A Survey on Type-3 Fuzzy Logic Systems and Their Control Applications

In this paper,we offer a review of type-3 fuzzy logic systems and their applications in control.The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems.In this case,we review their most important applications in control and other related topics with type-3 fuzzy systems.Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important.This paper reviews the main applications that make use of Intelligent Computing methods.Specifically,type-3 fuzzy logic systems.The aim of this research is to be able to appreciate,in detail,the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques.This is done with the construction and visualization of bibliometric networks,developed with VosViewer Software,which it is a free Java-based program,mainly intended to be used for analyzing and visualizing bibliometric networks.With this tool,we can create maps of publications,authors,or journals based on a co-citation network or construct maps of keywords,countries based on a co-occurrence networks,research groups,etc.

Methanol steam reforming for hydrogen production driven by an atomically precise Cu catalyst

Plasmon-induced hot-electron transfer from metal nanostructures is being intensely pursed in current photocatalytic research,however it remains elusive whether molecular-like metal clusters with excitonic behavior can be used as light-harvesting materials in solar energy utilization such as photocatalytic methanol steam reforming.In this work,we report an atomically precise Cu_(13)cluster protected by dual ligands of thiolate and phosphine that can be viewed as the assembly of one top Cu atom and three Cu_(4)tetrahedra.The Cu_(13)H_(10)(SR)_(3)(PR’_(3))_(7)(SR=2,4-dichlorobenzenethiol,PR’_(3)=P(4-FC_(6)H_(4))_(3))cluster can give rise to highly efficient light-driven activity for methanol steam reforming toward H_(2)production.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain networks.Cross-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium blockchains.However,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous blockchains.The fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy threats.This paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain security.In terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack diffusion.Then various security threats and attack paths faced by the cross-chain system are analyzed.At last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

Three-Way Behavioral Decision Making With Hesitant Fuzzy Information Systems:Survey and Challenges

Three-way decision(T-WD)theory is about thinking,problem solving,and computing in threes.Behavioral decision making(BDM)focuses on effective,cognitive,and social processes employed by humans for choosing the optimal object,of which prospect theory and regret theory are two widely used tools.The hesitant fuzzy set(HFS)captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades.Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together,this paper reviews and examines advances in three-way behavioral decision making(TW-BDM)with hesitant fuzzy information systems(HFIS)from the perspective of the past,present,and future.First,we provide a brief historical account of the three topics and present basic formulations.Second,we summarize the latest development trends and examine a number of basic issues,such as one-sidedness of reference points and subjective randomness for result values,and then report the results of a comparative analysis of existing methods.Finally,we point out key challenges and future research directions.

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH(Noise,Vibration,and Harshness).When analyzing the NVH performance of the vehicle body,the traditional SEA(Statistical Energy Analysis)simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions.In order to effectively solve these shortcomings,based on the analysis of the vehicle noise transmission path,a multi-level objective decomposition architecture of the interior noise at the driver’s right ear is established.Combined with the data-driven method,the ResNet neural network model is introduced.The stacked residual blocks avoid the problem of gradient dis-appearance caused by the increasing network level of the traditional CNN network,thus establishing a higher-precision prediction model.This method alleviates the inherent limitations of traditional SEA simulation design,and enhances the prediction performance of the ResNet model by dynamically adjusting the learning rate.Finally,the proposed method is applied to a specific vehicle model and verified.The results show that the proposed meth-od has significant advantages in prediction accuracy and robustness.

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.

Research on Human-Vehicle-Road Friendliness Based on Improved SH-GH-ADD Control

The hub-driven virtual rail train is a novel urban transportation system that amalgamates the benefits of modern trams and buses.However,this system is plagued by issues such as decreased ride comfort and severe deformation of urban roads due to the increase in sprung mass and long-term rolling at the same position.To address these concerns and improve the human-vehicle-road friendliness of the virtual rail train,we propose an Improved Sky-Ground Hook and Acceleration-Driven Damper control(Improved SH-GH-ADD control)strategy for the semi-active suspension system.This control monitors the vibration acceleration signal of the unsprung mass in real-time and selects the mixed Sky-Hook and Acceleration-Driven Damper(SH-ADD)control or the mixed Ground-Hook and Acceleration-Driven Damper(GH-ADD)control based on the positive and negative values of the vibration acceleration of the unsprung mass.The Improved SH-GH-ADD control combines the advantages of SH-ADD control and GH-ADD control to achieve control of the sprung mass and unsprung mass in the full fre-quency band.Finally,through simulation and comparative analysis with traditional SH-ADD,GH-ADD,and mixed SH-GH control,we demonstrate the exceptional performance of the proposed algorithm.

A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement

Fidelity plays an important role in quantum information processing,which provides a basic scale for comparing two quantum states.At present,one of the most commonly used fidelities is Uhlmann-Jozsa(U-J)fidelity.However,U-J fidelity needs to calculate the square root of the matrix,which is not trivial in the case of large or infinite density matrices.Moreover,U-J fidelity is a measure of overlap,which has limitations in some cases and cannot reflect the similarity between quantum states well.Therefore,a novel quantum fidelity measure called quantum Tanimoto coefficient(QTC)fidelity is proposed in this paper.Unlike other existing fidelities,QTC fidelity not only considers the overlap between quantum states,but also takes into account the separation between quantum states for the first time,which leads to a better performance of measure.Specifically,we discuss the properties of the proposed QTC fidelity.QTC fidelity is compared with some existing fidelities through specific examples,which reflects the effectiveness and advantages of QTC fidelity.In addition,based on the QTC fidelity,three discrimination coefficients d_(1)^(QTC),d_(2)^(QTC),and d_^(3)^(QTC)are defined to measure the difference between quantum states.It is proved that the discrimination coefficient d_(3)^(QTC)is a true metric.Finally,we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.

A review:Multi-hierarchy design strategy of electrocatalysts for energy molecule conversion

Under the new energy resource structure,electrocatalysts are key materials for the development of proton membrane fuel cells,electrolysis of aquatic hydrogen devices,and carbon dioxide reduction equipment,to address energy shortages and even environmental pollution issues.Although controlling the morphology or doping with heteroatoms for catalyst active centers have accelerated the reaction rate,it is difficult to solve the problems of multiple by-products,and poor stability of catalytic sites.From this,it will be seen that single regulation of metal active centers is difficult to comprehensively solve application problems.Orderly assembly and coordination of catalyst multi-hierarchy structures at the mesoscale above the nanometer level probably be more reasonable strategies,and numerous studies in thermal catalysis have supported this viewpoint.This article reviews the multi-hierarchy design of electrocatalyst active centers,high-energy supports,and peripheral structures in recent years,providing unconventional inspiration about electrocatalyst creation,which perhaps serves as a simple tutorial of electrocatalysis exploration for abecedarian.

Junction matters in hydraulic circuit bio-design of microfluidics

Microfluidic channels are at micrometer scales;thus,their fluid flows are laminar,resulting in the linear dependence of pressure drop on flow rate in the length of the channel.The ratio of the pressure drop to flow rate,referred to as resistance,depends on channel size and dynamic viscosity.Usually,a microfluidic chip is analogous to an electric circuit in design,but the design is adjusted to optimize channel size.However,whereas voltage loss is negligible at the nodes of an electric circuit,hydraulic pressure drops at the nodes of microfluidic chips by a magnitude are comparable to the pressure drops in the straight channels.Here,we prove by experiment that one must fully consider the pressure drops at nodes so as to accurately design a precise microfluidic chip.In the process,we numerically calculated the pressure drops at hydraulic nodes and list their resistances in the range of flows as concerned.We resorted to machine learning to fit the calculated results for complex junctions.Finally,we obtained a library of node resistances for common junctions and used them to design three established chips that work for single-cell analysis and for precision allocation of solutes(in gradient and averaging concentration microfluidic networks).Endothelial cells were stimulated by generating concentrations of adriamycin hydrochloride from the last two microfluidic networks,and we analyzed the response of endothelial cells.The results indicate that consideration of junction resistances in design calculation brings experimental results closer to the design values than usual.This approach may therefore contribute to providing a platform for the precise design of organ chips.

Cooperative extended rough attribute reduction algorithm based on improved PSO

Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

Exclusively catalytic oxidation of toluene to benzaldehyde in an O/W emulsion stabilized by hexadecylphosphate acid terminated mixed-oxide nanoparticles

A series of hexadecylphosphate acid(HDPA) terminated mixed-oxide nanoparticles have been investigated to catalyze the oxidation of toluene exclusive to benzaldehyde under mild conditions in an emulsion of toluene/water with the catalysts as stabilizers. With the HDPA-Fe2 O3/Al2 O3 as the basic catalyst, a series of transition metals, such as Mn, Co, Ni, Cu, Cr, Mo, V, and Ti, was respectively doped to the basic catalyst to modify the performance of the catalytic system, in expectation of influencing the mobility of the lattice oxygen species in the oxide catalysts. Under normally working conditions of the catalytic system, the nanoparticles of catalysts located themselves at the interface between the oil and water phases, constituting the Pickering emulsion. Both the doped iron oxide and its surface adsorbed hexadecylphosphate molecules were essential to the catalytic system for excellent performances with high toluene conversions as well as the exclusive selectivity to benzaldehyde. Under optimal conditions, ~83% of toluene conversion and >99% selectivity to benzaldehyde were obtained, using molecular oxygen as oxidant and HDPA-(Fe2 O3-Ni O)/Al2 O3 as the catalyst. This process is green and low cost to produce high quality benzaldehyde from O2 oxidation of toluene.

Surface titanium oxide loaded on a special alumina as high-performance catalyst for reduction of cinnamaldehyde by isopropanol

A nanocomposite catalyst with a nonstoichiometric titanium oxide loaded on a special nanotubular alumina(γ‐Al2O3‐nt)was developed and used to reduce cinnamaldehyde to cinnamyl alcohol with sacrificial isopropanol,i.e.,a Meerwein‐Ponndorf‐Verley type reaction.The deposition process produced a highly disperse layer of titanium oxide on the surface of aγ‐Al2O3‐nt support.After a reduction treatment,the as‐prepared TiOx/γ‐Al2O3‐nt was a highly efficient catalyst for the hydrogen transfer reaction between isopropanol and cinnamaldehyde.Selectivity for cinnamic alcohol was higher than99%and the conversion of cinnamaldehyde was higher than95%.The regular morphology of theγ‐Al2O3‐nt support with homogeneous surface sites and the uniformly dispersed titanium oxide featured a high concentration surface Ti(III)species.These factors contributed to the high performance of the TiOx/γ‐Al2O3‐nt catalyst.

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Catalytic performance of iron oxide loaded on electron-rich surfaces of carbon nitride

Carbon nitride (CN) in CN encapsulated Ni/ Al2O3 (denoted as CN/Ni/Al2O3) catalyst was evidenced previously as a material in electron-rich state and possessed H-2-dissociative adsorption activity due to the electron doping effect from underlying nickel. In this report, iron oxide loaded on the CN/Ni/Al2O3 was synthesized and investigated by Fischer-Tropsch (F-T) synthesis to test the special effect of electron-rich support on the catalytic activity of iron oxide. The Fe/CN/Al2O3 and CN/Ni/Al2O3 samples were accordingly synthesized for comparison. In Fe/CN/Ni/Al2O3, the iron oxide was reduced to magnetite by syngas as evidenced by the in-situ XPS measurements and XRD pattern of used catalyst. Compared with Fe/CN/Al2O3, more light hydrocarbons over Fe/CN/Ni/Al2O3 were observed. It should be understood by the interaction between iron oxide and support mainly due to the effect of electron-rich state and thus enhanced H-2 adsorption ability. In addition, such a novel support facilitated the CO conversion and retarded the water-gas shift reaction and CO2 formation. The new type of adjustment on electronic state should be useful for novel catalyst design. (C) 2016 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights reserved.

Direct conversion of corn cob to formic and acetic acids over nano oxide catalysts

Considering energy shortage, large molecules in corn cob and easy separation of solid catalysts, nano oxides are used to transform corn cob into useful chemicals. Because of the microcrystals, nano oxides offer enough accessible sites for cellulose, hemicellulose and monosaccharide from corn cob hydrolysis and oxidant. Chemical conversion of corn cob to organic acids is investigated over nano ceria, alumina, titania and zirconia under various atmospheres. Liquid products are mainly formic and acetic acids. A small amount of other compounds, such as D-xylose,D-glucose, arabinose and xylitol are also detected simultaneously. The yield of organic acids reaches 25%–29% over the nano oxide of ceria,zirconia and alumina with 3 h reaction time under 453 K and 1.2 MPa O2. The unique and fast conversion of corn cob is directly approached over the nano oxides. The results are comparative to those of biofermentation and offer an alternative method in chemically catalytic conversion of corn cob to useful chemicals in a one-pot chemical process.

Alginate core-shell microcapsule reduces the DMSO addition-induced osmotic damage to cells by inhibiting cellular blebs

In cryopreservation,the addition of cryoprotectant can change the intra-and extra-cellular osmotic pressure,affect the cell morphology,and induce blebs on the plasma membrane.In this study,the blebs of cells microencapsulated in the alginate microsphere induced by osmotic shock were studied,and the effects of microencapsulation on bleb size and cell viability were determined.Firstly,a coaxial co-flow focusing device was applied to generate cell-laden microcapsules using alginate hydrogel in this paper.Then,cellular blebs induced by DMSO with various concentrations under microencapsulation were compared with that when non-encapsulated,and the dynamic process of cellular bleb was investigated.Finally,the qualitative relationship between bleb size and cell viability in the presence of DMSO was built,and thus the effects of microencapsulation on bleb size and viability were evaluated.The results show that the bleb size is smaller and the cell viability is higher,and cell microencapsulation can significantly inhibit the excessively large blebs generated on the cell membrane and reduce the osmotic damage to cells when loading cryoprotectant and then to improve cell viability during cryopreservation.This work can provide insights for optimizing cryoprotectant-loading protocols,offer a new avenue to study cell blebbing,and advance future research on cryopreservation of rare cells and biomaterials.