With the enactment and enforcement of the 「Public Records and Archives Act」, the Tottori Prefectural Archives newly enacted the "Records Management Ordinance" through a comprehensive review of the previous archive function. In accordance with this ordinance, which came into effect in April 2012, Tottori Prefecture expanded the records management institutions (Public Security Committee and Police Headquarters, etc.) A series of archive systems were improved, such as the expansion and reinforcement of the authority to appraisal and select. In addition, the Tottori Prefectural Archives went further and implemented the "Ordinance on the Preservation of Historical Documents, etc." from April 2017. Through this, the municipalities unit basic local government's record management support work was set as a unique function of the local archive, and a linkage and cooperation system was established for the preservation of private records of the prefecture area as well as the basic local area together with cultural heritage institutions such as museums and libraries. As a reference case that continuously guarantees the performance of various activities based on the mission and vision of the local archives in Korea that aim for 'autonomy of records' on the poor archival culture soil, it is worth paying attention to the case of continuous record management reform of the Tottori Prefectural Archives through the enactment of the original role and function of the archive.
Alkaline fuel cells using liquid fuels such as hydrazine and ammonia are gaining great attention as a clean and renewable energy solution possibly owing to advantages such as excellent energy density, simple structure, compact size in fuel container, and ease of storage and transportation. However, common shortcomings including cathode flooding, fuel crossover, side yield reactions, and fuel security and toxicity are still challenging issues. Real time monitoring of fuel concentrations integrated into a fuel cell device can help improving fuel cell performance via predicting any loss of fuels used at a cathode for efficient energy production. There have been extensive research efforts made on developing real-time sensing platforms for hydrazine and ammonia. Among these, recent advancements in electrochemical sensors offering high sensitivity and selectivity, easy fabrication, and fast monitoring capability for analysis of hydrazine and ammonia concentrations will be introduced. In particular, research trend on the integration of metallic and metal oxide nanoparticles and also their hybrids with carbon-based nanomaterials into electrochemical sensing platforms for improvement in sensitivity and selectivity will be highlighted.
Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.
Objective: The aim of this trial was to investigate the effect of supplementation and withdrawal of selenium-enriched kale sprouts (SeKS) on productivity and egg Se concentration of laying hens. Selenium from commercial Se-enriched yeast (SeY) was used as a comparative Se source. Methods: One-hundred and eighty 61-week-old laying hens were randomly divided into 5 treatment groups with 4 replicates (9 hens each) in a 2×2+1 Augmented Factorial Experiment in a completely randomized design. The experimental diets were basal diet, basal diet supplemented with 0.2 and 0.4 mg Se/kg from SeKS and SeY, respectively. The 8-week feeding trial was divided into 2 periods, namely the Se supplemental period (week 1 to 4) and the Se withdrawal period (week 5 to 8). Results: Productive performance, egg quality and egg Se concentration of laying hens were not affected by sources of Se (SeKS and SeY) during both, the Se supplemental and withdrawal periods. Egg production and egg Se concentration increased (p<0.05) with increasing levels of Se supplementation. The egg Se concentration increased and reached a peak 1 week after Se supplementation. However, concentration of Se in eggs of hens fed Se from both sources decreased rapidly from the second week of the Se withdrawal period to reach the same egg Se concentration of hens fed the basal diet by the fourth week of the Se withdrawal period. Conclusion: The efficacy of Se from SeKS on productivity and egg Se concentration in laying hens was comparable to commercial SeY. Thus, SeKS can provide an alternate organic Se source for production of Se-enriched eggs.
Kim, Jaeyoung;Jung, Dooyup;Jin, Young-Hyun;Kang, Byung-Goo
Informatization Policy
/
v.30
no.4
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pp.40-61
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2023
The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.
The creation of vertically aligned one-dimensional (1D) nanostructures through the decoration of n-type tin oxide (SnO2) on p-type chromium oxide (Cr2O3) constitutes an effective strategy for enhancing gas sensing performance. These heterostructures are deposited in multiple stages using a glancing angle deposition technique with an electron beam evaporator, resulting in a reduction in the surface porosity of the nanorods as SnO2 is incorporated. In comparison to Cr2O3 films, the bare Cr2O3 nanorods exhibits a response 3.3 times greater to 50 ppm H2S at 300℃, while the SnO2-decorated Cr2O3 nanorods demonstrate an eleven-fold increase in response. Furthermore, when subjected to various gases (CH4, H2S, CO2, H2), a notable selectivity toward H2S is observed. This study paves the way for the development of p-type semiconductor sensors with heightened selectivity and sensitivity towards H2S, thus advancing the prospects of gas sensor technology.
The Journal of the Institute of Internet, Broadcasting and Communication
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v.24
no.3
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pp.77-85
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2024
In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.
The Journal of the Convergence on Culture Technology
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v.10
no.5
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pp.153-161
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2024
This study proposes improvements to the personnel management system for military officers in South Korea, addressing challenges posed by rapidly changing security environments and population decline due to low birth rates. The research identifies key issues in the current system, including lack of predictability in long-term service selection, limited opportunities for professional development, uncertainties in post-retirement careers, rigid organizational culture, and inadequate responses to demographic changes. To address these issues, the study analyzes personnel management practices in foreign militaries, including the United States, United Kingdom, and Israel, deriving valuable insights. Based on this analysis, the research suggests several improvement measures: enhancing predictability in long-term service selection, providing tailored job transition support for different military specialties, and expanding personnel exchanges between military and civilian sectors. Specific recommendations include clarifying long-term service selection criteria, introducing a phased selection system, strengthening connections between military specialties and civilian job sectors, expanding support for professional certifications, and increasing personnel exchange programs with public institutions and private companies. The study also outlines necessary legal and institutional reforms, strategies for securing and allocating budgets, and a phased implementation plan. The research proposes amendments to the Military Personnel Management Act, legislation for supporting retired military personnel, and the introduction of a performance-based budget management system. A systematic implementation plan is presented, divided into short-term, medium-term, and long-term phases.
In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.
More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.
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