Search results for: type-2 fuzzy programming

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Bita Payami-Shabestari, Dariush Eslami

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The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.

Keywords: economic production quantity, random cost, supply chain management, vendor-managed inventory

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Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model

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Authors: Angel G. De Leon Hernandez

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A structure is defined as a physical system or, in certain cases, an arrangement of connected elements, capable of bearing certain loads. The structures are presented in every part of the daily life, e.g., in the designing of buildings, vehicles and mechanisms. The main goal of a structure designer is to develop a secure, aesthetic and maintainable system, considering the constraint imposed to every case. With the advances in the technology during the last decades, the capabilities of solving engineering problems have increased enormously. Nowadays the computers, play a critical roll in the structural analysis, pitifully, for university students the vast majority of these software are inaccessible due to the high complexity and cost they represent, even when the software manufacturers offer student versions. This is exactly the reason why the idea of developing a more reachable and easy-to-use computing tool. This program is designed as a tool for the university students enrolled in courser related to the structures analysis and designs, as a complementary instrument to achieve a better understanding of this area and to avoid all the tedious calculations. Also, the program can be useful for graduated engineers in the field of structural design and analysis. A graphical user interphase is included in the program to make it even simpler to operate it and understand the information requested and the obtained results. In the present document are included the theoretical basics in which the program is based to solve the structural analysis, the logical path followed in order to develop the program, the theoretical results, a discussion about the results and the validation of those results.

Keywords: stiffness matrix method, structural analysis, Matlab® applications, programming

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Authors: Alaa Sheta

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Economic Load Dispatch (ELD) is one of the vital optimization problems in power system planning. Solving the ELD problems mean finding the best mixture of power unit outputs of all members of the power system network such that the total fuel cost is minimized while sustaining operation requirements limits satisfied across the entire dispatch phases. Many optimization techniques were proposed to solve this problem. A famous one is the Quadratic Programming (QP). QP is a very simple and fast method but it still suffer many problem as gradient methods that might trapped at local minimum solutions and cannot handle complex nonlinear functions. Numbers of metaheuristic algorithms were used to solve this problem such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper, another meta-heuristic search algorithm named Differential Evolution (DE) is used to solve the ELD problem in power systems planning. The practicality of the proposed DE based algorithm is verified for three and six power generator system test cases. The gained results are compared to existing results based on QP, GAs and PSO. The developed results show that differential evolution is superior in obtaining a combination of power loads that fulfill the problem constraints and minimize the total fuel cost. DE found to be fast in converging to the optimal power generation loads and capable of handling the non-linearity of ELD problem. The proposed DE solution is able to minimize the cost of generated power, minimize the total power loss in the transmission and maximize the reliability of the power provided to the customers.

Keywords: economic load dispatch, power systems, optimization, differential evolution

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Ugoh Kelechi Melford, Anene O.

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Introduction: Evidence has shown that there are increasing rates of new HIV and other STI infections occurring among Men who have Sex with Men (MSM) in Nigeria, with the prevalence 3 times higher than the general population as reported by the 2011 National Integrated Bio Behavioral Surveillance Survey. The poor state of health care and support services hinders our effort to control the high rates of these new infections among MSM. Methods: The Initiative for Improved Male Health (IMH-Initiative) works to provide a safe space for young MSM living with HIV to access comprehensive palliative care and support, as well as referrals for other services through drama and dance competitions. An STI assessment was conducted in IMH-Initiative’s Community Center in Calabar, for gay men and other MSM. An STI history was conducted for all clients who visited the community clinic specifically for HCT and STI counseling and referrals within a 5 month period, and their data were collated. Results: 61 MSM were diagnosed, and reported the following in the last 6 months. 49 where living with HIV. 46 had previous histories of untreated anal warts. 20 had previous histories of treated Gonorrhea by self-medication and herbs. 21 had untreated boils and rashes around the genitals. 10 clients where living with HIV, and reported untreated penile and rectal gonorrhea. All clients indicated that there were not comfortable discussing STI infections with staff of public hospitals. Conclusion: It is evident that a reasonable number of STI infections among MSM are not completely treated or ignored. This thereby increases the individual’s risk of HIV infection, and cripples HIV prevention programming in Nigeria. HIV programs targeting MSM must incorporate STI syndromic management, so as to increase access to non-stigmatized diagnosis and treatment of STIs. Also, access to STI drugs for clients cannot be overemphasized.

Keywords: MSM, IBBSS, STI, IMH

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Authors: Claudius Peter Van Wyk

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Given the increasingly manifest phenomena, as described in complexity theory, of volatility, uncertainty, complexity and ambiguity (VUCA), Taleb's notion of 'antifragility, has a significant contribution to make to positive psychology applied to wellbeing. Antifragility is argued to be fundamentally different from the concepts of resiliency; as the ability to recover from failure, and robustness; as the ability to resist failure. Rather it describes the capacity to reorganise in the face of stress in such a way as to cope more effectively with systemic challenges. The concept, which has been applied in disciplines ranging from physics, molecular biology, planning, engineering, and computer science, can now be considered for its application in individual human and social wellbeing. There are strong correlations to Antonovsky's model of 'salutogenesis' in which an attitude and competencies are developed of transforming burdening factors into greater resourcefulness. We demonstrate, from the perspective of neuroscience, how technology measuring nervous system coherence can be coupled to acquired psychodynamic approaches to not only identify contextual stressors, utilise biofeedback instruments for facilitating greater coherence, but apply these insights to specific life stressors that compromise well-being. Employing an on-going case study with BMW South Africa, the neurological mapping is demonstrated together with 'reframing' and emotional anchoring techniques from neurolinguistic programming. The argument is contextualised in the discipline of psychoneuroimmunology which describes the stress pathways from the CNS and endocrine systems and their impact on immune function and the capacity to restore homeostasis.

Keywords: antifragility, complexity, neuroscience, psychoneuroimmunology, salutogenesis, volatility

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Zeinab Haji Abolhasani, Romeo Marian, Lee Luong

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This paper and its companions (Part II, Part III) will concentrate on optimizing a class of supply chain problems known as Multi- Commodities Consumer Supply Chain (MCCSC) problem. MCCSC problem belongs to production-distribution (P-D) planning category. It aims to determine facilities location, consumers’ allocation, and facilities configuration to minimize total cost (CT) of the entire network. These facilities can be manufacturer units (MUs), distribution centres (DCs), and retailers/end-users (REs) but not limited to them. To address this problem, three major tasks should be undertaken. At the first place, a mixed integer non-linear programming (MINP) mathematical model is developed. Then, system’s behaviors under different conditions will be observed using a simulation modeling tool. Finally, the most optimum solution (minimum CT) of the system will be obtained using a multi-objective optimization technique. Due to the large size of the problem, and the uncertainties in finding the most optimum solution, integration of modeling and simulation methodologies is proposed followed by developing new approach known as GASG. It is a genetic algorithm on the basis of granular simulation which is the subject of the methodology of this research. In part II, MCCSC is simulated using discrete-event simulation (DES) device within an integrated environment of SimEvents and Simulink of MATLAB® software package followed by a comprehensive case study to examine the given strategy. Also, the effect of genetic operators on the obtained optimal/near optimal solution by the simulation model will be discussed in part III.

Keywords: supply chain, genetic algorithm, optimization, simulation, discrete event system

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Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

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Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Merve Kadioğlu, Nevin H. Şahin

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Purpose: This research is organized to determine the influence of the web-based learning program. The program has been developed to gain information about normal delivery skill that is one of the topics of nursing students who take the woman health and illness. Material and Methods: The methodology of this study was applied as pre-test post-test single-group quasi-experimental. The pilot study consisted of 28 nursing student study groups who agreed to participate in the study. The findings were gathered via web-based technologies: student information form, information evaluation tests, Web Based Training Material Evaluation Scale and web-based learning environment feedback form. In the analysis of the data, the percentage, frequency and Wilcoxon Signed Ranks Test were used. The Web Based Instruction Program was developed in the light of full learning model, Mayer's research-based multimedia development principles and Gagne's Instructional Activities Model. Findings: The average scores of it was determined in accordance with the web-based educational material evaluation scale: ‘Instructional Suitability’ 4.45, ‘Suitability to Educational Program’ 4.48, ‘Visual Adequacy’ 4.53, ‘Programming Eligibility / Technical Adequacy’ 4.00. Also, the participants mentioned that the program is successful and useful. A significant difference was found between the pre-test and post-test results of the seven modules (p < 0.05). Results: According to pilot study data, the program was rated ‘very good’ by the study group. It was also found to be effective in increasing knowledge about normal labor.

Keywords: normal delivery, web-based learning, nursing students, e-learning

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Authors: Vijaya K. Srivastava, Davide Spinello

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This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: constrained integer problems, enumerative search algorithm, Heuristic algorithm, Tunneling algorithm

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Authors: Abdul Basit Kiani, Maryam Kiani

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Content Management Systems (CMS) have transformed the landscape of web development by providing an accessible and efficient platform for creating and managing digital content. This abstract explores the key features and benefits of CMS in web development, highlighting its impact on website creation and maintenance. CMS offers a user-friendly interface that empowers individuals to create, edit, and publish content without requiring extensive technical knowledge. With customizable templates and themes, users can personalize the design and layout of their websites, ensuring a visually appealing online presence. Furthermore, CMS facilitates efficient content organization through categorization and tagging, enabling visitors to navigate and search for information effortlessly. It also supports version control, allowing users to track and manage revisions effectively. Scalability is a notable advantage of CMS, as it offers a wide range of plugins and extensions to integrate additional features into websites. From e-commerce functionality to social media integration, CMS adapts to evolving business needs. Additionally, CMS enhances collaborative workflows by allowing multiple user roles and permissions. This enables teams to collaborate effectively on content creation and management, streamlining processes and ensuring smooth coordination. In conclusion, CMS serves as a powerful tool in web development, simplifying content creation, customization, organization, scalability, and collaboration. With CMS, individuals and businesses can create dynamic and engaging websites, establishing a strong online presence with ease.

Keywords: web development, content management systems, information technology, programming

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Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Imen Amari, Kaouther Gasmi, Asma Rebaya, Salem Hasnaoui

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The problem of scheduling and mapping data flow applications on multi-core architectures is notoriously difficult. This difficulty is related to the rapid evaluation of Telecommunication and multimedia systems accompanied by a rapid increase of user requirements in terms of latency, execution time, consumption, energy, etc. Having an optimal scheduling on multi-cores DSP (Digital signal Processors) platforms is a challenging task. In this context, we present a novel technic and algorithm in order to find a valid schedule that optimizes the key performance metrics particularly the Latency. Our contribution is based on Satisfiability Modulo Theories (SMT) solving technologies which is strongly driven by the industrial applications and needs. This paper, describe a scheduling module integrated in our proposed Workflow which is advised to be a successful approach for programming the applications based on NoC-DSP platforms. This workflow transform automatically a Simulink model to a synchronous dataflow (SDF) model. The automatic transformation followed by SMT solver scheduling aim to minimize the final latency and other software/hardware metrics in terms of an optimal schedule. Also, finding the optimal numbers of cores to be used. In fact, our proposed workflow taking as entry point a Simulink file (.mdl or .slx) derived from embedded Matlab functions. We use an approach which is based on the synchronous and hierarchical behavior of both Simulink and SDF. Whence, results of running the scheduler which exist in the Workflow mentioned above using our proposed SMT solver algorithm refinements produce the best possible scheduling in terms of latency and numbers of cores.

Keywords: multi-cores DSP, scheduling, SMT solver, workflow

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Siliang Luan, Zhongtai Jiang

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The risk of highway incidents is commonly recognized as a major concern for transportation authorities due to the hazardous consequences and negative influence. It is crucial to respond to these unpredictable events as soon as possible faced by emergency management decision makers. In this paper, we focus on path planning for emergency vehicles, one of the most significant processes to avoid congestion and reduce rescue time. A Mixed-Integer Linear Programming with Semi-Soft Time Windows Model (MIPSSTWM) is conducted to plan an optimal routing respectively considering the time consumption of arcs and nodes of the urban road network and the highway network, especially in developing countries with an enormous population. Here, the arcs indicate the road segments and the nodes include the intersections of the urban road network and the on-ramp and off-ramp of the highway networks. An attempt in this research has been made to develop a comprehensive and executive strategy for emergency vehicle routing in heavy traffic conditions. The proposed Cuckoo Search (CS) algorithm is designed by imitating obligate brood parasitic behaviors of cuckoos and Lévy Flights (LF) to solve this hard and combinatorial problem. Using a Chinese city as our case study, the numerical results demonstrate the approach we applied in this paper outperforms the previous method without considering the nodes of the road network for a real-world situation. Meanwhile, the accuracy and validity of the CS algorithm also show better performances than the traditional algorithm.

Keywords: emergency vehicle, path planning, cs algorithm, urban traffic management and urban planning

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Sharul T. Tajuddin, Hartiny Md Azmi

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Taking into consideration the real time situation specifically the higher academic institutions, small, medium to large companies, public to private sectors and the remaining sectors, do experience the inventory or asset shrinkages due to theft, loss or even inventory tracking errors. This happening is due to a zero or poor security systems and measures being taken and implemented in their organizations. Henceforth, implementing the Radio Frequency Identification (RFID) technology into any manual or existing web-based system or web application can simply deter and will eventually solve certain major issues to serve better data retrieval and data access. Having said, this manual or existing system can be enhanced into a mobile-based system or application. In addition to that, the availability of internet connections can aid better services of the system. Such involvement of various technologies resulting various privileges to individuals or organizations in terms of accessibility, availability, mobility, efficiency, effectiveness, real-time information and also security. This paper will look deeper into the integration of mobile devices with RFID technologies with the purpose of asset tracking and control. Next, it is to be followed by the development and utilization of MongoDB as the main database to store data and its association with RFID technology. Finally, the development of a web based system which can be viewed in a mobile based formation with the aid of Hypertext Preprocessor (PHP), MongoDB, Hyper-Text Markup Language 5 (HTML5), Android, JavaScript and AJAX programming language.

Keywords: RFID, asset tracking system, MongoDB, NoSQL

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Authors: Maulik L. Sisodia, Tirth K. Raval, Aarsh S. Mistry

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This paper summarizes the ongoing researches about the defects in both flexible and rigid pavement and the maintenance in both flexible and rigid pavements. Various defects in pavements have been identified since the existence of both flexible and rigid pavement. Flexible Pavement failure is defined in terms of decreasing serviceability caused by the development of cracks, ruts, potholes etc. Flexible Pavement structure can be destroyed in a single season due to water penetration. Defects in flexible pavements is a problem of multiple dimensions, phenomenal growth of vehicular traffic (in terms of no. of axle loading of commercial vehicles), the rapid expansion in the road network, non-availability of suitable technology, material, equipment, skilled labor and poor funds allocation have all added complexities to the problem of flexible pavements. In rigid pavements due to different type of destress the failure like joint spalling, faulting, shrinkage cracking, punch out, corner break etc. Application of correction in the existing surface will enhance the life of maintenance works as well as that of strengthening layer. Maintenance of a road network involves a variety of operations, i.e., identification of deficiencies and planning, programming and scheduling for actual implementation in the field and monitoring. The essential objective should be to keep the road surface and appurtenances in good condition and to extend the life of the road assets to its design life. The paper describes lessons learnt from pavement failures and problems experienced during the last few years on a number of projects in India. Broadly, the activities include identification of defects and the possible cause there off, determination of appropriate remedial measures; implement these in the field and monitoring of the results.

Keywords: Flexible Pavements, Rigid Pavements, Defects, Maintenance

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Authors: Lokendra Kumar Devangan, Ajay Mishra

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This paper describes the implementation of a large-scale SAS/OR model with significant pre-processing, scenario analysis, and post-processing work done using SAS. A large cement manufacturer with ten geographically distributed manufacturing plants for two variants of cement, around 400 warehouses serving as transshipment points, and several thousand distributor locations generating demand needed to optimize this multi-echelon, multi-modal transport supply chain separately for planning and allocation purposes. For monthly planning as well as daily allocation, the demand is deterministic. Rail and road networks connect any two points in this supply chain, creating tens of thousands of such connections. Constraints include the plant’s production capacity, transportation capacity, and rail wagon batch size constraints. Each demand point has a minimum and maximum for shipments received. Price varies at demand locations due to local factors. A large mixed integer programming model built using proc OPTMODEL decides production at plants, demand fulfilled at each location, and the shipment route to demand locations to maximize the profit contribution. Using base SAS, we did significant pre-processing of data and created inputs for the optimization. Using outputs generated by OPTMODEL and other processing completed using base SAS, we generated several reports that went into their enterprise system and created tables for easy consumption of the optimization results by operations.

Keywords: production planning, mixed integer optimization, network model, network optimization

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Authors: Lucero Luciano, Cesar Celis, Jose Ramos

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Polygeneration improves energy efficiency and reduce both energy consumption and pollutant emissions compared to conventional generation technologies. A polygeneration system is a variation of a cogeneration one, in which more than two outputs, i.e., heat, power, cooling, water, energy or fuels, are accounted for. In particular, polygeneration systems integrating solar energy and water desalination represent promising technologies for energy production and water supply. They are therefore interesting options for coastal regions with a high solar potential, such as those located in southern Peru and northern Chile. Notice that most of the Peruvian and Chilean mining industry operations intensive in electricity and water consumption are located in these particular regions. Accordingly, this work focus on the design and integration of a polygeneration system producing industrial heating, cooling, electrical power and water for an industrial plant. The design procedure followed in this work involves integer linear programming modeling (MILP), operational planning and dynamic operating conditions. The technical and economic feasibility of integrating renewable energy technologies (photovoltaic and solar thermal, PV+CPS), thermal energy store, power and thermal exchange, absorption chillers, cogeneration heat engines and desalination technologies is particularly assessed. The polygeneration system integration carried out seek to minimize the system total annual cost subject to CO2 emissions restrictions. Particular economic aspects accounted for include investment, maintenance and operating costs.

Keywords: desalination, design and integration, polygeneration systems, renewable energy

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Authors: Wei Kunkun, Cao Wanyang, Xu Yujie, Qiao Yuzhi, Liu Yingning

Abstract:

The reasonable design of bus parking spaces can improve the traffic capacity of the station and reduce traffic congestion. In order to reasonably determine the number of berths at BRT (Bus Rapid Transit) stops, it is based on the actual bus rapid transit station observation data, scheduling data, and passenger flow data. Optimize the number of station berths from the perspective of optimizing the balance of supply and demand at the site. Combined with the classical capacity calculation model, this paper first analyzes the important factors affecting the traffic capacity of BRT stops by using SPSS PRO and MATLAB programming software, namely the distribution of BRT stops and the distribution of BRT stop time. Secondly, the method of calculating the number of the classic human capital management (HCM) model is optimized based on the actual passenger demand of the station, and the method applicable to the actual number of station berths is proposed. Taking Gangding Station of Zhongshan Avenue Bus Rapid Transit Corridor in Guangzhou as an example, based on the calculation method proposed in this paper, the number of berths of sub-station 1, sub-station 2 and sub-station 3 is 2, which reduces the road space of the station by 33.3% compared with the previous berth 3 of each sub-station, and returns to social vehicles. Therefore, under the condition of ensuring the passenger flow demand of BRT stations, the road space of the station is reduced, and the road is returned to social vehicles, the traffic capacity of social vehicles is improved, and the traffic capacity and efficiency of the BRT corridor system are improved as a whole.

Keywords: urban transportation, bus rapid transit station, HCM model, capacity, number of berths

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Authors: Yasin Tadayonrad

Abstract:

Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading /unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is loading/unloading capacity in each source/ destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming

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Authors: Nabeel Abdulkadhim Athab

Abstract:

The purpose of this research is to conduct a comparative study according analysis of programmed to some of physical and mechanical variables for joint wrist injury. As it can be through this research to distinguish between the amount of variation in the work of the joint after sample underwent rehabilitation program to improve the effectiveness of the joint and naturally restore its effectiveness. Supposed researcher that there is statistically significant differences between the results of the tests pre and post the members research sample, as a result of submission the sample to the program of rehabilitation, which led to the development of muscle activity that are working on wrist joint and this is what led to note the differences between the results of the tests pre and post. The researcher used the descriptive method. The research sample included (6) of injured players in the wrist joint, as the average age (21.68) and standard deviation (1.13) either length average (178cm) and standard deviation (2.08). And the sample as evidenced homogeneous among themselves. And where the data were collected, introduced in program for statistical processing to get to the most important conclusions and recommendations and that the most important: 1-The commitment of the sample program the qualifying process variables studied in the search for the heterogeneity of study activity and effectiveness of wrist joint for injured players. 2-The analysis programmed a high accuracy in the measurement of the research variables, and which led to the possibility of discrimination into account differences in motor ability camel and injured in the wrist joint. To search recommendations including: 1-The use of computer systems in the scientific research for the possibility of obtaining accurate research results. 2-Programming exercises rehabilitation according to an expert system for possible use by patients without reference to the person processor.

Keywords: analysis of joint wrist injury, physical and mechanical variables, wrist joint, wrist injury

Procedia PDF Downloads 427

Authors: Sema Kayapinar, Ismail Karaoglan, Turan Paksoy, Hadi Gokcen

Abstract:

Green Supply Chain Management is gaining growing interest among researchers and supply chain management. The concept of Green Supply Chain Management is to integrate environmental thinking into the Supply Chain Management. It is the systematic concept emphasis on environmental problems such as reduction of greenhouse gas emissions, energy efficiency, recycling end of life products, generation of solid and hazardous waste. This study is to present a green supply chain network model integrated Internet of Things applications. Internet of Things provides to get precise and accurate information of end-of-life product with sensors and systems devices. The forward direction consists of suppliers, plants, distributions centres and sales and collect centres while, the reverse flow includes the sales and collects centres, disassembled centre, recycling and disposal centre. The sales and collection centre sells the new products are transhipped from factory via distribution centre and also receive the end-of life product according their value level. We describe green logistics activities by presenting specific examples including “recycling of the returned products and “reduction of CO2 gas emissions”. The different transportation choices are illustrated between echelons according to their CO2 gas emissions. This problem is formulated as a mixed integer linear programming model to solve the green supply chain problems which are emerged from the environmental awareness and responsibilities. This model is solved by using Gams package program. Numerical examples are suggested to illustrate the efficiency of the proposed model.

Keywords: green supply chain optimization, internet of things, greenhouse gas emission, recycling

Procedia PDF Downloads 325

Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

Abstract:

This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence

Procedia PDF Downloads 321

Authors: Gülay Dalgıç, Gildis Tachir

Abstract:

Architectural design studios are ambiencein where architecture design is realized as a palpable product in architectural education. In the design studios that the architect candidate will use in the design processthe information, the methods of approaching the design problem, the solution proposals, etc., are set uptogetherwith the studio coordinators. The architectural design process, on the other hand, is complex and uncertain.Candidate architects work in a process that starts with abstre and ill-defined problems. This process starts with the generation of alternative solutions with the help of representation tools, continues with the selection of the appropriate/satisfactory solution from these alternatives, and then ends with the creation of an acceptable design/result product. In the studio ambience, many designs and thought relationships are evaluated, the most important step is the early design phase. In the early design phase, the first steps of converting the information are taken, and converted information is used in the constitution of the first design decisions. This phase, which positively affects the progress of the design process and constitution of the final product, is complex and fuzzy than the other phases of the design process. In this context, the aim of the study is to investigate the effects of face-to-face learning model and e-learning model on the early design phase. In the study, the early design phase was defined by literature research. The data of the defined early design phase criteria were obtained with the feedback graphics created for the architect candidates who performed e-learning in the first year of architectural education and continued their education with the face-to-face learning model. The findings of the data were analyzed with the common graphics program. It is thought that this research will contribute to the establishment of a contemporary architectural design education model by reflecting the evaluation of the data and results on architectural education.

Keywords: education modeling, architecture education, design education, design process

Procedia PDF Downloads 132