• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• Convergence Security Journal
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• v.23 no.4
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• pp.109-119
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• 2023

C4I system is an integrated battlefield information system that automates the five elements of command, control, communications, computers, and information to efficiently manage the battlefield. C4I systems play an important role in collecting and analyzing enemy positions, situations, and operational results to ensure that all services have the same picture in real time and optimize command decisions and mission orders. However, the current C4I has limitations whenever a new weapon system is introduced, as it only provides battlefield visualization in a single area focusing on the battlefield situation for each military service. In a future battlefield that expands not only to land, sea, and air domains but also to cyber and space domains, improved command and control decisions will be possible if organic data from various weapon systems is gathered to quickly visualize the battlefield situation desired by the user. In this study, the visualization technology applicable to the future C4I system is divided into map area, situation map area, and display area. The technological implementation of this future C4I system is based on various data and communication means such as 5G networks, and is expected to enable hyper-connected battlefield visualization that utilizes a variety of high-quality information to enable realistic and efficient battlefield situation awareness.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.297-305
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• 2024

As society changes rapidly, the role and status of general education has produced many changes, and accordingly, it is forming a close relationship with various social and cultural environments around the school. Especially, the direction in which general education should proceed and the development of regional-linked general education models have been actively studied at a time when the relationship between local regions and universities is getting strong. In this study, we have provided significant policy implications related to the development and application of future university-local cooperation models by developing a subject of community service activities based learning (CSAL) to get closer to community service and by analyzing actual educational cases and applying them to real community service organizations. In addition, we closely collaborated with local volunteer organizations operated by this local government system to connect with local community service, and through this, we reviewed actual cooperative classes with a local university and local volunteer organizations for the class curriculum, and developed a model to activate the university-local educational ecosystem.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.781-790
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• 2024

As modern people's interest in exercise and health increases, the demand for exercise-related information and devices is increasing, and exercising in the wrong posture can lead to body imbalance and injury. Therefore, in this study, the purpose of this study is to correct the posture for health promotion and injury prevention through the correct exercise posture of users. It was developed using Arduino Uno R3, a pressure sensor, and an acceleration sensor as the main memory device of the system. The pressure sensor was used to determine the squat posture, and the acceleration sensor was used to determine three types of gait: normal step, nasolabial step, and saddle step. Data is transmitted to a smartphone through a Bluetooth module and displayed on an app to guide the user in the correct exercise posture. The gait was determined based on the 20˚ angle at which the foot was opened, and the correct squat posture was compared with the ratio of the pressure sensor values of the forefoot and hindfoot based on the data of the skilled person. Therefore, based on an experiment with about 90% accuracy when determining gait and 95% accuracy based on a 7:3 ratio of pressure sensor values in squat posture, a system was established to guide users to exercise in the correct posture by checking in real time through a smartphone application and correcting exercise in the wrong posture.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.37-53
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• 2024

This study selected bicycle road hazard detection factors for mobile IoT sensor device operation and developed service application plans. Twelve bicycle road hazard detection factors were derived through a focused group interview, and a fuzzy AHP-based importance analysis was conducted on 30 road and transportation experts. As a result, 'damage to pavement' (1st overall) and 'environmental obstacle' (2nd) with low visibility but a high risk of accidents were selected the most. The factors in terms of facility management, such as 'disconnected route occurrence' (4th), 'artificial obstacle' (5th), 'effective width' (6th), and 'poor drainage' (7th), were selected as the upper and middle areas. Factors that are not direct accident-inducing factors, such as 'loss of road markings' (11th) and 'free space width' (12th), were selected the least. Based on this, a plan was presented to apply the bicycle road hazard detection service and a service operation strategy according to real-time performance. Nevertheless, follow-up studies, such as human behavioral analysis based on bicycle operators, analysis according to the bicycle road type, service demonstration, and pilot operation, will be needed to develop safe bicycle road management is expected.

• Research in Plant Disease
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• v.30 no.3
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• pp.294-299
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• 2024

Polymerase chain reaction (PCR) methods, including conventional PCR (cPCR) and quantitative real-time PCR (qRT-PCR), with both plasmid- and chromosome-targeting primers, are currently the most reliable methods for detecting Erwinia amylovora due to their high sensitivity and specificity. Despite qRT-PCR's quantitative advantage, cPCR remains an attractive method to detect this bacterium in initial screenings of suspected host plants, as it is cost-effective and does not require skilled personnel in well-equipped laboratories. This study aimed to significantly improve cPCR robustness via application of bovine serum albumin (BSA) as a PCR facilitator, with a modified EaF/R primer pair, as previously reported. Experiments have shown that simple supplementation with BSA (10 mg/ml) enhances cPCR reactions using templates such as genomic DNA, bacterial cells, and infected symptomless host organs, including immature apple fruits and seedlings, with EaF/R primers. The cPCR method described in this study is simple, specific, and reliable, and can be applied in routine assays to diagnose fire blight.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.107-118
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• 2016

The advent of 5G mobile communications, which is expected in 2020, will provide many services such as Internet of Things (IoT) and vehicle-to-infra/vehicle/nomadic (V2X) communication. There are many requirements to realizing these services: reduced latency, high data rate and reliability, and real-time service. In particular, a high level of reliability and delay sensitivity with an increased data rate are very important for M2M, IoT, and Factory 4.0. Around the world, 5G standardization organizations have considered these services and grouped them to finally derive the technical requirements and service scenarios. The first scenario is broadcast services that use a high data rate for multiple cases of sporting events or emergencies. The second scenario is as support for e-Health, car reliability, etc.; the third scenario is related to VR games with delay sensitivity and real-time techniques. Recently, these groups have been forming agreements on the requirements for such scenarios and the target level. Various techniques are being studied to satisfy such requirements and are being discussed in the context of software-defined networking (SDN) as the next-generation network architecture. SDN is being used to standardize ONF and basically refers to a structure that separates signals for the control plane from the packets for the data plane. One of the best examples for low latency and high reliability is an intelligent traffic system (ITS) using V2X. Because a car passes a small cell of the 5G network very rapidly, the messages to be delivered in the event of an emergency have to be transported in a very short time. This is a typical example requiring high delay sensitivity. 5G has to support a high reliability and delay sensitivity requirements for V2X in the field of traffic control. For these reasons, V2X is a major application of critical delay. V2X (vehicle-to-infra/vehicle/nomadic) represents all types of communication methods applicable to road and vehicles. It refers to a connected or networked vehicle. V2X can be divided into three kinds of communications. First is the communication between a vehicle and infrastructure (vehicle-to-infrastructure; V2I). Second is the communication between a vehicle and another vehicle (vehicle-to-vehicle; V2V). Third is the communication between a vehicle and mobile equipment (vehicle-to-nomadic devices; V2N). This will be added in the future in various fields. Because the SDN structure is under consideration as the next-generation network architecture, the SDN architecture is significant. However, the centralized architecture of SDN can be considered as an unfavorable structure for delay-sensitive services because a centralized architecture is needed to communicate with many nodes and provide processing power. Therefore, in the case of emergency V2X communications, delay-related control functions require a tree supporting structure. For such a scenario, the architecture of the network processing the vehicle information is a major variable affecting delay. Because it is difficult to meet the desired level of delay sensitivity with a typical fully centralized SDN structure, research on the optimal size of an SDN for processing information is needed. This study examined the SDN architecture considering the V2X emergency delay requirements of a 5G network in the worst-case scenario and performed a system-level simulation on the speed of the car, radius, and cell tier to derive a range of cells for information transfer in SDN network. In the simulation, because 5G provides a sufficiently high data rate, the information for neighboring vehicle support to the car was assumed to be without errors. Furthermore, the 5G small cell was assumed to have a cell radius of 50-100 m, and the maximum speed of the vehicle was considered to be 30-200 km/h in order to examine the network architecture to minimize the delay.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

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.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.