Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

Optimization of multi-color laser waveform for high-order harmonic generation

With the development of laser technologies,multi-color light-field synthesis with complete amplitude and phase control would make it possible to generate arbitrary optical waveforms.A practical optimization algorithm is needed to generate such a waveform in order to control strong-field processes.We review some recent theoretical works of the optimization of amplitudes and phases of multi-color lasers to modify the single-atom high-order harmonic generation based on genetic algorithm.By choosing different fitness criteria,we demonstrate that：（i） harmonic yields can be enhanced by 10 to 100 times,（ii） harmonic cutoff energy can be substantially extended,（iii） specific harmonic orders can be selectively enhanced,and（iv） single attosecond pulses can be efficiently generated.The possibility of optimizing macroscopic conditions for the improved phase matching and low divergence of high harmonics is also discussed.The waveform control and optimization are expected to be new drivers for the next wave of breakthrough in the strong-field physics in the coming years.

An efficient algorithm for generating a spherical multiple-cell grid

This paper presents an efficient algorithm for generating a spherical multiple-cell(SMC)grid.The algorithm adopts a recursive loop structure and provides two refinement methods:(1)an arbitrary area refinement method and(2)a nearshore refinement method.Numerical experiments are carried out,and the results show that compared with the existing grid generation algorithm,this algorithm is more flexible and operable.

Genetic Algorithms of Structural Fuzzy Reliability Index

In this paper the simple generation algorithms are improved. According to the geometric meaning of the structural reliability index, a method is proposed to deal with the variables in the standard normal space. With consideration of variable distribution, the correlation coefficient of the variables and its fuzzy reliability index, the feasibility and the reliability of the algorithms are proved with an example of structural reliability analysis and optimization.

Complete Real Solution of the Five-orientation Motion Generation Problem for a Spherical Four-bar Linkage

For a spherical four-bar linkage,the maximum number of the spherical RR dyad（R：revolute joint）of five-orientation motion generation can be at most 6.However,complete real solution of this problem has seldom been studied.In order to obtain six real RR dyads,based on Strum＇s theorem,the relationships between the design parameters are derived from a 6th-degree univariate polynomial equation that is deduced from the constraint equations of the spherical RR dyad by using Dixon resultant method.Moreover,the Grashof condition and the circuit defect condition are taken into account.Given the relationships between the design parameters and the aforementioned two conditions,two objective functions are constructed and optimized by the adaptive genetic algorithm（AGA）.Two examples with six real spherical RR dyads are obtained by optimization,and the results verify the feasibility of the proposed method.The paper provides a method to synthesize the complete real solution of the five-orientation motion generation,which is also applicable to the problem that deduces to a univariate polynomial equation and requires the generation of as many as real roots.

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomfilter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efficient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousfile sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

A Complete Critical Path Algorithm for Test Generation of Combinational Circuits

It is known that critical path test generation method is not a complete algorithm for combinational circuits with reconvergent-fanout.In order to make it a complete algorithm,we put forward a reconvergent-fanout- oriented technique,the principal critical path algorithm,propagating the critical value back to primary inputs along a single path,the principal critical path,and allowing multiple path sensitization if needed.Relationship among test patterns is also discussed to accelerate test generation.

Generation method of minimal-complete-coverage interoperability test sequence based on digraph

Even if two implementations of a protocol pass the conformance testing,it cannot guarantee that they can interoperate properly;so direct testing of interoperation is considered indispensable.During the interoperability testing,a minimal number of test sequences are expected to check as many as possible implementation errors.By using minimal-complete-coverage criterion,the test sequence generation based on digraph can produce more effective test sequences.

A Machine Learning-Based Botnet Malicious Domain Detection Technique for New Business

In the new network business,the danger of botnets should not be underestimated.Botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receive commands from the botmaster,carrying out further attack activities.Therefore,a system based onmachine learning to dichotomizeDNSdomain access is designed,which can instantly detectDGAdomain names and thus quickly dispose of infected computers to avoid spreading the virus and further damage.In the comparison,the bidirectional LSTM model slightly outperformed the unidirectional LSTM network and achieved 99%accuracy in the open dataset classification task.

A DGA domain names detection modeling method based on integrating an attention mechanism and deep neural network

Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79%for the detection and macro average precision and recall of 83%for the classification task of DGA domain names.

DGA-Based Botnet Detection Toward Imbalanced Multiclass Learning

Botnets based on the Domain Generation Algorithm(DGA) mechanism pose great challenges to the main current detection methods because of their strong concealment and robustness. However, the complexity of the DGA family and the imbalance of samples continue to impede research on DGA detection. In the existing work, the sample size of each DGA family is regarded as the most important determinant of the resampling proportion;thus,differences in the characteristics of various samples are ignored, and the optimal resampling effect is not achieved.In this paper, a Long Short-Term Memory-based Property and Quantity Dependent Optimization(LSTM.PQDO)method is proposed. This method takes advantage of LSTM to automatically mine the comprehensive features of DGA domain names. It iterates the resampling proportion with the optimal solution based on a comprehensive consideration of the original number and characteristics of the samples to heuristically search for a better solution around the initial solution in the right direction;thus, dynamic optimization of the resampling proportion is realized.The experimental results show that the LSTM.PQDO method can achieve better performance compared with existing models to overcome the difficulties of unbalanced datasets;moreover, it can function as a reference for sample resampling tasks in similar scenarios.

A DGA domain names detection modeling method based on integrating an attention mechanism and deep neural network

Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.

Construction of Multimodal Chinese Tourism Knowledge Graph

With the development of tourism knowledge graphs(KGs),recommendation,question answering(QA)and other functions under its support enable various applications to better understand users and provide services.Existing Chinese tourism KGs do not contain enough entity information and relations.Besides,the knowledge storage usually contains only the text modality but lacks other modalities such as images.In this paper,a multi-modal Chinese tourism knowledge graph(MCTKG)is proposed based on Beijing tourist attractions to support QA and help tourists plan tourism routes.An MCTKG ontology was constructed to maintain the semantic consistency of heterogeneous data sources.To increase the number of entities and relations related to the tourist attractions in MCTKG,entities were automatically expanded belonging to the concepts of building,organization,relic,and person based on Baidu Encyclopedia.In addition,based on the types of tourist attractions and the styles of tourism route,a tourism route generation algorithm was proposed,which can automatically schedule the tourism routes by incorporating tourist attractions and the route style.Experimental results show that the generated tourist routes have similar satisfaction comparedwith the tourism routes crawled from specific travel websites.