• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.519-536
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• 2007

In this paper, we combine the Hybrid-Automatic Repeat reQuest (HARQ) algorithm with joint Tx and Rx antenna selection based on the reliability of the individual antennas links. The cyclic redundancy check (CRC) is applied on the data before being encoded using the Turbo encoder. In the receiver the CRC is used to detect errors of each antenna stream and to decide whether a retransmission is required or not. The receiver feeds back the transmitter with the Tx antennas ordering and the acknowledgement of each antenna (ACK or NACK). If the number of ACK antennas is higher than the NACK antennas, then the retransmission takes place from the ACK antennas using the Chase Combining (CC). If the number of the NACK antennas is higher than the ACK antennas then the ACK antennas are used to retransmit the data streams using the CC algorithm and additional NACK antennas are used to retransmit the remaining streams using Incremental Redundancy (IR, i.e. the encoder rate is reduced). Furthermore, the HARQ is used with the I-BLAST (Iterative-BLAST) which grantees a high transmission rate.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.103-110
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• 2009

We are witnessing rapid increase of the number of wireless devices available today such as cell phones, PDAs, Wibro enabled devices. Because of the inherent limitation of the bandwidth available for wireless channels, broadcast systems have attracted the attention of the research community. The main problem in this area is to develop an efficient broadcast program. In this paper, we propose a dynamic broadcast method that overcomes the limitations of static broadcast programs. It optimizes the scheduling based on the probabilistic model of user requests. We show that dynamic broadcast system can indeed improve the quality of service using user requests. This paper extends our previous work in [1] to include more thorough explanation of the proposed methodology and diverse performance evaluation models.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.49-56
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• 2023

In this study, we propose a genetic algorithm (GA) to optimize the allocation and operation order of transporters. The solution in the GA is represented by a set of lists each of which the operation order of the corresponding transporter. In addition, it was implemented in the form of a hybrid genetic algorithm combining effective local search operations for performance improvement. The local search reduces the number of operating transporters by moving blocks from a transporter with a low workload into that with a high workload. To evaluate the effectiveness of the proposed algorithm, it was compared with Multi-Start and a pure genetic algorithm through a simulation environment similar in scale to an actual shipyard. For the largest problem, compared to them, the number of transporters was reduced by 40% and 34%, and the total task time was reduced by 27% and 17%, respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.91-101
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• 2008

For a friendly interaction between human and robot, emotional interchange has recently been more important. So many researchers who are investigating the emotion generation model tried to naturalize the robot's emotional state and to improve the usability of the model for the designer of the robot. And also the various emotion generation of the robot is needed to increase the believability of the robot. So in this paper we used the hybrid emotion generation architecture, and defined the generalized context input of emotion generation model for the designer to easily implement it to the robot. And we developed the personality and loyalty model based on the psychology for various emotion generation. Robot's personality is implemented with the emotional stability from Big-Five, and loyalty is made of familiarity generation, expression, and learning procedure which are based on the human-human social relationship such as balance theory and social exchange theory. We verify this emotion generation model by implementing it to the 'user calling and scheduling' scenario.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.121-130
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• 2011

Recent research indicates that transient errors will increasingly become a critical concern in microprocessor design. As embedded processors are widely used in reliability-critical or noisy environments, it is necessary to develop cost-effective fault-tolerant techniques to protect processors against transient errors. The register file is one of the critical components that can significantly affect microprocessor system reliability, since registers are typically accessed very frequently, and transient errors in registers can be easily propagated to functional units or the memory system, leading to silent data error (SDC) or system crash. This paper focuses on investigating the impact of register file soft errors on system reliability and developing cost-effective techniques to improve the register file immunity to soft errors. This paper proposes the register vulnerability factor (RVF) concept to characterize the probability that register transient errors can escape the register file and thus potentially affect system reliability. We propose an approach to compute the RVF based on register access patterns. In this paper, we also propose two compiler-directed techniques and a hybrid approach to improve register file reliability cost-effectively by lowering the RVF value. Our experiments indicate that on average, RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly, without sacrificing the register value integrity.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.145-154
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• 2004

The growing demand for customer-responsive, made-to-order manufacturing is stimulating the need for improved dynamic decision-making processes in commercial fleet operations. Moreover, the rapid growth of electronic commerce through the internet is also requiring advanced and precise real-time operation of vehicle fleets. Accompanying these demand side developments/pressures, the growing availability of technologies such as AVL(Automatic Vehicle Location) systems and continuous two-way communication devices is driving developments on the supply side. These technologies enable the dispatcher to identify the current location of trucks and to communicate with drivers in real time affording the carrier fleet dispatcher the opportunity to dynamically respond to changes in demand, driver and vehicle availability, as well as traffic network conditions. This research investigates key aspects of real time dynamic routing and scheduling problems in fleet operation particularly in a truckload pickup-and-delivery problem under various settings, in which information of stochastic demands is revealed on a continuous basis, i.e., as the scheduled routes are executed. The most promising solution strategies for dealing with this real-time problem are analyzed and integrated. Furthermore, this research develops. analyzes, and implements hybrid algorithms for solving them, which combine fast local heuristic approach with an optimization-based approach. In addition, various partitioning algorithms being able to deal with large fleet of vehicles are developed based on 'divided & conquer' technique. Simulation experiments are developed and conducted to evaluate the performance of these algorithms.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.834-845
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• 2011

Massive broadcasting contents such as UHD(Ultra High Definition) TV which requires multi-channel capacity for transmission has been introduced in recent years. A transmission scheme with channel bonding has been considered for transmission of massive broadcasting contents. In HFC(Hybrid Fiber Coaxial) networks, DOCSIS 3.0(Data Over Cable Service Interface Specification 3.0) has already applied channel bonding schemes for up/downstream of data service. A method unlike DOCSIS 3.0 is required to introduce a channel bonding scheme in the broadcasting service having unidirectional transmission with a downstream. Since a massive broadcasting content requires several channels for transmission, VBR(Variable Bit Rate) transmission has been emerging for the bandwidth efficiency. In addition, research on channel allocation and resource scheduling is required to guarantee QoS(Quality of Service) for the broadcasting service based on VBR. In this paper, we propose a transmission method for mass broadcasting service in HFC network and show the UHD transmission simulator developed to evaluate the performance. In order to evaluate the performance, we define various scenarios. Using the simulator, we assess the possibility of channel bonding and VBR transmission for UHD broadcasting system to provide mass broadcasting service efficiently. The developed simulator is expected to contribute to the efficient transmission system development of mass broadcasting service.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.13-34
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• 2012

The Paper Explores The Problem Of Recommending Talks To Attend At International Research Conferences. When Researchers Participate In Conferences, Finding Interesting Talks To Attend Is A Real Challenge. Given That Several Presentation Sessions And Social Activities Are Typically Held At A Time, And There Is Little Time To Analyze All Alternatives, It Is Easy To Miss Important Talks. In Addition, Compared With Recommendations Of Products Such As Movies, Books, Music, Etc. The Recipients Of Talk Recommendations (i.e. Conference Attendees) Already Formed Their Own Research Community On The Center Of The Conference Topics. Hence, Recommending Conference Talks Contains Highly Social Context. This Study Suggests That This Domain Would Be Suitable For Social Network-Based Recommendations. In Order To Find Out The Most Effective Recommendation Approach, Three Sources Of Information Were Explored For Talk Recommendation-Whateach Talk Is About (Content), Who Scheduled The Talks (Collaborative), And How The Users Are Connected Socially (Social). Using These Three Sources Of Information, This Paper Examined Several Direct And Hybrid Recommendation Algorithms To Help Users Find Interesting Talks More Easily. Using A Dataset Of A Conference Scheduling System, Conference Navigator, Multiple Approaches Ranging From Classic Content-Based And Collaborative Filtering Recommendations To Social Network-Based Recommendations Were Compared. As The Result, For Cold-Start Users Who Have Insufficient Number Of Items To Express Their Preferences, The Recommendations Based On Their Social Networks Generated The Best Suggestions.