• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.279-287
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• 1993

In this paper, double laxity threshold MLT(Minimum Laxity Threshold) algorithm and double queue threshold QLT(Queue Length Threshold) algorithm are proposed as DPS(Dynamic Priority Scheduling) techniques for advanced processing of multiple class traffics. Also, the performance of the proposed algorithms is analyzed by a computer simulation. According to the simulation results, it can be shown that the proposed double laxity threshold ML T algorithm advances the processing performance versus ML T algorithm for 2 or more classes delay sensitive traffics, and that double queue length threshold QL T algorithm provides more efficient performance than QL T for 2 or more classes of non real time traffics.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.424-433
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• 2009

In order to improve productivity and efficiency of ship production process, production technology converged with Information Technology can be considered. Mid-term scheduling based on long-term schedule of ship building and execution planning based on short-term production schedule have an important role in ship production processes and techniques. However, data used in the scheduling are from the experiences of the past, cognitive, and often inaccurate, moreover the updates of the data by formatted documents are not being performed efficiently. This paper designs the tracking and monitoring system for the curved plates forming process with shop level. At first step to it, we redefine and analyze the curved plates forming process by using SysML. From the definition and analysis of the curved plates forming process, we design the system with respect to operational view considering operational environment and interactions between systems included and scenario about operation, and with respect to system view considering functionalities and interfaces of the system. In order to study the feasibility of the system designed, a prototype of the system has been implemented with 13.56 MHz RHD hardware and application software.

• The Microorganisms and Industry
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• v.15 no.1
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• pp.38-42
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• 1989

Industrial strain Improvement is concerned with developing or modifying microorga-nisms used In production of commercially important fermentation products. The aim is to reduce the production cost by improving productivity of a strain and manipulating specific cilarafteristic such as the ability to utilize cheaper raw materials or resist bacteriophages. The traditional empiri-cal approach to strain improvement is mutation combined with selection and breeding techniques. It is still used by us to improve the productivity of organisms in amino acids. organic acids andenzymes production. The breeding of high L-lysine-producing strain Au112 is one of the outstanding examples of this approach. It is it homoserine auxotroph with AEC, TA double metabolicanalogue resistant markers. The yield reaches 100g/1. Resides, the citric acid-producing organism Aspergillus nuger, Co827, its productivity reches the advanced level in the world, is also the result of a series mutations expecially with Co Y-radiation. The thermostable a-amylaseroducing strain A 4041 is the third example. By combining physical and chemical multations. the strain ,A 4041becomes an asporogenous, catabolite derepressed mutant with rifamycin resistant and methionine, arginine auxotroph markers. The a-amylase activity reaches 200 units/ml. The fourth successful example of mutation in strain improvement is the glucoamylase-producing strain Aspergillus nigerSP56 its enzyme activity is 20,000 units/ml, 4 times of that of the parental strain UV_11. Recently recombinant DNA approach Provides a worth while alternative strategy to Industrial strain improve-ment. This technique had been used by us to increase the thermostable a-amylase production and on some genetic researches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10A
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• pp.1000-1010
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• 2002

Partial response maximum-likelihood (PRML) and fixed-delay tree search with decision feedback (FDTS/DF) yield a sub-optimum performance in storage systems. However, they suffer from the inevitable complexity problems. this paper focuses on detection schemes to overcome the drawbacks of the sequence detections by exploiting minimum run-length d=2. It is expected that the proposed systems yield substantial reductions of both processing speed and receiver complexity. When combined with a decision feedback equalization (DFE), they prove to keep pace with the FDTS/DF with ${\tau}$=2 and even outperform the PR(1111)ML at normalized density S>5.6.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.153-158
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• 2021

In this study, we will determined the total phenolic content (TPC) and the antioxidant activity of the methanolic extract (ME) of date palm (Phoenix dactylifera. L) fruits (DPF) of four native cultivars from Algeria: Ghars (Gh), Chtaya (Cht), DeglaBeïda (DB) and Tinissine (Tns). The TPC of ME of DPF was measured by using Folin-Ciocalteu. Thereafter, the antioxidant capacity of various extracts was determined using DPPH test, reducing power and superoxide anion test. These results showed that dates had strongly scavenging activity on DPPH. The value of IC50 for DPPH radical test was 0.077 mg/ml in Cht. Also, Cht cultivar showed the best-reducing power, which was significantly higher than the other varieties. The less IC50 value in cyclic voltammetry method (CV), which meets the highest effective antioxidant, was 0.006 mg/ml in methanolic extract of Cht.