Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary...Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.展开更多
We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment....We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro=mechanisms of network growth and the macrostructures of online social networks.展开更多
Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopte...Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopted to approximate the relationship of current efficiency, current density and acidity. Penalty function introduced and optimal objective function reconstructed, a single loop simulated annealing algorithm(SAA) by using mutation and extending searching spaces was used to obtain optimal time sharing power scheme. Industrial practical results show that the whole system can greatly decrease the power consumption of EZP and increase the time sharing profits.展开更多
Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all...Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Previous researches on WSN energy models have focused less on the aggregate transceiver energy consumption models as compared to studies on other components of the node, hence a large portion of energy in a WSN still get depleted through data transmission. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node and the transceiver design parameters that significantly influences this energy consumption. The contribution of this paper is an innovative energy consumption model based on simple finite automata which reveals the relationship between the aggregate energy consumption and important power parameters that characterize the energy consumption map of the transceiver in a WSN;an ideal tool to design low power WSN.展开更多
With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recogn...With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.展开更多
The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. ...The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. The identification of model parameters of IGBT in PSPICE using BP neural network is illustrated.展开更多
With the explosive development of wireless communication and low power embedded techniques, Body Area Network (BAN) has opened up new frontiers in the race to provide real-time health monitoring. IEEE 802 has establis...With the explosive development of wireless communication and low power embedded techniques, Body Area Network (BAN) has opened up new frontiers in the race to provide real-time health monitoring. IEEE 802 has established a Task Group called IEEE 802.15.6 inNovember 2007 and aims to establish a communication standard optimized for low power, high reliability applied to medical and non-medical application for BANs. This paper overviews the path loss model and the communication scheme for implant-to-body surface channel presented by IEEE 802.15.6 standard. Comparing with the standard scheme where BCH (Bose-Chaudhuri-Hochquenghem) code is employing, we propose a new coding solution using convolutional code operating with Bit Interleaver based on the properties of implant-to-body surface channel. To analyze the performance of the two Error Correct Coding (ECC) schemes, we performed simulations in terms of Bit Error Rate (BER) and power consumption on MATLAB and FPGA platform, respectively. The simulation results proved that with appropriate constraint length, convolutional code has a better performance not only in BER, but also in minimization of resources and power consumption.展开更多
At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the kn...At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).展开更多
This paper proposes to use the power system simulation software CYME to plan, model and simulate for an actual distribution network for improving the reliability and efficiency, enhancing the efficiency and capacity, ...This paper proposes to use the power system simulation software CYME to plan, model and simulate for an actual distribution network for improving the reliability and efficiency, enhancing the efficiency and capacity, simulating the abnormal condition of distribution network, and presenting operation program of safe, reliable and having simulation record statements. The modeling simulation results show that the software module has lots of advantages including high accuracy, ideal reliability, powerful practicality in simulation and analysis of distribution network, it only need to create once model, the model can sufficiently satisfy multifarious types of simulation analysis required for the distribution network planning.展开更多
为了提高短期风电功率预测的准确性,提出一种基于贝叶斯优化和特征融合的xLSTM(extended Long Short-Term Memory)-Transformer模型。该模型综合应用长短期记忆(LSTM)网络的时序处理能力和Transformer的自注意力机制的动态特征融合能力...为了提高短期风电功率预测的准确性,提出一种基于贝叶斯优化和特征融合的xLSTM(extended Long Short-Term Memory)-Transformer模型。该模型综合应用长短期记忆(LSTM)网络的时序处理能力和Transformer的自注意力机制的动态特征融合能力。借助贝叶斯优化方法,模型可在较少的迭代次数条件下优化超参数,显著降低模型对计算资源的依赖。实验结果表明,内蒙古某风电场数据集上,与单一的LSTM模型、Transformer模型、门控循环单元(GRU)模型以及未采用贝叶斯优化和特征融合的xLSTM-Transformer模型相比,当步长(LookBack)为4和8时,所提模型的决定系数R2较基准模型平均提升1.2%~11.3%;平均绝对误差(MAE)平均降低12.8%~38.4%;均方根误差(RMSE)平均降低8.6%~35.8%。结果表明,所提模型在短历史输入条件下具有更高的预测精度与稳定性。展开更多
抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关...抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61376024 and No.61306024Natural Science Foundation of Guangdong Province under Grant No.S2013040014366Basic Research Programme of Shenzhen No.JCYJ20140417113430642 and JCYJ20140901003939020
文摘Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61104139,70871082,and 71101053)the ECUST for Excellent Young Scientists,China
文摘We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro=mechanisms of network growth and the macrostructures of online social networks.
文摘Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopted to approximate the relationship of current efficiency, current density and acidity. Penalty function introduced and optimal objective function reconstructed, a single loop simulated annealing algorithm(SAA) by using mutation and extending searching spaces was used to obtain optimal time sharing power scheme. Industrial practical results show that the whole system can greatly decrease the power consumption of EZP and increase the time sharing profits.
文摘Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Previous researches on WSN energy models have focused less on the aggregate transceiver energy consumption models as compared to studies on other components of the node, hence a large portion of energy in a WSN still get depleted through data transmission. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node and the transceiver design parameters that significantly influences this energy consumption. The contribution of this paper is an innovative energy consumption model based on simple finite automata which reveals the relationship between the aggregate energy consumption and important power parameters that characterize the energy consumption map of the transceiver in a WSN;an ideal tool to design low power WSN.
文摘With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.
文摘The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. The identification of model parameters of IGBT in PSPICE using BP neural network is illustrated.
文摘With the explosive development of wireless communication and low power embedded techniques, Body Area Network (BAN) has opened up new frontiers in the race to provide real-time health monitoring. IEEE 802 has established a Task Group called IEEE 802.15.6 inNovember 2007 and aims to establish a communication standard optimized for low power, high reliability applied to medical and non-medical application for BANs. This paper overviews the path loss model and the communication scheme for implant-to-body surface channel presented by IEEE 802.15.6 standard. Comparing with the standard scheme where BCH (Bose-Chaudhuri-Hochquenghem) code is employing, we propose a new coding solution using convolutional code operating with Bit Interleaver based on the properties of implant-to-body surface channel. To analyze the performance of the two Error Correct Coding (ECC) schemes, we performed simulations in terms of Bit Error Rate (BER) and power consumption on MATLAB and FPGA platform, respectively. The simulation results proved that with appropriate constraint length, convolutional code has a better performance not only in BER, but also in minimization of resources and power consumption.
基金supported by X-Project funded by the Ministry of Science,ICT&Future Planning under Grant No.NRF-2015R1A2A1A16074929
文摘At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).
文摘This paper proposes to use the power system simulation software CYME to plan, model and simulate for an actual distribution network for improving the reliability and efficiency, enhancing the efficiency and capacity, simulating the abnormal condition of distribution network, and presenting operation program of safe, reliable and having simulation record statements. The modeling simulation results show that the software module has lots of advantages including high accuracy, ideal reliability, powerful practicality in simulation and analysis of distribution network, it only need to create once model, the model can sufficiently satisfy multifarious types of simulation analysis required for the distribution network planning.
文摘为了提高短期风电功率预测的准确性,提出一种基于贝叶斯优化和特征融合的xLSTM(extended Long Short-Term Memory)-Transformer模型。该模型综合应用长短期记忆(LSTM)网络的时序处理能力和Transformer的自注意力机制的动态特征融合能力。借助贝叶斯优化方法,模型可在较少的迭代次数条件下优化超参数,显著降低模型对计算资源的依赖。实验结果表明,内蒙古某风电场数据集上,与单一的LSTM模型、Transformer模型、门控循环单元(GRU)模型以及未采用贝叶斯优化和特征融合的xLSTM-Transformer模型相比,当步长(LookBack)为4和8时,所提模型的决定系数R2较基准模型平均提升1.2%~11.3%;平均绝对误差(MAE)平均降低12.8%~38.4%;均方根误差(RMSE)平均降低8.6%~35.8%。结果表明,所提模型在短历史输入条件下具有更高的预测精度与稳定性。
文摘抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。
