• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.35-41
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• 2018

The analysis of plankton species distribution in sea or fresh water is very important in preserving marine ecosystem health. Since manual analysis is infeasible, many automatic approaches were proposed. They usually use images from in situ towed underwater imaging sensor or specially designed, lab mounted microscopic imaging system. Normally they assume that only single plankton is present in an image so that, if there is a clumping among multiple plankton of same species (homogeneous clumping) or if there are multiple plankton of different species scattered in an image (heterogeneous interspersion), they have a difficulty in recognition. In this work, we propose a deep learning based method that can detect and recognize individual plankton in images with homogeneous clumping, heterogeneous interspersion, or combination of both.

• Journal of Welding and Joining
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• v.22 no.3
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• pp.45-49
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• 2004

Generally, it is used the compensation spring to compensate the inaccuracy of screen image induced by thermal deformation in CRT monitor. Its mechanism is hi-metallic system made of heterogeneous metals and these springs are bonded by laser welding. But laser welding induces a non-uniform temperature distribution, and residual stress is yielded locally by these temperature deviation. Therefore, this study measures the curvature constant to assess functions of the compensation spring of shadow mask with respect to increment temperature and estimates the effect of residual stress on the performance of tri-metal used to compensation spring.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.273-289
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• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Nuclear Engineering and Technology
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• v.21 no.3
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• pp.193-204
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• 1989

This paper develops a new method for reconstructing neutron flux distribution, that is based on the maximum entropy Principle in information theory. The Probability distribution that maximizes the entropy Provides the most unbiased objective Probability distribution within the known partial information. The partial information are the assembly volume-averaged neutron flux, the surface-averaged neutron fluxes and the surface-averaged neutron currents, that are the results of the nodal calculation. The flux distribution on the boundary of a fuel assembly, which is the boundary condition for the neutron diffusion equation, is transformed into the probability distribution in the entropy expression. The most objective boundary flux distribution is deduced using the results of the nodal calculation by the maximum entropy method. This boundary flux distribution is then used as the boundary condition in a procedure of the imbedded heterogeneous assembly calculation to provide detailed flux distribution. The results of the new method applied to several PWR benchmark problem assemblies show that the reconstruction errors are comparable with those of the form function methods in inner region of the assembly while they are relatively large near the boundary of the assembly. The incorporation of the surface-averaged neutron currents in the constraint information (that is not done in the present study) should provide better results.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.649-663
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• 2002

We consider an unlimited fluid queueing model which has Fractional Brownian motion(FBM) as an input and a single server of constant service rate. By using the result of Duffield and O'Connell(6), we investigate the asymptotic tail-distribution of the stationary work-load. When there are multiple homogeneous FBM inputs, the workload distribution is similar to that of the queue with one FBM input; whereas for the heterogeneous sources the asymptotic work-load distributions is dominated by the source with the largest Hurst parameter.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.407-414
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• 2017

Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3366-3383
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• 2020

The Poisson point process (PPP) is widely used in wireless network modeling and performance analysis because it can provide tractable results for heterogeneous cellular networks (HetNets) analysis. However, it cannot accurately reflect the spatial distribution characteristics of the actual base stations (BSs). Considering the fact that the distribution of macro base stations (MBSs) is exclusive, the deployment of MBSs is modeled by the Matérn hard-core point process (MHCPP), and the deployment of pico base stations (PBSs) is modeled by PPP. This paper studies the performance of multiantenna HetNets and improves the energy efficiency (EE) of HetNets by optimizing the transmit power of PBSs. We use a simple approximate method to study the signal-to-interference ratio (SIR) distribution in two-tier MHCPP-PPP HetNets and derive the coverage probability, average data rate and EE of HetNets. Then, an optimization algorithm is proposed to improve the EE of HetNets. Finally, three transmission technologies are simulated and analyzed. The results show that multiantenna transmission has better system performance than single antenna transmission and that selecting the appropriate transmit power for a PBS can effectively improve the EE of the system. In addition, two-tier MHCPP-PPP HetNets have higher EE than two-tier PPP-PPP HetNets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3370-3392
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• 2017

Next Generation Beyond 4G/5G systems will rely on the deployment of small cells over conventional macrocells for achieving high spectral efficiency and improved coverage performance, especially for indoor and hotspot environments. In such heterogeneous networks, the expected performance gains can only be derived with the use of efficient interference coordination schemes, such as Fractional Frequency Reuse (FFR), which is very attractive for its simplicity and effectiveness. In this work, femtocells are deployed according to a spatial Poisson Point Process (PPP) over hexagonally shaped, 6-sector macro base stations (MeNBs) in an uncoordinated manner, operating in hybrid mode. A newly introduced intermediary region prevents cross-tier, cross-boundary interference and improves user equipment (UE) performance at the boundary of cell center and cell edge. With tools of stochastic geometry, an analytical framework for the signal-to-interference-plus-noise-ratio (SINR) distribution is developed to evaluate the performance of all UEs in different spatial locations, with consideration to both co-tier and cross-tier interference. Using the SINR distribution framework, average network throughput per tier is derived together with a newly proposed harmonic mean, which ensures fairness in resource allocation amongst all UEs. Finally, the FFR network parameters are optimized for maximizing average network throughput, and the harmonic mean using a fair resource assignment constraint. Numerical results verify the proposed analytical framework, and provide insights into design trade-offs between maximizing throughput and user fairness by appropriately adjusting the spatial partitioning thresholds, the spectrum allocation factor, and the femtocell density.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4642-4660
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• 2016

One major concern of applying Multipath TCP (MPTCP) to data delivery in heterogeneous wireless networks is that the utilization of asymmetric paths with diverse networking-related parameters may cause severe packet reordering and receive buffer blocking (RB²LOC). Although many efforts are devoting to addressing MPTCP's packet reordering problems, their sender-controlled solutions do not consider balancing overhead between an MPTCP sender and receiver, and their fully MPTCP mode cannot make MPTCP achieve a desired performance. This paper proposes a novel receiver-centric buffer blocking-aware data scheduling strategy for MPTCP (dubbed MPTCP-rec) necessitating the following aims: (1) alleviating MPTCP's packet reordering and RB²LOC problems, (2) improving the MPTCP performance, and (3) balancing load between the MPTCP sender and receiver. Simulation results show that the proposed MPTCP-rec solution outperforms the existing MPTCP solutions in terms of data delivery performance in heterogeneous wireless networks.

• 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.