Search results for: Insulation Patterns

Authors: Toshio Akimitsu, Yoichi Okabe, Akira Hirose

Abstract:

Many experimental results suggest that more precise spike timing is significant in neural information processing. We construct a self-organization model using the spatiotemporal pat-terns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. We show that, for highly syn-chronized inputs, the fluctuation of conduction delays causes globally continuous and locally distributed firing patterns through the self-organization.

Keywords: Self-organization, synfire-chain, Spike-Timing DependentPlasticity, distributed information representation.

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Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

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In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, Gaussian processes, robot control learning, tracked vehicles.

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Authors: Kang, Eun Sung, Song, Hyeong Wook, Kim, Hong Kyu

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The purpose of this study is to analyze the visual preference of patterns in pedestrian roads. In this study, animation was applied for the estimation of dynamic streetscape. Six patterns of pedestrian were selected in order to analyze the visual preference. The shapes are straight, s-curve, and zigzag. The ratio of building's height and road's width are 2:1 and 1:1. Twelve adjective pairs used in the field investigation were selected from adjectives which are used usually in the estimation of streetscape. They are interesting-boring, simple-complex, calm-noisy, open-enclosed, active-inactive, lightly-depressing, regular-irregular, unique-usual, rhythmic-not rhythmic, united-not united, stable-unstable, tidy-untidy. Dynamic streetscape must be considered important in pedestrian shopping mall and park because it will be an attraction. So, s-curve pedestrian road, which is the most beautiful as a result of this study, should be designed in this area. Also, the ratio of building's height and road's width along pedestrian road should be reduced.

Keywords: Visual preference, streetscape, animation, simulation, pedestrian.

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. 

Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.

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Authors: Chen Bingru

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This study is intended to describe and analyze the effects of polysyllabic shortening and word or phrase boundary on the duration patterns of spoken utterances by Mandarin learners of English in comparison with native speakers of English. To investigate the relative contribution of these effects, two production experiments were conducted. The study included 11 native British English speakers and 20 Mandarin learners of English who were asked to produce four sets of tokens consisting of a mono-syllabic base form, disyllabic, and trisyllabic words derived from the base by the addition of suffixes, and a set of short sentences with a particular combination of phrase size, stress pattern, and boundary location. The duration of words and segments was measured, and results from the data analysis suggest that the amount of polysyllabic shortening and the effect of word or phrase position are likely to affect a Chinese accent for Mandarin ESL speakers. This study sheds light on research on the duration patterns of language by demonstrating the effect of duration-related factors on the foreign accent of Mandarin ESL speakers. It can also benefit both L2 learners and language teachers by increasing their sensitivity to the duration differences and difficulties experienced by L2 learners of English. An understanding of the amount of polysyllabic shortening and the effect of position in words and phrase on syllable duration can also facilitate L2 teachers to establish priorities for teaching pronunciation to ESL learners.

Keywords: Duration patterns, Chinese accent, Mandarin ESL speakers, polysyllabic shortening.

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Authors: Wanli Ma, Dat Tran, Dharmendra Sharma

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The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.

Keywords: Artificial Immune Systems, ComputationalIntelligence, Negative Selection, Pattern Discovery.

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Authors: K. L. Wong, I. N. Umar

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Online forum is part of a Learning Management System (LMS) environment in which students share their opinions. This study attempts to investigate the perceptions of students towards online forum and their patterns of listening behavior during the forum interaction. The students’ perceptions were measured using a questionnaire, in which seven dimensions were used involving online experience, benefits of forum participation, cost of participation, perceived ease of use, usefulness, attitude, and intention. Meanwhile, their patterns of listening behaviors were obtained using the log file extracted from the LMS. A total of 25 postgraduate students undertaking a course were involved in this study, and their activities in the forum session were recorded by the LMS and used as a log file. The results from the questionnaire analysis indicated that the students perceived that the forum is easy to use, useful, and bring benefits to them. Also, they showed positive attitude towards online forum, and they have the intention to use it in future. Based on the log data, the participants were also divided into six clusters of listening behavior, in which they are different in terms of temporality, breadth, depth and speaking level. The findings were compared to previous clusters grouping and future recommendations are also discussed.

Keywords: e-learning, learning management system, listening behavior, online forum.

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Authors: Manal Hassan Abdel Aziz, Fatma Mohamed Magdy Badr El Dine, Nourhan Mohamed Mohamed Saeed

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Establishing association between lip print patterns and those of fingerprints as well as blood groups is of fundamental importance in the forensic identification domain. The first aim of the current study was to determine the prevalent types of ABO blood groups, lip prints and fingerprints patterns in both studied populations. Secondly, to analyze any relation found between the different print patterns and the blood groups, which would be valuable in identification purposes. The present study was conducted on 60 healthy volunteers, (30 males and 30 females) from each of the studied population. Lip prints and fingerprints were obtained and classified according to Tsuchihashi's classification and Michael Kuchen’s classification, respectively. The results show that the ulnar loop was the most frequent among both populations. Blood group A was the most frequent among Egyptians, while blood groups O and B were the predominant among Malaysians. Significant relations were observed between lip print patterns and fingerprint (in the second quadrant for Egyptian males and the first one for Malaysian). For Malaysian females, a statistically significant association was proved in the fourth quadrant. Regarding the blood groups, 89.5% of ulnar loops were significantly related to blood group A among Egyptian males. The results proved an association between the fingerprint pattern and the lip prints, as well as between the ABO blood group and the pattern of fingerprints. However, further researches with larger sample sizes need to be directed to approve the current results.

Keywords: ABO, cheiloscopy, dactylography, Egyptians, Malaysians.

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Authors: Thammasak Thianniwet, Satidchoke Phosaard, Wasan Pattara-Atikom

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We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists- judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. Then the ratings and velocity were fed into a decision tree learning model (J48). We successfully extracted vehicle movement patterns to feed into the learning model using a sliding windows technique. The parameters capturing the vehicle moving patterns and the windows size were heuristically optimized. The model achieved accuracy as high as 99.68%. By implementing the model on the existing traffic report systems, the reports will cover comprehensive areas. The proposed method can be applied to any parts of the world.

Keywords: intelligent transportation system (ITS), traffic congestion level, human judgment, decision tree (J48), geographic positioning system (GPS).

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Authors: Seyed Ali Mir Heydari, Mohsen Sayadiharikandeh

Abstract:

Static analysis of source code is used for auditing web applications to detect the vulnerabilities. In this paper, we propose a new algorithm to analyze the PHP source code for detecting LFI and RFI potential vulnerabilities. In our approach, we first define some patterns for finding some functions which have potential to be abused because of unhandled user inputs. More precisely, we use regular expression as a fast and simple method to define some patterns for detection of vulnerabilities. As inclusion functions could be also used in a safe way, there could occur many false positives (FP). The first cause of these FP-s could be that the function does not use a usersupplied variable as an argument. So, we extract a list of usersupplied variables to be used for detecting vulnerable lines of code. On the other side, as vulnerability could spread among the variables like by multi-level assignment, we also try to extract the hidden usersupplied variables. We use the resulted list to decrease the false positives of our method. Finally, as there exist some ways to prevent the vulnerability of inclusion functions, we define also some patterns to detect them and decrease our false positives.

Keywords: User-supplied Variables, hidden user-supplied variables, PHP vulnerabilities.

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Authors: Zhaoyu Chen

Abstract:

In this study, an attempt was made to find reasons why tourists go to particular attractions. Tourists may be either motivated by the attractions or simply make the choice to satisfy their needs and desires. Based on the attractions in Hong Kong, this research was conducted to explore the attraction-related concepts to discuss how the attraction system works. Due to the limited studies on exploring the attractiveness of attractions through tourist movement patterns, the study aims to evaluate such indicators to determine whether tourists are motivated by attractiveness or their own needs. The investigation is conducted through the comparison of different source markets - Mainland China, short haul markets (excluding Mainland China) and long haul markets. The latest finding of Departing Visitor Survey (DVS) implemented by the Hong Kong Tourism Board (HKTB) is employed for the analysis. Various tourist movement patterns are drawn from the practical data. The managerial implication to destination management organizations (DMOs) is suggested to better allocate attractions according to the needs of tourists.

Keywords: Attractions, attraction system, Hong Kong, tourist movement patterns.

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Authors: Simone C. F. Neves, Lúcio F. A. Campos, Ewaldo Santana, Ginalber L. O. Serra, Allan K. Barros

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We propose a method for discrimination and classification of ovarian with benign, malignant and normal tissue using independent component analysis and neural networks. The method was tested for a proteomic patters set from A database, and radial basis functions neural networks. The best performance was obtained with probabilistic neural networks, resulting I 99% success rate, with 98% of specificity e 100% of sensitivity.

Keywords: Cancer ovarian, Proteomic patterns in serum, independent component analysis and neural networks.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

Abstract:

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.

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Authors: Ho-Dae Chong, Jong-Kil Kim

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Collaborative research has become more prevalent and important across disciplines because it stimulates innovation and interaction between scholars. Seeing as existing studies relatively disregarded the institutional conditions triggering collaborative research, this work aims to analyze the changing trend in collaborative work patterns among Korean social scientists. The focus of this research is the performance of social scientists who received research grants through the government’s Social Science Korea (SSK) program. Using quantitative statistical methods, collaborative research patterns in a total of 2,354 papers published under the umbrella of the SSK program in peer-reviewed scholarly journals from 2013 to 2016 were examined to identify changing trends and triggering factors in collaborative research. A notable finding is that the share of collaborative research is overwhelmingly higher than that of individual research. In particular, levels of collaborative research surpassed 70%, increasing much quicker compared to other research done in the social sciences. Additionally, the most common composition of collaborative research was for two or three researchers to conduct joint research as coauthors, and this proportion has also increased steadily. Finally, a strong association between international journals and co-authorship patterns was found for the papers published by SSK program researchers from 2013 to 2016. The SSK program can be seen as the driving force behind collaboration between social scientists. Its emphasis on competition through a merit-based financial support system along with a rigorous evaluation process seems to have influenced researchers to cooperate with those who have similar research interests.

Keywords: Co-authorship, collaboration, competition, cooperation, Social Science Korea, policy.

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Authors: F. J. Sowiński, M. Dziubiński

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In the study the influence of the physical-chemical properties of a liquid, the width of a channel gap and the superficial liquid and gas velocities on the patterns formed during two phase flows in vertical, narrow mini-channels was investigated. The research was performed in the channels of rectangular cross-section and of dimensions: 15 x 0.65 mm and 7.5 x 0.73 mm. The experimental data were compared with the published criteria of the transitions between the patterns of two-phase flows.

Keywords: Two-phase flow, flow regimes, mini-channel, viscosity.

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Authors: Suwimon Saneewong Na Ayuttaya, Chainarong Chaktranond, Phadungsak Rattanadecho

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This study numerically investigates the effects of Electrohydrodynamic on flow patterns and heat transfer enhancement within a cavity which is on the lower wall of channel. In this simulation, effects of using ground wire and ground plate on the flow patterns are compared. Moreover, the positions of electrode wire respecting with ground are tested in the range of angles θ = 0 - 180^o. High electrical voltage exposes to air is 20 kV. Bulk mean velocity and temperature of inlet air are controlled at 0.1 m/s and 60 ^OC, respectively. The result shows when electric field is applied, swirling flow is appeared in the channel. In addition, swirling flow patterns in the main flow of using ground plate are widely spreader than that of using ground wire. Moreover, direction of swirling flow also affects the flow pattern and heat transfer in a cavity. These cause the using ground wire to give the maximum temperature and heat transfer higher than using ground plate. Furthermore, when the angle is at θ = 60^o, high shear flow effect is obtained. This results show high strength of swirling flow and effective heat transfer enhancement.

Keywords: Swirling Flow, Heat Transfer, Electrohydrodynamic, Numerical Analysis.

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Authors: Katalina Grigorova, Kaloyan Mironov

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The paper focuses on the benefits of business process modeling. Although this discipline is developing for many years, there is still necessity of creating new opportunities to meet the ever increasing users’ needs. Because one of these needs is related to the conversion of business process models from one standard to another, the authors have developed a converter between BPMN and EPC standards using workflow patterns as intermediate tool. Nowadays there are too many systems for business process modeling. The variety of output formats is almost the same as the systems themselves. This diversity additionally hampers the conversion of the models. The presented study is aimed at discussing problems due to differences in the output formats of various modeling environments.

Keywords: Business process modeling, business process modeling standards, workflow patterns, converting models.

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Authors: Jia-Rong Yeh, Tzu-Yu Lin, Jiann-Shing Shieh, Yun Chen

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A mammal-s body can be seen as a blood vessel with complex tunnels. When heart pumps blood periodically, blood runs through blood vessels and rebounds from walls of blood vessels. Blood pressure signals can be measured with complex but periodic patterns. When an artery is clamped during a surgical operation, the spectrum of blood pressure signals will be different from that of normal situation. In this investigation, intestinal artery clamping operations were conducted to a pig for simulating the situation of intestinal blocking during a surgical operation. Similarity theory is a convenient and easy tool to prove that patterns of blood pressure signals of intestinal artery blocking and unblocking are surely different. And, the algorithm of Hilbert Huang Transform can be applied to extract the character parameters of blood pressure pattern. In conclusion, the patterns of blood pressure signals of two different situations, intestinal artery blocking and unblocking, can be distinguished by these character parameters defined in this paper.

Keywords: Blood pressure, spectrum, intestinal artery, similarity theory and Hilbert Huang Transform.

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Authors: Affordability, Expenditure Patterns towards Sustainable Consumption in Malaysia

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Safe drinking water is needed for survival. Households have to pay the water bill monthly. However, lower income households are sometimes unable to afford the cost. This study examines water access and affordability among households in Malaysia and the determinants of water affordability using cross-sectional data and multiple regression. The paper expects that the bill for basic water consumption is inversely related to average income. This means that policy makers need to redesign the water tariff to improve the quality of life of lower income households.

Keywords: Affordability, households, income, water tariff.

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Authors: Giovanni Luca Masala

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Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

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Authors: Rein Kuusik, Grete Lind

Abstract:

Generator of hypotheses is a new method for data mining. It makes possible to classify the source data automatically and produces a particular enumeration of patterns. Pattern is an expression (in a certain language) describing facts in a subset of facts. The goal is to describe the source data via patterns and/or IF...THEN rules. Used evaluation criteria are deterministic (not probabilistic). The search results are trees - form that is easy to comprehend and interpret. Generator of hypotheses uses very effective algorithm based on the theory of monotone systems (MS) named MONSA (MONotone System Algorithm).

Keywords: data mining, monotone systems, pattern, rule.

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Authors: H. W. Ker

Abstract:

Longitudinal data typically have the characteristics of changes over time, nonlinear growth patterns, between-subjects variability, and the within errors exhibiting heteroscedasticity and dependence. The data exploration is more complicated than that of cross-sectional data. The purpose of this paper is to organize/integrate of various visual-graphical techniques to explore longitudinal data. From the application of the proposed methods, investigators can answer the research questions include characterizing or describing the growth patterns at both group and individual level, identifying the time points where important changes occur and unusual subjects, selecting suitable statistical models, and suggesting possible within-error variance.

Keywords: Data exploration, exploratory analysis, HLMs/LMEs, longitudinal data, visual-graphical methods.

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Authors: P. Sengottuvelan, T. Gopalakrishnan

Abstract:

Web Usage Mining is the application of data mining techniques to find usage patterns from web log data, so as to grasp required patterns and serve the requirements of Web-based applications. User’s expertise on the internet may be improved by minimizing user’s web access latency. This may be done by predicting the future search page earlier and the same may be prefetched and cached. Therefore, to enhance the standard of web services, it is needed topic to research the user web navigation behavior. Analysis of user’s web navigation behavior is achieved through modeling web navigation history. We propose this technique which cluster’s the user sessions, based on the K-medoids technique.

Keywords: Clustering, K-medoids, Recommendation, User Session, Web Usage Mining.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: Taher M. Abou-deif, Mahmoud A. Fouad, Essam E. Khalil

Abstract:

The present paper was concerned primarily with the analysis, simulation of the air flow and thermal patterns in a lecture room. The paper is devoted to numerically investigate the influence of location and number of ventilation and air conditioning supply and extracts openings on air flow properties in a lecture room. The work focuses on air flow patterns, thermal behaviour in lecture room where large number of students. The effectiveness of an air flow system is commonly assessed by the successful removal of sensible and latent loads from occupants with additional of attaining air pollutant at a prescribed level to attain the human thermal comfort conditions and to improve the indoor air quality; this is the main target during the present paper. The study is carried out using computational fluid dynamics (CFD) simulation techniques as embedded in the commercially available CFD code (FLUENT 6.2). The CFD modelling techniques solved the continuity, momentum and energy conservation equations in addition to standard k – ε model equations for turbulence closure. Throughout the investigations, numerical validation is carried out by way of comparisons of numerical and experimental results. Good agreement is found among both predictions.

Keywords: Air Conditioning, CFD, Lecture Rooms, Thermal Comfort

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

Abstract:

Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.

Keywords: Business intelligence, data mining, functionalprogramming, intelligent system.

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Authors: M. Sharanreddy, P. K. Kulkarni

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Brain tumor is inherently serious and life-threatening disease. Brain tumor builds the intracranial pressure in the brain, by shifting the brain or pushing against the skull, and also damaging nerves and healthy brain tissues. This intracranial pressure affects and interferes with normal brain functionality, which results in generation of abnormal electrical activities from brain. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. Research scholars and general physicians, often face difficulty in understanding EEG patterns. This paper presents the EEG patterns associated with brain tumor by combing medicine theory and neurologist experience. Paper also explains the pros-cons of the EEG based brain tumor identification.

Keywords: Brain tumor, Electroencephalogram (EEG).

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Authors: S. Sadhu, M. Ramgopal, S. Bhattacharyya

Abstract:

Natural circulation loops (NCLs) are buoyancy driven flow systems without any moving components. NCLs have vast applications in geothermal, solar and nuclear power industry where reliability and safety are of foremost concern. Due to certain favorable thermophysical properties, especially near supercritical regions, carbon dioxide can be considered as an ideal loop fluid in many applications. In the present work, a high temperature NCL that uses supercritical carbon dioxide as loop fluid is analysed. The effects of relevant design and operating variables on loop performance are studied. The system operating under steady state is modelled taking into account the axial conduction through loop fluid and loop wall, and heat transfer with surroundings. The heat source is considered to be a heater with controlled heat flux and heat sink is modelled as an end heat exchanger with water as the external cold fluid. The governing equations for mass, momentum and energy conservation are normalized and are solved numerically using finite volume method. Results are obtained for a loop pressure of 90 bar with the power input varying from 0.5 kW to 6.0 kW. The numerical results are validated against the experimental results reported in the literature in terms of the modified Grashof number (Gr_m) and Reynolds number (Re). Based on the results, buoyancy and friction dominated regions are identified for a given loop. Parametric analysis has been done to show the effect of loop diameter, loop height, ambient temperature and insulation. The results show that for the high temperature loop, heat loss to surroundings affects the loop performance significantly. Hence this conjugate heat transfer between the loop and surroundings has to be considered in the analysis of high temperature NCLs.

Keywords: Conjugate heat transfer, heat loss, natural circulation loop, supercritical carbon dioxide.

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Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

Abstract:

Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: Dynamic Time Warping, Glottal Area Waveform, Linear Predictive Coding, High-Speed Laryngeal Images, Hilbert Transform.

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Authors: Yogi Satrya Aryadinata, Anne Laurent, Michel Sala

Abstract:

Gradual patterns have been studied for many years as they contain precious information. They have been integrated in many expert systems and rule-based systems, for instance to reason on knowledge such as “the greater the number of turns, the greater the number of car crashes”. In many cases, this knowledge has been considered as a rule “the greater the number of turns → the greater the number of car crashes” Historically, works have thus been focused on the representation of such rules, studying how implication could be defined, especially fuzzy implication. These rules were defined by experts who were in charge to describe the systems they were working on in order to turn them to operate automatically. More recently, approaches have been proposed in order to mine databases for automatically discovering such knowledge. Several approaches have been studied, the main scientific topics being: how to determine what is an relevant gradual pattern, and how to discover them as efficiently as possible (in terms of both memory and CPU usage). However, in some cases, end-users are not interested in raw level knowledge, and are rather interested in trends. Moreover, it may be the case that no relevant pattern can be discovered at a low level of granularity (e.g. city), whereas some can be discovered at a higher level (e.g. county). In this paper, we thus extend gradual pattern approaches in order to consider multiple level gradual patterns. For this purpose, we consider two aggregation policies, namely horizontal and vertical.

Keywords: Gradual Pattern.

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