• Archives of design research
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• v.19 no.2 s.64
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• pp.121-128
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• 2006

The elements to enhance preference in symbol design are mainly related to consumers' response and aesthetic elements. Because certain aesthetic elements in design affect consumers' response and it is actually presented through (the different level of) preference. This study through surveying case studies examines whether a certain aesthetic element in symbol design gives rise to much preference. According to the result of study, high preference in symbolic design depends on high level of Rhythm, Balance, Harmony, Elaboration, Round, Gestalt, Organic, and Artificial/Natural among aesthetic elements. In comparison, it is founded that Simplicity/complexity, Objective/Abstract, depth, and symmetry should be designed at the moderate level, and proportion, repetition of elements be at the low level. Additionally(or Besides) this study makes out that symbol design cases with high preference have shapes from natural material or patterns of traditional culture, while cases with low preference have shapes from geometric figures. On the basis of these results, a guideline of symbol design could De offered(or suggested) to fit preference of consumers. But, this study is mostly concerned with only affect among emotional reactions of consumer in a scope of study, and is considered only in the aspect of form excluding color and texture.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.357-366
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• 2020

As the proportion and complexity of software embedded in aircraft increase, risk factors such as mission failure, function failure and performance failure due to software errors also increase. In the mission-critical software systems such as aircraft software, managing requirement traceability is essential to maintain the software systems with minimal period and cost. However, the development company is not accurately complying with the guideline for managing requirement traceability due to various reasons such as development cost and schedule. Therefore, it is not easy to systematically establish and maintain requirement traceability. In the paper, we analyze actual test cases of aviation software systems from the viewpoint of requirements traceability in order to learn if there are failure cases of test cases due to the absence of systematic traceability management activities. We also check the risks associated with the failure cases according to the type and severity of the cases. As a result of analyzing a total of 7 aircraft-mounted software, failure cases could be divided into three types: omission of requirements, lack of connection between requirements and test procedures, and omission of test procedures. There were a total of 18 failure cases, 6 for each type. The numbers of high, middle and low risks were 1, 13 and 4, respectively, where the number of middle risks is largest.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.12
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• pp.1057-1064
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• 2014

Recently, there has been a growing interest in sustainable energy. One method that has been used is an organic Rankine cycle using conventional turbine technology with a low-temperature waste heat source. A 200-kW organic Rankine cycle (ORC) system was designed for a waste heat recovery application using R245fa as the working fluid. A radial turbine running at 15,000 rpm was employed to generate more than 200 kW with an expansion ratio of nine. Because an ORC turbine uses a refrigerant as the working fluid, the ideal gas law was not employed to design the turbine. In addition, the complexity of the molecular structure of R245fa made it difficult to design the turbine. Because R245fa has an Ma value of one at a low velocity for the working fluid (about 1/3 of the speed of sound in air) at about $100^{\circ}C$, it easily reaches a supersonic flow condition with a small pressure expansion. To increase the efficiency of the turbine, a dual stage radial-type turbine with a subsonic speed was suggested. This paper will describe the design procedure and performance evaluation of the ORC turbine using R245fa.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.600-608
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• 2016

This paper proposes a new high-efficient interleaved phase-shift full-bridge (PSFB) converter for low-voltage and high-current power systems. The proposed converter is composed of three switch-bridges and two transformers in the primary side and two rectifiers in the secondary side. Each transformer handles half of the total power with an interleaved operation, so that the proposed converter has high system reliability, as much as the conventional interleaved PSFB converter. The soft-switching characteristics of the proposed converter are better than those of the conventional converter due to the modulated primary side configuration. The proposed converter represents a single lagging-leg bridge, which has a poor soft switching condition in its operation, while the conventional converter has two lagging-leg bridges in its operation. Therefore, the number of switches having hard-switching conditions is reduced by half in the proposed converter. In addition, the reduced switch counts in the primary side of the proposed converter helps decrease the complexity of the proposed converter compared to that of the conventional converter. The operational principle and analysis are presented in this paper and the characteristics are verified using a PSIM simulation with 3kW server power specification.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.703-712
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• 2019

Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.

• Journal of Climate Change Research
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• v.3 no.4
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• pp.281-291
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• 2012

Forests act as carbon sinks and also improve water resources and biodiversity to climate change. Secure funding, administrative support, and sustainable management systems are essential to conserve forests and to implement international forestry related projects to climate change. Public private partnership (PPP) could be an effective way for forestry sector in developing countries. Awareness analysis should be preceded in order to encourage participation of enterprises for the diversification of funding and the enhancing quality of projects. We conducted a survey targeting more than 129 private enterprises for awareness analysis. As a result, lack of information, complexity of processes and low profit resulted in low interest on forest projects from private enterprises. Improving awareness of recipient countries on forest resources, financial and institutional supports from the public sector, information sharing, performance management and equal partnership between sectors were suggested to encourage PPP in international forestry related projects to climate change.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.40-50
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• 2017

Compared to the traditional format, the revised IPA is an effective method for selecting a management strategy as compared to the traditional IPA. Comparison between the traditional IPA and revised IPA with a management strategy has been, however, limited. Therefore, the difference between the traditional IPA and revised IPA was compared to select an effective management strategy in the Suncheon Bay Wetland Reserve. First of all, related papers were reviewed to select an appropriate revised IPA. It was found that Deng (2007)'s revised IPA was appropriate for quantifying service quality and a management strategy that affects the measurable satisfaction of visitors in the space. Second, the results of the traditional IPA were compared with the revised IPA in the Suncheon Bay Wetland Reserve and the management strategy of the revised IPA and the changes of management factors were discussed. It was found that some management factors deviated from the order of the quadrant "low priority for managers", "Concentrate management here", "Keep up the good work" were moved to the order of the quadrants "Concentrate management here", "low priority for managers" and "Possible overkill" in the revised IPA grid. The complexity as a management factor resulted in higher demand management than the traditional IPA, which moved from "low priority for managers" to "Concentrate management here". Management factors resulted in lower demand management than the traditional IPA moved from "Concentrate management here" to "low priority for managers"; these consisted of shade trees, exhibition exteriors, programs, and a guided tour. Also, management factors moved from "Keep up the good work" to "Possible overkill" consisted of relaxation facilities, glow of the setting sun, a hedge, and an exhibition interior. Over all, the revised IPA responded properly to changes in the measurable satisfaction of visitors to the Suncheon Bay Wetland Reserve. Therefore, a revised IPA should be provided for accurate and reliable guidelines when decision makers establish management strategies.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.