• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.140-147
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• 1995

This paper deals with conversion from the STL file to the Slice to the Slice cross-sectional information for Stereolithography. The STL file is widely used for Stereolithography, but it is very difficult to convert STL file into Slice file directly. Because it consists of an ordered list of triangular net without any topological information other than the orientation of each facet. So, The system is accomplished by data flow through several intermediate stages such as Reference. SL1. .SL2L. .SL3. and .SLC file. The data processing is performed in 5 steps: 1) Create a Reference file including common information. 2) Modify STL file within the effective range of SL machine. 3) Calculate a point of intersection between plane equation and line equation. 4) Sort z values in ascending order using quick sort algorithm. 5) Search the adjacent points and formulate a closed loop usingsingly linked linear list. The system is developed by using Borland C++ 3.1 compiler in the environment of Pentium PC, and verified to be satisfactory by making some prototypes of electric household appliances.

• Language and Information
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• v.2 no.1
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• pp.1-41
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• 1998

Evaluation of multilingual translation fundamentally involves measurement of meaning equivalences between the formally mapped discourses/texts of SL(source language) and TL(target language) both represented by a metalanguage called IL(interlingua). Unlike a usaal uni-directional MT(machine translation) model(e.g.:SL $\rightarrow$ analysis $\rightarrow$ transfer $\rightarrow$ generation $\rightarrow$ TL), a bi-directional(by 'negotiation') model(i.e.: SL $\rightarrow$ IL/S $\leftrightarrow$ IL $\leftrightarrow$ IL/T \leftarrow TL) is proposed here for the purpose of evaluating multilingual, not merely bilingual, translation. The IL, as conceived of in this study, is an English-based predicate logic represented in the framework of MRS(minimal recursion semantics), an MT-oriented off-shoot of HPSG(Head-driven Phrase Structure Grammar). In addition, a list of semantic and pragmatic checkpoints are set up, some being optional depending on the kind and use of the translation, so sa to have the evaluation of translation fine-grained by computing matching or mismatching of such checkpoints.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.436-442
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• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.

• The Korean Journal of Pesticide Science
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• v.7 no.4
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• pp.280-287
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• 2003

To assess the risk of pesticides on earthworm, the acute toxicities of 10 pesticides were investigated and their toxicity exposure ratios(TERs) were calculated. As the TERs of paraquat dichloride and pendimethalin were more than 100, their risks were rated negligible. Risk of benfuracarb, cadusafos, chlorpyrifos-methyl, endosulfan, isazofos and parathion which have TERs of $10\sim100$ were rated low. However, risk of imidacloprid and phorate which have TER of less than 10 were estimated highly to need a reproduction study. Earthworms were exposed to twenty two pesticides including dazomet 98% GR having PECs of more than $5mg{\cdot}kg^{-1}$ in artificial soil at standard and double dose for 14 days. All the earthworms exposed to dazomet 98% GR and metam-sodium 25% SL were died to show their high risk, while no serious adverse effects were observed in the soil treated with 15 pesticides, calcite 95% WP, calcium polysulfide 36% CF, chlorothalonil 75% WP, daminozide 85% WP, dichlonil 6.7% GR, etridiazole 25% EC, fosetyl-Al 80% WP, glyphosate 41 % SL, hymexazol 30% SL, iprodione 50% WP, machine oil 95% EC, mancozeb 75% WP, propineb 70% WP, terbuthylazine 80% WP and triazophos 40% EC. In case of thiophanate-methyl 70% WP, copper hydroxide 77% WP, dimethoate 46% EC, tolclofos-methyl 50% WP and propamocarb hydrochloride 67% SL, any effect did not show clearly, suggesting an additional subchronic toxicity study. The risk of thiophanate-methyl 70% WP to earthworm was estimated high, considering its subchronic effect, while effects of copper hydroxide 77% WP, dimethoate 46% EC, tolclofos-methyl 50% WP and propamocarb hydrochloride 67% SL to earthworms were negligible, considering no adverse effects in subchronic tests.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.3
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• pp.135-141
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• 2010

Researchers continue to explore possibilities for expanding additive manufacturing (AM) technologies into direct product manufacturing. One limitation is in the materials available for use in AM that can meet the needs of end-use applications. Stereolithography (SL) is an AM technology well known for its precision and high quality surface finish capabilities. SL builds parts by selectively crosslinking or solidifying photo-curable liquid resins, and the resin industry has been continuously developing new resins with improved performance characteristics. This paper introduces a unique SL machine that can fabricate parts out of multiple SL materials. The technology is based on using multiple vats positioned on a rotating vat carousel that contain different photo-curable materials. To change the material during the process, the build platform is raised out of the current vat, a new vat with a different material is rotated under the platform, and the platform is submerged into the new vat so that the new material can be used. This paper introduces a new vat exchange mechanism, cleaning process, recoating process, resin leveling mechanism and process planning technologies for the implementation of multiple material SL. An overview of the system framework is provided and the system integration and control software is described. In addition, several multiple material test parts are designed, fabricated, and described.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.61-67
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• 2005

During stereolithography processes, one of the main sources of dimensional error of prototype is the distortion effect owing to the shrinkage of resin. In this study, the effects of dimension of specimen, such as length, width, and thickness, on the curl distortion is examined. During the SL processes, the variation of curl distortion ewer is measured according to the number of layers, Through this study, it is verified that there is a big difference of the distortion error in both direction and magnitude between before and after the supports are removed. It Is also observed that end profile of the test part and the upper side around the border are also distorted due to the shrinkage of the resin.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.493-497
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• 2002

In the part industry, pipe has required high accuracy in surface roughness and size. Especially, when producing the high frequency welding pipe, cutting process is very important as the finishing process that remove the hot welding bead. The objective of this paper is to investigate the hot machining high frequency welded pipe by simulation and experimental tests. To test the cutting process as hot machining, all cutting environment is reproduced in turning with heating system, and the test is accomplished by comparing with room temperature machining and hot machining in consideration of cutting force, tool wear and cutting temperature.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1127-1134
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• 2002

The effects of pressure and temperature on the autoignition of propane and n-butane blends were investigated using a rapid compression machine (RCM) , which is widely used to examine the autoignition characteristics. The RCM was designed to be capable of varying the compression ratio between 5 and 20 and minimize the vortex formation on the cylinder wall using a wedge-shaped crevice. The initial temperature and pressure of the compressed gas were varied in range of 720∼900 K and 1.6∼ 1.8 MPa, respectively, by adjusting the ratio of the specific heat of the mixture by altering the ratio of the non-reactive components (N$_2$, Ar) under a constant effective equivalence ratio (ø$\_$f/= 1.0) The gas temperature after the compression stroke could be obtained from the measured time-pressure record. The results showed a two-stage ignition delay and a Negative Temperature Coefficient (NTC) behavior which were the unique characteristic of the alkane series fuels. As the propane concentration in the blend were increased from 20% and 40% propane, the autoignition delay time increased by approximately 41 % and 55% at 750 K. Numerical reduced kinetic modeling was performed using the Shell model, which introduced some important chemical ideas, represented by the generic species. Several rate coefficients were calibrated based on the experimental results to establish an autoignition model of the propane and n-butane blends. These coefficients can be used to predict the autoignition characteristics in LPG fueled Sl engines.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.