Search results for: real time traffic reporting

Authors: M. Mohebbi, K. Shakeri

Abstract:

Since the actuator capacity is limited, in the real application of active control systems under sever earthquakes it is conceivable that the actuators saturate, hence the actuator saturation should be considered as a constraint in design of optimal controllers. In this paper optimal design of active controllers for nonlinear structures by considering actuator saturation, has been studied. The proposed method for designing optimal controllers is based on defining an optimization problem which the objective has been to minimize the maximum displacement of structure when a limited capacity for actuator has been used. To this end a single degree of freedom (SDF) structure with a bilinear hysteretic behavior has been simulated under a white noise ground acceleration of different amplitudes. Active tendon control mechanism, comprised of prestressed tendons and an actuator, and extended nonlinear Newmark method based instantaneous optimal control algorithm have been used. To achieve the best results, the weights corresponding to displacement, velocity, acceleration and control force in the performance index have been optimized by the Distributed Genetic Algorithm (DGA). Results show the effectiveness of the proposed method in considering actuator saturation. Also based on the numerical simulations it can be concluded that the actuator capacity and the average value of required control force are two important factors in designing nonlinear controllers which consider the actuator saturation.

Keywords: Active control, Actuator Saturation, Distributedgeneticalgorithms, Nonlinear.

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Authors: Radomir Perzina, Jaroslav Ramik

Abstract:

The Analytic Hierarchy Process is frequently used approach for solving decision making problems. There exists wide range of software programs utilizing that approach. Their main disadvantage is that they are relatively expensive and missing intermediate calculations. This work introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work with scenarios or multiple decision makers and displays intermediate calculations. Users can structure their decision models into three levels – scenarios/users, criteria and variants. Items on all levels can be evaluated either by weights or pair-wise comparisons. There are provided three different methods for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. The proposed software package is demonstrated on couple of illustrating examples of real life decision problems.

Keywords: Analytic hierarchy process, multi-criteria decision making, pair-wise comparisons, Microsoft Excel, Scenarios.

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Authors: Vikas Tewari, Shivendra Tewari, K. R. Pardasani

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Calcium is a vital second messenger used in signal transduction. Calcium controls secretion, cell movement, muscular contraction, cell differentiation, ciliary beating and so on. Two theories have been used to simplify the system of reaction-diffusion equations of calcium into a single equation. One is excess buffer approximation (EBA) which assumes that mobile buffer is present in excess and cannot be saturated. The other is rapid buffer approximation (RBA), which assumes that calcium binding to buffer is rapid compared to calcium diffusion rate. In the present work, attempt has been made to develop a model for calcium diffusion under excess buffer approximation in neuron cells. This model incorporates the effect of [Na+] influx on [Ca2+] diffusion,variable calcium and sodium sources, sodium-calcium exchange protein, Sarcolemmal Calcium ATPase pump, sodium and calcium channels. The proposed mathematical model leads to a system of partial differential equations which have been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, association rate, calcium permeability.

Keywords: Excess buffer approximation, Na+ influx, sodium calcium exchange protein, sarcolemmal calcium atpase pump, forward time centred space.

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Authors: Dariush Semnani, Mohammad Sheikhzadeh

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Circular knitting machine makes the fabric with more than two knitting tools. Variation of yarn tension between different knitting tools causes different loop length of stitches duration knitting process. In this research, a new intelligent method is applied to control loop length of stitches in various tools based on ideal shape of stitches and real angle of stitches direction while different loop length of stitches causes stitches deformation and deviation those of angle. To measure deviation of stitch direction against variation of tensions, image processing technique was applied to pictures of different fabrics with constant front light. After that, the rate of deformation is translated to needed compensation of loop length cam degree to cure stitches deformation. A fuzzy control algorithm was applied to loop length modification in knitting tools. The presented method was experienced for different knitted fabrics of various structures and yarns. The results show that presented method is useable for control of loop length variation between different knitting tools based on stitch deformation for various knitted fabrics with different fabric structures, densities and yarn types.

Keywords: Circular knitting, Radon transformation, Knittedfabric, Regularity, Fuzzy control

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Ju Young Lee, Jung-Seok Yang, Jaeyoung Choi

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The purpose of this paper primarily intends to develop GIS interface for estimating sequences of stream-flows at ungauged stations based on known flows at gauged stations. The integrated GIS interface is composed of three major steps. The first, precipitation characteristics using statistical analysis is the procedure for making multiple linear regression equation to get the long term mean daily flow at ungauged stations. The independent variables in regression equation are mean daily flow and drainage area. Traditionally, mean flow data are generated by using Thissen polygon method. However, method for obtaining mean flow data can be selected by user such as Kriging, IDW (Inverse Distance Weighted), Spline methods as well as other traditional methods. At the second, flow duration curve (FDC) is computing at unguaged station by FDCs in gauged stations. Finally, the mean annual daily flow is computed by spatial interpolation algorithm. The third step is to obtain watershed/topographic characteristics. They are the most important factors which govern stream-flows. In summary, the simulated daily flow time series are compared with observed times series. The results using integrated GIS interface are closely similar and are well fitted each other. Also, the relationship between the topographic/watershed characteristics and stream flow time series is highly correlated.

Keywords: Integrated GIS interface, spatial interpolation algorithm, FDC.

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Authors: Remica Aggarwal, Sanjeet Singh

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The decision to recruit manpower in an organization requires clear identification of the criteria (attributes) that distinguish successful from unsuccessful performance. The choice of appropriate attributes or criteria in different levels of hierarchy in an organization is a multi-criteria decision problem and therefore multi-criteria decision making (MCDM) techniques can be used for prioritization of such attributes. Analytic Hierarchy Process (AHP) is one such technique that is widely used for deciding among the complex criteria structure in different levels. In real applications, conventional AHP still cannot reflect the human thinking style as precise data concerning human attributes are quite hard to be extracted. Fuzzy logic offers a systematic base in dealing with situations, which are ambiguous or not well defined. This study aims at defining a methodology to improve the quality of prioritization of an employee-s performance measurement attributes under fuzziness. To do so, a methodology based on the Extent Fuzzy Analytic Hierarchy Process is proposed. Within the model, four main attributes such as Subject knowledge and achievements, Research aptitude, Personal qualities and strengths and Management skills with their subattributes are defined. The two approaches conventional AHP approach and the Extent Fuzzy Analytic Hierarchy Process approach have been compared on the same hierarchy structure and criteria set.

Keywords: AHP, Extent analysis method, Fuzzy AHP, Prioritization.

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Authors: C. Ardil

Abstract:

In this research, the Balkan peninsula countries' developmental integration into European Union represents the strategic economic development objectives of the countries in the region. In order to objectively analyze the level of economic development competition of Balkan Peninsula countries, the mathematical compromise programming technique of multicriteria evaluation is used in this ranking problem. The primary aim of this research is to explain the role and significance of the multicriteria method evaluation using a real example of compromise solutions. Using the mathematical compromise programming technique, twelve countries of the Balkan Peninsula are economically evaluated and mutually compared. The economic development evaluation of the countries is performed according to five evaluation criteria forming the basis for economic development evaluation. The multiattribute model is solved using the mathematical compromise programming technique for producing different Pareto solutions. The results obtained by the multicriteria evaluation gives the possibility of identification and evaluation of the most eminent economic development indicators for each country separately. Finally, in this way, the proposed method has proved to be a successful model for the evaluation of the Balkan peninsula countries' economic development competition.

Keywords: Balkan peninsula countries, standard deviation, multicriteria decision making, mathematical compromise programming, multicriteria decision making, multicriteria analysis, multicriteria decision analysis.

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Authors: S. Z. Moussavi, A. Badri, F. Rastegar Kashkooli

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Wind farms (WFs) with high level of penetration are being established in power systems worldwide more rapidly than other renewable resources. The Independent System Operator (ISO), as a policy maker, should propose appropriate places for WF installation in order to maximize the benefits for the investors. There is also a possibility of congestion relief using the new installation of WFs which should be taken into account by the ISO when proposing the locations for WF installation. In this context, efficient wind farm (WF) placement method is proposed in order to reduce burdens on congested lines. Since the wind speed is a random variable and load forecasts also contain uncertainties, probabilistic approaches are used for this type of study. AC probabilistic optimal power flow (P-OPF) is formulated and solved using Monte Carlo Simulations (MCS). In order to reduce computation time, point estimate methods (PEM) are introduced as efficient alternative for time-demanding MCS. Subsequently, WF optimal placement is determined using generation shift distribution factors (GSDF) considering a new parameter entitled, wind availability factor (WAF). In order to obtain more realistic results, N-1 contingency analysis is employed to find the optimal size of WF, by means of line outage distribution factors (LODF). The IEEE 30-bus test system is used to show and compare the accuracy of proposed methodology.

Keywords: Probabilistic optimal power flow, Wind power, Pointestimate methods, Congestion management

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Authors: Aleksander Nawrat, Karol Jędrasiak, Artur Ryt, Dawid Sobel

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The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

Keywords: Firearms shot detection, geometric recalibration, photometric recalibration, IR tracking algorithm, thermography, ballistics.

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Authors: P. Kaladevi, N. Giridharan

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The system for analyzing and eliciting public grievances serves its main purpose to receive and process all sorts of complaints from the public and respond to users. Due to the more number of complaint data becomes big data which is difficult to store and process. The proposed system uses HDFS to store the big data and uses MapReduce to process the big data. The concept of cache was applied in the system to provide immediate response and timely action using big data analytics. Cache enabled big data increases the response time of the system. The unstructured data provided by the users are efficiently handled through map reduce algorithm. The processing of complaints takes place in the order of the hierarchy of the authority. The drawbacks of the traditional database system used in the existing system are set forth by our system by using Cache enabled Hadoop Distributed File System. MapReduce framework codes have the possible to leak the sensitive data through computation process. We propose a system that add noise to the output of the reduce phase to avoid signaling the presence of sensitive data. If the complaints are not processed in the ample time, then automatically it is forwarded to the higher authority. Hence it ensures assurance in processing. A copy of the filed complaint is sent as a digitally signed PDF document to the user mail id which serves as a proof. The system report serves to be an essential data while making important decisions based on legislation.

Keywords: Big Data, Hadoop, HDFS, Caching, MapReduce, web personalization, e-governance.

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Authors: S. Alfalah, J. Falah, T. Alfalah, M. Elfalah, O. Falah

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The field of education is an ever-evolving area constantly enriched by newly discovered techniques provided by active research in all areas of technologies. The recent years have witnessed the introduction of a number of promising technologies and applications to enhance the teaching and learning experience. Virtual Reality (VR) applications are considered one of the evolving methods that have contributed to enhancing education in many fields. VR creates an artificial environment, using computer hardware and software, which is similar to the real world. This simulation provides a solution to improve the delivery of materials, which facilitates the teaching process by providing a useful aid to instructors, and enhances the learning experience by providing a beneficial learning aid. In order to assure future utilization of such systems, students’ perceptions were examined toward utilizing VR as an educational tool in the Faculty of Information Technology (IT) in The University of Jordan. A questionnaire was administered to IT undergraduates investigating students’ opinions about the potential opportunities that VR technology could offer and its implications as learning and teaching aid. The results confirmed the end users’ willingness to adopt VR systems as a learning aid. The result of this research forms a solid base for investing in a VR system for IT education.

Keywords: Education, information, technology, virtual reality.

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Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat

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Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully.

Keywords: Neural Network, User Authentication, Smart Home, Security

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Authors: Ni Ni Soe , Masahiro Nakagawa

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Functional near infrared spectroscopy (fNIRS) is a practical non-invasive optical technique to detect characteristic of hemoglobin density dynamics response during functional activation of the cerebral cortex. In this paper, fNIRS measurements were made in the area of motor cortex from C4 position according to international 10-20 system. Three subjects, aged 23 - 30 years, were participated in the experiment. The aim of this paper was to evaluate the effects of different motor activation tasks of the hemoglobin density dynamics of fNIRS signal. The chaotic concept based on deterministic dynamics is an important feature in biological signal analysis. This paper employs the chaotic properties which is a novel method of nonlinear analysis, to analyze and to quantify the chaotic property in the time series of the hemoglobin dynamics of the various motor imagery tasks of fNIRS signal. Usually, hemoglobin density in the human brain cortex is found to change slowly in time. An inevitable noise caused by various factors is to be included in a signal. So, principle component analysis method (PCA) is utilized to remove high frequency component. The phase pace is reconstructed and evaluated the Lyapunov spectrum, and Lyapunov dimensions. From the experimental results, it can be conclude that the signals measured by fNIRS are chaotic.

Keywords: Chaos, hemoglobin, Lyapunov spectrum, motorimagery, near infrared spectroscopy (NIRS), principal componentanalysis (PCA).

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Authors: Hironori Karachi, Haruka Yamashita

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Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.

Keywords: Data science, non-negative matrix factorization, missing data, quality of services.

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Authors: Sunitha Ramanujam, Hany El Yamany, Miriam A. M. Capretz

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Software reliability, defined as the probability of a software system or application functioning without failure or errors over a defined period of time, has been an important area of research for over three decades. Several research efforts aimed at developing models to improve reliability are currently underway. One of the most popular approaches to software reliability adopted by some of these research efforts involves the use of operational profiles to predict how software applications will be used. Operational profiles are a quantification of usage patterns for a software application. The research presented in this paper investigates an innovative multiagent framework for automatic creation and management of operational profiles for generic distributed systems after their release into the market. The architecture of the proposed Operational Profile MAS (Multi-Agent System) is presented along with detailed descriptions of the various models arrived at following the analysis and design phases of the proposed system. The operational profile in this paper is extended to comprise seven different profiles. Further, the criticality of operations is defined using a new composed metrics in order to organize the testing process as well as to decrease the time and cost involved in this process. A prototype implementation of the proposed MAS is included as proof-of-concept and the framework is considered as a step towards making distributed systems intelligent and self-managing.

Keywords: Software reliability, Software testing, Metrics, Distributed systems, Multi-agent systems

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Authors: S. Ishak, M. G. Nasuruddin

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Traditional Malay performances are carried out for both entertainment and curing purposes. In curing rituals, the men and women serving as shamans, communicates with the spirits and beings from the nether world to facilitate the curing process. The dependency on engaging with these other-worldly beings however, have raised religious issues of being syirik, namely practicing in rituals which are religiously forbidden. This study aims to observe how ritual leaders attempt to negotiate the fine balance between what has been religiously forbidden and the psychological and sociological needs of the patient. Two curing rituals, the main peteri and the malibobou were chosen to exemplify the communication between the physical and spiritual realities. In both rituals, the healers engaged in procedures of curing as they attempted to diagnose sicknesses and proffer cures with the help of the spirits. The main peteri was conducted by a male shaman, the tuk teri whereas the malibobou was conducted by a female ritual specialist, the bobohizan. Main peteri and the malibobou both ended with ritually thanking and sending off the spirits back to their nether, invisible domains. These curing rituals heal not only the sick individual, but by extension, the village community. Therefore, there is a need to reconcile these rituals with religious tenets, beliefs and sociological-political-cultural dimensions.

Keywords: Traditional healing, trance, spirits, main peteri, bobohizan.

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Authors: Raja Das, M. K. Pradhan

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This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Ion Baş, Claudiu Zoicaş, Angela Ioniţâ

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In most of the cases, natural disasters lead to the necessity of evacuating people. The quality of evacuation management is dramatically improved by the use of information provided by decision support systems, which become indispensable in case of large scale evacuation operations. This paper presents a best practice case study. In November 2007, officers from the Emergency Situations Inspectorate “Crisana" of Bihor County from Romania participated to a cross-border evacuation exercise, when 700 people have been evacuated from Netherlands to Belgium. One of the main objectives of the exercise was the test of four different decision support systems. Afterwards, based on that experience, software system called TEVAC (Trans Border Evacuation) has been developed “in house" by the experts of this institution. This original software system was successfully tested in September 2008, during the deployment of the international exercise EU-HUROMEX 2008, the scenario involving real evacuation of 200 persons from Hungary to Romania. Based on the lessons learned and results, starting from April 2009, the TEVAC software is used by all Emergency Situations Inspectorates all over Romania.

Keywords: Emergency evacuation, Searching Features, TEVAC(Trans Border Evacuation) software system, User Interface Design.

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Authors: František Šebek, Petr Kubík, Jindřich Petruška, Jiří Hůlka

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Present study is aimed on the cutting process of circular cross-section rods where the fracture is used to separate one rod into two pieces. Incorporating the phenomenological ductile fracture model into the explicit formulation of finite element method, the process can be analyzed without the necessity of realizing too many real experiments which could be expensive in case of repetitive testing in different conditions. In the present paper, the steel AISI 1045 was examined and the tensile tests of smooth and notched cylindrical bars were conducted together with biaxial testing of the notched tube specimens to calibrate material constants of selected phenomenological ductile fracture models. These were implemented into the Abaqus/Explicit through user subroutine VUMAT and used for cutting process simulation. As the calibration process is based on variables which cannot be obtained directly from experiments, numerical simulations of fracture tests are inevitable part of the calibration. Finally, experiments regarding the cutting process were carried out and predictive capability of selected fracture models is discussed. Concluding remarks then make the summary of gained experience both with the calibration and application of particular ductile fracture criteria.

Keywords: Ductile fracture, phenomenological criteria, cutting process, explicit formulation, AISI 1045 steel.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: Gökhan Polat, Dicle Kocaoğlu Yılmazer, Muhlis Nezihi Sarıdede

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Iron oxides are the main input to produce iron in integrated iron and steel plants. During production of iron from iron oxides, some wastes with high iron content occur. These main wastes can be classified as basic oxygen furnace (BOF) sludge, flue dust and rolling scale. Recycling of these wastes has a great importance for both environmental effects and reduction of production costs. In this study, recycling experiments were performed on basic oxygen furnace sludge, flue dust and rolling scale which contain 53.8%, 54.3% and 70.2% iron respectively. These wastes were mixed together with coke as reducer and these mixtures are pressed to obtain cylindrical briquettes. These briquettes were pressed under various compacting forces from 1 ton to 6 tons. Also, both stoichiometric and twice the stoichiometric cokes were added to investigate effect of coke amount on reduction properties of the waste mixtures. Then, these briquettes were reduced at 1000°C and 1100°C during 30, 60, 90, 120 and 150 min in a muffle furnace. According to the results of reduction experiments, the effect of compacting force, temperature and time on reduction ratio of the wastes were determined. It is found that 1 ton compacting force, 150 min reduction time and 1100°C are the optimum conditions to obtain reduction ratio higher than 75%.

Keywords: Iron oxide wastes, reduction, coke, recycling.

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Authors: Bibhya Sharma, Jito Vanualailai, Ravindra Rai

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This research paper designs a unique motion planner of multiple platoons of nonholonomic car-like robots as a feasible solution to the lane changing/merging maneuvers. The decentralized planner with a leaderless approach and a path-guidance principle derived from the Lyapunov-based control scheme generates collision free avoidance and safe merging maneuvers from multiple lanes to a single lane by deploying a split/merge strategy. The fixed obstacles are the markings and boundaries of the road lanes, while the moving obstacles are the robots themselves. Real and virtual road lane markings and the boundaries of road lanes are incorporated into a workspace to achieve the desired formation and configuration of the robots. Convergence of the robots to goal configurations and the repulsion of the robots from specified obstacles are achieved by suitable attractive and repulsive potential field functions, respectively. The results can be viewed as a significant contribution to the avoidance algorithm of the intelligent vehicle systems (IVS). Computer simulations highlight the effectiveness of the split/merge strategy and the acceleration-based controllers.

Keywords: Lane merging, Lyapunov-based control scheme, path-guidance principle, split/merge strategy.

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Authors: J. Fenianos, C. Khater, D. Brouillet

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Ecological projects are often faced with reluctance from local communities hosting the project, especially when this project involves variation from preset ideas or classical practices. This paper aims at appreciating the contribution of environmental psychology through cognitive flexibility exercises to improve the acceptability of local communities in adopting more ecological rehabilitation scenarios. The study is based on a quarry site located in Bekaa- Lebanon. Four groups were considered with different levels of involvement, as follows: Group 1 is Training (T) – 50 hours of on-site training over 8 months, Group 2 is Awareness (A) – 2 hours of awareness raising session, Group 3 is Flexibility (F) – 2 hours of flexibility exercises and Group 4 is the Control (C). The results show that individuals in Group 3 (F) who followed flexibility sessions accept comparably the ecological rehabilitation option over the more classical one. This is also the case for the people in Group 1 (T) who followed a more time-demanding “on-site training”. Another experience was conducted on a second quarry site combining flexibility with awareness-raising. This research confirms that it is possible to reduce resistance to change thanks to a limited in-time intervention using cognitive flexibility. This methodological approach could be transferable to other environmental problems involving local communities and changes in preset perceptions.

Keywords: Acceptability, ecological restoration, environmental psychology, Lebanon, local communities, resistance to change.

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Authors: Emad E Abdallah, A.F. Otoom, ArwaSaqer, Ola Abu-Aisheh, Diana Omari, Ghadeer Salem

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As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Naïve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features.

Keywords: Digital investigation, cybercrimes, emails forensics, anonymous emails, writing style, and authorship analysis

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Authors: Qiangqiang Xu, Xu Ji, Jingyang Han, Changchun Yang, Ming Li

Abstract:

Replacing of complex solar concentrating unit, this paper designs a solar heat-concentrating medium-temperature steam-generating system. Solar radiation is collected by using a large solar collecting and heat concentrating plate and is converged to the metal evaporating pipe with high efficient heat transfer. In the meantime, the heat loss is reduced by employing a double-glazed cover and other heat insulating structures. Thus, a high temperature is reached in the metal evaporating pipe. The influences of the system's structure parameters on system performance are analyzed. The steam production rate and the steam production under different solar irradiance, solar collecting and heat concentrating plate area, solar collecting and heat concentrating plate temperature and heat loss are obtained. The results show that when solar irradiance is higher than 600 W/m², the effective heat collecting area is 7.6 m² and the double-glazing cover is adopted, the system heat loss amount is lower than the solar irradiance value. The stable steam is produced in the metal evaporating pipe at 100 ℃, 110 ℃, and 120 ℃, respectively. When the average solar irradiance is about 896 W/m², and the steaming cumulative time is about 5 hours, the daily steam production of the system is about 6.174 kg. In a single day, the solar irradiance is larger at noon, thus the steam production rate is large at that time. Before 9:00 and after 16:00, the solar irradiance is smaller, and the steam production rate is almost 0.

Keywords: Heat concentrating, heat loss, medium temperature, solar steam production.

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

Abstract:

The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: EIoT, machine learning, anomaly detection, environment monitoring.

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Authors: R. Thiamsri, N. Ruangkajonmathee, A. Oonsivilaiand B. Marungsri

Abstract:

This paper presents the experimental results on effect of applied voltage stress frequency to the occurrence of electrical treeing in 22 kV cross linked polyethylene (XLPE) insulated cable.Hallow disk of XLPE insulating material with thickness 5 mm taken from unused high voltage cable was used as the specimen in this study. Stainless steel needle was inserted gradually into the specimen to give a tip to earth plane electrode separation of 2.5
0.2 mm at elevated temperature 105-110°C. The specimen was then annealed for 5 minute to minimize any mechanical stress build up around the needle-plane region before it was cooled down to room temperature. Each specimen were subjected to the same applied voltage stress level at 8 kV AC rms, with various frequency, 50, 100, 500, 1000 and 2000 Hz. Initiation time, propagation speed and pattern of electrical treeing were examined in order to study the effect of applied voltage stress frequency. By the experimental results, initial time of visible treeing decreases with increasing in applied voltage frequency. Also, obviously, propagation speed of electrical treeing increases with increasing in applied voltage frequency.Furthermore, two types of electrical treeing, bush-like and branch-like treeing were observed.The experimental results confirmed the effect of voltage stress frequency as well.

Keywords: Voltage stress frequency, cross-linked polyethylene, electrical treeing, treeing propagation, treeing pattern

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Authors: M. Fareed Ahmed, M. Fadhil Nuruddin, Nasir Shafiq

Abstract:

Due to growing environmental concerns of the cement industry, alternative cement technologies have become an area of increasing interest. It is now believed that new binders are indispensable for enhanced environmental and durability performance. Self-compacting Geopolymer concrete is an innovative method and improved way of concreting operation that does not require vibration for placing it and is produced by complete elimination of ordinary Portland cement. This paper documents the assessment of the compressive strength and workability characteristics of low-calcium fly ash based selfcompacting geopolymer concrete. The essential workability properties of the freshly prepared Self-compacting Geopolymer concrete such as filling ability, passing ability and segregation resistance were evaluated by using Slump flow, V-funnel, L-box and J-ring test methods. The fundamental requirements of high flowability and segregation resistance as specified by guidelines on Self Compacting Concrete by EFNARC were satisfied. In addition, compressive strength was determined and the test results are included here. This paper also reports the effect of extra water, curing time and curing temperature on the compressive strength of self-compacting geopolymer concrete. The test results show that extra water in the concrete mix plays a significant role. Also, longer curing time and curing the concrete specimens at higher temperatures will result in higher compressive strength.

Keywords: Fly ash, Geopolymer Concrete, Self-compactingconcrete, Self-compacting Geopolymer concrete

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Authors: N. Khiati

Abstract:

The present paper attempts to report on some findings that emerged out of a larger scale doctorate research into English language needs of a renowned Algerian company of Hydrocarbon industry. From a multifaceted English for specific purposes (ESP) research perspective, the paper considers the English needs of the finance/legal department staff in the midst of the conflicting needs perspectives involving both objective needs indicators (i.e., the pressure of globalised business) and the general negative attitudes among the administrative -mainly jurists- staff towards English (favouring a non-adaptation strategy). The researcher’s unearthing of the latter’s needs is an endeavour to concretise the concepts of unmet, or unconscious needs, among others. This is why, these initially uncovered hidden needs will be detailed questioning educational background, namely previous language of instruction; training experiences and expectations; as well as the actual communicative practices derived from the retrospective interviews and preliminary quantitative data of the questionnaire. Based on these rough clues suggesting real needs, the researcher will tentatively propose some implications for both pre-service and in-service training organisers as well as for educational policy makers in favour of an English course in legal English for the jurists mainly from pre-graduate phases to in-service training.

Keywords: English for specific purposes, ESP, legal and finance staff, needs analysis, unmet/unconscious needs, training implications.

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