Search results for: advanced%20conversion%20technologies

Authors: Ammar Maziz, Mostapha Tarfaoui, Said Rechak

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The high mechanical performance of composite pipes can be adversely affected by their low resistance to impact loads. Loads in dynamic origin are dangerous and cause consequences on the operation of pipes because the damage is often not detected and can affect the structural integrity of composite pipes. In this work, an advanced 3-D finite element (FE) model, based on the use of intralaminar damage models was developed and used to predict damage under low-velocity impact. The performance of the numerical model is validated with the confrontation with the results of experimental tests. The results show that at low impact energy, the damage happens mainly by matrix cracking and delamination. The model capabilities to simulate the low-velocity impact events on the full-scale composite structures were proved.

Keywords: composite materials, low velocity impact, FEA, dynamic behavior, progressive damage modeling

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Authors: C. V. Pietreanu, S. E. Zaharia, C. Dinu

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Hazard assessment and risks quantification are key components for estimating the impact of existing regulations. But since regulatory compliance cannot cover all risks in aviation, the authors point out that by studying causal factors and eliminating uncertainty, an accurate analysis can be outlined. The research debuts by making delimitations on notions, as confusion on the terms over time has reflected in less rigorous analysis. Throughout this paper, it will be emphasized the fact that the variation in human performance and organizational factors represent the biggest threat from an operational perspective. Therefore, advanced risk assessment methods analyzed by the authors aim to understand vulnerabilities of the system given by a nonlinear behavior. Ultimately, the mathematical modeling of existing hazards and risks by eliminating uncertainty implies establishing an optimal solution (i.e. risk minimization).

Keywords: control, human factor, optimization, risk management, uncertainty

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Authors: Ali Moarrefzadeh

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In metalworking, rolling is a metal forming process in which metal stock is passed through a pair of rolls. Rolling is classified according to the temperature of the metal rolled. If the temperature of the metal is above its recrystallization temperature, then the process is termed as hot rolling. If the temperature of the metal is below its recrystallization temperature, the process is termed as cold rolling. In terms of usage, hot rolling processes more tonnage than any other manufacturing process, and cold rolling processes the most tonnage out of all cold working processes. This article describes the use of advanced tubing inspection NDT methods for boiler and heat exchanger equipment in the petrochemical industry to supplement major turnaround inspections. The methods presented include remote field eddy current, magnetic flux leakage, internal rotary inspection system and eddy current.

Keywords: hot forming, cold forming, metal, rolling, simulation

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Authors: Issam Ben Jami

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Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results.

Keywords: feature matching, historical documents, pattern recognition, word spotting

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Authors: Ahmet Yonetken, Ayhan Erol

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Intermetallic materials advanced technology materials that have outstanding mechanical and physical properties for high temperature applications. Especially creep resistance, low density and high hardness properties stand out in such intermetallics. The microstructure, mechanical properties of %80Cr-%10Ti and %10WC powders were investigated using specimens produced by tube furnace sintering at 1000-1400°C temperature. A composite consisting of ternary additions, a metallic phase, Ti,Cr and WC have been prepared under Ar shroud and then tube furnace sintered. XRD, SEM (Scanning Electron Microscope), were investigated to characterize the properties of the specimens. Experimental results carried out for composition %80Cr-%10Ti and %10WC at 1400°C suggest that the best properties as 292HV and 5,34g/cm3 density were obtained at 1400°C.

Keywords: ceramic-metal, composites, powder metallurgy, sintering

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Authors: Hussein Altartouri

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In this paper the kinematic and kinetic models of an omnidirectional holonomic mobile robot is presented. The kinematic and kinetic models form the OmniDrive model. Therefore, a mathematical model for the robot equipped with three- omnidirectional wheels is derived. This model which takes into consideration the kinematics and kinetics of the robot, is developed to state space representation. Relative analysis of the velocities and displacements is used for the kinematics of the robot. Lagrange’s approach is considered in this study for deriving the equation of motion. The drive train and the mechanical assembly only of the Festo Robotino® is considered in this model. Mainly the model is developed for motion control. Furthermore, the model can be used for simulation purposes in different virtual environments not only Robotino® View. Further use of the model is in the mechatronics research fields with the aim of teaching and learning the advanced control theories.

Keywords: mobile robot, omni-direction wheel, mathematical model, holonomic mobile robot

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Authors: Wesal Maash

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This paper is part of a PhD mixed-method project currently conducted in the Saudi context. This paper explores the challenges novice Arabic language teachers (ALT) face when starting the teaching profession through semi-structured interviews with ten teachers and a questionnaire with 208 teachers. The data provided details of the challenges faced by those teachers and reasons why they face such a challenge. From the data, it can be deduced that schools are advanced and updated continuously, and the preparation program does not cope with that. This situation makes teachers struggle to cover the gap between what they learnt in their preparation and what is expected from them as teachers when they started their teaching profession. This paper suggests conducting further research to better understand this phenomenon by shedding light on the content of teachers' preparation programs.

Keywords: educational technologies, novice teachers, arabic language teachers, Saudi Arabia

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Maythaya Preeyanon

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Chanthaburi Province is a province with people of many nationalities, such as Thai, Chinese, Vietnamese, Chong, and Khmer, resulting in a cultural mix in the past. What is a famous cultural transmission of Chanthaburi province is Chanthaburi reed mat weaving. Due to the suitability of both the climate and topography near the edge of the location, reeds have the qualities to be used to produce mats very well. There have been improvements in the methods of planting, dyeing, and weaving into various patterns that have been passed down as folk wisdom from generation to generation. Currently, there are brands of products belonging to the new generation that add creativity and add value through storytelling. Revive the value of reed mat products to increase in value until they can be exported to sell abroad. This research has therefore been conducted in order to decipher the success in transferring the Chanthaboon reed mat culture capital which has successfully advanced to being a creative economy so that it can be applied to other types of local wisdom or other local products.

Keywords: cultural capital, creative economy, communication, local product

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Majid Afkhami, Maryam Ehsanpour, Zahra Khoshnood

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The concentrations of 10 heavy metals (Cd, Pb, Hg, Cu, Fe, Mn, Al, As, Ni, Zn) were measured in muscle, gill and liver of 6 species from Hormoz Strait in north coast of Persian Gulf in 12 months (April 2009 – March 2010). All samples were analyzed three times for Cd, Pb, Cu, Fe, Mn, Al, As, Ni, Zn by inductively coupled plasma-atomic emission spectrometry (ICP-AES) and for Hg by LECO AMA254 Advanced Mercury Analyzer. Results of this study showed that iron had the highest concentration (total mean concentration) in all species, followed by Zn, Cu, Ni, Al, Pb, Mn, Cd, Hg and lowest concentration in three tissues was As. In addition, the accumulation of metals was species-dependent, and was higher in Scomberomorous commerson and Scomberomorous guttatus (p<0.05) and the lowest concentration was record in Pampus argenteus (p<0.05).

Keywords: Persian Gulf, heavy metals, Hormoz strait, Scomberomorous guttatus, Scomberomorous commerson, Pampus argenteus

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Authors: Kwaku Damoah

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This study presents a comprehensive data-driven analysis to understand the evolution of adverse events (AEs) in pharmacovigilance. Utilizing data from the FDA Adverse Event Reporting System (FAERS), we employed three analytical methods: rank-based, frequency-based, and percentage change analyses. These methods assessed temporal trends and patterns in AE reporting, focusing on various drug-active ingredients and patient demographics. Our findings reveal significant trends in AE occurrences, with both increasing and decreasing patterns from 2000 to 2023. This research highlights the importance of continuous monitoring and advanced analysis in pharmacovigilance, offering valuable insights for healthcare professionals and policymakers to enhance drug safety.

Keywords: event analysis, FDA adverse event reporting system, pharmacovigilance, temporal trend analysis

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Authors: Rodrigo Aguiar, Adelino Ferreira

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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Spira A., Marabelle A., Kientop D., Moser E., Mumm J.

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Background: Both interleukin-2 (IL-2) and interleukin-10 (IL-10) have been extensively studied for their stimulatory function on T cells and their potential to obtain sustainable tumor control in RCC, melanoma, lung, and pancreatic cancer as monotherapy, as well as combination with PD-1 blockers, radiation, and chemotherapy. While approved, IL-2 retains significant toxicity, preventing its widespread use. The significant efforts undertaken to uncouple IL-2 toxicity from its anti-tumor function have been unsuccessful, and early phase clinical safety observed with PEGylated IL-10 was not met in a blinded Phase 3 trial. Deka Biosciences has engineered a novel molecule coupling wild-type IL-2 to a high affinity variant of Epstein Barr Viral (EBV) IL-10 via a scaffold (scFv) that binds to epidermal growth factor receptors (EGFR). This patented molecule, termed DK210 (EGFR), is retained at high levels within the tumor microenvironment for days after dosing. In addition to overlapping and non-redundant anti-tumor function, IL-10 reduces IL-2 mediated cytokine release syndrome risks and inhibits IL-2 mediated T regulatory cell proliferation. Methods: DK210 (EGFR) is being evaluated in an open-label, dose-escalation (Phase 1) study with 5 (0.025-0.3 mg/kg) monotherapy dose levels and (expansion cohorts) in combination with PD-1 blockers, or radiation or chemotherapy in patients with advanced solid tumors overexpressing EGFR. Key eligibility criteria include 1) confirmed progressive disease on at least one line of systemic treatment, 2) EGFR overexpression or amplification documented in histology reports, 3) at least a 4 week or 5 half-lives window since last treatment, and 4) excluding subjects with long QT syndrome, multiple myeloma, multiple sclerosis, myasthenia gravis or uncontrolled infectious, psychiatric, neurologic, or cancer disease. Plasma and tissue samples will be investigated for pharmacodynamic and predictive biomarkers and genetic signatures associated with IFN-gamma secretion, aiming to select subjects for treatment in Phase 2. Conclusion: Through successful coupling of wild-type IL-2 with a high affinity IL-10 and targeting directly to the tumor microenvironment, DK210 (EGFR) has the potential to harness IL-2 and IL-10’s known anti-cancer promise while reducing immunogenicity and toxicity risks enabling safe concomitant cytokine treatment with other anti-cancer modalities.

Keywords: cytokine, EGFR over expression, interleukine-2, interleukine-10, clinical trial

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Authors: Ravipa Rojasavachai, Li Yang

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Working from home has become a global phenomenon after the coronavirus outbreak, and most employees have a choice to choose between working from home or the office. In this paper, we examine the demographics and socio-economics factors influencing individuals’ decision to choose working from home rather than the office before and after the coronavirus outbreak based on Australian household data. We find that all factors impact the working from home choice before the coronavirus outbreak, but the number of children turns to an uninfluenced factor on individuals’ choices after the outbreak. We also find that female employees have a higher probability of choosing to work from home after the coronavirus outbreak. This is because they have less concern for their career opportunities and higher wage premium of working from home due to the changing in cultural norms and advanced working from home technologies in companies after the coronavirus outbreak.

Keywords: work from home, telework, remote working, COVID-19, pandemic, wage

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Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Geetishree Mishra, S. Akhila

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Embedded System Design, the most challenging course of electronics engineering has always been appreciated and well acclaimed by the students of electronics and its related branches of engineering. Embedded system, being a product of multiple application domains, necessitates skilled man power to be well designed and tested in every important aspect of both hardware and software. In the current industrial scenario, the requirements are even more rigorous and highly demanding and needs to be to be on par with the advanced technologies. Fresh engineers are expected to be thoroughly groomed by the academic system and the teaching community. Graduates with the ability to understand both complex technological processes and technical skills are increasingly sought after in today's embedded industry. So, the need of the day is to restructure the under-graduate course- both theory and lab practice along with the teaching methodologies to meet the industrial requirements. This paper focuses on the importance of such a need in the present education system.

Keywords: embedded system design, industry requirement, syllabus restructuring, project-based learning, teaching methodology

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Djilani Kobibi Youcef Islam, Hadjeri Samir, Djehaf Mohamed Abdeldjalil

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The Flexible AC Transmission System (FACTS) technology is a new advanced solution that increases the reliability and provides more flexibility, controllability, and stability of a power system. The Unified Power Flow Controller (UPFC), as the most versatile FACTS device for regulating power flow, is able to control respectively transmission line real power, reactive power, and node voltage. The main purpose of this paper is to analyze the effect of the UPFC on the load flow, the power losses, and the voltage stability using NEPLAN software modules, Newton-Raphson load flow is used for the power flow analysis and the modal analysis is used for the study of the voltage stability. The simulation was carried out on the IEEE 14-bus test system.

Keywords: FACTS, load flow, modal analysis, UPFC, voltage stability

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Authors: Katja Ignatieva, Patrick Wong

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We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices.

Keywords: stochastic volatility, affine jump-diffusion models, high frequency data, model specification, markov chain monte carlo

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Authors: Hoon Yi, Minho Sung, Hangil Ko, Moon Kyu Kwak, Hoon Eui Jeong

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Inspired by the remarkable adhesion properties of gecko lizards, bio-inspired dry adhesives with smart adhesion properties have been developed in the last decade. Compared to earlier dry adhesives, the recently developed ones exhibit excellent adhesion strength, smart directional adhesion, and structural robustness. With these unique adhesion properties, bio-inspired dry adhesive pads have strong potential for use in precision industries such as semiconductor or display manufacturing. In this communication, we present a new manufacturing technology based on advanced dry adhesive systems that enable precise manipulation of large-area substrates over repeating cycles without any requirement for external force application. This new manufacturing technique is also highly accurate and environment-friendly, and thus has strong potential as a next-generation clean manufacturing technology.

Keywords: gecko, manufacturing robot, precision manufacturing

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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Authors: Benyamin Ahmadnia, Javier Serrano

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In this paper we compare two different approaches for translating from Persian to Spanish, as a language pair with scarce parallel corpus. The first approach involves direct transfer using an statistical machine translation system, which is available for this language pair. The second approach involves translation through English, as a pivot language, which has more translation resources and more advanced translation systems available. The results show that, it is possible to achieve better translation quality using English as a pivot language in either approach outperforms direct translation from Persian to Spanish. Our best result is the pivot system which scores higher than direct translation by (1.12) BLEU points.

Keywords: statistical machine translation, direct translation approach, pivot language translation approach, parallel corpus

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Authors: Sachinkumar Patil, Sonali Patil, Shitalkumar Patil

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Nanoparticles played important role in the biomedicine. New advanced methods having great potential apllication in the diagnosis and therapy of cancer. Now a day’s magnetic nanoparticles used in cancer therapy. Cancer is the major disease causes death. Magnetic nanoparticles show response to the magnetic field on the basis of this property they are used in cancer therapy. Cancer treated with hyperthermia by using magnetic nanoparticles it is unconventional but more safe and effective method. Magnetic nanoparticles prepared by using different innovative techniques that makes particles in uniform size and desired effect. Magnetic nanoparticles already used as contrast media in magnetic resonance imaging. A magnetic nanoparticle has been great potential application in cancer diagnosis and treatment as well as in gene therapy. In this review we will discuss the progress in cancer therapy based on magnetic nanoparticles, mainly including magnetic hyperthermia, synthesis and characterization of magnetic nanoparticles, mechanism of magnetic nanoparticles and application of magnetic nanoparticles.

Keywords: magnetic nanoparticles, synthesis, characterization, cancer therapy, hyperthermia, application

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Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

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Authors: Tatyana Gavrilova, Vadim Onufriev

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Knowledge-based systems’ design requires the developer’s owning the advanced analytical skills. The efficient development of that skills within university courses needs a deep understanding of main pitfalls and drawbacks, which students usually make during their analytical work in form of visual modeling. Thus, it was necessary to hold an analysis of 5-th year students’ learning exercises within courses of 'Intelligent systems' and 'Knowledge engineering' in Saint-Petersburg Polytechnic University. The analysis shows that both lack of system thinking skills and methodological mistakes in course design cause the errors that are discussed in the paper. The conclusion contains an exploration of the issues and topics necessary and sufficient for the implementation of the improved practices in educational design for future curricula of teaching programs.

Keywords: knowledge based systems, knowledge engineering, students’ errors, visual modeling

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Nilson Barbieri, Bruno Matos Martins, Gabriel de Sant'Anna Vitor Barbieri

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In more complex systems, such as automotive gearbox, a rigorous treatment of the data is necessary because there are several moving parts (gears, bearings, shafts, etc.), and in this way, there are several possible sources of errors and also noise. The basic objective of this work is the detection of damage in automotive gearbox. The detection methods used are the wavelet method, the bispectrum; advanced filtering techniques (selective filtering) of vibrational signals and mathematical morphology. Gearbox vibration tests were performed (gearboxes in good condition and with defects) of a production line of a large vehicle assembler. The vibration signals are obtained using five accelerometers in different positions of the sample. The results obtained using the kurtosis, bispectrum, wavelet and mathematical morphology showed that it is possible to identify the existence of defects in automotive gearboxes.

Keywords: automotive gearbox, mathematical morphology, wavelet, bispectrum

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