Search results for: Machine tools

Authors: Shaoying Guo, Yanyun Xu, Meng Zhang, Weiqing Huang

Abstract:

The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.

Keywords: Cross-validation support vector machine, refined composite multiscale dispersion entropy, specific emitter identification, transient signal, wireless communication device.

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Authors: Adnan Mazari, Engin Akcagun, Antonin Havelka, Funda Buyuk Mazari, Pavel Kejzlar

Abstract:

This article experimentally investigates various physical properties of special fire retardant sewing threads under different sewing speeds. The aramid threads are common for sewing the fire-fighter clothing due to high strength and high melting temperature. 3 types of aramid threads with different linear densities are used for sewing at different speed of 2000 to 4000 r/min. The needle temperature is measured at different speeds of sewing and tensile properties of threads are measured before and after the sewing process respectively. The results shows that the friction and abrasion during the sewing process causes a significant loss to the tensile properties of the threads and needle temperature rises to nearly 300^oC at 4000 r/min of machine speed. The Scanning electron microscope images are taken before and after the sewing process and shows no melting spots but significant damage to the yarn. It is also found that machine speed of 2000r/min is ideal for sewing firefighter clothing for higher tensile properties and production.

Keywords: Kevlar, needle temperature, Nomex, sewing.

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Authors: Aleksandra Nocoń (Szunke)

Abstract:

The study is a review of the literature concerning the consequences of non-standard monetary policy, which are used by central banks during unconventional periods, threatening banking sector instability. In particular, the attention was paid to the effects of non-standard monetary policy tools for financial markets. However, the empirical evidence about their effects and real consequences for financial markets is still not final. The main aim of the study is to survey consequences of standard and non-standard monetary policy instruments, implemented during the global financial crisis in the United States, United Kingdom and euro area, with particular attention to the results for the stabilization of global financial markets. The study consists mainly of the empirical review, indicating the impact of the implementation of these tools for financial markets. The following research methods were used in the study: literature studies, including domestic and foreign literature, cause and effect analysis and statistical analysis.

Keywords: Asset purchase facility, consequences of monetary policy instruments, non-standard monetary policy, Quantitative Easing.

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

Abstract:

For security purposes, it is important to detect passwords entered by unauthorized users. With traditional alphanumeric passwords, if the content of a password is acquired and correctly entered by an intruder, it is impossible to differentiate the password entered by the intruder from those entered by the authorized user because the password entries contain precisely the same character set. However, no two entries for the gesture-based passwords, even those entered by the person who created the password, will be identical. There are always variations between entries, such as the shape and length of each stroke, the location of each stroke, and the speed of drawing. It is possible that passwords entered by the unauthorized user contain higher levels of variations when compared with those entered by the authorized user (the creator). The difference in the levels of variations may provide cues to detect unauthorized entries. To test this hypothesis, we designed an empirical study, collected and analyzed the data with the help of machine-learning algorithms. The results of the study are significant.

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

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

Abstract:

The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool.

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Authors: Design of 3-Step Skew BLAC Motor for Better Performance in Electric Power Steering System

Abstract:

In Electric Power Steering (EPS), spoke type Brushless AC (BLAC) motors offer distinct advantages over other electric motor types in terms torque smoothness, reliability and efficiency. This paper deals with the shape optimization of spoke type BLAC motor, in order to reduce cogging torque. This paper examines 3 steps skewing rotor angle, optimizing rotor core edge and rotor overlap length for reducing cogging torque in spoke type BLAC motor. The methods were applied to existing machine designs and their performance was calculated using finite- element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly reduce using those methods.

Keywords: EPS, 3-Step skewing, spoke type BLAC, cogging torque, FEA, optimization.

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Authors: Marília Guterres Ferreira, Raul Sidnei Wazlawick

Abstract:

As seen in literature, about 70% of the improvement initiatives fail, and a significant number do not even get started. This paper analyses the problem of failing initiatives on Software Process Improvement (SPI), and proposes good practices supported by motivational tools that can help minimizing failures. It elaborates on the hypothesis that human factors are poorly addressed by deployers, especially because implementation guides usually emphasize only technical factors. This research was conducted with SPI deployers and analyses 32 SPI initiatives. The results indicate that although human factors are not commonly highlighted in guidelines, the successful initiatives usually address human factors implicitly. This research shows that practices based on human factors indeed perform a crucial role on successful implantations of SPI, proposes change management as a theoretical framework to introduce those practices in the SPI context and suggests some motivational tools based on SPI deployers experience to support it.

Keywords: Change management, human factors, motivation, software process improvement.

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Authors: Sidhartha Panda, Narayana Prasad Padhy

Abstract:

With constraints on data availability and for study of power system stability it is adequate to model the synchronous generator with field circuit and one equivalent damper on q-axis known as the model 1.1. This paper presents a systematic procedure for modelling and simulation of a single-machine infinite-bus power system installed with a thyristor controlled series compensator (TCSC) where the synchronous generator is represented by model 1.1, so that impact of TCSC on power system stability can be more reasonably evaluated. The model of the example power system is developed using MATLAB/SIMULINK which can be can be used for teaching the power system stability phenomena, and also for research works especially to develop generator controllers using advanced technologies. Further, the parameters of the TCSC controller are optimized using genetic algorithm. The non-linear simulation results are presented to validate the effectiveness of the proposed approach.

Keywords: Genetic algorithm, MATLAB/SIMULINK, modelling and simulation, power system stability, single-machineinfinite-bus power system, thyristor controlled series compensator.

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Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

Abstract:

The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: Cloud forensics, data protection laws, GDPR, IoT forensics, machine learning.

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Authors: M. Barański

Abstract:

This paper presents a several diagnostic methods designed to electrical machinesespecially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives.Thosemethodsare preferred by the author in periodic diagnostic of electrical machines. The special attentionshould be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methodswere createdinInstitute of Electrical Drives and MachinesKomel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: Electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to- traction drive, turn insulation, vibrations.

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Authors: Rameswar Debnath, Haruhisa Takahashi

Abstract:

An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.

Keywords: support vector machine, generalization error-bound, feature selection, evolutionary algorithm, microarray data

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Authors: H. Iranmanesh, M. Madadi

Abstract:

Work Breakdown Structure (WBS) is one of the most vital planning processes of the project management since it is considered to be the fundamental of other processes like scheduling, controlling, assigning responsibilities, etc. In fact WBS or activity list is the heart of a project and omission of a simple task can lead to an irrecoverable result. There are some tools in order to generate a project WBS. One of the most powerful tools is mind mapping which is the basis of this article. Mind map is a method for thinking together and helps a project manager to stimulate the mind of project team members to generate project WBS. Here we try to generate a WBS of a sample project involving with the building construction using the aid of mind map and the artificial intelligence (AI) programming language. Since mind map structure can not represent data in a computerized way, we convert it to a semantic network which can be used by the computer and then extract the final WBS from the semantic network by the prolog programming language. This method will result a comprehensive WBS and decrease the probability of omitting project tasks.

Keywords: Expert System, Mind map, Semantic network, Work breakdown structure,

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Authors: N. Hatraf, L. Merabeti, Z. Neffeh, W. Taane

Abstract:

The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming.

In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold.

Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize.

The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.

Keywords: Absorption chillers, crystallization, experimental results, Lithium Bromide solution.

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Authors: Berna Cantürk-Günhan, Esra Bukova-Güzel

Abstract:

The purpose of the study is to determine the primary mathematics student teachers- views related to use instructional technology tools in course of the learning process and to reveal how the sample presentations towards different mathematical concepts affect their views. This is a qualitative study involving twelve mathematics students from a public university. The data gathered from two semi-structural interviews. The first one was realized in the beginning of the study. After that the representations prepared by the researchers were showed to the participants. These representations contain animations, Geometer-s Sketchpad activities, video-clips, spreadsheets, and power-point presentations. The last interview was realized at the end of these representations. The data from the interviews and content analyses were transcribed and read and reread to explore the major themes. Findings revealed that the views of the students changed in this process and they believed that the instructional technology tools should be used in their classroom.

Keywords: Integrating Technology, Mathematics Education, Primary Education, Teacher Education.

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Authors: Kevin Fong-Rey Liu, Cheng-Wu Chen, Ken Yeh, Han-Hsi Liang

Abstract:

Environmental impact assessment (EIA) is a procedure tool of environmental management for identifying, predicting, evaluating and mitigating the adverse effects of development proposals. EIA reports usually analyze how the amounts or concentrations of pollutants obey the relevant standards. Actually, many analytical tools can deepen the analysis of environmental impacts in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA to introduce the causal relationships among environmental hazards and damage. Incorporating the LCIA concept into ERA as an integrated tool for EIA can extend the focus of the regulatory compliance of environmental impacts to determine of the significance of environmental impacts. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations due to insufficient information; therefore, ERA should be generalized to fuzzy risk assessment (FRA). Finally, the use of the proposed methodology is demonstrated through the study case of the expansion plan of the world-s largest plastics processing factory.

Keywords: Fuzzy risk analysis, life cycle impact assessment, fuzzy logic, environmental impact assessment

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

Abstract:

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: Color moments, visual thing recognition system, SIFT, color SIFT.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

Abstract:

Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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Authors: Smain M. Bekhti, Nada T. Matta

Abstract:

Knowledge sharing in general and the contextual access to knowledge in particular, still represent a key challenge in the knowledge management framework. Researchers on semantic web and human machine interface study techniques to enhance this access. For instance, in semantic web, the information retrieval is based on domain ontology. In human machine interface, keeping track of user's activity provides some elements of the context that can guide the access to information. We suggest an approach based on these two key guidelines, whilst avoiding some of their weaknesses. The approach permits a representation of both the context and the design rationale of a project for an efficient access to knowledge. In fact, the method consists of an information retrieval environment that, in the one hand, can infer knowledge, modeled as a semantic network, and on the other hand, is based on the context and the objectives of a specific activity (the design). The environment we defined can also be used to gather similar project elements in order to build classifications of tasks, problems, arguments, etc. produced in a company. These classifications can show the evolution of design strategies in the company.

Keywords: Project Memory, Knowledge re-use, Design rationale, Knowledge representation.

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Authors: J. K. Adedeji, M. O. Oyekanmi

Abstract:

This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: Biometric characters, facial recognition, neural network, OpenCV.

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Authors: Kostyantyn Yermashov, Karsten Wolke, Karl Hayo Siemsen

Abstract:

Domain-specific languages describe specific solutions to problems in the application domain. Traditionally they form a solution composing black-box abstractions together. This, usually, involves non-deep transformations over the target model. In this paper we argue that it is potentially powerful to operate with grey-box abstractions to build a domain-specific software system. We present parametric code templates as grey-box abstractions and conceptual tools to encapsulate and manipulate these templates. Manipulations introduce template-s merging routines and can be defined in a generic way. This involves reasoning mechanisms at the code templates level. We introduce the concept of Neurath Modelling Language (NML) that operates with parametric code templates and specifies a visualisation mapping mechanism for target models. Finally we provide an example of calculating a domain-specific software system with predefined NML elements.

Keywords: software design, code templates, domain-specific languages, modelling languages, generic tools

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Authors: Imran Ahmed, Muhammad Islam, Syed Inayat Ali Shah, Awais Adnan

Abstract:

The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 90 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Weed detection, Image Processing, real-timerecognition, Standard Deviation.

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Authors: Z. Zakaria, S. W. Lye, S. Endy

Abstract:

Despite the continuous advancements in automated conflict resolution tools, there is still a low rate of adoption of automation from Air Traffic Control Officers (ATCOs). Trust or acceptance in these tools and conformance to the individual ATCO preferences in strategy execution for conflict resolution are two key factors that impact their use. This paper proposes a methodology to unearth and classify ATCO conflict resolution strategies from simulator data of trained and qualified ATCOs. The methodology involves the extraction of ATCO executive control actions and the establishment of a system of strategy resolution classification based on ATCO radar commands and prevailing flight parameters in deconflicting a pair of aircraft. Six main strategies used to handle various categories of conflict were identified and discussed. It was found that ATCOs were about twice more likely to choose only vertical maneuvers in conflict resolution compared to horizontal maneuvers or a combination of both vertical and horizontal maneuvers.

Keywords: Air traffic control strategies, conflict resolution, simulator data, strategy classification system.

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Authors: Ankush Agrawal, Nadia Bhuiyan

Abstract:

The new product development (NPD) literature emphasizes the importance of introducing new products on the market for continuing business success. New products are responsible for employment, economic growth, technological progress, and high standards of living. Therefore, the study of NPD and the processes through which they emerge is important. The goal of our research is to propose a framework of critical success factors, metrics, and tools and techniques for implementing metrics for each stage of the new product development (NPD) process. An extensive literature review was undertaken to investigate decades of studies on NPD success and how it can be achieved. These studies were scanned for common factors for firms that enjoyed success of new products on the market. The paper summarizes NPD success factors, suggests metrics that should be used to measure these factors, and proposes tools and techniques to make use of these metrics. This was done for each stage of the NPD process, and brought together in a framework that the authors propose should be followed for complex NPD projects. While many studies have been conducted on critical success factors for NPD, these studies tend to be fragmented and focus on one or a few phases of the NPD process. 

Keywords: New product development, performance, critical success factors, framework.

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Authors: Hyun-Woo Cho

Abstract:

Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

Abstract:

Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: Pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction.

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Authors: Somkiat Tangjitsitcharoen, Siripong Damrongthaveesak

Abstract:

This paper presents an advance in monitoring and process control of surface roughness in CNC machine for the turning and milling processes. An integration of the in-process monitoring and process control of the surface roughness is proposed and developed during the machining process by using the cutting force ratio. The previously developed surface roughness models for turning and milling processes of the author are adopted to predict the inprocess surface roughness, which consist of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the cutting force ratio. The cutting force ratios obtained from the turning and the milling are utilized to estimate the in-process surface roughness. The dynamometers are installed on the tool turret of CNC turning machine and the table of 5-axis machining center to monitor the cutting forces. The in-process control of the surface roughness has been developed and proposed to control the predicted surface roughness. It has been proved by the cutting tests that the proposed integration system of the in-process monitoring and the process control can be used to check the surface roughness during the cutting by utilizing the cutting force ratio.

Keywords: Turning, milling, monitoring, surface roughness, cutting force ratio.

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Authors: Mohamed M. Saleh, Khalid S. Al-Hagla

Abstract:

By taking advantage of computer-s processing power, an unlimited number of variations and parameters in both spatial and environmental can be provided while following the same set of rules and constraints. This paper focuses on using the tools of parametric urbanism towards a more responsive environmental and sustainable urban morphology. It presents an understanding to Parametric Urban Comfort Envelope (PUCE) as an interactive computational assessment urban model. In addition, it investigates the applicability potentials of this model to generate an optimized urban form to Borg El Arab city (a new Egyptian Community) concerning the human comfort values specially wind and solar envelopes. Finally, this paper utilizes its application outcomes -both visual and numerical- to extend the designer-s limitations by decrease the concern of controlling and manipulation of geometry, and increase the designer-s awareness about the various potentials of using the parametric tools to create relationships that generate multiple geometric alternatives.

Keywords: Assessment model, human comfort, parametric urbanism, sustainable urban morphology.

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Authors: Anis Gharbi

Abstract:

This paper considers the problem of scheduling maintenance actions for identical aircraft gas turbine engines. Each one of the turbines consists of parts which frequently require replacement. A finite inventory of spare parts is available and all parts are ready for replacement at any time. The inventory consists of both new and refurbished parts. Hence, these parts have different field lives. The goal is to find a replacement part sequencing that maximizes the time that the aircraft will keep functioning before the inventory is replenished. The problem is formulated as an identical parallel machine scheduling problem where the minimum completion time has to be maximized. Two models have been developed. The first one is an optimization model which is based on a 0-1 linear programming formulation, while the second one is an approximate procedure which consists in decomposing the problem into several two-machine subproblems. Each subproblem is optimally solved using the first model. Both models have been implemented using Lingo and have been tested on two sets of randomly generated data with up to 150 parts and 10 turbines. Experimental results show that the optimization model is able to solve only instances with no more than 4 turbines, while the decomposition procedure often provides near-optimal solutions within a maximum CPU time of 3 seconds.

Keywords: Aircraft turbines, Scheduling, Identical parallel machines, 0-1 linear programming, Heuristic.

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Authors: Omolola A. Tajelawi, Hari L. Garbharran

Abstract:

Revenue leakages are one of the major challenges manufacturers face in production processes, as most of the input materials that should emanate as products from the lines are lost as waste. Rather than generating income from material input which is meant to end-up as products, losses are further incurred as costs in order to manage waste generated. In addition, due to the lack of a clear view of the flow of resources on the lines from input to output stage, acquiring information on the true cost of waste generated have become a challenge. This has therefore given birth to the conceptualization and implementation of waste minimization strategies by several manufacturing industries. This paper reviews the principles and applications of three environmental management accounting tools namely Activity-based Costing (ABC), Life-Cycle Assessment (LCA) and Material Flow Cost Accounting (MFCA) in the manufacturing industry and their effectiveness in curbing revenue leakages. The paper unveils the strengths and limitations of each of the tools; beaming a searchlight on the tool that could allow for optimal resource utilization, transparency in production process as well as improved cost efficiency. Findings from this review reveal that MFCA may offer superior advantages with regards to the provision of more detailed information (both in physical and monetary terms) on the flow of material inputs throughout the production process compared to the other environmental accounting tools. This paper therefore makes a case for the adoption of MFCA as a viable technique for the identification and reduction of waste in production processes, and also for effective decision making by production managers, financial advisors and other relevant stakeholders.

Keywords: MFCA, environmental management accounting, resource efficiency, waste reduction, revenue losses.

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Authors: Aikaterini Fountoulaki, Nikos Karacapilidis, Manolis Manatakis

Abstract:

This paper proposes an innovative methodology for Acceptance Sampling by Variables, which is a particular category of Statistical Quality Control dealing with the assurance of products quality. Our contribution lies in the exploitation of machine learning techniques to address the complexity and remedy the drawbacks of existing approaches. More specifically, the proposed methodology exploits Artificial Neural Networks (ANNs) to aid decision making about the acceptance or rejection of an inspected sample. For any type of inspection, ANNs are trained by data from corresponding tables of a standard-s sampling plan schemes. Once trained, ANNs can give closed-form solutions for any acceptance quality level and sample size, thus leading to an automation of the reading of the sampling plan tables, without any need of compromise with the values of the specific standard chosen each time. The proposed methodology provides enough flexibility to quality control engineers during the inspection of their samples, allowing the consideration of specific needs, while it also reduces the time and the cost required for these inspections. Its applicability and advantages are demonstrated through two numerical examples.

Keywords: Acceptance Sampling, Neural Networks, Statistical Quality Control.

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