• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.23-28
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• 2013

Because characteristics of diversity, large-scale and unpredictability of disaster are very difficult to make predict of disaster, rapid disaster response activity is important to reduce damage from disaster. Therefore emergency communication technology is essential to information sharing between field personnel in the field. Because the heterogeneous radios defined as some of 100MHz VHF, 400MHz UHF, and 800MHz emergency communication RF band are used in Korea, the field personnels need to have multiple radios. In order to use one radio per personnel, radio voice intercommunication is very necessary. We study on modular 3-Band RF to communicate between heterogeneous radios through single multi-band antenna and verify scalable of the number of equipped radios.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.81-86
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• 2020

Ambient backscatter communication has a drawback in which the transmission power is limited because the data is transmitted using the ambient RF signal. In order to improve transmission efficiency between transceiver, a channel estimator capable of estimating channel state at a receiver is needed. In this paper, we consider the K-means algorithm to improve the performance of the channel estimator based on EM algorithm. The simulation uses MSE as a performance parameter to verify the performance of the proposed channel estimator. The initial value setting through K-means shows improved performance compared to the channel estimation method using the general EM algorithm.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.162-179
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• 2024

With the acceleration of China's urbanization process, the economic development and market potential of small and medium-sized cities have been constantly enhanced, with the urbanization competition having been caught in the vortex of homogenization. However, small and medium-sized cities are exposed to limited resources and funds, and therefore need to be more flexible and innovative in the strategy implementation, while urban brand communication is one of the key factors to promote the competitiveness of cities. Taking the small and medium-sized cities in China as the research objects, this study, based on the domestic and foreign theories about the brand communication of small and medium-sized cities, as well as other city-related theories, analyzes textual materials about the current situation and new changes in brand communication of small and medium-sized cities with grounded theory, reflecting on the problems in brand communication in China's small and medium-sized cities. Combining the basic elements of urban brand communication, a model for constructing brand communication strategies for small and medium-sized cities is further proposed, so as to provide the differentiated and distinctive strategies for the construction and communication of urban brands in small and medium-sized cities in China, as well as provide a new perspective and strategy on how to enhance the competitiveness and contribute to the sustainable economic development of small and medium-sized.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.96-100
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• 2020

Soundtrack dissonance, which appears in the background music of a movie scene, is a phenomenon of using songs or compositions that contrast with the general sentiment of the situation. A sad scene usually uses a slow tempo of sad music to match the mood of the scene. However, sometimes, in order to play background music that follows a depressing, sad, or anxious scene, there is a case of inserting music with an opposite atmosphere such as bright music, exciting music, fast-tempo music, or magnificent music. The method of presenting music that is contrary to the mood of the scene is a kind of psychological technique that inflicts a kind of mental shock on the audience and makes them remember a particular situation. In this study, we have investigated the meaning coming from scenes and Soundtrack Dissonance in movies, in order to understand the role that music and images play.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.1-8
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• 2023

Smart IoT over high speed network and high performance smart devices explodes the ubiquitous services and applications. Nearest Neighbor(NN) query is one of the important type of queries that have to be supported for ubiquitous information services. In order to process efficiently NN queries in the wireless broadcast environment, it is important that the clients determine quickly the search space and filter out NN from the candidates containing the search space. In this paper, we propose a hybrid index of Voronoi and grid partition to provide quick search space decision and rapid filtering out NN from the candidates. Grid partition plays the role of helping quick search space decision and Voronoi partition providing the rapid filtering. We show the effectiveness of the proposed index by comparing the existing indexing schemes in the access time and tuning time. The evaluation shows the proposed index scheme makes the two performance parameters improved than the existing schemes.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.102-108
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• 2020

One of the key features of a mobile crowd-sensing (MCS) system is task allocation, which aims to recruit workers efficiently to carry out the tasks. Due to various constraints of the tasks (such as specific sensor requirement and a probabilistic guarantee of task completion) and workers heterogeneity, the task allocation become challenging. This assignment problem becomes more intractable because of the deadline of the tasks and a lot of possible task completion order or moving path of workers since a worker may perform multiple tasks and need to physically visit the tasks venues to complete the tasks. Therefore, in this paper, a hybrid search algorithm for task allocation called HST is proposed to address the problem, which employ a traveling salesman problem heuristic to find the task completion order. HST is developed based on the tabu search algorithm and exploits the premature convergence avoiding concepts from the genetic algorithm and simulated annealing. The experimental results verify that our proposed scheme outperforms the existing methods while satisfying given constraints.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.196-203
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• 2020

In this study, we tried to explore the actual direction for the play culture by looking at the social perception of the change of play culture due to the Corona 19 using big data analysis. For this research, we used Textom, a website specializing in collecting big data, and collected 10,216 data using keywords of "Corona + Play," "Play Culture" and "Leisure" from January 19, 2020 to September 30, 2020, when the first confirmed case of Corona 19 occurred in Korea on various portal sites at home and abroad. The results of this paper showed that the social perception of the play culture in Corona 19 was 51.61%, not much different from the negative image of 48.15%. It is necessary to develop a play culture program that can identify people's various desires and emotions under the premise that situations similar to the current With Corona period and Corona19 can occur at any time, and find mental and physical stability and vitality in unstable situations. In addition, the results of this study can be used as basic data for the development of play culture policies or programs, with the significance that this study helped vitalize big data utilization research in the fields of play, leisure, and culture.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.41-47
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• 2013

A great number of people all over the world are using smart phones. Researchers develop innovative technology of App. It's make rapid progress now that the country's infrastructure is computerized, we expect IT Technological Convergence. In this paper, designs underground utilities management system based on mobile augmented reality technology, and architecture configuration, interface development. Proposal system minimizes overhead of smart devices belonging to engineer's representative using wireless personal area networks. Center Server technology manages transmitted data from engineer's representative, it monitors client data path. And it provies information processing capacity for event generation module. Such event has connotations of instability and uncertainty.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.55-60
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• 2020

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.