2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing最新文献

{"title":"On abstract intelligence and brain informatics: Mapping the cognitive functions onto the neural architectures","authors":"Yingxu Wang","doi":"10.1109/ICCI-CC.2012.6311158","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311158","url":null,"abstract":"Summary form only given. A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neural architectures. According to cognitive informatics and abstract intelligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science are used to explain that the brain works in a certain way based on empirical observations of corresponding activities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational science and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vigor and empirical approach without the support of a formal model and a rigorous means. In order to formally explain the architectures and functions of the brain, as well as their intricate relations and interactions, systematic models of the brain are sought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of the brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics and cognitive psychology. A logical model of the brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of the abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated.The logical model of the brain and the abstract intelligence theory of the natural intelligence will enable the development of cognitive computers that perceive, think and learn. The functional and theoretical difference between cognitive computers and classic computers are that the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wide range of applications of the cognitive co","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blind MMSE multi-user detection based on NOOja algorithm","authors":"Junlin Zhang, Ling Nie","doi":"10.1109/ICCI-CC.2012.6311206","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311206","url":null,"abstract":"A new blind adaptive MMSE multi-user detection (MUD) based on subspace tracking is presented. The new detector doesn't employ signal eigenvalue estimation but the signal subspace estimation, and it avoids performance deterioration induced by eigenvalue estimation error. The proposed MUD exploits the normalized orthogonal Oja (NOOja) subspace tracking algorithm for subspace estimation, since it guarantees the orthogonality of the weight matrix spanned by the singnal subspace in every iteration, which must be meet in the new detector. The simulation results the proposed MMSE detector has faster convergence rate, better output SINR (signal-to-interference-and-noise ratio) and bit error rate (BER) and lower the computational complexity.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133958323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The testing and analysis of the centrifugal high-speed atomizer","authors":"Y. Ding, G. Hu","doi":"10.1109/ICCI-CC.2012.6311184","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311184","url":null,"abstract":"The centrifugal high-speed atomizer is running at high speed, it makes micron liquid-drop in order that chemical reaction happened between micron drop and harmful gas. Its function has direct impact to the emission produced from WTE plant. Meanwhile, the emission from the plant must meet the standard. The vibration of atomize is the very important factor, is also the most important data to carry out real-time control on the atomizer. So, to test and analyze vibration of atomizer is very important. This paper gives the result of test about atomizer used in 650T/D WTE plant.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123641687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Ersatz Brain Project: A brain-like computer architecture for cognition","authors":"J.A. Anderson, Paul D. Allopenna, G. Guralnik, Daniel Ferrente, John A. Santini","doi":"10.4018/jcini.2012100102","DOIUrl":"https://doi.org/10.4018/jcini.2012100102","url":null,"abstract":"The Ersatz Brain Project is an attempt to develop programming techniques and software applications for a brain-like computing system. Its brain-like hardware architecture design is based on a select set of ideas taken from the anatomy of mammalian neo-cortex. In common with other such attempts it is based on a massively parallel, two-dimensional array of CPUs and their associated memory. The design used in this project (1) uses an approximation to cortical computation called the network of networks which holds that the basic computing unit in the cortex is not a single neuron but groups of neurons working together in attractor networks; (2) assumes connections and data representations in cortex are sparse; (3) makes extensive use of local lateral connections and topographic data representations, and (4) scales in a natural way from small groups of neurons to the involvement of entire cortical regions.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126015288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory temporal data mining process in hospital information systems","authors":"S. Tsumoto, H. Iwata, S. Hirano, Y. Tsumoto","doi":"10.1109/ICCI-CC.2012.6311140","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311140","url":null,"abstract":"This paper proposes an exploratory temporal data mining process which aims at capturing behavior of medical staff. The process consists of the following four process. First, datasets will be extracted from hospital information systems through double-step datawarehousing. Second, similarities between temporal sequences are calculated from datasets. Third, data mining methods such as clustering, multidimensional scaling are applied for obtaining the class labels. Finally, other data mining methods, such as decision tree and correspondence analysis are applied to original data sets with the class labels.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130353112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of book purchasing model based on improved genetic neural network","authors":"Runhua Wang, Yi Tang, Guoquan Liu, Lei Li","doi":"10.1109/ICCI-CC.2012.6311188","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311188","url":null,"abstract":"A modeling method based on genetic neural network used for book purchasing is put forward on account of lacking of a set of scientific and uniform purchasing mode and model in current book purchasing process. This method improves standard genetic algorithm first, and then uses the improved standard genetic algorithm as a method of feed forward neural network training and threshold value of feed forward neural network weight adjustment, after that, explores potential relationship between various properties of book and whether it is purchased or not through optimized neural network, thereby to realize the forecast classification whether the book should be purchased or not. Simulation experiment shows good forecast performance and generalization ability of the book purchasing model, thus it is worth for promotion.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128334706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless brain signal acquisition circuits for body sensor network","authors":"Shuenn-Yuh Lee, Jia-Hua Hong, Liang-Hung Wang","doi":"10.1109/ICCI-CC.2012.6311129","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311129","url":null,"abstract":"The paper presents the proposed wireless brain signal acquisition circuits for body sensor network. Considering the power-efficient communication in the body sensor network, the required low-power analog integrated circuits (ICs) are developed for a wireless brain signal acquisition system. To acquire the electroencephalogram (EEG) signal, this paper proposes an analog front-end (AFE) circuit, including only one low-noise amplifier with chopping techniques and one high-pass sigma-delta modulator (HPSDM), which can be applied as a sensing circuit for EEG signal acquisition systems. To transmit the EEG signal through wireless communication, a quadrature CMOS voltage-controlled oscillator and a 2.4 GHz direct-conversion transmitter with a power amplifier and up-conversion mixer are also developed. In the receiver, a 2.4 GHz fully integrated CMOS radio-frequency front-end is also implemented. The circuits have been implemented to fit the specifications of the IEEE 802.15.4 2.4 GHz standard. The low-power ICs of the wireless EEG acquisition systems have been fabricated using a 0.18 μm TSMC CMOS standard process. The measured results reveal that the proposed low-power analog front-end ICs can be used for the wireless brain signal acquisition.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128234542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting river runoff through Support Vector Machines","authors":"Bryan Bell, Brian Wallace, Du Zhang","doi":"10.1109/ICCI-CC.2012.6311127","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311127","url":null,"abstract":"How “wet” or “dry” a year is predicted to be has many impacts. Public utilities need to determine what percentage of their electric energy generation will be hydro power. Good water years enable the utilities to use more hydro power and, consequently, save oil. Conversely, in a dry year, the utilities must depend more on steam generation and therefore use more oil, coal, and atomic fuel. Agricultural interests use the information to determine crop planting patterns, ground water pumping needs, and irrigation schedules. Operators of flood control projects determine how much water can safely be stored in a reservoir while reserving space for predicted inflows. Municipalities use the information to evaluate their water supply and determine whether (in a dry year) water rationing may be needed. Currently a combination of linear regression equations and human judgment is used for producing these forecasts. In this paper, we describe a Support Vector Machine based method for river runoff forecasting. Our method uses Smola/Scholkopf's Sequential Minimal Optimization algorithm for training a Support Vector Machine with a RBF kernel. The experimental results on predicting the full natural flow of the American River at the Folsom Dam measurement station in California indicates that our method outperforms the current forecasting practices.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"59 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Apply genetic algorithm with an adaptive stopping criterion to PCR-RFLP Primer Design","authors":"Yu-Huei Cheng, Li-Yeh Chuang, Cheng-Hong Yang","doi":"10.1109/ICCI-CC.2012.6311176","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311176","url":null,"abstract":"Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is usually applied to small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphisms (SNPs). Before performing PCR-RFLP for SNP genotyping, the feasible primer pair and the available restriction enzymes for discriminating the target SNP are required. This is a tedious and time-consuming task when using manual search without any computer assisting. Genetic Algorithm (GA) has been widely applied to many fields and yielded good solutions. However, in many used GAs, the number of generations are usually fixed and make them are inefficient in determining their adequate terminations. In this paper, we use GA with an adaptive stopping criterion to implement the PCR-RFLP primer design. The different numbers of generations are used to perform the PCR-RFLP primer design, and the adaptive generations are also observed and discussed.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Cancelable Biometrics","authors":"Padma Polash Paul, M. Gavrilova","doi":"10.1109/ICCI-CC.2012.6311208","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2012.6311208","url":null,"abstract":"Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains. It has to do with actual biometric characteristics of users being permanent, and their number being limited. Thus, if user's biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. Cancellable biometric for individual biometric has been a significantly understudied problem. The concept of cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that users can change their single biometric template in a biometric security system. However, cancelability in multimodal biometric has been barely addressed at all. In this paper, we tackle the problem and present a novel solution for cancelable biometrics in multimodal system. We develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on multi-modal face and ear database.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124588358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}