As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.
NAND flash memory is used as a medium for various storage devices due to its high data processing speed with low power consumption. However, since the read processing speed of data is about 10 times faster than the write processing speed, various studies are being conducted to improve the speed difference. In particular, flash dedicated buffer management policies have been studied to improve write speed. However, SSD(solid state disks), which has recently been used for various purposes, is more vulnerable to read performance than write performance. In this paper, we find out why read performance is slower than write performance in SSD composed of NAND flash memory and study buffer management policies to improve it. The buffer management policy proposed in this paper proposes a method of improving the speed of a flash-based storage device by analyzing the pattern of read data and applying a policy of pre-reading data to be requested in the future from NAND flash memory. It also proves the effectiveness of the read-ahead policy through simulation.
NB-IoT and LTE Cat.M1 based on LPWA(Low Power Wide Area) are commercialized and serviced by mobile carriers. As the demand for IoT devices is increased, the number of subscribers to these services is also increasing. In the beginning of service, there was no issue that eNB capacity for NB-IoT and LTE Cat.M1. However, as the number of subscribers increases, there is an issue that the eNB capacity for these service is insufficient. Active UE capacity issue may cause overload by continuous increase and temporary increase. In this paper, we propose a solution to solve the problem of LTE RRC(Radio Resource Control) Active UE capacity shortage and base station overload caused by the increase of NB-IoT and LTE Cat.M1 UE in same eNB. The proposed solution can increase a cell capacity without cell division and additional eNB, and can also improve the service quality of these UEs.
This study aimed to understand the increasing number of elder abuses in South Korea, where entry into the super-aged society is imminent, by implementing text mining analysis. Korean Academic journals were obtained from 2004, the establishment year of the senior care agency, to 2021. We performed natural language processing of the titles, keywords, and abstracts and divided them into three segments of periods to identify latent meanings in the data. The results illustrated that the first section included 81 papers, the second 64, and the third 104 respectively, averaging 13.8 annually, which increased its numbers from 2014 until the decrease below the annual average in 2020. Word frequency demonstrated that the common keywords of the entire segments were 'elder abuse,' 'elders,' 'influences,' 'factors,' 'recognition,' 'family,' 'society,' 'prevention plans,' 'experiences,' 'abused elders,' 'abuse prevention,' 'depression,' etc., in consecutive order. TF-IDF indicated that 'influences,' 'recognition,' 'society,' 'prevention plans,' 'abuse prevention,' 'experiences,' 'depression,' etc., were the common keywords of all divisions. Network text analysis displayed that the commonly represented keywords were 'elder abuse,' 'elders,' 'influences,' 'factors,' 'characteristics,' 'recognition,' 'family,' 'prevention plans,' 'society,' 'abuse prevention,' and 'experiences' in the entire sections. concor analysis presented that the first segment consisted of 5 groups, the second 7, and the third 6. We suggest future directions for elder abuse research based on the results.
Smoothing is a transmission plan that converts video data stored at a variable bit rate into a fixed bit rate. Algorithms for smoothing include CBA, which aims to minimize the number of transmission rate increases, MCBA, which minimizes the number of transmission rate changes, and MVBA algorithms that minimize the amount of transmission rate change. This paper compares the proposed algorithm with the CBA algorithm with various video data, buffer size, and performance evaluation factors as a follow-up to the proposed smoothing algorithm that minimizes (maximizes) the transmission rate increase (decrease) when the server requires more bandwidth The evaluation factors used were compared with the number of changes in the fps rate, the minimum fps, the average fps, fps variability, and the number of frames to be discarded. As a result of the comparison, the proposed algorithm showed superiority in comparing the number of fps rate changes and the number of frames discarded.
This study aimed to examine the meaning of adult learners' experience in which they performed a convergence program for the self-confidence improvement of disabled persons with brain lesion who were daycare center users. For the goal, the study collected data through a 5-session profound interview with those disabled persons and then through this author's observation. This study analyzed all the data and, as a result, categorized three significant themes that best represented the above mentioned meaning, which are 'tension of beginning', 'joy of being in company with others' and 'I as the present being'. With those meaningful themes taken into serious consideration. Finally, this study suggested that field programs for social welfare practice in better connection with adult learners' major should be researched and developed.
The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.
The purpose of this study aimed to identify the level of knowledge & preventive health behaviors related to COVID-19, self-efficacy, anxiety, and perceived stress of students at a nursing college and to investigate the correlation between them. The data were collected from 133 students at a nursing college in Seoul, Korea, from April 15, 2022 to May 10, 2022 through a Google online questionnaire. The data were analyzed using SPSS/WIN 25.0 to perform descriptive statistics, t-test, one-way ANOVA, Pearson's Correlation Coefficients. As a result of analyzing the difference according to general characteristics, preventive health behavior showed a significant difference according to the necessity of COVID-19 infection control education, and self-efficacy showed a difference according to the subjective health status. The degree of instability of the subjects was shown to have significant differences according to grade and subjective health status, and perceived stress showed significant differences according to subjective health status. The result of analyzing the correlation between preventive health behavior and knowledge, self-efficacy, anxiety, and perceived stress showed that there was no significant correlation, but self-efficacy had a significant negative correlation with anxiety and perceived stress, and anxiety had a significant positive correlation with perceived stress. The results of this study will be used as basic data for education programs and countermeasures to prevent COVID-19 infections.
In the era of the 4th industrial revolution, various universities' corporate field application education models are being presented. In particular, along with new teaching methods, various educational models for customized education of many companies are being studied, increasing their usability. Research on project-oriented teaching methods for competencies required in the field of business is the most developed field in recent years. In this study, we intend to propose a case-oriented curriculum model that applies the project-oriented teaching method to the requirements of these companies. In particular, we design an industry-oriented curriculum model through a companycustomized education model for blockchain and web developers, and suggest the direction of development focusing on examples of the operation process. The model through this case was designed and operated as a curriculum model suitable for the field through in-depth interviews with industries, etc.
The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.