• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1507-1517
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• 2023

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%

• Annual Conference of KIPS
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• 2020.11a
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• pp.1052-1055
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• 2020

본 논문에서는 3D 메시 정보, RGB-D 손 자세 및 2D/3D 손/세그먼트 마스크를 포함하여 인간의 손과 관련된 다양한 컴퓨터 비전 작업에 사용할 수 있는 새로운 다중 모달 합성 벤치마크를 제안 하였다. 생성된 데이터셋은 기존의 대규모 데이터셋인 BigHand2.2M 데이터셋과 변형 가능한 3D 손 메시(mesh) MANO 모델을 활용하여 다양한 손 포즈 변형을 다룬다. 첫째, 중복되는 손자세를 줄이기 위해 전략적으로 샘플링하는 방법을 이용하고 3D 메시 모델을 샘플링된 손에 피팅한다. 3D 메시의 모양 및 시점 파라미터를 탐색하여 인간 손 이미지의 자연스러운 가변성을 처리한다. 마지막으로, 다중 모달리티 데이터를 생성한다. 손 관절, 모양 및 관점의 데이터 공간을 기존 벤치마크의 데이터 공간과 비교한다. 이 과정을 통해 제안된 벤치마크가 이전 작업의 차이를 메우고 있음을 보여주고, 또한 네트워크 훈련 과정에서 제안된 데이터를 사용하여 RGB 기반 손 포즈 추정 실험을 하여 생성된 데이터가 양질의 질과 양을 가짐을 보여준다. 제안된 데이터가 RGB 기반 3D 손 포즈 추정 및 시맨틱 손 세그멘테이션과 같은 품질 좋은 큰 데이터셋이 부족하여 방해되었던 작업에 대한 발전을 가속화할 것으로 기대된다.

• The Journal of Economics, Marketing and Management
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• v.4 no.3
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• pp.12-24
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• 2016

Fraudulent financial reporting practices can have significant consequences for organizations and all stakeholders, as well as, for public confidence in the capital and security markets. In fact, comprehensive, accurate and reliable financial reporting is the bedrock upon which our markets are based. Keen to project a rosy picture of the Satyam to investors, employees and analysts, Mr. Raju (CEO and Chairman) fudged the account books so that it appeared to be a far bigger enterprise, with high profits and fast growth rate, than it actually was. The Satyam fraud has shattered the dreams of different categories of investors, shocked the government and regulators alike, and led to questioning of the accounting practices of statutory auditors and corporate governance norms in India. This is an exploratory study based on secondary sources of information. An attempt has been made to provide an explanation for various intriguing questions about Satyam scam. After thorough investigations by the CBI and SEBI, they have unveiled the methodology by which Satyam fraud was engineered. Finally, we recommend "Fraudulent reporting practices should be considered as a serious crime, and accounting bodies, courts and other regulatory authorities in India need to adopt very strict punitive measures to stop such unethical practices."

• Analytical Science and Technology
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• v.19 no.1
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• pp.58-64
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• 2006

A mugwort-tissue-based modified carbon paste electrode was constructed for the amperometric detection of hydrogen peroxide and its electrochemical properties are described. Especially the amperometric signal was very stable and bigger than any other enzyme electrode studied in this lab. The effect of tissue composition on the response was linear within the wide range of experiment and the linearity of Lineweaver-Burk plot showed that the sensing process of the biosensor is by enzymatic catalysis. And pH dependent current profile connoted that two isozymes are active in this system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.281-293
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• 2023

This study addresses the optimal design methodology for switching between active electronically scanned array (AESA) radar operating modes to easily select the necessary information to reduce pilots' cognitive load and physical workload in situations where diverse and complex information is continuously provided. This study presents a procedure for defining a hidden Markov chain model (HMM) for modeling operating mode changes based on time series data on the operating modes of the AESA radar used by pilots while performing mission scenarios with inherent uncertainty. Furthermore, based on a transition probability matrix (TPM) of the HMM, this study presents a mathematical programming model for proposing the optimal structural design of AESA radar operating modes considering the manipulation method of a hands on throttle-and-stick (HOTAS). Fighter pilots select and activate the menu key for an AESA radar operation mode by manipulating the HOTAS's rotary and toggle controllers. Therefore, this study presents an optimization problem to propose the optimal structural design of the menu keys so that the pilot can easily change the menu keys to suit the operational environment.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1094-1095
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• 2023

우리나라는 지리적 여건 상 대외무역에 대한 의존도가 높기 때문에, 해상운송에서의 물동량을 예측하여 항만시설을 개발하는 것이 매우 중요하다. 한편 우리나라 컨테이너 운송의 75%는 부산항을 통해 운송되고 있기 때문에 경기 회복을 위해서는 부산항의 경쟁력 강화가 급선무이다. [1] 물동량은 경제적 수입 뿐만 아니라, 지속가능성을 예측하는 측면에서도 가치가 있다. 본 연구에서는 물동량, 경제지수, 기후정보 등 다양한 입력변수와 LSTM 모델을 이용하여 보다 정확한 부산항 컨테이너 물동량 딥러닝 예측모델을 구현하였다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.140-142
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• 2023

Federated learning (FL) is a new paradigm in machine learning (ML) that enables multiple devices to collaboratively train a shared ML model without sharing their local data. FL is well-suited for applications where data is sensitive or difficult to transmit in large volumes, or where collaborative learning is required. The Internet of Underwater Things (IoUT) is a network of underwater devices that collect and exchange data. This data can be used for a variety of applications, such as monitoring water quality, detecting marine life, and tracking underwater vehicles. However, the harsh underwater environment makes it difficult to collect and transmit data in large volumes. FL can address these challenges by enabling devices to train a shared ML model without having to transmit their data to a central server. This can help to protect the privacy of the data and improve the efficiency of training. In this view, this paper provides a brief overview of Fed-IoUT, highlighting its various applications, challenges, and opportunities.

• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.9-22
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• 2023

Purpose: This paper aims to suggest ways to apply the leading technologies of Industry 5.0 to the housing welfare field, tasks for this, and policy implications. Research design, data, and methodology: The analysis method of this study is a literature study. The analysis steps are as follows. Technology trends and characteristics of Industry 5.0 were investigated and analyzed. The following is a method of applying technology 5.0 in the industrial field. Finally, the application areas of each technology and the challenges to be solved in the process were presented. Results: The results of the analysis are 1) the accessibility and diffusion of technology. This means that all citizens have equal access to and use of the latest technology. To this end, the appropriate use of technology and the development of a user-centered interface are needed. 2) Data protection and privacy. Residential welfare-related technologies may face risks such as personal information leakage and hacking in the process of collecting and analyzing residents' data. 3) Stability, economic feasibility, and sustainability of the technology. Conclusions: The policy implications include: 1) Enhancing technology education and promotion to improve tech accessibility for groups like the low-income, rural areas, and the elderly, 2) Strengthening security policies and regulations to safeguard resident data and mitigate hacking risks, 3) Standardization of technology, 4) Investment and support in R&D.

• Annual Conference of KIPS
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• 2008.05a
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• pp.1104-1107
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• 2008

These people where generally the image or the document nik technique silver document image, against the digital data of audio back all type the research is advanced being used with objective and the use which are various, is a d. Needs a low-end leveling instrument security text from the research which it sees and with substitution quantity the silver nik being simple it will be able to deliver the technique which is simple it embodied. It combined the text image first and the nose which is in the collar image of 24 bit depth which will reach ting it did and it rehabilitatedded and a higher officer technique and the result it used that the loss ratio of the text image to analyze is slight it was ascertained.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.