Search results for: measured data.

Authors: Shamsuddin Ahmed, M. G. M. Khan, Biman Prasad, Avlin Prasad

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The focus of this paper is to construct daily time series exchange rate forecast models of Samoan Tala/USD and Tala/AUD during the year 2008 to 2012 with neural network The performance of the models was measured by using varies error functions such as Root Square mean error (RSME), Mean absolute error (MAE), and Mean absolute percentage error (MAPE). Our empirical findings suggest that AR (1) model is an effective tool to forecast the Tala/USD and Tala/AUD.

Keywords: Neural Network Forecasting Model, Autoregressive time series, Exchange rate, Tala/AUD, winters model.

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Authors: F. Halek, A. Kavousi, F. Hassani

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A simultaneous study on indoor and outdoor particulate matter concentrations was done in five elementary schools in central parts of Tehran, Iran. Three sizes of particles including PM10, PM2.5 and PM1.0 were measured in 13 classrooms within this schools during winter (January, February and March) 2009. A laserbased portable aerosol spectrometer Model Grimm-1.108, was used for the continuous measurement of particles. The average indoor concentration of PM10, PM2.5 and PM1.0 in studied schools were 274 μg/m3, 42 μg/m3 and 19 μg/m3 respectively; and average outdoor concentrations of PM10, PM2.5 and PM1.0 were evaluated to be 22 μg/m3, 38 μg/m3 and 140 μg/m3 respectively.

Keywords: Elementary school, Indoor pollution, particulate matter, PM10, PM2.5, PM1.0, outdoor pollution, Tehran air pollution.

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Authors: Manish Kumar Thakur, Chiranjit Sarkar

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Sedimentation of magnetorheological (MR) fluid affects its working. MR fluid is a smart fluid that has unique qualities such as quick responsiveness and easy controllability. It is used in the MR damper, MR brake, and MR clutch. In this work effect of sedimentation on torque transmission in the shear mode operated MR clutch is investigated. A test rig is developed to test the impact of sedimentation on torque transmission in the MR clutch. Torque transmission capability of MR clutch has been measured under two conditions to confirm the result of sedimentation. The first experiment is done just after filling and the other after one week. It has been observed that transmission torque is decreased after sedimentation. Hence sedimentation affects the working of the MR clutch.

Keywords: Clutch, magnetorheological fluid, sedimentation, torque.

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Authors: Alla A. Mityureva

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The available data on the cross sections of electronimpact excitation of krypton 5s and 5p configuration levels out of the ground state are represented in convenient and compact form. The results are obtained by regression through all known published data related to this process.

Keywords: Cross section, electron excitation, krypton, regression

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Authors: Norshamsuri Ali, Hafizulfika, Salim Ali Al-Kathiri, Abdulla Al-Attas, Suhairi Saharudin, Mohamed Ridza Wahiddin

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We demonstrate single-photon interference over 10 km using a plug and play system for quantum key distribution. The quality of the interferometer is measured by using the interferometer visibility. The coding of the signal is based on the phase coding and the value of visibility is based on the interference effect, which result a number of count. The setup gives full control of polarization inside the interferometer. The quality measurement of the interferometer is based on number of count per second and the system produces 94 % visibility in one of the detectors.

Keywords: single photon, interferometer, quantum key distribution.

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Authors: T.Sheela, Dr.J.Raja

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Continuously growing needs for Internet applications that transmit massive amount of data have led to the emergence of high speed network. Data transfer must take place without any congestion and hence feedback parameters must be transferred from the receiver end to the sender end so as to restrict the sending rate in order to avoid congestion. Even though TCP tries to avoid congestion by restricting the sending rate and window size, it never announces the sender about the capacity of the data to be sent and also it reduces the window size by half at the time of congestion therefore resulting in the decrease of throughput, low utilization of the bandwidth and maximum delay. In this paper, XCP protocol is used and feedback parameters are calculated based on arrival rate, service rate, traffic rate and queue size and hence the receiver informs the sender about the throughput, capacity of the data to be sent and window size adjustment, resulting in no drastic decrease in window size, better increase in sending rate because of which there is a continuous flow of data without congestion. Therefore as a result of this, there is a maximum increase in throughput, high utilization of the bandwidth and minimum delay. The result of the proposed work is presented as a graph based on throughput, delay and window size. Thus in this paper, XCP protocol is well illustrated and the various parameters are thoroughly analyzed and adequately presented.

Keywords: Bandwidth-Delay Product, Congestion Control, Congestion Window, TCP/IP

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Authors: Sumanta Daw, Gopal Chandra Saha

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The present study was conducted to explore the basic pulmonary functions which may generally vary according to the bio-physical characteristics including age, height, body weight, and environment etc. of the sports performers. Regular and specific training exercises also change the characteristics of an athlete’s prowess and produce a positive effect on the physiological functioning, mostly upon cardio-pulmonary efficiency and thereby improving the body mechanism. The objective of the present study was to compare the differences in cardio-respiratory functions between aquatics and track and field performers. As cardio-respiratory functions are influenced by pulse rate and blood pressure (systolic and diastolic), so both of the factors were also taken into consideration. The component selected under cardio-respiratory functions for the present study were i) FEVI/FVC ratio (forced expiratory volume divided by forced vital capacity ratio, i.e. the number represents the percentage of lung capacity to exhale in one second) ii) FVC1 (this is the amount of air which can force out of lungs in one second) and iii) FVC (forced vital capacity is the greatest total amount of air forcefully breathe out after breathing in as deeply as possible). All the three selected components of the cardio-respiratory efficiency were measured by spirometry method. Pulse rate was determined manually. The radial artery which is located on the thumb side of our wrist was used to assess the pulse rate. Blood pressure was assessed by sphygmomanometer. All the data were taken in the resting condition. 36subjects were selected for the present study out of which 18were water polo players and rest were sprinters. The age group of the subjects was considered between 18 to 23 years. In this study the obtained data inform of digital score were treated statistically to get result and draw conclusions. The Mean and Standard Deviation (SD) were used as descriptive statistics and the significant difference between the two subject groups was assessed with the help of statistical ‘t’-test. It was found from the study that all the three components i.e. FEVI/FVC ratio (p-value 0.0148 < 0.01), FVC1 (p-value 0.0010 < 0.01) and FVC (p-value 0.0067 < 0.01) differ significantly as water polo players proved to be better in terms of cardio-respiratory functions than sprinters. Thus study clearly suggests that the exercise training as well as the medium of practice arena associated with water polo players has played an important role to determine better cardio respiratory efficiency than track and field athletes. The outcome of the present study revealed that the lung function in land-based activities may not provide much impact than that of in water activities.

Keywords: Cardio-respiratory efficiency, spirometry, water polo players, sprinters.

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Authors: Tharunika Sridhar

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This paper addresses sustainable eco-connectivity within the telecommunications sector studying its importance to tackle the contemporary challenges and data regulation issues. The paper also investigates the role of Big Data and its integration in this context, specific to telecom industry. One of the major focus areas in this paper is studying and examining the pathways explored, that are state-of-the-art ecological infrastructure solutions and sector-led measures derived from expert analyses and reviews. Additionally, the paper analyses critical factors involving cost-effective route planning, and the development of green telecommunications infrastructure that adds qualitative reasoning to the research idea. Furthermore, the study discusses in detail a potential green roadmap towards sustainability by exploring green routing software, eco-friendly infrastructure and other eco-focused initiatives. The paper is also directed at the special linguistic needs of the telecommunications sector by focusing on targeted select range of telecom environment.

Keywords: Big Data, telecom, sustainable telecom sector, telecom networks.

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Authors: Nik Kamariah Nik-Mat, Noor Hasmini Abd-Ghani, Jamal Mohammed Esmail Al-Ekam

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The Malaysian government had consistently revived its campaign for “Buy Malaysian Goods” from time to time. The purpose of the campaign is to remind consumers to be ethnocentric and patriotic when purchasing product and services. This is necessary to ensure high demand for local products and services compared to foreign products. However, the decline of domestic investment in 2012 has triggered concern for the Malaysian economy. Hence, this study attempts to determine the drivers of actual purchasing behavior, intention to purchase domestic products and ethnocentrism. The study employs the cross-sectional primary data, self-administered on household, selected using stratified random sampling in four Malaysian regions. A nine factor driver of actual domestic purchasing behavior (culture openness, conservatism, collectivism, patriotism, control belief, interest in foreign travel, attitude, ethnocentrism and intention) were measured utilizing 60 items, using 7-point Likertscale. From 1000 questionnaires distributed, a sample of 486 were returned representing 48.6 percent response rate. From the fit generated structural model (SEM analysis), it was found that the drivers of actual purchase behavior are collectivism, cultural openness and patriotism; the drivers of intention to purchase domestic product are attitude, control belief, collectivism and conservatism; and drivers of ethnocentrism are cultural openness, control belief, foreign travel and patriotism. It also shows that Malaysian consumers scored high in ethnocentrism and patriotism. The findings are discussed in the perspective of its implication to Malaysian National Agenda.

Keywords: Actual purchase, ethnocentrism, culture openness, conservatism, collectivism, patriotism.

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Authors: Omer K. Jasim, Safia Abbas, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem

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Cloud computing technology is very useful in present day to day life, it uses the internet and the central remote servers to provide and maintain data as well as applications. Such applications in turn can be used by the end users via the cloud communications without any installation. Moreover, the end users’ data files can be accessed and manipulated from any other computer using the internet services. Despite the flexibility of data and application accessing and usage that cloud computing environments provide, there are many questions still coming up on how to gain a trusted environment that protect data and applications in clouds from hackers and intruders. This paper surveys the “keys generation and management” mechanism and encryption/decryption algorithms used in cloud computing environments, we proposed new security architecture for cloud computing environment that considers the various security gaps as much as possible. A new cryptographic environment that implements quantum mechanics in order to gain more trusted with less computation cloud communications is given.

Keywords: Cloud Computing, Cloud Encryption Model, Quantum Key Distribution.

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Authors: R.Anita, V.Ganga Bharani, N.Nityanandam, Pradeep Kumar Sahoo

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The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based approach for extracting data from the deep web. Deep iCrawl splits the process into two phases. The first phase includes Query analysis and Query translation and the second covers vision-based extraction of data from the dynamically created deep web pages. There are several established approaches for the extraction of deep web pages but the proposed method aims at overcoming the inherent limitations of the former. This paper also aims at comparing the data items and presenting them in the required order.

Keywords: Crawler, Deep web, Web Database

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Authors: Ren-Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: Fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption.

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: Artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization.

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Authors: M. Zeghid, M. Machhout, L. Khriji, A. Baganne, R. Tourki

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. In this paper, we analyze the Advanced Encryption Standard (AES), and we add a key stream generator (A5/1, W7) to AES to ensure improving the encryption performance; mainly for images characterised by reduced entropy. The implementation of both techniques has been realized for experimental purposes. Detailed results in terms of security analysis and implementation are given. Comparative study with traditional encryption algorithms is shown the superiority of the modified algorithm.

Keywords: Cryptography, Encryption, Advanced EncryptionStandard (AES), ECB mode, statistical analysis, key streamgenerator.

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Authors: Dhananjoy Shaw, Seema Sharma (Kaushik)

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Anthropometry helps in associating the physical properties of an individual with their racial, cultural, and psychological attributes. Numerous research studies have included different skeletal diameters as a variable. However, most of the studies suggest their inclusion describing specific characteristics/traits of the body. However, there seems to be a scarcity of literature related to the effect of any kind of longitudinal physical training on human skeletal diameters. Hence, the present investigation was conducted to study the variability of covariance of selected skeletal diameters of females in a longitudinal physical training programme. The sample for the study was 78 college going students of the University of Delhi, classified equally in three groups, i.e. viz. (a) Progressive load of training or conditioning group coded as PLT; (b) Constant load of training or non-conditioning group coded as CLT; and (c) No-load or control or sedentary group coded as NL. Collectively, mean age of the sample was 19.54±1.79 years. The randomly selected samples were given maximum consideration to maintain their homogeneity. The variables included biacromial diameter, biiliocristal diameter, bitrochantaerion diameter, humeral bicondylar, femoral bicondylar, wrist diameter, ankle diameter, and foot breadth. Multi-group repeated measure design was adopted for the experimentation. Each group was measured four times after completion of each of the three meso-cycles of six-weeks duration. The measurements were taken following the standard landmarks and procedures. Mean, standard deviation, analysis of co-variance and its post-hoc analysis were computed to analyze the data statistically. The study concluded that both the progressive and constant load of physical training bring changes in the selected skeletal diameters of females. It also reflected the increase due to growth also along with training.

Keywords: Longitudinal, physical training, skeletal diameters, step progression load.

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Authors: Eiad Yafi, Ahmed Sultan Al-Hegami, M. A. Alam, Ranjit Biswas

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Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising.

Keywords: Knowledge discovery in databases (KDD), Data mining, Incremental Association rules, Domain knowledge, Interestingness, Shocking rules (SHR).

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Authors: Y. N. Phang, E. F. Loh

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Zero inflated strict arcsine model is a newly developed model which is found to be appropriate in modeling overdispersed count data. In this study, we extend zero inflated strict arcsine model to zero inflated strict arcsine regression model by taking into consideration the extra variability caused by extra zeros and covariates in count data. Maximum likelihood estimation method is used in estimating the parameters for this zero inflated strict arcsine regression model.

Keywords: Overdispersed count data, maximum likelihood estimation, simulated annealing.

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

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Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled.

Keywords: Classification, functional data, feature extraction, genetic algorithm, wavelet.

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Authors: Neamat El Gayar

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Learning using labeled and unlabelled data has received considerable amount of attention in the machine learning community due its potential in reducing the need for expensive labeled data. In this work we present a new method for combining labeled and unlabeled data based on classifier ensembles. The model we propose assumes each classifier in the ensemble observes the input using different set of features. Classifiers are initially trained using some labeled samples. The trained classifiers learn further through labeling the unknown patterns using a teaching signals that is generated using the decision of the classifier ensemble, i.e. the classifiers self-supervise each other. Experiments on a set of object images are presented. Our experiments investigate different classifier models, different fusing techniques, different training sizes and different input features. Experimental results reveal that the proposed self-supervised ensemble learning approach reduces classification error over the single classifier and the traditional ensemble classifier approachs.

Keywords: Multiple Classifier Systems, classifier ensembles, learning using labeled and unlabelled data, K-nearest neighbor classifier, Bayes classifier.

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Authors: A. M. Azad, M. Alam, C. M. Hussain

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In this paper, we have presented the effect of varying time-delays on performance and stability in the single-channel multirate sampled-data system in hard real-time (RT-Linux) environment. The sampling task require response time that might exceed the capacity of RT-Linux. So a straight implementation with RT-Linux is not feasible, because of the latency of the systems and hence, sampling period should be less to handle this task. The best sampling rate is chosen for the sampled-data system, which is the slowest rate meets all performance requirements. RT-Linux is consistent with its specifications and the resolution of the real-time is considered 0.01 seconds to achieve an efficient result. The test results of our laboratory experiment shows that the multi-rate control technique in hard real-time operating system (RTOS) can improve the stability problem caused by the random access delays and asynchronization.

Keywords: Multi-rate, PID, RT-Linux, Sampled-data, Servo.

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Authors: Wann-Ming Wey

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In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.

Keywords: Adaptive reuse, analytic network process, big data, land use strategy.

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Authors: S. Sarumathi, P. Ranjetha, C. Saraswathy, M. Vaishnavi, S. Geetha

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Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.

Keywords: Clustering, cluster ensemble methods, consensus function, data mining, unsupervised learning.

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Authors: T. Chandrasekhar, K. Thangavel, E. N. Sathishkumar

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Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.

Keywords: Clustering, Feature selection, Gene expression data, Quick reduct.

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Authors: Sobia T. Javed, Sarmad Hussain, Ameera Maqbool, Samia Asloob, Sehrish Jamil, Huma Moin

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The electronically available Urdu data is in image form which is very difficult to process. Printed Urdu data is the root cause of problem. So for the rapid progress of Urdu language we need an OCR systems, which can help us to make Urdu data available for the common person. Research has been carried out for years to automata Arabic and Urdu script. But the biggest hurdle in the development of Urdu OCR is the challenge to recognize Nastalique Script which is taken as standard for writing Urdu language. Nastalique script is written diagonally with no fixed baseline which makes the script somewhat complex. Overlap is present not only in characters but in the ligatures as well. This paper proposes a method which allows successful recognition of Nastalique Script.

Keywords: HMM, Image processing, Optical CharacterRecognition, Urdu OCR.

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Authors: A. Aydarous, M. El Ghazaly

Abstract:

The purpose of this study was to investigate the response of the newly released Gafchromic HD-V2 film for alpha particle of 5.5 MeV. Gafchromic HD-V2 was exposed to alpha particles of energy 5 MeV from 241Am for different durations. Then the films were scanned with a flatbed scanner. The dose response curve up to 2200 Gy has been achieved. The film’s reproducibility and sensitivity were evaluated. The results obtained show that the net optical density increases almost exponentially with the increase in the exposure time, and it becomes saturated after prolonged exposure times. The red channel shows the highest sensitivity, with a value of 4 x 10-3 Gy-1 at netOD of 0.4. The inter-film reproducibility was measured and the relative uncertainty found was 1.7 %, 2.1 % and 2.3 % for grey, red and green channels, respectively.

Keywords: Alpha dosimetry, 241Am, Gafchromic film.

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: Diesel engine, machine learning, NOx emission, semi-empirical.

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Authors: Amos Barkai, Guy Meredith, Fatima Felaar, Zahrah Dantie, Dave de Buys

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Fisheries management all around the world is hampered by the lack, or poor quality, of critical data on fish resources and fishing operations. The main reasons for the chronic inability to collect good quality data during fishing operations is the culture of secrecy common among fishers and the lack of modern data gathering technology onboard most fishing vessels. In response, OLRAC-SPS, a South African company, developed fisheries datalogging software (eLog in short) and named it Olrac. The Olrac eLog solution is capable of collecting, analysing, plotting, mapping, reporting, tracing and transmitting all data related to fishing operations. Olrac can be used by skippers, fleet/company managers, offshore mariculture farmers, scientists, observers, compliance inspectors and fisheries management authorities. The authors believe that using eLog onboard fishing vessels has the potential to revolutionise the entire process of data collection and reporting during fishing operations and, if properly deployed and utilised, could transform the entire commercial fleet to a provider of good quality data and forever change the way fish resources are managed. In addition it will make it possible to trace catches back to the actual individual fishing operation, to improve fishing efficiency and to dramatically improve control of fishing operations and enforcement of fishing regulations.

Keywords: data management, electronic logbook (eLog), electronic reporting system (ERS), fisheries management

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Authors: Magdalena Pejas

Abstract:

This article concerns the presentation of an integrated method for detection of steganographic content embedded by new unknown programs. The method is based on data mining and aggregated hypothesis testing. The article contains the theoretical basics used to deploy the proposed detection system and the description of improvement proposed for the basic system idea. Further main results of experiments and implementation details are collected and described. Finally example results of the tests are presented.

Keywords: Steganography, steganalysis, data embedding, data mining, feature extraction, knowledge base, system learning, hypothesis testing, error estimation, black box program, file structure.

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Authors: Donghyun Hwang, Soo-Hun Lee, Moon G. Lee

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In the various working field, vibration may cause injurious to human body. Especially, in case of the vibration which is constantly and repeatedly transferred to the human. That gives serious physical problem, so called, Reynaud phenomenon. In this paper, we propose a vibration transmissibility reduction module with flexure mechanism for personal tools. At first, we select a target personal tool, grass cutter, and measure the level of vibration transmissibility on the hand. And then, we develop the concept design of the module that has stiffness for reduction the vibration transmissibility more than 20%, where the vibration transmissibility is measured with an accelerometer. In addition, the vibration reduction can be enhanced when the interior gap between inner and outer body is filled with silicone gel. This will be verified by the further experiment.

Keywords: Flexure spring, tool engineering, vibration damping.

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Authors: Hidir S. Nogay

Abstract:

In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: Cascaded neural network, internal temperature, three-phase induction motor, inverter.

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