• Title/Summary/Keyword: Manufacturing system in fields

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Research on regional spatial information analysis platform about NTIS raw data (국가과학기술지식 원시데이터에 관한 지역 공간정보 분석 플랫폼 연구)

  • Lim, Jung-Sun;Kim, Sanggook;Bae, Seoung Hun;Kim, Kwang-Hoon;Won, Dong-Kyu
    • Journal of Cadastre & Land InformatiX
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    • v.50 no.2
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    • pp.21-35
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    • 2020
  • Due to the coronavirus pandemic and diplomatic disputes, governments are actively developing a policy to revitalize·reshore manufacturing and to diversify international cooperations. In order to develop such a policy, it is very important to compare and analyze domestic·international geospatial information. Over the decade, the US·EC governments have conducted a series of national researches to build data-based tools that can monitor·analyze regional geospatial information driven by government R&D investments. In the case of the EC system, it can compare geospatial information in domestic and international(including Korea) regions. Compared to US·EC cases, Korean examples of national researches with available data analplatform need future improvements. Current study is investigating an automated analysis methodologies using "National Institute of Science and Technology Information (NTIS)" DB, which was national security data until recently. Research on data-mining regional geospatial information can contribute to support policy fields that need to discover new issues in response to unexpected social problems such as recently faced corona and trade disputes.

Improvement on performance management through quantitative evaluation method for technologies acquired from defense offset program (절충교역 획득기술 활용성과 정량화를 통한 성과관리 제고 방안)

  • Park, Tae-Woan;Jung, Tae-Yun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.2
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    • pp.609-618
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    • 2017
  • This paper presents the quantitative evaluation method for technologies acquired from the defense offset program. We firstly deduced the consideration in development by gathering the opinions of 128 experts by conducting a survey. Next, we made up an additional 49 experts for developing a performance management system of the offset program. The management system covered 4 technology fields which are defense R&D, depot maintenance, performance improvement, and manufacturing. The development procedure was composed of 4 parts: setting-up of work process, defining performance indicators, calculating weighted values of each indicator, and devising quantitative method. The results of this research could be used for enhancing the effectiveness of the offset program in 3 ways: establishing a systematic work process after acquisition of technology in offset program, establishing the positive feedback architecture by providing incentives to superior institute or company which is appointed through quantitative performance evaluating, and publicizing and promoting quantified outstanding performances for contributing to advance the offset program.

Recent Progress in Air-Conditioning and Refrigeration Research : A Review of Papers Published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2013 (설비공학 분야의 최근 연구 동향 : 2013년 학회지 논문에 대한 종합적 고찰)

  • Lee, Dae-Young;Kim, Sa Ryang;Kim, Hyun-Jung;Kim, Dong-Seon;Park, Jun-Seok;Ihm, Pyeong Chan
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.26 no.12
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    • pp.605-619
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    • 2014
  • This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2013. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of fluid machinery, pipes and relative parts including orifices, dampers and ducts, fuel cells and power plants, cooling and air-conditioning, heat and mass transfer, two phase flow, and the flow around buildings and structures. Research issues dealing with home appliances, flows around buildings, nuclear power plant, and manufacturing processes are newly added in thermal and fluid engineering research area. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results for general analytical model for desiccant wheels, the effects of water absorption on the thermal conductivity of insulation materials, thermal properties of Octadecane/xGnP shape-stabilized phase change materials and $CO_2$ and $CO_2$-Hydrate mixture, effect of ground source heat pump system, the heat flux meter location for the performance test of a refrigerator vacuum insulation panel, a parallel flow evaporator for a heat pump dryer, the condensation risk assessment of vacuum multi-layer glass and triple glass, optimization of a forced convection type PCM refrigeration module, surface temperature sensor using fluorescent nanoporous thin film. In the area of pool boiling and condensing heat transfer, researches on ammonia inside horizontal smooth small tube, R1234yf on various enhanced surfaces, HFC32/HFC152a on a plain surface, spray cooling up to critical heat flux on a low-fin enhanced surface were actively carried out. In the area of industrial heat exchangers, researches on a fin tube type adsorber, the mass-transfer kinetics of a fin-tube-type adsorption bed, fin-and-tube heat exchangers having sine wave fins and oval tubes, louvered fin heat exchanger were performed. (3) In the field of refrigeration, studies are categorized into three groups namely refrigeration cycle, refrigerant and modeling and control. In the category of refrigeration cycle, studies were focused on the enhancement or optimization of experimental or commercial systems including a R410a VRF(Various Refrigerant Flow) heat pump, a R134a 2-stage screw heat pump and a R134a double-heat source automotive air-conditioner system. In the category of refrigerant, studies were carried out for the application of alternative refrigerants or refrigeration technologies including $CO_2$ water heaters, a R1234yf automotive air-conditioner, a R436b water cooler and a thermoelectric refrigerator. In the category of modeling and control, theoretical and experimental studies were carried out to predict the performance of various thermal and control systems including the long-term energy analysis of a geo-thermal heat pump system coupled to cast-in-place energy piles, the dynamic simulation of a water heater-coupled hybrid heat pump and the numerical simulation of an integral optimum regulating controller for a system heat pump. (4) In building mechanical system research fields, twenty one studies were conducted to achieve effective design of the mechanical systems, and also to maximize the energy efficiency of buildings. The topics of the studies included heating and cooling, HVAC system, ventilation, and renewable energies in the buildings. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment is mostly focused on indoor environment and building energy. The main researches of indoor environment are related to infiltration, ventilation, leak flow and airtightness performance in residential building. The subjects of building energy are worked on energy saving, operation method and optimum operation of building energy systems. The remained studies are related to the special facility such as cleanroom, internet data center and biosafety laboratory. water supply and drain system, defining standard input variables of BIM (Building Information Modeling) for facility management system, estimating capability and providing operation guidelines of subway station as shelter for refuge and evaluation of pollutant emissions from furniture-like products.

An Empirical Study on Predictive Modeling to enhance the Product-Technical Roadmap (제품-기술로드맵 개발을 강화하기 위한 예측모델링에 관한 실증 연구)

  • Park, Kigon;Kim, YoungJun
    • Journal of Technology Innovation
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    • v.29 no.4
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    • pp.1-30
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    • 2021
  • Due to the recent development of system semiconductors, technical innovation for the electric devices of the automobile industry is rapidly progressing. In particular, the electric device of automobiles is accelerating technology development competition among automobile parts makers, and the development cycle is also changing rapidly. Due to these changes, the importance of strategic planning for R&D is further strengthened. Due to the paradigm shift in the automobile industry, the Product-Technical Roadmap (P/TRM), one of the R&D strategies, analyzes technology forecasting, technology level evaluation, and technology acquisition method (Make/Collaborate/Buy) at the planning stage. The product-technical roadmap is a tool that identifies customer needs of products and technologies, selects technologies and sets development directions. However, most companies are developing the product-technical roadmap through a qualitative method that mainly relies on the technical papers, patent analysis, and expert Delphi method. In this study, empirical research was conducted through simulations that can supplement and strengthen the product-technical roadmap centered on the automobile industry by fusing Gartner's hype cycle, cumulative moving average-based data preprocessing, and deep learning (LSTM) time series analysis techniques. The empirical study presented in this paper can be used not only in the automobile industry but also in other manufacturing fields in general. In addition, from the corporate point of view, it is considered that it will become a foundation for moving forward as a leading company by providing products to the market in a timely manner through a more accurate product-technical roadmap, breaking away from the roadmap preparation method that has relied on qualitative methods.

Exposure Characteristics of Particles during the After-treatment Processes of Aluminum Oxide Fibers and Nickel Powders (산화알루미늄 섬유와 니켈분말 후처리공정에서 입자의 노출특성)

  • Kim, Jong Bum;Kim, Kyung Hwan;Ryu, Sung Hee;Yun, Seong-Taek;Bae, Gwi-Nam
    • Journal of Korean Society of Occupational and Environmental Hygiene
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    • v.26 no.2
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    • pp.225-236
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    • 2016
  • Objectives: Nanomaterials have been used in various fields. As use of nanoproducts is increasing, workers dealing with nanomaterials are also gradually increasing. Exposure assessments for nanomaterials have been carried out for protection of worker's health in workplace. Exposure studies were mainly focused on manufacturing processes, but these studies on after-treatment processes such as refinement, weighing, and packing were insufficient. So, we investigated exposure characteristics of particles during after-treatment processes of $Al_2O_3$ fibers and Ni powders. Methods: Mass-production of Ni powder process was carried out in enclosed capture-type canopy hood. In a developing stage, $Al_2O_3$ was handled with a local ventilation unit. Exposure characteristics of particles were investigated for $Al_2O_3$ fiber and Ni powder processes during the periods of 10:00 to 16:00, 20 May 2014 and 13:00 to 16:00, 21 May 2014, respectively. Three real-time aerosol instruments were utilized in exposure assessment. A scanning mobility particle sizer(SMPS, nanoscan, model 3910, TSI) and an optical particle counter(OPC, portable aerosol spectrometer, model 1.109, Grimm) were used to determine the particle size distribution in the size range of 10-420 nm and $0.25-32{\mu}m$, respectively. In addition, a nanoparticle aerosol monitor(NAM, model 9000, TSI) was used to measure lung-deposited nanoparticle surface area. Membrane filters(isopore membrane filter, pore size of 100 nm) were also used for air sampling for the FE-SEM(model S-5000H, Hitachi) analysis using a personal sampling pump(model GilAir Plus by 2.5 L/min, Gilian). Conclusions: For Ni powder after-treatment process, only 27% increase in particle concentration was found during the process. However, for $Al_2O_3$ fiber after-treatment process, significant exposure(1.56-3.34 times) was observed during the process.

Assessment of Radiation Shielding Ability of Printing Materials Using 3D Printing Technology: FDM 3D Printing Technology (3D 프린팅 기술을 이용한 원료에 대한 방사선 차폐능 평가: FDM 방식의 3D 프린팅 기술을 중심으로)

  • Lee, Hongyeon;Kim, Donghyun
    • Journal of the Korean Society of Radiology
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    • v.12 no.7
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    • pp.909-917
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    • 2018
  • 3D printing technology is expected to be an innovative technology of the manufacturing industry during the 4th industrial revolution, and it is being used in various fields including biotechnology and medical field. In this study, we verified the printing materials through Monte Carlo simulation to evaluate the radiation shielding ability of the raw material using this 3D printing technology. In this paper, the printing materials were selected from the raw materials available in a general-purpose FDM-based 3D printer. Simulation of the ICRU phantom and the shielding system was carried out to evaluate the shielding effect by evaluating the particle fluence according to the type and energy of radiation. As a result, the shielding effect tended to decrease gradually with increasing energy in the case of photon beam, and the shielding effect of TPU, PLA, PVA, Nylon and ABS gradually decreased in order of materials. In the case of the neutron beam, the neutron intensity increases at a low thickness of 5 ~ 10 mm. However, the effective shielding effect is shown above a certain thickness. The shielding effect of printing material is gradually increased in the order of Nylon, PVA, ABS, PLA and TPU Respectively.

A Study of R&D Process Integration in Automotive E/E Systems: New Product Development Process (차량 전장품의 R&D 프로세스 통합 연구: 신제품 개발 프로세스)

  • Joo, Baegsu;Suh, Minseok
    • Journal of Technology Innovation
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    • v.23 no.3
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    • pp.287-316
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    • 2015
  • The trend of R&D in automotive electronics industry is shifting towards ECU(Electronic Control Units) based on softwares which requires technology convergence to accommodate customers' requests on safety and convenience. The trend requires systemized R&D paradigm which reflects increased role of softwares. As the softwares became the core components in automotive innovation, there has been wide range of efforts to introduce software R&D processes and methodologies such as CMMI, A-SPICE and ISO-26262 etc. However, R&D departments in the industry fields are confronted with conflicts which arise from discrepancies among the individual process. In this study, we focus on suggesting our integrated and systematic R&D process with the aim of alleviating the conflicts and confusions. For this purpose, we analyze the cases of Korean automotive electronics companies to compare various R&D processes in the field and their relationships. Based on the analysis, we derive and suggest our model of R&D process which effectively integrate ISO/TS-16949 for manufacturing quality and CMMI, A-SPICE, ISO-26262 for system with softwares.

The IEEE 802.15.4e based Distributed Scheduling Mechanism for the Energy Efficiency of Industrial Wireless Sensor Networks (IEEE 802.15.4e DSME 기반 산업용 무선 센서 네트워크에서의 전력소모 절감을 위한 분산 스케줄링 기법 연구)

  • Lee, Yun-Sung;Chung, Sang-Hwa
    • Journal of KIISE
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    • v.44 no.2
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    • pp.213-222
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    • 2017
  • The Internet of Things (IoT) technology is rapidly developing in recent years, and is applicable to various fields. A smart factory is one wherein all the components are organically connected to each other via a WSN, using an intelligent operating system and the IoT. A smart factory technology is used for flexible process automation and custom manufacturing, and hence needs adaptive network management for frequent network fluctuations. Moreover, ensuring the timeliness of the data collected through sensor nodes is crucial. In order to ensure network timeliness, the power consumption for information exchange increases. In this paper, we propose an IEEE 802.15.4e DSME-based distributed scheduling algorithm for mobility support, and we evaluate various performance metrics. The proposed algorithm adaptively assigns communication slots by analyzing the network traffic of each node, and improves the network reliability and timeliness. The experimental results indicate that the throughput of the DSME MAC protocol is better than the IEEE 802.15.4e TSCH and the legacy slotted CSMA/CA in large networks with more than 30 nodes. Also, the proposed algorithm improves the throughput by 15%, higher than other MACs including the original DSME. Experimentally, we confirm that the algorithm reduces power consumption by improving the availability of communication slots. The proposed algorithm improves the power consumption by 40%, higher than other MACs.

Evaluation for Applicability as the Inorganic Binder with Rapid Setting Property for Construction Material of LFS Produced from Various Manufacturing Process (다양한 철강제조공정에서 부산되는 전기로 환원슬래그의 급경성 무기결합재로의 적용성 검토)

  • Kim, Jin-Man;Choi, Sun-Mi;Kim, Ji-Ho
    • Journal of the Korean Recycled Construction Resources Institute
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    • v.7 no.2
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    • pp.70-77
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    • 2012
  • The Ladle Furnace Slag, about 20% of the electric arc furnace slag, has high content of free CaO and free MgO, which generates the expansion collapse by hydration reaction. Although many researchers have been endeavoring to recycle the EAF reducing slag in construction fields, there is not found the effective recycling method up to now. However, the LFS(Ladle Furnace Slag) contains mineral composition of the system of calcium aluminate with high-reactivity. Therefore, it is possible to developed the quick setting property and the high strength at the early age by the rapid cooling. This study aimed to check the reactive minerals and predict the reactivity with water on the LFS discharged from different steel product plants. The test results show that many types of LFS has hydration reactivity and can use in construction field as a inorganic binder with the rapid setting property.

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The Effect of Meta-Features of Multiclass Datasets on the Performance of Classification Algorithms (다중 클래스 데이터셋의 메타특징이 판별 알고리즘의 성능에 미치는 영향 연구)

  • Kim, Jeonghun;Kim, Min Yong;Kwon, Ohbyung
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.23-45
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    • 2020
  • Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.