• Journal of Communications and Networks
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• v.18 no.2
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• pp.173-181
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• 2016

In this paper, we address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission, where a multi-antenna base station (BS) sends energy and information simultaneously to multiple users equipped with a single antenna. Assuming that perfect channel-state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an Euclidean ball-shaped uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. Due to the channel uncertainty, the original problem becomes a homogeneous quadratically constrained quadratic problem, which is NP-hard. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Numerical results are provided to validate the robustness of the proposed algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.175-182
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• 2021

Deadlock is a situation in which two or more processes competing for resources are waiting for the others to finish, and neither ever does. There are two different forms of systems, multi-unit and single-unit resource systems. The difference is the number of instances (or units) of each type of resource. Deadlock problem can be modeled as a constrained combinatorial problem that seeks to find a possible scheduling for the processes through which the system can avoid entering a deadlock state. To solve deadlock problem, several algorithms and techniques have been introduced, but the use of metaheuristics is one of the powerful methods to solve it. Genetic algorithms have been effective in solving many optimization issues, including deadlock Problem. In this paper, an improved parallel framework of the genetic algorithm is introduced and adapted effectively and efficiently to deadlock problem. The proposed modified method is implemented in java and tested on a specific dataset. The experiment shows that proposed approach can produce optimal solutions in terms of burst time and the number of feasible solutions in each advanced generation. Further, the proposed approach enables all types of crossovers to work with high performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.9-15
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• 2021

Recently, it is increasingly necessary to run high-end neural network applications with huge computation overhead on top of resource-constrained embedded systems, such as wearable devices. While the huge computational overhead can be alleviated by distributed neural networks running on multiple separate devices, existing distributed neural network techniques suffer from a large traffic between the devices; thus are very vulnerable to communication failures. These drawbacks make the distributed neural network techniques inapplicable to wearable devices, which are connected with each other through unstable and low data rate communication medium like human body communication. Therefore, in this paper, we propose a distributed neural network partitioning technique that is resilient to communication failures. Furthermore, we show that the proposed technique also improves the inference accuracy even in case of no communication failure, thanks to the improved network partitioning. We verify through comparative experiments with a real-life neural network application that the proposed technique outperforms the existing state-of-the-art distributed neural network technique in terms of accuracy and resiliency to communication failures.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.31-40
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• 2012

This paper proposes a visual landmark, and presents a parking assistance system using the landmarks. The visual landmark is a feature corresponding to the parking slots, it must be selected considering the parking lot's environment. The parking lot has simple repetitive pattern environment without noticeable features. The previous landmarks are not proper to the parking lot's environment. We propose the visual landmark for this environment. We estimate the vehicle's location using the proposed landmarks, and expect the vehicle's trajectory according to the vehicle's state. The system's inputs are images from the camera fixed to the vehicle. The presented system estimates the vehicle's location using the input images, and assists a driver through displaying the expected vehicle's trajectory from the steering angle. The experimental results showed the proposed landmark's performance and the parking assistance system's performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.1-11
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• 2013

Reducing the energy that consumed by tag is a key requirement for the wider acceptance of the active RFID systems that use battery constrained tags. When the reader is not interrogating, the active RFID standard protocols try to reduce energy consumption of tags by using sleep mode. On sleep mode tags is active by receiving a specific signals from reader, until tag receive a sleep mode command from the reader, a tag waste energy for remaining in RX mode. Overhearing is a state of a tag in which it wastes energy for maintaining active RX state while there is no frame destined to it. According to our analysis, the amount of energy consumed by a tag due to overhearing is several time larger than that consumed by the effective communication. We propose RANO(Reservation Aloha for No Overhearing) that is designed to inform a tag of its effective communication intervals to eliminate overhearing problem in active RFID communication. The performance of the proposed protocol was evaluated through the real world by changing the number of tags and size of data. The result of an experiment, the proposed protocol performed saving about 22 times less than the standard protocol did.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.73-80
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• 2004

A nonlinear time-domain system identification algorithm using measured acceleration data is developed for structural damage assessment. To take account of nonlinear behavior of structural systems, an output error between measured and computed acceleration increments has been defined and a constrained nonlinear optimization problem is solved for optimal structural parameters. The algorithm estimates time-varying properties of stiffness and damping parameters. Nonlinear response of restoring force of a structural system is recovered by using the estimated time-varying structural properties and computed displacement by Newmark-$\beta$ method. In the recovery, no pre-defined model for inelastic behavior has been assumed. In developing the algorithm, noise and incomplete measurement in space and state have been considered. To examine the developed algorithm, numerical simulation and laboratory experimental studies on a three-story shear building have been carried out.

• Journal of Semiconductor Engineering
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• v.1 no.1
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• pp.13-30
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• 2020

Radio frequency (RF) energy harvesting technology have become a reliable and promising alternative to extend the lifetime of power-constrained wireless networks by eliminating the need for batteries. This emerging technology enables the low-power wireless devices to be self-sustaining and eco-friendly by scavenging RF energy from ambient environment or dedicated energy sources. These attributes make RF energy harvesting technology feasible and attractive to an extended range of applications. However, despite being the most reliable energy harvesting technology, there are several challenges (especially power conversion efficiency, output DC voltage and sensitivity) poised for the implementation of RF energy harvesting systems. In this article, a detailed literature on RF energy harvesting technology has been surveyed to provide guidance for RF energy harvesters design. Since signal strength of the received RF power is limited and weak, high efficiency state-of-the-art RF energy harvesters are required to design for providing sufficient DC supply voltage to wireless networks. Therefore, various designs and their trade-offs with comprehensive analysis for RF energy harvesters have been discussed. This paper can serve as a good reference for the researchers to catch new research topics in the field of RF energy harvesting.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.245-254
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• 2004

This paper deals with the problem of estimating structure and motion from long continuous image sequences, applying the Expectation Maximization algorithm based on extended Kalman smoother to impose the time-continuity of the motion parameters. By repeatedly estimating the state transition matrix of the dynamic equation and the parameters of noise processes in the dynamic and measurement equations, this optimization gives the maximum likelihood estimates of the motion and structure parameters. Practically, this research is essential for dealing with a long video-rate image sequence with partially unknown system equation and noise. The algorithm is implemented and tested for a real image sequence.