Search results for: Machine Tool

Authors: Ali Akbar Jamali

Abstract:

This paper discusses site selection process for biological soil conservation planning. It was supported by a valuefocused approach and spatial multi-criteria evaluation techniques. A first set of spatial criteria was used to design a number of potential sites. Next, a new set of spatial and non-spatial criteria was employed, including the natural factors and the financial costs, together with the degree of suitability for the Bonkuh watershed to biological soil conservation planning and to recommend the most acceptable program. The whole process was facilitated by a new software tool that supports spatial multiple criteria evaluation, or SMCE in GIS software (ILWIS). The application of this tool, combined with a continual feedback by the public attentions, has provided an effective methodology to solve complex decisional problem in biological soil conservation planning.

Keywords: GIS, Biological soil conservation planning, Spatial multi-criteria evaluation, Iran

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Authors: Andreas Heindl, Alexander K. Seewald, Martin Schepelmann, Radu Rogojanu, Giovanna Bises, Theresia Thalhammer, Isabella Ellinger

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Bone remodeling occurs by the balanced action of bone resorbing osteoclasts (OC) and bone-building osteoblasts. Increased bone resorption by excessive OC activity contributes to malignant and non-malignant diseases including osteoporosis. To study OC differentiation and function, OC formed in in vitro cultures are currently counted manually, a tedious procedure which is prone to inter-observer differences. Aiming for an automated OC-quantification system, classification of OC and precursor cells was done on fluorescence microscope images based on the distinct appearance of fluorescent nuclei. Following ellipse fitting to nuclei, a combination of eight features enabled clustering of OC and precursor cell nuclei. After evaluating different machine-learning techniques, LOGREG achieved 74% correctly classified OC and precursor cell nuclei, outperforming human experts (best expert: 55%). In combination with the automated detection of total cell areas, this system allows to measure various cell parameters and most importantly to quantify proteins involved in osteoclastogenesis.

Keywords: osteoclasts, machine learning, ellipse fitting.

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: Moving object detection, histogram of oriented gradient histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine.

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Authors: Mehdi Ghommem, Daniel Garcia, Nathan Collier, Victor Calo

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We develop a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM). The computational framework uses the Python programming language which has easy integration with the scripts requiring computationally-expensive operations written in Fortran. The mixed-language approach enables high performance in terms of solution time and high flexibility in terms of easiness of code adaptation to different system configurations and applications. This computational tool is intended to predict the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges...) subject to an incoming air. We simulate different aerodynamic problems to validate and illustrate the usefulness and effectiveness of the developed computational tool.

Keywords: Unsteady aerodynamics, numerical simulations, mixed-language approach, potential flow.

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Authors: Jung-Ho Moon, Tae Kwon Ha

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In the present study, M2 high speed steels were fabricated by using electro-slag rapid remelting process. Carbide structure was analysed and the fracture toughness and hardness were also measured after austenitization treatment at 1190 and 1210oC followed by tempering treatment at 535oC for billets with various diameters from 16 to 60 mm. Electro-slag rapid remelting (ESRR) process is an advanced ESR process combined by continuous casting and successfully employed in this study to fabricate a sound M2 high speed ingot. Three other kinds of commercial M2 high speed steels, produced by traditional method, were also analysed for comparison. Distribution and structure of eutectic carbides of the ESRR billet were found to be comparable to those of commercial alloy and so was the fracture toughness.

Keywords: High speed tool steel, eutectic carbide, microstructure, hardness, fracture toughness.

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Authors: M. H. Arslan, U. K. Sahin, H. Acikgoz-Tufan, I. Gocek, I. Erdem

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Various exterior factors have perpetual effects on textile materials during wear, use and laundering in everyday life. In accordance with their frequency of use, textile materials are required to be laundered at certain intervals. The medium in which the laundering process takes place have inevitable detrimental physical and chemical effects on textile materials caused by the unique parameters of the process inherently existing. Connatural structures of various textile materials result in many different physical, chemical and mechanical characteristics. Because of their specific structures, these materials have different behaviors against several exterior factors. By modeling the behavior of commercial and test textiles as group-wise against laundering process, it is possible to disclose the relation in between these two groups of materials, which will lead to better understanding of their behaviors in terms of similarities and differences against the washing parameters of the laundering. Thus, the goal of the current research is to examine the behavior of two groups of textile materials as commercial textiles and as test textiles towards the main washing machine parameters during laundering process such as temperature, load quantity, mechanical action and level of water amount by concentrating on shrinkage, pilling, sewing defects, collar abrasion, the other defects other than sewing, whitening and overall properties of textiles. In this study, cotton fabrics were preferred as commercial textiles due to the fact that garments made of cotton are the most demanded products in the market by the textile consumers in daily life. Full factorial experimental set-up was used to design the experimental procedure. All profiles always including all of the commercial and the test textiles were laundered for 20 cycles by commercial home laundering machine to investigate the effects of the chosen parameters. For the laundering process, a modified version of ‘‘IEC 60456 Test Method’’ was utilized. The amount of detergent was altered as 0.5% gram per liter depending on varying load quantity levels. Datacolor 650®, EMPA Photographic Standards for Pilling Test and visual examination were utilized to test and characterize the textiles. Furthermore, in the current study the relation in between commercial and test textiles in terms of their performance was deeply investigated by the help of statistical analysis performed by MINITAB® package program modeling their behavior against the parameters of the laundering process. In the experimental work, the behaviors of both groups of textiles towards washing machine parameters were visually and quantitatively assessed in dry state.

Keywords: Behavior against washing machine parameters, performance evaluation of textiles, statistical analysis, commercial and test textiles.

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: Coupled Markov random field, environment, object-based analysis, Polarimetric SAR images.

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Authors: M. A. S. Fahim, J. Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realization often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: Air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter.

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Authors: R. Monib, C. I. Goodier, A. Gibb

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The aim of this paper is to investigate the key drivers in planning the construction phase for infrastructure projects to reduce project delays. To achieve this aim, the research conducted three case studies using semi-structured and unstructured interviews (n = 59). The results conclude that a lack of modularization awareness is among the key factors attributed to project delays. The current emotive and ill-informed approach to decision-making, coupled with the lack of knowledge regarding appropriate construction method selection, prevents the potential benefits of modularization being fully realized. To assist with decision-making for the best construction method, the research presents project management tools to help decision makers to choose the most appropriate construction approach through optimizing the use of modularization in engineering and construction (EC). A decision-making checklist is presented in this paper. This checklist tool assists the project team in determining the best construction method, taking into consideration the module type.

Keywords: Infrastructure, modularization, decision support, planning.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Amal E. Shehata, Manal E. Fareed

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Osteoarthritis (OA) is the most prevalent and far common debilitating form of arthritis which can be defined as a degenerative condition affecting synovial joint. Patients suffering from osteoarthritis often complain of dull ache pain on movement. Physical agents can fight the painful process when correctly indicated and used such as heat or cold therapy Aim. This study was carried out to: Compare the effect of cold, warm and contrast therapy on controlling knee osteoarthritis associated problems. Setting: The study was carried out in orthopedic outpatient clinics of Menoufia University and teaching Hospitals, Egypt. Sample: A convenient sample of 60 adult patients with unilateral knee osteoarthritis. Tools: three tools were utilized to collect the data. Tool I : An interviewing questionnaire. It comprised of three parts covering  sociodemographic data, medical data and adverse effects of the treatment protocol. Tool II : Knee Injury and Osteoarthritis Outcome Score (KOOS) It consists of five main parts. Tool II1 : 0-10 Numeric pain rating scale. Results: reveled that the total knee symptoms score was decreased from moderate symptoms pre intervention to mild symptoms after warm and contrast method of therapy, but the contrast therapy had significant effect in reducing the knee symptoms and pain than the other symptoms. Conclusions: all of the three methods of therapy resulted in improvement in all knee symptoms and pain but the most appropriate protocol of treatment to relive symptoms and pain was contrast therapy.

Keywords: Knee Osteoarthritis, Cold, Warm and Contrast Therapy.

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Authors: Paola Gattinoni, Vincenzo Francani

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The paper deals with the analysis of triggering conditions and evolution processes of piping phenomena, in relation to both mechanical and hydraulic aspects. In particular, the aim of the study is to predict slope instabilities triggered by piping, analysing the conditions necessary for a flow failure to occur. Really, the mechanical effect involved in the loads redistribution around the pipe is coupled to the drainage process arising from higher permeability of the pipe. If after the pipe formation, the drainage goes prevented for pipe clogging, the porewater pressure increase can lead to the failure or even the liquefaction, with a subsequent flow slide. To simulate the piping evolution and to verify relevant stability conditions, a iterative coupled modelling approach has been pointed out. As example, the proposed tool has been applied to the Stava Valley disaster (July, 1985), demonstrating that piping might be one of triggering phenomena of the tailings dams collapse.

Keywords: Flow failure, liquefaction, modeling, piping, porewater pressure.

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Authors: Montri Phothisonothai, Masahiro Nakagawa

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The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.

Keywords: electroencephalogram (EEG), motor imagery tasks, mental tasks, biomedical signals processing, human-machine interface, fractal analysis, critical exponent method (CEM).

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias

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Gene Ontology terms have been actively used to annotate various protein sets. SWISS-PROT, TrEMBL, and InterPro are protein databases that are annotated according to the Gene Ontology terms. However, direct implementation of the Gene Ontology terms for annotation of anonymous protein sequences is not easy, especially for species not commonly represented in biological databases. UTMGO is developed as a tool that allows the user to quickly and easily search for a group of semantically related Gene Ontology terms. The applicability of the UTMGO is demonstrated by applying it to annotation of anonymous protein sequence. The extended UTMGO uses the Gene Ontology terms together with protein sequences associated with the terms to perform the annotation task. GOPET, GOtcha, GoFigure, and JAFA are used to compare the performance of the extended UTMGO.

Keywords: Anonymous protein sequence, Gene Ontology, Protein sequence annotation, Protein sequence alignment

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Authors: Hyunchul Ahn, Kyoung-Jae Kim

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Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, most of them have only focused on classifying samples into nominal categories, thus the unique characteristic of the credit rating - ordinality - has been seldom considered in their approaches. This study proposes new types of ANN and MSVM classifiers, which are named OMANN and OMSVM respectively. OMANN and OMSVM are designed to extend binary ANN or SVM classifiers by applying ordinal pairwise partitioning (OPP) strategy. These models can handle ordinal multiple classes efficiently and effectively. To validate the usefulness of these two models, we applied them to the real-world bond rating case. We compared the results of our models to those of conventional approaches. The experimental results showed that our proposed models improve classification accuracy in comparison to typical multiclass classification techniques with the reduced computation resource.

Keywords: Artificial neural network, Corporate credit rating, Support vector machines, Ordinal pairwise partitioning

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Authors: Hui-Yu Huang, Shih-Hang Hsu

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In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.

Keywords: Gabor filter, improved active shape model (IASM), principal component analysis (PCA), face alignment, face recognition, support vector machine (SVM)

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Authors: Eric P. Jiang

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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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Authors: Malinwo Estone Ayikpa

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With the rapid increase of grid-connected PV systems over the last decades, genuine challenges have arisen for engineers and professionals of energy field in the planning and operation of existing distribution networks with the integration of new generation sources. However, the conventional distribution network, in its design was not expected to receive other generation outside the main power supply. The tools generally used to analyze the networks become inefficient and cannot take into account all the constraints related to the operation of grid-connected PV systems. Some of these constraints are voltage control difficulty, reverse power flow, and especially voltage unbalance which could be due to the poor distribution of single-phase PV systems in the network. In order to analyze the impact of the connection of small and large number of PV systems to the distribution networks, this paper presents an efficient optimization tool that minimizes voltage unbalance in three-phase distribution networks with active and reactive power injections from the allocation of single-phase and three-phase PV plants. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. Good reduction of voltage unbalance can be achieved by reactive power control of the PV systems. The presented tool is based on the three-phase current injection method and the PV systems are modeled via an equivalent circuit. The primal-dual interior point method is used to obtain the optimal operating points for the systems.

Keywords: Photovoltaic generation, primal-dual interior point method, three-phase optimal power flow, unbalanced system.

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Authors: Ilyas Khan. Liliane Pintelon, Harry Martin, Michael Shömig

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The increasing costs of healthcare on one hand, and the rise in aging population and associated chronic disease, on the other hand, are putting increasing burden on the current health care system in many Western countries. For instance, chronic kidney disease (CKD) is a common disease and in Europe, the cost of renal replacement therapy (RRT) is very significant to the total health care cost. However, the recent advancement in healthcare technology, provide the opportunity to treat patients at home in their own comfort. It is evident that home healthcare offers numerous advantages apparently, low costs and high patients’ quality of life. Despite these advantages, the intake of home hemodialysis (HHD) therapy is still low in particular in Germany. Many factors are accounted for the low number of HHD intake. However, this paper is focusing on patients’ safety-related factors of current HHD practices in Germany. The aim of this paper is to analyze the current HHD practices in Germany and to identify risks related factors if any exist. A case study has been conducted in a dialysis center which consists of four dialysis centers in the south of Germany. In total, these dialysis centers have 350 chronic dialysis patients, of which, four patients are on HHD. The centers have 126 staff which includes six nephrologists and 120 other staff i.e. nurses and administration. The results of the study revealed several risk-related factors. Most importantly, these centers do not offer allied health services at the pre-dialysis stage, the HHD training did not have an established curriculum; however, they have just recently developed the first version. Only a soft copy of the machine manual is offered to patients. Surprisingly, the management was not aware of any standard available for home assessment and installation. The home assessment is done by a third party (i.e. the machines and equipment provider) and they may not consider the hygienic quality of the patient’s home. The type of machine provided to patients at home is similar to the one in the center. The model may not be suitable at home because of its size and complexity. Even though portable hemodialysis machines, which are specially designed for home use, are available in the market such as the NxStage series. Besides the type of machine, no assistance is offered for space management at home in particular for placing the machine. Moreover, the centers do not offer remote assistance to patients and their carer at home. However, telephonic assistance is available. Furthermore, no alternative is offered if a carer is not available. In addition, the centers are lacking medical staff including nephrologists and renal nurses.

Keywords: Home hemodialysis, home hemodialysis practices, patients’ related risks in the current home hemodialysis practices, patient safety in home hemodialysis.

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: Dimensional affect prediction, Output-associative RVM, Multivariate regression.

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Authors: Ashly Joseph, Jithu Paulose

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The increased usage of cloud computing has revolutionized the IT landscape, but it has also raised new security concerns. DevSecOps emerged as a way for tackling these difficulties by integrating security into the software development process. However, the rising complexity and sophistication of cyber threats need more advanced solutions. This paper looks into the usage of artificial intelligence (AI) techniques in the DevSecOps framework to increase cloud security. This study uses quantitative and qualitative techniques to assess the usefulness of AI approaches such as machine learning, natural language processing, and deep learning in reducing security issues. This paper thoroughly examines the symbiotic relationship between AI and DevSecOps, concentrating on how AI may be seamlessly integrated into the continuous integration and continuous delivery (CI/CD) pipeline, automated security testing, and real-time monitoring methods. The findings emphasize AI's huge potential to improve threat detection, risk assessment, and incident response skills. Furthermore, the paper examines the implications and challenges of using AI in DevSecOps workflows, considering factors like as scalability, interpretability, and adaptability. This paper adds to a better understanding of AI's revolutionary role in cloud security and provides valuable insights for practitioners and scholars in the field.

Keywords: Cloud Security, DevSecOps, Artificial Intelligence, AI, Machine Learning, Natural Language Processing, NLP, cybersecurity, AI-driven Security.

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Authors: Tristan O'Neill, Trina Myers, Jarrod Trevathan

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The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for crossdomain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.

Keywords: Virtual research environment, Semantic Web, performance analysis, tropical data hub.

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Authors: Kuo-Yang Cheng, Yi-Lun Pan, Chang-Hsing Wu, His-En Yu, Hui-Shan Chen, Weicheng Huang

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The main mission of Ezilla is to provide a friendly interface to access the virtual machine and quickly deploy the high performance computing environment. Ezilla has been developed by Pervasive Computing Team at National Center for High-performance Computing (NCHC). Ezilla integrates the Cloud middleware, virtualization technology, and Web-based Operating System (WebOS) to form a virtual computer in distributed computing environment. In order to upgrade the dataset and speedup, we proposed the sensor observation system to deal with a huge amount of data in the Cassandra database. The sensor observation system is based on the Ezilla to store sensor raw data into distributed database. We adopt the Ezilla Cloud service to create virtual machines and login into virtual machine to deploy the sensor observation system. Integrating the sensor observation system with Ezilla is to quickly deploy experiment environment and access a huge amount of data with distributed database that support the replication mechanism to protect the data security.

Keywords: Cloud, Virtualization, Cassandra, WebOS

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Authors: Deniz T. Sodiri, Venkat V S S Sastry

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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.

Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.

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Authors: M. Gregor, J. Polcar, P. Horejsi, M. Simon

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Machine visualization is an area of interest with fast and progressive development. We present a method of machine visualization which will be applicable in real industrial conditions according to current needs and demands. Real factory data were obtained in a newly built research plant. Methods described in this paper were validated on a case study. Input data were processed and the virtual environment was created. The environment contains information about dimensions, structure, disposition, and function. Hardware was enhanced by modular machines, prototypes, and accessories. We added functionalities and machines into the virtual environment. The user is able to interact with objects such as testing and cutting machines, he/she can operate and move them. Proposed design consists of an environment with two degrees of freedom of movement. Users are in touch with items in the virtual world which are embedded into the real surroundings. This paper describes development of the virtual environment. We compared and tested various options of factory layout virtualization and visualization. We analyzed possibilities of using a 3D scanner in the layout obtaining process and we also analyzed various virtual reality hardware visualization methods such as: Stereoscopic (CAVE) projection, Head Mounted Display (HMD) and augmented reality (AR) projection provided by see-through glasses.

Keywords: Augmented reality, spatial scanner, virtual environment, virtual reality.

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Authors: M. K. Pradhan, C. K. Biswas,

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In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively

Keywords: Electrical discharge machining, material removal rate, neuro-fuzzy model, regression model, mountain clustering.

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Authors: Youngkuen Cho, Seonhong Hwang, Jinyoung Min, Youngho Kim, Dohyung Lim, Hansung Kim

Abstract:

Many studies have emphasized the importance of resistive exercise to maintain a healthy human body, particular in prevention of weakening of physical strength. Recently, some studies advocated that an application of vibration as a supplementary means in a regular training was effective in encouraging physical strength. Aim of the current study was, therefore, to identify if an application of vibration in a resistive exercise was effective in encouraging physical strength as that in a regular training. A 3-dimensional virtual lower extremity model for a healthy male and virtual leg-press model were generated and synchronized. Dynamic leg-press exercises on a slide machine with/without extra load and on a footboard with vibration as well as on a slide machine with extra load were analyzed. The results of the current indicated that the application of the vibration on the dynamic leg-press exercise might be not greatly effective in encouraging physical strength, compared with the dynamic leg press exercise with extra load. It was, however, thought that the application of the vibration might be helpful to elderly individuals because the reduced maximum muscle strength appeared by the effect of the vibration may avoid a muscular spasm, which can be driven from a high muscle strength sometimes produced during the leg-press exercise with extra load.

Keywords: Resistive exercise, leg-press exercise, muscle strength.

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Authors: Chia-Chen Wei, Pack Hsieh, Jeffrey Chen

Abstract:

Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.

Keywords: Big data analytics, Industry 4.0, SPI threshold setting, surface mount technology.

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Authors: Alvaro J. P. Ramos, Wellington S. Mota, Yendys S. Dantas

Abstract:

This paper analyses the torsional efforts in gas turbine-generator shafts caused by high speed automatic reclosing of transmission lines. This issue is especially important for cases of three phase short circuit and unsuccessful reclosure of lines in the vicinity of the thermal plant. The analysis was carried out for the thermal plant TERMOPERNAMBUCO located on Northeast region of Brazil. It is shown that stress level caused by lines unsuccessful reclosing can be several times higher than terminal three-phase short circuit. Simulations were carried out with detailed shaft torsional model provided by machine manufacturer and with the “Alternative Transient Program – ATP" program [1]. Unsuccessful three phase reclosing for selected lines in the area closed to the plant indicated most critical cases. Also, reclosing first the terminal next to the gas turbine gererator will lead also to the most critical condition. Considering that the values of transient torques are very sensible to the instant of reclosing, simulation of unsuccessful reclosing with statistics ATP switch were carried out for determination of most critical transient torques for each section of the generator turbine shaft.

Keywords: Torsional Efforts, Thermal Machine, GasTurbine, Automatic Reclosing.

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Authors: C. Ramsauer, J. Karandikar, D. Leitner, T. Schmitz, F. Bleicher

Abstract:

Machining instability, or chatter, can impose an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the TU Wien, Vienna, Austria. The recorded data from a milling test cut were used to classify the cut as stable or unstable based on a frequency analysis. The test cut result was used in a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculated the probability of stability as a function of axial depth of cut and spindle speed based on the test result and recommended parameters for the next test cut. The iterative process between two transatlantic locations was repeated until convergence to a stable optimal process parameter set was achieved.

Keywords: Bayesian learning, instrumented tool holder, machining stability, optimization strategy.

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