{"title":"Application of symmetry patterns to recognition of functional sites and phylogenetic analysis of DNA sequences","authors":"N. Akberova, L. Yu","doi":"10.1109/IJSIS.1998.685435","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685435","url":null,"abstract":"The set of genetic texts, taking part in replication initiation has been analyzed from the point of view of their symmetry. The subsets, corresponding to the phenotypical classification, were found to contain the same symmetrical structures. Along with the traditional symmetries (potential hairpins, direct and inverted repeats) the purine-pyrimidine and aminoketo repeats were found at equal distances in related sequences. Among the symmetrical structures the replication protein binding sites have been identified as well as sites taking part in the transcription initiation. The results obtained show the efficiency of symmetrical analysis in solving the problem of pattern recognition in genetic text.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122530638","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}
D. Kortenkamp, M. MacMahon, D. Ryan, R. Bonasso, L. Moreland
{"title":"Applying a layered control architecture to a free-flying space camera","authors":"D. Kortenkamp, M. MacMahon, D. Ryan, R. Bonasso, L. Moreland","doi":"10.1109/IJSIS.1998.685442","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685442","url":null,"abstract":"Describes a prototype robot for space applications. The robot is the first generation of a free-flying robotic camera that will assist astronauts in constructing and maintaining the space station. The goal of the robot is to provide remote views to astronauts and ground controllers. The robot can autonomously move to locations on an air bearing table, can track targets and can plan paths. The robot contains a large collection of different technologies and this paper concentrates on a layered control architecture that integrates all robot functions.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133709478","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":"Control and diagnosis of electrical drives: some applications by using neural networks","authors":"M. Cirrincione","doi":"10.1109/IJSIS.1998.685447","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685447","url":null,"abstract":"Some applications of neural networks to the control and diagnosis of electrical drives are presented. In the first part a direct inverse control scheme is presented for controlling a DC motor, which is based on a clustering neural network, called the progressive learning network (PLN) because of its inherent capacity of learning online. This approach can control the whole system without having to use a very rich training set; moreover it is able to adapt itself online to new working conditions by varying the number of neurons. In the second part of the paper some applications of self-organising neural networks are described for the diagnosis of AC drives. In particular it is shown that the vector quantisation projection algorithm can be useful for diagnosis purposes since it permits an easier representation of the output space than that available with the Kohonen's map.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134468927","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":"Smoothing images by a probability filter","authors":"Min-Cheng Pan, A. Lettington","doi":"10.1109/IJSIS.1998.685472","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685472","url":null,"abstract":"Image smoothing is a useful and necessary part of image processing. Both the median and mean filters are popular smoothing methods, but there can be problems when these two filters are applied to an image corrupted by unknown noise. We present a spatial domain probability filter which achieves the advantages of median and mean filters. The aim of our probability filter is to provide a compromise between the two more conventional filters. We present experimental results and performance evaluation of the median, mean filters and this probability filter operating on an image corrupted by impulse-like or random noise.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132324639","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":"Probability estimation in hybrid NN-HMM speech recognition systems with real-time neural networks","authors":"S. Georgescu","doi":"10.1109/IJSIS.1998.685486","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685486","url":null,"abstract":"FAPES system is based on a specialized fuzzy ARTMAP NN trained to estimate the observation probabilities in continuous parameter HMM (CHMM) speech recognition systems. The fuzzy ARTMAP classifier transfers after ART resonance, the choice function of all eligible nodes to a single layer perceptron (SLP) defuzzifier. There, the fuzzy scores are mapped to the a-posteriori probabilities of visiting CHMM states. Lower computing time results from estimating observation probabilities with such local error propagation NN, than with well-known multilayer perceptron. The fuzzy ARTMAP NN determines inherent discrimination among generated probabilities, discrimination usually added into HMM training by using the complex MMIE algorithm. Iterative training has been used to instruct the fuzzy ARTMAP and the SLP defuzzifier. The CHMM component was not trained due to small changes of transition probabilities.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201638","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":"Computing similarity between RNA secondary structures","authors":"Kaizhong Zhang","doi":"10.1109/IJSIS.1998.685429","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685429","url":null,"abstract":"The primary structure of a ribonucleic acid (RNA) molecule is a sequence of nucleotides (bases) over the four-letter alphabet {A,C,G,U}. The secondary structure of an RNA is a set of base-pairs (nucleotide pairs) which formed bonds between A-U and C-G. These bonds have been traditional assumed to be non-crossing in the secondary structure. This implies a tree representation of the secondary structure of RNA molecule. This paper considers several notions of similarity between two RNA molecule structures taking into account both the primary and the secondary structures. We consider a natural tree representation with both primary and secondary structure data. We present efficient algorithms for comparing such tree representation. We then show that some of these similarity notions can be used to solve the structure prediction problem when the structure of a closely related RNA is known.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155237","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":"Using neural networks for wine identification","authors":"M. Gaeta, M. Marsella, S. Miranda, S. Salerno","doi":"10.1109/IJSIS.1998.685488","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685488","url":null,"abstract":"This paper presents a study and research on mathematical models for the classification of wine absorption spectrum characteristics in order to improve some critical aspects of the production in the bottling process. The classifier model described, based on neural networks, performs both recognition and classification of various typologies of wine produced from the wine firm \"M. Mastroberardino\" of Atripalda.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126345129","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":"An application of genetic algorithm to DNA sequencing by oligonucleotide hybridization","authors":"H. Douzono, S. Hara, Y. Noguchi","doi":"10.1109/IJSIS.1998.685424","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685424","url":null,"abstract":"The authors propose a sequencing algorithm for oligonucleotide hybridization using the genetic algorithm. The target DNA sequence reconstructed by the hybridization method is relatively long for genetic algorithm (GA), so special setups of the genetic operation are necessary. The authors introduce the grouping GA and a special crossover method for this problem. They carried out some experiments of sequence reconstruction, and examined the reconstructed sequences by comparing the motif length subsequences.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124173214","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":"Issues in text-to-speech synthesis","authors":"M. Macchi","doi":"10.1109/IJSIS.1998.685467","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685467","url":null,"abstract":"The ultimate goal of text-to-speech synthesis is to convert ordinary orthographic text into an acoustic signal that is indistinguishable from human speech. Originally, synthesis systems were architected around a system of rules and models that were based on research on human language and speech production and perception processes. The quality of speech produced by such systems is inherently limited by the quality of the rules and the models. Given that our knowledge of human speech processes is still incomplete, the quality of text-to-speech is far from natural-sounding. Hence, today's interest in high quality speech for applications, in combination with advances in computer resource, has caused the focus to shift from rules and model-based methods to corpus-based methods that presumably bypass rules and models. For example, many systems now rely on large word pronunciation dictionaries instead of letter-to-phoneme rules and large prerecorded sound inventories instead of rules predicting the acoustic correlates of phonemes. Because of the need to analyze large amounts of data, this approach relies on automated techniques such as those used in automatic speech recognition.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166676","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":"An obstacle detection system using a light stripe identification based method","authors":"J. Haverinen, J. Roning","doi":"10.1109/IJSIS.1998.685450","DOIUrl":"https://doi.org/10.1109/IJSIS.1998.685450","url":null,"abstract":"A light stripe tracking and identification method is proposed for a structured light based obstacle detection system operating in an outdoor environment. The method makes the structured light based detection system more robust and applicable to use outdoors as aid for navigation. The method differentiates between the structured light produced by a light stripe projector and the light stripe kind patterns caused by ambient illumination. The centre of gravity of the segmented light stripe is tracked by using a Kalman filter. The position information together with the other properties of the stripe segment, including intensity, length and orientation are used to identify the same light stripe segment in adjacent images. By using a pulsed light source it is possible to differentiate between true and false light stripes depending on their time of appearance. During the project, a working obstacle detection system for a partly structured outdoor environment was implemented.","PeriodicalId":289764,"journal":{"name":"Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980581","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}