• Title, Summary, Keyword: Deep Neural Network(DNN)

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Improved Network Intrusion Detection Model through Hybrid Feature Selection and Data Balancing (Hybrid Feature Selection과 Data Balancing을 통한 효율적인 네트워크 침입 탐지 모델)

  • Min, Byeongjun;Ryu, Jihun;Shin, Dongkyoo;Shin, Dongil
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.2
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    • pp.65-72
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    • 2021
  • Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

A Comparative Study of Machine Learning Algorithms Based on Tensorflow for Data Prediction (데이터 예측을 위한 텐서플로우 기반 기계학습 알고리즘 비교 연구)

  • Abbas, Qalab E.;Jang, Sung-Bong
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.3
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    • pp.71-80
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    • 2021
  • The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

A Personal Video Event Classification Method based on Multi-Modalities by DNN-Learning (DNN 학습을 이용한 퍼스널 비디오 시퀀스의 멀티 모달 기반 이벤트 분류 방법)

  • Lee, Yu Jin;Nang, Jongho
    • Journal of KIISE
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    • v.43 no.11
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    • pp.1281-1297
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    • 2016
  • In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.

Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks

  • Utah, M.N.;Jung, J.C.
    • Nuclear Engineering and Technology
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    • v.52 no.9
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    • pp.1998-2008
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    • 2020
  • Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

Development of Water Level Prediction Models Using Deep Neural Network in Mountain Wetlands (딥러닝을 활용한 산지습지 수위 예측 모형 개발)

  • Kim, Donghyun;Kim, Jungwook;Kwak, Jaewon;Necesito, Imee V.;Kim, Jongsung;Kim, Hung Soo
    • Journal of Wetlands Research
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    • v.22 no.2
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    • pp.106-112
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    • 2020
  • Wetlands play an important function and role in hydrological, environmental, and ecological, aspects of the watershed. Water level in wetlands is essential for various analysis such as for the determination of wetland function and its effects on the environment. Since several wetlands are ungauged, research on wetland water level prediction are uncommon. Therefore, this study developed a water level prediction model using multiple regression analysis, principal component regression analysis, artificial neural network, and DNN to predict wetland water level. Geumjeong-Mountain Wetland located in Yangsan-city, Gyeongsangnam-do province was selected as the target area, and the water level measurement data from April 2017 to July 2018 was used as the dependent variable. On the other hand, hydrological and meteorological data were used as independent variables in the study. As a result of evaluating the predictive power, the water level prediction model using DNN was selected as the final model as it showed an RMSE value of 6.359 and an NRMSE value of 18.91%. This research study is believed to be useful especially as a basic data for the development of wetland maintenance and management techniques using the water level of the existing unmeasured points.

Development of a deep neural network model to estimate solar radiation using temperature and precipitation (온도와 강수를 이용하여 일별 일사량을 추정하기 위한 심층 신경망 모델 개발)

  • Kang, DaeGyoon;Hyun, Shinwoo;Kim, Kwang Soo
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.21 no.2
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    • pp.85-96
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    • 2019
  • Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

Time-domain Sound Event Detection Algorithm Using Deep Neural Network (심층신경망을 이용한 시간 영역 음향 이벤트 검출 알고리즘)

  • Kim, Bum-Jun;Moon, Hyeongi;Park, Sung-Wook;Jeong, Youngho;Park, Young-Cheol
    • Journal of Broadcast Engineering
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    • v.24 no.3
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    • pp.472-484
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    • 2019
  • This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

Predictive System for Unconfined Compressive Strength of Lightweight Treated Soil(LTS) using Deep Learning (딥러닝을 이용한 경량혼합토의 일축압축강도 예측 시스템)

  • Park, Bohyun;Kim, Dookie;Park, Dae-Wook
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.24 no.3
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    • pp.18-25
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    • 2020
  • The unconfined compressive strength of lightweight treated soils strongly depends on mixing ratio. To characterize the relation between various LTS components and the unconfined compressive strength of LTS, extensive studies have been conducted, proposing normalized factor using regression models based on their experimental results. However, these results obtained from laboratory experiments do not expect consistent prediction accuracy due to complicated relation between materials and mix proportions. In this study, deep neural network model(Deep-LTS), which was based on experimental test results performed on various mixing conditions, was applied to predict the unconfined compressive strength. It was found that the unconfined compressive strength LTS at a given mixing ratio could be resonable estimated using proposed Deep-LTS.

Video Transmission Technique based on Deep Neural Networks for Optimizing Image Quality and Transmission Efficiency (영상 품질 및 전송효율 최적화를 위한 심층신경망 기반 영상전송기법)

  • Lee, Jong Man;Kim, Ki Hun;Park, Hyun;Choi, Jeung Won;Kim, Kyung Woo;Bae, Sung Ho
    • Journal of Broadcast Engineering
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    • v.25 no.4
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    • pp.609-619
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    • 2020
  • In accordance with a demand for high quality video streaming, it needs high data rate in limited bandwidth and more traffic congestion occurs. In particular, when providing real time video service, packet loss rate and bit error probability increase significantly. To solve these problems, a raptor code, which is one of FEC(Forward Error Correction) techniques, is pervasively used in the application layers as a method for improving real-time service quality. In this paper, we propose a method of determining image transmission parameters based on various deep neural networks to increase transmission efficiency at a similar level of image quality by using raptor codes. The proposed neural network uses the packet loss rate, video encoding rate and data rate as inputs, and outputs raptor FEC parameters and packet sizes. The results of the proposed method present that the throughput is 1.2% higher than that of the existing multimedia transmission technique by optimizing the transmission efficiency at a PSNR(Peak Signal-to-Noise Ratio) level similar to that of the existing technique.

Performance Analysis of Hint-KD Training Approach for the Teacher-Student Framework Using Deep Residual Networks (딥 residual network를 이용한 선생-학생 프레임워크에서 힌트-KD 학습 성능 분석)

  • Bae, Ji-Hoon;Yim, Junho;Yu, Jaehak;Kim, Kwihoon;Kim, Junmo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.5
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    • pp.35-41
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    • 2017
  • In this paper, we analyze the performance of the recently introduced Hint-knowledge distillation (KD) training approach based on the teacher-student framework for knowledge distillation and knowledge transfer. As a deep neural network (DNN) considered in this paper, the deep residual network (ResNet), which is currently regarded as the latest DNN, is used for the teacher-student framework. Therefore, when implementing the Hint-KD training, we investigate the impact on the weight of KD information based on the soften factor in terms of classification accuracy using the widely used open deep learning frameworks, Caffe. As a results, it can be seen that the recognition accuracy of the student model is improved when the fixed value of the KD information is maintained rather than the gradual decrease of the KD information during training.