Derivation of Reliability Index Vector Formula for Series System and Its Application

In this study, a reliability index vector formula is proposed for series system with two failure modes in term of the concept of reliability index vector and equivalent failure modes. Firstly, the reliability index vector is introduced to determine the correlation coefficient between two failure modes, and then, the reliability index vector of a series system can be obtained. Several numerical cases and an analysis on offshore platform are performed, and the results show that this scheme provided here has better computational accuracy, and its calculation process is simpler for the series systems reliability calculations compared with the other methods. Also this scheme is more convenient for the engineering applications.

Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.

MODIFIED QUANTIZATION INDEX MODULATION WATERMARKING ADAPTIVE TO CONTRAST MASKING THRESHOLDS

In this paper,we suggest an adaptive watermarking method to improve both transparence and robustness of quantization index modulation(QIM) scheme. Instead of a fixed quantization step-size,we apply a step-size adapted to image content in each 8×8 block to make a balance of robust extraction and transparent embedding.The modified step-size is determined by contrast masking thresholds of Watson’s perceptual model.From a normalized crossed-correlation value between the original watermark and the detected watermark,we could observe that our method is robust to attacks of additive white Gaussian noise(AWGN),Salt and Pepper noise and Joint Photographic Experts Group(JPEG) compression than the original QIM.By taking into account the contrast insensitivity and visible thresholds of human visual system,the suggested improvement achieves a maximum embedding strength and an appropriate quantization step-size which is consistent with local values of a host signal.

Robust Quantzation Index Modulation VIA Adaptive Watermarking

In this paper,we suggest an adaptive watermarkingmethod to improve both transparence and robustnessof quantization index modulation(QIM)scheme.Instead of a fixed quantization step-size,we apply astep-size adapted to image content in each 8×8block to make a balance of robust extraction andtransparent embedding.The modified step-size isdetermined by contrast masking thresholds ofWatson’s perceptual model.From a normalizedcrossed-correlation value between the original watermarkand the detected watermark,we could observethat our method is robust to attacks of additivewhite Gaussian noise(AWGN),Salt and Peppernoise and Joint Photographic Experts Group(JPEG)compression than the original QIM.By taking intoaccount the contrast insensitivity and visible thresholdsof human visual system,the suggested improvementachieves a maximum embedding strength andan appropriate quantization step-size which is consistentwith local values of a host signal.

基于二次分解、LSTM-ELM和误差修正的空气质量指数预测模型

精准预测空气质量指数(Air Quality Index,AQI)对于制定有效的空气污染治理策略至关重要。为了进一步提升AQI的预测精度,提出了一种新的预测模型,并结合了二次分解(Secondary Decomposition,SD)、优化算法、双尺度预测和误差修正的方法。首先,采用改良的自适应白噪声完全集合经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)和样本熵(Sample Entropy,SE)对原始AQI序列进行分解并重构,获得高频、中频和低频3个频率分量。其次,利用经过北方苍鹰算法(Northern Goshawk Optimization,NGO)优化的变分模态分解(Variational Mode Decomposition,VMD)对高频分量进行二次分解,进一步降低其复杂度。再次,引入向量加权平均算法(Weighed Mean of Vectors Algorithm,INFO)对长短期记忆网络(Long Short-Term Memory,LSTM)和极限学习机(Extreme Learning Machine,ELM)的关键参数进行优化,同时利用INFO-LSTM预测高频分量分解后的子序列,进而利用INFO-ELM分别预测中、低频分量,并将所得预测结果进行线性叠加。最后,利用NGO-VMD和INFO-ELM对误差序列进行分解和预测,并对初次预测结果进行修正,得到最终的AQI预测值。研究选取北京、上海和成都3个典型城市为例进行实证分析,并对比了7个对照试验,发现基于二次分解、LSTM-ELM和误差修正的模型具有最高的预测精度。该模型可为治理空气污染提供理论和技术上的帮助。

Assessment on the Water Resources Carrying Capacity in Karst Area Based on the Cosine Vector Included Angle

[Objective] The research aimed to assess the water resources carrying capacity in Guizhou Province based on the cosine vector included angle method. [Method] By using the cosine vector included angle method, the index weight was determined. The projection value of water resources carrying capacity in Guizhou Province was counted by using the multi-objective gray relational projection method. Moreover, the projection value which was counted by the index weight determined by the mean-variance method was as the control. [Result] The projection values which were obtained by two kinds of methods were very close, and the ordering result was consistent. [Conclusion] In the assessment of water resources carrying capacity, it was feasible to use the cosine vector included angle method to determine the index weight.

GRIN(Gradient Index)介质中的Maxwell方程组与光线光学

利用坡印廷矢量(Poynting vector)的方向就是光线轨迹曲线的切线方向,推出程函方程(Eikonal equation)的矢量式.经分析发现此式包含了光的粒子性与光的波动性因素,光线的传播规律还受介质折射率函数的制约.再由程函方程进一步推得光线方程,并给出了应用实例.

HIV-1整合酶链转移抑制剂的QSAR研究与分子设计

为了研究人类免疫缺陷病毒1型(HIV-1)整合酶链转移抑制剂(INSTIs)抑制活性的定量构效关系(QSAR),从而获得活性更好的抑制剂,基于拓扑理论,计算了32种INSTIs分子的电拓扑状态指数(Ei)和电性距离矢量(Mj)。通过最佳变量子集回归(LBR)方法建立了化合物抑制活性的六元(M57,M14,E7,E13,E21,M36)QSAR模型。以模型中的6个参数为人工神经网络输入层构建反向传播(BP)算法模型,设定6∶2∶1的网络结构,相关系数R2由多元线性回归的0.946提升到0.992。分析模型的6个变量可知,影响INSTIs抑制活性的主要结构片段是>N—、—N—、—C—、—X、—C<、—CH和—OH。通过结构修饰提出3种具有较高抑制活性的新化合物。

Vector系统应用于牙周基础治疗的临床研究

目的研究Vector系统进行牙周基础治疗的临床效果。方法采用口内自身对照方法,选择58例慢性牙周炎患者,口内A、D区设为试验组,B、C区设为对照组。所有患者行超声洁治后,试验组应用Vector系统行龈下刮治及根面平整术(scaling and root planning,SRP),对照组应用Gracey刮治器械行SRP。对2组SRP的治疗时间,SRP前(基线期)及SRP后1、3、6个月的龈沟出血指数、探诊出血、探诊深度及附着水平进行比较,视觉模拟疼痛评级法(visual analogue scale,VAS)评定2组的疼痛程度。结果试验组每区的SRP治疗时间为(25.15±1.35)min,明显短于对照组的(40.11±1.08)min(Z=3.625,P<0.05)。SRP后各观察时点,2组的各项牙周指数较治疗前均有明显改善(P<0.05),但2组间各项牙周指数的差异均无统计学意义(P>0.05)。试验组SRP结束时(Zc=2.356,P<0.05)及治疗后1d(Zc=3.138,P<0.05)的VAS评分明显低于对照组。结论Vector系统能缩短临床操作时间,提高牙周基础治疗舒适度,有效改善慢性牙周炎的临床症状。

基于i-vector全局参数联合的说话人识别

以高斯通用背景模型(Gaussian mixture model-universal background model,GMM-UBM)和i-vector模型为主的说话人识别算法在实际应用中取得了不错的成绩,但i-vector说话人识别模型中存在没有充分考虑通用背景(universal background,UB)数据与训练数据耦合性的问题导致模型性能不佳。提出了基于i-vector全局参数联合(global parameter joint of identify vector,GPJ-IV)的说话人识别方法。该方法利用背景说话人特征训练得到说话人通用背景模型(universal background model,UBM),构建基于全局联合差异空间和联合信道补偿的GPJ-IV模型。通过实验测试并与传统方法进行对比,实验结果显示,所提出的GPJ-IV模型相比i-vector模型,等错误率(equal error rate,EER)和最小检测代价函数(minimum detection cost function,MinDCF)性能分别提升了58.99%和15.9%。

Application of SVR Models in Stock Index Forecast Based on Different Parameter Search Methods

Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

Vector治疗仪对慢性牙周炎患者的牙周袋深度及对其菌斑指数的影响

目的探讨Vector治疗仪对慢性牙周炎患者的牙周袋深度及对其菌斑指数的影响。方法前瞻性选择2016年12月至2019年12月在延安市中医医院口腔科治疗的慢性牙周炎患者,共60例。按照随机数字表法将研究对象分为观察组(n=30例)和对照组(n=30例)。观察组采用新型超声系统Vector牙周治疗仪治疗,对照组选用传统常规刮治疗法治疗。比较2组治疗时间和治疗过程中的主观疼痛度。在治疗前和治疗后1个半月,探查和记录所有患者牙周袋探测深度(PD)以及牙周菌斑指数(PLI),比较2组患者之间的差异。结果治疗过程中观察组用时较对照组明显缩短,差异有统计学意义(P=0.003);观察组患者主观疼痛度较对照组明显减轻,差异有统计学意义(P<0.001);治疗前观察组和对照组PD差异无统计学意义(P>0.05);治疗后1个半月随访,2组PD值均减小,差异有统计学意义(P<0.05);治疗后观察组PD值明显小于对照组(P<0.05);治疗前观察组和对照组PLI差异无统计学意义(P>0.05);治疗后1个半月随访,2组PLI值均减小,差异有统计学意义(P<0.05);治疗后观察组PLI值明显小于对照组(P<0.05)。结论Vector治疗仪在治疗慢性牙周炎时能明显减小患者牙周袋深度及菌斑指数,与传统刮治术相比更为安全、有效。

Lossless Data Hiding Scheme Based on VQ Index Coding

Lossless data hiding can restore the original status of cover media after embedded secret data are extracted. In 2010, Wang et al. proposed a lossless data hiding scheme which hides secret data in vector quantization （VQ） indices, but the encoding strategies adopted by their scheme expand the final codestream. This paper designs four embedding and encoding strategies to improve Wang et aL＇s scheme. The experiment result of the proposed scheme compared with that of the Wang et aL＇s scheme reduces the bit rates of the final codestream by 4.6% and raises the payload by 1.09% on average.

Vector系统在轻中度牙周病治疗中的应用研究

目的观察Vector系统治疗轻中度牙周炎的临床疗效。方法 200例牙周病患者随机分为对照组和实验组,分别采用Gracey刮治器和Vector系统治疗技术,在治疗后15、45、90d时分别测量并记录牙牙周探诊深度(probing depth,PD)和出血指数(bleeding index,BI)。结果对照组和实验组治疗前后的各项指标均有显著改善,实验组PD在治疗后15d时明显小于对照组(P<0.05),而45d和90d时两组的差异无统计学意义(P>0.05),实验组的BI改善优于对照组(P<0.05)。结论 Vector系统在牙周治疗中有确切的临床效果。

RELIABILITY ASSESSMENT OF STRUCTURAL SYSTEMS BASED ON DIRECTIONAL VECTOR SIMULATION TECHNIQUE

The new improved directional vector simulation method foranalyzing the reliability of struc- tural systems failure probabilityis researched. This paper also points out the defects of generaldirectional vector simulation, and gives rise to a new higheraccuracy approximate integral formula of structural systems failureprobability. A new geometric meaning of characteristic function isobtained. A new simple method of generating uniformly distributedrandom vector sample sin n-dimensional unit hyper-spherical surfaceis put forward and strictly proved. This method is easy to put intopractice. Numerical examples are given to show the applicability andeffectiveness of the suggested approach to structural systemsreliability problems.

Evolutionary algorithm based index assignment algorithm for noisy channel

A globally optimal solution to vector quantization (VQ) index assignment on noisy channel, the evolutionary algorithm based index assignment algorithm (EAIAA), is presented. The algorithm yields a significant reduction in average distortion due to channel errors, over conventional arbitrary index assignment, as confirmed by experimental results over the memoryless binary symmetric channel (BSC) for any bit error.

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.

BC-iDistance:an optimized high-dimensional index for KNN processing

To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overcome the defect of much information loss for iDistance in one-dimensional transformation,the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector,and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively.By employing the classical B+tree,this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing.Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.

Residual Spray for the Control of <i>Aedes</i>Vectors in Dengue Outbreak Residential Areas

The incidence of dengue in Malaysia has shown an increasing trend since the year 2000. Vector control is the primary approach for dengue control in Malaysia. There is an urgent need for new or modified approaches such as the residual spraying on the outer walls that can potentially last long enough to control the Aedes population, particularly in the outbreak-prone areas. In this field study, we conducted outdoor residual spraying (ORS) using a newly formulated polymer-enhanced suspension concentrate (SC-PE) of deltamethrin. The objectives of this study were to evaluate the efficacy of ORS using deltamethrin SC-PE and its effect on wild Aedes populations and to assess its residual bio-efficacy on painted cement walls against the pyrethroid-susceptible strains of laboratory-reared Aedes mosquitoes. Three rounds of spraying in a four-month cycle were conducted between 2014 and 2015 in four residential areas (low-rise and high-rise housing types) in Hulu Langat, Selangor. The bio-efficacy of the insecticide was evaluated by assessing its impact on vector population using ovitrap surveillance. Standard WHO wall deposit bioassay was adapted to determine bio-efficacy of deltamethrin, i.e. post 30 min knockdown and post 24 h mortality after exposure. During the treatment period, we observed significant reductions in the population of Ae. albopictus in the sprayed low-rise housing in both semi-indoor and outdoor environments, while in the high-rise housing, there was also a significant decline in Ae. aegypti population in the semi-indoor environment. The evaluation of the residual bio-efficacy of deltamethrin SC-PE against laboratory-reared Aedes mosquitoes showed that the insecticide lasted longer in the high-rise housing compared to the low-rise housing with >80% mortality achieved continuously for 16 weeks. We provide initial evidence on residual efficacy of deltamethrin SC-PE in reducing Aedes population size in the low-rise and high-rise housing. Our results showed that ORS is a promising tool in the dengue vector control and like IRS in malaria control;it is a powerful and effective method if conducted correctly. However, large scale and well-designed studies with entomological and epidemiological endpoints are still warranted before its routine use in dengue control.