A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

A situation awareness assessment method based on fuzzy cognitive maps

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

Using concept maps in cognitive treatment for children with developmental coordination disorder

Children with Developmental Coordination Disorder (DCD) often seem to possess a narrow repertoire of cognitive strategies. In particular, they have difficulties in learning and internalizing the rules and strategies that other people intuitively use to approach common everyday problems. As a result, they often appear to have organizational, planning, memory and learning difficulties. The article proposes using a Concept Map (CM) as a visual strategy to facilitate interaction between a child with DCD, his/her family and therapist, as reflected in Client Centred and cognitive approaches. The CM is used as a method of assisting the child to identify, develop and utilize cognitive strategies in order to manage daily tasks effectively, as a tool in organizing his own therapy and in order to encourage participation. A demonstration of the concept mapping usefulness is brought by a case report. Further uses of concept mapping as a useful strategy within the framework of intervention remain to be studied.

Using fuzzy cognitive maps to model performance measurement system of Internet-based supply chain

Fuzzy cognitive maps （FCM） is a well-established artificial intelligence technique, which can be effectively applied in the domains of performance measurement, decision making and other management science. FCM can be a useful tool in a group decision-making environment by using scientifically integrated expert knowledge. The theories of FCM and balance scorecard （BSC） both emphasize cause-and-effect relationships among indicators in a complex system, but few reports have been published addressing the combined application of these two techniques. In this paper we propose a FCM simulation model for the sample performance measurement system of Intemet-based supply chain, which is constructed by BSC theory. We gave examples to explain how FCM can be adapted to execute the causal mechanism of BSC, and also how FCM can support group decision-making and forecasting in performance measurement.

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated soil prediction tool helps to considerably improve the efficacy of the farmers.At the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive mapping.In other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering phase.In this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classifica-tion(FCMCSO-ASC)technique.The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil.To accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature vectors.In addition,the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm.For exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

A New Control Strategy for Modeling Wind Energy Systems Using Fuzzy Cognitive Maps

Wind energy is currently a fast-growing interdisciplinary field that encompasses many different branches of engineering and science. Modeling and controlling wind energy systems are difficult and challenging problems. The basic structure of wind turbines and some wind control system methods are briefly reviewed. The need for using advanced theories from fuzzy and intelligent systems in studying wind energy systems is identified and justified. FCMs （fuzzy cognitive maps） are used to model wind energy systems. Simulation studies are performed and obtained results are discussed. A new mathematical approach has been proposed to model dynamical complex systems, the DYFUKN （dynamic fuzzy knowledge networks）. Many open problems in the areas of modeling and controlling wind energy systems are outlined.

CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

Application of fuzzy cognitive map in information intelligent push

Since computer system functions are becoming increasingly complex, the user has to spend much more time on the process of seeking information, instead of utilizing the required infor- mation. Information intelligent push technology could replace the traditional method to speed up the information retrieval process. The fuzzy cognitive map has strong knowledge representation ability and reasoning capability. Information intelligent push with the basis on fuzzy cognitive map could ab- stract the computer user＇ s operations to a fuzzy cognitive map, and infer the user＇ s operating inten- tions. The reasoning results will be translated into operational events, and drive the computer system to push appropriate information to the user.

Application of Cognitive Map in Campus Environment Evaluation

On the basis of interpreting concept and features of cognitive map, this study took landscape investigation in Tianjin Agricultural University for example to analyze the application of cognitive map on campus. By analyzing five major elements (path, edge, node, district and landmark), the direction of and suggestions for campus reform were given as the reference for applying cognitive map in campus environment research.

Cognitive Mapping: Testing Application of Causal Algebra

This paper takes the basic mathematical operations required to manipulate the cognitive maps. This paper start by presenting all the values that a causal relationship can take. By the using of causal algebra, cognitive map (CC) become not only a graphical representation of a person’s beliefs, an agent or a particular area but also can capture the causal relationships existing between the concepts of a given system in a simple manner. Cognitive maps do not use a conventional algebra;algebra causal is needed to treat.

A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus

The entorhinal-hippocampus structure in the mammalian brain is the core area for realizing spatial cognition.However,the visual perception and loop detection methods in the current biomimetic robot navigation model still rely on traditional visual SLAM schemes and lack the process of exploring and applying biological visual methods.Based on this,we propose amap constructionmethod thatmimics the entorhinal-hippocampal cognitive mechanismof the rat brain according to the response of entorhinal cortex neurons to eye saccades in recent related studies.That is,when mammals are free to watch the scene,the entorhinal cortex neurons will encode the saccade position of the eyeball to realize the episodicmemory function.The characteristics of thismodel are as follows:1)A scenememory algorithmthat relies on visual saccade vectors is constructed to imitate the biological brain’s memory of environmental situation information matches the current scene information with the memory;2)According to the information transmission mechanism formed by the hippocampus and the activation theory of spatial cells,a localization model based on the grid cells of the entorhinal cortex and the place cells of the hippocampus was constructed;3)Finally,the scene memory algorithm is used to correct the errors of the positioning model and complete the process of constructing the cognitive map.The model was subjected to simulation experiments on publicly available datasets and physical experiments using a mobile robot platform to verify the environmental adaptability and robustness of the algorithm.The algorithm will provide a basis for further research into bionic robot navigation.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems:A Cognitive Mapping Approach

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

QoS-based MAC protocol for cognitive radio networks

In order to improve the throughput performance of the secondary users （SUs） in the cognitive radio （CR） environment, a quality of service （QoS） based media access control （MAC） protocol is proposed. In this protocol, the CR node maps the channel state as a vector, and the transmitter and the receiver obtain the final channel map through an AND operation to prepare for an optional channel set. Data from the upper application layer are classified into two priority levels according to the QoS requirement. The data of each level relate to different contention windows so that the priority of real time data can be guaranteed. A two-dimensional discrete-time Markov chain is utilized to evaluate the system performance, and mathematical expressions of the system throughput are derived. Simulation results show that compared with the IEEE 802. 11 distributed coordination function （DCF）, the proposed MAC protocol can achieve higher throughput.

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

The neuroimaging of neurodegenerative and vascular disease in the secondary prevention of cognitive decline

Structural brain changes indicative of dementia occur up to 20 years before the onset of clinical symptoms. Efforts to modify the disease process after the onset of cognitive symptoms have been unsuccessful in recent years. Thus, future trials must begin during the preclinical phases of the disease before symptom onset. Age related cognitive decline is often the result of two coexisting brain pathologies: Alzheimer's disease(amyloid, tau, and neurodegeneration) and vascular disease. This review article highlights some of the common neuroimaging techniques used to visualize the accumulation of neurodegenerative and vascular pathologies during the preclinical stages of dementia such as structural magnetic resonance imaging, positron emission tomography, and white matter hyperintensities. We also describe some emerging neuroimaging techniques such as arterial spin labeling, diffusion tensor imaging, and quantitative susceptibility mapping. Recent literature suggests that structural imaging may be the most sensitive and cost-effective marker to detect cognitive decline, while molecular positron emission tomography is primarily useful for detecting disease specific pathology later in the disease process. Currently, the presence of vascular disease on magnetic resonance imaging provides a potential target for optimizing vascular risk reduction strategies, and the presence of vascular disease may be useful when combined with molecular and metabolic markers of neurodegeneration for identifying the risk of cognitive impairment.

Animal Idioms＇ Teaching in Cognitive Perspective——from the Contrastive Analysis on Chinese ＂Hu＂ Idioms and English ＂Lion＂ Idioms

In the actual idioms teaching, most students feel that idioms are difficult to comprehend, memorize and make actual application just because the overall meanings of idioms are unpredictable. Since cognitive linguistics is based on embodiment, it can change the abstract things into comparatively concrete things, which provides a shortest way to grasp these idioms. Actually, most idioms are based on conceptual metaphor and metonymy composed of conceptual mappings between the source domain and the target one. Therefore, this paper is trying to probe into the role of cognitive principles in idioms teaching, especially animal idioms from the contrastive analysis on Chinese ＂hu＂ idioms and English ＂lion＂ idioms.

On the Cognitive Mechanism of Grammatical Metaphor

The previous studies of grammatical metaphor mainly emphasized its metafunctions or the classifications and identifications in discourse, and treated rankshift as its mechanism of occurrence. However, the question is ＂Why does the rankshift make it possible？＂ This paper is going to study grammatical metaphor from a cognitive perspective and will treat cross-grammatical domain mapping as its mechanism of occurrence.

Teaching College English Grammar in a Cognitive Task-Based Approach

Teaching College English in a cognitive task-based approach is actually a way to incorporate the grammar teaching within the well-sequenced activities. In this approach, language teachers draw learners＇ attention to the grammatical structures and try to be explicit about the relation between the form and the function either to confirm or to correct learners＇ hypotheses and develop their cognitive form-meaning mapping. The tasks should be designed to allow the structure to happen incidentally rather than give an overt explanation of rules. By the individualized mapping of meaning to the forms in the real meaningful context, the learners＇ initiatives in learning grammar would be greatly triggered.