• Clean Technology
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• v.17 no.4
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• pp.306-313
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• 2011

Flux or solder is used in soldering process for manufacturing electronic parts such as printed circuit boards (PCB). After soldering process, residual flux and solder paste on the parts should be removed since their residuals could cause performance degradation or failure of parts due to their corrosion and electric leakage. Ozone depletion substances such as 1,1,1- trichloroethane (TCE) and HCFC-141b have widely been using for removal of residual flux and solder paste after soldering process In manufacturing of electronic parts until now. In this study, non-aqueous cleaning agents without flash point were developed and applied to industrial field for replacement of cleaning agents with ozone depletion. In order to develop non-aqueous cleaning agents without ethers, esters, fluoride- type solvents. And their physical properties and cleaning abilities were evaluated, and they were applied to industrial fields for cleaning of flux and solder on the PCB. And vacuum distillation apparatus were operated to determine their operating conditions and recycling yields for recycling of used cleaning agents formulated in this study. As a result of physical properties measurement of our formulated cleaning agents, they were expected to have good wetting and penetrating power since their surface tensions were relatively low as 18.0~20.4 dyne/$cm^2$ and their wetting indices are relatively large. And some cleaning agents holding fluoride-type solvents as their components did not have any flash point and they seemed to be safe in their handling and storage. The cleaning experimental results showed that some cleaning agents were better in their cleaning of flux and solder paste than 1,1,1-TCE and HCFC-141b. And industrial application results of the formulated cleaning agents for cleaning PCB indicated that they can be applicable to industry due to their good cleaning capability in comparison with HCFC-141b. The recycling experiments of the used formulated cleaning agents through a vacuum distillation apparatus also showed that their 91.9~97.5% could be recycled with its proper operating conditions.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• Journal of Bio-Environment Control
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• v.27 no.4
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• pp.302-311
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• 2018

Using cylindrical paper pot nursery method, three kinds of commercial tomatoes 'Dafnis', 'DOTAERANG DIA' and 'Maescala' were grafted onto a commercial rootstock 'B blocking'. From 10 to 40 days after graft-take, growth traits of seedlings were investigated by 0.5, 1.0 and 2.0S treatments of standard nutrient solution(S) for seedling growth, and top to root ratio(TRR), compactness(CP) and Dickson Quality Index(DQI) were calculated. Two weeks after transplanting of the seedlings under three different night temperature targeting to 10, 15, and $25^{\circ}C$, which were not precisely controlled, the relative growth rate (RGR) was investigated. The quantitative growth traits of grafted seedlings increased with increasing fertilizer concentration, and various range of seedling size could be produced. Compactness and DQI were significantly regressed (Adj $R^2=0.9480$). Short-term RGR after transplanting was higher at 1.0S treatment of standard nutrient solution at the seedling age of 30 days and 40 days after graft-take(DAGT). DQI and RGR were significantly regressed linearly at respective fertigation strength. Specially the diminishing slope of RGR was lower at 1.0S fertigation strength with the increase of DQI than others. The results indicate that DQI could be applied as a quality index of grafted tomato seedlings and the relation analysis between DQI and short-term RGR also could be used to determine the good quality seedlings of grafted tomato grown in cylindrical paper pot.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.77-88
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• 2000

In this paper, the 3-mode variable gain high power amplifier for a transmitter of INMARSAT-B operating at L-band(1626.5-1646.5 MHz) was developed. This SSPA can amplify 42 dBm in high power mode, 38 dBm in medium power mode and 36 dBm in low power mode for INMARSAT-B. The allowable errol sets +1 dBm as the upper limit and -2 dBm as the lower limit, respectively. To simplify the fabrication process, the whole system is designed by two parts composed of a driving amplifier and a high power amplifier. The HP's MGA-64135 and Motorola's MRF-6401 were used for driving amplifier, and the ERICSSON's PTE-10114 and PTF-10021 for the high power amplifier. The SSPA was fabricated by the RP circuits, the temperature compensation circuits and 3-mode variable gain control circuits and 20 dB parallel coupled-line directional coupler in aluminum housing. In addition, the gain control method was proposed by digital attenuator for 3-mode amplifier. Then il has been experimentally verified that the gain is controlled for single tone signal as well as two tone signals. In this case, the SSPA detects the output power by 20 dB parallel coupled-line directional coupler and phase non-splitter amplifier. The realized SSPA has 41.6 dB, 37.6 dB and 33.2 dB for small signal gain within 20 MHz bandwidth, and the VSWR of input and output port is less than 1.3:1. The minimum value of the 1 dB compression point gets more than 12 dBm for 3-mode variable gain high power amplifier. A typical two tone intermodulation point has 36.5 dBc maximum which is single carrier backed off 3 dB from 1 dB compression point. The maximum output power of 43 dBm was achieved at the 1636.5 MHz. These results reveal a high power of 20 Watt, which was the design target.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.627-636
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• 2020

Debris flow disasters primarily occur in mountainous terrains far from cities. As such, they have been underestimated to cause relatively less damage compared with other natural disasters. However, owing to urbanization, several residential areas and major facilities have been built in mountainous regions, and the frequency of debris flow disasters is steadily increasing owing to the increase in rainfall with environmental and climate changes. Thus, the risk of debris flow is on the rise. However, only a few studies have explored the characteristics of flooding and reduction measures for debris flow in areas designated as steep slopes. In this regard, it is necessary to conduct research on securing independent disaster prevention technology, suitable for the environment in South Korea and reflective of the topographical characteristics thereof, and update and improve disaster prevention information. Accordingly, this study aimed to calculate the amount of debris flow, depending on disaster prevention performance targets for regions designated as steep slopes in South Korea, and develop an independent model to not only evaluate the impact of debris flow but also identify debris barriers that are optimal for mitigating damage. To validate the reliability of the two-dimensional debris flow model developed for the evaluation of debris barriers, the model's performance was compared with that of the hydraulic model. Furthermore, a 2-D debris model was constructed in consideration of the regional characteristics around the steep slopes to analyze the flow characteristics of the debris that directly reaches the damaged area. The flow characteristics of the debris delivered downstream were further analyzed, depending on the specifications (height) and installation locations of the debris barriers employed to reduce the damage. The experimental results showed that the reliability of the developed model is satisfactory; further, this study confirmed significant performance degradation of debris barriers in areas where the barriers were installed at a slope of 20° or more, which is the slope at which debris flows occur.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.3
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• pp.1-7
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• 2021

In this paper, by embedding a bare-die chip-type drive amplifier into the PCB composed of ABF and FR-4, it implements an embedded active device that can be applied in 28 GHz band modules. The ABF has a dielectric constant of 3.2 and a dielectric loss of 0.016. The FR-4 where the drive amplifier is embedded has a dielectric constant of 3.5 and a dielectric loss of 0.02. The proposed embedded module is processed into two structures, and S-parameter properties are confirmed with measurements. The two process structures are an embedding structure of face-up and an embedding structure of face-down. The fabricated module is measured on a designed test board using Taconic's TLY-5A(dielectric constant : 2.17, dielectric loss : 0.0002). The PCB which embedded into the face-down expected better gain performance due to shorter interconnection-line from the RF pad of the Bear-die chip to the pattern of formed layer. But it is verified that the ground at the bottom of the bear-die chip is grounded Through via, resulting in an oscillation. On the other hand, the face-up structure has a stable gain characteristic of more than 10 dB from 25 GHz to 30 GHz, with a gain of 12.32 dB at the center frequency of 28 GHz. The output characteristics of module embedded into the face-up structure are measured using signal generator and spectrum analyzer. When the input power (P_in) of the signal generator was applied from -10 dBm to 20 dBm, the gain compression point (P_1dB) of the embedded module was 20.38 dB. Ultimately, the bare-die chip used in this paper was verified through measurement that the oscillation is improved according to the grounding methods when embedding in a PCB. Thus, the module embedded into the face-up structure will be able to be properly used for communication modules in millimeter wave bands.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.