• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.852-858
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• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.680-682
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• 2018

기후변화에 따라 매개 질병의 발병 빈도가 증가하고 있으며 모기와 같은 매개체에 의해 전염되는 매개 질병은 인구집단에 대한 중요한 위협 요인이다. 이러한 질병 관리를 위해 지역별 모기 서식 현황을 모니터링 하는 시스템의 필요성이 강조되고 있다. 하지만 현재의 모기 모니터링은 개체 파악을 위한 분류와 동정을 사람이 직접 수행하기에 오랜 시간이 소요된다. 이 연구는 그러한 문제점을 해결하고 미래 매개곤충 서식 현황 파악 시스템의 기반을 마련하기 위해 심층 신경망(Deep Neural Networks)을 활용하여 한국 주요 매개모기 종 분류를 수행하고 결과를 분석하였다. 종 분류를 위한 모델은 잘 알려진 신경망 모델인 DenseNet(Densely Connected Networks)을 사용하였고 이를 직접 촬영한 모기 데이터와 약간의 변형을 가한 모기 데이터를 사용하여 학습시켰다. 학습 데이터를 각각 5배, 20배, 100배로 증강하여 실제 데이터의 부족을 보완하였으며, 이를 통해 최대 99.48%의 정확도를 달성하였다.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.890-898
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• 2018

Nondestructive inspection (NDI) is an integral part of structural integrity analyses of dry storage casks that house spent nuclear fuel. One significant concern for the structural integrity is stress corrosion cracking in the heat-affected zone of welds in the stainless steel canister that confines the spent fuel. In situ NDI methodology for detection of stress corrosion cracking is investigated, where the inspection uses a delivery robot because of the presence of the harsh environment and geometric constrains inside the cask protecting the canister. Shear horizontal (SH) guided waves that are sensitive to cracks oriented either perpendicular or parallel to the wave vector are used to locate welds and to detect cracks. SH waves are excited and received by electromagnetic acoustic transducers (EMATs) using noncontact ultrasonic transduction and pulse-echo mode. A laboratory-scale canister mock-up is fabricated and inspected using the proposed methodology to evaluate the ability of EMATs to excite and receive SH waves and to locate welds. The EMAT's capability to detect notches from various distances is evaluated on a plate containing 25%-through-thickness surface-breaking notches. Based on the results of the distances at which notch reflections are detectable, NDI coverage for spent nuclear fuel storage canisters is determined.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.83-92
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• 2000

Sound intensity method is well known as a visualization technique of sound field or sound propagation in noise control. Sound intensity or energy flux is a vector quantity which describes the amount and the direction of net flow of acoustic energy at a given position. Especially two dimensional sound intensity method is very useful in evaluating periodic characteristics and acoustic propagation mode of noise source. In this paper, we have studied the noise source Identification, acoustic sound field analysis, and characteristics of noise source of rotary compressor and scroll compressor for air conditioner using complex sound intensity method. Also we proposed a now method of time domain analysis which is used in evaluating of position of noise source in rotary and scroll compressor in this paper This paper presents the advantage, simplicity and economical efficiency of this method by analysing the characteristics of noise source with two dimensional complex sound intensity simultaneously.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.510-517
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• 2019

Increasing proliferation resistance of plutonium by way of increased $^{238}Pu$ content is of interest to the nuclear nonproliferation and international safeguards community. Considering the high alpha decay heat of $^{238}Pu$, increasing the isotopic fraction leads to a noticeably higher amount of heat generation within the plutonium. High heat generation is especially unattractive in the scenario of weaponization. Upon weaponization of the plutonium, the plutonium may generate enough heat to elevate the temperature in the high explosives to above its self-explosion temperature, rendering the weapon useless. In addition, elevated temperatures will cause thermal expansion in the components of a nuclear explosive device that may produce thermal stresses high enough to produce failure in the materials, reducing the effectiveness of the weapon. Understanding the technical limit of $^{238}Pu$ required to reduce the possibility of weaponization is key to reducing the current limit on safeguarded plutonium (greater than 80 at. % $^{238}Pu$). The plutonium vector evaluated in this study was found by simulating public information on Lightbridge's fuel design for pressurized water reactors. This study explores the temperature profile and maximum stress within a simple (first generation design) hypothetical nuclear explosive device of four unique scenarios over time. Analyzing the transient development of both the temperature profile and maximum stress not only establishes a technical limit on the $^{238}Pu$ content, but also establishes a time limit for which each scenario would be useable.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• ETRI Journal
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• v.44 no.3
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• pp.413-425
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• 2022

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification-based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa-based classification of Hindi poetry. For the task, we primarily used lexical features in a bag-of-words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec-based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.