• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.147-154
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• 2015

Recently agile development process became increasing effectiveness, quality, attention to experts in customer satisfaction, as evidenced in this methodology when selecting projects promoting productive efficiency. With regard to contemporary needs and user requirements on the methodology selected to meet this paper is the product based Cross functional team suggested methodology Feature Team model to solve problems of this model, and organizing the Cross functional team, this team but this outcome (product) basis, were examined for the model that points to progress the development across multiple product as a functional unit, value-driven agile project through the Skills-based model and proposed a difference. And it examined the Agile Maturity metrics. PRINCE2 Agile Health-check entries future development direction of Agile techniques is a requirement of the project outset has studied the subject objective evaluation by the assumption that they can be changed at any time, not fixed this way and for the project team through research The proposed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.229-236
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• 2011

In this paper we present light weighted English-to-Korean and Korean-to-English mobile translators on smart phones. For natural translation and higher translation quality, translation engines are hybridized with Translation Memory (TM) and Rule-based translation engine. In order to maximize the usability of the system, we combined an Optical Character Recognition (OCR) engine and Text-to-Speech (TTS) engine as a Front-End and Back-end of the mobile translators. With the BLEU and NIST evaluation metrics, the experimental results show our E-K and K-E mobile translation equality reach 72.4% and 77.7% of Google translators, respectively. This shows the quality of our mobile translators almost reaches the that of server-based machine translation to show its commercial usefulness.

• Convergence Security Journal
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• v.11 no.1
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• pp.39-48
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• 2011

QoS(Quality of Service) is defined "The collective effect of service performance which determines the degree of satisfaction of a user of the service" by ITU-T Rec. E.800. The final goal of information system is to secure the performance efficiency within the required time. The security QoS framework is the modeling of the QoS measurement metrics, the measurement time schedule, instrument, method of measurement and the series of methodology about analysis of the result of measurement. This paper relates to implementing issue and performance measuring about blended mechanism between networking technology and security technology. We got more effectiveness in overall network security, when applying and composing amalgamated security mechanism between network technology and security technology. In this paper, we suggest techniques being used on infrastructure system and also offers a security QoS methodology as a model of more effective way. Methodology proposed in this research has proven that it is possible to measure response time through the scheduled method.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.456-469
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• 2009

We develop a new wireless link quality metric, ECOT(Estimated Channel Occupancy Time) that enables a high throughput route setup in wireless mesh networks. The key feature of ECOT is to be applicable to diverse mesh network environments where IEEE 802.11 MAC (Medium Access Control) variants are used. We take into account the exact operational features of 802.11 MAC protocols, such as 802.11 DCF(Distributed Coordination Function), 802.11e EDCA(Enhanced Distributed Channel Access) with BACK (Block Acknowledgement), and 802.11n A-MPDU(Aggregate MAC Protocol Data Unit), and derive the integrated link metric based on which a high throughput end-to-end path is established. Through extensive simulation in random-topology settings, we evaluate the performance of proposed link metric and present that ECOT shows 8.5 to 354.4% throughput gain over existing link metrics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.527-530
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• 2014

DASH is a well-known streaming technology, which was proposed in 2010 by MPEG and standardized in 2011. Major multimedia contents service providers, including Apple, Microsoft, and Adobe are all using this technology to support their media streaming services. Whenever a new service is requested to the server, the DASH technology helps servicing the multimedia streaming to client by recognizing the capacity of network and by adapting the quality of the multimedia contents. In DASH, the quality of multimedia contents will be automatically lowered to meet the fluctuating network status, when undesirable breaks interrupt the network. In this paper, we classified and analysed the advantages and disadvantages of DASH researches in three aspects: bit-rate measurement method, bandwidth aggregation method; rate adaptation metrics, algorithms and logics; user's experiences and QoE.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1091-1097
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• 2022

In this paper, we implement the eXclusive OR-based Cast (XC) system that is a video streaming system using exclusive OR operations, and measure various performance metrics in wireless local area network (WLAN) environments. In addition, we investigate the performance improvement of the XC system considering various practical video streaming environments, while conventional studies analyzed the performance of XC through computer simulations in limited environments. To this end, we propose new control messages such as STR_REQ_MSG (SRM) that clients transmit to a video streaming server and STR_CON_MSG (SCM) that is used for the video streaming server to control the clients, and develop a new protocol by using the new control messages. According to the various measurement results using the implemented XC system, XC video streaming system can reduce the consumption of network bandwidth by 8.6% on average and up to 25% compared to the conventional video streaming system. In addition, the outage probability can be also reduced up to 76%.

• Fisheries and Aquatic Sciences
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• v.26 no.4
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• pp.268-281
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• 2023

Coral reef fisheries are prominent for the archipelagic countries' food sufficiency and security. Studies showed that fish abundance and biomass are affected by biophysical variables. The present study determines which biophysical variables are important predictors of fish diversity, abundance, and biomass. The study used available monitoring data from the Indonesian Research Center for Oceanography, the National Board for Research and Innovation. Data were collected from 245 transects in 19 locations distributed across the Indonesian Archipelago, including the eastern Indian Ocean, Sunda Shelf (Karimata Sea), Wallacea (Flores and Banda Seas), and the western Pacific Ocean. Principal component analysis and multiple regression model were administered to 13 biophysical metrics against 11 variables of coral reef fishes, i.e., diversity, abundance, and biomass of coral reef fishes at three trophic levels. The results showed for the first time that the covers of other fauna, coral rubbles, and soft corals were the three most important predictor variables for nearly all coral reef fish variables. Other fauna cover was the important predictor for all 11 coral reef fish variables. Coral rubble cover was the predictor for ten variables, but carnivore fish abundance. Soft coral cover was a good predictor for corallivore, carnivore, and targeted fishes. Despite important predictors for corallivore and carnivore fish variables, hard coral cover was not the critical predictor for herbivore fish variables. The other important predictor variables with a consistent pattern were dead coral covered with algae and rocks. Dead coral covered with algae was an important predictor for herbivore fishes, while the rock was good for only carnivore fishes.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.67-74
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• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.