• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.17-25
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• 2021

When a person wears clothing, it is important to configure an outfit appropriate to the intended occasion. Therefore, T.P.O(Time, Place, Occasion) of the outfit is considered in various fashion recommendation systems based on artificial intelligence. However, there are few studies that directly infer the T.P.O from outfit images, as the nature of the problem causes multi-label and class imbalance problems, which makes model training challenging. Therefore, in this study, we propose a model that can infer the T.P.O of outfit images by employing a label-distribution-aware margin(LDAM) loss function. Datasets for the model training and evaluation were collected from fashion shopping malls. As a result of measuring performance, it was confirmed that the proposed model showed balanced performance in all T.P.O classes compared to baselines.

• The Journal of Korean Institute of Information Technology
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• v.17 no.3
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• pp.31-41
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• 2019

In this paper, we design and implement the simulator for the transmitter and receiver of 4D-8PSK TCM with 2.0, 2.25, 2.5, and 2.75 bits/symbol-channel transmission efficiency recommended by the CCSDS for satellite communications, and then analyze the BER performance of 4D-8PSK TCM system in AWGN channel. The transmitter of 4D-8PSK TCM is designed in accordance with the recommendation in the CCSDS standard. Meanwhile, for the receiver design of 4D-8PSK TCM, we design the differential decoder generalizing the differential encoder/decoder scheme. The trellis decoding algorithm is designed by applying the auxiliary trellis information and the Viterbi algorithm, and an 8-dimensional constellation mapper equation given in the CCSDS standard is deconstructed to design constellation mapper. Especially, we present the optimized receiver for 4D-8PSK TCM system by investigating the BER performances for the traceback lengths in the Viterbi decoder through computer simulations..

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.311-336
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• 2022

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.33-41
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• 2022

Infrastructure procurement has been a major engagement route between China and Africa. This contributes immensely to the gradual infrastructure development seen on the continent. However, maturing discourse purports that these infrastructure collaborations lack intentionality in the continuous development of strategic guidelines and policies for effective implementation despite their uniqueness and criticality. This study proposes that an efficient approach to policy recommendations is through the political and economic analysis (PEA) of these partnerships using public-private partnership (PPP) optics. Unquestionably, these partnerships are representative of the concept of diplomatic transnational public-private partnership (DT-PPP) where infrastructure is procured through the collaboration of public (African governments) and private sector (Chinese state-owned corporations) who provide the managerial, financial, and technical resources for the project implementation. Given the quest for sustainable win-win, this study identifies strategies towards the realization of win-win in the implementation (i.e enablers of win-win) such that fairness and co-benefit, as well as interests, will be achieved. Thus, based on the PEA framework, case scenarios from Ghana and Nigeria using expert interviews identify the criticalities and best practices for the realization of these enablers at the development phase. Findings indicate more effort is required of the public sector (African host countries) in terms of people, structure/institutions, and the implementation processes. Recommendations include improvement of environmental management structures, contract administration procedures, external stakeholders/local community engagement mechanisms, knowledge and technology transfer procedures, and sector-based project operation and maintenance culture and systems. Additionally, actors must have emotional intelligence, good problem-solving abilities, and overall ensure cordial relationships for continued bilateral cooperation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.1-14
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• 2023

As markets and industries continue to evolve rapidly, technology opportunity discovery (TOD) has become critical to a firm's survival. From a common consensus that TOD based on a firm's capabilities is a valuable method for small and medium-sized enterprises (SMEs) and reduces the risk of failure in technology development, studies for TOD based on a firm's capabilities have been actively conducted. However, previous studies mainly focused on a firm's technological capabilities and rarely on business capabilities. Since discovered technologies can create market value when utilized in a firm's business, a firm's current business capabilities should be considered in discovering technology opportunities. In this context, this study proposes a TOD method that considers both a firm's business and technological capabilities. To this end, this study uses patent data, which represents the firm's technological capabilities, and trademark data, which represents the firm's business capabilities. The proposed method comprises four steps: 1) Constructing firm technology and business capability matrices using patent classification codes and trademark similarity group codes; 2) Transforming the capability matrices to preference matrices using the fuzzy function; 3) Identifying a target firm's candidate technology opportunities using the collaborative filtering algorithm; 4) Recommending technology opportunities using a portfolio map constructed based on technology similarity and applicability indices. A case study is conducted on a security firm to determine the validity of the proposed method. The proposed method can assist SMEs that face resource constraints in identifying technology opportunities. Further, it can be used by firms that do not possess patents since the proposed method uncovers technology opportunities based on business capabilities.

• Informatization Policy
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• v.30 no.3
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• pp.92-121
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• 2023

As music streaming services (MSS) involve various interactions with users during the music consumption process, it is important to understand the user experience and manage the service accordingly. This study developed a user experience model for MSS by theoretically linking the quality characteristics considered important by music service users with the structure of user experience. PLS analysis was then performed using survey data to test the model. As a result, functionality (search, browsing, and personalized recommendation), UI usability, content quality (currentness, sufficiency, relevance), and monetary cost were found to be key experience factors that determine the experience consequence, i.e., user satisfaction. In addition, in a supplementary analysis comparing domestic and global services, differences in user experience were found between the two groups in terms of functionality and content quality. The user experience model of MSS proposed in this study serves as a new foundation for theory-based research in this field and provides meaningful implications for the competitive landscape among music streaming service platforms and for their competitive strategies.

• Management & Information Systems Review
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• v.39 no.3
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• pp.1-16
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• 2020

The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.244-251
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• 2007

Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user's intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user's explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.8
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• pp.1066-1072
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• 2010

In this study, the effects of replacing inorganic copper, zinc and manganese with different levels of organic complexes of the same trace minerals on the lipid peroxidation and antioxidant defense systems in broilers were investigated. Two-hundred Ross-308 one-day-old broiler chickens were placed on controlled diets until 42 d of age. The experimental animals were divided into four groups comprising three experimental groups and one control group, each consisting of 50 chickens. All groups were also divided into five subgroups each containing 10 broiler chicks. The mineral content of the control group diet was controlled using a standard inorganic mineral premix with supplement levels and sources of trace minerals typical of commercial broiler diets according to the National Research Council (NRC) (containing 8 mg Cu as $CuSO_4$, 40 mg Zn as $ZnSO_4$, and 60 mg Mn as MnO, per kg). In the experimental diets, mineral premix was also comprised of inorganic formulations, except for those of Cu, Zn and Mn. Organically-complexed Cu, Zn, and Mn were separately added to the basal diet at 1/3 (L1), 2/3 (L2) and 3/3 (L3) levels with respect to the NRC recommendation, as Bioplex $Cu^{TM}$, Bioplex $Zn^{TM}$, Bioplex $Mn^{TM}$. At the end of the trial, the plasma Zn level significantly increased when the plasma Cu level significantly decreased (p<0.05) in chickens fed at 2/3 and 3/3 levels of organically complexed minerals. The liver trace mineral concentrations were significantly higher in chickens fed inorganic trace minerals in comparison to those fed organically-complexed minerals. The plasma malondialdehyde (MDA) level of experimental chickens was decreased in groups receiving levels of organic Cu, Zn and Mn in comparison to those fed inorganic forms (p<0.01). The erythrocyte superoxide dismutase (SOD) activity was higher in all groups receiving the organic mineral supplements in comparison to those fed inorganic forms (p<0.01). No differences were observed on either the erythrocyte catalase (CAT) activity or the plasma ceruloplasmin (Cp) levels, and the liver MDA levels and liver CAT and SOD activities in any of the groups that received the organic supplements of Cu, Zn, and Mn. It was concluded that supplementation of lower levels of organically-complexed copper, zinc, and manganese instead of their inorganic forms in diets had no negative effects on the antioxidant defense system in broilers.