• Title/Summary/Keyword: network performance

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Estimation of KOSPI200 Index option volatility using Artificial Intelligence (이기종 머신러닝기법을 활용한 KOSPI200 옵션변동성 예측)

  • Shin, Sohee;Oh, Hayoung;Kim, Jang Hyun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.10
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    • pp.1423-1431
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    • 2022
  • Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.

A Study on the Analysis of the Congestion Level of Tourist Sites and Visitors Characteristics Using SNS Data (SNS 데이터를 활용한 관광지 혼잡도 및 방문자 특성 분석에 관한 연구)

  • Lee, Sang Hoon;Kim, Su-Yeon
    • Journal of Korea Society of Industrial Information Systems
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    • v.27 no.5
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    • pp.13-24
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    • 2022
  • SNS has become a very close service to our daily life. As marketing is done through SNS, places often called hot places are created, and users are flocking to these places. However, it is often crowded with a large number of people in a short period of time, resulting in a negative experience for both visitors and service providers. In order to improve this problem, it is necessary to recognize the congestion level, but the method to determine the congestion level in a specific area at an individual level is very limited. Therefore, in this study, we tried to propose a system that can identify the congestion level information and the characteristics of visitors to a specific tourist destination by using the data on the SNS. For this purpose, posting data uploaded by users and image analysis were used, and the performance of the proposed system was verified using the Naver DataLab system. As a result of comparative verification by selecting three places by type of tourist destination, the results calculated in this study and the congestion level provided by DataLab were found to be similar. In particular, this study is meaningful in that it provides a degree of congestion based on real data of users that is not dependent on a specific company or service.

Grade Analysis and Two-Stage Evaluation of Beef Carcass Image Using Deep Learning (딥러닝을 이용한 소도체 영상의 등급 분석 및 단계별 평가)

  • Kim, Kyung-Nam;Kim, Seon-Jong
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.2
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    • pp.385-391
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    • 2022
  • Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.

Visibility Analysis of Iridium Communication for SNIPE Nano-Satellite (SNIPE 초소형위성용 Iridium 통신 모듈의 가시성 분석)

  • Cho, Dong-Hyun;Kim, Hongrae;Kim, Hae-Dong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.50 no.2
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    • pp.127-135
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    • 2022
  • Compared to the continuous increase of domestic nano-satellite development cases, the initial communication success rate is relatively low. In a situation where communication cases of LEO satellites using commercial satellite communication networks are increasing recently. In this situation, the SNIPE project developed by the KASI(Korea Astronomy and Space Science Institute), KARI(Korea Aerospace Research Institute), and Yonsei University apply an Iridium module for communication test to the SNIPE nano-satellites. Therefore, in this paper, the visibility analysis of the iridium module on the SNIPE satellite was analyzed under considering the orbital and communication environment of the iridium satellite constellation and the attitude control mode. In the case of LEO satellites, the communication possibility was limited due to the relatively small iridium communication coverage for high altitude and the high doppler shift considered in the iridium communication network. For this reason, in this paper, it could be simulated that there was a more performance difference according to the difference in relative RAAN(Right Ascension of Ascending Node) angle with the Iridium constellation. Finally, by checking the visibility of communication module under the tumbling situation that occurred during the initial deployment of the nano-satellite, the possibility of using the iridium communication technology was analyzed.

The Prediction of Durability Performance for Chloride Ingress in Fly Ash Concrete by Artificial Neural Network Algorithm (인공 신경망 알고리즘을 활용한 플라이애시 콘크리트의 염해 내구성능 예측)

  • Kwon, Seung-Jun;Yoon, Yong-Sik
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.26 no.5
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    • pp.127-134
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    • 2022
  • In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior were quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.

Makeup transfer by applying a loss function based on facial segmentation combining edge with color information (에지와 컬러 정보를 결합한 안면 분할 기반의 손실 함수를 적용한 메이크업 변환)

  • Lim, So-hyun;Chun, Jun-chul
    • Journal of Internet Computing and Services
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    • v.23 no.4
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    • pp.35-43
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    • 2022
  • Makeup is the most common way to improve a person's appearance. However, since makeup styles are very diverse, there are many time and cost problems for an individual to apply makeup directly to himself/herself.. Accordingly, the need for makeup automation is increasing. Makeup transfer is being studied for makeup automation. Makeup transfer is a field of applying makeup style to a face image without makeup. Makeup transfer can be divided into a traditional image processing-based method and a deep learning-based method. In particular, in deep learning-based methods, many studies based on Generative Adversarial Networks have been performed. However, both methods have disadvantages in that the resulting image is unnatural, the result of makeup conversion is not clear, and it is smeared or heavily influenced by the makeup style face image. In order to express the clear boundary of makeup and to alleviate the influence of makeup style facial images, this study divides the makeup area and calculates the loss function using HoG (Histogram of Gradient). HoG is a method of extracting image features through the size and directionality of edges present in the image. Through this, we propose a makeup transfer network that performs robust learning on edges.By comparing the image generated through the proposed model with the image generated through BeautyGAN used as the base model, it was confirmed that the performance of the model proposed in this study was superior, and the method of using facial information that can be additionally presented as a future study.

A Study on the Policy Direction for the Introduction and Activation of Smart Factories by Korean SMEs (우리나라 중소기업의 스마트 팩토리 수용 및 활성화 제고를 위한 정책 방향에 대한 연구)

  • Lee, Yong-Gyu;Park, Chan-Kwon
    • Korean small business review
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    • v.42 no.4
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    • pp.251-283
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    • 2020
  • The purpose of this study is to provide assistance to the establishment of related policies to improve the level of acceptance and use of smart factories for SMEs in Korea. To this end, the Unified Technology Acceptance Model (UTAUT) was extended to select additional factors that could affect the intention to accept technology, and to demonstrate this. To achieve the research objective, a questionnaire composed of 7-point Likert scales was prepared, and a survey was conducted for manufacturing-related companies. A total of 136 questionnaires were used for statistical processing. As a result of the hypothesis test, performance expectation and social influence had a positive (+) positive effect on voluntary use, but effort expectation and promotion conditions did not have a significant effect. As an extension factor, the network effect and organizational characteristics had a positive (+) effect, and the innovation resistance had a negative effect (-), but the perceived risk had no significant effect. When the size of the company is large, the perceived risk and innovation resistance are low, and the level of influencing factors for veterinary intentions, veterinary intentions, and veterinary behaviors are excluded. Through this study, factors that could have a positive and negative effect on the adoption (reduction) of smart factory-related technologies were identified and factors to be improved and factors to be reduced were suggested. As a result, this study suggests that smart factory-related technologies should be accepted.

A Study on Deep Learning based Aerial Vehicle Classification for Armament Selection (무장 선택을 위한 딥러닝 기반의 비행체 식별 기법 연구)

  • Eunyoung, Cha;Jeongchang, Kim
    • Journal of Broadcast Engineering
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    • v.27 no.6
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    • pp.936-939
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    • 2022
  • As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

A Road Environment Analysis for the Introduction of Connected and Automated Driving-based Mobility Services from an Operational Design Domain Perspective (자율주행기반 모빌리티 서비스 도입을 위한 운행설계영역 관점의 도로환경 분석)

  • Bo-Ram, WOO;Ah-Reum, KIM;Yong-Jun, AHN;Se-Hyun, TAK
    • Journal of the Korean Association of Geographic Information Studies
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    • v.25 no.4
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    • pp.107-118
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    • 2022
  • As connected and automated driving(CAD) technology is entering its commercialization stage, service platforms providing CAD-based mobility services have increased these days. However, CAD-baded mobility services with these platforms need more consideration for the demand for mobility services when determining target areas for CAD-based mobility services because current CAB-based mobility design focus on driving performance and driving stability. For a more efficient design of CAD-based mobility services, we analyzed the applicability for the introduction of CAD-based mobility services in terms of driving difficulty of CAD and demand patterns of current non-CAD based-mobility services, e.g., taxi, demand-responsive transit(DRT), and special transportation systems(STS). In addition, for the spatial analysis of the applicability of the CAD-based mobility service, we propose the Index for Autonomous Driving Applicability (IADA) and analyze the characteristics of the spatial distribution of IADA from the network perspective. The analysis results show that the applicability of CAD-based mobility services depends more on the demand patterns than the driving difficulty of CAV. In particular, the results show that the concentration pattern of demand in a specific road link is more important than the size of demand. As a result, STS service shows higher applicability compared to other mobility services, even though the size of demand for this mobility service is relatively small.

A Study on Non-Fungible Token Platform for Usability and Privacy Improvement (사용성 및 프라이버시 개선을 위한 NFT 플랫폼 연구)

  • Kang, Myung Joe;Kim, Mi Hui
    • KIPS Transactions on Computer and Communication Systems
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    • v.11 no.11
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    • pp.403-410
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    • 2022
  • Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.