• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.462-469
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• 2016

This study researched police intervention to ensure the safety of Multi-swarm events. And this study researched best practices of foreign countries. This study propose a solution. It used the situation to police intervention and domestic Multi-swarm events leading research and safety management systems. Safety management for multi-swarm events did ministry of public safety and security, local government, police department, fire department etc. Activities of Safety management for multi-swarm events was checked the risk level, safety management, safety planning and training, safety management in accordance with the Manual. But, safety management of police had Manual maintenance is necessary for the police forces and take advantage of, it is necessary to clarify the mission, it is necessary to configure the risk of a step-by-step checklist for police safety tips in detail. This study for solve the problem proposed the United States, United Kingdom, Germany, France, Australia, the case of the Japanese.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.975-980
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• 2019

In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.23-29
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• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.628-638
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• 2017

In response to difficult employment conditions in the maritime industry and a desire to expand their career options, domestic mates are persuing DP operator training at institutions both domestically and abroad based on their shipboard experience. However, since the offshore plant service industry has not yet been established in Korea, those seeking to enter this field have difficulty acquiring qualifications and most seek work overseas for offshore shipping companies. Individuals wishing to work as DP operators are likely to face more conservative recruitment processes with overseas offshore shipping companies, focusing on career language restrictions as they will be non-native speakers relative to the foreign company, difficulty living in a multi-cultural environment, and lack of systematic information on essential job requirements. For these reasons, domestic mates have difficulty seeking jobs. Therefore, this study analyzes the capabilities and qualification required to be a DP operator to provide basic data for developing NCS standards representing a minimum level of competency. These standards can be applied by the government to develop plans for professional training for DP operators. In study, job classifications, competency standards and career development paths for DP operators have been proposed along with joint use of DP training vessels, to train specialized DP instructors. An NCS export model led by the government to activate professional training for DP operators is also presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3A
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.179-189
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• 1994

A two-phase neural network finds exact feasible solutions for a constrained optimization programming problem. The time-varying programming neural network is a modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, we propose a time-varying two-phase optimization neural network which incorporates the merits of the two-phase neural network and the time-varying neural network. The proposed algorithm is applied to system identification and function approximation using a multi-layer perceptron. Particularly training of a multi-layer perceptrion is regarded as a time-varying optimization problem. Our algorithm can also be applied to the case where the weights are constrained. Simulation results prove the proposed algorithm is efficient for solving various optimization problems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.310-313
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• 1996

In this paper, a neural control algorithm is proposed on the automation of adjustment process. The adjustment processes in camcoder production line are modelled, and the processes are adjusted automatically by means of off-line supervisory trained multi-layer neural network. We have made many experiments on the several adjustment processes by using the control algorithm. There are many unexpected troubles to achieve the desirable adjust time in the practical application. To overcome those, some auxiliary algorithms are demanded. As a result, our proposed algorithm has some advantages - simple architecture, easy extraction of the training data without expertises, adaptability to the varying systems, and wide application for the other resemble processes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.922-931
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• 1995

In this paper, we propose a modified error function to improve the EBP(Error Back-Propagation) algorithm of Multi-Layer Perceptrons. Using the modified error function, the output node of MLP generates a strong error signal in the case that the output node is far from the desired value, and generates a weak error signal in the opposite case. This accelerates the learning speed of EBP algorothm in the initial stage and prevents overspecialization for training patterns in the final stage. The effectiveness of our modification is verified through the simulation of handwritten digit recognition.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.31-33
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• 2009

Face detection using AdaBoost algorithm is capable of processing images rapidly while having high detection rates. It seemed to be the fastest and the most robust and it is still today. Many improvements or extensions of this method have been proposed. However, previous approaches only deal with upright faces. They suffer from limited discriminant capability for rotated faces as these methods apply the same features for both upright and rotated faces. To solve this problem, it is necessary that we rotate input images or make independently trained detectors. However, this can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. This paper proposes a robust algorithm for finding rotated faces within an image. It reduces the computational and sample complexity, by finding common features that can be shared across the classes. And it will be able to apply with multi-class object detection.