• Journal of Information Technology Applications and Management
• /
• v.23 no.4
• /
• pp.55-70
• /
• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.2
• /
• pp.37-44
• /
• 2020

The fifth generation (5G) mobile communication has been commercialized and the 5G applications, such as the artificial intelligence (AI) and the internet of things (IoT), are deployed all over the world. The 5G new radio (NR) wireless networks are characterized by 100 times more traffic, 1000 times higher system capacity, and 1 ms latency. One of the promising 5G technologies is non-orthogonal multiple access (NOMA). In order for the NOMA performance to be improved, sometimes the additive signal-dependent Gaussian noise (ASDGN) channel model is required. However, the channel capacity calculation of such channels is so difficult, that only lower and upper bounds on the capacity of ASDGN channels have been presented. Such difficulties are due to the specific constraints on the dependency. Herein, we provide the capacity of ASDGN channels, by removing the constraints except the dependency. Then we obtain the ASDGN channel capacity, not lower and upper bounds, so that the clear impact of ASDGN can be clarified, compared to additive white Gaussian noise (AWGN). It is shown that the ASDGN channel capacity is greater than the AWGN channel capacity, for the high signal-to-noise ratio (SNR). We also apply the analytical results to the NOMA scheme to verify the superiority of ASDGN channels.

• Journal of Korea Multimedia Society
• /
• v.23 no.4
• /
• pp.574-580
• /
• 2020

Stress induces change to the body functions and causes chronic problems such as worsening a disease. Thus, humans want to evade anxiety and would try any means to reduce stressful situations. Generally, a person would handle their stress by either regulating their emotions or merely coping with the situation, for which the former is most widely used. Our research aims to effectively reduce stress by using the emotional response structure developed by Plutichik and the vitalization method. We extracted the relevant components of the stress-reduction method that would be applicable in any space using digital technologies such as sensors, IoT, and augmented reality. An architect or designer may incorporate these structural components into any structure to effectively reduce people's stress. The research aims to provide a new perspective of architectural space and to show applications of the stress-reducing architectural spaces, which should also fulfill the people's needs. Further research is needed to develop an automatic system to utilize spatial components more effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.11
• /
• pp.4367-4386
• /
• 2015

The concept of Internet of Things (IoT) has shed new light on WSN technologies. MAC protocol issues improving the network performance are important in WSNs because of the increase in demand for various applications to secure spectrum resources. Cognitive radio (CR) technology is regarded as a solution to the problems in this future wireless network. In recent years, energy efficiency has become an issue in CR networks. However, few relevant studies have been conducted. In this paper, an energy-efficient non-overlapping channel MAC (ENC-MAC) for CR-enabled sensor networks (CRSNs) is proposed. Applying the dedicated control channel approach, ENC-MAC allows the SUs to utilize channels in a non-overlapping manner, and thus spectrum efficiency is improved. Moreover, the cooperative spectrum sensing that allows an SU to use only two minislots in the sensing phase is addressed to en-hance energy efficiency. In addition, an analytical model for evaluating the performance, such as saturation throughput, average packet delay, and network lifetime, is developed. It is shown in our results that ENC-MAC remarkably outperforms existing MAC protocols.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.748-750
• /
• 2017

With the development of intelligent technologies, the Internet of Things(IoT) has been applied to various applications. A platform technology including a sensor-server-DB for easily managing data at a remote site is required. In this paper, we implemented a servo motor control system that moves by the smart phone tilt value using NodeJS and MongoDB. The system consists of Rasberry Pi, servo motor and smart phone and the servo motor sensor data is transmitted to NodeJS so that data can be stored in database.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.3
• /
• pp.373-379
• /
• 2019

Nowadays, the IoT portable electronic devices have become more useful and diverse, so they require various supply voltage levels to operate. This paper presents a DC-DC buck converter with pulse width modulation (PWM) for portable electronic devices. The proposed step-down DC-DC converter consists of passive elements such as capacitors, inductors, and resistors and an integrated chip (IC) for signal control to reduce power consumption and improves ripple voltage with the resolution. The proposed DC-DC converter is simulated and analyzed in PSPICE circuit design platform, and implemented on the prototype PCB board with a Texas Instruments LM5165 IC. The proposed buck converter is showed 92.6% of peak efficiency including a load current range of 4-10 mA, 3.29 mV of the voltage ripple at 5 V output voltage for the supply voltage 12 V. Measured and Simulated power efficiency are made good agreement with each other.

• Journal of Sensor Science and Technology
• /
• v.28 no.1
• /
• pp.36-40
• /
• 2019

Energy harvesting is an advantageous technology for wireless sensor networks (WSNs) that dispenses with the need for periodic replacement of batteries. WSNs are composed of numerous sensors for the collection of data and communication; hence, they are important in the Internet of Things (IoT). However, due to low power generation and energy conversion efficiency, harvesting technologies have so far been utilized in limited applications. In this study, a piezoelectric energy harvester was modeled in a vibration environment. This harvester has an oval-shaped configuration as compared to the conventional cantilever-type piezoelectric energy harvester. An analytical model based on an equivalent circuit was developed to appraise the advantages of the oval-shaped piezoelectric energy harvester in which several structural parameters were optimized for higher output performance in given vibration environments. As a result, an oval-shaped energy harvester with an average output power of 2.58 mW at 0.5 g and 60 Hz vibration conditions was developed. These technical approaches provided an opportunity to appreciate the significance of autonomous sensor networks.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1494-1507
• /
• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.724-727
• /
• 2014

This paper is analyzing the situation of the Internet of Things Industry and draw the policy implications to promote Internet of Things industry. Major IT companies as Apple, Google, IBM, Sony, and Samsung have developed various smart glass and smart watch as a Iot products. In order to promote Iot Industry, we should take the build up of eco system between IT makers and the various contents provider, protection of personal information and data, development of killer applications and business models, and the conversion from IPv4 to IPv6 as a next internet address infra, build up of international standard platform on IoT.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.5
• /
• pp.249-258
• /
• 2019

In the cyber-physical system, big data collected from numerous sensors and IoT devices is transferred to the Cloud for processing and analysis. When transferring data to the Cloud, merging data into one single file is more efficient than using the data in the form of split files. However, current merging and splitting operations are performed at the user-level and require many I / O requests to memory and storage devices, which is very inefficient and time-consuming. To solve this problem, this paper proposes kernel-level partitioning and combining operations. At the kernel level, splitting and merging files can be done with very little overhead by modifying the file system metadata. We have designed the proposed algorithm in detail and implemented it in the Linux Ext4 file system. In our experiments with the real Cloud storage system, our technique has achieved a transfer time of up to only 17% compared to the case of transferring split files. It also confirmed that the time required can be reduced by up to 0.5% compared to the existing user-level method.