Search results for: machine resistance training

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Chandra Gupt Porwal, Preeti C. Porwal

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Transient data reveals much about the machine's condition that steady-state data cannot. New technologies make this information much more available for evaluating the mechanical integrity of a machine train. Recent surveys at various stations indicate that simplicity is preferred over completeness in machine audits throughout the power generation industry. This is most clearly shown by the number of rotating machinery predictive maintenance programs in which only steady-state vibration amplitude is trended while important transient vibration data is not even acquired. Efforts have been made to explain what transient data is, its importance, the types of plots used for its display, and its effective utilization for analysis. In order to demonstrate the value of measuring transient data and its practical application in rotating machinery for resolving complex and persistent issues with turbine generators, the author presents a few case studies that highlight the presence of rotor instabilities due to the shaft moving towards the bearing centre in a 100 MM LMZ unit located in the Northern Capital Region (NCR), heavy misalignment noticed—especially after 2993 rpm—caused by loose coupling bolts, which prevented the machine from being synchronized for more than four months in a 250 MW KWU unit in the Western Region (WR), and heavy preload noticed at Intermediate pressure turbine (IPT) bearing near HP- IP coupling, caused by high points on coupling faces at a 500 MW KWU unit in the Northern region (NR), experienced at Indian power plants.

Keywords: transient data, steady-state-data, intermediate -pressure-turbine, high-points

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Authors: Fatima Al-Aswadi, Chan Yong

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With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future.

Keywords: concept discovery, deep learning, ontology learning, semantic relation, semantic web

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Yusri Yusof, Kamran Latif

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Computer Numeric Control (CNC) machine has been widely used in the industries since its inception. Currently, in CNC technology has been used for various operations like milling, drilling, packing and welding etc. with the rapid growth in the manufacturing world the demand of flexibility in the CNC machines has rapidly increased. Previously, the commercial CNC failed to provide flexibility because its structure was of closed nature that does not provide access to the inner features of CNC. Also CNC’s operating ISO data interface model was found to be limited. Therefore, to overcome that problem, Open Architecture Control (OAC) technology and STEP-NC data interface model are introduced. At present the Personal Computer (PC) has been the best platform for the development of open-CNC systems. In this paper, both ISO data interface model interpretation, its verification and execution has been highlighted with the introduction of the new techniques. The proposed is composed of ISO data interpretation, 3D simulation and machine motion control modules. The system is tested on an old 3 axis CNC milling machine. The results are found to be satisfactory in performance. This implementation has successfully enabled sustainable manufacturing environment.

Keywords: CNC, ISO 6983, ISO 14649, LabVIEW, open architecture control, reconfigurable manufacturing systems, sustainable manufacturing, Soft-CNC

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Authors: Prathibha Ganesan

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This paper discusses discontents of inhabitants in the landfills and its culmination into resistance against centralised waste disposal during the last three decades in Kerala. The study is based on a sample survey of 175 households located in the landfill sites and city limits of two Municipal Corporations viz. Thrissur and Cochin. The study found that waste is dumped in the periphery of the urban area where economically and socially vulnerable people are densely populated. Moreover, landfill sites are unscientifically managed to cause severe socio-economic and health issues to the local people, finally leading to their mobilisation and persistent struggle. The struggles often culminate in the closure of landfills or forced relocation or abandonment of the region by the community. The study concluded that persistent people’s struggles compel the local state to either find alternatives to centralised solid waste management system or use political power to subsume the local resistance. The persistence of the struggles determined the type waste governance adopted by the local governments.

Keywords: solid waste management, municipal corporation, resistance movements, urban, Kerala

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Authors: Mohammad M. Khan, Pankaj Agarwal

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The present investigation deals with the microstructures, mechanical and detailed wear characteristics of in situ TiC particles reinforced zinc aluminum-based metal matrix composites. The composites have been synthesized by liquid metallurgy route using vortex technique. The composite was found to be harder than the matrix alloy due to high hardness of the dispersoid particles therein. The former was also lower in ultimate tensile strength and ductility as compared to the matrix alloy. This could be explained to be due to the use of coarser size dispersoid and larger interparticle spacing. Reasonably uniform distribution of the dispersoid phase in the alloy matrix and good interfacial bonding between the dispersoid and matrix was observed. The composite exhibited predominantly brittle mode of fracture with microcracking in the dispersoid phase indicating effective easy transfer of load from matrix to the dispersoid particles. To study the wear behavior of the samples three different types of tests were performed namely: (i) sliding wear tests using a pin on disc machine under dry condition, (ii) high stress (two-body) abrasive wear tests using different combinations of abrasive media and specimen surfaces under the conditions of varying abrasive size, traversal distance and load, and (iii) low-stress (three-body) abrasion tests using a rubber wheel abrasion tester at various loads and traversal distances using different abrasive media. In sliding wear test, significantly lower wear rates were observed in the case of base alloy over that of the composites. This has been attributed to the poor room temperature strength as a result of increased microcracking tendency of the composite over the matrix alloy. Wear surfaces of the composite revealed the presence of fragmented dispersoid particles and microcracking whereas the wear surface of matrix alloy was observed to be smooth with shallow grooves. During high-stress abrasion, the presence of the reinforcement offered increased resistance to the destructive action of the abrasive particles. Microcracking tendency was also enhanced because of the reinforcement in the matrix. The negative effect of the microcracking tendency was predominant by the abrasion resistance of the dispersoid. As a result, the composite attained improved wear resistance than the matrix alloy. The wear rate increased with load and abrasive size due to a larger depth of cut made by the abrasive medium. The wear surfaces revealed fine grooves, and damaged reinforcement particles while subsurface regions revealed limited plastic deformation and microcracking and fracturing of the dispersoid phase. During low-stress abrasion, the composite experienced significantly less wear rate than the matrix alloy irrespective of the test conditions. This could be explained to be due to wear resistance offered by the hard dispersoid phase thereby protecting the softer matrix against the destructive action of the abrasive medium. Abraded surfaces of the composite showed protrusion of dispersoid phase. The subsurface regions of the composites exhibited decohesion of the dispersoid phase along with its microcracking and limited plastic deformation in the vicinity of the abraded surfaces.

Keywords: abrasive wear, liquid metallurgy, metal martix composite, SEM

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Authors: Chung-Young Lee, Se-Hee Ahn, Jun-Gu Choi, Youn-Jeong Lee, Hyuk-Joon Kwon, Jae-Hong Kim

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A/chicken/Korea/01310/2001 (H9N2) (01310) was passaged through embryonated chicken eggs (ECEs) by 20 times (01310-E20), and it has been used for an inactivated oil emulsion vaccine in Korea. After sequential passages, 01310-E20 showed higher pathogenicity in ECEs and acquired multiple mutations including a potential N-glycosylation at position 133 (H3 numbering) in HA and 18aa-deletion in NA stalk. To evaluate the effect of these mutations on the mammalian pathogenicity and resistance to non-specific inhibitors, we generated four PR8-derived recombinant viruses with different combinations of HA and NA from 01310-E2 and 01310-E20 (rH2N2, rH2N20, rH20N2, and rH20N20). According to our results, recombinant viruses containing 01310 E20 HA showed higher growth property in MDCK cells and higher virulence on mice than those containing 01310 E2 HA regardless of NA. The hemagglutination activity of rH20N20 was less inhibited by egg white and mouse lung extract than that of other recombinant viruses. Thus, the increased pathogenicity of 01310-E20 may be related to both higher replication efficiency and resistance to non-specific inhibitors in mice.

Keywords: avian influenza virus, egg adaptation, H9N2, N-glycosylation, stalk deletion of neuraminidase

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Authors: Merve Yünlü, Nihat Kandemir, Aylin Ersoy

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Temperature sensors are used in home appliances not only to monitor the basic functions of the machine but also to minimize energy consumption and ensure safe operation. In parallel with the development of smart home applications and IoT algorithms, these sensors produce important data such as the frequency of use of the machine, user preferences, and the compilation of critical data in terms of diagnostic processes for fault detection throughout an appliance's operational lifespan. Commercially available thin-film resistive temperature sensors have a well-established manufacturing procedure that allows them to operate over a wide temperature range. However, these sensors are over-designed for white goods applications. The operating temperature range of these sensors is between -70°C and 850°C, while the temperature range requirement in home appliance applications is between 23°C and 500°C. To ensure the operation of commercial sensors in this wide temperature range, usually, a platinum coating of approximately 1-micron thickness is applied to the wafer. However, the use of platinum in coating and the high coating thickness extends the sensor production process time and therefore increases sensor costs. In this study, an attempt was made to develop a low-cost temperature sensor design and production method that meets the technical requirements of white goods applications. For this purpose, a custom design was made, and design parameters (length, width, trim points, and thin film deposition thickness) were optimized by using statistical methods to achieve the desired resistivity value. To develop thin film resistive temperature sensors, one side polished sapphire wafer was used. To enhance adhesion and insulation 100 nm silicon dioxide was coated by inductively coupled plasma chemical vapor deposition technique. The lithography process was performed by a direct laser writer. The lift-off process was performed after the e-beam evaporation of 10 nm titanium and 280 nm platinum layers. Standard four-point probe sheet resistance measurements were done at room temperature. The annealing process was performed. Resistivity measurements were done with a probe station before and after annealing at 600°C by using a rapid thermal processing machine. Temperature dependence between 25-300 °C was also tested. As a result of this study, a temperature sensor has been developed that has a lower coating thickness than commercial sensors but can produce reliable data in the white goods application temperature range. A relatively simplified but optimized production method has also been developed to produce this sensor.

Keywords: thin film resistive sensor, temperature sensor, household appliance, sustainability, energy efficiency

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: Fouad Berrabeh, Sabir Messalti

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The performances of the photovoltaic systems are very dependent on different conditions, such as solar irradiation, temperature, etc. Therefore, it is very important to provide detailed studies for different cases in order to provide continuously power, so the photovoltaic system must be properly sized. This paper presents the modelling and simulation of the photovoltaic cell using single diode model. I-V characteristics and P-V characteristics are presented and it verified at different conditions (irradiance effect, temperature effect, series resistance effect).

Keywords: photovoltaic cell, BP SX 150 BP solar photovoltaic module, irradiance effect, temperature effect, series resistance effect, I–V characteristics, P–V characteristics

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Authors: Yu-Hsien Lin, Yu-Ru Lin, Yung-Chun Wu

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This work demonstrates the ultra-thin poly-Si (polycrystalline Silicon) nanowire junctionless thin film transistors (NWs JL-TFT) with nickel silicide contact. For nickel silicide film, this work designs to use two-step annealing to form ultra-thin, uniform and low sheet resistance (Rs) Ni silicide film. The NWs JL-TFT with nickel silicide contact exhibits the good electrical properties, including high driving current (>10⁷ Å), subthreshold slope (186 mV/dec.), and low parasitic resistance. In addition, this work also compares the electrical characteristics of NWs JL-TFT with nickel silicide and non-silicide contact. Nickel silicide techniques are widely used for high-performance devices as the device scaling due to the source/drain sheet resistance issue. Therefore, the self-aligned silicide (salicide) technique is presented to reduce the series resistance of the device. Nickel silicide has several advantages including low-temperature process, low silicon consumption, no bridging failure property, smaller mechanical stress, and smaller contact resistance. The junctionless thin-film transistor (JL-TFT) is fabricated simply by heavily doping the channel and source/drain (S/D) regions simultaneously. Owing to the special doping profile, JL-TFT has some advantages such as lower thermal the budget which can integrate with high-k/metal-gate easier than conventional MOSFETs (Metal Oxide Semiconductor Field-Effect Transistors), longer effective channel length than conventional MOSFETs, and avoidance of complicated source/drain engineering. To solve JL-TFT has turn-off problem, JL-TFT needs ultra-thin body (UTB) structure to reach fully depleted channel region in off-state. On the other hand, the drive current (Iᴅ) is declined as transistor features are scaled. Therefore, this work demonstrates ultra thin poly-Si nanowire junctionless thin film transistors with nickel silicide contact. This work investigates the low-temperature formation of nickel silicide layer by physical-chemical deposition (PVD) of a 15nm Ni layer on the poly-Si substrate. Notably, this work designs to use two-step annealing to form ultrathin, uniform and low sheet resistance (Rs) Ni silicide film. The first step was promoted Ni diffusion through a thin interfacial amorphous layer. Then, the unreacted metal was lifted off after the first step. The second step was annealing for lower sheet resistance and firmly merged the phase.The ultra-thin poly-Si nanowire junctionless thin film transistors NWs JL-TFT with nickel silicide contact is demonstrated, which reveals high driving current (>10⁷ Å), subthreshold slope (186 mV/dec.), and low parasitic resistance. In silicide film analysis, the second step of annealing was applied to form lower sheet resistance and firmly merge the phase silicide film. In short, the NWs JL-TFT with nickel silicide contact has exhibited a competitive short-channel behavior and improved drive current.

Keywords: poly-Si, nanowire, junctionless, thin-film transistors, nickel silicide

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Authors: Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha Lee

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Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS) -- a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis -- with Gaussian process machine learning. We collect over 20,000 EIS spectra of commercial Li-ion batteries at different states of health, states of charge and temperatures -- the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

Keywords: battery degradation, machine learning method, electrochemical impedance spectroscopy, battery diagnosis

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Authors: J. Kloypayan, W. Pimpakan

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The pipe taper thread measurement and uncertainty normally used the four-wire probe according to the JIS B 0262. Besides, according to the EA-10/10 standard, the pipe thread could be measured using the three-wire probe. This research proposed to use the three-wire probe measuring the pitch diameter of the pipe taper thread. The measuring accessory component was designed and made, then, assembled to one side of the ULM 828 CiM machine. Therefore, this machine could be used to measure and calibrate both the pipe thread and the pipe taper thread. The equations and the expanded uncertainty for pitch diameter measurement were formulated. After the experiment, the results showed that the pipe taper thread had the pitch diameter equal to 19.165 mm and the expanded uncertainty equal to 1.88µm. Then, the experiment results were compared to the results from the National Institute of Metrology Thailand. The equivalence ratio from the comparison showed that both results were related. Thus, the proposed method of using the three-wire probe measured the pitch diameter of the pipe taper thread was acceptable.

Keywords: pipe taper thread, three-wire probe, measure and calibration, the universal length measuring machine

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Authors: E. Nigris, F. Passalacqua

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Relating to the recent researches in the field of faculty development, this paper aims to present a pilot training programme realized at the University of Milano-Bicocca to improve teaching skills of faculty members. A total of 57 professors (both full professors and associate professors) were trained during the pilot programme in three editions of the workshop, focused on promoting skills for teaching large classes. The study takes into account: 1) the theoretical framework of the programme which combines the recent tradition about professional development and the research on in-service training of school teachers; 2) the structure and the content of the training programme, organized in a 12 hours-full immersion workshop and in individual consultations; 3) the educational specificity of the training programme which is based on the relation between 'general didactic' (active learning metholodies; didactic communication) and 'disciplinary didactics' (didactic transposition and reconstruction); 4) results about the impact of the training programme, both related to the workshop and the individual consultations. This study aims to provide insights mainly on two levels of the training program’s impact ('behaviour change' and 'transfer') and for this reason learning outcomes are evaluated by different instruments: a questionnaire filled out by all 57 participants; 12 in-depth interviews; 3 focus groups; conversation transcriptions of workshop activities. Data analysis is based on a descriptive qualitative approach and it is conducted through thematic analysis of the transcripts using analytical categories derived principally from the didactic transposition theory. The results show that the training programme developed effectively three major skills regarding different stages of the 'didactic transposition' process: a) the content selection; a more accurated selection and reduction of the 'scholarly knowledge', conforming to the first stage of the didactic transposition process; b) the consideration of students’ prior knowledge and misconceptions within the lesson design, in order to connect effectively the 'scholarly knowledge' to the 'knowledge to be taught' (second stage of the didactic transposition process); c) the way of asking questions and managing discussion in large classrooms, in line with the transformation of the 'knowledge to be taught' in 'taught knowledge' (third stage of the didactic transposition process).

Keywords: didactic communication, didactic transposition, instructional development, teaching large classroom

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: Gulnaz Khan, Meha F. Aftab, Munazza Murtaza, Rizwana S. Waraich

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Advanced glycation end products (AGEs) precursor and its abnormal accumulation cause damage to various tissues and organs. AGEs have pathogenic implication in several diseases including diabetes. Existing AGEs inhibitors are not in clinical use, and there is a need for development of novel inhibitors. The present investigation aimed at identifying the novel AGEs inhibitors and assessing their mechanism of action for treating insulin resistance in mice model of diabetes. Novel derivatives of benzylidene of indan-1,3-dione were synthesized. The compounds were selected to study their action mechanism in improving insulin resistance, in vitro, in human hepatocytes and murine adipocytes and then, in vivo, in mice genetic model of diabetes (db/db). Mice were treated with novel derivatives of benzylidene of indane 1,3-dione. AGEs mediated ROS production was measured by dihydroethidium fluorescence assay. AGEs level in the serum of treated mice was observed by ELISA. Gene expression of receptor for AGEs (RAGE), PPAR-gamma, TNF-alpha and GLUT-4 was evaluated by RT-PCR. Glucose uptake was measured by fluorescent method. Microscopy was used to analyze glycogen synthesis in muscle. Among several derivatives of benzylidene of indan-1,3-dione, IDD-24, demonstrated highest inhibition of AGESs. IDD-24 significantly reduced AGEs formation and expression of receptor for advanced glycation end products (RAGE) in fat, liver of db/db mice. Suppression of AGEs mediated ROS production was also observed in hepatocytes and fat cell, after treatment with IDD-24. Glycogen synthesis was increased in muscle tissue of mice treated with IDD-24. In adipocytes, IDD-24 prevented AGEs induced reduced glucose uptake. Mice treated with IDD-24 exhibited increased glucose tolerance, serum adiponectin levels and decreased insulin resistance. The result of present study suggested that IDD-24 can be a possible treatment target to address glycotoxins induced insulin resistance.

Keywords: advance glycation end product, hyperglycemia, indan-1, 3-dione, insulin resistance

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Authors: B. S. So, Y. H. Yoon, J. O. Jung, K. S. Bae

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Metal Injection Molding (MIM) is a technology that combines powder metallurgy and injection molding. Particularly, it is widely applied to the manufacture of precision mobile parts and automobile turbocharger parts because compact precision parts with complicated three-dimensional shapes that are difficult to machining are formed into a large number of finished products. The swing valve is a valve that adjusts the boost pressure of the turbocharger. Since the head portion is exposed to the harsh temperature condition of about 900 degrees in the gasoline GDI engine, it is necessary to use Inconel material with excellent heat resistance and abrasion resistance, resulting in high manufacturing cost. In this study, we developed a swing valve using a metal powder injection molding based hybrid material (Inconel 713C material with heat resistance is applied to the head part, and HK30 material with low price is applied to the rest of the body part). For this purpose, the process conditions of the metal injection molding were optimized to minimize the internal defects, and the effectiveness was confirmed by the fracture strength and fatigue test.

Keywords: hybrid metal injection molding, swing valve, turbocharger, double injection

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Authors: Asis Kumar Khan, Rajeev Kumar Goel

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Recent infrastructure growth all around the world lead to increase in demand for concrete day by day. Cement being binding material for concrete the usage of cement also gone up significantly. Cement manufacturing utilizes abundant natural resources and causes environment pollution by releasing a huge quantity of CO₂ into the atmosphere. So, it is high time to look for alternates to reduce the cement consumption in concrete. Geopolymer concrete is one such material which utilizes the industrial waste such as fly ash, ground granulated blast furnace slag and low-cost alkaline liquids such as sodium hydroxide and sodium silicate to produce the concrete. On the other side, river sand is becoming very expensive due to its large-scale depletion at source and the high cost of transportation. In this view, river sand is replaced by crushed sand in this study. In this work, an attempt has been made to understand the durability parameters of geopolymer concrete by partially replacing fly ash with cement. Fly ash is replaced by cement at various levels e.g., from 0 to 50%. Concrete cubes of 100x100x100mm were used for investigating different durability parameters. The various parameters studied includes compressive strength, split tensile strength, drying shrinkage, sodium sulphate attack resistance, sulphuric acid attack resistance and chloride permeability. Highest compressive strength & highest split tensile strength is observed in 30% replacement level. Least drying is observed with 30% replacement level. Very good resistance for sulphuric acid & sodium sulphate is found with 30% replacement. However, it was not possible to find out the chloride permeability due to the high conductivity of geopolymer samples of all replacement levels.

Keywords: crushed sand, compressive strength, drying shrinkage, geopolymer concrete, split tensile strength, sodium sulphate attack resistance, sulphuric acid attack resistance

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Authors: Jian Qu, Akira Shimazu

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OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%.

Keywords: information retrieval, definition retrieval, OOV (out of vocabulary), biomedical information retrieval

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Authors: L. C. Nwosu, C. O. Adedire, M. O. Ashamo, E. O. Ogunwolu

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The maize weevil, Sitophilus zeamais Motschulsky is a notorious pest of stored maize (Zea mays L.). The development of resistant maize varieties to manage weevils is a major breeding objective. The study investigated the parameters and mechanisms that confer resistance on a maize variety to S. zeamais infestation using twenty elite maize varieties. Detailed morphological, physical and chemical studies were conducted on whole-maize grain and the grain pericarp. Resistance was assessed at 33, 56, and 90 days post infestation using weevil mortality rate, weevil survival rate, percent grain damage, percent grain weight loss, weight of grain powder, oviposition rate and index of susceptibility as indices rated on a scale developed by the present study and on Dobie’s modified scale. Linear regression models that can predict maize grain damage in relation to the duration of storage were developed and applied. The resistant varieties identified particularly 2000 SYNEE-WSTR and TZBRELD3C5 with very high degree of resistance should be used singly or best in an integrated pest management system for the control of S. zeamais infestation in stored maize. Though increases in the physical properties of grain hardness, weight, length, and width increased varietal resistance, it was found that the bases of resistance were increased chemical attributes of phenolic acid, trypsin inhibitor and crude fibre while the bases of susceptibility were increased protein, starch, magnesium, calcium, sodium, phosphorus, manganese, iron, cobalt and zinc, the role of potassium requiring further investigation. Characters that conferred resistance on the test varieties were found distributed in the pericarp and the endosperm of the grains. Increases in grain phenolic acid, crude fibre, and trypsin inhibitor adversely and significantly affected the bionomics of the weevil on further assessment. The flat side of a maize grain at the point of penetration was significantly preferred by the weevil. Why the south area of the flattened side of a maize grain was significantly preferred by the weevil is clearly unknown, even though grain-face-type seemed to be a contributor in the study. The preference shown to the south area of the grain flat side has implications for seed viability. The study identified antibiosis, preference, antixenosis, and host evasion as the mechanisms of maize post harvest resistance to Sitophilus zeamais infestation.

Keywords: maize weevil, resistant, parameters, mechanisms, preference

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Ferhat Bülbül

Abstract:

The use of magnesium alloys is limited due to their susceptibility to corrosion although they have many attractive physical and mechanical properties. To increase mechanical and corrosion properties of these alloys, many deposition method and coating types are used. Electroless Ni–B coatings have received considerable interest recently due to its unique properties such as cost-effectiveness, thickness uniformity, good wear resistance, lubricity, good ductility and corrosion resistance, excellent solderability and electrical properties and antibacterial property. In this study, electroless Ni-B coating could been deposited on AZ91 magnesium alloy. The obtained coating exhibited an amorphous and rougher structure.

Keywords: magnesium, electroless Ni–B, X-ray diffraction, amorphous

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Authors: Saadat Ali, Rashid Abdulla Ahmed Alshehhi

Abstract:

Cable faults are always probable and common during or after commissioning, causing significant delays and disrupting power distribution or transmission network, which is intolerable for the utilities&service providers being their reliability and business continuity measures. Therefore, the adoption of rapid localization & rectification methodology is the main concern for them. This paper explores the present techniques available for high voltage cable localization & rectification and which is preferable with regards to easier, faster, and also less harmful to cables. It also provides insight experience of high resistance fault locating by utilization of the Very Low Frequency (VLF) method.

Keywords: faults, VLF, real cases, cables

Procedia PDF Downloads 111

Authors: H. Mahdjoub, B. Hamaidi, B. Zerouali, S. Rouabhia

Abstract:

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method.

Keywords: fault detection and diagnosis, lubrication system, turbine, ANFIS, training, pattern recognition

Procedia PDF Downloads 488

Authors: Shaher A. I. Shalfawi, Fredrik Hviding, Bjornar Kjellstadli

Abstract:

The main purpose of this study was to measure the effect of two different strength training methods on muscle strength, muscle mass, fat mass and endurance factors. Fourteen physical education students accepted to participate in this study. The participants were then randomly divided into three groups, traditional training group (TTG), cluster training group (CTG) and control group (CG). TTG consisted of 4 participants aged ( ± SD) (22.3 ± 1.5 years), body mass (79.2 ± 15.4 kg) and height (178.3 ± 11.9 cm). CTG consisted of 5 participants aged (22.2 ± 3.5 years), body mass (81.0 ± 24.0 kg) and height (180.2 ± 12.3 cm). CG consisted of 5 participants aged (22 ± 2.8 years), body mass (77 ± 19 kg) and height (174 ± 6.7 cm). The participants underwent a hypertrophy strength training program twice a week consisting of 4 sets of 10 reps at 70% of one-repetition maximum (1RM), using barbell squat and barbell bench press for 8 weeks. The CTG performed 2 x 5 reps using 10 s recovery in between repetitions and 50 s recovery between sets, while TTG performed 4 sets of 10 reps with 90 s recovery in between sets. Pre- and post-tests were administrated to assess body composition (weight, muscle mass, and fat mass), 1RM (bench press and barbell squat) and a laboratory endurance test (Bruce Protocol). Instruments used to collect the data were Tanita BC-601 scale (Tanita, Illinois, USA), Woodway treadmill (Woodway, Wisconsin, USA) and Vyntus CPX breath-to-breath system (Jaeger, Hoechberg, Germany). Analysis was conducted at all measured variables including time to peak VO2, peak VO2, heart rate (HR) at peak VO2, respiratory exchange ratio (RER) at peak VO2, and number of breaths per minute. The results indicate an increase in 1RM performance after 8 weeks of training. The change in 1RM squat was for the TTG = 30 ± 3.8 kg, CTG = 28.6 ± 8.3 kg and CG = 10.3 ± 13.8 kg. Similarly, the change in 1RM bench press was for the TTG = 9.8 ± 2.8 kg, CTG = 7.4 ± 3.4 kg and CG = 4.4 ± 3.4 kg. The within-group analysis from the oxygen consumption measured during the incremental exercise indicated that the TTG had only a statistical significant increase in their RER from 1.16 ± 0.04 to 1.23 ± 0.05 (P < 0.05). The CTG had a statistical significant improvement in their HR at peak VO2 from 186 ± 24 to 191 ± 12 Beats Per Minute (P < 0.05) and their RER at peak VO2 from 1.11 ± 0.06 to 1.18 ±0.05 (P < 0.05). Finally, the CG had only a statistical significant increase in their RER at peak VO2 from 1.11 ± 0.07 to 1.21 ± 0.05 (P < 0.05). The between-group analysis showed no statistical differences between all groups in all the measured variables from the oxygen consumption test during the incremental exercise including changes in muscle mass, fat mass, and weight (kg). The results indicate a similar effect of hypertrophy strength training irrespective of the methods of the training used on untrained subjects. Because there were no notable changes in body-composition measures, the results suggest that the improvements in performance observed in all groups is most probably due to neuro-muscular adaptation to training.

Keywords: hypertrophy strength training, cluster set, Bruce protocol, peak VO2

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Authors: Fahad Qureshi

Abstract:

This paper provides detailed study on the diagnosis of intermittent high vibration spikes at exhaust bearing (Non-Drive End) of Mitsubishi H-25 gas turbine installed in a petrochemical plant in Pakistan. The diagnosis is followed by successful root cause analysis of the issue and recommendations for improving the reliability of machine. Engro Polymer and Chemicals (EPCL), a Chlor Vinyl complex, has a captive power plant consisting of one combined cycle power plant (CCPP), having two gas turbines each having 25 MW capacity (make: Hitachi) and one extraction condensing steam turbine having 15 MW capacity (make: HTC). Besides, one 6.75 MW SGT-200 1S gas turbine (make: Alstom) is also available. In 2018, the organization faced an issue of intermittent high vibration at exhaust bearing of one of H-25 units having tag GT-2101 A, which eventually led to tripping of machine at configured securities. Since the machine had surpassed 64,000 running hours and major inspection was also due, so bearings inspection was performed. Inspection revealed excessive coke deposition at labyrinth where evidence of rotor rub was also present. Bearing clearance was also at upper limit, and slight babbitt (soft metal) chip off was observed at one of its pads so it was preventively replaced. The unit was restated successfully and exhibited no abnormality until October 2020, when these spikes reoccurred, leading to machine trip. Recurrence of the issue within two years indicated that root cause was not properly addressed, so this paper furthers the discussion on in-depth analysis of findings and establishes successful root cause analysis, which captured significant learnings both in terms of machine design deficiencies and gaps in operation & maintenance (O & M) regime. Lastly, revised O& M regime along with set of recommendations are proposed to avoid recurrence.

Keywords: exhaust side bearing, Gas turbine, rubbing, vibration

Procedia PDF Downloads 185

Authors: Farid Khosravikia, Patricia Clayton

Abstract:

The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

Procedia PDF Downloads 103

Authors: M. Jayandran

Abstract:

Continual professional development is an important concept for safety professionals to strengthen the knowledge base and to achieve the required qualifications or international memberships in a given time. But the main problems which have observed among most of the safety aspirants are as follows: lack of focus, inferiority complex, superiority complex, lack of interest and lethargy, family and off job stress, health issues, usage of drugs and alcohol, and absenteeism. A HSE trainer should be an expert in soft skills and other stress, emotional handling techniques, so as to manage the above aspirants during training. To do this practice, a trainer has to brainstorm himself of few of the soft skills like personnel resilience, mnemonic techniques, mind healing, and subconscious suggestion techniques by integrating with an emotional intelligence quotient of the aspirants. By adopting these techniques, a trainer can successfully deliver the course and influence the different types of audience to achieve success in training.

Keywords: personnel resilience, mnemonic techniques, mind healing, sub conscious suggestion techniques

Procedia PDF Downloads 305

Authors: Saroj Bijarnia, Preety Singh

Abstract:

We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

Procedia PDF Downloads 348