• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.3-9
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• 2015

The wireless communication system adopts an appropriate retransmission scheme on each system protocol layer to improve reliability of data transmission. In each system protocol layer, the retransmission scheme operates in independently other layers and operates based on the parameters without reference to end-to-end performance of wireless communication system. For this reason, it is difficult to design the optimal system parameters that satisfy the QoS requirements for each service class. Thus, the performance analysis of wireless communication system is needed to design the optimal system parameters according to the end-to-end QoS requirements for each service class. In this paper, we derive the mathematical model to formulate the end-to-end performance of wireless communication system. We also evaluate the performance at the MAC and transport layers in terms of average spectral efficiency and average transmission delay. Based on the results of performance evaluations, we design the optimal system parameters according to the QoS requirements of service classes. From the results, the HARQ combined with AMC is appropriate for the delay-sensitive service and the ARQ combined with AMC is appropriate for a service that is insensitive to transmission delay. Also, the TCP can be applied for the delay-insensitive service only.

• Tuberculosis and Respiratory Diseases
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• v.83 no.1
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• pp.81-88
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• 2020

Background: Use of appropriate antibiotics for the treatment of pneumonia is integral in patients admitted to intensive care units (ICUs). Although it is recommended that empirical treatment regimens should be based on the local distribution of pathogens in patients with suspected hospital-acquired pneumonia, few studies observe patients admitted to ICUs with nursing home-acquired pneumonia (NHAP). We found factors associated with the use of inappropriate antibiotics in patients with pneumonia admitted to the ICU via the emergency room (ER). Methods: We performed a retrospective cohort study of 83 pneumonia patients with confirmed causative bacteria admitted to ICUs via ER March 2015-May 2017. We compared clinical parameters, between patients who received appropriate or inappropriate antibiotics using the Mann-Whitney U, Pearson's chi-square, and Fisher's exact tests. We investigated independent factors associated with inappropriate antibiotic use in patients using multivariate logistic regression. Results: Among 83 patients, 30 patients (36.1%) received inappropriate antibiotics. NHAP patients were more frequently treated with inappropriate antibiotics than with appropriate antibiotics (47.2% vs. 96.7%, p<0.001). Methicillin-resistant Staphylococcus aureus was more frequently isolated from individuals in the inappropriate antibiotics-treated group than in the appropriate antibiotics-treated group (7.5% vs. 70.0%, p<0.001). In multivariate analysis, NHAP was independently associated with the use of inappropriate antibiotics in patients with pneumonia admitted to the ICU via ER. Conclusion: NHAP is a risk factor associated with the use of inappropriate antibiotics in patients with pneumonia admitted to the ICU via the ER.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.369-386
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• 2005

Although Intelligent Tutoring System(ITS) offers individualized learning environment that overcome limited function of existent CAI, and consider many learners' variable, there is little development to be using at the sites of schools because of inefficiency of investment and absence of pedagogical content knowledge representation techniques. To solve these problem, we should study a method, which represents knowledge for ITS, and which reuses knowledge base. On the pedagogical content knowledge, the knowledge in education differs from knowledge in a general sense. In this paper, we shall primarily address the multi-complex structure of knowledge and explanation of learning vein using multi-complex structure. Multi-Complex, which is organized into nodes, clusters and uses by knowledge base. In addition, it grows a adaptive knowledge base by self-learning. Therefore, in this paper, we propose the 'Extended Neural Logic Network(X-Neuronet)', which is based on Neural Logic Network with logical inference and topological inflexibility in cognition structure, and includes pedagogical content knowledge and object-oriented conception, verify validity. X-Neuronet defines that a knowledge is directive combination with inertia and weights, and offers basic conceptions for expression, logic operator for operation and processing, node value and connection weight, propagation rule, learning algorithm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.69-77
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• 1998

This paper presents a new efficient and stable multilevel partitioning algorithm for VLSI circuit design. The performance of multilevel partitioning algorithms that are proposed to enhance the performance of previous iterative-improvement partitioning algorithms for large scale circuits, depend on choice of construction methods for partition hierarchy. As the most of previous multilevel partitioning algorithms forces experimental constraints on the process of hierarchy construction, the stability of their performances goes down. The lack of stability causes the large variation of partition results during multiple runs. In this paper, we minimize the use of experimental constraints and propose a new method for constructing partition hierarchy. The proposed method clusters the cells with the connection status of the circuit. After constructing the partition hierarchy, a partition improvement algorithm, HYIP$^{[11]}$ using hybrid bucket structure, unclusters the hierachy to get partition results. The experimental results on ACM/SIGDA benchmark circuits show improvement up to 10-40% in minimum outsize over the previous algorithm $^{[3] [4] [5] [8] [10]}$. Also our technique outperforms ML$^{[10]}$ represented multilevel partition method by about 5% and 20% for minimum and average custsize, respectively. In addition, the results of our algorithm with 10 runs are better than ML algorithm with 100 runs.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1221-1230
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• 2004

In mobile environments, the locations of moving objects such as vehicles, airplanes and users of wireless devices continuously change over time. For efficiently processing moving object information, the database system should be able to deal with large volume of data, and manage indexing efficiently. However, previous research on indexing method mainly focused on query performance, and did not pay attention to update operation for moving objects. In this paper, we propose a novel moving object indexing method, named ACAR-Tree. For processing efficiently frequently updating of moving object location information as well as query performance, the proposed method is based on fixed grid structure with auxiliary R-Tree. This hybrid structure is able to overcome the poor update performance of R-Tree which is caused by reorganizing of R-Tree. Also, the proposed method is able to efficiently deal with skewed-. or gaussian distribution of data using auxiliary R-Tree. The experimental results using various data size and distribution of data show that the proposed method has reduced the size of index and improve the update and query performance compared with R-Tree indexing method.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.