• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.2
• /
• pp.70-75
• /
• 2014

Recently, the demand for high-integrated, low-powered, and high-powered SoC design has been increasing due to the multi-functionality and the miniaturization of digital devices and the high capacity of service informations. With the rapid evolution of the system, the required hardware performances have become diversified, the FPGA system has been increasingly adopted for the rapid verification, and SoC system using the FPGA and the ARM core for control has been growingly chosen. While the AXI bus is used in these kinds of systems in various ways, it is traditionally designed with AXI slave structure. In slave structure, there are problems with the CPU resources because CPU is continually involved in the data transfer and can't be used in other jobs, and with the decreased transmission efficiency because the time not used of AXI bus beomes longer. In this paper, an efficient AXI master interface is proposed to solve this problem. The simulation results show that the proposed system achieves reductions in the consumption clock by an average of 51.99% and in the slice by 31% and that the maximum operating frequency is increased to 107.84MHz by about 140%.

• Journal of Korea Water Resources Association
• /
• v.31 no.1
• /
• pp.13-25
• /
• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.65-78
• /
• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.4
• /
• pp.153-163
• /
• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.519-522
• /
• 2017

In this paper, we propose an algorithm for an alternative method using the clustering algorithm in a system that needs classification to extract individual user context information. In the conventional user classification system, the user has to input his own information. In this paper, we will research and develop a system applying a clustering algorithm which can extract user 's perceived information applying the improved algorithm for user management base. Generally, the algorithm that distinguishes users with the same data makes sure that recorded information matches the newly entered information, and then responds accordingly. However, it is troublesome to manually input information of the new user. Therefore, in this paper, we propose a method to distinguish users by using the clustering algorithm based on the analyzed data from the working memory in the accumulated system without directly inputting the user information. The study shows that the management method applied to the applied algorithm is more adaptive in environments where the number of people is different from that of the existing system (as a subjective observer test method).

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.3
• /
• pp.258-268
• /
• 2001

The compression method is necessarily used to send the high quality moving picture that contains a number of data in image processing. In the field of moving picture compression method, the motion estimation algorithm is used to reduce the temporal redundancy. Block matching algorithm to be usually used is distinguished partial search algorithm with full search algorithm. Full search algorithm be used in this paper is the method to compare the reference block with entire block in the search window. It is very efficient and has simple data flow and control circuit. But the bigger the search window, the larger hardware size, because large computational operation is needed. In this paper, we design the full search block matching motion estimator. Using the DCT DC values, we decide luminance. And we apply 3 bit compare-selector using bit plane to I(Intra coded) picture, not using 8 bit luminance signals. Also it is suggested that use the same selective bit for the P(Predicted coded) and B(Bidirectional coded) picture. We compare based full search method with PSNR(Peak Signal to Noise Ratio) for C language modeling. Its condition is the reference block 8$\times$8, the search window 24$\times$24 and 352$\times$288 gray scale standard video images. The result has small difference that we cannot see. And we design the suggested motion estimator that hardware size is proved to reduce 38.3％ for structure I and 30.7％ for structure II. The memory is proved to reduce 31.3％ for structure I and II.

• The KIPS Transactions:PartA
• /
• v.18A no.5
• /
• pp.193-204
• /
• 2011

Since web applications are accessed by anonymous users via web, more security risks are imposed on those applications. In particular, because security vulnerabilities caused by insecure source codes cannot be properly handled by the system-level security system such as the intrusion detection system, it is necessary to eliminate such problems in advance. In this paper, to enhance the security of web applications, we develop a static analyzer for detecting the well-known security vulnerability of PHP file inclusion vulnerability. Using a semantic based static analysis, our vulnerability analyzer guarantees the soundness of the vulnerability detection and imposes no runtime overhead, differently from the other approaches such as the penetration test method and the application firewall method. For this end, our analyzer adopts abstract interpretation framework and uses an abstract analysis domain designed for the detection of the target vulnerability in PHP programs. Thus, our analyzer can efficiently analyze complicated data-flow relations in PHP programs caused by extensive usage of string data. The analysis results can be browsed using a JAVA GUI tool and the memory states and variable values at vulnerable program points can also be checked. To show the correctness and practicability of our analyzer, we analyzed the source codes of open PHP applications using the analyzer. Our experimental results show that our analyzer has practical performance in analysis capability and execution time.

• Korean Journal of Community Nutrition
• /
• v.19 no.2
• /
• pp.142-162
• /
• 2014

Objectives: To evaluate the critical environmental factors on healthy-aging of Korean people, we investigated the significant factors influencing health status of septuagenarians living in rural area of Jeonla province, known to be one of the representative longevity regions in Korea. Methods: We divided subjects into healthy group (36M/25F) or poor-health group (26M/73F) based on self-reported health status, body mass index, a number of prescription, and blood test data. General characteristics, physical measurements, lifestyle, dietary behavior and nutrient intake, physical health and mental health data were statistically compared between the two groups. Results: Average age was not different between healthy group and poor-health group in men and women, respectively. In men, significantly favorable factors to health were observed to be higher education, regular exercise, higher grip strength and walking function, body mass index (${\geq}18.5kg/m^2$), moderate frequency of drinking and eating-out, non-smoking, normal red blood cell (RBC) count, higher serum dehydroepiandrosterone-sulfate (DHEAS) level, good digestive function and appetite, normal hearing function, regular meals, adequate vegetable and fruit intake, diverse food intake, adequate energy and nutrients (protein, vitamin $B_1$, $B_6$, C and E, folate, niacin, P, Zn and K) intake, higher mini-nutrient status assessment (MNA) score and low level of depression. On the other hand, in women, those were literacy, living arrangement, moderate frequency of drinking, healthy teeth, higher grip strength and walking function, bone mineral density, normal RBC and white blood cell (WBC) count, higher DHEAS concentration, higher MNA score, normal cognition and memory function, having snack and adequate fruit intake. Conclusions: These results could be useful to plan effective strategies to increase health-life expectancy of Korean old people living in rural areas.

• The KIPS Transactions:PartA
• /
• v.16A no.2
• /
• pp.69-78
• /
• 2009

One of the most effective way to improve cache performance is to exploit both temporal and spatial locality given by any program executive characteristics. In this paper we present a high performance and low power cache structure with a bank selection mechanism that enhances exploitation of spatial and temporal locality. The proposed cache system consists of two parts, i.e., a main direct-mapped cache with a small block size and a fully associative buffer with a large block size as a multiple of the small block size. Especially, the main direct-mapped cache is constructed as two banks for low power consumption and stores a small block which is selected from fully associative buffer by the proposed bank selection algorithm. By using the bank selection algorithm and three state bits, We selectively extend the lifetime of those small blocks with high temporal locality by storing them in the main direct-mapped caches. This approach effectively reduces conflict misses and cache pollution at the same time. According to the simulation results, the average miss ratio, compared with the Victim and STAS caches with the same size, is improved by about 23% and 32% for Mibench applications respectively. The average memory access time is reduced by about 14% and 18% compared with the he victim and STAS caches respectively. It is also shown that energy consumption of the proposed cache is around 10% lower than other cache systems that we examine.

• The KIPS Transactions:PartA
• /
• v.16A no.2
• /
• pp.113-124
• /
• 2009

With increasing multicore system, much effort has been put on the performance improvement of its application. Because multicore system has multiple processing devices in one system, its processing power increases compared to the single core system. However in many cases the advantages of multicore can not be exploited fully because the existing software and hardware were designed to be suitable for single core. When the existing software runs on multicore, its performance improvement is limited by the bottleneck of sharing resources and the inefficient use of cache memory on multicore. Therefore, according as the number of core increases, it doesn't show performance improvement and shows performance drop in the worst case. In this paper we propose a method of performance improvement of multicore system by applying Flow-Level Parallelism to the existing TCP/IP network application and operating system. The proposed method sets up the execution environment so that each core unit operates independently as much as possible in network application, TCP/IP stack on operating system, device driver, and network interface. Moreover it distributes network traffics to each core unit through L2 switch. The proposed method allows to minimize the sharing of application data, data structure, socket, device driver, and network interface between each core. Also it allows to minimize the competition among cores to take resources and increase the hit ratio of cache. We implemented the proposed methods with 8 core system and performed experiment. Experimental results show that network access speed and bandwidth increase linearly according to the number of core.