Simulation of Haloxylon Ammodendron Stand Basic Diameter Structure Based on Fuzzy Distribution Function

Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R2 values showed that Fuzzy-Γ1, Fuzzy-Γ2, Fuzzy-Γ3, Fuzzy-Γ4 had good fitting performance, among which Fuzzy-Γ1 had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ2 distribution function was the second, and Fuzzy-Γ4 distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ5 is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ1 and Fuzzy-Γ2.

Twin-Field Quantum Key Distribution Protocol Based on Wavelength-Division-Multiplexing Technology

Quantum key distribution(QKD) generates information-theoretical secret keys between two parties based on the physical laws of quantum mechanics. Following the advancement in quantum communication networks, it becomes feasible and economical to combine QKD with classical optical communication through the same fiber using dense wavelength division multiplexing(DWDM) technology. This study proposes a detailed scheme of TF-QKD protocol with DWDM technology and analyzes its performance, considering the influence of quantum channel number and adjacent quantum crosstalk on the secret key rates. The simulation results show that the scheme further increases the secret key rate of TF-QKD and its variants. Therefore, this scheme provides a method for improving the secret key rate for practical quantum networks.

考虑需求侧灵活性资源配置的配电网分布鲁棒优化规划方法研究

考虑需求侧灵活性资源(简称“资源”)配置,开展网-荷-储联合规划是提升配电网灵活性以适应新能源出力波动的关键途径。为此提出一种灵活性缺失场景筛选与不确定性分析方法改进相结合的配电网规划研究模型:首先,立足资源视角进行配电网灵活性供需关系建模;其次,基于影子价格理论提出配电网灵活性缺失场景筛选策略,建立筛选指标以描述规划资源、运行约束、场景筛选间闭环关系;然后,将该策略嵌入配电网-多资源两阶段联合规划模型,一阶段考虑投资成本最优,二阶段协调运行及灵活性综合成本期望;将规划模型重构并于改进场景概率驱动型分布鲁棒优化(ISPD-DRO)框架下求解,其优势在于实现了概率优化求解与场景动态更新的有机统一;最后经算例分析验证所提模型在提升决策经济性及配电网灵活性层面的优势。

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

A distributed routing algorithm based-on simplified topology in LEO satellite networks

In this paper, a distributed muting strategy based on simplified topology （DRBST） was proposed for LEO satellite networks. The topology of LEO satellite networks was simplified aiming at minimizing intersatellite links handover number. To optimize the route based on the simplified topology, we considered not only the transmission delay but also the queuing delay and the processing delay, which were analyzed using Markov chain and determined using a novel methodology. The DRBST algorithm was simulated in a LEO satellite networks model built using OPNET. The simulation results demonstrate that the low complexity DRBST algorithm can guarantee end-to-end delay bound. Moreover, the muting protocol cost is much less than traditional algorithms.

Developing a Secure Framework Using Feature Selection and Attack Detection Technique

Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.

Influence of alkali metal doping on surface properties and catalytic activity/selectivity of CaO catalysts in oxidative coupling of methane

Surface properties （viz. surface area, basicity/base strength distribution, and crystal phases） of alkali metal doped CaO （alkali metal/Ca= 0.1 and 0.4） catalysts and their catalytic activity/selectivity in oxidative coupling of methane （OCM） to higher hydrocarbons at different reaction conditions （viz. temperature, 700 and 750 ℃; CH4/O2 ratio, 4.0 and 8.0 and space velocity, 5140-20550 cm^3 ·g^-1·h^-1） have been investigated. The influence of catalyst calcination temperature on the activity/selectivity has also been investigated. The surface properties （viz. surface area, basicity/base strength distribution） and catalytic activity/selectivity of the alkali metal doped CaO catalysts are strongly influenced by the alkali metal promoter and its concentration in the alkali metal doped CaO catalysts. An addition of alkali metal promoter to CaO results in a large decrease in the surface area but a large increase in the surface basicity （strong basic sites） and the C2＋ selectivity and yield of the catalysts in the OCM process. The activity and selectivity are strongly influenced by the catalyst calcination temperature. No direct relationship between surface basicity and catalytic activity/selectivity has been observed. Among the alkali metal doped CaO catalysts, Na-CaO （Na/Ca = 0.1, before calcination） catalyst （calcined at 750 ℃）, showed best performance （C2＋ selectivity of 68.8% with 24.7% methane conversion）, whereas the poorest performance was shown by the Rb-CaO catalyst in the OCM process.

Underwater target feature recognition based on distribution of highlights

An algorithm for underwater target feature recognition is proposed using its highlights distribution.For an underwater target with large size and slender body,it is assumed that the heading course and the length of the target are both determined by the distribution of its highlights.By supposing that these highlights obey Gaussian mixture distribution,the feature recognition problem can be transformed into a clustering problem.Therefore,using the collinearly constrained expectation maximization algorithm,the clustering centers of these highlights can be calculated and then the estimation of the heading and length of the target can be obtained with high accuracy.The effectiveness of the proposed method is demonstrated using simulations.

Reconstruction of 3D light distribution produced by cylindrical diffuser in deep tissues based on photoacoustic imaging

Measurement of light distribution in biological tissue contributes to selecting strategy and optimizing dose for biomedical application. In this letter, a photoacoustic method combined with Monte Carlo simulation was used to estimate the three-dimensional light distribution in biological tissue. The light distribution was produced by a cylindrical diffuser which interposed into tissues. The light profiles obtained by the method were compared to those detected by photo diodes. The experimental results demonstrate the feasibility of this method. The approach can play a significant role for photo-dosimetry in biomedical phototherapy.

Application of hydrometeorological coupled European flood forecasting operational real time system in Yellow River Basin

This study evaluated the application of the European flood forecasting operational real time system （EFFORTS） to the Yellow River. An automatic data pre-processing program was developed to provide real-time hydrometeorological data. Various GIS layers were collected and developed to meet the demands of the distributed hydrological model in the EFFORTS. The model parameters were calibrated and validated based on more than ten years of historical hydrometeorological data from the study area. The San-Hua Basin （from the Sanmenxia Reservoir to the Huayuankou Hydrological Station）, the most geographically important area of the Yellow River, was chosen as the study area. The analysis indicates that the EFFORTS enhances the work efficiency, extends the flood forecasting lead time, and attains an acceptable level of forecasting accuracy in the San-Hua Basin, with a mean deterministic coefficient at Huayuankou Station, the basin outlet, of 0.90 in calibration and 0.96 in validation. The analysis also shows that the ;simulation accuracy is better for the southern part than for the northern part of the San-Hua Basin. This implies that, along with the characteristics of the basin and the mechanisms of runoff generation of the hydrological model, the hydrometeorological data play an important role in simulation of hydrological behavior.

基于场景聚类的主动配电网分布鲁棒综合优化

为较好地考虑分布式电源(DG)和负荷的不确定性对配电网的影响,基于风电场景预测,通过调整网络拓扑结构和风电、光伏发电、微型燃气轮机、储能、无功补偿等的出力,提出了实现动态重构与无功优化相结合的主动配电网综合优化模型。在考虑DG间相关性的基础上,基于原始数据采用拉丁超立方采样得到初始场景,利用手肘法确定K-means算法的聚类数,从而得到典型场景,场景分布的概率置信区间由1-范数和∞-范数约束。另外,为了消除储能与机组组合的时间相关性约束,重点介绍了基于盒式分解算法的两阶段分解模型。第1阶段以机组启停成本和运行成本为目标函数对机组与储能的各时段运行域进行约束。将第2阶段转化为单时间尺度优化问题,通过求解得到经济性最优的结果。模型采用列与约束生成算法进行求解,通过对IEEE 33节点系统与Taipower 84节点系统的仿真验证了模型的鲁棒性与可行性。

Wideband reverberation suppression in beams domain based on angular distribution index

Based on the properties of wideband source’s spatial spread,an algorithm for reverberation suppression in beams domain is proposed.In an effective observation range,a compact bank of receiving spatial beams is operated to transform the data from element domain to beam domain.Angular Distribution Index(ADI) is obtained by comparing to a reference value which is computed by Monte Carlo Integration.ADI decreases with a stronger target echo.Hence,choosing an appropriate threshold of ADI,the target echo can be separated from reverberation by cutting off the high AD components.The filter is compatible with the procedures of beamforming and detection for the current sonar.The computation complexity of the filter is low.The relationships between ADI and frequency,bandwidth,signal-reverberation ratio,are illustrated by a simulation example.The processing results based on the lake-trial and sea-trial data show that reverberation can be significantly suppressed,and threshold choosing for wideband ADI filter is fault tolerant.

Soft Computing: Fuzzy Logic Approach in Wireless Sensors Networks

The recent growth of communication and sensor technology results in the enlargement of a new attractive and challenging area-wireless sensor networks (WSNs). A network comprising of several minute wireless sensor nodes which are organized in a dense manner is called as a Wireless Sensor Network (WSN). Every node estimates the state of its surroundings in this network. The estimated results are then converted into the signal form in order to determine the features related to this technique after the processing of the signals. It’s high computational environment with limited and controlled broadcast range, processing, as well as limited energy. The embedded soft computing approach in wireless sensor networks is suggested. This approach means a grouping of embedded fuzzy logic and neural networks models for information processing in complex environment with unsure, rough, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems.

Nonlinear Time-Frequency Distributions of Spectrum Energy Operator in Large Vocabulary Mandarin Speaker Independent Speech Recognition System

This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.

Polymer gratings based on photopolymerization for low-order distributed feedback polymer lasers

Novel polymer distributed feedback （DFB） gratings axe fabricated based on photopolymerization to reduce lasing threshold of polymer lasers. A photopolymer formulation sensitive to 355-nm ultraviolet （UV） light is proposed for the fabrication of polymer gratings and it can be used to form polymer films by spin-coating process. A very low surface-relief depth ranging from 12.5 to about 1.0 nm has been demonstrated with a refractive-index modulation of about 0.012. The experimental results indicate that such polymer gratings have promising potentials for the fabrication of low-order DFB organic semiconductor lasers.

Adaptive Compensation Algorithm in Open Vocabulary Mandarin Speaker-Independent Speech Recognition

In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech recognition algorithms have been developed to solve this problem by filtering the input signal with a single-zero high pass filter. Unfortunately, this technique increases the noise energy at high frequencies above 4 kHz, which in some cases degrades the recognition accuracy. This paper solves the problem using a pre-emphasis filter in the front end of the recognizer. The aim is to develop a modified parameterization approach taking into account the whole energy zone in the spectrum to improve the performance of the existing baseline recognition system in the acoustic phase. The results show that a large vocabulary speaker-independent continuous speech recognition system using this approach has a greatly improved recognition rate.

Privacy Preserving Query over Encrypted Multidimensional Massive Data in Cloud Storage

Cloud storage is widely used in massive data outsourcing, but how to efficiently query encrypted multidimensional data stored in an untrusted cloud environment remains a research challenge. We propose a high performance and privacy-preserving query（p LSH-PPQ） scheme over encrypted multidimensional data to address this challenge. In our scheme, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-Based Encryption（CP-ABE）policy is used to control access to the search results. Therefore, only the requester with the permission attribute can obtain correct secret keys to decrypt the data. Security analysis proves that the p LSH-PPQ scheme achieves data confidentiality and reserves the data owner＇s privacy in a semi-trusted cloud. In addition, evaluations demonstrate that the p LSH-PPQ scheme can significantly reduce response time and provide high search efficiency without compromising on search quality.