Search results for: stochastic programming

Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan

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With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.

Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization

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Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Saul A. Torres Z., Eduardo Liceaga C., Alfredo Arias M.

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We seek to develop a UAV for agricultural spraying at a maximum altitude of 5000 meters above sea level, with a payload of 100 liters of fumigant. For the developing the aerodynamic design of the aircraft is using computational tools such as the "Vortex Lattice Athena" software, "MATLAB", "ANSYS FLUENT", "XFoil" package among others. Also methods are being used structured programming, exhaustive analysis of optimization methods and search. The results have a very low margin of error, and the multi-objective problems can be helpful for future developments. Also we developed method for Stability Analysis (Lateral-Directional and Longitudinal).

Keywords: aerodynamics design, optimization, algorithm genetic, multi-objective problem, longitudinal stability, lateral-directional stability

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Iannick Gagnon, Alain April

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The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization.

Keywords: metaheuristics, stochastic optimization, particle swarm optimization, exploratory landscape analysis

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Authors: Ioana Pisica, Dimitrios Gkritzapis

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The significance of UAS in scientific applications has been amply demonstrated in recent years. The combinations of portability and quasi-static positioning by means of flying in close loop path make them versatile and efficient in the inspection of power systems infrastructure. In this paper, we critically assess several platforms and sensor capabilities to identify their pros and cons in relation to the power systems assets to be monitored. In this respect, it is paramount the flights to be conducted by using UAS which bear certain suitable features, such as responsive and easy control, video capturing in real time, autonomous routing of pre-planned flight programming with differentiating payloads. The outcome of this research is a set of optimal requirements for power system assets monitoring with UAS.

Keywords: platforms, power system, sensors, UAVs

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Authors: Taha Benarbia, Kay W. Axhausen, Anugrah Ilahi

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Electric car sharing system as ecologic transportation increasing in the world. The complexity of managing electric car sharing systems, especially one-way trips and battery autonomy have direct influence to on supply and demand of system. One must be able to precisely model the demand and supply of these systems to better operate electric car sharing and estimate its effect on mobility management and the accessibility that it provides in urban areas. In this context, our work focus to develop performances optimization model of the system based on discrete event simulation and stochastic Petri net. The objective is to search optimal decisions and management parameters of the system in order to fulfil at best demand while minimizing undesirable situations. In this paper, we present new model of electric cars sharing with relocation based on monitoring system. The proposed approach also help to precise the influence of battery charging level on the behaviour of system as important decision parameter of this complex and dynamical system.

Keywords: electric car-sharing systems, smart mobility, Petri nets modelling, discrete event simulation

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Authors: Aradhna Pandarum

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South Africa is experiencing an exponential growth of distributed solar PV installations. This is due to various factors with the predominant one being increasing electricity tariffs along with decreasing installation costs, resulting in attractive business cases to some end-users. Despite there being a variety of economic and environmental advantages associated with the installation of PV, their potential impact on distribution grids has yet to be thoroughly investigated. This is especially true since the locations of these units cannot be controlled by Network Service Providers (NSPs) and their output power is stochastic and non-dispatchable. This report details two case studies that were completed to determine the possible voltage and technical losses impact of increasing PV penetration in the Northern Cape of South Africa. Some major impacts considered for the simulations were ramping of PV generation due to intermittency caused by moving clouds, the size and overall hosting capacity and the location of the systems. The main finding is that the technical impact is different on a constrained feeder vs a non-constrained feeder. The acceptable PV penetration level is much lower for a constrained feeder than a non-constrained feeder, depending on where the systems are located.

Keywords: medium voltage networks, power system losses, power system voltage, solar photovoltaic

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Authors: Yang-Kuei Lin, Yin-Yi Chou

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In this research, we have considered operating room scheduling problem. The objective is to minimize total operating cost. The total operating cost includes idle cost and overtime cost. We have proposed a dispatching rule that can guarantee to find feasible solutions for the studied problem efficiently. We compared the proposed dispatching rule with the optimal solutions found by solving Inter Programming, and other solutions found by using modified existing dispatching rules. The computational results indicates that the proposed heuristic can find near optimal solutions efficiently.

Keywords: assignment, dispatching rule, operation rooms, scheduling

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Authors: H. R. Kamali, A. Sadegheih, M. A. Vahdat-Zad, H. Khademi-Zare

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In this paper, a single product, multi-echelon, multi-period closed loop supply chain is surveyed, including a variety of costs, time conditions, and capacities, to plan and determine the values and time of the components procurement, production, distribution, recycling and disposal specially for high-tech products that undergo a decreasing production cost and sale price over time. For this purpose, the mathematic model of the problem that is a kind of mixed integer linear programming is presented, and it is finally proved that the problem belongs to the category of NP-hard problems.

Keywords: closed loop supply chain, continuous price decrease, NP-hard, planning

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Authors: Ashraf Ward

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Effect of calving interval on 305 day milk yield for first three lactations was studied in order to increase efficiency of selection schemes and to more efficiently manage Holstein cows that have been raised on small farms in Libya. Results obtained by processing data of 1476 cows, managed in 935 small scale farms, pointed out that current calving interval significantly affects on milk production for first three lactations (p<0.05). Preceding calving interval affected 305 day milk yield (p<0.05) in second lactation only. Linear regression model accounted for 20-25 % of the total variance of 305 day milk yield. Extension of calving interval over 420, 430, 450 days for first, second and third lactations respectively, did not increase milk production when converted to 305 day lactation. Stochastic relations between calving interval and calving age and month are moderated. Values of Pierson’s correlation coefficients ranged 0.38 to 0.69. Adjustment of milk production in order to reduce effect of calving interval on total phenotypic variance of milk yield is valid for first lactation only. Adjustment of 305 day milk yield for second and third lactations in order to reduce effects of factors “calving age and month” brings about, at the same time, elimination of calving interval effect.

Keywords: milk yield, Holstien, non genetic, calving

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: M. Chen, S-Q. Zhang, X. Wang, D. Tate

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This paper presents a numerical solution, namely limit and shakedown analysis, to predict the safety state of smart structures made of heterogeneous materials under unknown cyclic loadings, for instance, the flexure hinge in the micro-positioning stage driven by piezoelectric actuator. In combination of homogenization theory and finite-element method (FEM), the safety evaluation problem is converted to a large-scale nonlinear optimization programming for an acceptable bounded loading as the design reference. Furthermore, a general numerical scheme integrated with the FEM and interior-point-algorithm based optimization tool is developed, which makes the practical application possible.

Keywords: limit state, shakedown analysis, homogenization, heterogeneous structure

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Authors: Kien Vu, Jien Min Soh, Mohamed Jahangir Abubacker, Piyawut Pattamanon, Soojin Lee, Suvro Banerjee

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Recent advances in Generative Artificial Intelligence (GenAI), in particular Large Language Models (LLMs) have shown promise to disrupt multiple industries at scale. However, LLMs also present unique challenges, notably, these so-called "hallucination" which is the generation of outputs that are not grounded in the input data that hinders its adoption into production. Common practice to mitigate hallucination problem is utilizing Retrieval Agmented Generation (RAG) system to ground LLMs'response to ground truth. RAG converts the grounding documents into embeddings, retrieve the relevant parts with vector similarity between user's query and documents, then generates a response that is not only based on its pre-trained knowledge but also on the specific information from the retrieved documents. However, the RAG system is not suitable for tabular data and subsequent data analysis tasks due to multiple reasons such as information loss, data format, and retrieval mechanism. In this study, we have explored a novel methodology that combines planning-and-execution and code generation agents to enhance LLMs' data analysis capabilities. The approach enables LLMs to autonomously dissect a complex analytical task into simpler sub-tasks and requirements, then convert them into executable segments of code. In the final step, it generates the complete response from output of the executed code. When deployed beta version on DataSense, the property insight tool of PropertyGuru, the approach yielded promising results, as it was able to provide market insights and data visualization needs with high accuracy and extensive coverage by abstracting the complexities for real-estate agents and developers from non-programming background. In essence, the methodology not only refines the analytical process but also serves as a strategic tool for real estate professionals, aiding in market understanding and enhancement without the need for programming skills. The implication extends beyond immediate analytics, paving the way for a new era in the real estate industry characterized by efficiency and advanced data utilization.

Keywords: large language model, reasoning, planning and execution, code generation, natural language processing, prompt engineering, data analysis, real estate, data sense, PropertyGuru

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Authors: Maitree Thamma, Witchupong Wiboonjaroen, Thanat Suknuan, Karan Homchat

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First order plus dead time (FOPDT) is a high dynamic system. Therefore, the controller must be intelligent. This paper presents the development and implementation of self-tuning Fuzzy-PID controller for controlling the FOPDT system. The water level process used represented FOPDT system and the mathematical model of the system was approximated by using System Identification toolbox in Matlab. The control programming and Fuzzy-PID algorithm used Matlab/Simulink and run on Microcontroller STM32.

Keywords: real-time control, self-tuning fuzzy-PID, FOPDT system, the water lever process

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Authors: Ana M. Korol, Bibiana Riquelme, Osvaldo A. Rosso

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Since erythrocyte deformability analysis is mostly qualitative, the development of quantitative nonlinear methods is crucial for restricting subjectivity in the study of cell behaviour. An electro-optic mechanic system called erythrodeformeter has been developed and constructed in our laboratory in order to evaluate the erythrocytes' viscoelasticity. A numerical method formulated on the basis of fractal approximation for ordinary (OBM) and fractionary Brownian motion (FBM), as well as wavelet transform analysis, are proposed to distinguish chaos from noise based on the assumption that diffractometric data involves both deterministic and stochastic components, so it could be modelled as a system of bounded correlated random walk. Here we report studies on 25 donors: 4 alpha thalassaemic patients, 11 beta thalassaemic patients, and 10 healthy controls non-alcoholic and non-smoker individuals. The Correlation Coefficient, a nonlinear parameter, showed evidence of the changes in the erythrocyte deformability; the Wavelet Entropy could quantify those differences which are detected by the light diffraction patterns. Such quantifiers allow a good deal of promise and the possibility of a better understanding of the rheological erythrocytes aspects and also could help in clinical diagnosis.

Keywords: red blood cells, deformability, nonlinear dynamics, chaos theory, wavelet trannsform

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Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

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Authors: Mohammad Alshawaf, Rahmat Poudineh, Nawaf Alhajeri

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The power generated from photovoltaic (PV) modules is non-dispatchable on demand due to the stochastic nature of solar radiation. The random variations in the measured intensity of solar irradiance are due to clouds and, in the case of arid regions, dust storms which decrease the intensity of intensity of solar irradiance. Therefore, modeling PV power output using average, maximum, or minimum solar irradiance values is inefficient to predict power generation reliably. The overall objective of this paper is to predict the power output of PV modules using Monte Carlo approach based the weather and solar conditions measured in Kuwait. Given the 250 Wp PV module used in study, the average daily power output is 1021 Wh/day. The maximum power was generated in April and the minimum power was generated in January 1187 Wh/day and 823 Wh/day respectively. The certainty of the daily predictions varies seasonally and according to the weather conditions. The output predictions were far more certain in the summer months, for example, the 80% certainty range for August is 89 Wh/day, whereas the 80% certainty range for April is 250 Wh/day.

Keywords: Monte Carlo, solar energy, variable renewable energy, Kuwait

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Authors: Abdulmajid Mukhtar Afat

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This paper aims at introducing nondeterministic finite automata with ε value which is used to perform some operations on languages. a program is created to implement the algorithm that converts nondeterministic finite automata with ε value (ε-NFA) to deterministic finite automata (DFA).The program is written in c++ programming language. The program inputs are FA 5-tuples from text file and then classifies it into either DFA/NFA or ε -NFA. For DFA, the program will get the string w and decide whether it is accepted or rejected. The tracking path for an accepted string is saved by the program. In case of NFA or ε-NFA automation, the program changes the automation to DFA to enable tracking and to decide if the string w exists in the regular language or not.

Keywords: DFA, NFA, ε-NFA, eclose, finite automata, operations on languages

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Authors: Sudaff Mohammed, Wahda Shuker Al-Hinkawi, Nada Abdulmueen Hasan

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The compatibility of the Rhizome and Complex Adaptive system theory as a strategy within the urban context is the essential interest of this paper since there are only a few attempts to establish a hybrid, multi-scalar, and developable strategy based on the concept of the Rhizome and the CAS theory. This paper aims to establish a Rhizomic CAS strategy for different urban contexts by investigating the principles, characteristics, properties, and mechanisms of Rhizome and Complex Adaptive Systems. The research focused mainly on analyzing three properties: aggregation, non-linearity, and flow through the lens of Rhizome, Rhizomatization of CAS properties. The most intriguing result is that the principal and well-investigated characteristics of Complex Adaptive systems can be ‘Rhizomatized’ in two ways; highlighting commonalities between Rhizome and Complex Adaptive systems in addition to using Rhizome-related concepts. This paper attempts to emphasize the potency of the Rhizome as an apparently stochastic and barely anticipatable structure that can be developed to analyze cities of distinctive contexts for formulating better customized urban strategies.

Keywords: rhizome, complex adaptive system (CAS), system Theory, urban system, rhizomatic CAS, assemblage, human occupation impulses (HOI)

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Authors: Srivatsav Sanjay Sridhar, Asvitha Raja, Prathit Kalra, Soni Gupta

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Database management is a system that keeps track of details about a person in an organisation. Not a lot of orphanages these days are shifting to a computer and program-based system, but unfortunately, most have only pen and paper-based records, which not only consumes space but it is also not eco-friendly. It comes as a hassle when one has to view a record of a person as they have to search through multiple records, and it will consume time. This program will organise all the data and can pull out any information about anyone whose data is entered. This is also a safe way of storage as physical data gets degraded over time or, worse, destroyed due to natural disasters. In this developing world, it is only smart enough to shift all data to an electronic-based storage system. The program comes with all features, including creating, inserting, searching, and deleting the data, as well as printing them.

Keywords: database, orphans, programming, C⁺⁺

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Authors: Yangrae Cho, Jinseok Kim, Yongtae Park

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Mobile app service ecosystem has been abruptly emerged, explosively grown, and dynamically transformed. In contrast with product markets in which product sales directly cause increment in firm’s income, customer’s usage is less visible but more valuable in service market. Especially, the market situation with cutthroat competition in mobile app store makes securing and keeping of users as vital. Although a few service firms try to manage their apps’ usage patterns by fitting on S-curve or applying other forecasting techniques, the time series approaches based on past sequential data are subject to fundamental limitation in the market where customer’s attention is being moved unpredictably and dynamically. We therefore propose a new conceptual approach for detecting usage pattern of mobile app service with Hidden Markov Model (HMM) which is based on the dual stochastic structure and mainly used to clarify unpredictable and dynamic sequential patterns in voice recognition or stock forecasting. Our approach could be practically utilized for app service firms to manage their services’ lifecycles and academically expanded to other markets.

Keywords: mobile app service, usage pattern, Hidden Markov Model, pattern detection

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Authors: Murtaza Hassan Sheikh, Ahmed A. A. AlSaleh, Naser H. N. Jasem

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Kuwait has not yet made its mark in the world of technology and research. Therefore, advancements have been made to fill in this gap. Since Robotics covers a wide variety of fields and helps innovation, efforts have been made to promote its education. Despite of the efforts made in Kuwait, robotics education is still on hold. The paper discusses the issues and obstacles in the implementation of robotics education in Kuwait and how a robotics kit “E-Robot” is making an impact in the Kuwait’s future education and innovation. Problems such as robotics competitions rather than education, complexity of robot programming and lack of organized open source platform are being addressed by the introduction of the E-Robot Kit in Kuwait. Due to its success since 2012 a total of 15 schools have accepted the Kit as a core subject, with 200 teaching it as an extracurricular activity.

Keywords: robotics education, Kuwait's education, e-robot kit, research and development, innovation and creativity

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Authors: Ahmed Fradi

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In a digital world in perpetual evolution and acceleration, data more and more voluminous, rich and varied, the new software solutions emerged with the Big Data phenomenon offer new opportunities to the company enabling it not only to optimize its business and to evolve its production model, but also to reorganize itself to increase competitiveness and to identify new strategic axes. Design and manufacturing industrial companies, like the others, face these challenges, data represent a major asset, provided that they know how to capture, refine, combine and analyze them. The objective of our paper is to propose a solution allowing geometric and topological information extraction from 3D CAD model (precisely STEP files) databases, with specific algorithm based on the programming paradigm MapReduce. Our proposal is the first step of our future approach to 3D CAD object retrieval.

Keywords: Big Data, MapReduce, 3D object retrieval, CAD, STEP format

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Authors: Doyoung Kim, Hyo Seon Park

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Studies of System Identification(SI) based on Structural Health Monitoring(SHM) have actively conducted for structural safety. Recently SI techniques have been rapidly developed with output-only SI paradigm for estimating modal parameters. The features of these output-only SI methods consist of Frequency Domain Decomposition(FDD) and Stochastic Subspace Identification(SSI) are using the algorithms based on orthogonal decomposition such as singular value decomposition(SVD). But the SVD leads to high level of computational complexity to estimate modal parameters. This paper proposes the technique to estimate mode shape with lower computational cost. This technique shows pseudo modal Operating Deflections Shape(ODS) through bandpass filter and suggests time history Modal Assurance Criterion(MAC). Finally, mode shape could be estimated from pseudo modal ODS and time history MAC. Analytical simulations of vibration measurement were performed and the results with mode shape and computation time between representative SI method and proposed method were compared.

Keywords: modal assurance criterion, mode shape, operating deflection shape, system identification

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Authors: António Fiúza, Aurora Futuro, Joana Monteiro, Joaquim Góis

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Lepidolite is an important lithium mineral that, to the author’s best knowledge, has not been used to produce lithium hydroxide, which is necessary for energy conversion to electric vehicles. Alkaline leaching of lithium concentrates allows the establishment of a production diagram avoiding most of the environmental drawbacks that are associated with the usage of acid reagents. The tested processes involve a pretreatment by digestion at high temperatures with additives, followed by leaching at hot atmospheric pressure. The solutions obtained must be compatible with solutions from the leaching of spodumene concentrates, allowing the development of a common treatment diagram, an important accomplishment for the feasible exploitation of Portuguese resources. Statistical programming and interpretation techniques minimize the laboratory effort required by conventional approaches and allow phenomenological comprehension.

Keywords: alkaline leaching, lithium, research design, statistical interpretation

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: A. T. Omarova, G. N. Nakipova

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This article discusses the principles of object-oriented human capital development using the technology program. Also the article includes priorities of the strategy of industrial-innovative development of Kazakhstan in conditions of integration activity into the world community. The article shows the tasks of human resource management in the implementation of industrial and innovation development, particularities of Kazakhstan's theory of management staff, as well as due to the specificity of the Kazakhstan authorities. In the article, we have considered the factors which are affecting the people in the organization and also have considered mechanisms of HRM within organization in the conditions of innovative development in Kazakhstan.

Keywords: programming, management of human resources, innovation, investment, innovation process, HRD model, innovative development, integration, management, transformation, economic potential, competitiveness

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Authors: E. Ramprasath, P. Manojkumar, P. Veena

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Proposed paper dealt with the modelling and analysis of induction motor based on the mathematical expression using the graphical programming environment of Laboratory Virtual Instrument Engineering Workbench (LabVIEW). Induction motor modelling with the mathematical expression enables the motor to be simulated with the various required parameters. Owing to the invention of variable speed drives study about the induction motor characteristics became complex.In this simulation motor internal parameter such as stator resistance and reactance, rotor resistance and reactance, phase voltage, frequency and losses will be given as input. By varying the speed of motor corresponding parameters can be obtained they are input power, output power, efficiency, torque induced, slip and current.

Keywords: induction motor, LabVIEW software, modelling and analysi, electrical and mechanical characteristics of motor

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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