Search results for: smart camera networks

Authors: Farouk Lawan Gambo

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Data compression shrinks files into fewer bits then their original presentation. It has more advantage on internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature therefore making them difficult to digest by some students (Engineers in particular). To determine the best approach toward learning data compression technique, this paper first study the learning preference of engineering students who tend to have strong active, sensing, visual and sequential learning preferences, the paper also study the advantage that mobility of learning have experienced; Learning at the point of interest, efficiency, connection, and many more. A survey is carried out with some reasonable number of students, through random sampling to see whether considering the learning preference and advantages in mobility of learning will give a promising improvement over the traditional way of learning. Evidence from data analysis using Ms-Excel as a point of concern for error-free findings shows that there is significance different in the students after using learning content provided on smart phone, also the result of the findings presented in, bar charts and pie charts interpret that mobile learning has to be promising feature of learning.

Keywords: data analysis, compression techniques, learning content, traditional learning approach

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Authors: Bakhtiar Amen, Abduladim Ali, Joan Lu

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Recently, wireless technologies have made tremendous influences in advanced technology era, precisely on the learning environment through PADs and smart phones to engage learners to collaborate effectively. In fact, the wireless communication technologies are widely adopted in the education sectors within most of the countries to deliver education support electronically. Today, Introducing multilingual Wireless Response System (WRS) application is an enormous challenge and complex. The purpose of this paper is to implementing internationalization testing strategy through WRS application case study and proposed a questionnaire in multilingual speakers like (Arabic, Kurdish, Chines, Malaysian, Turkish, Dutch, Polish, Russian) to measure the internationalization testing results which includes localization and cultural testing results. This paper identifies issues with each language’s specification attributes for instance right to left (RTL) screen direction related languages, Linguistic test or word spaces in Chines and Dutch languages. Finally, this paper attempt to emphasizes many challenges and solutions that associated with globalization testing model.

Keywords: mobile WRS, internationalization, globalization testing

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Fariborz Ahmadi, Hamid Salehi, Khosrow Karimi

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A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm.

Keywords: evolutionary computation, genetic algorithm, particle swarm optimization, sensor network optimization

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Authors: Sergey Chirun

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Using of a combination of institutional analysis and network access has allowed the author to identify the characteristics of the informal institutions of regional political power and political regimes. According to the author, ‘field’ of activity of post-Soviet regimes, formed under the influence of informal institutions, often contradicts democratic institutional regional changes which are aimed at creating of a legal-rational type of political domination and balanced model of separation of powers. This leads to the gap between the formal structure of institutions and the real nature of power, predetermining the specific character of the existing political regimes.

Keywords: authoritarianism, institutions, political regime, social networks, transformation

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Authors: Ken Munro

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Cruise ships are possibly the most complex collection of systems it is possible to find in one physical, moving location. Propulsion, navigation, power generation and more, combined with a hotel, restaurant, casino, theatre etc, with safety and fire control systems to boot. That complexity creates huge challenges with keeping OT and IT systems apart. Ships engines are often remotely managed, network segregation is often defeated through troubleshooting when at sea. This session will refer to multiple entertaining and informative tales of taking control of ships, including accessing a ships Azipods via a game simulator for passengers. Fortunately, genuine attacks against vessels are very rare, but the effects and impacts to world trade are becoming increasingly obvious.

Keywords: maritime security, cybersecurity, OT, IT, networks

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Authors: Gulcin Yildiz

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Drying (dehydration) is the process of removing water from food in order to preserve the food and an alternative to reduce post-harvest loss of fruits. Different pre-treatment methods have been developed for fruit drying, such as ultrasound. If no pre-treatment is done, the fruits will continue to darken after they are dried. However, the effects of ultrasound as pre-treatment on drying of apples has not been well documented. This study was undertaken to investigate the effect of ultrasound as pre-treatment before oven drying of red delicious and golden delicious apples. Red delicious and golden delicious apples were dried in different temperatures. Before performing drying experiments in an oven at 50, 75 and 100 °C, ultrasound as pretreatment was applied in 5, 10, and 15 minutes. Colors of the dried apples were measured with a Minolta Chroma Meter CR-300 (Minolta Camera Co. Ltd., Osaka, Japan) by directly holding the device vertically to the surface of the samples. Content of total phenols was determined spectrophotometrically with the FolinCiocalteau assay, and the antioxidant capacity was evaluated by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay. The samples (both red delicious and golden delicious apples) with longer ultrasound treatment produced higher weight loss due to the changes in tissue structure. However less phenolic content and antioxidant capacity were observed for the samples with longer ultrasound pre-treatment. The highest total phenolic content (TPC) was determined in dried apples at 75 °C with 5 minutes pre-treatment ultrasound and the lowest TPC was determined in dried apples at 50 °C with 15 minutes pre-treatment ultrasound which was subjected to the longest ultrasound pre-treatment and drying. The combination of 5 min of ultrasound pre-treatment and 75 °C of oven-drying showed to be the best combination for an energy efficient process. This combination exhibited good antioxidant properties as well. The present study clearly demonstrated that applying ultrasound as pre-treatment for drying of apples is an effective process in terms of quality of dried products, time, and energy.

Keywords: golden delicious apples, red delicious apples, total phenolic content, Ultrasound

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Authors: Michael Okeke, Andrew Blyth

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Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection.

Keywords: industrial control system (ics, no-trust-zone (ntz), particle swarm optimisation (pso), supervisory control and data acquisition (scada), swarm intelligence (SI)

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Authors: Kulwinder Singh

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Punjab is one of the major labour exporting states of India. Every year more than 20,000 youths from Punjab attempt irregular migration. About 84 irregular migrants are from rural areas and 16 per cent from urban areas. Irregular migration could only be achieved if be organized through highly efficient international networks with the countries of origin, transit, and destination. A good number of Punjabis continue to immigrate into the UK for work through unauthorized means entering the country on visit visas and overstaying or getting ‘smuggled into’ the country with the help of transnational networks of agents. Although, the efforts are being made by the government to curb irregular migration through The Punjab Prevention of Human Smuggling Rules (2012, 2014) and Punjab Travel Regulation Act (2012), but yet it exists parallel to regular migration. Despite unprecedented miseries of irregular migrants and strict laws implemented by the state government to check this phenomenon, ‘why do Punjabis migrate abroad irregularly’ is the important question to answer. This study addresses this question through the comparison of irregular migration with regular one. In other words, this analysis reveals major causes, specifically economic ones, of irregular migration from rural Punjab. This study is unique by presenting economics of irregular migration, given previous studies emphasize the role of sociological and psychological factors. Addressing important question “why do Punjabis migrate abroad irregularly?”, the present study reveals that Punjabi, being far-sighted, endeavor irregular migration as it is, though, economically nonviable in short run, but offers lucrative economic gains as gets older. Despite its considerably higher cost viz-a-viz regular migration, it is the better employment option to irregular migrants with higher permanent income than local low paid jobs for which risking life has become the mindset of the rural Punjabis. Although, it carries considerably lower economic benefits as compared to regular migration, but provides the opportunity of migrating abroad to less educated, semi-skilled and language-test ineligible Punjabis who cannot migrate through regular channels. As its positive impacts on source and destination countries are evident, it might not be restricted, rather its effective management, through liberalising restrictive migration policies by destination nations, can protect the interests of all involved stakeholders.

Keywords: cost, migration, income, irregular, regular, remittances

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: N. Attari, S. Amziane, M. Chemrouk

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A large number of old buildings have been identified as having potentially critical detailing to resist earthquakes. The main reinforcement of lap-spliced columns just above the joint region, discontinuous bottom beam reinforcement, and little or no joint transverse reinforcement are the most critical details of interior beam column joints in such buildings. This structural type constitutes a large share of the building stock, both in developed and developing countries, and hence it represents a substantial exposure. Direct observation of damaged structures, following the Algiers 2003 earthquake, has shown that damage occurs usually at the beam-column joints, with failure in bending or shear, depending on geometry and reinforcement distribution and type. While substantial literature exists for the design of concrete frame joints to withstand this type of failure, after the earthquake many structures were classified as slightly damaged and, being uneconomic to replace them, at least in the short term, suitable means of repairs of the beam column joint area are being studied. Furthermore; there exists a large number of buildings that need retrofitting of the joints before the next earthquake. The paper reports the results of the experimental programme, constituted of three beam-column reinforced concrete joints at a scale of one to three (1/3) tested under the effect of a pre-stressing axial load acting over the column. The beams were subjected at their ends to an alternate cyclic loading under displacement control to simulate a seismic action. Strain and cracking fields were monitored with the help a digital recording camera. Following the analysis of the results, a comparison can be made between the performances in terms of ductility, strength and mode of failure of the different strengthening solution considered.

Keywords: fibre reinforced polymers, joints, reinforced concrete, beam columns

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Authors: Kaddi Mohammed, Benahmed Khelifa D. Benatiallah

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A sensor network consists of a large number of sensors deployed in areas to monitor and communicate with each other through a wireless medium. The collected routing data in the network consumes most of the energy of the sensor nodes. For this purpose, multiple routing approaches have been proposed to conserve energy resource at the sensors and to overcome the challenges of its limitation. In this work, we propose a new low energy consumption routing protocol for wireless sensor networks based on a meta-heuristic methods. Our protocol is to operate more fairly energy when routing captured data to the base station.

Keywords: WSN, routing, energy, heuristic

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Authors: Ankita Sharma, Reenu Singh

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In order to conserve the ecology and the environment, there is a need to focus on sustainable lighting solutions such as LED bulbs instead of incandescent bulbs, candle-powered lamps, self-cooling smart bulbs, and many more, that are both eco-friendly and practical. This paper focuses on such sustainable solutions to lighting, which will have a major positive impact on the environment in the coming future. A questionnaire survey was conducted to note the responses of people living in high-rise buildings in metropolitan cities with regards to such sustainable lighting choices in their homes. The result of such questionnaire survey has helped to design parameters which are used to ideate design interventions in this field of sustainable lighting choices. This paper includes proposals to facilitate the reduction of electric power in interior lighting through various lighting accessory design interventions. Thus, such design interventions will allow us to design more sustainable interior spaces, and renewable energy strategies can be developed in the field of lighting, which will not only help to save energy but also positively affect other aspects of human well-being such as productivity, heritage conservation and economic well-being too!

Keywords: sustainable, interior lighting, lighting design, environmental impact, metropolitan cities

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Authors: Soukaina Boutaky, Abdelhak Sahibeddine

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A study was carried out among students who have especially a scientific and technical educational background and who had opportunities to benefit from a program entrepreneurship course of 50 hours; at Higher School of Technology Khenifra, Morocco. This article has as a goal to explain the relationship between entrepreneurial education, entrepreneurial passion and entrepreneurial intention. The authors chose Bandura’s theory of social cognition as a theoretical framework. The modeling methods equation is adopted to analyze the hypotheses by SMART PLS for 188 students. The results show a strong positive relationship between entrepreneurial education and entrepreneurial passion. They also reveal that entrepreneurship education affects entrepreneurial intention through the effect of entrepreneurial passion, particularly among women than men. In addition, this study contributes in a theoretical way to the level of the relationship between entrepreneurial education and entrepreneurial passion, and these results provide educators and public decision-makers with advice on the importance of entrepreneurship training based on emotional traits such as passion; which constitutes a key and essential element to encourage young graduates to choose an entrepreneurial career as an alternative option or to develop entrepreneurial passion among the business leaders of tomorrow.

Keywords: entrepreneurship education, entrepreneurial passion, entrepreneurial intention, equation modeling methods

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: Vishnu Pratap Singh Kirar

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Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.

Keywords: wireless sensor network (WSN), wireless network attacks, wireless network security, security requirements

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Authors: Yanto Santosa, Yohanna Dalimunthe, Intan Purnamasari

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Expansion of Indonesia oil palm plantations has contributed significantly to the national revenue annually and has been able to absorb millions of workers. Behind all these positive contributions, such expansion was accused as the cause of the decline in wildlife populations such as mammal and herpetofauna. Research was carried out in 8 oil palm plantations in Riau Province of Indonesia from March to April 2016, to determine the impacts of oil palm plantations on mammal and herpetofauna biodiversity. Direct observation was conducted simultaneously equipped with camera traps placed (for mammal) on various land cover types. For mammals' survey, line transect method was used, and for herpetofauna, Visual Encounter Survey (VES) method was used. Landsat imagery was used to interpret land cover types 3 years prior to the establishment of the oil palm plantations. The study revealed that one year before the oil palm plantations was established, most the land covers were comprised of 49.96% rubber plantations, 35.99% secondary forest, 10.17% bare land, 3.03% shrubs and 0.84% mixed dryland farming-shrubs. Based on the number of species found, it was identified that on the average, mammal diversity in 4 of 8 oil palm plantations, showed a decrease by 14.29%-100%, whereas 2 plantations did not experienced any changes in the number of species and one plantation showed an increased in the number of mammal species. The plantations that experienced a reduction in the number of mammal’s diversity were previously dominated covered by secondary forest (40%) and rubber plantation (40%), while those experiencing no changes in the number of species were also dominated by secondary forest. The area with an increased number of mammal species was historically dominated by rubber plantation. On the contrary, significant results were shown for herpetofauna, where all study sites showed a sharp increase in the number of herpetofauna species, by 100%-225.00%.

Keywords: herpetofauna, impact, mammal, oil palm plantations

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Authors: Mojtaba Hakimi-Moghaddam

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Positive real and strictly positive real transfer functions are important concepts in the control theory. In this paper, the results of researches in these areas are summarized. Definitions together with their graphical interpretations are mentioned. The equivalent conditions in the frequency domain and state space representations are reviewed. Their equivalent electrical networks are explained. Also, a comprehensive discussion about a difference between behavior of real part of positive real and strictly positive real transfer functions in high frequencies is presented. Furthermore, several illustrative examples are given.

Keywords: real rational transfer functions, positive realness property, strictly positive realness property, equivalent conditions

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Authors: Guoqiang Yu, Mehrana Mohtasebi, Jinghong Sun, Thomas Pittman

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Malignant glioma (MG) is the most common type of primary malignant brain tumor. Surgical resection of MG remains the cornerstone of therapy, and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical-grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes.

Keywords: fluorescence guided surgery, malignant glioma, neurosurgical operative microscope, wearable fluorescence imaging device

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Authors: Jinsiang Shaw, Sheng-Xiang Xu

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This paper presents the development of an autonomous automated guided vehicle (AGV) with a robot arm attached on top of it within the framework of robot operation system (ROS). ROS can provide libraries and tools, including hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, etc. For this reason, this AGV can provide automatic navigation and parts transportation and pick-and-place task using robot arm for typical industrial production line use. More specifically, this AGV will be controlled by an on-board host computer running ROS software. Command signals for vehicle and robot arm control and measurement signals from various sensors are transferred to respective microcontrollers. Users can operate the AGV remotely through the TCP / IP protocol and perform SLAM (Simultaneous Localization and Mapping). An RGBD camera and LIDAR sensors are installed on the AGV, using these data to perceive the environment. For SLAM, Gmapping is used to construct the environment map by Rao-Blackwellized particle filter; and AMCL method (Adaptive Monte Carlo localization) is employed for mobile robot localization. In addition, current AGV position and orientation can be visualized by ROS toolkit. As for robot navigation and obstacle avoidance, A* for global path planning and dynamic window approach for local planning are implemented. The developed ROS AGV with a robot arm on it has been experimented in the university factory. A 2-D and 3-D map of the factory were successfully constructed by the SLAM method. Base on this map, robot navigation through the factory with and without dynamic obstacles are shown to perform well. Finally, pick-and-place of parts using robot arm and ensuing delivery in the factory by the mobile robot are also accomplished.

Keywords: automated guided vehicle, navigation, robot operation system, Simultaneous Localization and Mapping

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Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: path planning, obstacle avoidance, autonomous mobile robots, algorithms

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Authors: Joachim F. Sartor

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According to German guidelines, external natural catchments are greater sub-catchments without significant portions of impervious areas, which possess a surface drainage system and empty in a sewer network. Basically, such catchments should be disconnected from sewer networks, particularly from combined systems. If this is not possible due to local conditions, their flow hydrographs have to be considered at the design of sewer systems, because the impact may be significant. Since there is a lack of sufficient measurements of storm-runoff events for such catchments and hence verified simulation methods to analyze their design flows, German standards give only general advices and demands special considerations in such cases. Compared to urban sub-catchments, external natural catchments exhibit greatly different flow characteristics. With increasing area size their hydrological behavior approximates that of rural catchments, e.g. sub-surface flow may prevail and lag times are comparable long. There are few observed peak flow values and simple (mostly empirical) approaches that are offered by literature for Central Europe. Most of them are at least helpful to crosscheck results that are achieved by simulation lacking calibration. Using storm-runoff data from five monitored rural watersheds in the west of Germany with catchment areas between 0.33 and 1.07 km2 , the author investigated by multiple event simulation three different approaches to determine the rainfall excess. These are the modified SCS variable run-off coefficient methods by Lutz and Zaiß as well as the soil moisture model by Ostrowski. Selection criteria for storm events from continuous precipitation data were taken from recommendations of M 165 and the runoff concentration method (parallel cascades of linear reservoirs) from a DWA working report to which the author had contributed. In general, the two run-off coefficient methods showed results that are of sufficient accuracy for most practical purposes. The soil moisture model showed no significant better results, at least not to such a degree that it would justify the additional data collection that its parameter determination requires. Particularly typical convective summer events after long dry periods, that are often decisive for sewer networks (not so much for rivers), showed discrepancies between simulated and measured flow hydrographs.

Keywords: external natural catchments, sewer network design, storm-runoff modelling, urban drainage

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Authors: Alexander Vandesompele, Joni Dambre

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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Reiko Yamamoto

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This paper reports on how synchrony occurs between children and their teacher, and what prevents or facilitates synchrony. The aim of the experiment conducted in this study was to precisely analyze their movements and synchrony and reveal the process of synchrony in a real-world classroom. Specifically, the experiment was conducted for around 20 minutes during an English as a foreign language (EFL) lesson. The participants were 11 fourth-grade school children and their classroom teacher in a public elementary school in Japan. Previous researchers assert that synchrony causes the state of flow in a class. For checking the level of flow, Short Flow State Scale (SFSS) was adopted. The experimental procedure had four steps: 1) The teacher read aloud the first half of an English storybook to the children. Both the teacher and the children were at their own desks. 2) The children were subjected to an SFSS check. 3) The teacher read aloud the remaining half of the storybook to the children. She made the children remove their desks before reading. 4) The children were again subjected to an SFSS check. The movements of all participants were recorded with a video camera. From the movement analysis, it was found that the children synchronized better with the teacher in Step 3 than in Step 1, and that the teacher’s movement became free and outstanding without a desk. This implies that the desk acted as a barrier between the children and the teacher. Removal of this barrier resulted in the children’s reactions becoming synchronized with those of the teacher. The SFSS results proved that the children experienced more flow without a barrier than with a barrier. Apparently, synchrony is what caused flow or social emotions in the classroom. The main conclusion is that synchrony leads to cognitive outcomes such as children’s academic performance in EFL learning.

Keywords: engagement in a class, English as a foreign language (EFL) learning, interactional synchrony, social emotions

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Authors: Sang Koo Jeon, Seung Hoon Nahm, Oh Heon Kwon

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The unit cell of solid oxide fuel cell (SOFC) must be stacked as several layers type to obtain the high power. The most of researcher have concerned about the performance of stacked SOFC rather than the structural stability of stacked SOFC and especially interested how to design for reducing the electrical loss and improving the high efficiency. Consequently, the stacked SOFC able to produce the electrical high power and related parts like as manifold, gas seal, bipolar plate were developed to optimize the stack design. However, the unit cell of SOFC was just layered on the interconnector without the adhesion and the hydrogen and oxygen were injected to the interfacial layer in the high temperature. On the operating condition, the interfacial layer can be the one of the weak point in the stacked SOFC. Therefore the evaluation of the structural safety for the failure is essentially needed. In this study, interfacial adhesion between SOFC and metal adhesive was estimated in the high temperature environment. The metal adhesive was used to strongly connect the unit cell of SOFC with interconnector and provide the electrical conductivity between them. The four point bending test was performed to measure the interfacial adhesion. The unit cell of SOFC and SiO2 wafer were diced and then attached by metal adhesive. The SiO2 wafer had the center notch to initiate a crack from the tip of the notch. The modified stereomicroscope combined with the CCD camera and system for measuring the length was used to observe the fracture behavior. Additionally, the interfacial adhesion was evaluated in the high temperature condition because the metal adhesive was affected by high temperature. Also the specimen was exposed in the furnace during several hours and then the interfacial adhesion was evaluated. Finally, the interfacial adhesion energy was quantitatively determined and compared in the each condition.

Keywords: solid oxide fuel cell (SOFC), metal adhesive, adhesion, high temperature

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Authors: Aofan Liu, Qianqian Tan, Burra Venkata Durga Kumar

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Data security is of the utmost importance in the healthcare area, as sensitive patient information is constantly sent around and analyzed by many different parties. The use of federated learning, which enables data to be evaluated locally on devices rather than being transferred to a central server, has emerged as a potential solution for protecting the privacy of user information. To protect against data breaches and unauthorized access, federated learning alone might not be adequate. In this context, the application of blockchain technology could provide the system extra protection. This study proposes a distributed federated learning system that is built on blockchain technology in order to enhance security in healthcare. This makes it possible for a wide variety of healthcare providers to work together on data analysis without raising concerns about the confidentiality of the data. The technical aspects of the system, including as the design and implementation of distributed learning algorithms, consensus mechanisms, and smart contracts, are also investigated as part of this process. The technique that was offered is a workable alternative that addresses concerns about the safety of healthcare while also fostering collaborative research and the interchange of data.

Keywords: data privacy, distributed system, federated learning, machine learning

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Authors: Firas A. Al Laban, Mohamed Chabi, Sammani Danwawu Abdullahi

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There are a real needs to integrate types of Open Educational Resources (OER) with an intelligent system to extract information and knowledge in the semantic searching level. Those needs raised because most of current learning standard adopted web based learning and the e-learning systems does not always serve all educational goals. Semantic Web systems provide educators, students, and researchers with intelligent queries based on a semantic knowledge management learning system. An ontology-based learning system is an advanced system, where ontology plays the core of the semantic web in a smart learning environment. The objective of this paper is to discuss the potentials of ontologies and mapping different kinds of ontologies; heterogeneous or homogenous to manage and control different types of Open Educational Resources. The important contribution of this research is to approach a methodology uses logical rules and conceptual relations to map between ontologies of different educational resources. We expect from this methodology to establish for an intelligent educational system supporting student tutoring, self and lifelong learning system.

Keywords: knowledge management systems, ontologies, semantic web, open educational resources

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Authors: O. Guellati, A. Harat, F. Djefaflia, N. Habib, A. Nait-Merzoug, J. El Haskouri, D. Momodu, N. Manyala, D. Bégin, M. Guerioune

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We report a facile and low-cost free-template synthesis method was used to synthesize mesoporous smart multifunctional nanohybrids based on Graphene/PANI nanofibers micro/nanostructures with very interesting physic-chemical properties and faradic electrochemical behavior of these products was investigated. These nanohybrid products have been characterized quantitatively and qualitatively using different techniques, such as XRD / FTIR, Raman, XPS spectroscopy, Field Emission SEM and High-Resolution TEM microscopy, BET textural analysis, electrochemical measurements (CV, CD, EIS). Moreover, the electrochemical measurements performed in a 6 M KOH aqueous electrolyte depicted excellent electrochemical performance ascribed to the optimized composition of hydroxides et PANI nanofibers. An exceptionally notable specific capacitance between 800  and 2000 F. g-1 was obtained at 5  mV. s-1 scan rate for these synthesized products depends on the optimized growth conditions. We found much better nanohybrids by reinforcing hydroxides or conduction polymer nanofibers with carbonaceous nanomaterials depicting their potential as suitable materials for energy storage devices.

Keywords: nanohybrid materials, conducting polymers, carbonaceous nanomaterials, supercapacitors, energy storage

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Authors: Krzysztof Skiba, Zbigniew Czyz, Ksenia Siadkowska, Piotr Borowiec

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The article presents selected concepts of technologies that use intelligent materials in aircraft in order to improve their performance. Most of the research focuses on solutions that improve the performance of fixed wing aircraft due to related to their previously dominant market share. Recently, the development of the rotorcraft has been intensive, so there are not only helicopters but also gyroplanes and unmanned aerial vehicles using rotors and vertical take-off and landing. There are many different technologies to change a shape of the aircraft or its elements. Piezoelectric, deformable actuator systems can be applied in the system of an active control of vibration dampening in the aircraft tail structure. Wires made of shape memory alloys (SMA) could be used instead of hydraulic cylinders in the rear part of the aircraft flap. The aircraft made of intelligent materials (piezoelectrics and SMA) is one of the NASA projects which provide the possibility of changing a wing shape coefficient by 200%, a wing surface by 50%, and wing deflections by 20 degrees. Active surfaces made of shape memory alloys could be used to control swirls in the flowing stream. An intelligent control system for helicopter blades is a method for the active adaptation of blades to flight conditions and the reduction of vibrations caused by the rotor. Shape memory alloys are capable of recovering their pre-programmed shapes. They are divided into three groups: nickel-titanium-based, copper-based, and ferromagnetic. Due to the strongest shape memory effect and the best vibration damping ability, a Ni-Ti alloy is the most commercially important. The subject of this work was to prepare a conceptual design of a rotor blade with SMA actuators. The scope of work included 3D design of the supporting rotor blade, 3D design of beams enabling to change the geometry by changing the angle of rotation and FEM (Finite Element Method) analysis. The FEM analysis was performed using NX 12 software in the Pre/Post module, which includes extended finite element modeling tools and visualizations of the obtained results. Calculations are presented for two versions of the blade girders. For FEM analysis, three types of materials were used for comparison purposes (ABS, aluminium alloy 7057, steel C45). The analysis of internal stresses and extreme displacements of crossbars edges was carried out. The internal stresses in all materials were close to the yield point in the solution of girder no. 1. For girder no. 2 solution, the value of stresses decreased by about 45%. As a result of the displacement analysis, it was found that the best solution was the ABS girder no. 1. The displacement of about 0.5 mm was obtained, which resulted in turning the crossbars (upper and lower) by an angle equal to 3.59 degrees. This is the largest deviation of all the tests. The smallest deviation was obtained for beam no. 2 made of steel. The displacement value of the second girder solution was approximately 30% lower than the first solution. Acknowledgement: This work has been financed by the Polish National Centre for Research and Development under the LIDER program, Grant Agreement No. LIDER/45/0177/L-9/17/NCBR/2018.

Keywords: aircraft, helicopters, shape memory alloy, SMA, smart material, unmanned aerial vehicle, UAV

Procedia PDF Downloads 138