Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Статья научная
Nowadays sustainable underground geothermal energy resources have received special attention thanks for being characterized as clean, zero-carbon footprint, reliable, and free source of renewable energy that can run all year long and around the clock. Barren desert lands, which make up 33% and contribute to almost 30 Million km² of global land surface area, is increasingly seen as supply of green energy but not yet efficiently and globally utilized although it can save up to 70% compared to traditional HVAC systems bills. This paper presents a novel artificial intelligent machine learning and big data algorithm to analyze and control geothermal heat pump system (GHP). In particular, the main objective of this research is to model, design, analyze, control and optimize the performance of desert underground GTH system based on thermodynamics laws and AI machine learning. As a case study, the analysis and design of desert GHP is performed based on the annual weather data collected for Al Ain city in UAE. By selecting a horizontal layout, the design analysis results show that GHP unit needs a 66 m total trench length with a cooling capacity estimated of 12.4 kW, heat pump COP of 2.8 and 1.6 for the system COP with 30.3 L/min water flow rate. Similar results for the heating system are obtained as well. Furthermore, financial calculations show the GHP system is very economic and competitive comparing with the traditional cooling/heating systems. It is figured out that the annual cost of the GHP system costs around $1676 compared with $7992 if air-cooled chiller and boiler are used. To maintain the geothermal system for one life cycle (usually 20 years) it needs to spend only $14,659 compared with $109,944 in case HVAC system is utilized. The overall life cycle cost in case of the desert GHP system does not exceed (45%) of the traditional HVAC system ($81,881 compared to $181,974). One of the direct applications is use this proposed desert GHP to cool the roof water tank for domestic and personal usage. Furthermore, artificial intelligent and big data machine learning is executed to analyze the weather conditions related to the GHP performance based on huge number of thermal observations recorded for the years 2015-2018. Moreover, the mean switch-off control hours of the GHP is examined by developing a supervised learning predictive model. For the purpose of validation a four ton Bosch GHP unit is selected as a benchmark. Switch-off control hours per month for the entire geothermal data set are demonstrated by using a linear regression model that help to guide the controller to switch-on/switch-off the system without having the need for the real data measurement. One primary outcome obtained is the ability to optimize the GHP performance, save primary input energy and operation periods. Furthermore, the results interprets that almost one third of the year is in a switched-off saving mode (33%), compared to 67% in switch-on mode. This smart big data control will lead to a life-cycle saving of $27,020. This AI saving strategy is found to be competitive and leading compared to other schemes. It is worthy to recommend linking GHP controller with real-time radar or weather station that will fed the system with real data conditions which would lead to improving its performance and dispense costly measuring sensors.
Бесплатно
Статья научная
Utilization of the sustainable and renewable sea wave energy has recently received special attention by the virtue of being a free, clean and zero-carbon footprint power source. This paper presents a novel approach to model, design, analyze and control a sea wave electric power generating system using an artificial intelligent nonlinear auto regressive with external input neural networks (NARX-NN). Modeling design, and analysis of an electro-mechanical power-generating system using linear permanent magnet generator attached to a dual spring-mass-damper platforms is introduced. The purpose of this proposed generator is to convert sea and ocean wave kinetic energy into a useful electrical power generated as a result of the linear motion core through an electromagnetic stator. One of the direct applications of the sea wave generator is to install one or more units on shipboard to contribute to its power utility needs whether it is moving or floating. The dynamical stability and compensator control of the spring-mass damper generator platform is analyzed along with its associated electric power. Faraday’s law based results show that the output induced voltage ranges from -60 to 60 volts (120 volts p-p). Moreover, artificial intelligent nonlinear auto-regressive neural networks are used to train, validate, and test the sea wave electric generator output. Two-layer NN are used to train the dynamical input-output relationship of the proposed system using one hidden layer that contains of 10 neurons. Two delays are used, one for motion input and one for voltage output. The NARX-NN training demonstrates that the network is being trained efficiently and tracks the actual sea wave electric generator output with a very low mean-square-error performance response without the need to measure the variables.
Бесплатно
Artificial neural estimator and controller for Field Oriented Control of three-phase I.M.
Статья научная
Speed control for an I.M is a few what complex strategies; the complexity is regularly increasing in line with the required system achievement. The main forms of control strategies are scalar, direct torque, adaptive, sensorless, and vector or Field Oriented Control (FOC). The FOC method is the most efficient technique in which machine parameters: Rotor flux, unit vector, and electromagnetic torque, usually are estimated by means of using Digital Signal Processing (DSP). The Artificial Neural Network (ANN) becomes an effective tool for controlling nonlinear device in present time. This paper proposes the using of ANN instead of DSP to estimate the machine parameters in order to reduce the hardware complexity and the Electromagnetic Interference (EMI) impact. Also, it presents the PI-NN controller which is based totally on ANN. The systems simulations for both DSP and ANN are depicted. The performance of the ANN-based system gives excellent results: overshot less than 0.5%, rise time 0.514 s, steady state error less than 0.2%, settling time 0.7 s. in conjunction with that of DSP-based performance: overshot about 2%, rise time 0.64 s, steady state error less than 0.4%, settling time 0.75 s.
Бесплатно
Aspect sentiment identification using random Fourier features
Статья научная
The objective of the paper was to show the effectiveness of using random Fourier features in detection of sentiment polarities. The method presented in this paper proves that detection of aspect based polarities can be improved by selective choice of relevant features and mapping them to lower dimensions. In this study, random Fourier features were prepared corresponding to the polarity data. A regularized least square strategy was adopted to fit a model and perform the task of polarity detection Experiments were performed with 10 cross-validations. The proposed method with random Fourier features yielded 90% accuracy over conventional classifiers. Precision, Recall, and F-measure were deployed in our empirical evaluations.
Бесплатно
Assessing Different Crossover Operators for Travelling Salesman Problem
Статья научная
Many crossover operators have been proposed in literature on evolutionary algorithms, however, it is still unclear which crossover operator works best for a given optimization problem. In this study, eight different crossover operators specially designed for travelling salesman problem, namely, Two-Point Crossover, Partially Mapped Crossover, Cycle Crossover, Shuffle Crossover, Edge Recombination Crossover, Uniform Order-based Crossover, Sub-tour Exchange Crossover, and Sequential Constructive Crossover are evaluated empirically. The select crossover operators were implemented to build an experimental setup upon which simulations were run. Four benchmark instances of travelling salesman problem, two symmetric (ST70 and TSP225) and two asymmetric (FTV100 and FTV170), were used to thoroughly assess the select crossover operators. The performance of these operators was analyzed in terms of solution quality and computational cost. It was found that Sequential Constructive Crossover outperformed other operators in attaining 'good' quality solution, whereas Two-Point Crossover outperformed other operators in terms of computational cost. It was also observed that the performance of different crossover operators is much better for relatively small number of cities, both in terms of solution quality and computational cost, however, for relatively large number of cities their performance greatly degrades.
Бесплатно
Assessing Query Translation Quality Using Back Translation in Hindi-English CLIR
Статья научная
Cross-Language Information Retrieval (CLIR) is a most demanding research area of Information Retrieval (IR) which deals with retrieval of documents different from query language. In CLIR, translation is an important activity for retrieving relevant results. Its goal is to translate query or document from one language into another language. The correct translation of the query is an essential task of CLIR because incorrect translation may affect the relevancy of retrieved results. The purpose of this paper is to compute the accuracy of query translation using the back translation for a Hindi-English CLIR system. For experimental analysis, we used FIRE- 2011 dataset to select Hindi queries. Our analysis shows that back translation can be effective in improving the accuracy of query translation of the three translators used for analysis (i.e. Google, Microsoft and Babylon). Google is found best for the purpose.
Бесплатно
Auditory Model Identification Using REVCOR Method
Статья научная
Auditory models are very useful in many applications such as speech coding and compression, cochlea prosthesis, and audio watermarking. In this paper we will develop a new auditory model based on the REVCOR method. This technique is based on the estimation of the impulse response of a suitable filter characterizing the auditory neuron and the cochlea. The first step of our study is focused on the development of a mathematical model based on the gammachirp system. This model is then programmed, implemented and simulated under Matlab. The obtained results are compared with the experimental values (REVCOR experiments) for the validation and a better optimization of the model parameters. Two objective criteria are used in order to optimize the audio model estimation which are the SNR (signal to noise ratio) and the MQE (mean quadratic error). The simulation results demonstrated that for the auditory model, only a reduced number of channels are excited (from 3 to 6). This result is very interesting for auditory implants because only significant channels will be stimulated. Besides, this simplifies the electronic implementation and medical intervention.
Бесплатно
Author Attribution of Arabic Texts Using Extended Probabilistic Context Free Grammar Language Model
Статья научная
Author attribution is the problem of assigning an author to an unknown text. We propose a new approach to solve such a problem using an extended version of the probabilistic context free grammar language model, supplied by more informative lexical and syntactic features. In addition to the probabilities of the production rules in the generated model, we add probabilities to terminals, non-terminals, and punctuation marks. Also, the new model is augmented with a scoring function which assigns a score for each production rule. Since the new model contains different features, optimum weights, found using a genetic algorithm, are added to the model to govern how each feature participates in the classification. The advantage of using many features is to successfully capture the different writing styles of authors. Also, using a scoring function identifies the most discriminative rules. Using optimum weights supports capturing different authors' styles, which increases the classifier's performance. The new model is tested over nine authors, 20 Arabic documents per author, where the training and testing are done using the leave-one-out method. The initial error rate of the system is 20.6%. Using the optimum weights for features reduces the error rate to 12.8%.
Бесплатно
Статья научная
Forecasting CPU availability in volunteer computing systems using a single prediction algorithm is insufficient due to the diversity of the world-wide distributed resources. In this paper, we draw-up the main guidelines to develop an appropriate CPU availability prediction system for such computing infrastructures. To reduce solution time and to enhance precision, we use simple prediction techniques, precisely vector autoregressive models and a tendency-based technique. We propose a predictor construction process which automatically checks assumptions of vector autoregressive models in time series. Three different past analyses are performed. For a given volunteer resource, the proposed prediction system selects the appropriate predictor using the multi-state based prediction technique. Then, it uses the selected predictor to forecast CPU availability indicators. We evaluated our prediction system using real traces of more than 226000 hosts of Seti@home. We found that the proposed prediction system improves the prediction accuracy by around 24%.
Бесплатно
Статья научная
Inspection task is traditionally carried out by human. However, Automated Visual Inspection (AVI) has gradually become more popular than human inspection due to the advantageous in the aspect of high precision and short processing time. Therefore, this paper proposed a system which identifies the object's position for industrial robot based on colors and shapes where, red, green, blue and circle, square, triangle are recognizable. The proposed system is capable to identify the object's position in three modes, either based on color, shape or both color and shape of the desired objects. During the image processing, RGB color space is utilized by the proposed system while winner take all approach is used to classify the color of the object through the evaluation of the pixel's intensity value of the R, G and B channel. Meanwhile, the shapes and position of the objects are determined based on the compactness and the centroid of the region respectively. Camera settings, such as brightness, contrast and exposure is another important factor which can affect the performance of the proposed system. Lastly, a Graphical User Interface was developed. The experimental result shows that the developed system is highly efficient when implemented in the selected database.
Бесплатно
Automated bug assignment in software maintenance using graph databases
Статья научная
Processes involved in maintaining a system play a crucial role in enhancing customer satisfaction and longevity of the system. Maintenance engineers are the most critical resources in Software Maintenance. They play a significant role in fixing bugs and ensuring the normal functioning of systems. Software maintenance is a tedious task for novice engineers who are new to the system domain. The lack of up-to-date documentation makes system comprehension more challenging for inexperienced engineers. Assignment of high priority bugs to novice engineers may lead to inappropriate fixes and delay in the revival of an impacted system. Such issues may degrade customer satisfaction and also poor fixes can have a severe impact on the functioning of the system at a later stage. Our research is focussed on identification of engineers with the right level of experience to fix a given bug. We have used the concept of page ranking and graph databases to compute the importance of bugs and assignees in a graph. A newly reported bug will be scored and matched with bugs that have a similar score in the graph database. Assignees who have fixed a bug that closely maps the score of the reported bug will be assigned the task of fixing the bug. We have implemented this methodology using bug reports from QT framework on neo4j graph database. Our results are promising and will definitely pave way for a new bug assignment strategy in software maintenance.
Бесплатно
Automatic Ethical Filtering using Semantic Vectors Creating Normative Tag Cloud from Big Data
Статья научная
Ethical filtering has been a painful and controversial issue seen by different angles worldwide. Stalwarts for freedom find newer methods to circumvent banned URLs while generative power of the Internet outpaces velocity of censorship. Hence, keeping online content safe from anti-religious and sexually provocative content is a growing issue in conservative countries in Asia and The Middle East. Solutions for online ethical filters are linearly upper bound given computation and big data growth scales. In this scenario, Semantic Vectors are applied as automatic ethical filters to calculate accuracy and efficiency metrics. The results show a normative tag cloud generated with superior performance to industry solutions.
Бесплатно
Automatic Ration Material Distributions Based on GSM and RFID Technology
Статья научная
Now a day ration card is very important for every home and used for various field such as family members details, to get gas connection, it act as address proof for various purposes etc. All the people having a ration card to buy the various materials (sugar, rice, oil, kerosene, etc) from the ration shops. But in this system having two draw backs, first one is weight of the material may be inaccurate due to human mistakes and secondly, if not buy the materials at the end of the month, they will sale to others without any intimation to the government and customers. In this paper, proposed an Automatic Ration Materials Distribution Based on GSM (Global System for Mobile) and RFID (Radio Frequency Identification) technology instead of ration cards. To get the materials in ration shops need to show the RFID tag into the RFID reader, then controller check the customer codes and details of amounts in the card. After verification, these systems show the amount details. Then customer need to enter they required materials by using keyboard, after receiving materials controller send the information to government office and customer through GSM technology. In this system provides the materials automatically without help of humans.
Бесплатно
Automatic brain tissues segmentation based on self initializing K-Means clustering technique
Статья научная
This paper proposed a self-initialization process to K-Means method for automatic segmentation of human brain Magnetic Resonance Image (MRI) scans. K-Means clustering method is an iterative approach and the initialization process is usually done either manually or randomly. In this work, a method has been proposed to make use of the histogram of the gray scale MRI brain images to automatically initialize the K-means clustering algorithm. This is done by taking the number of main peaks as well as their values as number of clusters and their initial centroids respectively. This makes the algorithm faster by reducing the number of iterations in segmenting the MRI image. The proposed method is named as Histogram Based Self Initializing K-Means (HBSIKM) method. Experiments were done with the MRI brain volumes available from Internet Brain Segmentation Repository (IBSR). Similarity validation was done by Dice coefficient with the available gold standards from the IBSR website. The performance of the proposed method is compared with the traditional K-Means method. For the IBSR volumes, the proposed method yields 3 to 4 times faster results and higher dice value than traditional K-Means method.
Бесплатно
Available Link Bandwidth Based Network Selection in Multi-access Networks
Статья научная
In a heterogeneous wireless environment, one of the important aspects of seamless communication for ubiquitous computing is the dynamic selection of the best access network. The problem of access network selection has been addressed through various decision methods based on available network information. Available link bandwidth is one of the important information parameters, which can be used as criterion for network selection. In this paper, we consider available bandwidth as a dynamic parameter to select the network in heterogeneous environment. First, we propose a bootstrap approximation based technique to estimate available bandwidth and then utilize it for the selection of the best suitable network in the heterogeneous environment consisting of 2G and 3G standards based wireless networks. The proposed algorithm is implemented in temporal and spatial domains to check its robustness. Estimation time with varying size of files is used as the performance metric. Through numerical results, it is shown that the proposed algorithm gives improved performance as compared to the existing algorithm.
Бесплатно
BER Analysis of OFDM Digital Communication Systems with Improved ICI Cancellation Technique
Статья научная
In this paper, performance of OFDM digital communication systems have been analyzed with improved ICI cancellation technique. The bit error rate has been regarded as a fundamental information theoretic measure of a communication system. A novel parallel ICI cancellation technique has been proposed for mitigating frequency offset of OFDM digital communication systems. The simulated results of the proposed technique is compared with ICI self cancellation scheme. The simulated results show better performance over ICI self cancellation scheme.
Бесплатно
Balanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem
Статья научная
0/1 Multiple Knapsack Problem, a generalization of more popular 0/1 Knapsack Problem, is NP-hard and considered harder than simple Knapsack Problem. 0/1 Multiple Knapsack Problem has many applications in disciplines related to computer science and operations research. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, are considered effective to solve difficult problems particularly NP-hard combinatorial optimization problems. A hybrid QIEA is presented for multiple knapsack problem which incorporates several features for better balance between exploration and exploitation. The proposed QIEA, dubbed QIEA-MKP, provides significantly improved performance over simple QIEA from both the perspectives viz., the quality of solutions and computational effort required to reach the best solution. QIEA-MKP is also able to provide the solutions that are better than those obtained using a well known heuristic alone.
Бесплатно
Balinese historian chatbot using full-text search and artificial intelligence markup language method
Статья научная
In the era of technology, various information could be obtained quickly and easily. The history of Bali is one of the information that could be obtained. Balinese have known their history through Babad and stories which are told through generations. Babad is traditional-historical writing which tells important event that has happened. As technology evolves, Balinese’s interest in studying their own history has been decreased. It is caused by people interest in studying history books and chronicles tend to decrease over time. Therefore, an innovation of technology, which able to convert historical data from printed media to digital media, is needed. The technology that could be used is Chatbot technology; a computer program that could carry out conversations. Chatbot technology is used to make people learning history easily by using Instant Messenger LINE as a platform to communicate. This Chatbot uses two methods, namely the Artificial Intelligence Markup Language method and the Full-Text Search method. The Artificial Intelligence Markup Language method is used as the process of making characteristic of questions and answers. The Full-Text Search method is the process of matching answers based on user input. This chatbot only uses Indonesian to communicate. The results of this study are a Chatbot that could be accessed by using Instant Messenger LINE and could communicate like historian expert.
Бесплатно
Bandwidth Extension of Speech Signals: A Comprehensive Review
Статья научная
Telephone systems commonly transmit narrowband (NB) speech with an audio bandwidth limited to the traditional telephone band of 300-3400 Hz. To improve the quality and intelligibility of speech degraded by narrow bandwidth, researchers have tried to standardize the telephonic networks by introducing wideband (50-7000 Hz) speech codecs. Wideband (WB) speech transmission requires the transmission network and terminal devices at both ends to be upgraded to the wideband that turns out to be time-consuming. In this situation, novel Bandwidth extension (BWE) techniques have been developed to overcome the limitations of NB speech. This paper discusses the basic principles, realization, and applications of BWE. Challenges and limitations of BWE are also addressed.
Бесплатно
Bank Customer Credit Scoring by Using Fuzzy Expert System
Статья научная
Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
Бесплатно
