• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.687-708
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• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.553-557
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• 2009

USS(Ubiquitous Smart Space) give services, that fit in with customer's goal, by cognizing various situations that happens in a space and cooperating autonomously objects or services in a space. In USS, U-Safety is a system that cognizes more exact situations with multiple sensors in USS, deals with this and take proper actions. When men reason on situations objectively, it is most ideal that image data among collected data with used various sensors in U-Safety. A senter collects a lot of image data from image input devices equipped in various points and work a multiple situation cognition and inference that are based on this. So, senters spend many resources for processing massive data. This paper proposes hierarchical image processing method that does the first situation cognization in image input devices, blocks only points that situation cognization possibility is high among a total image, and transfers to senters. It improves the efficiency of smooth situation cognization by reducing resources that a senter spends on image processing. So, it reduces proportion of image data in U-Safety.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.27-31
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• 2019

Various safety accidents occur frequently at construction sites due to poor operation of construction machines and insufficient safety facilities. Among them, safety accidents on the Tawerkrain have resulted in massive casualties and property damage. Therefore, the purpose of this study is to develop safety devices to prevent safety accidents in the Tower Crane. To this end, the existing safety device was explored and the model of the Tower Crane was constructed using Arduino. The weight of the object, the weight of the pendulum, and the distance from the center axis, as well as the equation of turning and experimentation, suggested mobile weights as a safety device to identify and resolve problems with existing safety devices. This is expected to minimize the limit on the salvage distance of the tower crane, thus providing a safer working environment away from the risk of a safety accident.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.181-186
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• 2014

In this paper, we propose an overload control scheme to resolve an overload problem in a random access channel of cellular machine-to-machine (M2M) communication networks. The M2M applications are characterized by small-sized data intermittently transmitted by a massive number of machines. Due to this characteristics, an overload situation in random access channel (RACH) can happen when a large number of devices try to send a signal via the RACH. To address this overload problem, we propose a scheme in which a base station estimates the total load in the network and controls the load by using a p-persistent method based on the estimated load.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.230-232
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• 2011

Owing to the advent of digital devices which equipped with GPS, such as smartphone and tablet pc, a number of LBSNS applications have been released and even SNS applications serve various Location-Based Services. In twitter's case, the news of interesting area is provided to user not by being subscribed them automatically, but by being searched on web-site. This paper describes the system designed for users want to subscribe the local news without procedure like searching using operators. This system uses PBSM(Partition Based Spatial-Merge Join) which has no index for batch processing and against a massive query. The results from Spatial Join are stored in Materialized View then provided to user.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.52.2-52.2
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• 2017

Recent success of gravitational wave (GW) detection by LIGO opened a new window to expand our understanding of the Universe. In addition to LIGO, several other developments are going on or under planning. However, each of these detectors has a specific sensitive frequency range. There is a missing frequency band, 0.1-10 Hz, where detectors loose sensitivity significantly due to Newtonian noise on the Earth. We introduce a plan to develop a Superconducting Low-frequency Gravitational- wave Telescope (SLGT), which can observe massive black holes in 0.1-10 Hz. The SLGT system consists of magnetically levitated six test masses, superconducting quantum interference devices (SQUIDs), rigid support frame, cooling system, vibration isolation, and signal acquisition. By taking the advantage of nearly quantum-limited low-noise SQUIDs and capacitor bridge transducers, SLGT's detection sensitivity can be improved to allow astrophysical observation of black holes in cosmological distances. We present preliminary design study and expected sensitivity, and its technical feasibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2591-2611
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• 2020

Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. The obtained results show the proposed framework obtained 98.20% accuracy with a low 0.1140 false-positive rate.

• Journal of Yeungnam Medical Science
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• v.31 no.1
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• pp.28-32
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• 2014

Pulmonary thromboembolism (PTE) increases the pressure of the right ventricle and leads to symptoms and signs, such as dyspnea and hypoxia. If PTE causes hemodynamic instability, thrombolytic therapy should be considered. A mechanical thrombectomy is an alternative treatment to thrombolytic therapy and should be considered when thrombolytic therapy is contraindicated. Various devices are used in mechanical maceration and catheter-directed thrombolysis, but there is no standard mechanical device for PTE as yet. We report here on 2 clinical experiences of mechanical thrombectomy using the Arrow-Trerotola percutaneous thrombolytic device to remove residual clots after systemic thrombolysis in patients with massive PTE.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.