• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.149-172
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• 2014

Four algorithms for damage detection of trusses are presented in this paper. These approaches can detect damage by using both complete and incomplete measurements. The suggested methods are based on the minimization of the difference between the measured and analytical static responses of structures. A non-linear constrained optimization problem is established to estimate the severity and location of damage. To reach the responses, the successive quadratic method is used. Based on the objective function, the stiffness matrix of the truss should be estimated and inverted in the optimization procedure. The differences of the proposed techniques are rooted in the strategy utilized for inverting the stiffness matrix of the damaged structure. Additionally, for separating the probable damaged members, a new formulation is proposed. This scheme is employed prior to the outset of the optimization process. Furthermore, a new tactic is presented to select the appropriate load pattern. To investigate the robustness and efficiency of the authors' method, several numerical tests are performed. Moreover, Monte Carlo simulation is carried out to assess the effect of noisy measurements on the estimated parameters.

• Computers and Concrete
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• v.13 no.2
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• pp.209-220
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• 2014

Vibration based damage detection is very popular in the civil engineering area. Especially, special structures like dams, long-span bridges and high-rise buildings, need continues monitoring in terms of mechanical properties of material, static and dynamic behavior. It has been stated in the International Commission on Large Dams that more than half of the large concrete dams were constructed more than 50 years ago and the old dams have subjected to repeating loads such as earthquake, overflow, blast, etc.,. So, some unexpected failures may occur and catastrophic damages may be taken place because of theloss of strength, stiffness and other physical properties of concrete. Therefore, these dams need repairs provided with global damage evaluation in order to preserve structural integrity. The paper aims to show the effectiveness of the model updating method for global damage detection on a laboratory arch dam model. Ambient vibration test is used in order to determine the experimental dynamic characteristics. The initial finite element model is updated according to the experimentally determined natural frequencies and mode shapes. The web thickness is selected as updating parameter in the damage evaluation. It is observed from the study that the damage case is revealed with high accuracy and a good match is attained between the estimated and the real damage cases by model updating method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.47-52
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• 2008

A radio-frequency identification(RFID) tag is a small, inexpensive microchip that emits an identifier in response to a query from a nearby reader. The price of these tags promises to drop to the range of $0.05 per unit in the next several years, offering a viable and powerful replacement for barcodes. The challenge in providing security for low-cost RFID tags is that they are computationally weak devices, unable to perform even basic symmetric-key cryptographic operations. Security researchers often therefore assume that good privacy protection in RFID tags is unattainable. In this paper, we explore a notion of minimalist cryptography suitable for RFID tags. We consider the type of security obtainable in RFID devices with a small amount of rewritable memory, but very limited computing capability. Our aim is to show that standard cryptography is not necessary as a starting point for improving security of very weak RFID devices. Our contribution is threefold: 1. We propose a new formal security model for authentication and privacy in RFID tags. This model takes into account the natural computational limitations and the likely attack scenarios for RFID tags in real-world settings. It represents a useful divergence from standard cryptographic security modeling, and thus a new view of practical formalization of minimal security requirements for low-cost RFID-tag security. 2. We describe protocol that provably achieves the properties of authentication and privacy in RFID tags in our proposed model, and in a good practical sense. Our proposed protocol involves no computationally intensive cryptographic operations, and relatively little storage. 3. Of particular practical interest, we describe some reduced-functionality variants of our protocol. We show, for instance, how static pseudonyms may considerably enhance security against eavesdropping in low-cost RFID tags. Our most basic static-pseudonym proposals require virtually no increase in existing RFID tag resources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.775-784
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• 2019

Malware infringement attacks are continuously increasing in various environments such as mobile, IOT, windows and mac due to the emergence of new and variant malware, and signature-based countermeasures have limitations in detection of malware. In addition, analytical performance is deteriorating due to obfuscation, packing, and anti-VM technique. In this paper, we propose a system that can detect malware based on machine learning by using similarity hashing-based pattern detection technique and static analysis after file classification according to packing. This enables more efficient detection because it utilizes both pattern-based detection, which is well-known malware detection, and machine learning-based detection technology, which is advantageous for detecting new and variant malware. The results of this study were obtained by detecting accuracy of 95.79% or more for benign sample files and malware sample files provided by the AI-based malware detection track of the Information Security R&D Data Challenge 2018 competition. In the future, it is expected that it will be possible to build a system that improves detection performance by applying a feature vector and a detection method to the characteristics of a packed file.

• Food Science and Preservation
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• v.21 no.6
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• pp.903-907
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• 2014

For the selection of a starter for vinegar, six strains of acetic-acid bacteria were isolated from traditional Korean vinegar fermented through the static method. These strains were investigated for their acetic-acid fermentation and identification characteristics. The 16S rRNA sequences of six strains were identified as Acetobacter pasteurianus, A. malorum, Gluconacetobacter entanii, Ga. intermedius, and Ga. xylinus respectively. The overoxidation of acetic acid, acetic-acid and pH tolerances, and acetic-acid production of these strains were investigated. None seemed to have been overoxidized. The Gluconacetobacter genus showed acetic-acid tolerance. Among the acetic-acid bacteria, A. malorum V5-7 exhibited the highest pH tolerance. The Ga. intermedius V11-5 and Ga. xylinus V8-1 strains produced colloids that exopolysaccharides of fiber. The acetic-acid production by isolated acetic-acid bacteria and type strain was a achieved at a shaking culture at $30^{\circ}C$ for 5 days. A. malorum V5-7, A. pasteurianus Gam2, and Ga. intermedius V11-5 exhibited the highest acetic acid production. The study results indicate that appropriate strains of acetic-acid bacteria improved the thraditional Korean vinegar fermented through the static method.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.2
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• pp.67-74
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• 2010

To a series of vibration records obtained from experimental modal testing using a fixed hammer and roving accelerometers for greenhouse arch structures, modal parameters such as natural frequencies, damping ratios and mode shapes are extracted by applying the two most advanced system identification methods in the frequency-domain up to now, so-called PolyMAX and FDD. The former involves both input and output data, while the latter utilizes only the output data. The possibility of determining the static buckling load, detecting damages, etc., for very slender steel-pipe arches by means of a non-destructive testing method based on vibration measurements is primarily investigated. The extracted modal parameters generally correlated well with those obtained using finite element analysis, demonstrating promising results for further on-going research.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.145-155
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• 2010

This paper presents a new approach to obstacle avoidance for mobile robot in unknown or partially unknown environments. The method combines two navigation subsystems: low level and high level. The low level subsystem takes part in the control of linear, angular velocities using a multivariable PI controller, and the nonlinear position control. The high level subsystem uses ultrasonic and IR sensors to detect the unknown obstacle include static and dynamic obstacle. This approach provides both obstacle avoidance and target-following behaviors and uses only the local information for decision making for the next action. Also, we propose a new algorithm for the identification and solution of the local minima situation during the robot's traversal using the set of fuzzy rules. The system has been successfully demonstrated by simulations and experiments.