• Journal of Power Electronics
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• v.5 no.1
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• pp.36-44
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• 2005

This paper presents an implementation of high performance control of a reluctance synchronous motor (RSM) using a neural network with a direct torque control. The equivalent circuit in a RSM, which considers iron losses, is theoretically analyzed. Also, the optimal current ratio between torque current and exiting current is analytically derived. In the case of a RSM, unlike an induction motor, torque dynamics can only be maintained by controlling the flux level because torque is directly proportional to the stator current. The neural network is used to efficiently drive the RSM. The TMS320C3l is employed as a control driver to implement complex control algorithms. The experimental results are presented to validate the applicability of the proposed method. The developed control system shows high efficiency and good dynamic response features for a 1.0 [kW] RSM having a 2.57 ratio of d/q.

• Proceedings of the KIEE Conference
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• 2006.05a
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• pp.78-82
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• 2006

The Partial Discharge Monitoring System is technology which is available to measure & analysis the partial discharge of Power equipment. This technology is in the limelight as a pre-forecast system of equipment defect but there are some problems like no protocol standard, layered network management and the limitation of physical size. This paper presents whole system structure includes engineering centers, LN(Logical Node) to apply PDMS for, base-digital substation system, ICE61850 compatible Condition Monitoring &Diagnosis (CMD), Local Unit(LU), Intelligent Electronic Device (IED) for the solution scheme of these limitations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.9
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• pp.661-667
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• 2018

Tactical communication radio systems that employ software defined radios(SDRs) have been developed for achieving high-speed data transmissions and voice communications. Such systems possess multiband and multichannel features, and can potentially replace several existing radio systems. This paper proposes a design for wideband antennas by incorporating a diplexer matching network for tactical vehicles. The proposed antenna design includes two radiators(upper and lower) and a diplexer matching network connected to the end of the feed line such that the LC matching networks are interleaved in the lower radiator and spring mount. By employing the diplexer matching network, the designed antenna can perform wideband impedance matching for the fifty ohm feed line. The designed LC networks aid in varying the effective electrical length of the antenna according to the operation frequency. The primary objective behind adjusting the electrical length is to vary the current distribution above and below the LC networks. The proposed antenna was fabricated and tested in an open site. The obtained evaluation results show that the designed antenna can achieve a relative bandwidth of 190% with a VSWR value of 3.5:1, and can attain good antenna gains over VHF and UHF bands.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.214-222
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• 2022

In these proceedings, we will address the problem of air pollution on the premises of the Cement factory SHARRCEM L.L.C. in Hani Elezit in the Republic of Kosovo respectively around the clinker cooler, rotary kiln, and raw material mill. By air pollution, we mean the introduction of chemicals, particles, or other harmful materials into the atmosphere which in one way or another causing damage to the development of plants and organisms. Air pollution occurs when certain substances are released into the air, which depending on the quantitative level, can be harmful to human health, animals, and the environment in general. The analysis of air shows the influence of the extractive and processing industry on the chemical composition of air. Parameters analyzed though under control such as the case of carbon dioxide, due to the increasing production capacity of cement, the production of hundreds of thousands of cubic meters of CO₂ gas made CO₂ production a concern. With the purchase of the latest technology by the SHARCEM Factory in Hani Elezit, the amount of air pollution has been reduced and the allowed parameters of environmental pollution have been kept under control. Air pollutants are introduced into the atmosphere from various sources which change the composition of the atmosphere and affect the biotic environment.The concentration of air pollutants depends not only on the quantities that are emitted from the sources of air pollution but also on the ability of the atmosphere to absorb or disperse these emissions. Sources of air pollutants include vehicles, industry, indoor sources, and natural resources. There are some natural pollutants, such as natural fog, particles from volcanic eruptions, pollen grains, bacteria, and so on.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1046-1049
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• 1998

This paper deals with the new fault detection technique for transmission line using Neuro-fuzzy Scheme. Neuro-fuzzy Scheme is ANFIS(Adaptive-network Fuzzy Inference System) based on fusion of fuzzy logic and neural networks. The proposed scheme has five layers. Each layer is the component of fuzzy Inference system and performs different action. Using learning method of neural network, fuzzy premise and consequent parameters is tuned properly.

• Korean Journal of Crystallography
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• v.13 no.1
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• pp.12-16
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• 2002

The complex [Ni(L)](BDC)·4H₂O (1) (L = 3,14-dimethyl-2,6,13,17-tetraazatricyclo[16,4,O/sup 1.18/,O/sup 7.12/] docosane; BDC = 1,3-benzenedicarboxylate) has been synthesized and characterized by X-ray crystallography. Compound 1 crystallizes in the orthorhombic space group Pcnb, with a = 8.764(2) , b = 17.687(2) , c = 19.475(1) , V = 3018.7(8) ³, Z = 4, R₁, (wR₂) for 2148 observed reflections of [1>2σ(I) was 0.0822 (0.2236). Compound 1 is interconnected to give a three-dimensional network through weak hydrogen-bonding interactions.

• Microbiology and Biotechnology Letters
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• v.47 no.2
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• pp.296-302
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• 2019

In this study, the effects of ammonium concentration ($117.5-1155.0mg-N{\cdot}l^{-1}$), nitrite concentration ($0-50.0mg-N{\cdot}l^{-1}$), and temperature ($15-35^{\circ}C$) on nitrification performance and its functional genes (amoA-arc, amoA-bac, hao) in an enriched consortium inoculated with humic acid were determined. Notably, the maximum nitrification rate value was observed at $315mg-N{\cdot}l^{-1}$ of ammonium, but the highest functional gene copy numbers were obtained at $630mg-N{\cdot}l^{-1}$ of ammonium. No inhibition of the nitrification rate and functional gene copy numbers was observed via the added nitrites. The optimum temperature for maximum nitrification performance was observed to be $30^{\circ}C$. The amoA-bac copy numbers were also greater than those of amoA-arc under all test conditions. Notably, amoA-arc copy numbers and nitrification efficiency showed a positive relationship in network analysis. These results indicate that ammonium-oxidizing archaea and bacteria play important roles in the nitrification process.