• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.337-346
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• 2004

Punctured convolutional codes increase transmission efficiency without increasing hardware complexity. However, Viterbi decoder supporting punctured codes requires long decoding length and large survivor memory to achieve sifficiently low bit error rate (BER), when compared to the Viterbi decoder for a rate 1/2 convolutional code. This Paper presents novel architecture adopting a pipelined trace-forward unit reducing survivor memory requirements in the Viterbi decoder. The proposed survivor path architecture reduces the memory requirements by removing the initial decoding delay needed to perform trace-back operation and by accelerating the trace-forward process to identify the survivor path in the Viterbi decoder. Experimental results show that the area of survivor path unit has been reduced by 16％ compared to that of conventional hybrid survivor path unit.

• Steel and Composite Structures
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• v.48 no.6
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• pp.611-623
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• 2023

This work considered an optimal control formulation in the sense of Caputo derivatives. The optimality of the fractional optimal control problem. The tumor immune interaction in fractional form provides an excellent tool for the description of memory and hereditary properties of inter and intra cells. So the interaction between effector-cells, tumor cells and are modeled by using the definition of Caputo fractional order derivative that provides the system with long-time memory and gives extra degree of freedom. In addiltion, existence and local stability of fixed points are investigated for discrete model. Moreover, in order to achieve more efficient computational results of fractional-order system, a discretization process is performed to obtain its discrete counterpart. Our technique likewise allows the advancement of results, such as return time to baseline that are unrealistic with current model solvers.

• Advances in nano research
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• v.10 no.4
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• pp.359-371
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• 2021

A tumor immune interaction is a main topic of interest in the last couple of decades because majority of human population suffered by tumor, formed by the abnormal growth of cells and is continuously interacted with the immune system. Because of its wide range of applications, many researchers have modeled this tumor immune interaction in the form of ordinary, delay and fractional order differential equations as the majority of biological models have a long range temporal memory. So in the present work, tumor immune interaction in fractional form provides an excellent tool for the description of memory and hereditary properties of inter and intra cells. So the interaction between effector-cells, tumor cells and interleukin-2 (IL-2) are modeled by using the definition of Caputo fractional order derivative that provides the system with long-time memory and gives extra degree of freedom. Moreover, in order to achieve more efficient computational results of fractional-order system, a discretization process is performed to obtain its discrete counterpart. Furthermore, existence and local stability of fixed points are investigated for discrete model. Moreover, it is proved that two types of bifurcations such as Neimark-Sacker and flip bifurcations are studied. Finally, numerical examples are presented to support our analytical results.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.75-83
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• 2018

Recently, autonomous vehicles have been actively studied. Pedestrian detection and recognition technology is important in autonomous vehicles. Pedestrian detection using CNN(Convolutional Neural Netwrok), which is mainly used recently, generally shows good performance, but there is a performance degradation depending on the environment of the image. In this paper, we propose a pedestrian detection system applying long-term memory structure of hippocampal neural network based on CNN network with LGP-FL (Local Gradient Pattern-Feature Layer) added. First, change the input image to a size of $227{\times}227$. Then, the feature is extracted through a total of 5 layers of convolution layer. In the process, LGP-FL adds the LGP feature pattern and stores the high-frequency pattern in the long-term memory. In the detection process, it is possible to detect the pedestrian more accurately by detecting using the LGP feature pattern information robust to brightness and color change. A comparison of the existing methods and the proposed method confirmed the increase of detection rate of about 1~4%.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2431-2449
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• 2024

As problems such as water pollution and fish species depletion have become serious, a land-based fish farming is receiving a great attention for ensuring stable productivity. In the fish farming, it is important to determine the timing of shipments, as one of key factors to increase net profit on the aquaculture. In this paper, we propose a system for predicting net profit to support decision of timing of shipment using fish farming-related statistical data. The prediction system consists of growth and farm-gate price prediction models, a cost statistics table, and a net profit estimation algorithm. The Gaussian process regression (GPR) model is exploited for weight prediction based on the analysis that represents the characteristics of the weight data of cultured fish under the assumption of Gaussian probability processes. Moreover, the long short-term memory (LSTM) model is applied considering the simple time series characteristics of the farm-gate price data. In the case of GPR model, it allows to cope with data missing problem of the weight data collected from the fish farm in the time and temperature domains. To solve the problem that the data acquired from the fish farm is aperiodic and small in amount, we generate the corresponding data by adopting a data augmentation method based on the Gaussian model. Finally, the estimation method for net profit is proposed by concatenating weight, price, and cost predictions. The performance of the proposed system is analyzed by applying the system to the Korean flounder data.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.521-527
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• 2021

This study presents a process management method for the detection of casting defects during in high-pressure die casting based on machine learning. The model predicts the defects of the next cycle by extracting the features appearing over the previous cycles. For design of the gearbox, the proposed model detects shrinkage defects with data from three cycles in advance with 98.9% accuracy and 96.8% recall rates.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.795-804
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• 2009

An HSS(Home Subscriber Server) system requires a main-memory database on main-memory unit for the real-tine management of the subscriber information in the mobile communication service, in that the system controls not only basic data for handling calls of users, but also additional service data related to user authentication and operational data. Nonetheless, HSS-DBS system, requiring the reliability and stability, need more secure data store method and a back-up technique because the system have a long startup time and the big problem on the failures of main-memory. This paper proposes an efficient back-up replication technique, on the basis of enhancing the stability and performance of HSS system. The proposed shadowing back-up technique adopting the delayed recovery process, can help minimize the real-time back-up overloads by location registration, while the proposed backup replication method enables more stable system operations with replicating the data to remote server in real time.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.36-46
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• 2017

The turbo code, an error correction code, needs a long decoding time since the same decoding process must be repeated several times in order to obtain a good BER performance. Thus, parallel processing may be used to reduce the decoding time, in which case there may be a memory contention that requires additional buffers. The QPP interleaving has been proposed to avoid such case, but there is still a possibility of memory contention when a decoder is constructed using the so-called double flow technique. In this paper, we propose an even-odd cross mapping technique to avoid memory conflicts even in decoding using the double-flow technique. This method uses the address generation characteristic of the QPP interleaving and can be used to implement the interleaving circuit between the decoding blocks and the LLR memory blocks. When the decoder implemented by applying the double flow and the proposed methods is compared with the decoder by the conventional MDF techniques, the decoding time is reduced by up to 32% with the total area increase by 8%.