• Bulletin of the Korean Chemical Society
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• v.25 no.8
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• pp.1156-1160
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• 2004

The technique of an on-line preconcentration by the direct sulfide precipitation has been developed. Sn was homogeneously precipitated by sulfide, which was generated in situ from the hydrolysis of thioaceteamide. Precipitate was collected on a filter in the line and dissolved out instantaneously by KOH to be sent to an ICP. The enrichment factor was 4 with the sampling speed of 15/hr for 1.0 mL of sample. It was increased to more than 40 times when the sampling volume was increased to 10 mL with the sampling speed of 5/hr. $Sn^{2+}/Sn^{4+}$ could be separately determined with the on-line precipitation technique. The method was applied to the analysis of NIST SRM 1566 Oyster sample and yielded good agreement with the certified value.

• 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.

• Geomechanics and Engineering
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• v.23 no.3
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• pp.289-300
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• 2020

With the rapid development of the distributed strain sensing (DSS) technology, the strain becomes an alternative monitoring parameter to analyze slope stability conditions. Previous studies reveal that the horizontal strain measurements can be used to evaluate the deformation pattern and failure mechanism of soil slopes, but they fail to consider various influential factors. Regarding the horizontal strain as a key parameter, this study aims to investigate the stability condition of a locally loaded slope by adopting the variable-controlling method and conducting a strength reduction finite element analysis. The strain distributions and factors of safety in different conditions, such as slope ratio, soil strength parameters and loading locations, are investigated. The results demonstrate that the soil strain distribution is closely related to the slope stability condition. As the slope ratio increases, more tensile strains accumulate in the slope mass under surcharge loading. The cohesion and the friction angle of soil have exponential relationships with the strain parameters. They also display close relationships with the factors of safety. With an increasing distance from the slope edge to the loading position, the transition from slope instability to ultimate bearing capacity failure can be illustrated from the strain perspective.

• Journal of Power Electronics
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• v.14 no.2
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• pp.351-361
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• 2014

The discrete torque generation mechanism and inherently nonlinear magnetic characterization of switched reluctance motors lead to unacceptable torque ripples and limit the application of these motors. In this study, a phase current profiling technique and torque sharing function are proposed in consideration of magnetic saturation effects and by minimizing power loss in the commutation area between the adjacent phases. Constant torque trajectories are considered in incoming and outgoing phase current planes based on nonlinear T-i-theta curves obtained from experimental measurements. Optimum points on constant torque trajectories are selected by improving drive efficiency and minimizing copper loss in each rotor position. A novel analytic invertible function is introduced to express phase torque based on rotor position and its corresponding phase current. The optimization problem is solved by the proposed torque function, and optimum torque sharing functions are derived. A modification method is also introduced to enhance the torque ripple-free region based on simple logic rules. Compared with conventional torque sharing functions, the resultant reference current from the proposed method has less peak and effective values and exhibits lower copper loss. Experimental and simulation results from a four-phase 4 KW 8/6 SRM validate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.682-684
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• 2015

A 12/14 bearingless switched reluctance motor (BLSRM) with hybrid stator poles has been proposed due to the outstanding decoupling characteristics between the torque and suspending force. However, the motor is a two-phase motor. The output torque of the motor has torque dead zone and high torque ripple. Hence, the motor cannot self-start at some rotor positions. To solve the self-starting problems and reduce the torque ripple, a stepped rotor is proposed in this paper. Then, the motor with the stepped rotor is optimally designed. In the new designed motor, the majority parameters are kept the same with those of original motor; only the torque pole arc and rotor pole shape are optimally designed. The characteristics of the redesigned motor, such as inductance, torque and suspending force, are analyzed and compared with those in the original motor. Finally, the effectiveness of the proposed method is verified by the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.47-53
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• 2012

A novel 6/5 switched reluctance motor (SRM) with short flux path is presented in this paper. The concept of this proposed motor is a novel SR motor with six stator and five rotor poles. The stator is constructed with three independent and physically separate C-core segments, and the rotor is composed of five poles. This motor, with a new selection for the number of stator/rotor poles, achieves a short flux path, which reduces the magnetomotive force required to drive the motor. To verify the performance of the proposed motor, a comparison with conventional SR motors with the same dimensions is executed. The comparison demonstrates that the proposed motor offers better performance in terms of maximum torque production. Furthermore, Finite Element Analysis (FEA) and Matlab/Simulink software are used to predict and simulate the performance of the proposed motor.

• Journal of Magnetics
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• v.17 no.3
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• pp.200-205
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• 2012

Recently, applications of the reluctance torque motor have been quite limited due to their inherent limitation of noise and vibration and thus, researches on the reluctance motor have been limited as well. However, with the tremendous increase in the cost of rare earth material magnets, studies of the reluctance torque motor are being conducted more and more. In principle, reluctance torque is generated when the inductance is changed. Therefore, in order to generate continuous torque in the switched reluctance motor, it is necessary to figure out the exact inductance level corresponding to the rotor position and the current level to be applied in that rotor position, respectively. If the current level or the rotor position is not accurately determined, then the generated reluctance torque becomes unstable and undesirable torque ripples prevail to eventually cause noise and vibrations. In this research, a flux switched reluctance motor (FSRM), which is classified into the switched reluctance motor (SRM), was studied. A methodology using the current shaping control according to the rotor position was proposed. Based on the proposed methodology, the optimal current waveform and the torque distribution function for the FSRM to minimize torque ripple was established and demonstrated in this paper.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1167-1175
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• 2021

The process of automation and monitoring in industrial control system involves the use of many types of sensors. A programmable logic controller plays an important role in the automation of the different processes in the power plant system. The major control units are boiler for temperature and pressure, turbine for speed of motor, generator for voltage, conveyer belt for fuel. The power plant units are controlled using microcontrollers and PLCs, but FPGA can be the feasible solution. The paper focused on the design and simulation of hardware chip to monitor boiler, turbine, generator and conveyer belt. The hardware chip of the plant is designed in Xilinx Vivado Simulator 17.4 software using VHDL programming. The methodology includes VHDL code design, simulation, verification and testing on Virtex-5 FPGA hardware. The system has four independent buzzers used to indicate the status of the boiler, generator, turbine motor and conveyer belt in on/off conditions respectively. The GSM is used to display corresponding message on the mobile to know the status of the device in on/off condition. The system is very much helpful for the industries working on plant automation with FPGA hardware integration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.