• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.21-28
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• 2001

In this study, GNLP(GIS linked non-linear network analysis program) for pipeline system analysis has been developed. This GNLP gets the input data for pipeline analysis from existing GIS(geographic information system) data automatically, and has GUI(graphic user interface) for user. Non-Linear Method was used for hydraulic analysis of pipe network based on Hazen-Williams equation, and Microsoft Access of relational database management system(RDBMS) was used for the framework of database applied program. GNLP system environment program was improved so that a pipe network designer can input information data for hydraulic analysis of pipeline system more easily than that of existing models. Furthermore this model generate output such as pressure and water quantities in the form of a table and a chart, and also produces output data in Excel file. This model is also able to display data effectively for analysed data confirmation and query function which is the core of GIS program.

• Journal of the Ergonomics Society of Korea
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• v.34 no.5
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• pp.377-399
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• 2015

Objective: The aim of this study is to understand and identify the critical issues in vision research area using content analysis and network analysis. Background: Vision, the most influential factor in information processing, has been studied in a wide range of area. As studies on vision are dispersed across a broad area of research and the number of published researches is ever increasing, a bibliometric analysis towards literature would assist researchers in understanding and identifying critical issues in their research. Method: In this study, content and network analysis were applied on the meta-data of literatures collected using three search keywords: 'visual search', 'eye movement', and 'eye tracking'. Results: Content analysis focuses on extracting meaningful information from the text, deducting seven categories of research area; 'stimuli and task', 'condition', 'measures', 'participants', 'eye movement behavior', 'biological system', and 'cognitive process'. Network analysis extracts relational aspect of research areas, presenting characteristics of sub-groups identified by community detection algorithm. Conclusion: Using these methods, studies on vision were quantitatively analyzed and the results helped understand the overall relation between concepts and keywords. Application: The results of this study suggests that the use of content and network analysis helps identifying not only trends of specific research areas but also the relational aspects of each research issue while minimizing researchers' bias. Moreover, the investigated structural relationship would help identify the interrelated subjects from a macroscopic view.

• The Journal of Industrial Distribution & Business
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• v.10 no.8
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• pp.55-65
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• 2019

Purpose - Recently, the fourth industrial revolution is rapidly progressing, and the central government-led innovation system is not able to flexibly cope with changes in science and the economy and society. To solve these problems, it is necessary for local governments, which can easily identify and flexibly respond to local sites, to become self-centered and ready to respond more quickly to massive changes. Through this research, this study investigated the awareness of the elements of Jeonnam Province's capabilities in the field of science and technology policy, the importance of R&D, and how network cooperation among the base institutions might affect performance. Research design, data, and methodology - In fact, the data used in this study only 115 people were polled, excluding five who did not respond to the necessary variables. The methods of the survey were sampled, and the means of the survey were investigated via a self-contained electronic file (e-mail). Statistical analysis, including hypothesis verification, was performed by SPSS 19. The regression analysis was used. Results - All factors significantly affect performance by dividing them into five sub-fields: R&D strategic establishment, R&D demand survey, R&D planning, R&D evaluation, and R&D project management. These results suggest the importance and need for local scientific technology policy capabilities. Besides, it was confirmed that the relationship between regional science and technology policy capabilities and performance was moderated by the recognition of the importance of science technology and network cooperation among the core organizations. Conclusions - As a result, independent variables regarding the capabilities of each scientific technology policy were found to be statistically significant and have a significant effect on performance. Second, the regression analysis has shown the moderation effects of R&D importance awareness between the capabilities of science and technology policies and their performance. On the other hand, a regression analysis showing that the capabilities of science and technology policies and network cooperation between the base regions were not significant, indicating that there is no effect of moderation of network cooperation between the base regions between the capabilities of science and technology policies and performance.

• The Journal of Information Technology
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• v.7 no.2
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• pp.43-54
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• 2004

Since Dr. John J. Hopfield has proposed the HOpfield network, it has been widely applied to the pattern recognition and the routing optimization. The method of Jian-Hua Li improved efficiency of Hopfield network which input pattern's weights are regenerated by SVD(singluar value decomposition). This paper deals with Li's Hopfield Network by linear pre-processing. Linear pre-processing is used for increasing orthogonality of input pattern set. Two methods of pre-processing are used, Hadamard method and random method. In manner of success rate, radom method improves maximum 30 percent than the original and hadamard method improves maximum 15 percent. In manner of success time, random method decreases maximum 5 iterations and hadamard method decreases maximum 2.5 iterations.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.2
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• pp.182-189
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• 2016

A modeling method of electric power network inside a fishing boat less than 5 tons is proposed for its high-energy efficiency with renewable energy sources. The power network inside the fishing boat consists of a diesel engine, a starter motor, an alternator, battery packs, and electric loads, which are connected in parallel. To obtain proper power network model, the voltage -current characteristics of the electric components are considered to develop elaborate electrical models under several load conditions. Measured data of the battery and alternator current include noise. By using an average method, the AC components from the power network of the fishing boat can be reduced, which is verified by KCL rule. Using the proposed power network model, the power generation of the alternator and the reduction of diesel consumption in the boat's engine are predictable under various operating conditions. The validity of the proposed methodology is verified by comparing simulation results with experimental measurements using statistical inferences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2581-2594
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• 2018

The microcell terrain is the most common wireless network terrain in our life. In order to solve wireless network optimization of weak coverage in the microcell terrain, improve call quality and reduce the cost of the premise, power amplifiers in base stations should be adjusted according to user volume. In this paper, characteristics of microcell topography are obtained after analysis. According to the topography characteristics of different microcells, changes in the number of users at different times have been estimated, meanwhile, the number of scatter users are also obtained by monitoring the PCCPCH RSCP and other parameters. Then B-Spline interpolation method has been applied to scatter users to obtain the continuous relationship between the number of users and time. On this basis, power amplification can be chosen according to changes in the number of users. The methods adopted by this paper are also applied in the engineering practice, sampling and interpolation are used to obtain the number of users at all times, so that the power amplification can be adjusted by the number of users in a microcell. Such a method is able to optimize wireless network and achieve a goal of expanding the area of base stations, reduce call drop rate and increase capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.