Search results for: Digital image processing

Authors: S. S. Sravanthi, Swati Ghosh Acharyya

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Automotive light weighting is of major prominence in the current times due to its contribution in improved fuel economy and reduced environmental pollution. Various arc welding technologies are being employed in the production of automobile components with reduced weight. The present study is of practical importance since it involves preferential substitution of Zinc coated mild steel with a light weight alloy such as 6061 Aluminium by means of Gas Metal Arc Welding (GMAW) – Brazing technique at different processing parameters. However, the fabricated joints have shown the generation of Al – Fe layer at the interfacial regions which was confirmed by the Scanning Electron Microscope and Energy Dispersion Spectroscopy. These Al-Fe compounds not only affect the mechanical strength, but also predominantly deteriorate the corrosion resistance of the joints. Hence, it is essential to understand the phases formed in this layer and their crystal structure. Micro area X - ray diffraction technique has been exclusively used for this study. Moreover, the crevice corrosion analysis at the joint interfaces was done by exposing the joints to 5 wt.% FeCl₃ solution at regular time intervals as per ASTM G 48-03. The joints have shown a decreased crevice corrosion resistance with increased heat intensity. Inner surfaces of welds have shown severe oxide cracking and a remarkable weight loss when exposed to concentrated FeCl₃. The weight loss was enhanced with decreased filler wire feed rate and increased heat intensity. 

Keywords: Automobiles, welding, corrosion, lap joints, Micro XRD.

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Authors: A. Dashti, M. Eskandari, L. Farahmand, P. Parvin, A. Jafargholi

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Breast Cancer is one of the most considerable diseases in the United States and other countries and is the second leading cause of death in women. Common breast cancer treatments would lead to adverse side effects such as loss of hair, nausea, and weakness. These complications arise because these cancer treatments damage some healthy cells while eliminating the cancer cells. In an effort to address these complications, laser radiation was utilized and tested as a targeted cancer treatment for breast cancer. In this regard, tissue engineering approaches are being employed by using an electrospun scaffold in order to facilitate the growth of breast cancer cells. Polycaprolacton (PCL) was used as a material for scaffold fabricating because of its biocompatibility, biodegradability, and supporting cell growth. The specific breast cancer cells have the ability to create a three-dimensional cell cluster due to the spontaneous accumulation of cells in the porosity of the scaffold under some specific conditions. Therefore, we are looking for a higher density of porosity and larger pore size. Fibers showed uniform diameter distribution and final scaffold had optimum characteristics with approximately 40% porosity. The images were taken by SEM and the density and the size of the porosity were determined with the Image. After scaffold preparation, it has cross-linked by glutaraldehyde. Then, it has been washed with glycine and phosphate buffer saline (PBS), in order to neutralize the residual glutaraldehyde. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromidefor (MTT) results have represented approximately 91.13% viability of the scaffolds for cancer cells. In order to create a cluster, Michigan Cancer Foundation-7 (MCF-7, breast cancer cell line) and Michigan Cancer Foundation-10A (MCF-10A, human mammary epithelial cell line) cells were cultured on the scaffold in 24 well plate for five days. Then, we have exposed the cluster to the laser diode 808 nm radiation to investigate the effect of laser on the tumor with different power and time. Under the same conditions, cancer cells lost their viability more than the healthy ones. In conclusion, laser therapy is a viable method to destroy the target cells and has a minimum effect on the healthy tissues and cells and it can improve the other method of cancer treatments limitations.

Keywords: Breast cancer, electrospun scaffold, polycaprolacton, laser diode, cancer treatment.

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Authors: Lanka Dinushke Weerasiri, Subrat Das, Daniel Fabijanic, William Yang

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Fluidization at vacuum pressure has been a topic that is of growing research interest. Several industrial applications (such as drying, extractive metallurgy, and chemical vapor deposition (CVD)) can potentially take advantage of vacuum pressure fluidization. Particularly, the fine chemical industry requires processing under safe conditions for thermolabile substances, and reduced pressure fluidized beds offer an alternative. Fluidized beds under vacuum conditions provide optimal conditions for treatment of granular materials where the reduced gas pressure maintains an operational environment outside of flammability conditions. The fluidization at low-pressure is markedly different from the usual gas flow patterns of atmospheric fluidization. The different flow regimes can be characterized by the dimensionless Knudsen number. Nevertheless, hydrodynamics of bubbling vacuum fluidized beds has not been investigated to author’s best knowledge. In this work, the two-fluid numerical method was used to determine the impact of reduced pressure on the fundamental properties of a fluidized bed. The slip flow model implemented by Ansys Fluent User Defined Functions (UDF) was used to determine the interphase momentum exchange coefficient. A wide range of operating pressures was investigated (1.01, 0.5, 0.25, 0.1 and 0.03 Bar). The gas was supplied by a uniform inlet at 1.5U_mf and 2U_mf. The predicted minimum fluidization velocity (U_mf) shows excellent agreement with the experimental data. The results show that the operating pressure has a notable impact on the bed properties and its hydrodynamics. Furthermore, it also shows that the existing Gorosko correlation that predicts bed expansion is not applicable under reduced pressure conditions.

Keywords: Computational fluid dynamics, fluidized bed, gas-solid flow, vacuum pressure, slip flow, minimum fluidization velocity.

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Authors: C. Farinea, A. Conserva, F. Demeur

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Nature has often been something humans have fought against. However, with the changing climate and urban challenges such as air pollution and food shortages, to name but a few, it has never been more crucial to work with nature to find solutions that can help us to adapt to the current planetary situation and mitigate the challenges that we will continue to face in the future. Nature-based solutions (NBS) have been gaining ground as one strategy that can help to create more sustainable solutions for our planet and simultaneously, provide several ecosystem services. As designers, there are a lot of insights that can be extracted and gained from nature. However, nature is a complex and sometimes difficult to predict system and its implementation in cities requires a multidisciplinary knowledge. To keep up with the solutions and prepare the future generations of architects and designers with the skills to be able to implement NBS, educational systems also have to adapt with the times. Architecture is no longer solely about drawing buildings with beautiful forms. It is no longer discipline bound. With the input from different disciplines, the implementation of NBS can be significantly more successful. Transdisciplinary strategies can encourage architects and designers to think beyond their discipline, and ensure the success and realization of the NBS. The paper will demonstrate how transdisciplinary teaching methodologies, including also taking part in participatory processes with experts intended as gathering local knowledge, can be implemented with architectural master students to achieve implementable NBS. Through two projects co-funded by the European Union, strategies such as participatory co-design and transdisciplinary start-ups were implemented into seminars that focused on the development of NBS with a transdisciplinary approach. Within the “Design with Living Systems” seminar, students took part in participatory co-design strategies with experts to design solutions that will be implemented in Porto as part of a healthy corridor, and that respond to the needs of the users and site. On the other hand, within the “Design for Living Systems” seminar, the transdisciplinary start-up approach created start-ups with students of architecture, business and biology focusing on identifying a problem and designing a NBS as a product. Both seminars proved to be successful in achieving implementable NBS through strategies of transdisciplinary education and gave the students the skill sets to be able to work with nature in their future careers.

Keywords: Architectural higher education, digital fabrication, nature-based solutions, transdisciplinary approaches.

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi

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The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.

Keywords: Accu-Chek, diabetes, neural network, pattern recognition.

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Authors: E. Tarantino, G. Disciglio, G. Gatta, L. Frabboni, A. Libutti, A. Tarantino

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Worldwide, about two-thirds of industrial and domestic wastewater effluent is discharged without treatment, which can cause contamination and eutrophication of the water. In particular, for Mediterranean countries, irrigation with treated wastewater would mitigate the water stress and support the agricultural sector. Changing global weather patterns will make the situation worse, due to increased susceptibility to drought, which can cause major environmental, social, and economic problems. The study was carried out in open field in an intensive agricultural area of the Apulian region in Southern Italy where freshwater resources are often scarce. As well as providing a water resource, irrigation with treated wastewater represents a significant source of nutrients for soil–plant systems. However, the use of wastewater might have further effects on soil. This study thus investigated the long-term impact of irrigation with reclaimed agro-industrial wastewater on the chemical characteristics of the soil. Two crops (processing tomato and broccoli) were cultivated in succession in Stornarella (Foggia) over four years from 2012 to 2016 using two types of irrigation water: groundwater and tertiary treated agro-industrial wastewater that had undergone an activated sludge process, sedimentation filtration, and UV radiation. Chemical analyses were performed on the irrigation waters and soil samples. The treated wastewater was characterised by high levels of several chemical parameters including TSS, EC, COD, BOD₅, Na⁺, Ca²⁺, Mg²⁺, NH₄-N, PO₄-P, K⁺, SAR and CaCO₃, as compared with the groundwater. However, despite these higher levels, the mean content of several chemical parameters in the soil did not show relevant differences between the irrigation treatments, in terms of the chemical features of the soil.

Keywords: Agro-industrial wastewater, broccoli, long-term re-use, tomato.

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Authors: Paula Pascoal-Faria, Rúben Pereira, Elodie Pinto, Miguel Belbut, Ana Rosa, Inês Sousa, Nuno Alves

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Apple bruise damage from harvesting, handling, transporting and sorting is considered to be the major source of reduced fruit quality, resulting in loss of profits for the entire fruit industry. The three factors which can physically cause fruit bruising are vibration, compression load and impact, the latter being the most common source of bruise damage. Therefore, prediction of the level of damage, stress distribution and deformation of the fruits under external force has become a very important challenge. In this study, experimental and numerical methods were used to better understand the impact caused when an apple is dropped from different heights onto a plastic surface and a conveyor belt. Results showed that the extent of fruit damage is significantly higher for plastic surface, being dependent on the height. In order to support the development of a biomimetic electronic device for the determination of fruit damage, the mechanical properties of the apple fruit were determined using mechanical tests. Preliminary results showed different values for the Young’s modulus according to the zone of the apple tested. Along with the mechanical characterization of the apple fruit, the development of the first two prototypes is discussed and the integration of the results obtained to construct the final element model of the apple is presented. This work will help to reduce significantly the bruise damage of fruits or vegetables during the entire processing which will allow the introduction of exportation destines and consequently an increase in the economic profits in this sector.

Keywords: Apple, fruit damage, impact during crop and post-crop, mechanical characterization of the apple, numerical evaluation of fruit bruise damage, electronic device.

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: Deep learning, data mining, gender predication, MOOCs.

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Authors: G. Bumanis, D. Bajare

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With demand for primary energy continuously growing, search for renewable and efficient energy sources has been high on agenda of our society. One of the most promising energy sources is biogas technology. Residues coming from dairy industry and milk processing could be used in biogas production; however, low efficiency and high cost impede wide application of such technology. One of the main problems is management and conversion of organic residues through the anaerobic digestion process which is characterized by acidic environment due to the low whey pH (<6) whereas additional pH control system is required. Low buffering capacity of whey is responsible for the rapid acidification in biological treatments; therefore alkali activated material is a promising solution of this problem. Alkali activated material is formed using SiO2 and Al2O3 rich materials under highly alkaline solution. After material structure forming process is completed, free alkalis remain in the structure of materials which are available for leaching and could provide buffer capacity potential. In this research porous alkali activated material was investigated. Highly porous material structure ensures gradual leaching of alkalis during time which is important in biogas digestion process. Research of mixture composition and SiO2/Na2O and SiO2/Al2O ratio was studied to test the buffer capacity potential of alkali activated material. This research has proved that by changing molar ratio of components it is possible to obtain a material with different buffer capacity, and this novel material was seen to have considerable potential for using it in processes where buffer capacity and pH control is vitally important.

Keywords: Alkaline material, buffer capacity, biogas production.

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Authors: F. M. Pisano, M. Ciminello

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Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: Interactive dashboards, optical fibers, structural health monitoring, visual analytics.

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Authors: Walenty Oniszczuk

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The use of buffer thresholds, blocking and adequate service strategies are well-known techniques for computer networks traffic congestion control. This motivates the study of series queues with blocking, feedback (service under Head of Line (HoL) priority discipline) and finite capacity buffers with thresholds. In this paper, the external traffic is modelled using the Poisson process and the service times have been modelled using the exponential distribution. We consider a three-station network with two finite buffers, for which a set of thresholds (tm1 and tm2) is defined. This computer network behaves as follows. A task, which finishes its service at station B, gets sent back to station A for re-processing with probability o. When the number of tasks in the second buffer exceeds a threshold tm2 and the number of task in the first buffer is less than tm1, the fed back task is served under HoL priority discipline. In opposite case, for fed backed tasks, “no two priority services in succession" procedure (preventing a possible overflow in the first buffer) is applied. Using an open Markovian queuing schema with blocking, priority feedback service and thresholds, a closed form cost-effective analytical solution is obtained. The model of servers linked in series is very accurate. It is derived directly from a twodimensional state graph and a set of steady-state equations, followed by calculations of main measures of effectiveness. Consequently, efficient expressions of the low computational cost are determined. Based on numerical experiments and collected results we conclude that the proposed model with blocking, feedback and thresholds can provide accurate performance estimates of linked in series networks.

Keywords: Blocking, Congestion control, Feedback, Markov chains, Performance evaluation, Threshold-base networks.

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Authors: Reshu Yadav, Himanshu Joshi, S. K.Tripathi

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A pilot field study was conducted at the Jagjeetpur Municipal Sewage treatment plant situated in the Haridwar town in Uttarakhand state, India. The objectives of the present study were to study the effect of treated wastewater on the production of various paddy varieties (Sharbati, PR-114, PB-1, Menaka, PB1121 and PB 1509) and the emission of GHG gases (CO2, CH4 and N2O) as compared to the same varieties grown in the control plots irrigated with fresh water. Of late, the concept of water footprint assessment has emerged, which explains enumeration of various types of water footprints of an agricultural entity from its production to processing stages. Paddy, the most water demanding staple crop of Uttarakhand state, displayed a high green water footprint value of 2474.12 m3/ Ton. Most of the wastewater irrigated varieties displayed up to 6% increase in production, except Menaka and PB-1121, which showed a reduction in production (6% and 3% respectively), due to pest and insect infestation. The treated wastewater was observed to be rich in Nitrogen (55.94 mg/ml Nitrate), Phosphorus (54.24 mg/ml) and Potassium (9.78 mg/ml), thus rejuvenating the soil quality and not requiring any external nutritional supplements. A Percentage increase of GHG gases of irrigation with treated municipal wastewater as compared to control plots was observed as 0.4% - 8.6% (CH4), 1.1% - 9.2% (CO2), and 0.07% - 5.8% (N2O). The variety, Sharbati, displayed maximum production (5.5 ton/ha) and emerged as the most resistant variety against pests and insects. The emission values of CH4, CO2 and N2O were 729.31 mg/m2/d, 322.10 mg/m2/d and 400.21 mg/m2/d in water stagnant condition. This study highlighted a successful possibility of reuse of wastewater for non-potable purposes offering the potential for exploiting this resource that can replace or reduce the existing use of fresh water sources in agriculture sector.

Keywords: Greenhouse gases, nutrients, water footprint, wastewater irrigation.

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Authors: Timothy E. Frank, Peter J. Amaddio, Elizabeth D. Decko, Alexis M. Tri, Darcy A. Farrell, Cole M. Landes

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Ultra-high performance concrete (UHPC) is a class of cementitious composites with a relatively large percentage of cement generating high compressive strength. Additionally, UHPC contains disbursed fibers, which control crack width, carry the tensile load across narrow cracks, and limit spalling. These characteristics lend themselves to a wide range of structural applications when UHPC members are reinforced with longitudinal steel. Efficient use of fibers and longitudinal steel is required to keep lifecycle cost competitive in reinforced UHPC members; this requires full utilization of both the compressive and tensile qualities of the reinforced cementitious composite. The objective of this study is to investigate the shear response of steel-reinforced UHPC beams to guide design decisions that keep initial costs reasonable, limit serviceability crack widths, and ensure a ductile structural response and failure path. Five small-scale, reinforced UHPC beams were experimentally tested. Longitudinal steel, transverse steel, and casting direction were varied. Results indicate that an increase in transverse steel in short-spanned reinforced UHPC beams provided additional shear capacity and increased the peak load achieved. Beams with very large longitudinal steel reinforcement ratios did not achieve yield and fully utilized the tension properties of the longitudinal steel. Casting the UHPC beams from the end or from the middle affected load-carrying capacity and ductility, but image analysis determined that the fiber orientation was not significantly different. It is believed that the presence of transverse and longitudinal steel reinforcement minimized the effect of different UHPC casting directions. Results support recent recommendations in the literature suggesting that a 1% fiber volume fraction is sufficient within UHPC to prevent spalling and provide compressive fracture toughness under extreme loading conditions.

Keywords: Fiber orientation, reinforced ultra-high performance concrete beams, shear, transverse steel.

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Authors: Rudy Bahouth, Farouk Benmeddour, Emmanuel Moulin, Jamal Assaad

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Guided ultrasonic waves are used in Non-Destructive Testing and Structural Health Monitoring for inspection and damage detection. Recently, wireless data transmission using ultrasonic waves in solid metallic channels has gained popularity in some industrial applications such as nuclear, aerospace and smart vehicles. The idea is to find a good substitute for electromagnetic waves since they are highly attenuated near metallic components due to Faraday shielding. The proposed solution is to use ultrasonic guided waves such as Lamb waves as an information carrier due to their capability of propagation for long distances. In addition to this, valuable information about the health of the structure could be extracted simultaneously. In this work, the reliable frequency bandwidth for communication is extracted experimentally from dispersion curves at first. Then, an experimental platform for wireless communication using Lamb waves is described and built. After this, coherent demodulation algorithm used in telecommunications is tested for Amplitude Shift Keying, On-Off Keying and Binary Phase Shift Keying modulation techniques. Signal processing parameters such as threshold choice, number of cycles per bit and Bit Rate are optimized. Experimental results are compared based on the average bit error percentage. Results has shown high sensitivity to threshold selection for Amplitude Shift Keying and On-Off Keying techniques resulting a Bit Rate decrease. Binary Phase Shift Keying technique shows the highest stability and data rate between all tested modulation techniques.

Keywords: Lamb Wave Communication, wireless communication, coherent demodulation, bit error percentage.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

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The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.

Keywords: Decision support systems, frost protection, fruit, time-series prediction, weather modeling

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Authors: Iftikhar Ahmad, Youssef Elkezza

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Sarir oilfield is in North Africa. It has facilities of oil and gas production. The assets of the Sarir oilfield can be divided into five following categories, namely: (i) Well bore and wellheads; (ii) Vessels such as separators, desalters, and gas processing facilities; (iii) Pipelines including all flow lines, trunk lines, and shipping lines; (iv) storage tanks; (v) Other assets such as turbines and compressors, etc. The nature of the petroleum industry recognizes the potential human, environmental and financial consequences that can result from failing to maintain the integrity of wellheads, vessels, tanks, pipelines, and other assets. The importance of effective asset integrity management increases as the industry infrastructure continues to age. The primary objective of assets integrity management (AIM) is to maintain assets in a fit-for-service condition while extending their remaining life in the most reliable, safe, and cost-effective manner. Corrosion management is one of the important aspects of successful asset integrity management. It covers corrosion mitigation, monitoring, inspection, and risk evaluation. External corrosion on pipelines, well bores, buried assets, and bottoms of tanks is controlled with a combination of coatings by cathodic protection, while the external corrosion on surface equipment, wellheads, and storage tanks is controlled by coatings. The periodic cleaning of the pipeline by pigging helps in the prevention of internal corrosion. Further, internal corrosion of pipelines is prevented by chemical treatment and controlled operations. This paper describes the integrity management system used in the Sarir oil field for its oil and gas production facilities based on standard practices of corrosion mitigation and inspection.

Keywords: Assets integrity management, corrosion prevention in oilfield assets, corrosion management in oilfield, corrosion prevention and inspection activities.

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

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In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, null hypothesis, seismic lines, seismic reflection survey.

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Authors: Olivier Adam, Maciej Lopatka, Christophe Laplanche, Jean-François Motsch

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This article presents the results using a parametric approach and a Wavelet Transform in analysing signals emitting from the sperm whale. The extraction of intrinsic characteristics of these unique signals emitted by marine mammals is still at present a difficult exercise for various reasons: firstly, it concerns non-stationary signals, and secondly, these signals are obstructed by interfering background noise. In this article, we compare the advantages and disadvantages of both methods: Auto Regressive models and Wavelet Transform. These approaches serve as an alternative to the commonly used estimators which are based on the Fourier Transform for which the hypotheses necessary for its application are in certain cases, not sufficiently proven. These modern approaches provide effective results particularly for the periodic tracking of the signal's characteristics and notably when the signal-to-noise ratio negatively effects signal tracking. Our objectives are twofold. Our first goal is to identify the animal through its acoustic signature. This includes recognition of the marine mammal species and ultimately of the individual animal (within the species). The second is much more ambitious and directly involves the intervention of cetologists to study the sounds emitted by marine mammals in an effort to characterize their behaviour. We are working on an approach based on the recordings of marine mammal signals and the findings from this data result from the Wavelet Transform. This article will explore the reasons for using this approach. In addition, thanks to the use of new processors, these algorithms once heavy in calculation time can be integrated in a real-time system.

Keywords: Autoregressive model, Daubechies Wavelet, Fourier Transform, marine mammals, signal processing, spectrogram, sperm whale, Wavelet Transform.

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Authors: El-Said. I. Shabana, Mohammad S. Tayeb, Maher M. T. Qutub, Abdulraheem A. Kinsara

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Usually, phosphate deposits contain ²³⁸U and ²³²Th in addition to their decay products. Due to their different pathways in the environment, the ²³⁸U/²³²Th activity concentration ratio usually found to be greater than unity in phosphate sediments. The presence of these radionuclides creates a potential need to control exposure of workers in the mining and processing activities of the phosphate minerals in accordance with IAEA safety standards. The greatest dose to workers comes from exposure to radon, especially ²²²Rn from the uranium series, and has to be controlled. In this regard, radon (²²²Rn) was measured in the atmosphere (indoor and outdoor) of Al-Jalamid phosphate-mines working area using a portable radon-measurement instrument RAD7, in a purpose of radiation protection. Radon was measured in 61 sites inside the open phosphate mines, the phosphate upgrading facility (offices and rooms of the workers, and in some open-air sites) and in the dwellings of the workers residence-village that lies at about 3 km from the mines working area. The obtained results indicated that the average indoor radon concentration was about 48.4 Bq/m³. Inside the upgrading facility, the average outdoor concentrations were 10.8 and 9.7 Bq/m³ in the concentrate piles and crushing areas, respectively. It was 12.3 Bq/m³ in the atmosphere of the open mines. These values are comparable with the global average values. Based on the average values, the annual effective dose due to radon inhalation was calculated and risk estimates have been done. The average annual effective dose to workers due to the radon inhalation was estimated by 1.32 mSv. The potential excess risk of lung cancer mortality that could be attributed to radon, when considering the lifetime exposure, was estimated by 53.0x10^-4. The results have been discussed in detail.

Keywords: Dosimetry, environmental monitoring, phosphate deposits, radiation protection, radon-22.

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Authors: Chun-Cheng Lin

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Noise level has critical effects on the diagnostic performance of signal-averaged electrocardiogram (SAECG), because the true starting and end points of QRS complex would be masked by the residual noise and sensitive to the noise level. Several studies and commercial machines have used a fixed number of heart beats (typically between 200 to 600 beats) or set a predefined noise level (typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform SAECG analysis. However different criteria or methods used to perform SAECG would cause the discrepancies of the noise levels among study subjects. According to the recommendations of 1991 ESC, AHA and ACC Task Force Consensus Document for the use of SAECG, the determinations of onset and offset are related closely to the mean and standard deviation of noise sample. Hence this study would try to perform SAECG using consistent root-mean-square (RMS) noise levels among study subjects and analyze the noise level effects on SAECG. This study would also evaluate the differences between normal subjects and chronic renal failure (CRF) patients in the time-domain SAECG parameters. The study subjects were composed of 50 normal Taiwanese and 20 CRF patients. During the signal-averaged processing, different RMS noise levels were adjusted to evaluate their effects on three time domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS voltage of the last QRS 40 ms (RMS40), and (3) duration of the low amplitude signals below 40 μV (LAS40). The study results demonstrated that the reduction of RMS noise level can increase fQRSD and LAS40 and decrease the RMS40, and can further increase the differences of fQRSD and RMS40 between normal subjects and CRF patients. The SAECG may also become abnormal due to the reduction of RMS noise level. In conclusion, it is essential to establish diagnostic criteria of SAECG using consistent RMS noise levels for the reduction of the noise level effects.

Keywords: Signal-averaged electrocardiogram, Ventricular latepotentials, Chronic renal failure, Noise level effects.

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Authors: S. Y. Lin, C. H. Kuan, C. H. She, W. T. Wang

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In this study, ultrasonic assisted machining (UAM) technique is applied in side-surface milling experiment for glass-ceramic workpiece material. The tungsten carbide cutting-tool with diamond coating is used in conjunction with two kinds of cooling/lubrication mediums such as water-soluble (WS) cutting fluid and minimum quantity lubricant (MQL). Full factorial process parameter combinations on the milling experiments are planned to investigate the effect of process parameters on cutting performance. From the experimental results, it tries to search for the better process parameter combination which the edge-indentation and the surface roughness are acceptable. In the machining experiments, ultrasonic oscillator was used to excite a cutting-tool along the radial direction producing a very small amplitude of vibration frequency of 20KHz to assist the machining process. After processing, toolmaker microscope was used to detect the side-surface morphology, edge-indentation and cutting tool wear under different combination of cutting parameters, and analysis and discussion were also conducted for experimental results. The results show that the main leading parameters to edge-indentation of glass ceramic are cutting depth and feed rate. In order to reduce edge-indentation, it needs to use lower cutting depth and feed rate. Water-soluble cutting fluid provides a better cooling effect in the primary cutting area; it may effectively reduce the edge-indentation and improve the surface morphology of the glass ceramic. The use of ultrasonic assisted technique can effectively enhance the surface finish cleanness and reduce cutting tool wear and edge-indentation. 

Keywords: Glass-ceramic, ultrasonic assisted machining, cutting performance, edge-indentation

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Authors: Alloysius C. Ogodo, Ositadinma C. Ugbogu, Uzochukwu G. Ekeleme

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Food poisoning and infection by bacteria are of public health significance to both developing and developed countries. Samples of ogi (akamu) prepared from white and yellow variety of maize sold in Uturu and Okigwe were analyzed together with the laboratory prepared ogi for bacterial quality using the standard microbiological methods. The analyses showed that both white and yellow variety had total bacterial counts (cfu/g) of 4.0 ×107 and 3.9 x 107 for the laboratory prepared ogi while the commercial ogi had 5.2 x 107 and 4.9 x107, 4.9 x107 and 4.5 x107, 5.4 x107 and 5.0 x107 for Eke-Okigwe, Up-gate and Nkwo-Achara market respectively. The Staphylococcal counts ranged from 2.0 x 102 to 5.0 x102 and 1.0 x 102 to 4.0 x102 for the white and yellow variety from the different markets while Staphylococcal growth was not recorded on the laboratory prepared ogi. The laboratory prepared ogi had no Coliform growth while the commercially prepared ogi had counts of 0.5 x103 to 1.6 x 103 for white variety and 0.3 x 103 to 1.1 x103 for yellow variety respectively. The Lactic acid bacterial count of 3.5x106 and 3.0x106 was recorded for the laboratory ogi while the commercially prepared ogi ranged from 3.2x106 to 4.2x106 (white variety) and 3.0 x106 to 3.9 x106 (yellow). The presence of bacteria isolates from the commercial and laboratory fermented ogi showed that Lactobacillus sp, Leuconostoc sp and Citrobacter sp were present in all the samples, Micrococcus sp and Klebsiella sp were isolated from Eke- Okigwe and ABSU-up-gate markets varieties respectively, E. coli and Staphylococcus sp were present in Eke-Okigwe and Nkwo- Achara markets while Salmonella sp were isolated from the three markets. Hence, there are chances of contracting food borne diseases from commercially prepared ogi. Therefore, there is the need for sanitary measures in the production of fermented cereals so as to minimize the rate of food borne pathogens during processing and storage.

Keywords: Bacterial quality, fermentation, maize, Ogi.

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Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour

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In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.

Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.

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Authors: Dongxiao Sun-Waterhouse, Shiji Nair, Reginald Wibisono, Sandhya S. Wadhwa, Carl Massarotto, Duncan I. Hedderley, Jing Zhou, Sara R. Jaeger, Virginia Corrigan

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Attempts to add fibre and polyphenols (PPs) into popular beverages present challenges related to the properties of finished products such as smoothies. Consumer acceptability, viscosity and phenolic composition of smoothies containing high levels of fruit fibre (2.5-7.5 g per 300 mL serve) and PPs (250-750 mg per 300 mL serve) were examined. The changes in total extractable PP, vitamin C content, and colour of selected smoothies over a storage stability trial (4°C, 14 days) were compared. A set of acidic aqueous model beverages were prepared to further examine the effect of two different heat treatments on the stability and extractability of PPs. Results show that overall consumer acceptability of high fibre and PP smoothies was low, with average hedonic scores ranging from 3.9 to 6.4 (on a 1-9 scale). Flavour, texture and overall acceptability decreased as fibre and polyphenol contents increased, with fibre content exerting a stronger effect. Higher fibre content resulted in greater viscosity, with an elevated PP content increasing viscosity only slightly. The presence of fibre also aided the stability and extractability of PPs after heating. A reduction of extractable PPs, vitamin C content and colour intensity of smoothies was observed after a 14-day storage period at 4°C. Two heat treatments (75°C for 45 min or 85°C for 1 min) that are normally used for beverage production, did not cause significant reduction of total extracted PPs. It is clear that high levels of added fibre and PPs greatly influence the consumer appeal of smoothies, suggesting the need to develop novel formulation and processing methods if a satisfactory functional beverage is to be developed incorporating these ingredients.

Keywords: Apple fibre, apple and blackcurrant polyphenols, consumer acceptability, functional foods, stability.

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the surface hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor hobson talysurf tester, micro vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer. 

Keywords: Surface hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness.

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: Bernardo C. P. Albuquerque, Darym J. F. Campos

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Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management.

Keywords: Statistical slope stability analysis, Skew distributions, Probability of failure, Functions of random variables.

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: Anti-spoofing, CNN, fingerprint recognition, loss function, optimizer.

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