• Journal of the Semiconductor & Display Technology
• /
• v.19 no.1
• /
• pp.23-28
• /
• 2020

In Smart mobile devices embedding big.LITTLE architectures, the conventional multi-core assignment scheme for user applications may incur wasteful energy consumption and long response time. In this paper, we propose a user experience assisted energy-efficient multicore assignment scheme. Our simulation results show that the proposed scheme achieves at 40% less energy consumption and at 20% less response time comparing to the legacy scheme.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.05a
• /
• pp.827-830
• /
• 2006

With recent advanced in portable electric devices, wireless sensor, MEMS and bio-Mechanics device, the new typed power supply, not conventional battery but self-powered energy source is needed. Particularly, the system that harvests from their environments are interests for use in self powered devices. For very low powered devices, environmental energy may be enough to use power source. In the generality of cases, these energy harvesting systems are used in the piezoelectric materials as mechanisms to convert mechanical vibration energy into electric energy. Through the piezoelectric materials, the ambient vibration energy could be used to prolong the power supply or in the ideal case provide endless energy f9r the devices. Therefore, the piezoelectric power harvesting cantilever beam is developed. Also, the output voltage and power are predicted in this study. We also discuss the developing system of the piezoelectric energy scavenger. An experimental verification of the model is also performed to ensure its accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4044-4062
• /
• 2016

We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to quantize the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.6
• /
• pp.1106-1112
• /
• 2017

As the Internet of Things (IOT) is evolving into an industry-wide service and expanded to the concept of Internet of Everything (IoE), services using IoT devices are easily accessible in everyday life. IoT requires more devices to collect information and is expected to increase the number of devices by 50 billion by 2020, and is about the number of devices currently available. Gradually, the number of mobile devices, smart devices, and Internet devices is increasing, and energy resources are required to operate such a large number of Internet devices, and the energy consumed by each device is small. In this paper, we consider the number of devices to be increased and generate a signal irrespective of transmission information so that power other than the energy required for signal transmission is consumed. When transmission information is generated and near to a receiver to receive information, The method to be used as an analysis is designed through experiments.

• Nuclear Engineering and Technology
• /
• v.51 no.2
• /
• pp.539-545
• /
• 2019

Yttrium-90 is a useful therapeutic radioisotope for tumor treatment because of its high-energy-emitting beta rays. However, it has been difficult to select appropriate collimators and main energy windows for Y-90 Bremsstrahlung imaging using gamma cameras because of the broad energy spectra of Y-90. We used a Monte Carlo simulation to investigate the effects of collimator selection and energy windows on Y-90 Bremsstrahlung imaging. We considered both MELP and HE collimators. Various phantoms were employed in the simulation to determine the main energy window using primary-to-scatter ratios (PSRs). Imaging performance was evaluated using spatial resolution indices, imaging counts, scatter fractions, and contrast-to-noise ratios. Collimator choice slightly affected energy spectrum shapes and improved PSRs. The HE collimator performed better than the MELP collimator on all imaging performance indices (except for imaging count). We observed minor differences in SR and SF values for the HE collimator among the five simulated energy windows. The combination of an HE collimator and improved-PSR energy window produced the best CNR value. In conclusion, appropriate collimator selection is an important component of Bremsstrahlung Y-90 photon imaging and main energy window determination. We found HE collimators to be more appropriate for improving the imaging performance of Bremsstrahlung Y-90 photons.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.4
• /
• pp.159-164
• /
• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.472_473
• /
• 2009

It is fascinated research theme to store electric energy as much as possible and to utilize it at the point of proper time. Especially, the demand for energy storage devices has been increased based on the interest for distributed generation and smartgrid. As the results for a number of researches on it, various types of energy storage devices have been developed. Each devices have its own dynamic characteristics, power capacity, and storage capacity followed by storage mechanism. In this paper, the comparison research for various energy storage devices has been performed based on power capacity, storage capacity, discharging time, lifetime, efficiency, and cost. Application researches of SFES(superconducting flywheel energy storage), characterized as 300kW-100kWh, have been performed also.

• ETRI Journal
• /
• v.39 no.2
• /
• pp.145-150
• /
• 2017

As wearable devices are powered by batteries, they need to consume as little energy as possible. To address this challenge, in this article, we propose a synergistic technique for energy-efficient approximate speech signal processing (ASSP) for wearable devices. More specifically, to enable the efficient trade-off between energy consumption and sound quality, we synergistically integrate an approximate multiplier and a successive approximate register analog-to-digital converter using our enhanced conversion algorithm. The proposed ASSP technique provides ~40% lower energy consumption with ~5% higher sound quality than a traditional one that optimizes only the bit width of SSP.

• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.9
• /
• pp.78-85
• /
• 2008

Piezoelectric materials have been investigated as vibration energy converters to power wireless devices or MEMS devices due to the recent low power requirements of such devices and the advancement in miniaturization technology. Piezoelectric power generation can be an alternative to the traditional power source-battery because of the presence of facile vibration sources in our environment and the potential elimination of the maintenance required for large volume batteries. This paper represents the new power source which supplies energy device node. This system, called "energy harvesting system", with piezo materials scavenges extra energy such as vibration and acceleration from the environment. Then it converts the mechanical energy scavenged to electrical energy for powering device This paper explains the properties of piezo material through theoretical analysis and experiments The developed system provides a solution to overcome the critical problem of making up wireless device networks.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2003.03a
• /
• pp.393-400
• /
• 2003

Various types of dampers are widely adopted to reduce the seismic damages in bridges. However, dampers may be the improper solution especially in moderate seismic regions because dampers are costly for installation and require constant maintenance during life cycle. In this study, energy-dissipating sacrificial device is proposed, which sacrifices easily substitutable bridge members and dissipates the excessive energy during seismic excitations. In turns, the inelastic behavior of sacrificial members reduces the input energy of the major members, such as piers in bridges, and may prevent the major members from serious malfunction. A simplified mechanical model is developed to represent the behavior of sacrificial devices installed in a bridge. The hysteresis energy of piers is analyzed to certify performance of device under seismic loads applied to this mechanical model. The results from this study show that the proposed sacrificial energy-dissipating device can decrease excessive hysteresis energy and reduce the damage of piers under seismic excitation. Therefore, economical enhancement of the seismic performance of bridges may be possible by employing the proposed sacrificial energy-dissipating devices.