Research on Thermodynamic Properties of Polybrominated Diphenylamine by Neural Network

Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.

APPLICATION OF ASSOCIATIVE MEMORY NEURAL NETWORK IN HIGH VOLTAGE TRANSMISSIONLINE FAULT DIAGNOSIS

Effective methods of enhancing the fault-tolerance property are proposed for two kinds of associative memory (AM) neural network (NN) used in high voltage transmission line fault diagnosis. For feedforward NN (FNN),the conception of 'fake attaction region' is presented to expand the attraction region artificially,and for the feedback Hopfield bidirectional AM NN (BAM-NN),the measure to add redundant neurons is taken to enhance NN's memory capacity and fault-tolerance property. Study results show that the NNs built not only can complete fault diagnosis correctly but also have fairly high fault-tolerance ability for disturbed input information sequence. Moreover FNN is a more convenient and effective method of solving the problem of power system fault diagnosis.

EFFECT OF ELECTROACUPUNCTURE OF THE HEART MERIDIAN ACUPOINTS ON ISCHEMIC CARDIAC FUNCTION IN THE RABBIT

Subjective: To observe the effect of electroacupuncture (EA) of acupoints of the Heart Meridian and Lung Meridian on ischemic cardiac systolic ability for analyzing the relative specific relationship between the Heart Meridian and the heart. Methods: Acute myocardial ischemia (AMI) was produced by intravenous infusion of pituitrin (40 u + 5% glucose injection 500 ml, 60 drips/min) in the rabbit. Left intraventricular pressure (LVP), maximal rising velocity of LVP (dp/dt max), isovolumetric pressure (IP) and end-diastolic pressure (EDP) of the left cardiac ventricle were used as the indexes. Three points of Heart Meridian [HM, from 'Shenmen' (HT 7) to 'Lingdao' (HT 4)] and the three points of Lung Meridian [LM, from 'Taiyuan' (LU 9) to 'Lieque' (LU 7)] were punctured with filiform needles and stimulated with hand-manipulation and electrically with ZY2-1 EA Therapeutic Apparatus. 30 rabbits anesthetized with urethane (1 g/kg) were randomly and evenly divided into control group, HM group and LM group. Result-s: The effects of EA of HM points were evidently superior to those of EA of LM points in promoting the recovery of both AMI-induced decrease of LVP and dp/dtmax, and AMI-induced increase of IP and EDP. Conclusion: Acupoints of Heart Meridian has a relatively specific connection with the heart in comparison with those of Lung Meridian; and the Heart Meridian is a functional whole.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

ANN Based Performance Analysis of UPFC in Power Flow Control

The Unified Power Flow Controller （UPFC） is one of the most versatile Flexible AC Transmission Systems （FACTS） devices that has unique capability of independently controlling the real and reactive power flows, in addition to regulate the system bus voltage. This paper presents performance analysis of Unified Power Flow Controller based on two axis theory. Based on this analysis, a new Artificial Neural Network （ANN） based controller has been proposed to improve the system performance. The controller rules are structured depending upon the relationship between series inserted voltage and the desired changes in real/reactive power flow in the power system. The effects of different controllers along with parameters of series transformer and transmission line have been investigated through developed control block model in SIMULINK tool box of MATLAB. The effectiveness of the proposed scheme is demonstrated by case studies.

良导络测量系统的理论应用与远景分析

本文通过目前有关良导络测量系统的理论与研究近况，分析其应用于传统医学研究的价值，指出其用于经络腧穴电性研究相关理论的可行性及其意义。

Floating-gate photosensitive synaptic transistors with tunable functions for neuromorphic computing

Synaptic devices that merge memory and processing functions into one unit have broad application potentials in neuromorphic computing, soft robots, and humanmachine interfaces. However, most previously reported synaptic devices exhibit fixed performance once been fabricated,which limits their application in diverse scenarios. Here, we report floating-gate photosensitive synaptic transistors with charge-trapping perovskite quantum dots(PQDs) and atomic layer deposited(ALD) Al_(2)O_(3) tunneling layers, which exhibit typical synaptic behaviors including excitatory postsynaptic current(EPSC), pair-pulse facilitation and dynamic filtering characteristics under both electrical or optical signal stimulation. Further, the combination of the high-quality Al2O3 tuning layer and highly photosensitive PQDs charge-trapping layer provides the devices with extensively tunable synaptic performance under optical and electrical co-modulation. Applying light during electrical modulation can significantly improve both the synaptic weight changes and the nonlinearity of weight updates, while the memory effect under light modulation can be obviously adjusted by the gate voltage.The pattern learning and forgetting processes for "0" and "1"with different synaptic weights and memory times are further demonstrated in the device array. Overall, this work provides synaptic devices with tunable functions for building complex and robust artificial neural networks.