Search results for: gaussian selection operator

Authors: Mahbube Eghtesad

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For a number of years, the problem of simultaneous detection and tracking of a target has been one of the most relevant and challenging issues in a wide variety of military and civilian systems. We develop methods for detecting and tracking a target using an orthogonal frequency division multiplexing (OFDM) based radar. As a preliminary step we introduce the target trajectory and Gaussian noise model in discrete time form. Then resorting to match filter and Kalman filter we derive a detector and target tracker. After that we propose an OFDM radar in order to achieve further improvement in tracking performance. The motivation for employing multiple frequencies is that the different scattering centers of a target resonate differently at each frequency. Numerical examples illustrate our analytical results, demonstrating the achieved performance improvement due to the OFDM signaling method.

Keywords: matched filter, target trashing, OFDM radar, Kalman filter

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Authors: Samuel Capintero

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Public-private partnerships (PPP) arrangements have emerged all around the world as a response to infrastructure deficits and the need to refurbish existing infrastructure. During the last decade, the Spanish companies have dominated the international market of PPP projects in Latin America, Western Europe and North America, particularly in the transportation sector. Arguably, one of the most influential factors has been the organizational structure of the concessionaire implemented by the Spanish consortiums. The model followed by most Spanish groups has been a bundled model, where the concessionaire integrates the functions of concessionaire, construction and operator companies. This paper examines this model and explores how it has provided the Spanish companies with a comparative advantage in the international PPP market.

Keywords: PPP, project management, concessionaire, concession, infrastructure, construction

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Authors: M. N. A. Azman, M. S. S. Ahamad

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The Construction Industry Development Board of Malaysia has been actively promoting the use of precast manufacturing in the local construction industry over the last decade. In an era of rapid technological changes, precast manufacturing significantly contributes to improving construction activities and ensuring sustainable economic growth. Current studies on the location decision of precast manufacturing plants aimed to enhanced local economic development are scarce. To address this gap, the present research establishes a new set of spatial criteria, such as attribute maps and preference weights, derived from a survey of local industry decision makers. These data represent the input parameters for the MCE-GIS site selection model, for which the weighted linear combination method is used. Verification tests on the model were conducted to determine the potential precast manufacturing sites in the state of Penang, Malaysia. The tests yield a predicted area of 12.87 acres located within a designated industrial zone. Although, the model is developed specifically for precast manufacturing plant but nevertheless it can be employed to other types of industries by following the methodology and guidelines proposed in the present research.

Keywords: geographical information system, multi criteria evaluation, industrialised building system, civil engineering

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Lamia Bouzgarrou, Amira Omrane, Haithem Kalel, Salma Kammoun

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Background: During recent decades, mechanical and electrical industrial sector has greatly expanded with a significant employability potential. However, this sector faces the increasing prevalence of musculoskeletal disorders with heavy consequences associated with direct and indirect costs. Objective: The current intervention was motivated by large musculoskeletal upper limbs and back disorders frequency among the operators of an assembly workshop in a leader company specialized in sanitary equipment and water and gas connections. We aimed to identify biomechanical constraints among these operators through activity and biomechanical exposures semi-quantitative analysis based on video recordings and MUSKA-TMS software. Methods: We conducted, open observations and exploratory interviews at first, in order to overall understand work situation. Then, we analyzed operator’s activity through systematic observations and interviews. Finally, we conducted a semi-quantitative biomechanical constraints analysis with MUSKA-TMS software after representative activity period video recording. The assessment of biomechanical constrains was based on different criteria; biomechanical characteristics (work positions), aggravating factor (cold, vibration, stress, etc.) and exposure time (duration and frequency of solicitations, recovery phase); with a synthetic score of risk level variable from 1 to 4 (1: low risk of developing MSD and 4: high risk). Results: Semi-quantitative analysis objective many elementary operations with higher biomechanical constrains like high repetitiveness, insufficient recovery time and constraining angulation of shoulders, wrists and cervical spine. Among these risky elementary operations we sited the assembly of sleeve with the body, the assembly of axis, and the control on testing table of gas valves. Transformation of work situations were recommended, covering both the redevelopment of industrial areas and the integration of new tools and equipment of mechanical handling that reduces operator exposure to vibration. Conclusion: Musculoskeletal disorders are complex and costly disorders. Moreover, an approach centered on the observation of the work can promote the interdisciplinary dialogue and exchange between actors with the objective to maximize the performance of a company and improve the quality of life of operators.

Keywords: musculoskeletal disorders, biomechanical constrains, semi-quantitative analysis, ergonomics

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Authors: Saeed Sayyad Hagh Shomar

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Traffic congestion pricing – as a strategy in travel demand management in urban areas to reduce traffic congestion, air pollution and noise pollution – has drawn many attentions towards itself. Unlike the satisfying findings in this method, there are still problems in determining the best functional congestion pricing scheme with regard to the situation. The so-called problems in this process will result in further complications and even the scheme failure. That is why having proper knowledge of the significance of congestion pricing schemes and the effective factors in choosing them can lead to the success of this strategy. In this study, first, a variety of traffic congestion pricing schemes and their components are introduced; then, their functional usage is discussed. Next, by analyzing and comparing the barriers, limitations and advantages, the selection criteria of pricing schemes are described. The results, accordingly, show that the selection of the best scheme depends on various parameters. Finally, based on examining the effective parameters, it is concluded that the implementation of area-based schemes (cordon and zonal) has been more successful in non-diversion of traffic. That is considering the topology of the cities and the fact that traffic congestion is often created in the city centers, area-based schemes would be notably functional and appropriate.

Keywords: congestion pricing, demand management, flat toll, variable toll

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Authors: Mohsen Khaledian, Razali Ismail, Mehdi Saeidmanesh, Mahdiar Hosseinghadiry

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A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy Et and the distance traveled by electron gaining Et. In addition, ionization threshold energy is semi-analytically modeled for GNR. We justify our assumptions using analytic modeling and comparison with simulation results. Gaussian simulator together with analytical modeling is used in order to calculate ionization threshold energy and Kinetic Monte Carlo is employed to calculate ionization coefficient and verify the analytical results. Finally, the profile of ionization is presented using the proposed models and simulation and the results are compared with that of silicon.

Keywords: nanostructures, electronic transport, semiconductor modeling, systems engineering

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: Bhupeshwaran Mani, S. Radha, A. Jawahar, A. Sivasubramanian

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Here, we study the characteristic feature of conventional (ON-OFF keying) and soliton based transmission system. We consider 20 Gbps transmission system implemented with Conventional Single Mode Fiber (C-SMF) to examine the role of Gaussian pulse which is the characteristic of conventional propagation and hyperbolic-secant pulse which is the characteristic of soliton propagation in it. We note the influence of these pulses with respect to different dispersion lengths and soliton period in conventional and soliton system, respectively, and evaluate the system performance in terms of quality factor. From the analysis, we could prove that the soliton pulse has more consistent performance even for long distance without dispersion compensation than the conventional system as it is robust to dispersion. For the length of transmission of 200 Km, soliton system yielded Q of 33.958 while the conventional system totally exhausted with Q=0.

Keywords: dispersion length, retrun-to-zero (rz), soliton, soliton period, q-factor

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Authors: Katsumi Hirata

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To develop the useful acoustic environmental recognition system, the method of estimating 3D-position of a stationary random acoustic source using bispectral analysis of 4-point detected signals is proposed. The method uses information about amplitude attenuation and propagation delay extracted from amplitude ratios and angles of auto- and cross-bispectra of the detected signals. It is expected that using bispectral analysis affects less influence of Gaussian noises than using conventional power spectral one. In this paper, the basic principle of the method is mentioned first, and its validity and features are considered from results of the fundamental experiments assumed ideal circumstances.

Keywords: 4-point detection, a stationary random acoustic source, auto- and cross-bispectra, estimation of 3D-position

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Authors: Mohammed A. Zidan, Alaa Sagheer, Nasser Metwally

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Real-time learning is an important goal that most of artificial intelligence researches try to achieve it. There are a lot of problems and applications which require low cost learning such as learn a robot to be able to classify and recognize patterns in real time and real-time recall. In this contribution, we suggest a model of quantum competitive learning based on a series of quantum gates and additional operator. The proposed model enables to recognize any incomplete patterns, where we can increase the probability of recognizing the pattern at the expense of the undesired ones. Moreover, these undesired ones could be utilized as new patterns for the system. The proposed model is much better compared with classical approaches and more powerful than the current quantum competitive learning approaches.

Keywords: competitive learning, quantum gates, quantum gates, winner-take-all

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Authors: P. P. L. Kwan, D. K. S. Chan

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Organizations are constantly changing in today’s business environment. Research findings have revealed that overcoming resistance and getting employees ready for change is a crucial driver for organizational change success. Thus, change adaptability has become a more prominent selection criterion used in many organizations. Although change readiness could be situation-specific, employees’ personality, emotion, and cognition should also be crucial factors in shaping their readiness. However, relatively little research has focused on the roles of individual characteristics in organizational changes. The present study examines the relations between individual characteristics and change readiness with the aim to validate a model, which proposes three types of individual attributes as antecedents to change readiness. The three attributes considered are trait cynicism, positive affectivity, and personal valence covering personality, emotional, and cognitive aspects respectively. The model also hypothesizes that relations between the three antecedents and change readiness will be moderated by a positive mental resource known as psychological capital (PsyCap), which consists of hope, optimism, efficacy and resilience; and a learning culture within the organization. We are currently collecting data from a targeted sample size of 300 Hong Kong employees. Specifically, participants complete a questionnaire which was designed to measure their perceived change efficacy in response to three scenarios commonly happened in the workplace (i.e., business acquisition, team restructuring, and information system change) as a measure of change readiness, as well as the aforementioned individual characteristics. Preliminary analysis provides some support to the hypotheses. That is, employees who are less cynical in personality and more positive in their cognition and affectivity particularly welcome the potential changes in their organizations. Further data collection and analyses are continuously carried out for a more definitive conclusion. Our findings will shed light on employee selection; and on how strengthening positive psychological resources and promoting the culture of learning organization among employees may enhance the chance to succeed for organizations undergoing change.

Keywords: learning organization, psychological capital, readiness for change, employee selection

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Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Hasan Heydari

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In recent decades, locating urban services is one of the significant discussions in urban planning. Among these considerations, cities require more accurate planning in order to supply citizen needs, especially part of urban safety. In order to gain this goal, one of the main tasks of urban planners and managers is specifying suitable sites to locate firefighting stations. This study has been done to reach this purpose. Therefore effective criteria consist of coverage radius, population density, proximity to pathway network, land use (compatible and incompatible neighborhood) have been specified. After that, descriptive and local information of the criteria was provided and their layers were created in ArcGIS 9.3. Using Analytic Hierarchy Process (AHP) these criteria and their sub-criteria got the weights. These layers were classified regarding their weights and finally were overlaid by Index Overlay Model and provided the final site selection map for firefighting stations of Arak city. The results gained by analyzing in GIS environment indicate the existing fire station don’t cover the whole city sufficiently and some of the stations have established on the unsuitable sites. The output map indicates the best sites to locate firefighting stations of Arak.

Keywords: site-selection, firefighting stations, analytic hierarchy process (AHP), GIS, index overlay model

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Authors: Rakesh Dubey, Vaishali Bahl, Dalveer Kaur

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The spiralling urge for high rate data transmission over wireless mediums needs intelligent use of electromagnetic resources considering restrictions like power ingestion, spectrum competence, robustness against multipath propagation and implementation intricacy. Orthogonal frequency division multiplexing (OFDM) is a capable technique for next generation wireless communication systems. For such high rate data transfers there is requirement of proper allocation of resources like power and capacity amongst the sub channels. This paper illustrates various available methods of allocating power and the capacity requirement with the constraint of Shannon limit.

Keywords: Additive White Gaussian Noise, Multi-Carrier Modulation, Orthogonal Frequency Division Multiplexing (OFDM), Signal to Noise Ratio (SNR), Water Filling

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Authors: Naime Boudemagh

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In this work, we aim to improve the detection performances of radar systems. To this end, we propose and analyze a novel censoring technique of undesirable samples, of priori unknown positions, that may be present in the environment under investigation. Therefore, we consider heterogeneous backgrounds characterized by the presence of some irregularities such that clutter edge transitions and/or interfering targets. The proposed detector, termed automatic censoring constant false alarm (AC-CFAR), operates exclusively in a Gaussian background. It is built to allow the segmentation of the environment to regions and switch automatically to the appropriate detector; namely, the cell averaging CFAR (CA-CFAR), the censored mean level CFAR (CMLD-CFAR) or the order statistic CFAR (OS-CFAR). Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed processor exhibits considerable robustness in a heterogeneous background.

Keywords: CFAR, automatic censoring, heterogeneous environments, radar systems

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Authors: Evgeny Kozubenko, Dmitry Shaposhnikov, Mikhail Petrushan

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This work is focused on the study of the binocular characteristics of the phenomenon of perisaccadic suppression in humans when perceiving visual objects. This phenomenon manifests in a decrease in the subject's ability to perceive visual information during saccades, which play an important role in purpose-driven behavior and visual perception. It was shown that the impairment of perception of visual information in the post-saccadic time window is stronger (p < 0.05) in the ipsilateral eye (the eye towards which the saccade occurs). In addition, the observed heterogeneity of post-saccadic suppression in the contralateral and ipsilateral eyes may relate to depth perception. Taking the studied phenomenon into account is important when developing ergonomic control panels in modern operator systems.

Keywords: eye movement, natural vision, saccadic suppression, visual perception

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Shabnam Najafi, Metin Turkay

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Network design problem (NDP) is used to determine the set of optimal values for certain pre-specified decision variables such as capacity expansion of nodes and links by optimizing various system performance measures including safety, congestion, and accessibility. The designed transportation network should improve objective functions defined for the system by considering the route choice behaviors of network users at the same time. The NDP studies mostly investigated the random demand and route selection constraints separately due to computational challenges. In this work, we consider both random demand and route selection constraints simultaneously. This work presents a nonlinear stochastic model for land use and road network design problem to address the development of different functional zones in urban areas by considering both cost function and air pollution. This model minimizes cost function and air pollution simultaneously with random demand and stochastic route selection constraint that aims to optimize network performance via road capacity expansion. The Bureau of Public Roads (BPR) link impedance function is used to determine the travel time function in each link. We consider a city with origin and destination nodes which can be residential or employment or both. There are set of existing paths between origin-destination (O-D) pairs. Case of increasing employed population is analyzed to determine amount of roads and origin zones simultaneously. Minimizing travel and expansion cost of routes and origin zones in one side and minimizing CO emission in the other side is considered in this analysis at the same time. In this work demand between O-D pairs is random and also the network flow pattern is subject to stochastic user equilibrium, specifically logit route choice model. Considering both demand and route choice, random is more applicable to design urban network programs. Epsilon-constraint is one of the methods to solve both linear and nonlinear multi-objective problems. In this work epsilon-constraint method is used to solve the problem. The problem was solved by keeping first objective (cost function) as the objective function of the problem and second objective as a constraint that should be less than an epsilon, where epsilon is an upper bound of the emission function. The value of epsilon should change from the worst to the best value of the emission function to generate the family of solutions representing Pareto set. A numerical example with 2 origin zones and 2 destination zones and 7 links is solved by GAMS and the set of Pareto points is obtained. There are 15 efficient solutions. According to these solutions as cost function value increases, emission function value decreases and vice versa.

Keywords: epsilon-constraint, multi-objective, network design, stochastic

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Authors: Manpreet Singh, V. P. Agrawal, Gurmanjot Singh Bhatti

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From the last decades, there is a significant technological advancement in the field of robotics, and a number of modular self-reconfigurable robots were introduced that can help in space exploration, bucket to stuff, search, and rescue operation during earthquake, etc. As there are numbers of self-reconfigurable robots, choosing the optimum one is always a concern for robot user since there is an increase in available features, facilities, complexity, etc. The objective of this research work is to present a multiple attribute decision making based methodology for coding, evaluation, comparison ranking and selection of modular self-reconfigurable robots using a technique for order preferences by similarity to ideal solution approach. However, 86 attributes that affect the structure and performance are identified. A database for modular self-reconfigurable robot on the basis of different pertinent attribute is generated. This database is very useful for the user, for selecting a robot that suits their operational needs. Two visual methods namely linear graph and spider chart are proposed for ranking of modular self-reconfigurable robots. Using five robots (Atron, Smores, Polybot, M-Tran 3, Superbot), an example is illustrated, and raking of the robots is successfully done, which shows that Smores is the best robot for the operational need illustrated, and this methodology is found to be very effective and simple to use.

Keywords: self-reconfigurable robots, MADM, TOPSIS, morphogenesis, scalability

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Authors: Yang Zhou, Kaijian Li, Guiwen Liu

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Offsite construction is an innovative alternative to conventional site-based construction, with wide-ranging benefits. It requires building components, elements or modules were prefabricated and pre-assembly before installed into their final locations. To improve efficiency and achieve synergies, in recent years, construction companies were clustered into construction industrial parks (CIPs) in China. A CIP is a community of construction manufacturing and service businesses located together on a common property. Companies involved in industrial clusters can obtain environment and economic benefits by sharing resources and information in a given region. Therefore, the concept of industrial symbiosis (IS) can be applied to the traditional CIP to achieve sustainable industrial development or redevelopment through the implementation of eco-industrial parks (EIP). However, before designing a symbiosis network between companies in a CIP, candidate support tenants need to be selected to complement the existing construction companies. In this study, an access indicator system and a linear programming model are established to select candidate tenants in a CIP while satisfying the degree of connectivity among the enterprises in the CIP, minimizing the environmental impact, and maximizing the annualized profit of the CIP. The access indicator system comprises three primary indicators and fifteen secondary indicators, is proposed from the perspective of park-based level. The fifteen indicators are classified as three primary indicators including industrial symbiosis, environment performance and economic benefit, according to the three dimensions of sustainability (environment, economic and social dimensions) and the three R's of the environment (reduce, reuse and recycle). The linear programming model is a method to assess the satisfactoriness of all the indicators and to make an optimal multi-objective selection among candidate tenants. This method provides a practical tool for planners of a CIP in evaluating which among the candidate tenants would best complement existing anchor construction tenants. The reasonability and validity of the indicator system and the method is worth further study in the future.

Keywords: construction industrial park, China, industrial symbiosis, offsite construction, selection of support tenants

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Authors: John Narh

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There is high rate of deforestation in Ghana due to agricultural expansion, illegal mining and illegal logging. While it is attempting to address the illegalities, Ghana has also initiated a reforestation program known as the Modified Taungya System (MTS). Within the MTS framework, farmers are allocated degraded forestland and provided with tree seedlings to practice agroforestry until the trees form canopy. Yet, the political, ecological and economic models that inform the selection of tree species, the motivations of participating farmers as well as the factors that accounts for differential access to the land and performance of farmers engaged in the program lie underexplored. Using a sequential explanatory mixed methods approach in five forest-fringe communities in the Eastern Region of Ghana, the study reveals that economic factors and Ghana’s commitment to international conventions on the environment underpin the selection of tree species for the MTS program. Social network and access to remittances play critical roles in having access to, and enhances poor farmers’ chances in the program respectively. Farmers are more motivated by the access to degraded forestland to cultivate food crops than having a share in the trees that they plant. As such, in communities where participating farmers are not informed about their benefit in the tree that they plant, the program is largely unsuccessful.

Keywords: translocality, deforestation, forest management, social network

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Authors: Alireza Taghdiri, Sara Ghanbarzade Ghomi

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With the gravity load reduction in the structural and non-structural components, the lightweight construction will be achieved as well as the improvement of efficiency and functional specifications. The advantages of lightweight construction can be examined in two levels. The first is the mass reduction of load bearing structure which results in increasing internal useful space and the other one is the mass reduction of building which decreases the effects of seismic load as a result. In order to achieve this goal, the essential building materials specifications and also optimum load bearing geometry of structural systems and elements have to be considered, so lightweight materials selection particularly with lightweight aggregate for building components will be the first step of lightweight construction. In the next step, in addition to selecting the prominent samples of Iran's traditional architecture, the process of these works improvement is analyzed through the viewpoints of structural efficiency and lightweighting and also the practical methods of lightweight construction have been extracted. The optimum design of load bearing geometry of structural system has to be considered not only in the structural system elements, but also in their composition and the selection of dimensions, proportions, forms and optimum orientations, can lead to get a maximum materials efficiency for loads and stresses bearing.

Keywords: gravity load, light-weighting structural system, load bearing geometry, seismic behavior

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Authors: Forouzan Salehi Fergeni

Abstract:

Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Ashis Pradhan, Mohan P. Pradhan

Abstract:

Pattern Matching is useful for recognizing character in a digital image. OCR is one such technique which reads character from a digital image and recognizes them. Line segmentation is initially used for identifying character in an image and later refined by morphological operations like binarization, erosion, thinning, etc. The work discusses a recognition technique that defines a set of morphological operators based on its orientation in a character. These operators are further categorized into groups having similar shape but different orientation for efficient utilization of memory. Finally the characters are recognized in accordance with the occurrence of frequency in hierarchy of significant pattern of those morphological operators and by comparing them with the existing database of each character.

Keywords: binary image, morphological patterns, frequency count, priority, reduction data set and recognition

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Authors: Carolina Senabre, Sergio Valero, Miguel Lopez, Antonio Gabaldon

Abstract:

This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research.

Keywords: short-term load forecasting, power demand, neural networks, load forecasting

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Authors: S. R. A. Rahim, I. Musirin, M. M. Othman, M. H. Hussain

Abstract:

Loss minimization is a long progressing issue mainly in distribution system. Nevertheless, its effect led to temperature rise due to significant voltage drop through the distribution line. Thus, compensation scheme should be proper scheduled in the attempt to alleviate the voltage drop phenomenon. Distributed generation has been profoundly known for voltage profile improvement provided that over-compensation or under-compensation phenomena are avoided. This paper addresses the issue of voltage improvement through different type DG installation. In ensuring optimal sizing and location of the DGs, predeveloped EMEFA technique was made to be used for this purpose. Incremental loading condition subjected to the system is the concern such that it is beneficial to the power system operator.

Keywords: distributed generation, EMEFA, power loss, voltage profile

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Authors: Kun-Peng Jin, Jin Liang, Ti-Jun Xiao

Abstract:

In this work, we are concerned with the dynamic behaviors of solutions to some coupled systems with infinite memory, which consist of two partial differential equations where only one partial differential equation has damping. Such coupled systems are good mathematical models to describe the deformation and stress characteristics of some viscoelastic materials affected by temperature change, external forces, and other factors. By using the theory of operator semigroups, we give wellposedness results for the Cauchy problem for these coupled systems. Then, with the help of some auxiliary functions and lemmas, which are specially designed for overcoming difficulties in the proof, we show that the solutions of the coupled systems decay to zero in a strong way under a few basic conditions. The results in this dynamic analysis of coupled systems are generalizations of many existing results.

Keywords: dynamic analysis, coupled system, infinite memory, damping.

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