• Journal of IKEEE
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• v.15 no.4
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• pp.345-353
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• 2011

Recently portable mobile devices such as smart-phones and notebooks have rapidly increasing demands. Those devices consume more power because they are expected to offer more complex functionality including multimedia features. For these reasons engineering efforts are changing to focus on maximizing energy efficiency within a limited battery capacity instead of increasing computational performance. In this paper, we propose a battery management system using event driven programming technique on a embedded processor. We also show that the proposed system satisfies SBS (Smart Battery Specification) v1.1. The proposed system maintains minimum code size and memory size comparing to those of RTOSs. The proposed system can be also easily incorporated in the conventional RTOSs as a form of firmware.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.797-802
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• 2020

Over the past few years, smart devices, including smartphones, have been continuously required by users based on portability. The performance is improving. Ubiquitous computing environment and sensor network are also improved. Due to various network connection technologies, mobile terminals are widely used. Smart terminals need technology to make energy monitoring more detailed for more stable operation during use. The smart terminal which is light in small size generates the power shortage problem due to the various multimedia task among the terminal operation. Various estimation hardwares have been developed to prevent such situation in advance and to operate stable terminals. However, the method and performance of estimating the remaining amount are not relatively good. In this paper, we propose a method for estimating the remaining amount of smart terminals. The Capacity Estimation of lithium ion cells for stable operation was estimated based on machine learning. Learning the characteristics of lithium ion cells in use, not the existing hardware estimation method, through a map learning algorithm using machine learning technique The optimized results are estimated and applied.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.11-18
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• 2017

The Objective of this Study is to Prove the Effectiveness of a Smart Water Management(SWM) Technology. The SWM Technology can Reduce the Production Cost using Internet of Thing(IoT) Technology that Utilizes Remote Metering of Consumer's Water usage and Reduce the Leakage of Supply Facilities. The SWM Demonstration Model Installed a Remote Water Leakage Sensor, Smart Metering and Micro Multi Sensor in Water Supply Facility, and Provided Real-Time Monitoring of the Operation Status. Consumers can be Provided the usage of Tap Water and the Water Puality through a Smart Phone Application. At this Time, we Surveyed Whether Consumers save the Tap Water or Drinking Directly using the Tap Water usage Information. Also, this Study is to Verify the Degree of Improvement of Water Supply Rates and Drinking Water Rate, and to Decrease Consumer's Complaints, Operating Costs, and Water Consumption by the SWM Technology. It is also Established a SWM Model Combined with the IoT Sensor at Supply Facilities, operator monitoring system and explored recovery solution detected events. It means the upbringing of the domestic water industry by developing the related technologies and spreading the SWM to advanced levels.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.4
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• pp.333-336
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• 2017

A speaker diaphragm generates a divided vibration. The influence of the break-up mode is sufficient to cause a shape change in the diaphragm. In this paper, is widely used in ultra-thin multi-media devices, including smart phones is the advance guard of the IT sector, the micro-speakers and its target. Micro-speakers are different from general speakers. The plate has structural form and space constraints. In particular, they utilize a closed-type drive space. It is difficult to provide cooling for the auxiliary suspension structure because of the heat generated in the moving coil. The present study considered the relationship between the break-up mode and the heat transfer of the diaphragm. An experiment was conducted in two stages to compare the embodiment of the break-up mode and heat transfer in a certain frequency range. The changes in the heat were determined through measurements and thermal imaging of the break-up mode. The break-up mode tendency of the diaphragm could be rapidly predicted based on the imaging results using the thermal imaging camera. This will help in the optimal design of micro-speakers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.178-188
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• 2016

The traffic in the wired and wireless networks has increased exponentially because of increase of smart phone and improvement of PC performance. Multimedia services and file transmission such as Facebook, Youtube occupy a large part of the traffic. CDN is a technique that duplicates the contents on a remote web server of content provider to local CDN servers near clients and chooses the optimal CDN server for providing the content to the client in the event of a content request. In this paper, the content request message between CDN servers and the client used the SCRP algorithm utilizing the MST algorithm and the traffic throughput was optimized. The average response time for the content request is reduced by employing HC_LRU cache algorithm that improves the cache hit ratio. The proposed SCRP and HC_LRU algorithm may build a scalable content delivery network system that efficiently utilizes network resources, achieves traffic localization and prevents bottlenecks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.348-357
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• 2012

Recently, smartphone, notebook, PC and other supporting wireless LAN device have come into wide use. By increasing user that use wireless LAN device, wireless traffic also increased. If wireless traffic through one AP is increase, it causes throughput decrease. To solve this problem, wireless LAN service provider install more AP where overload occurred. But this is not enough. Because stations can't know AP's load factor, and APs do nothing for load balancing. In this paper, we propose Multi-Interface Access Point(MIAP) to solve this problem. MIAP operate same as multiple APs with multi-interface, and MIAP measure each interface's load periodically. If MIAP detect overloaded interface, MIAP transfer station from overloaded interface to under-loaded interface. We conducted an experiment for verifying existing problem, and we found this problem occurred. We plan an experiment scenario for a comparison between existing AP and MIAP, and excute these experiment. In the result, we show MIAP with load balancing can improve total throughput about 72% and stabilize delay jitter than existing AP.

• Journal of KIISE
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• v.43 no.1
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• pp.47-53
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• 2016

While the Internet develops rapidly, a huge amount of image data collected from smart phones, digital cameras and black boxes are being shared through social media sites. Generally, social images are handled by tagging them with information. Due to the ease of sharing multimedia and the explosive increase in the amount of tag information, it may be considered too much hassle by some users to put the tags on images. Image retrieval is likely to be less accurate when tags are absent or mislabeled. In this paper, we suggest a method of extracting tags from social images by using image content. In this method, CNN(Convolutional Neural Network) is trained using ImageNet images with labels in the training set, and it extracts labels from instagram images. We use the extracted labels for automatic image tagging. The experimental results show that the accuracy is higher than that of instagram retrievals.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1104-1114
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• 2007

Digital video compression technique has played an important role that enables efficient transmission and storage of multimedia data where bandwidth and storage space are limited. The new video coding standard, H.264/AVC, developed by Joint Video Team(JVT) significantly outperforms previous standards in compression performance. Especially, variable length code(VLC) plays a crucial pun in video and image compression applications. H.264/AVC standard adopted Context-based Adaptive Variable Length Coding(CAVLC) as the entropy coding method. CAVLC of H.264/AVC requires a large number of the memory accesses. This is a serious problem for applications such as DMB and video phone service because of the considerable amount of power that is consumed in accessing the memory. In order to overcome this problem in this paper, we propose a variable length technique that implements memory-free coeff_token, level, and run_before decoding based on arithmetic operations and using only 70% of the required memory at total_zero variable length decoding.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.813-815
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• 2016

This paper implements a device control system based on the brain wave data. Brain-Computer Interface (BCI) technology can pass directly to the system without going through the operation of the language or body. By controlling the device to detect brain waves in real time according to the change of status it helps to ease life for a variety of services, such as disabled people with limited mobility or students, people who need multi-tasking. In addition, it is possible to develop an application service such as the home device control system. A device control system implemented in the paper based on the data collected from the EEG Headset associated to control the power of the smart phone and audio. Control the power ON / OFF operation by the Attention, and support service functions to control the audio by the Meditation and Eye blink. It was confirmed that the device control using the brain wave data to be operated through a laboratory test successfully.