• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1290-1298
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• 2000

Turbo Code decoder is an iterate decoding technology, which extracts extrinsic information from the bit to be decoded by calculating both forward and backward metrics, and uses the information to the next decoding step Turbo Code shows excellent performance, approaching Shannon Limit at the view of BER, when the size of Interleaver is big and iterate decoding is run enough. But it has the problems which are increased complexity and delay and difficulty of real-time processing due to Interleaver and iterate decoding. In this paper, it is analyzed that MAP(maximum a posteriori) algorithm which is used as one of Turbo Code decoding, and the factor which determines its performance. MAP algorithm proceeds iterate decoding by determining soft decision value through the environment and transition probability between all adjacent bits and received symbols. Therefore, to improve the performance of MAP algorithm, the trust between adjacent received symbols must be ensured. However, MAP algorithm itself, can not do any action for ensuring so the conclusion is that it is needed more algorithm, so to decrease iterate decoding. Consequently, MAP algorithm and Turbo Code performance are analyzed in the nongaussian channel applying Robust equalization technique in order to input more trusted information into MAP algorithm for the received symbols.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.473-478
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• 2017

Drones require altitude holding in order to achieve flight objectives. The altitude holding of the drone is to repeat the operation of raising or lowering the drone according to the altitude information being measured in real-time. When the drones are maintained altitude, the drone's altitude will continue to change due to external factors such as imbalance in thrust due to difference in motor speed or wind. Therefore, in order to maintain the altitude of drone, we have to exactly measure the continuously changing altitude of the drone. Generally, the acceleration sensor is used for measuring the height of the drones. In this method, there is a problem that the measured value due to the integration error accumulates, and the drone's vibration is recognized by the altitude change. To solve the difficulty of the altitude measurement, commercial drones and existing studies are used for altitude measurement together with acceleration sensors by adding other sensors. However, most of the additional sensors have a limitation on the measurement distance and when the sensors are used together, the calculation processing of the sensor values increases and the altitude measurement speed is delayed. Therefore, it is necessary to accurately measure the altitude of the drone without considering additional sensors or devices. In this paper, we propose a measurement algorithm that improves general altitude measurement method using acceleration sensor and show that accuracy of altitude holding and altitude measurement is improved as a result of applying this algorithm.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.165-172
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• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.157-164
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• 2017

Mobile ECG signal measurement is a technique to measure small signals of several mV, and many studies have been conducted to remove noise including wandering scheme. Removal of the equipotential line noise caused by shaking or movement of the electrode cable is one of the core research contents for the electrocardiogram measurement. In this study, we proposed a modified step-size of combined NLMS(normalized least squares) and DLMS(delayed least squares) adaptive filter to eliminate baseline noise from ECG signals. The proposed method mainly adjusts initial filter step-size to reduce distortion of original ECG signals characteristic after eliminating baseline noise. The modified filter step-size is scaled by filter order size and distortion minimization factor. This method is suitable for portable ECG device with a small processor and less power consumption. This technique also decreases computation time which is essential for real-time filtering. The proposed filter also increase the signal to noise ratio (SNR) compared to conventional NLMS filter.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.275-286
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• 2019

In order to prevent local extinction of organisms and to preserve biodiversity, it is important to ensure connectivity between habitats. Even if the habitat is exposed to various disturbance factors, it is possible to avoid or respond to disturbances if they are linked to other habitats. Habitat connectivity can be assessed from a variety of perspectives, but the importance of functional connectivity based on species movement has been emphasized in recent years due to the development of computational capabilities and related software. Among them, Circuitscape, which is a connectivity evaluation tool, has an advantage it can provide detailed reference data for the city planning because it maps ecological flows on individual grid based on circuit theory. Therefore, in this study, the functional connectivity of Suwon was evaluated by applying Circuitscape and then, the ecological corridor to be conserved and supplemented was suggested based on it. The results of this study are expected to effectively complement the methodology related ecological corridor/axis, which was previously provided only in the form of a diagram, and to be effective in management of development project and urban planning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.100-109
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• 2019

Maintainability indicates the extent to which maintenance can be done easily and quickly. The consideration of maintainability is crucial to reduce the operation and support costs of weapon systems, but if the maintainability is evaluated after the prototype production is done and necessitates design changes, it may increase the cost and delay the schedule. The evaluation should verify whether maintenance work can be performed, and support the designers in developing a design to improve maintainability. In previous studies, the maintainability index was calculated using the graph theory at the early design phase, but evaluation accuracy appeared to be limited. Analyzing the methods of evaluating the maintainability using fuzzy logic and 3D modeling indicate that the design of a system with good maintainability should be done in an integrated manner during the whole system life cycle. This paper proposes a method to evaluate maintainability using SysML-based modeling and simulation technique and fuzzy logic. The physical design structure with maintainability attributes was modeled using SysML 'bdd' diagram, and the maintainability was represented by an AHP matrix for maintainability attributes. We then calculated the maintainability using AHP-based weighting calculation and fuzzy logic through the use of SysML 'par' diagram that incorporated MATLAB. The proposed maintainability model can be managed efficiently and consistently, and the state of system design and maintainability can be analyzed quantitatively, thereby improving design by early identifying the items with low maintainability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.2
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• pp.21-34
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• 2002

In this paper we propos a new hardware architecture of modular exponentiation using a division chain method which has been proposed in (2). Modular exponentiation using the division chain is performed by receding an exponent E as a mixed form of multiplication and addition with divisors d=2 or $d=2^I +1$ and respective remainders r. This calculates the modular exponentiation in about $1.4log_2$E multiplications on average which is much less iterations than $2log_2$E of conventional Binary Method. We designed a linear systolic array multiplier with pipelining and used a horizontal projection on its data dependence graph. So, for k-bit key, two k-bit data frames can be inputted simultaneously and two modular multipliers, each consisting of k/2+3 PE(Processing Element)s, can operate in parallel to accomplish 100% throughput. We propose a new encoding scheme to represent divisors and remainders of the division chain to keep regularity of the data path. When it is synthesized to ASIC using Samsung 0.5 um CMOS standard cell library, the critical path delay is 4.24ns, and resulting performance is estimated to be abort 140 Kbps for a 1024-bit data frame at 200Mhz clock In decryption process, the speed can be enhanced to 560kbps by using CRT(Chinese Remainder Theorem). Futhermore, to satisfy real time requirements we can choose small public exponent E, such as 3,17 or $2^{16} +1$, in encryption and verification process. in which case the performance can reach 7.3Mbps.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.153-166
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• 2011

Recent trends in mobile device market whose services are rapidly expanding to provide wireless internet access are drawing people's attention to mobile security. Especially, since threats to information leakage are reaching to the critical level due to the frequent interchange of important data such as personal and financial information through wireless internet, various encryption algorithms has been developed to protect them. The encryption algorithms confront the serious threats by the appearance of side channel attack (SCA) which uses the physical leakage information such as timing, and power consumption, though the their robustness to threats is theoretically verified. Against the threats of SCA, researches including the performance and development direction of SCA should precede. Among tile SCA methods, the power analysis (PA) attack overcome this misalignment problem. The conventional methods require large computational power and they do not effectively deal with the delay changes in a power trace. To overcome the limitation of the conventional methods, we proposed a novel alignment method using peak matching. By computer simulations, we show the advantages of the proposed method compared to the conventional alignment methods.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.