• Korean Chemical Engineering Research
• /
• v.47 no.6
• /
• pp.695-699
• /
• 2009

As a rule, when develop new product in company, multiple products that have similar function are developed simultaneously. These products are subjected to a group of tests covering quality, safety and durability. If the schedule of tests is changed, the expected net presented value(NPV) of new products is changed. The tests should be scheduled with the goal of maximizing the expected NPV of the new products. A model incorporated resource constraints with the sequencing of testing tasks of multiple products is proposed in this paper. Examples show that the proposed model can handle stochastic task duration data represented by scenarios with probabilities.

• Journal of Communications and Networks
• /
• v.9 no.4
• /
• pp.473-481
• /
• 2007

Energy efficiency is one of the most critical concerns for wireless sensor networks. By allowing sensor nodes in close proximity to cooperate in transmission to form a virtual multiple-input multiple-output(MIMO) system, recent progress in wireless MIMO communications can be exploited to boost the system throughput, or equivalently reduce the energy consumption for the same throughput and BER target. However, these cooperative transmission strategies may incur additional energy cost and system overhead. In this paper, assuming that data collectors are equipped with antenna arrays and superior processing capability, energy efficiency of relevant traditional and cooperative transmission strategies: Single-input-multiple-output(SIMO), space-time block coding(STBC), and spatial multiplexing(SM) are studied. Analysis in the wideband regime reveals that, while receive diversity introduces significant improvement in both energy efficiency and spectral efficiency, further improvement due to the transmit diversity of STBC is limited, as opposed to the superiority of the SM scheme especially for non-trivial spectral efficiency. These observations are further confirmed in our analysis of more realistic systems with limited bandwidth, finite constellation sizes, and a target error rate. Based on this analysis, general guidelines are presented for optimal transmission strategy selection in system level and link level, aiming at minimum energy consumption while meeting different requirements. The proposed selection rules, especially those based on system-level metrics, are easy to implement for sensor applications. The framework provided here may also be readily extended to other scenarios or applications.

• Journal of Communications and Networks
• /
• v.15 no.3
• /
• pp.283-291
• /
• 2013

In this paper, we propose new multiuser detectors (MUDs) based on compressed sensing approaches for the large-scale multiple antenna systems equipped with dozens of low-power antennas. We consider the scenarios where the number of receiver antennas is smaller than the total number of users, but the number of active users is relatively small. This prior information motivates sparsity-embracing MUDs such as sparsity-embracing linear/nonlinear MUDs where the detection of active users and their symbol detection are employed. In addition, sparsity-embracing MUDs with maximum a posteriori probability criterion (MAP-MUDs) are presented. They jointly detect active users and their symbols by exploiting the probability of user activity, and it can be solved efficiently by introducing convex relaxing senses. Furthermore, it is shown that sparsity-embracing MUDs exploiting common users' activity across multiple symbols, i.e., frame-by-frame, can be considered to improve performance. Also, in multiple multiple-input and multiple-output networks with aggressive frequency reuse, we propose the interference cancellation strategy for the proposed sparsity-embracing MUDs. That first cancels out the interference induced by adjacent networks and then recovers the desired users' information by exploiting the low user activity. In simulation studies for binary phase shift keying modulation, numerical evidences establish the effectiveness of our proposed MUDs exploiting low user activity, as compared with the conventional MUD.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.9
• /
• pp.939-945
• /
• 2009

In this paper, we proposed a method which corresponds people under the structured spaces with multiple cameras. The correspondence takes an important role for using multiple camera system. For solving this correspondence, the proposed method consists of three main steps. Firstly, moving objects are detected by background subtraction using a multiple background model. The temporal difference is simultaneously used to reduce a noise in the temporal change. When more than two people are detected, those detected regions are divided into each label to represent an individual person. Secondly, the detected region is segmented as features for correspondence by a criterion with the color distribution and context information of human body. The segmented region is represented as a set of blobs. Each blob is described as Gaussian probability distribution, i.e., a person model is generated from the blobs as a Gaussian Mixture Model (GMM). Finally, a GMM of each person from a camera is matched with the model of other people from different cameras by maximum likelihood. From those results, we identify a same person in different view. The experiment was performed according to three scenarios and verified the performance in qualitative and quantitative results.

• Nuclear Engineering and Technology
• /
• v.56 no.2
• /
• pp.558-567
• /
• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Korean Journal of Construction Engineering and Management
• /
• v.8 no.2
• /
• pp.146-154
• /
• 2007

The u-City construction project has become a hot topic In the construction market because it seems economic value-added field for construction firms. However, construction firms don't willingly participate in the u-City construction market because the future business environments for the u-City are very uncertain. Scenario planning is a very powerful method in managing this complex planning situation and is based on scenarios that help each enterprise appropriately adapt itself to its own business environments. Therefore it Is based on the main principles of systems thinking and multiple futures. For the purpose of dealing with such uncertainties, this paper attempts to develop the possible market scenarios of the u-City construction market through a scenario planning approach. From this perspective, we considered various aspects of the u-City construction such as market demands, technology development, policy level and management environment. After considering the relevant issues, we identified the main trends and key uncertainties. Then, we developed three coherent u-City construction market scenarios. On the basis of the proposed scenarios, the business strategies of potential construction firms in the u-City construction market has been formulated. Therefore, construction firms can use these scenarios as a basic data for market analysis and business strategy. Therefore, this paper is able to enhance the participation of construction firms in the u-City construction market.

• Journal of Korea Water Resources Association
• /
• v.52 no.6
• /
• pp.381-395
• /
• 2019

The purpose of this study is to predict agricultural reservoir storage rate (RSR) in a month. This algorithm was developed by multiple linear regression model (MLRM) which included the past 3 months RSRs data and the future climate change scenarios. In order to improve use of predicted RSR, this study need the severe criteria in terms of drought. So, the predicted RSR was indexed as the 3 months reservoir drought index (RDI3) and then it was disaggregated into drought duration, severity, and intensity. For the future RSR estimation by climate change scenarios, the 6 RCP 8.5 scenarios of HadGEM2-ES, CESM1-BGC, MPI-ESM-MR, INM-CM4, FGOALS-s2, and HadGEM3-RA were used in three future evaluation periods (S1: 2011~2040, S2: 2041~2070, S3: 2071~2099). The future S3 period of HadGEM2-ES scenario which has the biggest increase in precipitation and temperature showed the largest decrease to 60.2% among the 6 scenarios compared to the historical RSR (1976~2005) 77.3%. In contrast, INM-CM4 scenario which has smallest changes in precipitation and temperature in S3 period showed the smallest decrease to 72.8%. For the CESM1-BGC and MPI-ESM-MR, FGOALS-s2, and HadGEM3-RA, the S3 period RSR showed 72.6%, 72.6%, 67.4%, and 64.5% decrease respectively. The future severe drought condition of RDI3 below -0.25 showed the increase trend for the number and severity up to -2.0 during S3 period.

• Communications for Statistical Applications and Methods
• /
• v.28 no.2
• /
• pp.135-150
• /
• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Journal of Communications and Networks
• /
• v.12 no.5
• /
• pp.411-432
• /
• 2010

We consider three different secure broadcasting scenarios: i) Broadcast channels with common and confidential messages (BCC), ii) multi-receiver wiretap channels with public and confidential messages, and iii) compound wiretap channels. The BCC is a broadcast channel with two users, where in addition to the common message sent to both users, a private message, which needs to be kept hidden as much as possible from the other user, is sent to each user. In this model, each user treats the other user as an eavesdropper. The multi-receiver wiretap channel is a broadcast channel with two legitimate users and an external eavesdropper, where the transmitter sends a pair of public and confidential messages to each legitimate user. Although there is no secrecy concern about the public messages, the confidential messages need to be kept perfectly secret from the eavesdropper. The compound wiretap channel is a compound broadcast channel with a group of legitimate users and a group of eavesdroppers. In this model, the transmitter sends a common confidential message to the legitimate users, and this confidential message needs to be kept perfectly secret from all eavesdroppers. In this paper, we provide a survey of the existing information-theoretic results for these three forms of secure broadcasting problems, with a closer look at the Gaussian multiple-input multiple-output (MIMO) channel models. We also present the existing results for the more general discrete memoryless channel models, as they are often the first step in obtaining the capacity results for the corresponding Gaussian MIMO channel models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.12
• /
• pp.3061-3080
• /
• 2012

To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.