• Smart Media Journal
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• v.11 no.3
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• pp.18-25
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• 2022

Neuromorphic computing generally shows significantly better power, area, and speed performance than neural network computation using CPU and GPU. These characteristics are suitable for resource-constrained IoT environments where energy consumption is important. However, there is a problem in that it is necessary to modify the source code for environment setting and application operation according to heterogeneous IoT devices that support neuromorphic computing. To solve these problems, NAAL was proposed and implemented in this paper. NAAL provides functions necessary for IoT device control and neuromorphic architecture abstraction and inference model operation in various heterogeneous IoT device environments based on common APIs of NAAL. NAAL has the advantage of enabling additional support for new heterogeneous IoT devices and neuromorphic architectures and computing devices in the future.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.896-905
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• 2010

The sensor networks can be used attractively for various application areas. Time synchronization is important for any Ubiquitous Sensor Networks (USN) systems. USN makes extensive use of synchronized time in many contexts for data fusion. However existing time synchronization protocols are available only for homogeneous sensor nodes of USN. It needs to be extended or redesigned in order to apply to the USN with heterogeneous sensor nodes. Because heterogeneous sensor nodes have different clock sources with the SinkNode of USN, it is impossible to be synchronized global time. In addition, energy efficiency is one of the most significant factors to influence the design of sensor networks, as sensor nodes are limited in power, computational capacity, and memory. In this paper, we propose specific time synchronization based on master-slave topology for the global time synchronization of USN with heterogeneous sensor nodes. The time synchronization master nodes are always able to be synchronized with the SinkNode. Then time synchronization master nodes enable time synchronization slave nodes to be synchronized sleep periods. The proposed master-slave time synchronization for heterogeneous sensor nodes of USN is also helpful for power saving by maintaining maximum sleep time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.128-137
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• 2006

In this paper, we propose a wireless network discovery scheme which support effective device power management by employing battery efficient network scanning procedure. Multi-mode terminals need to discover other wireless systems, above all, to execute an inter-system handover in the environment of heterogeneous wireless networks. The existing methods introduced in some recent research reports have certain shortcomings, such as battery power consumption increased by frequent modem activation, or the multi-mode terminal's inability to promptly discover wireless system. We Propose a scheme in which multi-mode terminals more quickly and accurately discover other wireless systems than previous schemes, while consuming minimum power. It also proves that the scheme has better performance by comparing it with the existing schemes.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.328-333
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• 2017

Mobile game apps, which are popular in various mobile devices, tend to be power-hungry and rapidly drain the device's battery. Since a long battery lifetime is a key design requirement of mobile devices, reducing the power consumption of mobile game apps has become an important research topic. In this paper, we investigate the power consumption characteristics of popular mobile games with multiple threads, focusing on the inter-thread. From our power measurement study of popular mobile game apps, we observed that some of these apps have abnormally high-load threads that barely affect the user's gaming experience, despite the high energy consumption. In order to reduce the wasted power from these abnormal threads, we propose a novel technique that detects such abnormal threads during run time and reduces their power consumption without degrading user experience. Our experimental results on an Android smartphone show that the proposed technique can reduce the energy consumption of mobile game apps by up to 58% without any negative impact on the user's gaming experience.

• 한국연소학회:학술대회논문집
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• 1997.06a
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• pp.69-76
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• 1997

Industrial-scale pulse corona process to remove $SO_2$ and $NO_x$ simultaneously from combustion flue gas has been studied. The pilot plant built in the present study treats 2,000 $Nm^3$/hr of flue gas from a boiler. The geometry of the pulse corona reactor is similar to that of an electrostatic precipitator commonly used in industry, A thyratron switch and magnetic pulse compressors, which can generate up to 130 kV of peak pulse voltage and up to 30 kW of average pulse power, have been used to produce pulsed corona. The removal efficiencies of $S0_2$ and $NO_x$ with the present process are maximum of 95 % and 85 %, respectively. Electrical power consumption to produce the pulsed corona, which has been one of the major difficulties to apply this process to industry, has been evaluated in the present study. The results showed that the power consumption can be reduced significantly by simultaneous addition of hydrocarbon injection and heterogeneous phase reactions to the process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1707-1714
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• 2016

This paper presents the effect of spectrum sharing/overlapping in a heterogeneous OFDM system with nonlinear High Power Amplifier (HPA). According to the spectrum sharing strategies, the achievable rate performances are analyzed. In the non-orthogonal spectrum sharing, we address how the portion of the overlapped or overlaid spectrum band and the nonlinear properties of HPA affect the system performance and accordingly, propose the optimized spectrum sharing strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.

• Journal of the Microelectronics and Packaging Society
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• v.21 no.2
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• pp.13-21
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• 2014

Technology for high power devices has made impressive progress in increasing the current density of power semiconductor, system module, and design optimization, which realize high power systems with heterogeneous functional integration. Depending on the performance development of high power semiconductor, packaging technology of high power device is urgently required for efficiency improvement of the device. Power device packaging must provide superior thermal management due to high operating temperature of power modules. Here we, therefore, review critical challenges of typical power electronics packaging today including core assembly processes, component materials, and reliability evaluation regulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5276-5298
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• 2019

Internet of Things (IoT) based sensor networks have gained unprecedented popularity in recent years. With the exponential explosion of the objects (sensors and mobiles), the bandwidth and the speed of data transmission are dwarfed by the anticipated emergence of IoT. In this paper, we propose a novel heterogeneous IoT model integrated the power line communication (PLC) and WiFi network to increase the network capacity and cope with the rapid growth of the objects. We firstly propose the mean transmission delay calculation algorithm based the 3D Markov chain according to the multi-priority of the objects. Then, the attractor selection algorithm, which is based on the adaptive behavior of the biological system, is exploited. The combined the 3D Markov chain and the attractor selection model, named MASM, can select the optimal path adaptively in the heterogeneous IoT according to the environment. Furthermore, we verify that the MASM improves the transmission efficiency and reduce the transmission delay effectively. The simulation results show that the MASM is stable to changes in the environment and more applicable for the heterogeneous IoT, compared with the other algorithms.