• Journal of the Korean Society for Precision Engineering
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• v.31 no.10
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• pp.881-887
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• 2014

Micro-sized Peltier coolers are generally employed for uniformly distributing heat generated in the multi-chip packages. These coolers are commonly classified into vertical and planar devices, depending on the heat flow direction and the arrangement of thermoelectric materials on the used substrate. Owing to the strong need for evaluation of performance of thermoelectric modules, at present an establishment of proper theoretical model has been highly required. The design theory for micro-sized thermoelectric cooler should be considered with contact resistance. Cooling performance of these modules was significantly affected by their contact resistance such as electrical and thermal junction. In this paper, we introduce the useful and optimal design model of small dimension thermoelectric module.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.47-47
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• 2010

Over the past 20 years, constant progress in noise and vibration (NVH) engineering has enabled to constantly advance quality and comfort of operation and use of really any products - from automobiles to aircraft, to all kinds of industrial vehicles and machines - to the extend that for many products, supreme NVH performance has becomes part of its brand image in the market. At the same time, the product innovation agenda in the automotive, aircraft and really many other industries, has been extended very much in recent years by meeting ever more strict environmental regulations. Like in the automotive industry, the drive towards meeting emission and CO2 targets leads to very much accelerated adoption of new powertrain concepts (downsizing of ICE, hybrid-electrical...), and to new vehicle architectures and the application of new materials to reduce weight, which bring new challenges for not only maintaining but further improving NVH performance. This drives for innovation in NVH engineering, so as to succeed in meeting a product brand performance for NVH, while as the same time satisfying eco-constraints. Product innovation has also become increasingly dependent on the adoption of electronics and software, which drives for new solutions for NVH engineering that can be applied for NVH performance optimization of mechatronic products. Finally, relentless pressure to shorten time to market while maintaining overall product quality and reliability, mandates that the practice and solutions for NVH engineering can be optimally applied in all phases of product development. The presentation will first review the afore trends for product and process innovation, and discuss the challenges they represent for NVH engineering. Next, the presentation discusses new solutions for NVH engineering of products, so as to meet target brand values, while at the same time meeting ever more strict eco constraints, and this within a context of increasing adoption of electronics and controls to drive product innovation. NVH being very much defined by system level performance, these solutions implement the approach of "Model Based System Engineering" to increase the impact of system level analysis for NVH in all phases of product development: - At the Concept Phase, to be able to do business case analysis of new product concepts; to arrive at an optimized and robust product architecture (e.g. to hybrid powertrain lay-out, to optimize fuel economy); to enable target cascading, to subsystem and component level. - In Development Phase, to increase realism and productivity of simulation, so as to frontload virtual validation of components and subsystems and to further reduce reliance on physical testing. - During the final System Testing Phase, to enable subsystem testing by a combination of physical testing and simulation: using simulation models to simulate the final integration context when testing a subsystem, enabling to frontload subsystem testing before final system integration is possible. - To interconnect Mechanical, Electronical and Controls engineering, in all phases of development, by supporting model driven controls engineering (MIL, SIL, HIL). Finally, the presentation reviews examples of how LMS is implementing such new applications for NVH engineering with lead customers in Europe, Asia and US, with demonstrated benefits both in terms of shortening development cycles, and/or enabling a simulation based approach to reduce reliance on physical testing.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.951-956
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• 2014

Steady-state thermal analysis was performed on a thermal equivalent circuit to determine the heat generation during operation of an interior permanent magnet synchronous motor (IPMSM). New machines must be compact and light and produce high torque density under extreme environmental conditions. Thermal analysis of an IPMSM is particularly important because excessive heat generated from the core and magnet reduces the IPMSM's output and has adverse effects on the durability. Therefore, steady-state thermal analysis of an IPMSM was performed for changes in the design variables using a thermal equivalent circuit. The changed variables were the axis length and thickness of the housing. The results of this method were compared with those of the finite element method to verify the accuracy and reliability.

• Korean Journal of Cognitive Science
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• v.30 no.3
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• pp.157-174
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• 2019

Motivation of human individuals to perform on intellectual tasks can be hampered by identity threat from intellectual machines. A laboratory experiment examined whether individuals' performance loss on intellectual tasks appears under human identity threat. Additionally, by affirming alternative attributes of human identity, researchers checked whether group-affirmation alleviate the performance loss on intellectual tasks. This research predicted that under high social identity threat, individuals' performance loss on the intellectual tasks would be moderated by valuing alternative attributes of human identity. Experiment shows that when social identity threat is increased, human individuals affirmed alternative human attributes show higher performance on intellectual tasks than individuals non-affirmed. This effect of human-group level affirmation on performance loss did not appear in the condition of low social identity threat. Theoretical and practical implications were discussed.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.157-164
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• 2024

In this paper, we propose a discussion that the feasibility of deploying a deep learning-based detector on the resource-limited board. Although many studies evaluate the detector on machines with high-performed GPUs, evaluation on the board with limited computation resources is still insufficient. Therefore, in this work, we implement the deep-learning detectors and deploy them on the compact board by parsing and optimizing a detector. To figure out the performance of deep learning based detectors on limited resources, we monitor the performance of several detectors with different H/W resource. On COCO detection datasets, we compare and analyze the evaluation results of detection model in On-Board and the detection model in On-GPU in terms of several metrics with mAP, power consumption, and execution speed (FPS). To demonstrate the effect of applying our detector for the military area, we evaluate them on our dataset consisting of thermal images considering the flight battle scenarios. As a results, we investigate the strength of deep learning-based on-board detector, and show that deep learning-based vision models can contribute in the flight battle scenarios.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.