Search results for: Decision Support System

Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: Brain activity, EEG, information processing model, model human processor.

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Authors: Madiajagan Muthaiyan, Neha Goel, Deepti Sunder Prakash

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This paper discusses about an intelligent system to be installed in ambulances providing professional support to the paramedics on board. A video conferencing device over mobile 4G services enables specialists virtually attending the patient being transferred to the hospital. The data centre holds detailed databases on the patients past medical history and hospitals with the specialists. It also hosts various software modules that compute the shortest traffic –less path to the closest hospital with the required facilities, on inputting the symptoms of the patient, on a real time basis.

Keywords: 4G mobile services, cloud computing, data centre, intelligent system, optimization, real time traffic reporting, SaaS, video conferencing.

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Authors: Francesco Ruscio, Paolo Paoletti, Jens Thomas, Paul Myers, Sebastiano Fichera

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This paper presents the development of a low-cost monitoring system for a hydroponic urban vertical farm, enabling its automation and a quantitative assessment of the farm performance. Urban farming has seen increasing interest in the last decade thanks to the development of energy efficient and affordable LED lights; however, the optimal configuration of such systems (i.e. amount of nutrients, light-on time, ambient temperature etc.) is mostly based on the farmers’ experience and empirical guidelines. Moreover, even if simple, the maintenance of such systems is labor intensive as it requires water to be topped-up periodically, mixing of the nutrients etc. To unlock the full potential of urban farming, a quantitative understanding of the role that each variable plays in the growth of the plants is needed, together with a higher degree of automation. The low-cost monitoring system proposed in this paper is a step toward filling this knowledge and technological gap, as it enables collection of sensor data related to water and air temperature, water level, humidity, pressure, light intensity, pH and electric conductivity without requiring any human intervention. More sensors and actuators can also easily be added thanks to the modular design of the proposed platform. Data can be accessed remotely via a simple web interface. The proposed platform can be used both for quantitatively optimizing the setup of the farms and for automating some of the most labor-intensive maintenance activities. Moreover, such monitoring system can also potentially be used for high-level decision making, once enough data are collected.

Keywords: Automation, hydroponics, internet of things, monitoring system, urban farming.

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Authors: G. Balakrishnan, G. Sainarayanan, R. Nagarajan, Sazali Yaacob

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This paper presents a review on vision aided systems and proposes an approach for visual rehabilitation using stereo vision technology. The proposed system utilizes stereo vision, image processing methodology and a sonification procedure to support blind navigation. The developed system includes a wearable computer, stereo cameras as vision sensor and stereo earphones, all moulded in a helmet. The image of the scene infront of visually handicapped is captured by the vision sensors. The captured images are processed to enhance the important features in the scene in front, for navigation assistance. The image processing is designed as model of human vision by identifying the obstacles and their depth information. The processed image is mapped on to musical stereo sound for the blind-s understanding of the scene infront. The developed method has been tested in the indoor and outdoor environments and the proposed image processing methodology is found to be effective for object identification.

Keywords: Blind navigation, stereo vision, image processing, object preference, music tones.

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Authors: Abdelaziz Zaidi, Belkacem Ould Bouamama, Moncef Tagina

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Bond Graph as a unified multidisciplinary tool is widely used not only for dynamic modelling but also for Fault Detection and Isolation because of its structural and causal proprieties. A binary Fault Signature Matrix is systematically generated but to make the final binary decision is not always feasible because of the problems revealed by such method. The purpose of this paper is introducing a methodology for the improvement of the classical binary method of decision-making, so that the unknown and identical failure signatures can be treated to improve the robustness. This approach consists of associating the evaluated residuals and the components reliability data to build a Hybrid Bayesian Network. This network is used in two distinct inference procedures: one for the continuous part and the other for the discrete part. The continuous nodes of the network are the prior probabilities of the components failures, which are used by the inference procedure on the discrete part to compute the posterior probabilities of the failures. The developed methodology is applied to a real steam generator pilot process.

Keywords: Redundancy relations, decision-making, Bond Graph, reliability, Bayesian Networks.

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Authors: Li-Chung Su, Chia-Yu Chen, Tzu-Yuan Lai, Sheng-Jye Hwang

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Mechanical design of the thin-film solar framed module and mounting system is important to enhance module reliability and to increase areas of applications. The stress induced by different mounting positions played a main role controlling the stability of the whole mechanical structure. From the finite element method, under the pressure from the back of module, the stress at Lc (center point of the Long frame) increased and the stresses at Center, Corner and Sc (center point of the Short frame) decreased while the mounting position was away from the center of the module. In addition, not only the stress of the glass but also the stress of the frame decreased. Accordingly it was safer to mount in the position away from the center of the module. The emphasis of designing frame system of the module was on the upper support of the Short frame. Strength of the overall structure and design of the corner were also important due to the complexity of the stress in the Long frame.

Keywords: Finite element method, Framed module, Mountingposition

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Authors: Elangeshwaran Pathmanathan, Rosdiazli Ibrahim, Vijanth Sagayan Asirvadam

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This paper aims to develop a NOx emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS) to report emission level online to DOE . As a hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive technique is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a LNG Complex. Simulated Annealing (SA) is first used to determine the initial hyperparameters which are then further optimized based on the performance of the model using Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on backpropagation neural networks (BPNN) in both training and testing data.

Keywords: artificial neural networks, industrial pollution, predictive algorithms, support vector machines

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Authors: P. Visutsak

Abstract:

The study of the interaction between humans and computers has been emerging during the last few years. This interaction will be more powerful if computers are able to perceive and respond to human nonverbal communication such as emotions. In this study, we present the image-based approach to emotion classification through lower facial expression. We employ a set of feature points in the lower face image according to the particular face model used and consider their motion across each emotive expression of images. The vector of displacements of all feature points input to the Adaptive Support Vector Machines (A-SVMs) classifier that classify it into seven basic emotions scheme, namely neutral, angry, disgust, fear, happy, sad and surprise. The system was tested on the Japanese Female Facial Expression (JAFFE) dataset of frontal view facial expressions [7]. Our experiments on emotion classification through lower facial expressions demonstrate the robustness of Adaptive SVM classifier and verify the high efficiency of our approach.

Keywords: emotion classification, facial expression, adaptive support vector machines, facial expression classifier.

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Authors: N. Rugthaicharoencheep, T. Langtharthong

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This paper presents the optimal placement and sizing of distributed generation (DG) in a distribution system. The problem is to reliability improvement of distribution system with distributed generations. The technique employed to solve the minimization problem is based on a developed Tabu search algorithm and reliability worth analysis. The developed methodology is tested with a distribution system of Roy Billinton Test System (RBTS) bus 2. It can be seen from the case study that distributed generation can reduce the customer interruption cost and therefore improve the reliability of the system. It is expected that our proposed method will be utilized effectively for distribution system operator.

Keywords: Distributed generation Optimization technique Reliability improvement, Distribution system.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: Wernhuar Tarng, Yuan-Yuan Chen, Chien-Lung Li, Kun-Rong Hsie, Mingteh Chen

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An emotional speech recognition system for the applications on smart phones was proposed in this study to combine with 3G mobile communications and social networks to provide users and their groups with more interaction and care. This study developed a mechanism using the support vector machines (SVM) to recognize the emotions of speech such as happiness, anger, sadness and normal. The mechanism uses a hierarchical classifier to adjust the weights of acoustic features and divides various parameters into the categories of energy and frequency for training. In this study, 28 commonly used acoustic features including pitch and volume were proposed for training. In addition, a time-frequency parameter obtained by continuous wavelet transforms was also used to identify the accent and intonation in a sentence during the recognition process. The Berlin Database of Emotional Speech was used by dividing the speech into male and female data sets for training. According to the experimental results, the accuracies of male and female test sets were increased by 4.6% and 5.2% respectively after using the time-frequency parameter for classifying happy and angry emotions. For the classification of all emotions, the average accuracy, including male and female data, was 63.5% for the test set and 90.9% for the whole data set.

Keywords: Smart phones, emotional speech recognition, socialnetworks, support vector machines, time-frequency parameter, Mel-scale frequency cepstral coefficients (MFCC).

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Authors: Kaveh Mollazade, Hojat Ahmadi, Mahmoud Omid, Reza Alimardani

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Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. The aim of this work is to investigate the effectiveness of a new fault diagnosis method based on power spectral density (PSD) of vibration signals in combination with decision trees and fuzzy inference system (FIS). To this end, a series of studies was conducted on an external gear hydraulic pump. After a test under normal condition, a number of different machine defect conditions were introduced for three working levels of pump speed (1000, 1500, and 2000 rpm), corresponding to (i) Journal-bearing with inner face wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii) Journal-bearing with inner face wear plus Gear with tooth face wear (B&GW). The features of PSD values of vibration signal were extracted using descriptive statistical parameters. J48 algorithm is used as a feature selection procedure to select pertinent features from data set. The output of J48 algorithm was employed to produce the crisp if-then rule and membership function sets. The structure of FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed PSD-J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500, and 2000 rpm conditions were 96.42%, 100%, and 96.42% respectively. The results indicate that the combined PSD-J48-FIS model has the potential for fault diagnosis of hydraulic pumps.

Keywords: Power Spectral Density, Machine ConditionMonitoring, Hydraulic Pump, Fuzzy Logic.

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Authors: Arda Yunianta, Norazah Yusof, Mohd Shahizan Othman, Dewi Octaviani

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Learning is the acquisition of new mental schemata, knowledge, abilities and skills which can be used to solve problems potentially more successfully. The learning process is optimum when it is assisted and personalized. Learning is not a single activity, but should involve many possible activities to make learning become meaningful. Many e-learning applications provide facilities to support teaching and learning activities. One way to identify whether the e-learning system is being used by the learners is through the number of hits that can be obtained from the e-learning system's log data. However, we cannot rely solely to the number of hits in order to determine whether learning had occurred meaningfully. This is due to the fact that meaningful learning should engage five characteristics namely active, constructive, intentional, authentic and cooperative. This paper aims to analyze the e-learning activities that is meaningful to learning. By focusing on the meaningful learning characteristics, we match it to the corresponding Moodle e-learning activities. This analysis discovers the activities that have high impact to meaningful learning, as well as activities that are less meaningful. The high impact activities is given high weights since it become important to meaningful learning, while the low impact has less weight and said to be supportive e-learning activities. The result of this analysis helps us categorize which e-learning activities that are meaningful to learning and guide us to measure the effectiveness of e-learning usage.

Keywords: e-learning system, e-learning activity, meaningful learning characteristics, Moodle

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Authors: A. Gruzdz, A. Ihnatowicz, J. Siddiqi, B. Akhgar

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MATCH project [1] entitle the development of an automatic diagnosis system that aims to support treatment of colon cancer diseases by discovering mutations that occurs to tumour suppressor genes (TSGs) and contributes to the development of cancerous tumours. The constitution of the system is based on a) colon cancer clinical data and b) biological information that will be derived by data mining techniques from genomic and proteomic sources The core mining module will consist of the popular, well tested hybrid feature extraction methods, and new combined algorithms, designed especially for the project. Elements of rough sets, evolutionary computing, cluster analysis, self-organization maps and association rules will be used to discover the annotations between genes, and their influence on tumours [2]-[11]. The methods used to process the data have to address their high complexity, potential inconsistency and problems of dealing with the missing values. They must integrate all the useful information necessary to solve the expert's question. For this purpose, the system has to learn from data, or be able to interactively specify by a domain specialist, the part of the knowledge structure it needs to answer a given query. The program should also take into account the importance/rank of the particular parts of data it analyses, and adjusts the used algorithms accordingly.

Keywords: Bioinformatics, gene expression, ontology, selforganizingmaps.

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Authors: Raju Kaduru, Narsaiah Srinivas Gondlala

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This paper presents an analytical approach for evaluating distribution system reliability indices in the presence of distributed generation. Modeling distributed generation and evaluation of distribution system reliability indices using the frequency duration technique. Using model implements and case studies are discussed. Results showed that location of DG and its effect in distribution reliability indices. In this respect, impact of DG on distribution system is investigated using the IEEE Roy Billinton test system (RBTS2) included feeder 1. Therefore, it will help to the distribution system planners in the DG resource placement.

Keywords: Distributed Generation, DG Location, Distribution System, Reliability Indices.

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Authors: Mui Joo Tang, Eang Teng Chan

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Advertising is commonly used to foster sales and reputation of an institution. It is at first the growth of print advertising that has increased the population and number of periodicals of newspaper and its circulation. The rise of Internet and online media has somehow blurred the role of media and advertising though the intention is still to reach out to audience and to increase sales. The relationship between advertising and audience on a product purchase through persuasion has been developing from print media to online media. From the changing media environment and audience, it is the concern of this research to study the impact of online advertising to such a relationship cycle. The content of online advertisements is much of text, multimedia, photo, audio and video. The messages of such content format may indeed bring impacts to its audience and its credibility. This study is therefore reflecting the effectiveness of online advertisement and its influences on generation Y in their purchasing behavior. This study uses Media Dependency Theory to analyze the relationship between the impact of online advertisement and media usage pattern of generation Y. Hierarchy of Effectiveness Model is used as a marketing communication model to study the effectiveness of advertising and further to determine the impact of online advertisement on generation Y in their purchasing decision making. This research uses online survey to reach out the sample of generation Y. The results have shown that online advertisements do not affect much on purchase decision making even though generation Y relies much on the media content including online advertisement for its information and believing in its credibility. There are few other external factors that may interrupt the effectiveness of online advertising. The very obvious influence of purchasing behavior is actually derived from the peers.

Keywords: Generation Y, online advertising, online media, persuasion, print media, purchase decision.

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Authors: P.W. C. Prasad, A. Assi, A. Harb, V.C. Prasad

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This paper presents an improved variable ordering method to obtain the minimum number of nodes in Reduced Ordered Binary Decision Diagrams (ROBDD). The proposed method uses the graph topology to find the best variable ordering. Therefore the input Boolean function is converted to a unidirectional graph. Three levels of graph parameters are used to increase the probability of having a good variable ordering. The initial level uses the total number of nodes (NN) in all the paths, the total number of paths (NP) and the maximum number of nodes among all paths (MNNAP). The second and third levels use two extra parameters: The shortest path among two variables (SP) and the sum of shortest path from one variable to all the other variables (SSP). A permutation of the graph parameters is performed at each level for each variable order and the number of nodes is recorded. Experimental results are promising; the proposed method is found to be more effective in finding the variable ordering for the majority of benchmark circuits.

Keywords: Binary decision diagrams, graph representation, Boolean functions representation, variable ordering.

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Authors: Rachid Ahdid, Said Safi, Bouzid Manaut

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In this paper, we present a comparative study of three methods of 2D face recognition system such as: Iso-Geodesic Curves (IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram (GIH). These approaches are based on computing of geodesic distance between points of facial surface and between facial curves. In this study we represented the image at gray level as a 2D surface in a 3D space, with the third coordinate proportional to the intensity values of pixels. In the classifying step, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The images used in our experiments are from two wellknown databases of face images ORL and YaleB. ORL data base was used to evaluate the performance of methods under conditions where the pose and sample size are varied, and the database YaleB was used to examine the performance of the systems when the facial expressions and lighting are varied.

Keywords: 2D face recognition, Geodesic distance, Iso-Geodesic Curves, Geodesic-Intensity Histogram, facial surface, Neural Networks, K-Nearest Neighbor, Support Vector Machines.

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Authors: Ayad A. Abdulkafi, Shahir F. Nawaf, Mohammed K. Hussein, Ibrahim K. Sileh, Fouad A. Abdulkafi

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Visible light communication (VLC) is a new approach of optical wireless communication proposed to support the congested radio frequency (RF) spectrum. VLC systems are combined with orthogonal frequency division multiplexing (OFDM) to achieve high rate transmission and high spectral efficiency. In this paper, we investigate the Pilot-Assisted Channel Estimation for DC biased Optical OFDM (PACE-DCO-OFDM) systems to reduce the effects of the distortion on the transmitted signal. Least-square (LS) and linear minimum mean-squared error (LMMSE) estimators are implemented in MATLAB/Simulink to enhance the bit-error-rate (BER) of PACE-DCO-OFDM. Results show that DCO-OFDM system based on PACE scheme has achieved better BER performance compared to conventional system without pilot assisted channel estimation. Simulation results show that the proposed PACE-DCO-OFDM based on LMMSE algorithm can more accurately estimate the channel and achieves better BER performance when compared to the LS based PACE-DCO-OFDM and the traditional system without PACE. For the same signal to noise ratio (SNR) of 25 dB, the achieved BER is about 5×10^-4 for LMMSE-PACE and 4.2×10^-3 with LS-PACE while it is about 2×10^-1 for system without PACE scheme.

Keywords: Channel estimation, OFDM, pilot-assist, VLC.

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Authors: Yu-Chi Shiue, Jyong Lin, Shih-Chang Chen

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With the increase of economic behavior and the upgrade of living standar, the ratio for people in Taiwan who own automobiles and motorcycles have recently increased with multiples. Therefore, parking issues will be a big challenge to facilitate traffic network and ensure urban life quality. The Parking Guidance and Information System is one of important systems for Advanced Traveler Information Services (ATIS). This research proposes a parking guidance and information system which integrates GPS and 3G network for a map on the Geographic Information System to solution inadequate of roadside information kanban. The system proposed in this study mainly includes Parking Host, Parking Guidance and Information Server, Geographic Map and Information System as well as Parking Guidance and Information Browser. The study results show this system can increase driver-s efficiency to find parking space and efficiently enhance parking convenience in comparison with roadside kanban system.

Keywords: Geographic Information System, 3G, GPS, parkinginformation

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Authors: Tayfun Çay, Yaşar İnceyol, Abdurrahman Özbeyaz

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Land reallocation is one of the most important steps in land consolidation projects. Many different models were proposed for land reallocation in the literature such as Fuzzy Logic, block priority based land reallocation and Spatial Decision Support Systems. A model including four parts is considered for automatic block reallocation with genetic algorithm method in land consolidation projects. These stages are preparing data tables for a project land, determining conditions and constraints of land reallocation, designing command steps and logical flow chart of reallocation algorithm and finally writing program codes of Genetic Algorithm respectively. In this study, we designed the first three steps of the considered model comprising four steps.

Keywords: Genetic algorithm, land consolidation, landholding, land reallocation.

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Authors: Dewan Md. Farid, Nguyen Huu Hoa, Jerome Darmont, Nouria Harbi, Mohammad Zahidur Rahman

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In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of naïve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that naïve Bayesian tree improves the classification rates in large dataset. In naïve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain naïve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection.

Keywords: Detection rates, false positives, network intrusiondetection, naïve Bayesian tree.

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Authors: Stephen Akuma, Timothy Ndera

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Information seekers get “lost in hyperspace” due to the voluminous documents updated daily on the internet. Adaptive Hypermedia Systems (AHS) are used to direct learners to their target goals. One of the most common AHS designed to help information seekers to overcome the problem of information overload is the Adaptive Education Hypermedia System (AEHS). However, this paper focuses on AEHS that adopts the learning preference of high school students and deliver learning content according to this preference throughout their learning experience. The research developed a prototype system for predicting students’ learning preference from the Visual, Aural, Read-Write and Kinesthetic (VARK) learning style model and adopting the learning content suitable to their preference. The predicting strength of several classifiers was compared and we found Support Vector Machine (SVM) to be more accurate in predicting learning style based on users’ preferences.

Keywords: Hypermedia, adaptive education, learning style, lesson content, user profile, prediction, feedback, adaptive hypermedia, learning style.

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Authors: Daniel J. Glauber

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Knowledge transfer between personnel could benefit an organization’s improved competitive advantage in the marketplace from a strategic approach to knowledge management. The lack of information sharing between personnel could create knowledge transfer gaps while restricting the decision-making processes. Knowledge transfer between personnel can potentially improve information sharing based on an implemented knowledge management strategy. An organization’s capacity to gain more knowledge is aligned with the organization’s prior or existing captured knowledge. This case study attempted to understand the overall influence of a KMS within the corporate environment and knowledge exchange between personnel. The significance of this study was to help understand how organizations can improve the Return on Investment (ROI) of a knowledge management strategy within a knowledge-centric organization. A qualitative descriptive case study was the research design selected for this study. The lack of information sharing between personnel may create knowledge transfer gaps while restricting the decision-making processes. Developing a knowledge management strategy acceptable at all levels of the organization requires cooperation in support of a common organizational goal. Working with management and executive members to develop a protocol where knowledge transfer becomes a standard practice in multiple tiers of the organization. The knowledge transfer process could be measurable when focusing on specific elements of the organizational process, including personnel transition to help reduce time required understanding the job. The organization studied in this research acknowledged the need for improved knowledge management activities within the organization to help organize, retain, and distribute information throughout the workforce. Data produced from the study indicate three main themes including information management, organizational culture, and knowledge sharing within the workforce by the participants. These themes indicate a possible connection between an organizations KMS, the organizations culture, knowledge sharing, and knowledge transfer.

Keywords: Knowledge management strategies, knowledge transfer, knowledge management, knowledge capacity.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

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Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Goh Yen Nee

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This research aimed to find out the determining factors for ISO 14001 EMS implementation among SMEs in Malaysia from the Resource based view. A cross-sectional approach using survey was conducted. A research model been proposed which comprises of ISO 14001 EMS implementation as the criterion variable while physical capital resources (i.e. environmental performance tracking and organizational infrastructures), human capital resources (i.e. top management commitment and support, training and education, employee empowerment and teamwork) and organizational capital resources (i.e. recognition and reward, organizational culture and organizational communication) as the explanatory variables. The research findings show that only environmental performance tracking, top management commitment and support and organizational culture are found to be positively and significantly associated with ISO 14001 EMS implementation. It is expected that this research will shed new knowledge and provide a base for future studies about the role played by firm-s internal resources.

Keywords: ISO 14001 Environmental Management System, Malaysia, Resource based view, SMEs

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Authors: Essam Al-Daoud

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Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.

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Authors: Amjaad Alsalamah, Liyakathunsia Syed

Abstract:

Transportation is an essential need for many people to go to their work, school, and home. In particular, the main common method inside many cities is to drive the car. Driving a car can be an easy job to reach the destination and load all stuff in a reasonable time. However, deciding to find a parking lot for a car can take a long time using the traditional system that can issue a paper ticket for each customer. The old system cannot guarantee a parking lot for all customers. Also, payment methods are not always available, and many customers struggled to find their car among a numerous number of cars. As a result, this research focuses on providing an online smart parking system in order to save time and budget. This system provides a flexible management system for both parking owner and customers by receiving all request via the online system and it gets an accurate result for all available parking and its location.

Keywords: Smart parking system, IoT, tracking system, process model, cost, time.

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Authors: M. H. Moradi, S. M. Moosavi, A. R. Reisi

Abstract:

The main objective of this paper is to investigate the enhancement of power system stability via coordinated tuning of Power System Stabilizers (PSSs) in a multi-machine power system. The design problem of the proposed controllers is formulated as an optimization problem. Chaotic catfish particle swarm optimization (C-Catfish PSO) algorithm is used to minimize the ITAE objective function. The proposed algorithm is evaluated on a two-area, 4- machines system. The robustness of the proposed algorithm is verified on this system under different operating conditions and applying a three-phase fault. The nonlinear time-domain simulation results and some performance indices show the effectiveness of the proposed controller in damping power system oscillations and this novel optimization algorithm is compared with particle swarm optimization (PSO).

Keywords: Power system stabilizer, C-Catfish PSO, ITAE objective function, Power system control, Multi-machine power system

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Authors: Chih-Jian Hu, Chung-Chih Cheng, Hsiao-Yuan Wu., Nien-Tzu Chao

Abstract:

Increasing user comfort and reducing operation costs have always been primary objectives of lighting control strategies in a building. This paper proposes an architecture of the lighting control system for a daylit office. The system consists of the lighting controller, A/D & D/A converter, dimmable LED lights, and the lighting management software. Verification tests are conducted using the proposed system specialized for the interior lighting of a open-plan office. The results showed the proposed architecture of the lighting system would improve the overall system reliability, lower the system cost, and provide ease of installation and maintenance.

Keywords: control, dimming, LED, lighting.

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