• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.117-123
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• 2017

The personalized search algorithm is a search system that analyzes the user's IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called 'Filter bubble' phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider's server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.1-12
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• 2012

In this paper, we report a case study on the common due-date assignment and scheduling problem in a paper remanufacturing system that produces corrugated cardboards using collected waste papers for a given set of orders under the make-to-order (MTO) environment. Since the system produces corrugated cardboards in an integrated process and has sequence-dependent setups, the problem considered here can be regarded as common due-date assignment and sequencing on a single machine with sequence-dependent setup times. The objective is to minimize the sum of the penalties associated with due-date assignment, earliness, and tardiness. In the study, the earliness and tardiness penalties were obtained from inventory holding and backorder costs, respectively. To solve the problem, we adopted two types of algorithms: (a) branch and bound algorithm that gives the optimal solutions; and (b) heuristic algorithms. Computational experiments were done on the data generated from the case and the results show that both types of algorithms work well for the case data. In particular, the branch and bound algorithm gave the optimal solutions quickly. However, it is recommended to use the heuristic algorithms for large-sized instances, especially when the solution time is very critical.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.261-266
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• 2012

Purpose: Traumatic pancreatic injury is not common in abdominal trauma injury. However, the morbidity and the mortality rates of patients with pancreatic injury, which are related with difficulties of initial assessment, establishment of diagnosis, and treatment are relatively high. The aim of this study is to review our institution's experience and suggest a diagnosis and therapeutic algorithm for use in cases involving traumatic pancreatic injury. Methods: Eighteen(18) patients with blunt pancreatic injury from January, 2004 to October 2012 were included in this study. We analyzed treatment and diagnosis method, other organ injury, treatment interval, hospital stay and complications retrospectively. Results: Nine patients were treated with conservative medication and another nine patients were treated surgically. Complications occurred in nine patients, and one patient died due to intraventricular hemorrhage and subdural hemorrhage with multiple organ failure. Delayed surgery was performed in three cases. The early and delayed surgery groups showed difference in hospital stay and intensive care unit stay. Delayed surgery was associated with a longer hospital stay (p=0.007) than immediate surgery. Conclusion: In blunt pancreatic trauma, proper early diagnosis and prompt treatment are recommended necessity. Based on this review of our experience, we also suggest the adoption of our institution's algorithm for cases involving traumatic pancreatic injury.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• Earthquakes and Structures
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• v.13 no.1
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• pp.1-8
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• 2017

This article is all about using the MR damper with an external lever system for mitigation torsional and transitional lateral displacements by using of PID control algorithm. The torsional modes are so destructive and can be varied during an earthquake therefore, using a semi-active control system mostly recommended for them. In this paper the corner lateral displacement of each floor obtains and then it equivalents in a solid member and it connects to an MR damper, which relies to a rigid structure to reduce the response. An MR damper is a semi-active control system, which can absorb a lot of energy by injecting current to it. This amount of current is very low and needs low power supply, but it increases the amount of damper force, rather than inactive systems like viscous dampers. This paper will show the appropriate algorithm for current injection into MR damper when the eccentricity of the load is changed by using of Bouc-Wen and Bingham's methods and illustrates the coincidence of them.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.281-287
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• 2021

Bioinformatics is formed with a blend of biology and informatics technologies and it employs the statistical methods and approaches for attending the concerning issues in the domains of nutrition, medical research and towards reviewing the living environment. The ceaseless growth of DNA sequencing technologies has resulted in the production of voluminous genomic data especially the DNA sequences thus calling out for increased storage and bandwidth. As of now, the bioinformatics confronts the major hurdle of management, interpretation and accurately preserving of this hefty information. Compression tends to be a beacon of hope towards resolving the aforementioned issues. Keeping the storage efficiently, a methodology has been recommended which for attending the same. In addition, there is introduction of a competent algorithm that aids in exact matching of small pattern. The DNA representation sequence is then implemented subsequently for determining 2 bases to 6 bases matching with the remaining input sequence. This process involves transforming of DNA sequence into an ASCII symbols in the first level and compress by using LZ77 compression method in the second level and after that form the grid variables with size 3 to hold the 100 characters. In the third level of compression, the compressed output is in the grid variables. Hence, the proposed algorithm S_Pattern DNA gives an average better compression ratio of 93% when compared to the existing compression algorithms for the datasets from the UCI repository.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.1-8
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• 2019

Internet of Things (IoT) is proliferating to provide more intelligent services by interconnecting various Internet devices, and TCP based MQTT is being used as a standard communication protocol of the IoT. Although it is recommended to use TLS/SSL security protocol for TCP with MQTT-based IoT devices, encryption and decryption performance degenerates when applied to low-specification / low-capacity IoT devices. In this paper, we propose an end-to-end message security protocol using elliptic curve cryptosystem, a lightweight encryption algorithm, which improves performance on both sides of the client and server, based on the simulation of TLS/SSL and the proposed protocol.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.171-176
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• 2022

With the recent development of deep learning technology, the areas of recommendation systems have also diversified. This paper studied algorithms to improve the learning rate and studied the significance results according to words through comparison with the performance characteristics of the Word2Vec model. The problem recommendation algorithm was implemented with the values expressed through the reflection of meaning and similarity test between texts, which are characteristics of the Word2Vec model. Through Word2Vec's learning results, problem recommendations were conducted using text similarity values, and problems with high similarity can be recommended. In the experimental process, it was seen that the accuracy decreased with the quantitative amount of data, and it was confirmed that the larger the amount of data in the data set, the higher the accuracy.