Search results for: deep neural image models

Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

Procedia PDF Downloads 142

Authors: Seyed-Ali Sadegh-Zadeh, Kaveh Kavianpour, Hamed Atashbar, Elham Heidari, Saeed Shiry Ghidary, Amir M. Hajiyavand

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Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications.

Keywords: evaluation metrics, performance measurement, supervised learning, unsupervised learning, reinforcement learning, model robustness and stability, comparative analysis

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Authors: Guanqiao Wang, Hongyang Yu

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There is a lot of repetitive work in the traditional construction industry. These repetitive tasks can significantly improve production efficiency by replacing manual tasks with robots. There- fore, robots appear more and more frequently in the construction industry. Navigation and positioning are very important tasks for construction robots, and the requirements for accuracy of positioning are very high. Traditional indoor robots mainly use radiofrequency or vision methods for positioning. Compared with ordinary robots, the indoor plastering robot needs to be positioned closer to the wall for wall plastering, so the requirements for construction positioning accuracy are higher, and the traditional navigation positioning method has a large error, which will cause the robot to move. Without the exact position, the wall cannot be plastered, or the error of plastering the wall is large. A new positioning method is proposed, which is assisted by line lasers and uses image processing-based positioning to perform more accurate positioning on the traditional positioning work. In actual work, filter, edge detection, Hough transform and other operations are performed on the images captured by the camera. Each time the position of the laser line is found, it is compared with the standard value, and the position of the robot is moved or rotated to complete the positioning work. The experimental results show that the actual positioning error is reduced to less than 0.5 mm by this accurate positioning method.

Keywords: indoor plastering robot, navigation, precise positioning, line laser, image processing

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Authors: Sujit Kumar Pradhan, Anil Kumar, Vijay Kumar

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The testing process of the software during the software development time is a crucial step as it makes the software more efficient and dependable. To estimate software’s reliability through the mean value function, many software reliability growth models (SRGMs) were developed under the assumption that operating and testing environments are the same. Practically, it is not true because when the software works in a natural field environment, the reliability of the software differs. This article discussed an SRGM comprising change-point and imperfect debugging in a simulated testing environment. Later on, we extended it in a multi-release direction. Initially, the software was released to the market with few features. According to the market’s demand, the software company upgraded the current version by adding new features as time passed. Therefore, we have proposed a generalized multi-release SRGM where change-point and imperfect debugging concepts have been addressed in a simulated testing environment. The failure-increasing rate concept has been adopted to determine the change point for each software release. Based on nine goodness-of-fit criteria, the proposed model is validated on two real datasets. The results demonstrate that the proposed model fits the datasets better. We have also discussed the optimal release time of the software through a cost model by assuming that the testing and debugging costs are time-dependent.

Keywords: software reliability growth models, non-homogeneous Poisson process, multi-release software, mean value function, change-point, environmental factors

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Authors: Widi Astuti, Rizki Agus Hermawan, Hariono Mukti, Nurul Retno Sugiyono

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The removal of lead ion (Pb²⁺) from aqueous solution by activated carbon with phosphoric acid activation employing mangrove propagule as precursor was investigated in a batch adsorption system. Batch studies were carried out to address various experimental parameters including pH and contact time. The Langmuir and Freundlich models were able to describe the adsorption equilibrium, while the pseudo first order and pseudo second order models were used to describe kinetic process of Pb²⁺ adsorption. The results show that the adsorption data are seen in accordance with Langmuir isotherm model and pseudo-second order kinetic model.

Keywords: activated carbon, adsorption, equilibrium, kinetic, lead, mangrove propagule

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Authors: Funmilayo L. Amao, Amos O. Amao

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It has been observed that majority of the people are living in poor housing quality or totally homeless in urban center despite all governmental policies to provide housing to the public. On the supply side, various government policies in the past have been formulated towards overcoming the huge shortage through several Housing Reform Programmes. Despite these past efforts, housing continues to be a mirage to ordinary Nigerian. Currently, there are various mass housing delivery programmes such as the affordable housing scheme that utilize the Public Private Partnership effort and several Private Finance Initiative models could only provide for about 3% of the required stock. This suggests the need for a holistic solution in approaching the problem. The aim of this research is to find out the problems hindering the delivery of housing in Nigeria and its effects on housing affordability. The specific objectives are to identify the causes of housing delivery problems, to examine different housing policies over years and to suggest a way out for sustainable housing delivery. This paper also reviews the past and current housing delivery programmes in Nigeria and analyses the demand and supply side issues. It identifies the various housing delivery mechanisms in current practice. The objective of this paper, therefore, is to give you an insight into the delivery option for the sustainability of housing in Nigeria, given the existing delivery structures and the framework specified in the New National Housing Policy. The secondary data were obtained from books, journals and seminar papers. The conclusion is that we cannot copy models from other nations, but should rather evolve workable models based on our socio-cultural background to address the huge housing shortage in Nigeria. Recommendations are made in this regard.

Keywords: housing, sustainability, housing delivery, housing policy, housing affordability

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Authors: P. Pötters, A. Marquet, B. Leyendecker

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The objective of this paper is to investigate different implementation approaches for the implementation of Lean production in companies. Furthermore, a structured overview of those different approaches is to be made. Therefore, the present work is intended to answer the following research question: What differences and similarities exist between the various systematic approaches and phase models for the implementation of Lean Production? To present various approaches for the implementation of Lean Production discussed in the literature, a qualitative content analysis was conducted. Within the framework of a qualitative survey, a selection of texts dealing with lean production and its introduction was examined. The analysis presents different implementation approaches from the literature, covering the descriptive aspect of the study. The study also provides insights into similarities and differences among the implementation approaches, which are drawn from the analysis of latent text contents and author interpretations. In this study, the focus is on identifying differences and similarities among systemic approaches for implementing Lean Production. The research question takes into account the main object of consideration, objectives pursued, starting point, procedure, and endpoint of the implementation approach. The study defines the concept of Lean Production and presents various approaches described in literature that companies can use to implement Lean Production successfully. The study distinguishes between five systemic implementation approaches and seven phase models to help companies choose the most suitable approach for their implementation project. The findings of this study can contribute to enhancing transparency regarding the existing approaches for implementing Lean Production. This can enable companies to compare and contrast the available implementation approaches and choose the most suitable one for their specific project.

Keywords: implementation, lean production, phase models, systematic approaches

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Authors: Samaneh Yavari, Christiane Pferrer, Elisabeth Engelke, Alexander Starke, Juergen Rehage

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Background: Nowadays, bovine limb interventions, especially any claw surgeries, raises selection of the most qualified and appropriate local anesthesia technique applicable for any superficial or deep interventions of the limbs. Currently, two local anesthesia methods of Intravenous Regional Anesthesia (IVRA), as well as Nerve Blocks, have been routine to apply. However, the lack of studies investigating the quality and duration as well as quantity and onset of full (complete) local anesthesia, is noticeable. Therefore, the aim of our study was comparing the onset and quality of both IVRA and our modified NBA at the hind limb of dairy cows. For this abstract, only the onset of full local anesthesia would be consider. Materials and Methods: For that reason, we used six healthy non pregnant non lactating Holestein Frisian cows in a cross-over study design. Those cows divided into two groups to receive IVRA and our modified four-point NBA. For IVRA, 20 ml procaine without epinephrine was injected into the vein digitalis dorsalis communis III and for our modified four-point NBA, 10-15 ml procaine without epinephrine preneurally to the nerves, superficial and deep peroneal as well as lateral and medial branches of metatarsal nerves. For pain stimulation, electrical stimulator Grass S48 was applied. Results: The results of electrical stimuli revealed the faster onset of full local anesthesia (p < 0.05) by application of our modified NBA in comparison to IVRA about 10 minutes. Conclusion and discussion: Despite of available references showing faster onset of foot local anesthesia of IVRA, our study demonstrated that our modified four point NBA not only can be well known as a standard foot local anesthesia method applicable to desensitize the hind limb of dairy cows, but also, selection of this modified validated local anesthesia method can lead to have a faster start of complete desensitization of distal hind limb that is remarkable in any bovine limb interventions under time constraint.

Keywords: IVRA, four point NBA, dairy cow, hind limb, full onset

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Authors: Dianelys Vega, Carlos Magluta, Ney Roitman

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The simulation of loads induced by walking people in civil engineering structures is still challenging It has been the focus of considerable research worldwide in the recent decades due to increasing number of reported vibration problems in pedestrian structures. One of the most important key in the designing of slender structures is the Human-Structure Interaction (HSI). How moving people interact with structures and the effect it has on their dynamic responses is still not well understood. To rely on calibrated pedestrian models that accurately estimate the structural response becomes extremely important. However, because of the complexity of the pedestrian mechanisms, there are still some gaps in knowledge and more reliable models need to be investigated. On this topic several authors have proposed biodynamic models to represent the pedestrian, whether these models provide a consistent approximation to physical reality still needs to be studied. Therefore, this work comes to contribute to a better understanding of this phenomenon bringing an experimental validation of a pedestrian walking model and a Human-Structure Interaction model. In this study, a bi-dimensional bipedal walking model was used to represent the pedestrians along with an interaction model which was applied to a prototype footbridge. Numerical models were implemented in MATLAB. In parallel, experimental tests were conducted in the Structures Laboratory of COPPE (LabEst), at Federal University of Rio de Janeiro. Different test subjects were asked to walk at different walking speeds over instrumented force platforms to measure the walking force and an accelerometer was placed at the waist of each subject to measure the acceleration of the center of mass at the same time. By fitting the step force and the center of mass acceleration through successive numerical simulations, the model parameters are estimated. In addition, experimental data of a walking pedestrian on a flexible structure was used to validate the interaction model presented, through the comparison of the measured and simulated structural response at mid span. It was found that the pedestrian model was able to adequately reproduce the ground reaction force and the center of mass acceleration for normal and slow walking speeds, being less efficient for faster speeds. Numerical simulations showed that biomechanical parameters such as leg stiffness and damping affect the ground reaction force, and the higher the walking speed the greater the leg length of the model. Besides, the interaction model was also capable to estimate with good approximation the structural response, that remained in the same order of magnitude as the measured response. Some differences in frequency spectra were observed, which are presumed to be due to the perfectly periodic loading representation, neglecting intra-subject variabilities. In conclusion, this work showed that the bipedal walking model could be used to represent walking pedestrians since it was efficient to reproduce the center of mass movement and ground reaction forces produced by humans. Furthermore, although more experimental validations are required, the interaction model also seems to be a useful framework to estimate the dynamic response of structures under loads induced by walking pedestrians.

Keywords: biodynamic models, bipedal walking models, human induced loads, human structure interaction

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Authors: Urooj Saeed, Sajid Rashid Ahmad, Iqra Khalid, Sahar Mirza, Imtiaz Younas

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Pakistan, a flood-prone country, has experienced worst floods in the recent past which have caused extensive damage to the urban and rural areas by loss of lives, damage to infrastructure and agricultural fields. Poor flood management system in the country has projected the risks of damages as the increasing frequency and magnitude of floods are felt as a consequence of climate change; affecting national economy directly or indirectly. To combat the needs of flood emergency, this paper focuses on remotely sensed data based approach for rapid mapping and monitoring of flood extent and its damages so that fast dissemination of information can be done, from local to national level. In this research study, spatial extent of the flooding caused by heavy rains of 2014 has been mapped by using space borne data to assess the crop damages and affected population in sixteen districts of Punjab. For this purpose, moderate resolution imaging spectroradiometer (MODIS) was used to daily mark the flood extent by using Normalised Difference Water Index (NDWI). The highest flood value data was integrated with the LandScan 2014, 1km x 1km grid based population, to calculate the affected population in flood hazard zone. It was estimated that the floods covered an area of 16,870 square kilometers, with 3.0 million population affected. Moreover, to assess the flood damages, Object Based Image Analysis (OBIA) aided with spectral signatures was applied on Landsat image to attain the thematic layers of healthy (0.54 million acre) and damaged crops (0.43 million acre). The study yields that the population of Jhang district (28% of 2.5 million population) was affected the most. Whereas, in terms of crops, Jhang and Muzzafargarh are the ‘highest damaged’ ranked district of floods 2014 in Punjab. This study was completed within 24 hours of the peak flood time, and proves to be an effective methodology for rapid assessment of damages due to flood hazard

Keywords: flood hazard, space borne data, object based image analysis, rapid damage assessment

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Authors: Wang Ye, Wei Wei

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Residential block construction of big cities in China began in the 1950s, and four models had far-reaching influence on modern residential block in its development process, including unit compound and residential district in 1950s to 1980s, and gated community and open community in 1990s to now. Based on analysis of the four models’ fabric, the article takes residential blocks in Hangzhou west area as an example and carries on the studies from urban structure level and block special level, mainly including urban road network, land use, community function, road organization, public space and building fabric. At last, the article puts forward semi-open sub-community strategy to improve the current fabric.

Keywords: Hangzhou west area, residential block model, residential block fabric, semi-open sub-community strategy

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Authors: Judson P. LaGrone

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Simulation-based education in curricula was once a luxurious component of nursing programs but now serves as a vital element of an individual’s learning experience. A debriefing occurs after the simulation scenario or clinical experience is completed to allow the instructor(s) or trained professional(s) to act as a debriefer to guide a reflection with a purpose of acknowledging, assessing, and synthesizing the thought process, decision-making process, and actions/behaviors performed during the scenario or clinical experience. Debriefing is a vital component of the simulation process and educational experience to allow the learner(s) to progressively build upon past experiences and current scenarios within a safe and welcoming environment with a guided dialog to enhance future practice. The aim of this integrative review was to assess current practices of debriefing models in simulation-based education for health care professionals and students. The following databases were utilized for the search: CINAHL Plus, Cochrane Database of Systemic Reviews, EBSCO (ERIC), PsycINFO (Ovid), and Google Scholar. The advanced search option was useful to narrow down the search of articles (full text, Boolean operators, English language, peer-reviewed, published in the past five years). Key terms included debrief, debriefing, debriefing model, debriefing intervention, psychological debriefing, simulation, simulation-based education, simulation pedagogy, health care professional, nursing student, and learning process. Included studies focus on debriefing after clinical scenarios of nursing students, medical students, and interprofessional teams conducted between 2015 and 2020. Common themes were identified after the analysis of articles matching the search criteria. Several debriefing models are addressed in the literature with similarities of effectiveness for participants in clinical simulation-based pedagogy. Themes identified included (a) importance of debriefing in simulation-based pedagogy, (b) environment for which debriefing takes place is an important consideration, (c) individuals who should conduct the debrief, (d) length of debrief, and (e) methodology of the debrief. Debriefing models supported by theoretical frameworks and facilitated by trained staff are vital for a successful debriefing experience. Models differed from self-debriefing, facilitator-led debriefing, video-assisted debriefing, rapid cycle deliberate practice, and reflective debriefing. A reoccurring finding was centered around the emphasis of continued research for systematic tool development and analysis of the validity and effectiveness of current debriefing practices. There is a lack of consistency of debriefing models among nursing curriculum with an increasing rate of ill-prepared faculty to facilitate the debriefing phase of the simulation.

Keywords: debriefing model, debriefing intervention, health care professional, simulation-based education

Procedia PDF Downloads 137

Authors: Cecile Meier, Drago Diaz Aleman, Itahisa Perez Conesa, Jose Luis Saorin Perez, Jorge De La Torre Cantero

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In this work, two ways of creating small-sized metal sculptures are proposed: the first by means of microcasting and the second by electroforming from models printed in 3D using an FDM (Fused Deposition Modeling‎) printer or using a DLP (Digital Light Processing) printer. It is viable to replace the wax in the processes of the artistic foundry with 3D printed objects. In this technique, the digital models are manufactured with resin using a low-cost 3D FDM printer in polylactic acid (PLA). This material is used, because its properties make it a viable substitute to wax, within the processes of artistic casting with the technique of lost wax through Ceramic Shell casting. This technique consists of covering a sculpture of wax or in this case PLA with several layers of thermoresistant material. This material is heated to melt the PLA, obtaining an empty mold that is later filled with the molten metal. It is verified that the PLA models reduce the cost and time compared with the hand modeling of the wax. In addition, one can manufacture parts with 3D printing that are not possible to create with manual techniques. However, the sculptures created with this technique have a size limit. The problem is that when printed pieces with PLA are very small, they lose detail, and the laminar texture hides the shape of the piece. DLP type printer allows obtaining more detailed and smaller pieces than the FDM. Such small models are quite difficult and complex to melt using the lost wax technique of Ceramic Shell casting. But, as an alternative, there are microcasting and electroforming, which are specialized in creating small metal pieces such as jewelry ones. The microcasting is a variant of the lost wax that consists of introducing the model in a cylinder in which the refractory material is also poured. The molds are heated in an oven to melt the model and cook them. Finally, the metal is poured into the still hot cylinders that rotate in a machine at high speed to properly distribute all the metal. Because microcasting requires expensive material and machinery to melt a piece of metal, electroforming is an alternative for this process. The electroforming uses models in different materials; for this study, micro-sculptures printed in 3D are used. These are subjected to an electroforming bath that covers the pieces with a very thin layer of metal. This work will investigate the recommended size to use 3D printers, both with PLA and resin and first tests are being done to validate use the electroforming process of microsculptures, which are printed in resin using a DLP printer.

Keywords: sculptures, DLP 3D printer, microcasting, electroforming, fused deposition modeling

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Authors: Pegah Ebrahimi, Farshad Oveissi, Iman Manavi-Tehrani, Sina Naficy, David F. Fletcher, Fariba Dehghani, David S. Winlaw

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Despite advances in modern surgery, congenital heart disease remains a medical challenge and a major cause of infant mortality. Cardiovascular prostheses are routinely used in surgical procedures to address congenital malformations, for example establishing a pathway from the right ventricle to the pulmonary arteries in pulmonary valvar atresia. Current off-the-shelf options including human and adult products have limited biocompatibility and durability, and their fixed size necessitates multiple subsequent operations to upsize the conduit to match with patients’ growth over their lifetime. Non-physiological blood flow is another major problem, reducing the longevity of these prostheses. These limitations call for better designs that take into account the hemodynamical and anatomical characteristics of different patients. We have integrated tissue engineering techniques with modern medical imaging and image processing tools along with mathematical modeling to optimize the design of cardiovascular grafts in a patient-specific manner. Computational Fluid Dynamics (CFD) analysis is done according to models constructed from each individual patient’s data. This allows for improved geometrical design and achieving better hemodynamic performance. Tissue engineering strives to provide a material that grows with the patient and mimic the durability and elasticity of the native tissue. Simulations also give insight on the performance of the tissues produced in our lab and reduce the need for costly and time-consuming methods of evaluation of the grafts. We are also developing a methodology for the fabrication of the optimized designs.

Keywords: computational fluid dynamics, cardiovascular grafts, design optimization, tissue engineering

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Authors: Chen-Chang Lee, Po-Chou Chen, Jo-Chi Jao, Chun-Chung Lui, Leung-Chit Tsang, Lain-Chyr Hwang

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Fetal Magnetic Resonance Imaging (MRI) is a challenge task because the fetal movements could cause motion artifact in MR images. The remedy to overcome this problem is to use fast scanning pulse sequences. The Single-Shot Fast Spin-Echo (SSFSE) T2-weighted imaging technique is routinely performed and often used as a gold standard in clinical examinations. Fast spoiled gradient-echo (FSPGR) T1-Weighted Imaging (T1WI) is often used to identify fat, calcification and hemorrhage. Fast Imaging Employing Steady-State Acquisition (FIESTA) is commonly used to identify fetal structures as well as the heart and vessels. The contrast of FIESTA image is related to T1/T2 and is different from that of SSFSE. The advantages and disadvantages of these two scanning sequences for fetal imaging have not been clearly demonstrated yet. This study aimed to compare these three rapid MRI techniques (SSFSE, FIESTA, and FSPGR) for fetal MRI examinations. The image qualities and influencing factors among these three techniques were explored. A 1.5T GE Discovery 450 clinical MR scanner with an eight-channel high-resolution abdominal coil was used in this study. Twenty-five pregnant women were recruited to enroll fetal MRI examination with SSFSE, FIESTA and FSPGR scanning. Multi-oriented and multi-slice images were acquired. Afterwards, MR images were interpreted and scored by two senior radiologists. The results showed that both SSFSE and T2W-FIESTA can provide good image quality among these three rapid imaging techniques. Vessel signals on FIESTA images are higher than those on SSFSE images. The Specific Absorption Rate (SAR) of FIESTA is lower than that of the others two techniques, but it is prone to cause banding artifacts. FSPGR-T1WI renders lower Signal-to-Noise Ratio (SNR) because it severely suffers from the impact of maternal and fetal movements. The scan times for these three scanning sequences were 25 sec (T2W-SSFSE), 20 sec (FIESTA) and 18 sec (FSPGR). In conclusion, all these three rapid MR scanning sequences can produce high contrast and high spatial resolution images. The scan time can be shortened by incorporating parallel imaging techniques so that the motion artifacts caused by fetal movements can be reduced. Having good understanding of the characteristics of these three rapid MRI techniques is helpful for technologists to obtain reproducible fetal anatomy images with high quality for prenatal diagnosis.

Keywords: fetal MRI, FIESTA, FSPGR, motion artifact, SSFSE

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Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

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Authors: Delara Kazempour, Mashallah Abasi Dezfuli, Reza Javidan

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Video compression used for channels with limited bandwidth and storage devices has limited storage capabilities. One of the most popular approaches in video compression is the usage of different transforms. Discrete cosine transform is one of the video compression methods that have some problems such as blocking, noising and high distortion inappropriate effect in compression ratio. wavelet transform is another approach is better than cosine transforms in balancing of compression and quality but the recognizing of curve curvature is so limit. Because of the importance of the compression and problems of the cosine and wavelet transforms, the contourlet transform is most popular in video compression. In the new proposed method, we used contourlet transform in video image compression. Contourlet transform can save details of the image better than the previous transforms because this transform is multi-scale and oriented. This transform can recognize discontinuity such as edges. In this approach we lost data less than previous approaches. Contourlet transform finds discrete space structure. This transform is useful for represented of two dimension smooth images. This transform, produces compressed images with high compression ratio along with texture and edge preservation. Finally, the results show that the majority of the images, the parameters of the mean square error and maximum signal-to-noise ratio of the new method based contourlet transform compared to wavelet transform are improved but in most of the images, the parameters of the mean square error and maximum signal-to-noise ratio in the cosine transform is better than the method based on contourlet transform.

Keywords: video compression, contourlet transform, discrete cosine transform, wavelet transform

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Authors: C. Baidada, M. H. Abidi, A. Jakimi, E. H. El Kinani

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Reverse engineering has become a viable method to measure an existing system and reconstruct the necessary model from tis original. The reverse engineering of behavioral models consists in extracting high-level models that help understand the behavior of existing software systems. In this paper, we propose an approach for the reverse engineering of sequence diagrams from the analysis of execution traces produced dynamically by an object-oriented application using petri nets. Our methods show that this approach can produce state diagrams in reasonable time and suggest that these diagrams are helpful in understanding the behavior of the underlying application. Finally we will discuss approachs and tools that are needed in the process of reverse engineering UML behavior. This work is a substantial step towards providing high-quality methodology for effectiveand efficient reverse engineering of sequence diagram.

Keywords: reverse engineering, UML behavior, sequence diagram, execution traces, petri nets

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Authors: Chao-Ton Su, Li-Fei Chen

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The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson

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The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.

Keywords: PCR, optimisation, microfluidics, COMSOL

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Authors: Beata Bilska, Marzena Tomaszewska, Danuta Kolozyn-Krajewska

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Food loss and food waste are a global problem of the modern economy. The research undertaken aimed to analyze how food is handled in catering establishments when it comes to food waste and to demonstrate the main ways of management with foods/dishes not served to consumers. A survey study was conducted from January to June 2019. The selection of catering establishments participating in the study was deliberate. The study included establishments located only in Mazowieckie Voivodeship (Poland). Forty-two completed questionnaires were collected. In some questions, answers were based on a 5-point scale of 1 to 5 (from "always" / "every day" to "never"). The survey also included closed questions with a suggested cafeteria of answers. The respondents stated that in their workplaces, dishes served cold and hot ready meals are discarded every day or almost every day (23.7% and 20.5% of answers respectively). A procedure most frequently used for dealing with dishes not served to consumers on a given day is their storage at a cool temperature until the following day. In the research, 1/5 of respondents admitted that consumers "always" or "usually" leave uneaten meals on their plates, and over 41% "sometimes" do so. It was found additionally that food not used in the foodservice sector is most often thrown into a public container for rubbish. Most often thrown into the public container (with communal trash) were: expired products (80.0%), plate waste (80.0%) and inedible products (fruit and vegetable peels, eggshells) (77.5%). Most frequently into the container dedicated only to food waste were thrown out used deep-frying oil (62.5%). 10% of respondents indicated that inedible products in their workplaces are allocated for animal feeds. Food waste in the foodservice sector remains an insufficiently studied issue, as owners of these objects are often unwilling to disclose data about the subject. Incorrect ways of management with foods not served to consumers were observed. There is a need to develop educational activities for employees and management in the context of food waste management in the foodservice sector.

Keywords: food waste, inedible products, plate waste, used deep-frying oil

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Authors: Florian Mach, Eric Hund, Malte Stonis

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The dismantling of disused industrial facilities such as nuclear power plants or refineries is an enormous challenge for the planning and control of the logistic processes. Existing control models do not meet the requirements for a proper dismantling of industrial plants. Therefore, the paper presents an approach for the control of dismantling and post-processing processes (e.g. decontamination) in plant decommissioning. In contrast to existing approaches, the dismantling sequence and depth are selected depending on the capacity utilization of required post-processing processes by also considering individual characteristics of respective dismantling tasks (e.g. decontamination success rate, uncertainties regarding the process times). The results can be used in the dismantling of industrial plants (e.g. nuclear power plants) to reduce dismantling time and costs by avoiding bottlenecks such as capacity constraints.

Keywords: dismantling management, logistics planning and control models, nuclear power plant dismantling, reverse logistics

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Authors: I. A. Simsek, S. N. Dogan, A. S. Kipcak, E. Morodor Derun, N. Tugrul

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In this study, the drying characteristics of shrimp are studied by using the traditional drying method of oven. Drying temperatures are selected between 60-80°C. Obtained experimental drying results are applied to eleven mathematical models of Alibas, Aghbashlo et al., Henderson and Pabis, Jena and Das, Lewis, Logaritmic, Midilli and Kucuk, Page, Parabolic, Wang and Singh and Weibull. The best model was selected as parabolic based on the highest coefficient of determination (R²) (0.999990 at 80°C) and the lowest χ² (0.000002 at 80°C), and the lowest root mean square error (RMSE) (0.000976 at 80°C) values are compared to other models. The effective moisture diffusivity (Deff) values were calculated using the Fick’s second law’s cylindrical coordinate approximation and are found between 6.61×10⁻⁸ and 6.66×10⁻⁷ m²/s. The activation energy (Ea) was calculated using modified form of Arrhenius equation and is found as 18.315 kW/kg.

Keywords: activation energy, drying, effective moisture diffusivity, modelling, oven, shrimp

Procedia PDF Downloads 172

Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Laura M. F. Bertens

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There is a long tradition of visually representing the art historical canon, in schematic overviews and diagrams. This is indicative of the desire for scientific, ‘objective’ knowledge of the kind (seemingly) produced in the natural sciences. These diagrams will, however, always retain an element of subjectivity and the modelling methods colour our perception of the represented information. In recent decades visualisations of art historical data, such as hand-drawn diagrams in textbooks, have been extended to include digital, computational tools. These tools significantly increase modelling strength and functionality. As such, they might be used to deconstruct and amend the very problem caused by traditional visualisations of the canon. In this paper, the use of digital tools for modelling the art historical canon is studied, in order to draw attention to the artificial nature of the static models that art historians are presented with in textbooks and lectures, as well as to explore the potential of digital, dynamic tools in creating new models. To study the way diagrams of the canon mediate the represented information, two modelling methods have been used on two case studies of existing diagrams. The tree diagram Stammbaum der neudeutschen Kunst (1823) by Ferdinand Olivier has been translated to a social network using the program Visone, and the famous flow chart Cubism and Abstract Art (1936) by Alfred Barr has been translated to an ontological model using Protégé Ontology Editor. The implications of the modelling decisions have been analysed in an art historical context. The aim of this project has been twofold. On the one hand the translation process makes explicit the design choices in the original diagrams, which reflect hidden assumptions about the Western canon. Ways of organizing data (for instance ordering art according to artist) have come to feel natural and neutral and implicit biases and the historically uneven distribution of power have resulted in underrepresentation of groups of artists. Over the last decades, scholars from fields such as Feminist Studies, Postcolonial Studies and Gender Studies have considered this problem and tried to remedy it. The translation presented here adds to this deconstruction by defamiliarizing the traditional models and analysing the process of reconstructing new models, step by step, taking into account theoretical critiques of the canon, such as the feminist perspective discussed by Griselda Pollock, amongst others. On the other hand, the project has served as a pilot study for the use of digital modelling tools in creating dynamic visualisations of the canon for education and museum purposes. Dynamic computer models introduce functionalities that allow new ways of ordering and visualising the artworks in the canon. As such, they could form a powerful tool in the training of new art historians, introducing a broader and more diverse view on the traditional canon. Although modelling will always imply a simplification and therefore a distortion of reality, new modelling techniques can help us get a better sense of the limitations of earlier models and can provide new perspectives on already established knowledge.

Keywords: canon, ontological modelling, Protege Ontology Editor, social network modelling, Visone

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Authors: Majid AghaAlikhani, Mostafa Hojati, Saeid Satari-Yuzbashkandi

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This paper studies energy use patterns and relationship between energy input and yield for soybean (Glycine max (L.) Merrill) in Mazandaran province of Iran. In this study, data were collected by administering a questionnaire in face-to-face interviews. Results revealed that the highest share of energy consumption belongs to chemical fertilizers (29.29%) followed by diesel (23.42%) and electricity (22.80%). Our investigations showed that a total energy input of 23404.1 MJ.ha-1 was consumed for soybean production. The energy productivity, specific energy, and net energy values were estimated as 0.12 kg MJ-1, 8.03 MJ kg-1, and 49412.71 MJ.ha-1, respectively. The ratio of energy outputs to energy inputs was 3.11. Obtained results indicated that direct, indirect, renewable and non-renewable energies were (56.83%), (43.17%), (15.78%) and (84.22%), respectively. Three econometric models were also developed to estimate the impact of energy inputs on yield. The results of econometric models revealed that impact of chemical, fertilizer, and water on yield were significant at 1% probability level. Also, direct and non-renewable energies were found to be rather high. Cost analysis revealed that total cost of soybean production per ha was around 518.43$. Accordingly, the benefit-cost ratio was estimated as 2.58. The energy use efficiency in soybean production was found as 3.11. This reveals that the inputs used in soybean production are used efficiently. However, due to higher rate of nitrogen fertilizer consumption, sustainable agriculture should be extended and extension staff could be proposed substitution of chemical fertilizer by biological fertilizer or green manure.

Keywords: Cobbe Douglas function, economical analysis, energy efficiency, energy use patterns, soybean

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Authors: Bara' Al-Mistarehi

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Documentation and assessment of conservation state of an archaeological structure is a significant procedure in any management plan. However, it has always been a challenge to apply this with a low coast and safe methodology. It is also a time-demanding procedure. Therefore, a low cost, efficient methodology for documenting the state of a structure is needed. In the scope of this research, this paper will employ digital photogrammetry and laser scanner to one of highly significant structures in Germany, The Old Castle (German: Altes Schloss). The site is well known for its unique features. However, the castle suffers from serious deterioration threats because of the environmental conditions and the absence of continuous monitoring, maintenance and repair plans. Digital photogrammetry is a generally accepted technique for the collection of 3D representations of the environment. For this reason, this image-based technique has been extensively used to produce high quality 3D models of heritage sites and historical buildings for documentation and presentation purposes. Additionally, terrestrial laser scanners are used, which directly measure 3D surface coordinates based on the run-time of reflected light pulses. These systems feature high data acquisition rates, good accuracy and high spatial data density. Despite the potential of each single approach, in this research work maximum benefit is to be expected by a combination of data from both digital cameras and terrestrial laser scanners. Within the paper, the usage, application and advantages of the technique will be investigated in terms of building high realistic 3D textured model for some parts of the old castle. The model will be used as diagnosing tool of the conservation state of the castle and monitoring mean for future changes.

Keywords: Digital photogrammetry, Terrestrial laser scanners, 3D textured model, archaeological structure

Procedia PDF Downloads 159

Authors: Marine Kobalava

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Introduction. The paper substantiates the crucial role of anti-crisis PR in managing the image and reputation of companies. The critical situation caused by the Covid-19 virus in various countries of the world and the actions taken have had a significant negative impact on the image of companies and public groups. The mentioned circumstance has caused some problems for companies’ products in terms of customer demand. Accordingly, the main goal of PR has become to achieve the optimal relationship between companies and society with effective management. It should also be taken into account that the range of action of PR in crisis situations is much wider than that of advertising. In the paper, Public Relations is evaluated as a determining factor of the companies' prestige, its reliability, which has a decisive effect on the goodwill, trust, and general reputation of the public towards the company. The purpose of the study is to reveal the challenges of anti-crisis PR in Georgian companies and to develop recommendations on effective management mechanisms. Methodologies. Analysis, induction, synthesis, and other methods are used in the paper; Matrix and SWOT analysis are constructed. Ways of establishing and implementing an anti-crisis PR system in companies are proposed. The main aspects of anti-crisis management are identified by using the matrix of the choice of diversification strategy of the companies' activities, the possibilities of making adequate decisions using PR are studied according to the characteristics of the companies' activities and priority directions. Conclusion. The paper draws conclusions on modern problems of anti-crisis PR, offers recommendations on ways to solve it through PR strategies.

Keywords: anti-crisis PR, effective management, company, PR strategy

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Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

Procedia PDF Downloads 347