Adaptive Agent Model with Hybrid Routing Selection Strategy for Improving the Road-Network Congestion Problem

This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Novel IOV worm model and its corresponding hybrid anti-worm strategy in expressway interchange terminal

In order to take precaution and cure against intemet of vehicles （IOV） worm propagation in expressway, the IOV worm propagation and its corresponding anti-worm strategy were studied in expressway interchange terminal. According to omnirange driving in expressway interchange terminal and vehicular mobile communication environment, an IOV worm propagation model is constructed; and then according to the dynamic propagation law and destructiveness of IOV worm in this environment, a novel hybrid anti-worm strategy for confrontation is designed. This worm propagation model can factually simulates the IOV worm propagation in this interchange terminal environment; and this hybrid anti-worm strategy can effectively control IOV worm propagation in the environment, moreover, it can reduce the influence on network resource overhead.

A hybrid specific index-related process monitoring strategy based on a novel two-step information extraction method

A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.

A Novel Polymorphic Topology with Hybrid Control Strategy Based LLC Resonant Converter for Ultra-Wide Input Voltage Range Applications

To realize effective utilization of renewable energy sources,a novel polymorphic topology with hybrid control strategy based LLC resonant converter was analyzed and designed in this paper.By combining the merits of a full bridge LLC resonant converter,three-level half bridge LLC resonant converter,and variable frequency control mode,the converter realizes an intelligent estimation of input voltage by automatically changing its internal cir-cuit topology.Under this control strategy,different input voltages determine different operation modes.This is achieved in full bridge LLC mode when the input voltage is low.If the input voltage rises to a certain level,it operates in three-level half bridge LLC mode.These switches are digital and entirely carried out by the DSP(Digi-tal Signal Processor),which means that an auxiliary circuit is unnecessary,where a simple strategy of software modification can be utilized.Experimental results of a 500W prototype with 100V~600V input voltage and full load efficiency of up to 92%are developed to verify feasibility and practicability.This type of converter is suitable for applications with an ultra-wide input voltage range,such as wind turbines,photovoltaic generators,bioenergy,and other renewable energy sources.

Hybrid Control Strategy for Matrix Converter Fed Wind Energy Conversion System

In this paper, a hybrid control strategy for a matrix converter fed wind energy conversion system is presented. Since the wind speed may vary, output parameters like power, frequency and voltage may fluctuate. Hence it is necessary to design a system that regulates output parameters, such as voltage and frequency, and thereby provides a constant voltage and frequency output from the wind energy conversion system. Matrix converter is used in the proposed solution as the main power conditioner as a more efficient alternative when compared to traditional back-back converter structure. To control the output voltage, a vector modulation based refined control structure is used. A power tracker is included to maximize the mechanical output power of the turbine. Over current protection and clamp circuit input protection have been introduced to protect the system from over current. It reduces the spikes generated at the output of the converter. The designed system is capable of supplying an output voltage of constant frequency and amplitude within the expected ranges of input during the operation. The matrix converter control using direct modulation method, modified Venturini modulation method and vector modulation method was simulated, the results were compared and it was inferred that vector modulation method was superior to the other two methods. With the proposed technique, voltage transfer ratio and harmonic profile have been improved compared to the other two modulation techniques. The behaviour of the system is corroborated by MATLAB Simulink, and hardware is realized using an FPGA controller. Experimental results are found to be matching with the simulation results.

Hybrid Support Vector Machines-Based Multi-fault Classification

Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

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.

Designing an automated FAQ answering system for farmers based on hybrid strategies

Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.

Hybridization of iron phthalocyanine and MoS_(2)for high-efficiency and durable oxygen reduction reaction

Hybrid catalysts based on iron phthalocyanine(FePc)have raised much attention due to their promising applications in electrocatalytic oxygen reduction reaction(ORR).Various hybridization strategies have been developed for improving their activity and durability.However,the influence of different hybridization strategies on their catalytic performance remains unclear.In this study,Fe Pc was effectively distributed on molybdenum disulfide(MoS_(2))forming Fe Pc-based hybrid catalysts,namely Fe Pc-MoS_(2),Fe Pc*-MoS_(2),and Fe Pc-Py-MoS_(2),respectively,to disclose the related influence.Through direct hybridization,the stacked and highly dispersed Fe Pc on MoS_(2)resulted in Fe Pc-MoS_(2),and Fe Pc*-MoS_(2),respectively,in which the substrate and Fe Pc are mainly bound through van der Waals interactions.Through covalent hybridization strategy using pyridyl(Py)as a linker,Fe Pc-Py-MoS_(2)hybrid catalyst was prepared.Experimental and theoretical results disclosed that the linker hybridization of Fe Pc and MoS_(2)facilitated the exposure of Fe-N4 sites,maintained the intrinsic activity of Fe Pc by forming a more dispersed phase and increased the durability via Fe-N bonding,rendering the Fe Pc-Py-MoS_(2)an excellent ORR hybrid catalyst.Compared with van der Waals hybridized Fe Pc-MoS_(2)and Fe Pc*-MoS_(2)in alkaline media,the linker hybridized Fe Pc-Py-MoS_(2)showed an obviously enhanced ORR activity with a half-wave potential(E_(1/2))of 0.88 V vs RHE and an ultralow Tafel slope of 26 m V dec-1.Besides,the Fe Pc-Py-MoS_(2)exhibited a negligible decay of E_(1/2) after 50,000 CV cycles for ORR,showing its superior durability.This work gives us more insight into the influence of different hybrid strategies on Fe Pc catalysts and provides further guidance for the development of highly efficient and durable ORR catalysts.

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

Coordinated Capacitor Voltage Balancing Method for Cascaded H-bridge Inverter with Supercapacitor and DC-DC Stage

Cascaded H-bridge inverter(CHBI) with supercapacitors(SCs) and dc-dc stage shows significant promise for medium to high voltage energy storage applications. This paper investigates the voltage balance of capacitors within the CHBI, including both the dc-link capacitors and SCs. Balance control over the dc-link capacitor voltages is realized by the dcdc stage in each submodule(SM), while a hybrid modulation strategy(HMS) is implemented in the H-bridge to balance the SC voltages among the SMs. Meanwhile, the dc-link voltage fluctuations are analyzed under the HMS. A virtual voltage variable is introduced to coordinate the balancing of dc-link capacitor voltages and SC voltages. Compared to the balancing method that solely considers the SC voltages, the presented method reduces the dc-link voltage fluctuations without affecting the voltage balance of SCs. Finally, both simulation and experimental results verify the effectiveness of the presented method.

Accuracy improvement of the traditional rolling force and torque model powered by an industrial data platform

The calculation results of the rolling force and torque model based on Orowan's differential equation numerical solution method do not fit with the industrial measurements very well.In particular,a quite large deviation on the torque model was found.On the basis of analyzing the shortcomings of the existing method,an improved rolling force and torque model algorithm aided by the Process Integrated Data Application System platform is proposed.Accordingly,the calculation accuracy of the rolling torque model is improved.The improved models are verified by 1711136 records of a data platform.The improved models are also based on Orowan's differential equation.Two coefficients,namely,friction factor and forward slip,are recognized as the crucial factors to be determined from industrial measurements to improve the accuracy.Therefore,the proposed method is a hybrid method that can be used to deeply understand the rolling process and improve the model's accuracy by combining traditional plastic mechanics and data-driving global optimization algorithms.This paper proposes a new approach to studying theoretical rolling deformation models powered by the industrial data platform.

Solving algorithm for TA optimization model based on ACO-SA

An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.

A hybrid brain-computer interface control strategy in a virtual environment

This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world.The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states,and state switch is achieved in a sequential manner.In the current system,imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously,while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm.The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI.Subjects obtained similar performances in the hybrid and single control tasks,which indicates the hybrid control strategy works well in the virtual environment.

Adaptive Region Boosting method with biased entropy for path planning in changing environment

Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.

Responsive Prototypes： Urban Machines as a Framework for the Digital City

Over the last few decades, a diverse and expanded practice has emerged from the increasingly collaborative work developed among architects, urbanists, artists, and media designers. This collaboration has more and more developed a particular landscape of projects which share the same operative principles and modes of operation. The city at large has been the context where these projects have been implemented and developed with the will of bringing these types of experimentation to the people and to the public and social scale. Cities are continuously produced through entropic processes that mediate between complexity and immediacy. Information technology operates as a linkage able to set up new rules for communication between man and matter. In this framework, by looking at spatial interventions in the urban space, the aim of the paper is to dissect the modes in which spatial practitioners operate in the digital city through ＂Urban Machines＂ and how information technology becomes a tool for place-making. The paper looks at integrated systems or machines that have spatial, social, and environmental implication in how we experience the city. Machines imply ＂something that has been constructed＂ and ＂function with a specific purpose＂ while being composed by parts that respond to a ＂functioning whole＂. These interventions start from sharing the same principle： How do we generate through technologically mediated experiences opportunities for new types of production of（social）space？; How such systems shape our urban experience and deploy forms of participation in civic life？.

Energy Management Strategy for Hybrid Electric Vehicle Based on System Efficiency and Battery Life Optimization

A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.