• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.954-961
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• 2010

Stream cipher was worse than block cipher in terms of security, but faster in execution speed as an advantage. However, since so far there have been many algorithm researches about the execution speed of block cipher, these days, there is almost no difference between them in the execution speed of AES. Therefore an secure and fast stream cipher development is urgently needed. In this paper, we propose a 32bit output fast stream cipher, AA32, which is composed of ASR(Arithmetic Shifter Register) and simple logical operation. Proposed algorithm is a cipher algorithm which has been designed to be implemented by software easily. AA32 supports 128bit key and executes operations by word and byte unit. As Linear Feedback Sequencer, ASR 151bit is applied to AA32 and the reduction function is a very simple structure stream cipher, which consists of two major parts, using simple logical operations, instead of S-Box for a non-linear operation. The proposed stream cipher AA32 shows the result that it is faster than SSC2 and Salsa20 and satisfied with the security required for these days. Proposed cipher algorithm is a fast stream cipher algorithm which can be used in the field which requires wireless internet environment such as mobile phone system and real-time processing such as DRM(Digital Right Management) and limited computational environments such as WSN(Wireless Sensor Network).

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1770-1780
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• 1997

Our problem is improvement of image quality because it is inevitable cell loss of image data when traffic congestion occurs. If cells are discarded indiscriminately in transmission of MPEG video data, it occurs severe degradation in quality of service(QOS). In this paper, to solve this problem, we propose two method. The first, we analyze the traffic characteristics of an MPEG encoder and generate high priority and low priority data stream. During network congestion, only the least low priority cells are dropped, and this ensures that the high priority cells are successfully transmitted, which, in turn, guarantees satisfactory QoS. In this case, the prioritization scheme for the encoder assigns components of the data stream to each priority level based on the value of a parameter ${\beta}$. The second, Number of high priority cells are increased when value of ${\beta}$ is large. It occurs the loss of high priority cell in the congestion. To prevent it, this paper is regulated to data stream rate as buffer occupancy with UPC controller. Therefore, encoder's bandwidth can be calculated renegotiation of the encoder and networks. In this paper, the encoder's bandwidth requirements are characterized by a usage parameter control (UPC) set consisting of peak rate, burstness, and sustained rate. An adaptive encoder rate control algorithm at the Networks Interface Card(NIC) computes the necessary UPC parameter to maintain the user specified quality of service. Simulation results are given for a rate-controlled VBR video encoder operating through an ATM network interface which supports dynamic UPC. These results show that dynamic bandwidth renegotiation of prioritized data stream could provided bandwidth saving and significant quality gains which guarantee high priority data stream.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.203-206
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-transformed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-fransform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this condition of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1077-1081
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• 2004

In this Paper we discuss an algorithm for a real-time transmission of moving color image on the TCP/IP network using wavelet transform and neural network. The Image frames received from the camera are two-level wavelet-transformed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted with a neural network or fuzzy logic. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.487-492
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• 2003

To satisfy the requirements for QoS of Users using multimedia content stream service, it is required to control mechanism for QoS based on class priority, URFA classifies the user by two classes (super class, base class) and controls the admission ratio of user's requests by user's class information. URFA increases the admission ratio class and utilization ratio of stream server resources.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2106-2111
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-trans formed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.47-56
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• 2003

According to rapidly developed mobile terminals and network in the play environment for multimedia presentation, different end-to-end QoS situations appear. Then the generation of transcoding path algorithm was reviewed to transcode a source'data satisfying the needed QoS from a destination and play it considering given transcoders and network. This method used only workload as a parameter although two parameters, workload and throughput, were needed to process multimedia stream in a transcoder, Therefore generated transcoding path with this method had additional calculation to check playability whether it was safisfed the QoS of a destination or not. To solve the problem this paper suggests T algorithm with evaluation function using isochronous property that is needed for multimedia stream to arrive a destination. That means, most playable path is selected with heuristic search based isochronous property between many transcoding paths. Using the suggested algorithm, a transcoding path can be generated faster to play the multimedia data with different end-to-end QoS in real-time transmission.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.