• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• The Mathematical Education
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• v.63 no.2
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• pp.335-350
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• 2024

This study explored the factors that influence elementary school teachers' intention to use an artificial intelligence (AI) math learning system and analyzed the interactions and relationships among these factors. Based on the technology acceptance model, perceived usefulness for math learning, perceived ease of use of AI, and attitude toward using AI were analyzed as the main variables. Data collected from a survey of 215 elementary school teachers was used to analyze the relationships between the variables using structural equation modeling. The results of the study showed that perceived usefulness for math learning and perceived ease of use of AI significantly influenced teachers' positive attitudes toward AI math learning systems, and positive attitudes significantly influenced their intention to use AI. These results suggest that it is important to positively change teachers' perceptions of the effectiveness of using AI technology in mathematics instruction and their attitudes toward AI technology in order to effectively adopt and utilize AI-based mathematics education tools in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of Distribution Science
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• v.12 no.11
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• pp.85-100
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• 2014

Purpose - Recently, the prevalence of high-speed mobile communication technology (4G) and mobile devices (smart phones, tablet PC, etc.) is leading innovative changes across all fields in society as well as business environments. Furthermore, a diversified mobile application service has spread rapidly through mobile devices such as smart phones and tablet PCs. Accordingly, the traditional E-government services paradigm has rapidly changed into mobile intelligence. To identify the influencing factors on the using intention of G4C smart app services, based on previous studies, the variables that influence using G4C smart app services are defined; these are user cognitive factors (perceived usefulness, perceived easiness), user characteristics factors (user innovativeness, self-efficiency, social influence), service quality factors (convenience, interactivity, accessibility), and system quality factors (instant connectivity, safety). Research design, data, and methodology - This is designed not only to collect data with a questionnaire survey (9/22/13~10/23/13) but also to test hypotheses with SEM by SPSS 21.0 and AMOS 21.0 in both Korea and China. All items are used with Likert 5 scales. A total of 643 questionnaires (Korea 318, China 325) are used. Results - The perceived usefulness and perceived easiness in user cognitive factors have positive influence on using intention. The user innovativeness, self-efficiency, and social factors in user characteristics factors have positive influences on using intention. The convenience, interactivity, and accessibility in service quality factors have positive influences on both reliability and using intention. Safety in system quality has positive influence on both reliability and using intention. Reliability has positive influence on using intention. The control variables (Korea and China) affect its control hypothesis. Strategies and implications are suggested to assist the public using the intention of smartphone's e-government services based on the results of the empirical analysis. The mobile application service can be considered a new emergence of the paradigm just like the government's on-line portal websites appeared in the past. Under this prevailing situation of mobile smart devices, to promote the success of e-government mobile APP services, accurate analysis and understanding of users should precede anything, to provide services to grasp and satisfy users' desire properly. Conclusions - This study proposes implications to help E-governmental officers and companies make strategies. First, this is expected to give some information on the understanding and knowledge regarding the process of G4C smart APP service based on the empirical study. Second, this helps to make future policies and ways about E-government G4C smart APP service. Third, it is proved that super speed mobile communication technology and devices including phones will be crucial to change the structure of E-government services in 2-3 years. Fourth, it is necessary to increase the trust and using intention of users. Fifth, considering what type of environment users are placed in, to present proper public information matching their inclination, is important. Finally, various ways of experiencing service to explore potential users and ceaseless public relations are required.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.219-250
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• 2014

Corporations look for their competitiveness in many different areas, and one of them is customer call center, which provides professional, special service to the customers. The importance and the value of realization of customer satisfaction at each call center have become significant as it plays a leading role and is responsible for customer recreation and follow-up services. This study held in-depth interviews with 3 groups of professional call representatives (categorized by experience) from popular property and life insurance companies in Korea who operate in and outbound calls. Three variables, emotional quotient, organizational citizenship behavior (OCB), and IT utilization skill, were selected and it was observed how these variables affect the job performance of in and outbound call center representatives. The importance of the relationship between the variables and the key factors in the future direction of improvement of the call center representative organization was analyzed. Emotional quotient refers to the ability to recognize and use the emotion of oneself and others. It was considered it would be effective in enhancing the counseling ability of call center representatives, This study identifies the effect of emotional quotient on job performance and organizational citizenship behavior, which is defined as actions outside of normative roles in the organization. Also, the study aims to prove the effect of emotional quotient on the rapidly developing IT utilization ability in relation to job performance. As a result, it was found that service career (3 months, 3 years, 10 years) had a significant effect on the key factors that affect the organization-emotional quotient, IT utilization skill, and OCB. Especially for OCB, it was considered relatively less important to the new employees, while it greatly affected the job performance, rate of settlement, and sustainment of the working life for those with longer experience. Also, the result of the interview for each variable differed by the service career. Emotional quotient, IT utilization skill, and OCB were close connection for the representatives with longer experience than for those with shorter experience. And the level of intimacy of their relationship with job performance was in proportion to the years of service. Thus, to secure stability for the call center representatives and enhance their job performance continually, the importance of emotional quotient and IT utilization skill shall be reflected in the operation process actively. In particular, in order to lead a organization with outstanding culture, a management system shall apply OCB from the recruitment process.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.11-17
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• 2020

South Korea started to actively participate in the ODA project across Asia, Africa, the Middle East, CIS countries, and Latin America as it transitioned from a country receiving developmental aid to a donor country. As more and more recipient countries are looking into the e-government model of South Korea, a new approach to the Korean model is being investigated. Various promotions were made, ranging from the implementation of the e-government ODA project, review of its feasibility to evaluation of the results. In order to improve and maximize performance management OD projects, it is necessary to look into the analysis of its follow-up results. In this study, practical performance management and policy implications were explored, focusing on the e-government ODA project. In particular, a case study analysis was attempted with focus on the Nigerian e-government project promoted as part of the project. It focused on Korea's e-government linkage process, implementation procedures and strategies applied when establishing the e-government master plan and suggested the necessity of an approach suitable for new environmental changes. In terms of sustainability of the ODA project, it is necessary to build a Korean e-government model that reflects the new intelligence information technology.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.87-107
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• 2016

Understanding market convergence has became essential for small and mid-size enterprises. Identifying convergence items among heterogeneous markets could lead to product innovation and successful market introduction. Previous researches have two limitations. First, traditional researches focusing on patent databases are suitable for detecting technology convergence, however, they have failed to recognize market demands. Second, most researches concentrate on identifying the relationship between existing products or technology. This study presents a platform to identify the opportunity of market convergence by using product databases from a global B2B marketplace. We also attempt to identify convergence opportunity in different industries by applying Structural Hole theory. This paper shows the mechanisms for market convergence: attributes extraction of products and services using text mining and association analysis among attributes, and network analysis based on structural hole. In order to discover market demand, we analyzed 240,002 e-catalog from January 2013 to July 2016.