• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.257-261
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• 2005

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a WAVENET control technique to control efficiently its elevation angle and azimuth one. Wavelet neural network(WAVENET) can construct systematically initial neural network as applying wavelet theory to feedforward network. It is proved through computer simulation that WAVENET has more excellent approximation capability than existing neural network. The simulation results using MATLAB are introduced.

• 대한의용생체공학회:의공학회지
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• 제12권3호
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.59.1-59
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• 2001

The attackers on Internet-connected systems we are seeing today are more serious and more technically complex than those in the past. Computer security incidents are different from many other types of crimes because detection is unusually difficult. So, network security managers need a IDS and Firewall. IDS (Intrusion Detection System) monitors system activities to identify unauthorized use, misuse or abuse of computer and network system. It accomplishes these by collecting information from a variety of systems and network resources and then analyzing the information for symptoms of security problems. A Firewall is a way to restrict access between the Internet and internal network. Usually, the input ...

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.191-197
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• 2021

Ensuring the economic security of agro-industrial complexes of Ukrainian regions has become a top-priority task of state regional policy, as their stable functioning is an essential element of economic security of the whole country. It is overcoming threats to the development of the agro-industrial complex that ensures its further effective functioning and has a significant impact on the economic security of our state. Methods: logical method; methods of system analysis; synthesis; economic and statistical method; method of expert assessment; SWOT analysis; economic and mathematical modelling and planning. Results. Characteristic features of economic security have been given. The essence and significance of the agro-industrial complex in improving the economic security of the state have been determined. It has been noted that in recent years, the agro-industrial complex, which acts as a driver of the domestic economy and has a direct impact on the development of the country, has been growing (in 2019 the cereal and legume harvest exceeded 75 million tons, 20,269 thousand tons of potatoes were dug, more than 15 million tons of sunflower, 9,688 thousand tons of vegetables and 2,119 thousand tons of fruits and berries were harvested, meat and egg production increased by 137.5 thousand tons (or 5.8%) and 545.5 million pieces (or 3.4%), respectively, the number of employed population in agriculture increased by 139.8 thousand people (or 4.9%), the labour productivity in crop production increased by UAH 294.4 thousand (or 44.6%), in livestock production - by UAH 311.3 thousand (or 61.8%)). Based on the system of production and economic indicators, the analysis of the state of the agro-industrial complex has been carried out. Taking into account the results of the obtained data and using SWOT-analysis, the major threats to the development of the agro-industrial complex have been identified. Ways of overcoming threats enhancing the economic security of Ukraine have been proposed.

• 경영과학
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• 제26권1호
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• pp.113-126
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• 2009

By the newly emerging network access technology, we face the new heterogeneous network environment. Focusing on the co-existence of multiple access network technology and the complex service needs of users, the wireless service operators should present the stable service quality for every user. For this, the service operators should build the new operation framework which combine the pre-established network and newly adopted one. Our problem is finding the optimal heterogeneous network operation framework. We suggest market-based marginal cost function for evaluating the relative value of resource of each network and develop the whole new heterogeneous network operation framework. To test the applicability of developed operation framework, we build large-scale JAVA simulator. By this development, we can easily test the new network environment in practical engineering field.

• 한국안전학회지
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• 제19권1호
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• 대한안전경영과학회지
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• 제7권4호
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• pp.219-227
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• 2005

Three general algorithms for evaluating the reliability for complex bridge system are proposed. These methods, such as Keystone, Boolean, Network algorithms are powerful and effective to derive an reliability expression for many practical complex systems. The combination approach of RBD and FTA proposed in this paper provides an effective way to evaluate the functional dependency for applications of FMEA.

• 대한화학회지
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• 제57권6호
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• pp.721-725
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• 2013

Reaction between 2-pyridinecarboxylic acid (Hpic) and $K_3[Cr(O_2)_4]$ give complex $[Cr(pic)_3].H_2O$ (1) which is characterized by elemental analysis and spectroscopic methods (FT-IR, Raman) and X-ray crystallography. In the crystal structure of 1, chromium atom with coordinated by three nitrogen and three oxygen atoms has a distorted octahedral geometry. Also a water molecule is incorporated in crystal network. Each water molecule acts as hydrogen bond bridging and connects two adjacent complexes by two $O-H{\cdots}O$ hydrogen bonds.

• Spatial Information Research
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• 제23권1호
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• pp.49-57
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• 2015

건축 기술이 발달하고 도시의 인구 집약도가 늘어남에 따라 도심의 대형 건물 또한 늘고 있다. 이에 따라 대형 건물 내부의 위치를 쉽게 파악하고 실내 정보를 쉽게 취득할 수 있는 여러 서비스들이 많이 제공되고 있는데 실내 내비게이션 및 실내지도서비스 등이 그 예이다. 이러한 서비스들이 제공되기 위해서 가장 기초가 되어야할 정보 중 하나는 실내 네트워크 정보이다. 건물의 실내 네트워크는 실내의 각 공간들의 연결 관계에 대한 정보를 제공하며 건물의 기하 정보와는 달리 위상적 특성을 가진다. 하지만 현재 이러한 실내 네트워크를 구축하기 위해서는 건물의 기하 정보를 뒷받침하여 계산하거나 사람이 직접 도면을 이용하여 구축해야 된다. 이는 단순한 건물일 경우에는 쉬운 작업일 수 있지만 복잡한 대형 건물에서는 그 구축이 힘들다. 이를 해소할 방안으로 본 논문에서는, 사람들의 실내 이동정보를 크라우드소싱 방법으로 건물의 실내 네트워크를 자동으로 생성하는 방법론을 제안한다. 수집된 보행자의 이동 데이터를 분석하여, 실내 네트워크를 추출하는 방식이다. 실내에서의 보행자 이동 데이터 수집에 대한 실내측위 환경이 잘 구축되어 있다면 본 방법론은 현실적이고 실질적인 건물의 실내 네트워크를 생성하는데 기여할 것이라 생각된다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.