• Journal of Korea Multimedia Society
• /
• v.13 no.2
• /
• pp.258-264
• /
• 2010

In the Ad-hoc networks, the limitation on the availability of power for operation is a significant bottleneck, given the requirements of portability, weight, and size of mobile devices. Hence, the use of routing metrics that consider the capabilities of the power sources of the network nodes contributes to the efficient utilization of energy. This paper presents a QoS routing protocol that minimize the power consumed by a packet in traversing from source node to the destination node. Results obtained of simulation show that, with our approach we can reduce the power consumption of nodes and increases the life time of the network.

• International Journal of Contents
• /
• v.5 no.2
• /
• pp.6-15
• /
• 2009

Recently many different programming languages have emerged for the development of bioinformatics applications. In addition to the traditional languages, languages from open source projects such as BioPerl, BioPython, and BioJava have become popular because they provide special tools for biological data processing and are easy to use. However, it is not well-studied which of these programming languages will be most suitable for a given bioinformatics task and which factors should be considered in choosing a language for a project. Like many other application projects, bioinformatics projects also require various types of tasks. Accordingly, it will be a challenge to characterize all the aspects of a project in order to choose a language. However, most projects require some common and primitive tasks such as file I/O, text processing, and basic computation for counting, translation, statistics, etc. This paper presents the benchmarking results of six popular languages, Perl, BioPerl, Python, BioPython, Java, and BioJava, for several common and simple bioinformatics tasks. The experimental results of each language are compared through quantitative evaluation metrics such as execution time, memory usage, and size of the source code. Other qualitative factors, including writeability, readability, portability, scalability, and maintainability, that affect the success of a project are also discussed. The results of this research can be useful for developers in choosing an appropriate language for the development of bioinformatics applications.

• Journal of information and communication convergence engineering
• /
• v.6 no.2
• /
• pp.122-128
• /
• 2008

Multiprocessor architectures and platforms, such as, a multiprocessor system on chip (MPSoC) recently introduced to extend the applicability of the Moore's law, depend upon concurrency and synchronization in both software and hardware to enhance design productivity and system performance. With the rapidly approaching billion transistors era, some of the main problem in deep sub-micron technologies characterized by gate lengths in the range of 60-90 nm will arise from non scalable wire delays, errors in signal integrity and non-synchronized communication. These problems may be addressed by the use of Network on Chip (NOC) architecture for future System-on-Chip (SoC). We have modeled a concurrent architecture for a customizable and scalable NOC in a system level modeling environment using MLDesigner (from MLD Inc.). Varying network loads under various traffic scenarios were applied to obtain realistic performance metrics. We provide the simulation results for latency as a function of the buffer size. We have abstracted the area results for NOC components from its FPGA implementation. Modeled NOC architecture supports three different levels of quality-of-service (QoS).

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.41 no.12
• /
• pp.9-14
• /
• 2004

As the internet traffic type and size have bun diversified in recent years, the GMPLS-based distributed control and management protocol have surfaced as a serious issue for dynamic optical lightpath setup. In this reason, we investigated and analyzed network performance and protocols using global information-based link state approach and local information based link state approach. We calculated connection setup time, required control bandwidth and setup blocking probability that made from network update period and threshold metrics according to traffic arrival rate. The evaluated results will be used in broadband network and adopted for high speed network in the future widely.

• Journal of Communications and Networks
• /
• v.17 no.3
• /
• pp.306-320
• /
• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• Journal of Astronomy and Space Sciences
• /
• v.37 no.4
• /
• pp.219-228
• /
• 2020

A CubeSat platform has become a popular choice due to inexpensive commercial off-the-shelf (COTS) components and low launch cost. However, it requires more power-efficient and higher-data rate downlink capability for space applications related to remote sensing. In addition, the platform is limited by the size, weight and power (SWaP) constraints as well as the regulatory issue of licensing the radio frequency (RF) spectrum. The requirements and limitations have put optical communications on promising alternatives to RF communications for a CubeSat platform, owing to the power efficiency and high data rate as well as the license free spectrum. In this study, we analyzed the performance of optical downlink communications compatible with CubeSat platforms in terms of data rate, bit error rate (BER) and outage probability. Mathematical models of BER and outage probability were derived based on not only the log-normal model of atmospheric turbulence but also a transmitter with a finite extinction ratio. Given the fixed slot width, the optimal guard time and modulation orders were chosen to achieve the target data rate. And the two performance metrics, BER and outage data rate, were analyzed and discussed with respect to beam divergence angle, scintillation index and zenith angle.

• Journal of Dental Anesthesia and Pain Medicine
• /
• v.21 no.3
• /
• pp.227-236
• /
• 2021

Background: Emergencies in outpatient clinics are rare. However, potentially catastrophic events can be challenging to manage due to a variety of factors, including limited equipment and staff. The purpose of this quality improvement project was to improve the staff knowledge and familiarity with critical performance elements for emergencies encountered in the setting of a periodontics clinic. Methods: Emergency cognitive aids tailored to the clinic's resources were created for anaphylaxis, airway obstruction, and sublingual hemorrhage. The project pre-post-test repeated measures design evaluated the effectiveness of cognitive aids using a combination of hands-on simulation, written knowledge assessments, and self-efficacy surveys. Training sessions and simulations were provided to the clinic's existing care teams made up of a periodontist and two dental assistants with an anesthetist who was present for simulations involving sedation. Due to the small sample size (N = 14) and non-normal distribution, all metrics were evaluated using non-parametric statistics. Results: Significant improvements were found in knowledge assessment (-2.310, P = 0.021) and self-efficacy (-2.486, P = 0.013) scores when retention after a training session before and after the introduction of cognitive aid was compared. The mean simulation scores and times improved steadily or reached maximum scores during the project progression. Conclusion: Training sessions before and after cognitive aid introduction were effective in improving knowledge, self-efficacy, and simulation performance. Future projects should focus on validating the process for creating contextualized cognitive aids and evaluating the effectiveness of these cognitive aids in larger samples.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.1
• /
• pp.30-37
• /
• 2022

This paper compares and evaluates model performance from two perspectives according to the learning target and network structure for training Deep Neural Network (DNN)-based speech enhancement models in the frequency domain. In this case, spectrum mapping and Time-Frequency (T-F) masking techniques were used as learning targets, and a real network and a complex network were used for the network structure. The performance of the speech enhancement model was evaluated through two objective evaluation metrics: Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) depending on the scale of the dataset. Test results show the appropriate size of the training data differs depending on the type of networks and the type of dataset. In addition, they show that, in some cases, using a real network may be a more realistic solution if the number of total parameters is considered because the real network shows relatively higher performance than the complex network depending on the size of the data and the learning target.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.53-60
• /
• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• Korean Journal of Biological Psychiatry
• /
• v.27 no.1
• /
• pp.18-26
• /
• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.