no.1
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• pp.13-25
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• 2023

Background: This study was conducted to confirm the service quality management of care workers, who are direct service personnel of long-term care insurance for the elderly, using unstructured big data. Methods: Using a textome, this study collected and analyzed unstructured social data related to care workers' service quality. Frequency, TF-IDF, centrality, semantic network, and CONCOR analyses were conducted on the top 50 keywords collected by crawling the data. Results: As a result of frequency analysis, the top-ranked keywords were 'Long-term care services,' 'Care workers,' 'Quality of care services,' 'Long term care,' 'Long term care facilities,' 'Enhancement,' 'Elderly,' 'Treatment,' 'Improvement,' and 'Necessity.' The results of degree centrality and eigenvector centrality were almost the same as those of the frequency analysis. As a result of the CONCOR analysis, it was found that the improvement in the quality of long-term care services, the operation of the long-term care services, the long-term care services system, and the perception of the psychological aspects of the care workers were of high concern. Conclusion: This study contributes to setting various directions for improving the service quality of care workers by presenting perceptions related to the service quality of care workers as a meaningful group.

• Speech Sciences
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• v.12 no.3
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• pp.19-33
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• 2005

Tremor is a main factor of parkinsonism. Voice tremor may be the first, later or the only symptom of a neurological disease and its frequency, amplitude, and regularity may differ among the diseases of different neural subsystems. Differential diagnosis between idiopathic Parkinson's disease (IPD) and multiple system atrophy (MSA) has been difficult. This study included three groups: (1) 6 IPD patients; (2) 6 MSA patients; and (3) 20 ageand sex-matched normal controls. The MDVP (Multidimensional Voice Program) was used to analyze the sustained /a/phonation. The results were as follows: (1) frequency perturbation parameters (jitter, sPPQ, Vf0) and FTRI of tremor parameter of two patient groups were statistically different from those of the controls (p < .01); (2) measures were higher in short-term and long-term f0 and amplitude perturbation in MSA than IPD; (3) however, any acoustic parameters between IPD and MSA were not statistically different; except for the rate of frequency tremor, 4$\sim$5 Hz in IPD, 5$\sim$11 Hz in MSA and (4) the pattern of regularity for voice tremor through histogram indicated that amplitude of IPD was irregular while both f0 and amplitude of MSA were irregular. In conclusion, F0, rate of frequency tremor, and pattern of f0 regularity may be predictors for differential diagnosis. These findings might signify that voice tremor of parkinsonism was resulted from modulation of f0.

• Toxicological Research
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• v.11 no.2
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• pp.261-266
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• 1995

Age-related change in the frequency of spontaneous sister chromatid exchange (SCE) and chromosornal aberrations were investigated in bone marrow cells of accelerated senescence-resistant mice (SAM R1) and senescence accelerated ones (SAM P1). And the effect of chronic treatment of ginseng extract (Panax ginseng C.A. Meyer) on these chromosomal abnormalities was tested in SAM P1. SCE frequency in the cells was progressively increased with age in both mice, but it was consistently higher in SAM P1 than in SAM R1 at all corresponding age. Chromosomal aberrations were, however, not significantly changed with age except that it was slightly increased in only aged SAM P1. Interestingly, the rate of these genetic instabilities in SAM P1 was remarkably retarded by long-term administration of ginseng water extract (0.05% in drinking water). These results suggest that frequency of spontaneous SCE in bone marrow cells increase in parallel with senescence of the mice, and SAM P1 is in the condition of being more exposed than SAM R1 to DNA damaging factors. These also indicate that long-term treatment of ginseng may reduce the genetic damage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.587-596
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• 2024

Cyber Attack and Cyber Threat are getting confused and evolved. Therefore, using AI(Artificial Intelligence), which is the most important technology in Fourth Industry Revolution, to build a Cyber Threat Detection System is getting important. Especially, Government's SOC(Security Operation Center) is highly interested in using AI to build SOAR(Security Orchestration, Automation and Response) Solution to predict and build CTI(Cyber Threat Intelligence). In this thesis, We introduce the Cyber Threat Detection System by analyzing Network Traffic and Web Application Firewall(WAF) Log data. Additionally, we apply the well-known TF-IDF(Term Frequency-Inverse Document Frequency) method and AutoML technology to classify Web traffic attack type.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.589-599
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• 2024

This study presents the usability of the high-rate single-frequency Precise Point Positioning (SF-PPP) technique based on 20 Hz Global Positioning Systems (GPS)-only observations in detecting dynamic motions. SF-PPP solutions were obtained from post-mission and real-time GNSS corrections. These include the International GNSS Service (IGS)-Final, IGS real-time (RT), real-time MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), and real-time products from the Australian/New Zealand satellite-based augmentation systems (SBAS, known as SouthPAN). SF-PPP results were compared with LVDT (Linear Variable Differential Transformer) sensor and single-frequency relative positioning (SF-RP) solutions. The findings show that the SF-PPP technique successfully detects the harmonic motions, and the real-time products-based PPP solutions were as accurate as the final post-mission products. In the frequency domain, all GNSS-based methods evaluated in this contribution correctly detect the dominant frequency of short-term harmonic oscillations, while the differences in the amplitude values corresponding to the peak frequency do not exceed 1.1 mm. However, evaluations in the time domain show that SF-PPP needs high-pass filtering to detect accurate displacement since SF-PPP solutions include trends and low-frequency fluctuations, mainly due to atmospheric effects. Findings obtained in the time domain indicate that final, real-time, and MADOCA-based PPP results capture short-term dynamic behaviors with an accuracy ranging from 3.4 mm to 8.5 mm, and SBAS-based PPP solutions have several times higher RMSE values compared to other methods. However, after high-pass filtering, the accuracies obtained from PPP methods decreased to a few mm. The outcomes demonstrate the potential of the high-rate SF-PPP method to reliably monitor structural and earthquake-induced ground motions and vibration frequencies of structures.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.338-344
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• 2015

The national competency standards (NCS) is a systematize and standardize for skills required to perform their job. The NCS has developed a learning module with materialization and standardize by competence unit element, which is the unit of specific job competency. The existing curriculum is material to gab analysis for use in education training with competence unit element. The existing gab analysis has evaluated subjectively by experts. The gab analysis by experts bring up a subject subjective decision, accuracy lack, temporal and spatial inefficiency by psychological factor. This paper is proposed automated evaluation model for problem resolve of subjective evaluation. This paper use index term extraction, term frequency-inverse document frequency for feature value extraction, cosine similarity algorithm for gab analysis between existing curriculum and competence unit element. This paper was presented similarity mapping table between existing curriculum and competence unit element. The evaluation model in this paper should be complemented by an improved algorithm from the structural characteristics and speed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2A
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• pp.97-108
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• 2012

Dynamic responses were measured using long-term monitoring system for Uldolmok tidal current pilot power plant which is one of jacket-type offshore structures. Among the dynamic quantities, the tilt angle was chosen because the low frequency response components can be precisely measured by dynamic tiltmeter, and the natural frequencies and modal damping ratio were successfully identified using proposed LS-FDD (least squared frequency domain decomposition) method. And the effects of tidal height and tidal current velocity on the variation of natural frequencies and modal damping ratios were investigated in time and frequency domain. Also the non-parametric models were tested to model the relationship between tidal conditions and modal properties such as natural frequencies and damping ratios.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.