• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.4
• /
• pp.378-388
• /
• 2008

Flash memory has been attracting attention as the next mass storage media for mobile computing systems such as notebook computers and UMPC(Ultra Mobile PC)s due to its low power consumption, high shock and vibration resistance, and small size. A storage system with flash memory excels in random read, sequential read, and sequential write. However, it comes short in random write because of flash memory's physical inability to overwrite data, unless first erased. To overcome this shortcoming, we propose an SSD(Solid State Disk) architecture with two novel features. First, we utilize non-volatile FRAM(Ferroelectric RAM) in conjunction with NAND flash memory, and produce a synergy of FRAM's fast access speed and ability to overwrite, and NAND flash memory's low and affordable price. Second, the architecture categorizes host write requests into small random writes and large sequential writes, and processes them with two different buffer management, optimized for each type of write request. This scheme has been implemented into an SSD prototype and evaluated with a standard PC environment benchmark. The result reveals that our architecture outperforms conventional HDD and other commercial SSDs by more than three times in the throughput for random access workloads.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1115-1123
• /
• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.4
• /
• pp.325-331
• /
• 2016

Computational fluid numerical analysis using the commercial code CFX was performed to develop a Pelton turbine for a pico hydro power generator using the circulating water of a cooling tower in a large building. The performance of the Pelton turbine was examined for different design factors, such as the bucket shape, in which the Pelton wheel was connected in an appropriate manner to the pipe section, and the number of buckets in order to find the optimal design of Pelton turbine for a pico hydro power using surplus water. A benchmark test was carried out on the manufactured small scale Pelton turbine to validate the design method of the Pelton turbine by numerical analysis. The results obtained by comparing the flow characteristics and power output measured using the ultrasonic flowmeter, the pressure transducer and the oscilloscope with the numerical results confirmed the validity of the analytical design method. The possibility of developing Pelton turbines for kW class pico hydro power generators using surplus water with an average circulation velocity of 1.2 m/s for the chosen bucket shape and number of buckets in a 30 m high building was confirmed.

• Journal of KIISE:Software and Applications
• /
• v.32 no.12
• /
• pp.1217-1228
• /
• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.1-7
• /
• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.21 no.6
• /
• pp.113-125
• /
• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.2
• /
• pp.85-97
• /
• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.19 no.4 s.74
• /
• pp.429-440
• /
• 2006

This paper comprises two specific objectives. The first is to examine the applicability of ordinary kriging interpolation(OK) to the p-adaptivity of the finite element method that is based on variogram modeling. The second objective Is to present the adaptive procedure by the hierarchical p-refinement in conjunction with a posteriori error estimator using the modified S.P.R. (superconvergent patch recovery) method. The ordinary kriging method that is one of weighted interpolation techniques is applied to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In the p-refinement, the analytical domain has to be refined automatically to obtain an acceptable level of accuracy by increasing the p-level non-uniformly or selectively. To verify the performance of the modified S.P.R. method, the new error estimator based on limit value has been proposed. The validity of the proposed approach has been tested with the help of some benchmark problems of linear elastic fracture mechanics such as a centrally cracked panel, a single edged crack, and a double edged crack.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.8 no.3
• /
• pp.63-76
• /
• 2004

This paper presents LRB-based hybrid base isolation systems employing additional active/semiactive control devices for mitigating earthquake-induced vibration of a cable-stayed 29 bridge. Hybrid base isolation systems could improve the control performance compared with the passive type-base isolation system such as LRB-installed bridge system due to multiple control devices are operating. In this paper, the additional response reduction by the two typical additional control devices, such as active type hydraulic actuators controlled by LQG algorithm and semiactive-type magnetorheological dampers controlled by clipped-optimal algorithm, have been evaluated bypreliminarily investigating the slightly modified version of the ASCE phase I benchmark cable-stayed bridge problem (i.e., the installation of LRBs to the nominal cable-stayed bridge model of the problem). It shows from the numerical simulation results that all the LRB based hybrid seismic isolation systems considered are quite effective to mitigate the structural responses. In addition, the numerical results demonstrate that the LRB based hybrid seismic isolation systems employing MR dampers have the robustness to some degree of the stiffness uncertainty of in the structure, whereas the hybrid system employing hydraulic actuators does not. Therefore, the feasibility of the hybrid base isolation systems employing semiactive additional control devices could be more appropriate in realfor full-scale civil infrastructure applications is clearly verified due to their efficacy and robustness.

• International Commerce and Information Review
• /
• v.14 no.2
• /
• pp.147-172
• /
• 2012

The aim of this research is to identify the competencies required by freight forwarding company logistics managers or supply chain managers. And this research also attempts to show their relative importance and key knowledge areas that require improvement. Using a survey questionnaire, data was collected against forty three logistics and supply chain management skills or competencies, which were then grouped into four categories and analysed. The Analysis revealed that supply chain awareness, ability to make decisions, analytical skill, communication skill, supply chain cost, people skill, and integration of internal or external information flow which belong to logistics planning group are considered the most important competencies for effective and efficient logistics functioning. On the other hand, reverse logistics and IATA regulations from environmental awareness group show little influence on logistics managers for improving their logistics performances. The results have implications for a variety of parties including prospective logisticians, students, teachers and companies considering expanding their business to Chinese market. For example, the results permit companies to employ appropriate logistics managers who are qualified with sufficient skills and competencies suggested in this research. In the case of practitioners, the results provide a benchmark for comparison with their current level of abilities and suggested competencies.