• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.6
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• pp.434-440
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• 2007

One of the cold start problems of a FCV is the freezing of the water in the water tank when a FCV is not in operation and the surrounding temperature drops below $0^{\circ}C$. The ice in the tank should be melted as quickly as possible for a satisfactory operation of fuel cell vehicles. In this study, the melting process for the constant heat fluxes of the plate heaters was numerically calculated in the 2-D model of the tank and plate heaters. The enthalpy method and FVM code was used for this analysis. The changes of the temperature with heat fluxes and the heat transfer area could be investigated. The energy balance error was found to increase with the heat flux. From this numerical analysis, the proper heat flux value and some important design factors relating local overheating and pressurization of the water tank could be examined.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.21-29
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• 2013

The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.494-507
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• 2020

This study proposed the movie recommendation system based on the user's personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.83-90
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• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• Journal of Hydrogen and New Energy
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• v.23 no.5
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• pp.484-492
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• 2012

The TMS(Thermal Management System) heater in a fuel cell vehicle has been developed to prevent a decline of fuel cell durability and cold start durability. Main functions of the COD(Cathode Oxygen Depletion) heater are depletion of oxygen in a cathode as heat energy and consumption of electric power for rapid warming up of a fuel cell stack. This paper covers subjects including the design specification of a heater, heater controller for detection of overheat and reliability assessment including coolant pressure cycle test of a heater. To verify the design concept, burst pressure and deformation analysis of plastic housing were carried out. Also, temperature distribution analysis of heater surface and coolant inside of housing were carried out to verify the design concept. By designing the plastic housing instead of a steel housing, the 30% weight lightening and 50% cost reduction were attained. A module-based design of a TMS system including a heater or reducing the watt density of a heater is a problem to be solved in the near future work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Journal of ILASS-Korea
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• v.27 no.1
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• pp.11-17
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• 2022

In upcoming Post Stage-V and Tier 5 regulations of construction machineries, nitrogen oxide (NOx) emissions are strictly limited in cold start conditions. In response to this, a method of improving NOx conversion efficiency has been applied by installing an electric heating catalyst (EHC) in front of conventional urea-SCR systems so that the evaporation and thermal decomposition of urea-water solution can be promoted in cold start conditions. In this strategy, the evaporation and thermal decomposition of urea-water solution and corresponding NOx conversion efficiency are governed by temperature conditions inside the EHC. Therefore, characterizing the temperature distribution in the EHC under various operating conditions is crucial for the optimized operation and control of the EHC in Urea-SCR systems. In this study, a 1-D modeling analysis was performed to predict the heater surface temperature distribution in EHC under various operating conditions. The reliability of prediction results was verified by comparing them with measurement results obtained using an infrared (IR) camera. Based on 1-D analysis results, the effects of various EHC operation parameters on the heater surface temperature distribution were analyzed and discussed.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.382-387
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• 2024

Recently, the amount of adult reading has been continuously decreasing, but the consumption of video content is increasing. Accordingly, there is no information on preferences and behavior patterns for new users, and user evaluation or purchase of new books are insufficient, causing cold start problems and data scarcity problems. In this paper, a hybrid book recommendation system based on video content was proposed. The proposed recommendation system can not only solve the cold start problem and data scarcity problem by utilizing the contents of the video, but also has improved performance compared to the traditional book recommendation system, and even high-quality recommendation results that reflect genre, plot, and rating information-based user taste information were confirmed.