• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2581-2594
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• 2018

The microcell terrain is the most common wireless network terrain in our life. In order to solve wireless network optimization of weak coverage in the microcell terrain, improve call quality and reduce the cost of the premise, power amplifiers in base stations should be adjusted according to user volume. In this paper, characteristics of microcell topography are obtained after analysis. According to the topography characteristics of different microcells, changes in the number of users at different times have been estimated, meanwhile, the number of scatter users are also obtained by monitoring the PCCPCH RSCP and other parameters. Then B-Spline interpolation method has been applied to scatter users to obtain the continuous relationship between the number of users and time. On this basis, power amplification can be chosen according to changes in the number of users. The methods adopted by this paper are also applied in the engineering practice, sampling and interpolation are used to obtain the number of users at all times, so that the power amplification can be adjusted by the number of users in a microcell. Such a method is able to optimize wireless network and achieve a goal of expanding the area of base stations, reduce call drop rate and increase capacity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1189-1197
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• 2006

Ultra-wideband(UWB) is a killer technology for short-range wireless communications. In the past, most of the UWB research focused on physical layer but the unique characteristics of UWB make it different to design the upper layer protocols than conventional narrow band systems. Cross-layer protocols have received high attention for UWB networks. In this paper, we investigate the performance of two physical layer schemes: Time Hopping Binary Pulse Position Modulation(TH-BPPM) and Time Hopping Binary Pulse Amplitude Modulation (TH-BPAM) with proposed private MAC protocol for UWB ad-hoc networks. From pulse level to packet level simulation is done in network simulator ns-2 with realistic network environments for varying traffic load, mobility and network density. Our simulation result shows TH-BPAM outperforms TH-BPPM in high traffic load, mobility and dense network cases but in a low traffic load case identical performance is achieved.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.479-484
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• 2014

In this paper, we consider the construction of cyclic and quasi-cyclic structured q-ary low-density parity-check (LDPC) codes over a designated small field. The construction is performed with a pre-defined sliding-window, which actually executes the regular mapping from original field to the targeted field under certain parameters. Compared to the original codes, the new constructed codes can provide better flexibility in choice of code rate, code length and size of field. The constructed codes over small fields with code length from tenths to hundreds perform well with q-ary sum-product decoding algorithm (QSPA) over the additive white Gaussian noise channel and are comparable to the improved spherepacking bound. These codes may found applications in wireless sensor networks (WSN), where the delay and energy are extremely constrained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.649-663
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• 2012

We propose a percolation based M2M networking architecture and its data transmission method. The proposed network architecture can be server-free and router-free, which allows us to operate routing efficiently with percolations based on six degrees of separation theory in small world network modeling. The data transmission can be divided into two phases: routing and data transmission phases. In the routing phase, probe packets will be transmitted and forwarded in the network thus multiple paths are selected and performed based on the constriction of the maximum hop number. In the second phase, the information will be encoded, say, with the fountain codes, and transmitted using the paths generated in the first phase. In such a way, an efficient routing and data transmission mechanism can be built, which allow us to construct a low-cost, flexible and ubiquitous network. Such a networking architecture and data transmission can be used in many M2M communications, such as the stub network of internet of things, and deep space networking, and so on.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.558-567
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• 2015

In recent years, we have seen a proliferation of mobile-network-enabled smart objects, such as smart-phones and smart-watches, that form a cyber-physical integrated network to connect the cyber and physical worlds through the capabilities of sensing, communicating, and computing. Discovery of the relationship between smart objects is a critical and nontrivial task in cyber-physical integrated network applications. Aiming to find the most stable relationship in the heterogeneous and dynamic cyber-physical network, we propose a distributed and efficient relationship-discovery algorithm, called dynamically maximizing remaining unchanged time with minimum connected dominant set (DMRUT-MCDS) for constructing a backbone with the smallest scale infrastructure. In our proposed algorithm, the impact of the duration of the relationship is considered in order to balance the size and sustain time of the infrastructure. The performance of our algorithm is studied through extensive simulations and the results show that DMRUT-MCDS performs well in different distribution networks.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.229-234
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• 2006

There are still some key problems having to be solved in theory and technique applications when GPS is used to monitor the vertical deformation of ground with high precision. Utilizing the GPS technology to monitor the deformation of the land subsidence and ground fissure in Xi'an, this paper puts forward advice that the datum frame of GPS network has significant influence on the precision and accuracy of the vertical deformation results by some research. The co-authors make some theoretical study of the datum error and practice by establishing the datum error models, especially the influence of scale and azimuth datum errors on GPS monitoring network. Then the datum frame design methods and arithmetic of GPS monitoring network are presented and have taken a good effect.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.195-197
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• 2005

This paper is proposed adaptive fuzzy neural network(AFNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed. so that the actual state variable will coincide with the desired one. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Molecules and Cells
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• v.26 no.4
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• pp.368-372
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• 2008

Recent molecular and genetic studies in rice, a short-day plant, have elucidated both conservation and divergence of photoperiod pathway genes and their regulators. However, the biological roles of rice genes that act within the autonomous pathway are still largely unknown. In order to better understand the function of the autonomous pathway genes in rice, we conducted molecular genetic analyses of OsFVE, a rice gene homologous to Arabidopsis FVE. OsFVE was found to be ubiquitously expressed in vegetative and reproductive organs. Overexpression of OsFVE could rescue the flowering time phenotype of the Arabidopsis fve mutants by up-regulating expression of the SUPPRESSOR OF OVEREXPRESSION OF CO1 (SOC1) and down-regulating FLOWERING LOCUS C (FLC) expression. These results suggest that there may be a conserved function between OsFVE and FVE in the control of flowering time. However, OsFVE overexpression in the fve mutants did not rescue the flowering time phenotype in in relation to the response to intermittent cold treatment.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.