• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.10-21
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• 2005

Software architecture is increasingly being viewed as a key design in developing complex software systems, which largely affects the achievement of quality attributes. During the architecture-based software development, therefore, architectural transformation is needed to achieve quality attributes. Due to the variety of design alternatives and the poor predictability of the effects of the transformation, however, it is not easy to apply architectural transformation. Therefore, the method is needed to support architectural transformation through systematically analyzing the effects of applying various design alternatives to the architecture. This paper proposes an approach to support software architecture transformation. Based on architectural design decisions and the constraints on them included in the architecture, our approach defines a decision constraint graph representing the dependencies among architectural design decisions. Through using the decision constraint graph, architectural transformation can be systematically performed by understanding the effects of applying a transformation. While this work supports more understanding of applying architectural transformation, it also helps reconstruct a software architecture to improve the quality of the software.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.499-510
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• 2011

Any server offering a routing service in the Internet would naturally be in competition for clients, and clients may need to utilize service from a specific server in order to achieve a desired result. We study the various properties of this competition, such as the fraction of route requests handled by a routing service provider and the fraction of total revenue obtained. As the routing service providers (i.e., servers or routers in this context) compete, they may alter behavior in order to optimize one of the above properties. For example, a service provider may lower the price charged for its service, in order to increase the number of clients served. Our models are based on servers offering a routing service to clients within representative network topologies based on actual Internet sub-graphs. These models provide, a framework for evaluating competition in the Internet. We monitor key aspects of the service, as several variables are introduced into the models. The first variable is the fraction of client requests that will pay more for a better quality route. The remaining requests are normal client requests that are satisfied by the most economical route. The second variable is the fraction of servers who choose to lower service prices in order to maximize the number of client requests served. As this fraction increases, it is more likely that a server will lower the price. Finally, there are some resource constraints applied to the model, to increase the difficulty in providing a routing solution, i.e., to simulate a realistic scenario. We seek to understand the effect on the overall network, as service providers compete. In simple cases, we show that this competition could have a negative impact on the overall efficiency of a service. We show that the routing variety present in the larger models is unable to mask this tendency and the routing service performance is decreased due to competition.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.333-342
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• 2010

Building ontology is the key factor to construct semantic web. However, this is time-consuming process. Hence, there are several approaches which automatically generate the ontologies from relational databases. Current studies on the automatic generation of the ontologies from relational database are focused on generating the ontology by analyzing the database schema and stored data. These studies generate the ontology by analyzing only tables and constraints in the schema and ignore view definitions. However, view definitions are defined by a database designer considering the domain of the database. Hence, by considering view definitions, additional classes and hierarchical relationships can be generated. And these are useful in answering queries and integration of ontologies. In this paper, we formalize the generation of classes and hierarchical relationships by analyzing existing methods, and we propose the method which generates additional classes and hierarchical relationships by analyzing view definitions. Finally, we analyze the generated ontology by applying our method to synthetic data and real-world data. We show that our method generates meaningful classes and hierarchical relationships using view definitions.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.8
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• pp.391-400
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• 2017

Software architecture plays a key role in satisfying non-functional requirement (NFR), i.e. quality requirements and constraints. Architecture design methods and tactics for conventional NFR are largely available in literatures. However, the methods for the target system-specific non-conventional NFRs are not readily available; rather architects should invent the design methods from their experiences and intuitions. Hence, the hardship to design architectures for non-conventional NFRs is quite high. In this paper, we provide a systematic architecture design methodology for non-conventional NFRs. We provide a five-step process, and detailed instructions for the steps. In the process, we treat the traceability among artifacts and seamlessness as essential values for supporting effective architecture design. We apply the methodology on designing architectures for a platform software system. We believe that the proposed methodology can be effectively utilized in designing high quality architectures for non-conventional NFRs.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.785-795
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• 2019

This paper is a study on the modeling of the quantity estimation model for offshore plant Material handling equipment in FEED(Front End Engineering Design) verification stage using system engineering approach which is an engineering design methods. The relevant engineering execution procedure is not systemized although the operation method and Material handling equipment selection with weight and space constraints is a key part of the FEED. Using the system engineering process, the stakeholder requirements analysis process, the system requirements analysis, and the final system architecture design were sequentially performed, and the process developed through the functional development diagram and Requirement traceability matrix (RTM) was verified. In addition, based on the established process, we propose a Material handling quantity estimation model and Quantity calculation verification Table that can be applied at the FEED verification stage and we verify the applicability through case studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.8
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• pp.637-648
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• 2021

This paper aims to propose new reentry guidance for Korean Space plane (KSP). Similar to the Space Shuttle guidance concept, a reference drag profile is first determined to satisfy several flight path constraints and boundary conditions, and the proposed guidance commands are realized in a way to track the predetermined reference drag profile. To this end, the drag dynamics is examined. The investigation uncovers that the dynamics characteristics of the drag and the flight path angle are considerably different. Based on this fact, the proposed guidance commands are determined using the time-scale separation technique and the feedback linearization methodology. The key feature of the proposed guidance lies in its simple structure and a clear working mechanism. Therefore, the proposed method is simple to implement compared to existing methods. Numerical simulations are performed to investigate the performance of the proposed method.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1108-1121
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• 2019

The non-inverting buck-boost converter (NIBB) is a step-up and step-down DC-DC converter suitable for wide-input-voltage-range applications. However, when the input voltage is close to the output voltage, the NIBB needs to operate in the buck-boost mode, causing a significant efficiency reduction since all four switches operates in the PWM mode. Considering both the current stress limitation and the efficiency optimization, a novel design methodology for the optimal phase-shift modulation of a NIBB in the buck-boost mode is proposed in this paper. Since the four switches in the NIBB form two bridges, the shifted phase between the two bridges can serve as an extra degree of freedom for performance optimization. With general phase-shift modulation, the analytic current expressions for every duty ratio, shifted phase and input voltage are derived. Then with the two key factors in the NIBB, the converter efficiency and the switch current stress, taken into account, an objective function with constraints is derived. By optimizing the derived objective function over the full input voltage range, an offline design methodology for the optimal modulation scheme is proposed for efficiency optimization on the premise of current stress limitation. Finally, the designed optimal modulation scheme is implemented on a DSPs and the design methodology is verified with experimental results on a 300V-1.5kW NIBB prototype.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.1
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• pp.65-84
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• 2021

Our understanding of how barriers to access systematically varies with the compositional and contextual characteristics of users is incomplete. Using a public university library in Ghana, this study assessed the heterogeneous barriers or constraints students encounter in accessing electronic resources based on their demographic and contextual attributes. A descriptive survey design was adopted and structured questionnaires were administered randomly to 558 students in the four constituent colleges of the University of Cape Coast, Ghana. Data were collected and analysed using SPSS and descriptive statistics were generated. The results revealed that students faced six key challenges in accessing electronic information resources in the library namely delays in download of information, poor internet connectivity, and limited accessibility of university portal, inadequate computers in the library, poor lighting and limited ancillary services (on the spot printing facilities), with differences based on gender, academic level, and college affiliation. Only 24% males and 26% females had no challenges or problems with delays in download of electronic information. About three-fourth of all users had poor internet connectivity and complained about inadequate computers associated with accessing electronic resources. 40% percent of undergraduate students in the Colleges of Education Studies, Agriculture and Natural Sciences, and Humanities and Legal Studies each encountered four to six simultaneous challenges. Irrespective of gender, first year undergraduate students in all the four colleges were the least likely to report multiple challenges. This suggests the need for targeted and context-specific interventions to address the identified challenges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.