• Title/Summary/Keyword: Information input algorithm

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Classification of Parent Company's Downward Business Clients Using Random Forest: Focused on Value Chain at the Industry of Automobile Parts (랜덤포레스트를 이용한 모기업의 하향 거래처 기업의 분류: 자동차 부품산업의 가치사슬을 중심으로)

  • Kim, Teajin;Hong, Jeongshik;Jeon, Yunsu;Park, Jongryul;An, Teayuk
    • The Journal of Society for e-Business Studies
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    • v.23 no.1
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    • pp.1-22
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    • 2018
  • The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

Development and Validation of the Letter-unit based Korean Sentimental Analysis Model Using Convolution Neural Network (회선 신경망을 활용한 자모 단위 한국형 감성 분석 모델 개발 및 검증)

  • Sung, Wonkyung;An, Jaeyoung;Lee, Choong C.
    • The Journal of Society for e-Business Studies
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    • v.25 no.1
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    • pp.13-33
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    • 2020
  • This study proposes a Korean sentimental analysis algorithm that utilizes a letter-unit embedding and convolutional neural networks. Sentimental analysis is a natural language processing technique for subjective data analysis, such as a person's attitude, opinion, and propensity, as shown in the text. Recently, Korean sentimental analysis research has been steadily increased. However, it has failed to use a general-purpose sentimental dictionary and has built-up and used its own sentimental dictionary in each field. The problem with this phenomenon is that it does not conform to the characteristics of Korean. In this study, we have developed a model for analyzing emotions by producing syllable vectors based on the onset, peak, and coda, excluding morphology analysis during the emotional analysis procedure. As a result, we were able to minimize the problem of word learning and the problem of unregistered words, and the accuracy of the model was 88%. The model is less influenced by the unstructured nature of the input data and allows for polarized classification according to the context of the text. We hope that through this developed model will be easier for non-experts who wish to perform Korean sentimental analysis.

Object Detection Algorithm Using Edge Information on the Sea Environment (해양 환경에서 에지 정보를 이용한 물표 추출 알고리즘)

  • Jeong, Jong-Myeon;Park, Gyei-Kark
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.9
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    • pp.69-76
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    • 2011
  • According to the related reports, about 60 percents of ship collisions have resulted from operating mistake caused by human factor. Specially, the report said that negligence of observation caused 66.8 percents of the accidents due to a human factor. Hence automatic detection and tracking of an object from an IR images are crucial for safety navigation because it can relieve officer's burden and remedies imperfections of human visual system. In this paper, we present a method to detect an object such as ship, rock and buoy from a sea IR image. Most edge directions of the sea image are horizontal and most vertical edges come out from the object areas. The presented method uses them as a characteristic for the object detection. Vertical edges are extracted from the input image and isolated edges are eliminated. Then morphological closing operation is performed on the vertical edges. This caused vertical edges that actually compose an object be connected and become an object candidate region. Next, reference object regions are extracted using horizontal edges, which appear on the boundaries between surface of the sea and the objects. Finally, object regions are acquired by sequentially integrating reference region and object candidate regions.

Frequently Occurred Information Extraction from a Collection of Labeled Trees (라벨 트리 데이터의 빈번하게 발생하는 정보 추출)

  • Paik, Ju-Ryon;Nam, Jung-Hyun;Ahn, Sung-Joon;Kim, Ung-Mo
    • Journal of Internet Computing and Services
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    • v.10 no.5
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    • pp.65-78
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    • 2009
  • The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

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Algorithm of Generating Adaptive Background Modeling for crackdown on Illegal Parking (불법 주정차 무인 자동 단속을 위한 환경 변화에 강건한 적응적 배경영상 모델링 알고리즘)

  • Joo, Sung-Il;Jun, Young-Min;Choi, Hyung-Il
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.6
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    • pp.117-125
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    • 2008
  • The Object tracking by real-time image analysis is one of the major concerns in computer vision and its application fields. The Object detection process of real-time images must be preceded before the object tracking process. To achieve the stable object detection performance in the exterior environment, adaptive background model generation methods are needed. The adaptive background model can accept the nature's phenomena changes and adapt the system to the changes such as light or shadow movements that are caused by changes of meridian altitudes of the sun. In this paper, we propose a robust background model generation method effective in an illegal parking auto-detection application area. We also provide a evaluation method that judges whether a moving vehicle stops or not. As the first step, an initial background model is generated. Then the differences between the initial model and the input image frame is used to trace the movement of object. The moving vehicle can be easily recognized from the object tracking process. After that, the model is updated by the background information except the moving object. These steps are repeated. The experiment results show that our background model is effective and adaptable in the variable exterior environment. The results also show our model can detect objects moving slowly. This paper includes the performance evaluation results of the proposed method on the real roads.

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Development of T-commerce Processing Payment Module Using IC Credit Card(EMV) (IC신용카드(EMV)를 이용한 T-커머스 결제처리 모듈 개발)

  • Choi, Byoung-Kyu;Lee, Dong-Bok;Kim, Byung-Kon;Heu, Shin
    • The KIPS Transactions:PartA
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    • v.19A no.1
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    • pp.51-60
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    • 2012
  • IC(Integrated circuits)card, generally be named smard card, embedded MPU(Micro Processor Unit) of small-size, memory, EEPROM, Card Operating System(COS) and security algorithm. The IC card is used in almost all industry such as a finance(credit, bank, stock etc.), a traffic, a communication, a medical, a electronic passport, a membership management and etc. Recently, a application field of IC card is on the increase by method for payments of T-commerce, as T-commerce is becoming a new growth engine of the broadcating industry by trend of broadcasting and telecommunication convergence, smart mechanization of TV. For example, we can pay in IC credit card(or IC cash card) on T-Commerce. or we can be provided TV banking service in IC cash card such as ATM. However, so far, T-commerce payment services have weakness in security such as storage and disclosure of card information as well as dropping sharply about custom ease because of taking advantage of card information input method using remote control. To solve this problem, This paper developed processing payment module for implementing TV electronic payment system using IC credit card payment standard, EMV.

A Study on the Efficiency of Join Operation On Stream Data Using Sliding Windows (스트림 데이터에서 슬라이딩 윈도우를 사용한 조인 연산의 효율에 관한 연구)

  • Yang, Young-Hyoo
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.2
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    • pp.149-157
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    • 2012
  • In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

Enhanced Sound Signal Based Sound-Event Classification (향상된 음향 신호 기반의 음향 이벤트 분류)

  • Choi, Yongju;Lee, Jonguk;Park, Daihee;Chung, Yongwha
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.5
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    • pp.193-204
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    • 2019
  • The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

A Road Luminance Measurement Application based on Android (안드로이드 기반의 도로 밝기 측정 어플리케이션 구현)

  • Choi, Young-Hwan;Kim, Hongrae;Hong, Min
    • Journal of Internet Computing and Services
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    • v.16 no.2
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    • pp.49-55
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    • 2015
  • According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

A Scalable and Modular Approach to Understanding of Real-time Software: An Architecture-based Software Understanding(ARSU) and the Software Re/reverse-engineering Environment(SRE) (실시간 소프트웨어의 조절적${\cdot}$단위적 이해 방법 : ARSU(Architecture-based Software Understanding)와 SRE(Software Re/reverse-engineering Environment))

  • Lee, Moon-Kun
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.12
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    • pp.3159-3174
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    • 1997
  • This paper reports a research to develop a methodology and a tool for understanding of very large and complex real-time software. The methodology and the tool mostly developed by the author are called the Architecture-based Real-time Software Understanding (ARSU) and the Software Re/reverse-engineering Environment (SRE) respectively. Due to size and complexity, it is commonly very hard to understand the software during reengineering process. However the research facilitates scalable re/reverse-engineering of such real-time software based on the architecture of the software in three-dimensional perspectives: structural, functional, and behavioral views. Firstly, the structural view reveals the overall architecture, specification (outline), and the algorithm (detail) views of the software, based on hierarchically organized parent-chi1d relationship. The basic building block of the architecture is a software Unit (SWU), generated by user-defined criteria. The architecture facilitates navigation of the software in top-down or bottom-up way. It captures the specification and algorithm views at different levels of abstraction. It also shows the functional and the behavioral information at these levels. Secondly, the functional view includes graphs of data/control flow, input/output, definition/use, variable/reference, etc. Each feature of the view contains different kind of functionality of the software. Thirdly, the behavioral view includes state diagrams, interleaved event lists, etc. This view shows the dynamic properties or the software at runtime. Beside these views, there are a number of other documents: capabilities, interfaces, comments, code, etc. One of the most powerful characteristics of this approach is the capability of abstracting and exploding these dimensional information in the architecture through navigation. These capabilities establish the foundation for scalable and modular understanding of the software. This approach allows engineers to extract reusable components from the software during reengineering process.

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