• Advanced Industrial SCIence
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• v.1 no.1
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• pp.1-6
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• 2022

As different news is reported every day in our daily life, the amount of data generated around us is enormous. Some of this data is personal, but there are some things that are common to everyone, such as weather and traffic information. This paper is a study on information transmission using public data to use information effectively and quickly, and investigated open API based on public data and API technology. Based on this, we explain a method of transmitting information using an open API that can be easily used in daily life, and propose a method to transmit information using various open APIs by applying this. If public data is used using the suggested method, it is possible to transmit information more easily and accurately, so it is possible to apply it to various fields.

• Journal of the Korean Recycled Construction Resources Institute
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• v.1 no.1
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• pp.49-57
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• 2013

Recently, recycling of industrial by-products and CO2 reduction have been important issues in the world. In this reason, applications and reuse of Fly ash as a binder for concrete, which is generated in thermoelectric power plant, have been one of the effective recycle methods. In order for Fly ash to be applied to concrete, Korean Standard(KS) has selected and managed quality such as $SiO_2$, fineness, specific gravity, ignition loss and activity index. However, there is a limits for activity index, whose test period required is at least 28 days or 91 days. Activity index is the critical indication standard to determine mechanical strength of concrete that contained Fly ash. To complement the disadvantage of test method, this research provided "API test method", which quickly measure Pozzolanic reaction of Fly ash can be considered as a alternative of activity index. Then, the adaptable API test method need to be investigated through comparative analysis with the test result of API, activity index and K-value. The test method can make evaluation of Fly ash quality faster and more accurate. As a result, most Fly ash produced in Korea has not been satisfied in the KS quality standard except water content and specific gravity, and especially fluidized bed boiler ash has its characteristics. Also, API, activity index and K-value have superior interrelationship. The interrelationship between API and activity index and K-value gets increased as the material age gets higher, so API test can be considered as very useful test method for Pozzolanic reaction evaluation of Fly ash.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.97-104
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• 2021

Securities and investment services had the most data per company on the average, and used the most data. Investors are increasingly demanding to invest through their own analysis methods. Therefore, securities and investment companies provide stock data to investors through open API. The data received using the open API is in text format. Python is effective and convenient for requesting and receiving text data. We investigate there are 22 major securities and investment companies in Korea and only 6 companies. Only Daishin Securities Co. supports Python officially. We compare how to receive stock data through open API using Python, and Python programming features. The open APIs for the study are Daishin Securities Co. and eBest Investment & Securities Co. Comparing the two APIs for receiving the current stock data, we find the main two differences are the login method and the method of sending and receiving data. As for the login method, CYBOS plus has login information, but xingAPI does not have. As for the method of sending and receiving data, Cybos Plus sends and receives data by calling the request method, and the reply method. xingAPI sends and receives data in the form of an event. Therefore, the number of xingAPI codes is more than that of CYBOS plus. And we find that CYBOS plus executes a loop statement by lists and tuple, dictionary, and CYBOS plus supports the basic commands provided by Python.

• Journal of KIISE
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• v.43 no.10
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• pp.1067-1072
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• 2016

Software birthmarks, which are unique characteristics of the software, are used to detect software plagiarism or software similarity. Generally, software birthmarks are divided into static birthmarks or dynamic birthmarks, which have evident pros and cons depending on the extraction method. In this paper, we propose a method for extracting the API sequence birthmarks using a dynamic analysis and similarity detection between the executable codes. Dynamic birthmarks based on API sequences extract API functions during the execution of programs. The extracted API sequences often include all the API functions called from the start to the end of the program. Meanwhile, our dynamic birthmark scheme extracts the API functions only called directly from the executable code. Then, it uses a sequence alignment algorithm to calculate the similarity metric effectively. We evaluate the birthmark with several open source software programs to verify its reliability and credibility. Our dynamic birthmark scheme based on the extracted API sequence can be utilized in a similarity test of executable codes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.11-17
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• 2017

We proposed malware detection method, which use the feature vector that consist of Opcode(operation code) and Windows API Calls extracted from executable files. And, we implemented our feature vector and measured the performance of it by using Bernoulli Naïve Bayes and K-Nearest Neighbor classifier. In experimental result, when using the K-NN classifier with the proposed method, we obtain 95.21% malware detection accuracy. It was better than existing methods using only either Opcode or Windows API Calls.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.263-268
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• 2015

Malware variations could be defined as malicious executable files that have similar functions but different structures. In order to classify the variations, this paper analyzed sequence alignment, the method used in Bioinformatics. This method found common parts of the Malwares' API call information. This method's performance is dependent on the API call information's length; if the length is too long, the performance should be very poor. Therefore we removed the repeated patterns in API call information in order to improve the performance of sequence alignment analysis, before the method was applied. Finally the similarity between malware was analyzed using sequence alignment. The experimental results with the real malware samples were presented.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.71-76
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• 2010

This research proposed the method for application of SCORM in U-learning system. That is, I proposed the API broker to connect between U-learning and API Instance of RTE that is existing SCORM based learning object interface environment. The API broker operated handling process using request port and response port between SCORM and U-learning server. For efficient operation in each service, this system has learning contents service buffer in API broker.

• Asian Journal of Atmospheric Environment
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• v.9 no.2
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• pp.101-113
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• 2015

Air quality index (AQI) or air pollution index (API) is commonly used to report the level of severity of air pollution to public. A number of methods were developed in the past by various researchers/environmental agencies for determination of AQI or API but there is no universally accepted method exists, which is appropriate for all situations. Different method uses different aggregation function in calculating AQI or API and also considers different types and numbers of pollutants. The intended uses of AQI or API are to identify the poor air quality zones and public reporting for severity of exposure of poor air quality. Most of the AQI or API indices can be broadly classify as single pollutant index or multi-pollutant index with different aggregation method. Every indexing method has its own characteristic strengths and weaknesses that affect its suitability for particular applications. This paper attempt to present a review of all the major air quality indices developed worldwide.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.227-232
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• 2020

The one of the biggest challenges in using e-mail is that it is difficult to filter out unconfirmed senders. Therefore, in this study, an email group authentication API was designed using blockchain technology, which has been widely used as an authentication method. As the proposed model, the node-node weighting index was obtained through the relationship association network, and after designing the email reliability model, the reliability calculation model of the Rotten Tomato method was obtained. Based on this, the system structure was designed, chain code methods were defined, and API was developed. Through this study, it is expected that it will be used in various fields requiring authentication as well as email user authentication, and it is expected that the relationship of group authentication can be proved by allowing a large number of users to use the API in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.115-127
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• 2008

The Native API is a system call which can only be accessed with the authentication of the administrator. It can be used to detect a variety of malicious codes which can only be executed with the administrator's authority. Therefore, much research is being done on detection methods using the characteristics of the Native API. Most of these researches are being done by using supervised learning methods of machine learning. However, the classification standards of Anti-Virus companies do not reflect the characteristics of the Native API. As a result the population data used in the supervised learning methods are not accurate. Therefore, more research is needed on the topic of classification standards using the Native API for detection. This paper proposes a method for re-grouping malicious codes using fuzzy clustering methods with the Native API standard. The accuracy of the proposed re-grouping method uses machine learning to compare detection rates with previous classifying methods for evaluation.