Search results for: intelligent automation

Authors: Sungho Kim, Jaechoul Shin, Yujin Baek, Nakseok Kim, Kyungnam Kim, Shinhaeng Jo

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The climate change and increase in heavy traffic have been accelerating damages that cause the problems such as pothole on asphalt pavement. Pothole causes traffic accidents, vehicle damages, road casualties and traffic congestion. A quick and efficient maintenance method is needed because pothole is caused by stripping and accelerates pavement distress. In this study, we propose a rapid and systematic pothole management by developing a pothole automated repairing equipment including a volume measurement system of pothole. Three kinds of cold mix asphalt mixture were investigated to select repair materials. The materials were evaluated for satisfaction with quality standard and applicability to automated equipment. The volume measurement system of potholes was composed of multi-sensor that are combined with laser sensor and ultrasonic sensor and installed in front and side of the automated repair equipment. An algorithm was proposed to calculate the amount of repair material according to the measured pothole volume, and the system for releasing the correct amount of material was developed. Field test results showed that the loss of repair material amount could be reduced from approximately 20% to 6% per one point of pothole. Pothole rapid automated repair equipment will contribute to improvement on quality and efficient and economical maintenance by not only reducing materials and resources but also calculating appropriate materials. Through field application, it is possible to improve the accuracy of pothole volume measurement, to correct the calculation of material amount, and to manage the pothole data of roads, thereby enabling more efficient pavement maintenance management. Acknowledgment: The author would like to thank the MOLIT(Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: automated equipment, management, multi-beam sensor, pothole

Procedia PDF Downloads 225

Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 80

Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

Procedia PDF Downloads 557

Authors: C. B. Masera, M. Imprialou, L. Budd, C. Morton

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Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e. excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in the urban road network, an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.

Keywords: connected vehicles, GLOSA, intelligent transport systems, vehicle-to-infrastructure communication

Procedia PDF Downloads 175

Authors: Luisa Cabezas, Karol Leal, Harold Mendoza, Fabio Trochez, Angel Lozada

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According to the 2030 Agenda for Sustainable Development Goals (SDG) in which equal importance is given to economic, social, and environmental dimensions where the equality and dignity of each human person is placed at the center of discussion, changing the development concept for one with more responsibility with the environment. It can be found that the energy and food systems are deeply entangled, and they are transversal to the 17 proposed SDG. In this order of ideas, a research project is carried out at Unidad Central del Valle del Cauca (UCEVA) with these two systems in mind, on one hand the energy transition and, on the other hand the transformation of agri-food systems. This project it could be achieved by automation and control irrigation system of medicinal, aromatic, and condimentary plants (MACP) area within the UCEVA Agroecological Farm and located in rural area of Tulua municipality (Valle del Cauca Department, Colombia). This system have allowed to stablish a remote monitoring of MACP area, including MACP moisture measurement, and execute the required system actions. In addition, the electrical system of irrigation control system is powered by a scalable photovoltaic solar energy system based on its specifications. Thus, the developed system automates and control de irrigation system, which is energetically self-sustainable and allows to satisfy the MACP area requirements. Is important to highlight that at MACP area, several medicinal, aromatic, and condimentary plants species are preserved to become primary sources for the pharmaceutical industry and, in many occasions, the only medicines for many communities. Therefore, preserve medicinal plants area would generates medicinal security and preserve cultural heritage as these plants are part of ancestral knowledge that penetrate academic and research communities at UCEVA campus to other society sectors.

Keywords: ancestral knowledge, climate change, medicinal plants, solar energy

Procedia PDF Downloads 242

Authors: Jérémie Ochin

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Soccer analytics are built on two data sources: the frame-by-frame position of each player on the terrain and the sequences of events, such as ball drive, pass, cross, shot, throw-in... With more than 2000 ball-events per soccer game, their precise and exhaustive annotation, based on a monocular video stream such as a TV broadcast, remains a tedious and costly manual task. State-of-the-art methods for spatio-temporal action detection from a monocular video stream, often based on 3D convolutional neural networks, are close to reach levels of performances in mean Average Precision (mAP) compatibles with the automation of such task. Nevertheless, to meet their expectation of exhaustiveness in the context of data analytics, such methods must be applied in a regime of high recall – low precision, using low confidence score thresholds. This setting unavoidably leads to the detection of false positives that are the product of the well documented overconfidence behaviour of neural networks and, in this case, their limited access to contextual information and understanding of the game: their predictions are highly unstructured. Based on the assumption that professional soccer players’ behaviour, pose, positions and velocity are highly interrelated and locally driven by the player performing a ball-action, it is hypothesized that the addition of information regarding surrounding player’s appearance, positions and velocity in the prediction methods can improve their metrics. Several methods are compared to build a proper representation of the game surrounding a player, from handcrafted features of the local graph, based on domain knowledge, to the use of Graph Neural Networks trained in an end-to-end fashion with existing state-of-the-art 3D convolutional neural networks. It is shown that the inclusion of information regarding surrounding players helps reaching higher metrics.

Keywords: fine-grained action recognition, human action recognition, convolutional neural networks, graph neural networks, spatio-temporal action recognition

Procedia PDF Downloads 33

Authors: Wei Yao

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A steerable and flexible drill is needed in orthopaedic surgery. For example, osteoarthritis is a common condition affecting millions of people for which joint replacement is an effective treatment which improves the quality and duration of life in elderly sufferers. Conventional surgery is not very accurate. Computer navigation and robotics can help increase the accuracy. For example, In Total Hip Arthroplasty (THA), robotic surgery is currently practiced mainly on acetabular side helping cup positioning and orientation. However, femoral stem positioning mostly uses hand-rasping method rather than robots for accurate positioning. The other case for using a flexible drill in surgery is Anterior Cruciate Ligament (ACL) Reconstruction. The majority of ACL Reconstruction failures are primarily caused by technical mistakes and surgical errors resulting from drilling the anatomical bone tunnels required to accommodate the ligament graft. The proposed new steerable drill system will perform orthopedic surgery through curved tunneling leading to better accuracy and patient outcomes. It may reduce intra-operative fractures, dislocations, early failure and leg length discrepancy by making possible a new level of precision. This technology is based on a robotically assisted, steerable, hand-held flexible drill, with a drill-tip tracking device and a multi-modality navigation system. The critical differentiator is that this robotically assisted surgical technology now allows the surgeon to prepare 'patient specific' and more anatomically correct 'curved' bone tunnels during orthopedic surgery rather than drilling straight holes as occurs currently with existing surgical tools. The flexible and steerable drill and its navigation system for femoral milling in total hip arthroplasty had been tested on sawbones to evaluate the accuracy of the positioning and orientation of femoral stem relative to the pre-operative plan. The data show the accuracy of the navigation system is better than traditional hand-rasping method.

Keywords: navigation, robotic orthopedic surgery, steerable drill, tracking

Procedia PDF Downloads 173

Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 413

Authors: Faith Eweluegim Enahoro-Ofagbe

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Water is essential for sustaining life. As a limited resource, effective water management is vital. Water scarcity has become more common due to the effects of climate change, land degradation, deforestation, and population growth, especially in rural communities, which are more susceptible to water-related issues such as water shortage, water-borne disease, et c., due to the unsuccessful implementation of water policies and projects in Nigeria. Since rural communities generate the majority of agricultural products, they significantly impact on water management for sustainability. The development of methods to advance this goal for residential and agricultural usage in the present and the future is a challenge for rural residents. This study evaluated rural water supply systems and irrigation management techniques to conserve water in Kwara State, North-Central Nigeria. Suggesting some measures to conserve water resources for sustainability, off-season farming, and socioeconomic security that will remedy water degradation, unemployment which is one of the causes of insecurity in the country, by considering the use of fabricated or locally made irrigation equipment, which are affordable by rural farmers, among other recommendations. Questionnaires were distributed to respondents in the study area for quantitative evaluation of irrigation methods practices. For physicochemical investigation, samples were also gathered from their available water sources. According to the study's findings, 30 percent of farmers adopted intelligent irrigation management techniques to conserve water resources, saving 45% of the water previously used for irrigation. 70 % of farmers practice seasonal farming. Irrigation water is drawn from river channels, streams, and unlined and unprotected wells. 60% of these rural residents rely on private boreholes for their water needs, while 40% rely on government-supplied rural water. Therefore, the government must develop additional water projects, raise awareness, and offer irrigation techniques that are simple to adapt for water management, increasing socio-economic productivity, security, and water sustainability.

Keywords: water resource management, sustainability, irrigation, rural water management, irrigation management technique

Procedia PDF Downloads 116

Authors: Chih-Jer Lin, Jian-Hong Hou

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Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

Procedia PDF Downloads 152

Authors: Amna Khaliq

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In this research paper, a comparison study has been undertaken between Canada and Denmark to analyze the education system between the countries in entrepreneurship. Denmark, a land of high wages and high taxes, and Canada, a land of immigrants and opportunities, have seen a positive relationship in entrepreneurs' growth. They are both considered one of the top ten countries to start a business and to have government support globally. However, education is entirely free to Danish students, including university degrees, compared to Canadians, which can further hurdle for Canadian millennials to grow in the business world—the business experience more growth with educated entrepreneurs with international backgrounds in new immigrants. Denmark has seen a gradual increase in female entrepreneurs over the decade but is still lower than OECD countries. Compassionate management and work-life balance are prioritized in Denmark, unlike in Canada. Danish are early adopters of technology and have excellent infrastructure to support the technology industry, whereas Canada is still a service-oriented and manufacturer-based country. 2018 has been the highest number of opening businesses for Canada and Denmark. Some companies offer high wages, hiring bonuses, flexible working hours, wellness, and mental health benefits during Pandemic to keep the companies running and keep their workers' morale high. Pandemic has taught consumers new patterns to shop online. It is essential now to use technology and automation to increase productivity in businesses. Only those companies will survive that are applying this strategy. The Pandemic has ultimately changed entrepreneurs' and employees' behavior in the business world. Along with Ph.D. professors, entrepreneurs should be allowed to teach at learning intuitions. Millennials turn out to be the most entrepreneurial generation in both countries. Entrepreneurship education will only be beneficial when students create businesses and learn from real-life experiences. Managing physical, mental, emotional, and psychological health while dealing with high pressure in entrepreneurship are soft skills learned through practical work.

Keywords: entrepreneurship education, millennials, pandemic, Denmark, Canada

Procedia PDF Downloads 108

Authors: Moet Moet Myint lay

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Professional learning communities provide ongoing professional development for teachers, where they become learning leaders and actively participate in school improvement. The development of professional knowledge requires a significant focus on professional competence in the work of teachers, and a solid foundation of professional knowledge and skills is necessary for members of society to become intelligent members. Continuing professional development (CPD) plays a vital role in improving educational outcomes, as its importance has been proven over the years. This article explores the need for CPD for teachers in Myanmar and the utility of professional learning communities in improving teacher quality. This study aims to explore a comprehensive understanding of professional learning communities to support the continuing professional development of teacher educators in improving the quality of education. The research questions are: (1) How do teacher educators in Myanmar understand the concept of professional learning communities for continuing professional development? (2) What CPD training is required for all teachers in teachers' colleges? Quantitative research methods were used in this study. Survey data were collected from 50 participants (teacher trainers) from five educational institutions. The analysis shows that professional learning communities when done well, can have a lasting impact on teacher quality. Furthermore, the creation of professional learning communities is the best indicator of professional development in existing education systems. Some research suggests that teacher professional development is closely related to teacher professional skills and school improvement. As a result of the collective learning process, teachers gain a deeper understanding of the subject matter, increase their knowledge, and develop their professional teaching skills. This will help improve student performance and school quality in the future. The lack of clear understanding and knowledge about PLC among school leaders and leads teachers to believe that PLC activities are not beneficial. Lack of time, teacher accountability, leadership skills, and negative attitudes of participating teachers were the most frequently cited challenges in implementing PLCs. As a result of these findings, educators and stakeholders can use them to implement professional learning communities.

Keywords: professional learning communities, continuing professional development, teacher education, competence, school improvement

Procedia PDF Downloads 63

Authors: Xiyue Wang, Shaoning Yan

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Cities in China today is faced with an increasingly serious river ecological crisis accompanying with the development of urbanization: waterlogging on account of the fragmented urban natural hydrological system; the limited ecological function of the hydrological system caused by a destruction of water system and waterfront ecological environment. Additionally, the eco-hydrological processes of rivers are affected by various environmental factors, which are more complex in the context of urban environment. Therefore, efficient hydrological monitoring and analysis tools, accurate and visual hydrological and hydraulic models are becoming more important basis for decision-makers and an important way for landscape architects to solve urban hydrological problems, formulating sustainable and forward-looking schemes. The study mainly introduces the river and flood analysis model based on the platform of Autodesk Civil 3D. Taking the Luanhe River in Qian'an City of Hebei Province as an example, the 3D models of the landform, river, embankment, shoal, pond, underground stream and other land features were initially built, with which the water transfer simulation analysis, river floodplain analysis, and river ecology analysis were carried out, ultimately the real-time visualized simulation and analysis of rivers in various hypothetical scenarios were realized. Through the establishment of digital hydrological and hydraulic model, the hydraulic data can be accurately and intuitively simulated, which provides basis for rational water system and benign urban ecological system design. Though, the hydrological and hydraulic model based on Autodesk Civil3D own its boundedness: the interaction between the model and other data and software is unfavorable; the huge amount of 3D data and the lack of basic data restrict the accuracy and application range. The hydrological and hydraulic model based on Autodesk Civil3D platform provides more possibility to access convenient and intelligent tool for urban planning and monitoring, a solid basis for further urban research and design.

Keywords: visualization, hydrological and hydraulic model, Autodesk Civil 3D, urban river

Procedia PDF Downloads 298

Authors: Kuo-Dung Chiou, Yen-Xue Chen, Chia-Ying Chang

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This study uses AI technology to establish an expert system and establish a fruit appearance database for pineapples and custard apples. It collects images based on appearance defects and fruit maturity. It uses deep learning to detect the location of the fruit and can detect the appearance of the fruit in real-time. Flaws and maturity. In addition, a hyperspectral camera was used to scan pineapples and custard apples, and the light reflection at different frequency bands was used to find the key frequency band for pectin softening in post-ripe fruits. Conducted a large number of multispectral image collection and data analysis to establish a database of Pineapple Custard Apple and Big Eyed Custard Apple, which includes a high-definition color image database, a hyperspectral database in the 377~1020 nm frequency band, and five frequency band images (450, 500, 670, 720, 800nm) multispectral database, which collects 4896 images and manually labeled ground truth; 26 hyperspectral pineapple custard apple fruits (520 images each); multispectral custard apple 168 fruits (5 images each). Using the color image database to train deep learning Yolo v4's pre-training network architecture and adding the training weights established by the fruit database, real-time detection performance is achieved, and the recognition rate reaches over 97.96%. We also used multispectral to take a large number of continuous shots and calculated the difference and average ratio of the fruit in the 670 and 720nm frequency bands. They all have the same trend. The value increases until maturity, and the value will decrease after maturity. Subsequently, the sub-bands will be added to analyze further the numerical analysis of sugar content and moisture, and the absolute value of maturity and the data curve of maturity will be found.

Keywords: hyperspectral image, fruit firmness, deep learning, automatic detection, automatic measurement, intelligent labor saving

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Authors: Zhang Dongqing

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Concern for students' mental health and the application of intelligent advanced technologies are driving changes in teaching models. The traditional teacher-centered classroom is beginning to transform into a student-centered smart interactive learning environment. Nowadays, smart classrooms are compatible with constructivist learning. This theory emphasizes the role of teachers in the teaching process as helpers and facilitators of knowledge construction, and students learn by interacting with them. The spatial design of classrooms is closely related to the teaching model and should also be developed in the direction of smart classroom design. The goal is to explore the impact of smart classroom layout on student-centered teaching environment and teacher-student interaction under the guidance of constructivist learning theory, by combining the design process and feedback analysis of the smart transformation project on the campus of Tongji University in Shanghai. During the research process, the theoretical basis of constructivist learning was consolidated through literature research and case analysis. The integration and visual field analysis of the traditional and transformed indoor floor plans were conducted using space syntax tools. Finally, questionnaire surveys and interviews were used to collect data. The main conclusions are as followed: flexible spatial layouts can promote students' learning effects and mental health; the interactivity of smart classroom layouts is different and needs to be combined with different teaching models; the public areas of teaching buildings can also improve the interactive learning atmosphere by adding discussion space. This article provides a data-based research basis for improving students' learning effects and mental health, and provides a reference for future smart classroom design.

Keywords: spatial layout, smart classroom, space syntax, renovation, educational environment

Procedia PDF Downloads 77

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

Procedia PDF Downloads 172

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

Procedia PDF Downloads 84

Authors: Dedie Tooy, Rio Kolibu, Rio Putra, Herry Frits Pinatik, Daniel P. M. Ludong

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This study presents the design and development of a smart greenhouse prototype tailored to optimize Pak choi (Brassica chinensis L.) cultivation in tropical rural climates. Pak choi, a high-demand leafy vegetable in Indonesia, often experiences suboptimal growth due to elevated temperatures and humidity. The objective of this research is to design and develop an intelligent greenhouse to optimize pak choi cultivation in tropical rural climates. The design of a smart greenhouse provides a controlled environment to stabilize these conditions, but managing fluctuating temperature, humidity, and light in tropical regions remains challenging. This system regulates critical environmental factors, including temperature, humidity, irrigation system, and light, creating optimal conditions for Pak Choi. The prototype's effectiveness was evaluated by monitoring growth indicators such as leaf weight, freshness, and moisture content, alongside the consistency of the internal climate compared to external conditions. Results indicate that the smart greenhouse supports superior crop growth, enhances yield quality, and reduces environmental resource consumption. The irrigation control system test was carried out for 40 days. Researchers observed the results of the automatic system working according to the sensor value readings. The results of the temperature control system test work: when the air temperature in the greenhouse is more than 33 degrees, the condensation pump will turn on, and when the temperature is below 32 degrees, the pump will automatically turn itself off. The cycle repeats continuously. The results achieved pak coy can live up to 40 days. As part of our ongoing research, we are actively considering integrating double-layered roofs to improve insulation and reduce external temperature fluctuations, which could further enhance the effectiveness of the smart greenhouse.

Keywords: smart greenhouse, horticulture, rural tropical climate, sustainable agriculture

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Authors: Sharda Kumari

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As the Covid-19 pandemic continues to leave its mark on the global business landscape, companies have had to adapt to new realities and find ways to sustain their operations amid social distancing measures, government restrictions, and heightened public health concerns. This unprecedented situation has placed considerable stress on both employees and employers, underscoring the need for innovative approaches to manage the risks associated with Covid-19 transmission in the workplace. In response to these challenges, the pandemic has accelerated the adoption of digital technologies, with an increasing preference for remote interactions and virtual collaboration. Customer relationship management (CRM) systems have risen to prominence as a vital resource for organizations navigating the post-pandemic world, providing a range of benefits that include acquiring new customers, generating insightful consumer data, enhancing customer relationships, and growing market share. In the context of pandemic management, CRM systems offer three primary advantages: (1) integration features that streamline operations and reduce the need for multiple, costly software systems; (2) worldwide accessibility from any internet-enabled device, facilitating efficient remote workforce management during a pandemic; and (3) the capacity for rapid adaptation to changing business conditions, given that most CRM platforms boast a wide array of remotely deployable business growth solutions, a critical attribute when dealing with a dispersed workforce in a pandemic-impacted environment. These advantages highlight the pivotal role of CRM systems in helping organizations remain resilient and adaptive in the face of ongoing global challenges.

Keywords: healthcare, CRM, customer relationship management, customer experience, digital transformation, pandemic response, patient monitoring, patient management, healthcare automation, electronic health record, patient billing, healthcare information systems, remote workforce, virtual collaboration, resilience, adaptable business models, integration features, CRM in healthcare, telehealth, pandemic management

Procedia PDF Downloads 104

Authors: Shreya Poddar

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Legal drafting and revising are recognized as highly demanding tasks for legal professionals. This paper introduces an approach to automate and refine these processes through the use of advanced Artificial Intelligence (AI). The method employs Large Language Models (LLMs), with a specific focus on 'Chain of Thoughts' (CoT) and knowledge injection via prompt engineering. This approach differs from conventional methods that depend on comprehensive training or fine-tuning of models with extensive legal knowledge bases, which are often expensive and time-consuming. The proposed method incorporates knowledge injection directly into prompts, thereby enabling the AI to generate more accurate and contextually appropriate legal texts. This approach substantially decreases the necessity for thorough model training while preserving high accuracy and relevance in drafting. Additionally, the concept of guardrails is introduced. These are predefined parameters or rules established within the AI system to ensure that the generated content adheres to legal standards and ethical guidelines. The practical implications of this method for legal work are considerable. It has the potential to markedly lessen the time lawyers allocate to document drafting and revision, freeing them to concentrate on more intricate and strategic facets of legal work. Furthermore, this method makes high-quality legal drafting more accessible, possibly reducing costs and expanding the availability of legal services. This paper will elucidate the methodology, providing specific examples and case studies to demonstrate the effectiveness of 'Chain of Thoughts' and knowledge injection in legal drafting. The potential challenges and limitations of this approach will also be discussed, along with future prospects and enhancements that could further advance legal work. The impact of this research on the legal industry is substantial. The adoption of AI-driven methods by legal professionals can lead to enhanced efficiency, precision, and consistency in legal drafting, thereby altering the landscape of legal work. This research adds to the expanding field of AI in law, introducing a method that could significantly alter the nature of legal drafting and practice.

Keywords: AI-driven legal drafting, legal automation, futureoflegalwork, largelanguagemodels

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Authors: Yelba Maria Carrillo

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The focus of this research study was to explore barriers, if any, faced by parents or legal guardians who are of Latinx background and speak Spanish as a primary language or are bilingual speakers of Spanish and English; barriers that limit their understanding of and involvement in their gifted child’s academic life. This study was guided by a qualitative case study design. The primary investigator hosted focus group interviews at a Magnet Middle School in Southern California. The groups consisted of 25 parents, or legal guardians of bilingual (English/Spanish) or former English learner students enrolled in a school serving 6th-8th grades. The primary investigator interviewed Latinx Spanish-speaking parents or legal guardians of gifted students regarding their perception of their child’s giftedness, parental involvement in schools, and fostering their child’s exceptional abilities. Parents and legal guardians described children as creative, intellectual, and highly intelligent. Key themes such as student performance, language proficiency, socio-emotional, and general intellectual ability were strong indicators of giftedness. Barriers such as language and education inhibited parent and legal guardian ability to understand their child’s giftedness, which resulted in their inability to adequately contribute to the development of their children’s talents and advocate for the appropriate services for their children. However, they recognized the importance of being involved in their child’s academic life and the importance of nurturing their ‘dón’ or ‘gift.’ La Familia is the foundation and core of Latinx culture; and, without a strong foundation, children lack guidance, confidence, and awareness to tap into their gifted abilities. Providing Latinx parents with the proper tools and resources to appropriately identify gifted characteristics and traits could lead to early identification and intervention for students in schools and at home.

Keywords: gifted education, gifted Latino students, Latino parent involvement, high ability students

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Cristian Cazacu, Bela Kobulniczky

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Even though nowadays the median area of the Bucharest’s Sector 1 owns one of the best reputations in terms of quality of life level, the problems in urban planning from the last twenty years, as well as those related to the urban environment, became more and more obvious and shrill. And all this happened as long as non-compliance with urban and spatial planning laws, corroborated with uncontrolled territorial expansion on certain areas and faulty management of public and private spaces were more acute. The action of all these factors has been felt more and more strongly in the territory in the last twenty years, generating the degradation of the quality of the urban environment and affecting in parallel the general level of the inhabitants¬’ quality of life. Our methodology is based on analyzing a wide range of environmental parameters and it is also based on using advanced resources and skills for mapping planning and environmental dysfunctions as well as the possibility of integrating information into GIS programs, all data sets corroborated with problems related to spatial planning management and inaccuracies of the urbanistic sector. In the end, we managed to obtain a calculated and realistic image of the dysfunctions and a quantitative view of their magnitude in the territory. We also succeeded to create a full general map of the degree of degradation of the urban environment by typologies of urban tissues. Moreover, the methods applied by us can also be used globally to calculate and create realistic images and intelligent maps over the quality of the environment in areas larger than this one. Our study shows that environmental degradation occurred differently in the urban tissues from our study area, depending on several factors, reviewing the faulty way in which the processes of recovery / urban regeneration of the gap in recent years have led to the creation of new territorial dysfunctions. The general, centralized results show that the analyzed space has a much wider range of problems than initially thought, although notoriety and social etiquette place them far above other spaces from the same city of study.

Keywords: environment, GIS, planning, urban tissues

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Authors: P. Geetha, R. S. D. Wahida Banu

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The crowning advances in Silicon based electronic technology have dominated the computation world for the past decades. The captivating performance of Si devices lies in sustainable scaling down of the physical dimensions, by that increasing device density and improved performance. But, the fundamental limitations due to physical, technological, economical, and manufacture features restrict further miniaturization of Si based devices. The pit falls are due to scaling down of the devices such as process variation, short channel effects, high leakage currents, and reliability concerns. To fix the above-said problems, it is needed either to follow a new concept that will manage the current hitches or to support the available concept with different materials. The new concept is to design spintronics, quantum computation or two terminal molecular devices. Otherwise, presently used well known three terminal devices can be modified with different materials that suits to address the scaling down difficulties. The first approach will occupy in the far future since it needs considerable effort; the second path is a bright light towards the travel. Modelling paves way to know not only the current-voltage characteristics but also the performance of new devices. So, it is desirable to model a new device of suitable gate control and project the its abilities towards capability of handling high current, high power, high frequency, short delay, and high velocity with excellent electronic and optical properties. Carbon nanotube became a thriving material to replace silicon in nano devices. A well-planned optimized utilization of the carbon material leads to many more advantages. The unique nature of this organic material allows the recent developments in almost all fields of applications from an automobile industry to medical science, especially in electronics field-on which the automation industry depends. More research works were being done in this area. This paper reviews the carbon nanotube field effect transistor with various gate configurations, number of channel element, CNT wall configurations and different modelling techniques.

Keywords: array of channels, carbon nanotube field effect transistor, double gate transistor, gate wrap around transistor, modelling, multi-walled CNT, single-walled CNT

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: Mehrzad Soltani, Pegah Rezaei

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In response to the escalating challenges posed by resource scarcity, urban congestion, and the dearth of green spaces, contemporary urban areas have undergone a remarkable transformation into smart cities. This evolution necessitates a strategic and forward-thinking approach to urban development, with the primary objective of diminishing and eventually eradicating dependence on non-renewable energy sources. This steadfast commitment to sustainable development is geared toward the continual enhancement of our global urban milieu, ensuring a healthier and more prosperous environment for forthcoming generations. This transformative vision has been meticulously shaped by an extensive research framework, incorporating in-depth field studies and investigations conducted at both neighborhood and city levels. Our holistic strategy extends its purview to encompass major cities and states, advocating for the realization of exceptional development firmly rooted in the principles of sustainable intelligence. At its core, this approach places a paramount emphasis on stringent pollution control measures, concurrently safeguarding ecological equilibrium and regional cohesion. Central to the realization of this vision is the widespread adoption of environmentally friendly materials and components, championing the cultivation of plant life and harmonious green spaces, and the seamless integration of intelligent lighting and irrigation systems. These systems, including solar panels and solar energy utilization, are deployed wherever feasible, effectively meeting the essential lighting and irrigation needs of these dynamic urban ecosystems. Overall, the transformation of urban areas into smart cities necessitates a holistic and innovative approach to urban development. By actively embracing sustainable intelligence and adhering to strict environmental standards, these cities pave the way for a brighter and more sustainable future, one that is marked by resilient, thriving, and eco-conscious urban communities.

Keywords: smart city, green urban, sustainability, urban management

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Authors: Samuel John, S. Sharanya

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Automation has become an important part of our life. It has been used to control home entertainment systems, changing the ambience of rooms for different events etc. One of the main parameters to control in a smart home is the atmospheric comfort. Atmospheric comfort mainly includes temperature and relative humidity. In homes, the desired temperature of different rooms varies from 20 °C to 25 °C and relative humidity is around 50%. However, it varies widely. Hence, automated measurement of these parameters to ensure comfort assumes significance. To achieve this, a fuzzy logic controller using Arduino was developed using MATLAB. Arduino is an open source hardware consisting of a 24 pin ATMEGA chip (atmega328), 14 digital input /output pins and an inbuilt ADC. It runs on 5v and 3.3v power supported by a board voltage regulator. Some of the digital pins in Aruduino provide PWM (pulse width modulation) signals, which can be used in different applications. The Arduino platform provides an integrated development environment, which includes support for c, c++ and java programming languages. In the present work, soft sensor was introduced in this system that can indirectly measure temperature and humidity and can be used for processing several measurements these to ensure comfort. The Sugeno method (output variables are functions or singleton/constant, more suitable for implementing on microcontrollers) was used in the soft sensor in MATLAB and then interfaced to the Arduino, which is again interfaced to the temperature and humidity sensor DHT11. The temperature-humidity sensor DHT11 acts as the sensing element in this system. Further, a capacitive humidity sensor and a thermistor were also used to support the measurement of temperature and relative humidity of the surrounding to provide a digital signal on the data pin. The comfort sensor developed was able to measure temperature and relative humidity correctly. The comfort percentage was calculated and accordingly the temperature in the room was controlled. This system was placed in different rooms of the house to ensure that it modifies the comfort values depending on temperature and relative humidity of the environment. Compared to the existing comfort control sensors, this system was found to provide an accurate comfort percentage. Depending on the comfort percentage, the air conditioners and the coolers in the room were controlled. The main highlight of the project is its cost efficiency.

Keywords: arduino, DHT11, soft sensor, sugeno

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Authors: Henda J Thomas, Cornelia PJ Harmse, Cecile Schultz

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Wearables are one of the technologies that will flourish within the fourth industrial revolution and digital transformation arenas, allowing employers to integrate collected data into organisational information systems. The study aimed to investigate whether wearable technology readiness can predict employees’ acceptance to wear wearables in the workplace. The factors of technology readiness predisposition that predict acceptance and positive attitudes towards wearable use in the workplace were examined. A quantitative research approach was used. The population consisted of 8 081 South African Department of Employment and Labour employees (DEL). Census sampling was used, and questionnaires to collect data were sent electronically to all 8 081 employees, 351 questionnaires were received back. The measuring instrument called the Technology Readiness and Acceptance Model (TRAM) was used in this study. Four hypotheses were formulated to investigate the relationship between readiness and acceptance of wearables in the workplace. The results found consistent predictions of technology acceptance (TA) by eagerness, optimism, and discomfort in the technology readiness (TR) scales. The TR scales of optimism and eagerness were consistent positive predictors of the TA scales, while discomfort proved to be a negative predictor for two of the three TA scales. Insecurity was found not to be a predictor of TA. It was recommended that the digital transformation policy of the DEL should be revised. Wearables in the workplace should be embraced from the viewpoint of convenience, automation, and seamless integration with the DEL information systems. The empirical contribution of this study can be seen in the fact that positive attitude emerged as a factor that extends the TRAM. In this study, positive attitude is identified as a new dimension to the TRAM not found in the original TA model and subsequent studies of the TRAM. Furthermore, this study found that Perceived Usefulness (PU) and Behavioural Intention to Use and (BIU) could not be separated but formed one factor. The methodological contribution of this study can lead to the development of a Wearable Readiness and Acceptance Model (WRAM). To the best of our knowledge, no author has yet introduced the WRAM into the body of knowledge.

Keywords: technology acceptance model, technology readiness index, technology readiness and acceptance model, wearable devices, wearable technology, fourth industrial revolution

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Authors: Badrane Nohayla

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In a global economic context marked by growing instability and persistent inflationary pressures, Moroccan companies are facing unprecedented challenges. With galloping inflation exerting increasing pressure on the Moroccan economy, it is becoming crucial for companies to rethink their cash management approach. In fact, this complex economic situation, marked by rising commodity costs, currency volatility and market uncertainty, requires an innovative strategic response. In this regard, the present article delves into how innovation in cash management can play a pivotal role in mitigating the destabilising effects of inflation while bolstering the financial resilience of Moroccan companies. The primary objective of this paper is to illuminate the innovative strategies that can be adopted to counteract inflationary pressures. It focuses on exploring advanced financial and technological approaches, such as the use of artificial intelligence for financial forecasting, the automation of cash management processes, and the implementation of hedging strategies to safeguard against price and interest rate fluctuations. Furthermore, in the Moroccan context, where recent inflation has heightened economic vulnerabilities, these innovative strategies are vital for optimising performance and ensuring businesses survival. By integrating these cutting-edge practices into their cash management frameworks, Moroccan companies can not only mitigate the immediate impacts of inflation on their operations but also position themselves more securely to withstand future challenges. In doing so, they enhance their capacity to navigate an uncertain economic landscape and seize sustainable growth opportunities, thereby strengthening their long-term resilience. It is worth noting that embracing innovative cash management is not merely a response to current economic challenges but a strategic investment in future-proofing businesses. By leveraging innovation, Moroccan companies can develop adaptive capabilities that will enhance their resilience to future crises, whether these stem from economic fluctuations or other external shocks. Thus, innovation emerges not just as an adjustment tool but as a critical strategic driver for thriving in a future where economic uncertainty may well become the norm.

Keywords: innovative cash management, inflation, resilience, financial risks, moroccan companies

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