• Title/Summary/Keyword: AI Module

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Design of Driving methods of lower power consumption in Plasma AI(plasma adaptive intensifier) driving method (Plasma AI(plasma adaptive intensifier)구동의 전력 소모 개선을 위한 구동방식 설계)

  • Kim, Jun-Hyeong;O, Sun-Taek;Lee, Dong-Ho
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.844-847
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    • 2003
  • Display devices are becoming increasingly important as an interface between humans and machines in the growing information society. In display devices, PDP (Plasma Display Panel) has many advantages in that it has wide screen, wide viewing angle and is light weight, thin. In PDP driving method, if the brightness of input image is high, applying the fixed sustain pulse to the PDP panel will raise the PDP power consumption and may damages the PDP panel. To overcome these problems, the Plasma AI driving method was introduced by the Matshushita co. in Japan. The Plasma AI driving module calculates the peak value and average value of 1 frame image and adjusts the gradation and sustain pulses for 1 frame sustain. In this paper, the proposed PDP driving module is based on the Plasma AI driving module. The proposed driving module calculates peak value and average value, and the brightness distribution of 1 frame image. Using brightness distribution, the proposed driving module divides 1 frame input image into 15 image patterns. For each image pattern, minimum sustain pulses and sub-frames are used for the brightness of 1 frame image and the sustain weight for 64, 128, 192 gradation is proposed. Therefore, the sustain power consumption can be reduced.

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Research on Water-Repellent Coating Materials to Prevent Solar Module Pollution (태양광 모듈 오염 방지를 위한 발수 코팅 물질에 대한 연구 )

  • Young-A Park;Da Yeon Jung;Hyun Chul Ki
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.37 no.2
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    • pp.182-187
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    • 2024
  • Currently, the most developed new energy source is solar energy. Because solar power is installed outside, it is exposed to many pollutants. Pollutants are causing the characteristics of solar energy to deteriorate. Therefore, this study aims to develop a water-repellent coating to prevent contamination of solar modules. Silica and Titania materials are mainly used as water-repellent coating materials. In this study, it was based on silica and the contact angle characteristics were measured according to the change in the amount of silica and ammonia water added and the number of coatings. As a result of the measurement, it was confirmed that the contact angle was more than 60 degrees when 0.5 mol of TEOS was added to 50 mL and 0.15 M when 1 mL of ammonia water was added to 296.47 ml of distilled water. And it was confirmed that the contact angle improved when the number of coatings was applied twice. A water-repellent coating material was applied to low iron tempered glass used to protect dye-sensitized solar cell modules. The characteristics of the module were measured after spraying DI-Water on low-emission tempered glass with a water-repellent coating. As a result of the measurement, the efficiency of the module without application, the efficiency of the module coated once, and the module coated twice were 4.87%, 4.90%, and 4.91%, respectively. It was confirmed that the efficiency of the module increased by applying water-repellent coating. As a result of this study, it is determined that the water-repellent coating material will help improve solar power generation efficiency and lifespan by being self-cleaning and non-reflective.

Semantic Building Segmentation Using the Combination of Improved DeepResUNet and Convolutional Block Attention Module (개선된 DeepResUNet과 컨볼루션 블록 어텐션 모듈의 결합을 이용한 의미론적 건물 분할)

  • Ye, Chul-Soo;Ahn, Young-Man;Baek, Tae-Woong;Kim, Kyung-Tae
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1091-1100
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    • 2022
  • As deep learning technology advances and various high-resolution remote sensing images are available, interest in using deep learning technology and remote sensing big data to detect buildings and change in urban areas is increasing significantly. In this paper, for semantic building segmentation of high-resolution remote sensing images, we propose a new building segmentation model, Convolutional Block Attention Module (CBAM)-DRUNet that uses the DeepResUNet model, which has excellent performance in building segmentation, as the basic structure, improves the residual learning unit and combines a CBAM with the basic structure. In the performance evaluation using WHU dataset and INRIA dataset, the proposed building segmentation model showed excellent performance in terms of F1 score, accuracy and recall compared to ResUNet and DeepResUNet including UNet.

Development of Non-Face-To-Face Heat Sensor Module for AI Automated Access Control System and Linkage with Education Program (AI 자동화 출입통제 시스템을 위한 비대면 발열 감지기 모듈 개발 및 교육 프로그램 연계)

  • Lee, Hyo-Jai;Kim, Eungsuk;Hong, Chang-Ho
    • Journal of Practical Engineering Education
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    • v.13 no.2
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    • pp.301-304
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    • 2021
  • In this study, we developed a module that can perform two functions at the same time through interworking between a personal recognition module and a heat detection module in the era of COVID-19. This can simultaneously solve the problem of compatibility of the personal recognition module that occurs in the existing system and the problem of secondary infection that can occur during congestion due to the separate implementation of heat detection. Therefore, in this study, NFC and Bluetooth motherboards were developed, and an array-type non-contact temperature sensor was applied to detect heat. The developed system is expected to be able to realize both access control of floating population and effective quarantine at the same time in public institutions or private companies that require AI automated access control. In addition, it is judged that it is possible to link the embedded programming and web programming implementation method using the module of the development system to the educational program.

A Web Services based e-Business Application Integration Framework (웹 서비스 기반 e-비즈니스 응용 프로그램 통합 프레임워크)

  • Lee Sung-Doke;Han Dong-Soo
    • Journal of KIISE:Computing Practices and Letters
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    • v.11 no.6
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    • pp.514-530
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    • 2005
  • This paper proposes a compact eAI framework for the integration of various types of applications deployed on different platforms in the Internet. The applications are connected and invoked to achieve a business goal by the coordination of the workflow system in the framework. for the construction of the framework, five sub-modules are elicited and the functions and roles of each module are defined. The elicited five sub-modules include business process modeling tool, eAI platform, business processes transform module, UDDI connection module, and workflow system. In the framework, intra and inter organizational applications can be integrated together across firewalls. In this paper, the extension of a workflow system to implement the framework is also described in detail and the usefulness of the framework is ascertained by implementing an application process within the framework. A full-fledged eAI solution can be constructed by gradually adding supplementary functions within this framework.

Agent-Based Game Platform with Cascade-Fuzzy System Strategy Module (단계적 퍼지 시스템 전략모듈을 지원하는 에이전트기반 게임 플랫폼)

  • Lee, Won-Hee;Kim, Won-Seop;Kim, Tae-Yong
    • Journal of Korea Multimedia Society
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    • v.11 no.1
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    • pp.76-87
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    • 2008
  • As hardware performance rises, game users demand higher computer graphic, more convenient UI(User Interface), faster network, and smarter AI(Artificial Intelligence). At this time, however, AI development is accomplished by a co-development team or only one developer. For that reason, it's hard to verify that AI performance and basic game AI technology is lacking for developing high-level AI. Searching the merits and demerits of existing game AI platforms, we investigate main points to consider when designing game AI platforms in this paper. From this we suggest Darwin, a game platform, based on agent that developers embody AI easily and capable of proposing AI test with module that makes them find strategic position. And then evaluate achievement results through making agent used strategic module that Darwin offers.

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Baer and Quasi-Baer Modules over Some Classes of Rings

  • Haily, Abdelfattah;Rahnaou, Hamid
    • Kyungpook Mathematical Journal
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    • v.51 no.4
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    • pp.375-384
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    • 2011
  • We study Baer and quasi-Baer modules over some classes of rings. We also introduce a new class of modules called AI-modules, in which the kernel of every nonzero endomorphism is contained in a proper direct summand. The main results obtained here are: (1) A module is Baer iff it is an AI-module and has SSIP. (2) For a perfect ring R, the direct sum of Baer modules is Baer iff R is primary decomposable. (3) Every injective R-module is quasi-Baer iff R is a QI-ring.

Intelligent Monitoring System for Solitary Senior Citizens with Vision-Based Security Architecture (영상보안 구조 기반의 지능형 독거노인 모니터링 시스템)

  • Kim, Soohee;Jeong, Youngwoo;Jeong, Yue Ri;Lee, Seung Eun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.639-641
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    • 2022
  • With the increasing of aging population, a lot of researches on monitoring systems for solitary senior citizens are under study. In general, a monitoring system provides a monitoring service by computing the information of vision, sensors, and measurement values on a server. Design considering data security is essential because a risk of data leakage exists in the structure of the system employing the server. In this paper, we propose a intelligent monitoring system for solitary senior citizens with vision-based security architecture. The proposed system protects privacy by ensuring high security through an architecture that blocks communication between a camera module and a server by employing an edge AI module. The edge AI module was designed with Verilog HDL and verified by implementing on a Field Programmable Gate Array (FPGA). We tested our proposed system on 5,144 frame data and demonstrated that a dangerous detection signal is generated correctly when human motion is not detected for a certain period.

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Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice

  • Jonghwan Park;Jaegi Son;Dongmin Kim
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.6
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    • pp.1545-1559
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    • 2023
  • In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.

Alternative Production of Avermectin Components in Streptomyces avermitilis by Gene Replacement

  • Yong Joon-Hyoung;Byeon Woo-Hyeon
    • Journal of Microbiology
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    • v.43 no.3
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    • pp.277-284
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    • 2005
  • The avermectins are composed of eight compounds, which exhibit structural differences at three positions. A family of four closely-related major components, A1a, A2a, B1a and B2a, has been identified. Of these components, B1a exhibits the most potent antihelminthic activity. The coexistence of the '1' components and '2' components has been accounted for by the defective dehydratase of aveAI module 2, which appears to be responsible for C22-23 dehydration. Therefore, we have attempted to replace the dehydratase of aveAI module 2 with the functional dehydratase from the erythromycin eryAII module 4, via homologous recombination. Erythromycin polyketide synthetase should contain the sole dehydratase domain, thus generating a saturated chain at the C6-7 of erythromycin. We constructed replacement plasmids with PCR products, by using primers which had been derived from the sequences of avermectin aveAI and the erythromycin eryAII biosynthetic gene cluster. If the original dehydratase of Streptomyces avermitilis were exchanged with the corresponding erythromycin gene located on the replacement plasmid, it would be expected to result in the formation of precursors which contain alkene at C22-23, formed by the dehydratase of erythromycin module 4, and further processed by avermectin polyketide synthase. Consequently, the resulting recombinant strain JW3105, which harbors the dehydratase gene derived from erythromycin, was shown to produce only C22,23-unsaturated avermectin compounds. Our research indicates that the desired compound may be produced via polyketide gene replacement.