Search results for: motor for washing machine

Authors: Ali Emre Öztürk, Ergun Ercelebi

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Autonomous robotic systems needs an equipment like a human eye for their movement. Robotic camera systems, distance sensors and 3D laser scanners have been used in the literature. In this study a 3D laser scanner has been produced for those autonomous robotic systems. In general 3D laser scanners are using 2 dimension laser range finders that are moving on one-axis (1D) to generate the model. In this study, the model has been obtained by a one-dimensional laser range finder that is moving in two –axis (2D) and because of this the laser scanner has been produced cheaper. Furthermore for the laser scanner a motor driver, an embedded system control board has been used and at the same time a user interface card has been used to make the communication between those cards and computer. Due to this laser scanner, the density of the objects, the distance between the objects and the necessary path ways for the robot can be calculated. The data collected by the laser scanner system is converted in to cartesian coordinates to be modeled in AutoCAD program. This study shows also the synchronization between the computer user interface, AutoCAD and the embedded systems. As a result it makes the solution cheaper for such systems. The scanning results are enough for an autonomous robot but the scan cycle time should be developed. This study makes also contribution for further studies between the hardware and software needs since it has a powerful performance and a low cost.

Keywords: 3D laser scanner, embedded system, 1D laser range finder, 3D model

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Authors: Jonathan Sohn

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Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

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Authors: Zhikang Rong

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The 4th industrial revolution has introduced technologies like interconnectivity, machine learning, and real-time big data analytics that improve operations and business efficiency. This paper examines how these advancements have led to a concentration of wealth, specifically among the top 1%, and investigates whether this wealth provides value to society. Through analyzing impacts on employment, productivity, supply-demand dynamics, and potential externalities, it is shown that successful businesspeople, by enhancing productivity and creating jobs, contribute positively to long-term economic growth. Additionally, externalities such as environmental degradation are managed by social entrepreneurship and government policies.

Keywords: wealth creation, employment, productivity, social entrepreneurship

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Authors: Hoda A. Abdel Hafez

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Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

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Authors: Touati Mawloud

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Between electronics and electrical systems has developed a new technology that is power electronics, also called electronic of strong currents, this application covers a very wide range of use particularly in the industrial sector, where direct current engines are frequently used, they control their speed by the use of the converters (DC-DC), which aims to deal with various mechanical disturbances (fillers) or electrical (power). In future, it will play a critical role in transforming the current electric grid into the next generation grid. Existing silicon-based PE devices enable electric grid functionalities such as fault-current limiting and converter devices. Systems of future are envisioned to be highly automated, interactive "smart" grid that can self-adjust to meet the demand for electricity reliability, securely, and economically. Transforming today’s electric grid to the grid of the future will require creating or advancing a number of technologies, tools, and techniques—specifically, the capabilities of power electronics (PE). PE devices provide an interface between electrical system, and electronics system by converting AC to direct current (DC) and vice versa. Solid-state wide Bandgap (WBG), semiconductor electronics (such as silicon carbide [SiC], gallium nitride [GaN], and diamond) are envisioned to improve the reliability and efficiency of the next-generation grid substantially.

Keywords: Power Electronics (PE), electrical system generation electric grid, switching frequencies, converter devices

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Authors: Pablo Raul Yanyachi, Alfredo Mamani Saico, Jorch Mendoza, Wang Xinsheng

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The Attitude Determination and Control System (ADCS) plays a pivotal role in the operation of nanosatellites such as Cubesats, managing orientation and stability during space missions. Within the ADCS, Reaction Wheels (RW) are electromechanical devices responsible for adjusting and maintaining satellite orientation through the application of kinetic moments. This study focuses on the characterization and analysis of a specific Reaction Wheel integrated into an ADCS prototype developed at the National University of San Agust´ın, Arequipa (UNSA). To achieve this, a single-axis Test Bench was constructed, where the reaction wheel consists of a brushless motor and an inertia flywheel driven by an Electronic Speed Controller (ESC). The research encompasses RW characterization, energy consumption evaluation, dynamic modeling, and control. The results have allowed us to ensure the maneuverability of ADCS prototypes while maintaining energy consumption within acceptable limits. The characterization and linearity analysis provides valuable insights for sizing and optimizing future reaction wheel prototypes for nanosatellites. This contributes to the ongoing development of aerospace technology within the scientific community at UNSA.

Keywords: test bench, nanosatellite, control, reaction wheel

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Authors: Olha Yarova

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This study is a collaborative project between the American University of Central Asia and parent association of children with Down syndrome ‘Sunterra’ that took place in Bishkek, Kyrgyzstan. The purpose of the study was to explore whether principles and techniques of applied behavior analysis (ABA) could be used to teach children with Down syndrome socially significant behaviors. ABA is considered to be one of the most effective treatment for children with autism, but little research is done on the particularity of using ABA to children with Down syndrome. The data for the study was received during clinical observations; work with children with Down syndrome and interviews with their mothers. The results show that many ABA principles make the work with children with Down syndrome more effective. Although such children very rarely demonstrate aggressive behavior, they show a lot of escape-driven and attention seeking behaviors that are reinforced by their parents and educators. Thus functional assessment can be done to assess the function of problem behavior and to determine appropriate treatment. Prompting and prompting fading should be used to develop receptive and expressive language skills, and enhance motor development. Even though many children with Down syndrome work for praise, it is still relevant to use tangible reinforcement and to know how to remove them. Based on the results of the study, the training for parents of children with Down syndrome will be developed in Kyrgyzstan, country, where children with Down syndrome are not accepted to regular kindergartens and where doctors in maternity hospitals tell parents that their child will never talk, walk and recognize them

Keywords: down syndrome, applied behavior analysis, functional assessment, problem behavior, reinforcement

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Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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Authors: Reema Alghamdi, Rasees Aleisa, Layan Sukkar

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The primary objective of this research is to conduct an examination of the Flo menstrual cycle application. We do that by evaluating the user experience and their satisfaction with integrated AI features. The study seeks to gather data from primary resources, primarily through surveys, to gather different insights about the application, like its usability functionality in addition to the overall user satisfaction. The focus of our project will be particularly directed towards the impact and user perspectives regarding the integration of artificial intelligence features within the application, contributing to an understanding of the holistic user experience.

Keywords: period, women health, machine learning, AI features, menstrual cycle

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Authors: Catherine Vasnetsov, Victor Vasnetsov

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Context: Thermo-responsive polymers are materials that undergo significant changes in their physical properties in response to temperature changes. These polymers have gained significant attention in research due to their potential applications in various industries and medicine. However, the molecular mechanisms underlying their behavior are not well understood, particularly in relation to cosolvency, which is crucial for practical applications. Research Aim: This study aimed to theoretically investigate the phenomenon of cosolvency in long-chain polymers using the Flory-Huggins statistical-mechanical framework. The main objective was to understand the interactions between the polymer, solvent, and cosolvent under different conditions. Methodology: The research employed a combination of Monte Carlo computer simulations and advanced machine-learning methods. The Flory-Huggins mean field theory was used as the basis for the simulations. Spinodal graphs and ternary plots were utilized to develop an initial computer model for predicting polymer behavior. Molecular dynamic simulations were conducted to mimic real-life polymer systems. Machine learning techniques were incorporated to enhance the accuracy and reliability of the simulations. Findings: The simulations revealed that the addition of very low or very high volumes of cosolvent molecules resulted in smaller radii of gyration for the polymer, indicating poor miscibility. However, intermediate volume fractions of cosolvent led to higher radii of gyration, suggesting improved miscibility. These findings provide a possible microscopic explanation for the cosolvency phenomenon in polymer systems. Theoretical Importance: This research contributes to a better understanding of the behavior of thermo-responsive polymers and the role of cosolvency. The findings provide insights into the molecular mechanisms underlying cosolvency and offer specific predictions for future experimental investigations. The study also presents a more rigorous analysis of the Flory-Huggins free energy theory in the context of polymer systems. Data Collection and Analysis Procedures: The data for this study was collected through Monte Carlo computer simulations and molecular dynamic simulations. The interactions between the polymer, solvent, and cosolvent were analyzed using the Flory-Huggins mean field theory. Machine learning techniques were employed to enhance the accuracy of the simulations. The collected data was then analyzed to determine the impact of cosolvent volume fractions on the radii of gyration of the polymer. Question Addressed: The research addressed the question of how cosolvency affects the behavior of long-chain polymers. Specifically, the study aimed to investigate the interactions between the polymer, solvent, and cosolvent under different volume fractions and understand the resulting changes in the radii of gyration. Conclusion: In conclusion, this study utilized theoretical modeling and computer simulations to investigate the phenomenon of cosolvency in long-chain polymers. The findings suggest that moderate cosolvent volume fractions can lead to improved miscibility, as indicated by higher radii of gyration. These insights contribute to a better understanding of the molecular mechanisms underlying cosolvency in polymer systems and provide predictions for future experimental studies. The research also enhances the theoretical analysis of the Flory-Huggins free energy theory.

Keywords: molecular modelling, flory-huggins, cosolvency, stimuli-responsive polymers

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Authors: Deepali Rastogi, Akanksha Rastogi

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Comfort and safety are two essential factors in a newborn’s clothing. Natural dyes are considered safe for infant clothes because they are non-toxic and have medicinal properties. Natural dyes are sensitive to pH and may show changes in hue under different pH conditions. Infant garments face treatments different than adult clothing, for instance, exposure to infant’s saliva, milk, and urine. The present study was designed to study the suitability of natural dyes for infant clothes. Cotton fabric was dyed using fifteen natural dyes and two mordants, alum, and ferrous sulphate. The dyed samples were assessed for colour fastness to washing, rubbing, perspiration and light. In addition, fastness to milk, saliva, and urine was also tested. Simulated solutions of saliva and urine were prepared for the study. For milk, one of the commercial formulations for infants was taken and used as per the directions. A wide gamut of colours was obtained after dyeing the cotton with different natural dyes and mordants. The colour strength of all the dyed samples was determined in terms of K/S values. Most of the ferrous sulphate mordanted dyes gave higher K/S values than alum mordanted samples. The wash fastness of dyed cotton fabrics ranged from 3/4 -5. Perspiration fastness test for the samples was done in both acidic and alkaline mediums. The ratings ranged from 3-5, with most of the dyes falling in the range of 4-5. The rubbing fastness of the dyed samples was tested in dry and wet conditions. The results showed excellent rub fastness ranging between 4-5. Light fastness was found to be good to moderate. The main food for infants is milk, and this becomes one of the main agents to spot infants' garments. All dyes showed excellent fastness properties against milk with a grey scale rating of 4-5. Fastness against saliva is recommended by various eco-labels, standards, and organizations for fabrics of infants or babies. The fastness of most of the dyes was found to be satisfactory against saliva. Infant garments get frequently soiled with urine. Most of the natural dyes on cotton fabric had good to excellent fastness to simulated urine. The grey scale ratings ranged from 3/4 – 5. Thus, it can be concluded that most of the natural dyes can be successfully used for infant wear and accessories and are fast to various liquids to which infant wear are exposed. Therefore, we can surround little ones with beautiful hues from nature's garden and clothe them in natural fibres dyed with natural dyes.

Keywords: fastness properties, infant wear, mordants, natural dyes

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Authors: Seungwoo Kim, Yeongcheol Cho

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In this paper, a holonomic mobile robot is designed in omnidirectional wheels and an adaptive fuzzy controller is presented for its precise trajectories. A kind of adaptive controller based on fuzzy multi-layered algorithm is used to solve the big parametric uncertainty of motor-controlled dynamic system of 3-wheels omnidirectional mobile robot. The system parameters such as a tracking force are so time-varying due to the kinematic structure of omnidirectional wheels. The fuzzy adaptive control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system. Finally, the good performance of a holonomic mobile robot is confirmed through live tests of the tracking control task.

Keywords: fuzzy adaptive control, fuzzy multi-layered controller, holonomic mobile robot, omnidirectional wheels, robustness and stability.

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Authors: Elyas Akbari, Jafar Hasani, Newsha Dehestani, Mohammad Khaleghi, Alireza Moradi

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The aim of the present study was to assess the effect of emotional experience induction in the mirror neurons systems (MNS) activity with regard to the spectrum of depressive symptoms. For this purpose, at first stage, 449 students of Kharazmi University of Tehran were selected randomly and completed the second version of the Beck Depression Inventory (BDI-II). Then, 36 students with standard Z-score equal or above +1.5 and equal or equal or below -1.5 were selected to construct two groups of high and low spectrum of depressive symptoms. In the next stage, the basic activity of MNS was recorded (mu wave) before presenting the positive and negative emotional video clips by Electroencephalography (EEG) technique. The findings related to emotion induction (neutral, negative and positive emotion) demonstrated that the activity of recorded mirror neuron areas had a significant difference between the depressive and non-depressive groups. These findings suggest that probably processing of negative emotions in depressive individuals is due to the idea that the mirror neurons in motor cortex matched up the activity of cognitive regions with the person’s schema. Considering the results of the present study, it could be said that the MNS provides a substrate where emotional disorders can be studied and evaluated.

Keywords: emotional experiences, mirror neurons, depressive symptoms, negative and positive emotion

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Shuang Qiu, Zhipeng Chen, Lingfeng Wang, Shijian Ge

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Biowastes are a concern due to the large amounts of commercial food required for model animals during the biomedical research. Searching for sustainable food alternatives with negligible physiological effects on animals is critical to solving or reducing this challenge. Microalgae have been demonstrated as suitable for both human consumption and animal feed in addition to biofuel and bioenergy applications. In this study, the possibility of using Chlorella vulgaris and Senedesmus obliquus as a feed replacement to Drosophila melanogaster, one of the fly models commonly used in biomedical studies, was investigated to assess the fly locomotor activity, motor pattern, lifespan, and body weight. Compared to control, flies fed on 60% or 80% (w/w) microalgae exhibited varied walking performance including travel distance and apparent step size, and flies treated with 40% microalgae had shorter lifespans and decreased body weight. However, the 20% microalgae treatment showed no statistical differences in all parameters tested with respect to the control. When partially including 20% microalgae in the standard food, it can annually reduce the food waste (~ 202 kg) by 22.7 % and save $ 7,200 of the food cost, offering an environmentally superior and cost-effective food alternative without compromising physiological performance.

Keywords: animal feed, Chlorella vulgaris, Drosophila melanogaster, food waste, microalgae

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: Ehsan Kaviani, Azin Golmoradizade

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Introduction: Traditionally, tendons are considered to only contain tenocytes that are responsible for the maintenance, repair, and remodeling of tendons. Stem cells, which are termed tendon-derived stem cells, so this study we investigate the effect of transcranial direct current stimulation combined with swallowing training on post-stroke dysphagia. Methods: This review article is about effects of transcranial direct current stimulation (tDCS) on post-stroke dysphagia that were extracted from Science Direct, Pro quest, and Pub med Data Bases. 15 articles had been selected according to inclusion criteria from 2014 to 2019, and 6 of them had been deleted by exclusion criteria. Results: The results of our systematic review suggest that tDCS may represent a promising novel treatment for post-stroke dysphagia. However, to date, little is known about the optimal parameters of tDCS for relieving post-stroke dysphagia. Further studies are warranted to refine this promising intervention by exploring the optimal parameters of tDCS. Conclusion: anodal tDCS over the affected hemisphere may be as effective as cathodal tDCS on the unaffected hemisphere to enhance recovery after subacute ischemic stroke and anodal tdcs applied over the affected pharyngeal motor cortex can enhance the outcome of swallowing training in post-stroke dysphagia.

Keywords: dysphagia, stroke, cortical stimulation, transcranial direct current stimulation

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Authors: Joanna Cabaj, Sylwia Baluta, Karol Malecha

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Catecholamines are vital neurotransmitters that mediate a variety of central nervous system functions, such as motor control, cognition, emotion, memory processing, and endocrine modulation. Dysfunctions in catecholamine neurotransmission are induced in some neurologic and neuropsychiatric diseases. Changeable neurotransmitters level in biological fluids can be a marker of several neurological disorders. Because of its significance in analytical techniques and diagnostics, sensitive and selective detection of neurotransmitters is increasingly attracting a lot of attention in different areas of bio-analysis or biomedical research. Recently, optical techniques for the detection of catecholamines have attracted interests due to their reasonable cost, convenient control, as well as maneuverability in biological environments. Nevertheless, with the observed need for a sensitive and selective catecholamines sensor, the development of a convenient method for this neurotransmitter is still at its basic level. The manipulation of nanostructured materials in conjunction with biological molecules has led to the development of a new class of hybrid-modified enzymatic sensors in which both enhancement of charge transport and biological activity preservation may be obtained. Immobilization of biomaterials on electrode surfaces is the crucial step in fabricating electrochemical as well as optical biosensors and bioelectronic devices. Continuing systematic investigation in manufacturing of enzyme–conducting sensitive systems, here is presented a convenient fluorescence as well as electrochemical sensing strategy for catecholamines detection.

Keywords: biosensors, catecholamines, fluorescence, enzymes

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Authors: Mehdi Modabberifar, Sanaz Jabary, Mojtaba Ghodsi

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Stiffness sensing is an important issue in medical diagnostic, robotics surgery, safe handling, and safe grasping of objects in production lines. Detecting and obtaining the characteristics in dwelling lumps embedded in a soft tissue and safe removing and handling of detected lumps is needed in surgery. Also in industry, grasping and handling an object without damaging in a place where it is not possible to access a human operator is very important. In this paper, a method for object handling is presented. It is based on the use of an intelligent gripper to detect the object stiffness and then setting a programmable force for grasping the object to move it. The main components of this system includes sensors (sensors for measuring force and displacement), electrical (electrical and electronic circuits, tactile data processing and force control system), mechanical (gripper mechanism and driving system for the gripper) and the display unit. The system uses a rotary potentiometer for measuring gripper displacement. A microcontroller using the feedback received by the load cell, mounted on the finger of the gripper, calculates the amount of stiffness, and then commands the gripper motor to apply a certain force on the object. Results of Experiments on some samples with different stiffness show that the gripper works successfully. The gripper can be used in haptic interfaces or robotic systems used for object handling.

Keywords: gripper, haptic, stiffness, robotic

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Authors: Zelal Çınar

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This paper aims to expose the effects of the ideological program of Turkish Republic on city planning, through the first plan of Ankara. As the new capital, Ankara was planned to be the ‘showcase’ of modern Turkey. It was to represent all new ideologies and the country’s cultural similarities with the west. At the same time it was to underline the national identity and independence of Turkish republic. To this end, a new plan for the capital was designed by German city planner Carl Christopher Lörcher. Diametrically opposed with the existing fabric of the city, this plan was built on the basis of papers and plans, on ideological aims. On the contrary, this paper argues that the city is a machine of possibilities, rather than a clear, materialized system.

Keywords: architecture, ideology, modernization, urban planning

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Authors: Carlos Arias-Alcaide, Cristina Soguero-Ruiz, Paloma Santos-Álvarez, Adrián García-Romero, Inmaculada Mora-Jiménez

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Analysing clinical data with computers in such a way that have an impact on the practitioners’ workflow is a challenge nowadays. This paper provides a first approach for monitoring the health status of COVID-19 patients through the use of some biomarkers (blood tests) and the simplest Naïve Bayes classifier. Data of two Spanish hospitals were considered, showing the potential of our approach to estimate reasonable posterior probabilities even some days before the event.

Keywords: Bayesian model, blood biomarkers, classification, health tracing, machine learning, posterior probability

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Authors: Minjuan Sun

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Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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Authors: Natia Gavardashvili

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On June 13-14 of 2015, a landslide in village Akhaldaba was formed as a result of the intense rains in the water catch basin of the river Vere. As a result of the landslide movement, freshets and mudflows originated, and unfortunately, there were victims: zoo animals and birds were drawn in the flood and 12 people died due to the flooded motor road. The goal of the study is to give the analysis of the results of the field and scientific research held in 2015-2017 and to generalize them to the water catch basins of the erosive-mudflow rivers of other mountain landscapes of Georgia. By considering the field and scientific works, the main geographic, geological, climatic, hydrological and hydraulic properties of the erosive-mudflow tributaries of the water catch basin of the river Vere were evaluated and the probabilities of mudflow formation by considering relevant risk-factors were identified. The typology of the water catch basins of erosive-mudflow rivers of Georgia was identified on the example of the river Vere based on the field and scientific study, and their genesis, frequency of mudflow formation and volume of the drift material was identified. By using the empirical and theoretical dependencies, the amount of solid admixtures in the mudflow formed in the gorge of the river Jokhona, the right tributary of the river Vere was identified by considering the shape of the stones.

Keywords: water catchment basin, erosion, mudflow, typology

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Authors: Dawei Li, Cheng Liu, Yiteng Liu

Abstract:

This paper introduces an embedded digital image system for Chinese space environment vertical exploration sounding rocket. In order to record the flight status of the sounding rocket as well as the payloads, an onboard embedded image processing system based on ADV212, a JPEG2000 compression chip, is designed in this paper. Since the sounding rocket is not designed to be recovered, all image data should be transmitted to the ground station before the re-entry while the downlink band used for the image transmission is only about 600 kbps. Under the same condition of compression ratio compared with other algorithm, JPEG2000 standard algorithm can achieve better image quality. So JPEG2000 image compression is applied under this condition with a limited downlink data band. This embedded image system supports lossless to 200:1 real time compression, with two cameras to monitor nose ejection and motor separation, and two cameras to monitor boom deployment. The encoder, ADV7182, receives PAL signal from the camera, then output the ITU-R BT.656 signal to ADV212. ADV7182 switches between four input video channels as the program sequence. Two SRAMs are used for Ping-pong operation and one 512 Mb SDRAM for buffering high frame-rate images. The whole image system has the characteristics of low power dissipation, low cost, small size and high reliability, which is rather suitable for this sounding rocket application.

Keywords: ADV212, image system, JPEG2000, sounding rocket

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

Abstract:

Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: Crin N. Marcean, Mihaela A. Alexandru, Eugenia S. Cristescu

Abstract:

By definition, nursing means caring. It is a profession within the health care sector focused on the care of individuals, families, and communities so they may attain, maintain or recover optimal health and quality of life. However, there is a subtle difference between the two: nursing is widely considered as an art and a science, wherein caring forms the theoretical framework of nursing. Nursing and caring are grounded in a relational understanding, unity, and connection between the professional nurse and the patient. Task-oriented approaches challenge nurses in keeping care in nursing. This challenge is on-going as professional nurses strive to maintain the concept, art, and act of caring as the moral centre of the nursing profession. Keeping the care in nursing involves the application of art and science through theoretical concepts, scientific research, conscious commitment to the art of caring as an identity of nursing, and purposeful efforts to include caring behaviours during each nurse-patient interaction. The competencies, abilities, as well as the psycho-motor, cognitive, and relational skills necessary for the nursing practice are conveyed and improved by the nursing teachers’ art of teaching. They must select and use the teaching methods which shape the personalities of the trainers or students, enabling them to provide individualized, personalized care in real-world context of health problems. They have the ultimate responsibility of shaping the future health care system by educating skilful nurses.

Keywords: art of nursing, health care, teacher-student relationship, teaching innovations

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Authors: A. Belmeguenai, K. Mansouri, R. Djemili

Abstract:

This work present a new algorithm based on the nonlinear filter generator for speech encryption and decryption. The proposed algorithm consists on the use a linear feedback shift register (LFSR) whose polynomial is primitive and nonlinear Boolean function. The purpose of this system is to construct Keystream with good statistical properties, but also easily computable on a machine with limited capacity calculated. This proposed speech encryption scheme is very simple, highly efficient, and fast to implement the speech encryption and decryption. We conclude the paper by showing that this system can resist certain known attacks.

Keywords: nonlinear filter generator, stream ciphers, speech encryption, security analysis

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Authors: Mohammad Shameel, Peter K. K. Loh, James H. Ng

Abstract:

Cybercrime is a new category of crime which poses a different challenge for crime investigators and incident responders. Attackers can mask their identities using a suite of tools and with the help of the deep web, which makes them difficult to track down. Scouring the deep web manually takes time and is inefficient. There is a growing need for a tool to scour the deep web to obtain useful evidence or intel automatically. In this paper, we will explain the background and motivation behind the research, present a survey of existing research on related tools, describe the design of our own crime/threat intelligence hunting tool prototype, demonstrate its capability with some test cases and lastly, conclude with proposals for future enhancements.

Keywords: cybercrime, deep web, threat intelligence, web crawler

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Authors: Anita Sadat Sadati Rostami, Reza Almasi Ghaleh

Abstract:

Surface electromyography (sEMG) offers important insights into muscle activation and has applications in fields including rehabilitation and human-computer interaction. The purpose of this work is to predict the degree of activation of two joints in the index finger using an LSTM-Transformer architecture trained on sEMG data from the Ninapro DB8 dataset. We apply advanced preprocessing techniques, such as multi-band filtering and customizable rectification methods, to enhance the encoding of sEMG data into features that are beneficial for regression tasks. The processed data is converted into spike patterns and simulated using Leaky Integrate-and-Fire (LIF) neuron models, allowing for neuromorphic-inspired processing. Our findings demonstrate that adjusting filtering parameters and neuron dynamics and employing the LSTM-Transformer model improves joint angle prediction performance. This study contributes to the ongoing development of deep learning frameworks for sEMG analysis, which could lead to improvements in motor control systems.

Keywords: surface electromyography, LSTM-transformer, spiking neural networks, hand kinematics, leaky integrate-and-fire neuron, band-pass filtering, muscle activity decoding

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Authors: Vishwa Joshi

Abstract:

The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

Procedia PDF Downloads 493