• Journal of Mechanical Science and Technology
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• v.14 no.3
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• pp.320-330
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• 2000

It is desirable to perform nondestructive evaluation (NDE) to assess material properties and part homogeneity because the manufacturing of carbon/carbon brake disks requires complicated and costly processes. In this work several ultrasonic techniques were applied to carbon/carbon brake disks (322mm ad, 135mm id) for the evaluation of spatial variations in material properties that are attributable to the manufacturing process. In a large carbon/carbon disk manufactured by chemical vapor infiltration (CYI) method, the spatial variation of ultrasonic velocity was measured and found to be consistent with the densification behavior in CYI process. Low frequency (e.g., 1-5MHz) through-transmission scans based on both amplitude and time-of-flight of the ultrasonic pulse were used for mapping out the material property inhomogeneity. Images based on both the amplitude and the time-of-flight of the transmitted ultrasonic pulse showed significant variation in the radial direction. The radial variations in ultrasonic velocity and attenuation were attributed to a density variation caused by the more efficient densification of pitch impregnation near the id and od and by the less efficient densification away from the exposed edged of the disk. Ultrasonic velocities in the edges of the disk. Ultrasonic velocities in the thickness direction were also measured as a function of location using dry-coupling transducers ; the results were consistent with the densification behavior. However, velocities in the in-plane directions (circumferential and radial) seemed to be affected more by the relative contents of fabric and chopped fiber, and less by the void content.

• Spatial Information Research
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• v.16 no.1
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• pp.51-64
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• 2008

Recently, development and research of u-City established the ubiquitous environment which can be anytime, anywhere computing or network, has been much highlighted. Thus, current urban facilities should be managed by ubiquitous concept, and monitored location and status information in a real-time manner, and controled if necessary. In order to be establish in the purpose of management, indirect mapping through id-tag is better than facility management directly. For instance, RFID, UCODE, UFID. In this paper, we propose that represent facility object through UOID(Unique Object IDentification). UOID comprises three parts; 1) sensing object, 2) facility object, 3) cell object consists of facilities. and Life cycle management system in UOID, and network system connected with internet is proposed. We wish that proposed UOID and network system mange u-City facilities effectively, and also provide ubiquitous service to the citizen, one of the integrate service of u-City platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.241-244
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• 2011

Recently, Due to development of Internet techniques, user suddenly increased that Used of Web services and with out constraints of place and time has been provided. typically, Web services used ID/Password authentication. User confirmed personal data Stored on Web servers after user authorized. web service provider is to provide variety security techniques for the protection personal information. However, recently accident has happened is the malicious attackers may capture user information such as users entered personal information through new keyboard hooking. In this paper, we propose a keyboard hooking protected password input method using CAPTCHA. The proposed password input method is based on entering the password using mouse click or touch pad on the CAPTCHA image. The mapping of CAPTCHA image pixels is random.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.69-78
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• 2016

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Applied Biological Chemistry
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• v.53 no.4
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• pp.225-231
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• 2010

To understand the inhibitory activity with changing hydroxyl substituents ($R_l-R_9$) of polyhydroxy substituted 2-phenyl-l,4-benzopyrone analogues (1-25) against tyrosinase (PDB ID: oxy-form; 1WX2), molecular docking and the three dimensional quantitative structure-activity relationships (3D-QSARs: Comparative molecular field analysis (CoMFA) & Comparative molecular similarity indices analysis (CoMSIA)) were studied quantitatively. The statistically best models were CoMFA 1 and CoMSIA 1 model from the results. The optimized CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced ($dq^2'/dr_{yy'}^2$=1.009 & $q^2$=0.51l) by a progressive scrambling analysis were not dependent on chance correlation. The inhibitory activities with optimized CoMSIA 1 model were dependent upon electrostatic factor (51.4%) of substrate molecules. Contour mapping the 3D-QSAR models to the active site of tyrosinase provides new insight into the interaction between tyrosinase as receptor and 2-phenyl-l,4-benzopyrone analogues as inhibitor. Therefore, the results will he able to apply to the optimization of a new potent tyrosinase inhibitors.

• Journal of KIISE
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• v.44 no.1
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• pp.1-12
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• 2017

The continuously growing internet service requirements has resulted in a multitier system structure consisting of web server and database (DB) server. In this multitier structure, the existing intrusion detection system (IDS) detects known attacks by matching misused traffic patterns or signatures. However, malicious change to the contents at DB server through hypertext transfer protocol (HTTP) requests at the DB server cannot be detected by the IDS at the DB server's end, since the DB server processes structured query language (SQL) without knowing the associated HTTP, while the web server cannot identify the response associated with the attacker's SQL query. To detect these types of attacks, the malicious user is tracked using knowledge on interaction between HTTP request and SQL query. However, this is a practical challenge because system's source code analysis and its application logic needs to be understood completely. In this study, we proposed a scheme to find the HTTP request associated with a given SQL query using only system log files. We first generated an HTTP request-SQL query map from system log files alone. Subsequently, the HTTP request associated with a given SQL query was identified among a set of HTTP requests using this map. Computer simulations indicated that the proposed scheme finds the HTTP request associated with a given SQL query with 94% accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5605-5611
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• 2012

Background: In the last two decades, pioneering research on anti-tumour activity of saffron has shed light on the role of crocetin, picrocrocin and safranal, as broad spectrum anti-neoplastic agents. However, the exact mechanisms have yet to be elucidated. Identification and characterization of the targets of bioactive constituents will play an imperative role in demystifying the complex anti-neoplastic machinery. Methods: In the quest of potential target identification, a dual virtual screening approach utilizing two inverse screening systems, one predicated on idTarget and the other on PharmMapper was here employed. A set of target proteins associated with multiple forms of cancer and ranked by Fit Score and Binding energy were obtained from the two independent inverse screening platforms. The validity of the results was checked by meticulously analyzing the post-docking binding pose of the picrocrocin with Hsp90 alpha in AutoDock. Results: The docking pose reveals that electrostatic and hydrogen bonds play the key role in inter-molecular interactions in ligand binding. Picrocrocin binds to the Hsp90 alpha with a definite orientation appropriate for nucleophilic attacks by several electrical residues inside the Hsp90-alpha ATPase catalytic site. Conclusion: This study reveals functional information about the anti-tumor mechanism of saffron bioactive constituents. Also, a tractable set of anti-neoplastic targets for saffron has been generated in this study which can be further authenticated by in vivo and in vitro experiments.