• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.46 no.6
• /
• pp.103-110
• /
• 2009

We propose a novel routing protocol selection scheme based on the application feature in wireless mesh network. Each application has its own feature such as its packet size. For example, text messenger generates short size packets and file transfer application generates long size packets. Routing protocols in wireless mesh network discover the route with different features. Some find shortest hop routes; others find the routes consisting of high bandwidth though they have more hops. The proposed scheme selects the routing protocol by matching the feature of routing protocol and that of application. This paper shows the system that we have developed for supporting mesh routing as well as the proposed scheme and experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.2894-2909
• /
• 2015

As the number of mobile devices such as smart phones and tablets explodes, the need for new services or applications is also rapidly increasing. Smart class application is one of the emerging applications, in which most of contents are distributed to all members of a class simultaneously. It is highly required to select relay nodes to cover shadow area of radio as well as extend coverage, but existing algorithms in a smart class environment suffer from high control packet overhead and delay for exchanging topology information among all pairs of nodes to select relay nodes. In addition, the relay selection procedure should be repeated in order to adapt to the dynamic topology changes caused by link status changes or device's movement. This paper proposes the learning based relay selection algorithm to overcome aforementioned problems. The key idea is that every node keeps track of its relay quality in a fully distributed manner, where RQI (Relay Quality Indicator) is newly defined to measure both the ability of receiving packets from content source and the ability of successfully relaying them to successors. The RQI of each node is updated whenever it receives or relays broadcast packet, and the node having the higher RQI is selected as a relay node in a distributed and run-time manner. Thus, the proposed algorithm not only removes the overhead for obtaining prior knowledge to select relay nodes, but also provides the adaptability to the dynamic topology changes. The network simulation and experimental results prove that the proposed algorithm provides efficient and reliable content distribution to all members in a smart class as well adaptability against network dynamics.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2013.02a
• /
• pp.357-358
• /
• 2013

The non-linear characteristics of ON states are important for the application to the high density cross-point memory industry because the sneak current in neighbor cells occurred during reading, erasing, and writing process. Kw of above 20 in ON states, which is the writing current @ Vwrite/the current @ 1/2Vwrite, was required in cross-point ReRAM memory industry. The high current density non-linear IV curve of ZnSe selector was shown and the ALD HfO2 switching device has the linear properties of ON states and the compliance current of 100 uA. To evaluate the performance of the selection device, we connected itto HfO2 switching device in series. The bottom electrode of the selection device was connected to the top electrode of the RRAM. All of the bias was applied with respect to the top electrode of the selection device, whereas the bottom electrode of the RRAM was grounded. In the cross-point application, 1/2Vwrite and -1/2Vwrite were applied to the word-line and bit-line, respectively, which were connected to the selected cell, and a zero bias was applied to the unselected word-lines and bit-lines. The current @ 1/2Vwrite of the unselected cells was blocked by the selection device, thus eliminating the sneak path and obtaining a writing voltage margin. Using this method, the writing voltage margin was analyzed on the basis of the memory size.

• Journal of Climate Change Research
• /
• v.9 no.1
• /
• pp.47-58
• /
• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.3
• /
• pp.382-391
• /
• 2019

The naval combat system is a distributed system in which various subsystems are integrated and operated together. The integrated management system(IMS) is a software system for systematically and consistently managing the application software which control and operate various devices in such a combat system. Since the malfunction or failure of such an IMS can disable the entire combat system, the IMS is more important than other application software of the combat system. In this paper, we propose a method to guarantee the stable and correct operation of the combat system. To this end, we propose a redundancy scheme composed of one leader and several followers so as to tolerate the failure situation of the IMS. We also propose a leader selection algorithm to select a new leader when the leader fails and can no longer perform its role. To verify the validity of the study, we verify the fault tolerance behavior of the system and the accuracy of the leader selection algorithm.

• Journal of The Korean Society of Integrative Medicine
• /
• v.11 no.2
• /
• pp.37-47
• /
• 2023

Purpose : This study aimed to identify factors that influence selection of occupational therapy as a college major by freshmen. Methods : A survey was conducted on 308 freshmen majoring in occupational therapy from March 2022 to September 2022. The questionnaire consisted of five items about general characteristics, two about the choice of college and major, three about the timing of major selection, three about information acquisition, and one about the university application process. Results : While choosing a university, 37% of the students primarily considered the available choice of majors, whereas 41% considered the college application period. Employment rate was another important factor that was considered when choosing a department. Most students learned about occupational therapy through research on physical therapy and primarily gathered information by searching on "Naver and blogs." Among the descriptions of occupational therapy, the most interesting keyword was "hospital" (54 %). Conclusion : This study investigated the factors that influenced the college major selection by new students in the department of occupational therapy. This study provides meaningful basic data that can be referred to when promoting the department of occupational therapy. A limitation of this study is that a high percentage of students from Gyeongnam were included, and hence, further research that investigates according to region is needed. Moreover, detailed investigations on factors related to university applications in each region are required. It is also necessary to investigate the relationship between the characteristics of freshmen and the determining factors of the department and the admission process.

• Korean Journal of Clinical Pharmacy
• /
• v.34 no.2
• /
• pp.134-139
• /
• 2024

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clinical trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions-Drug, Biological, Dietary Supplement, or Combination Product-were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results: The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imaging or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to develop rapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.11
• /
• pp.41-49
• /
• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Journal of Korean Institute of Industrial Engineers
• /
• v.20 no.2
• /
• pp.19-30
• /
• 1994

Generally, a planning problem in a flexible manufacturing system is considered to be a composite of three interdependent tasks : part selection, loading, and routing mix. This research presents a mathematical model which can concurrently solve part selection, loading, and routing mix problems, so the problems that are caused by treating the planning problems independently are solved. The mathematical model is aimed to minimize system unbalance and the number of late parts, including constraints such as machine capacity, tool magazine capacity, and tool inventory. To illustrate the application of the model, an example is included. Solution procedure based on Lagrangian relaxation is also suggested for larger-sized problems.

• Industrial Engineering and Management Systems
• /
• v.13 no.1
• /
• pp.29-42
• /
• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.