Search results for: multi robot system

Authors: Mario Gianni, Federico Nardi, Federico Ferri, Filippo Cantucci, Manuel A. Ruiz Garcia, Karthik Pushparaj, Fiora Pirri

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In this paper, we describe a Mixed-Initiative Operational Model (MIOM) which directly intervenes on the state of the functionalities embedded into a robot for Urban Search&Rescue (USAR) domain applications. MIOM extends the reasoning capabilities of the vehicle, i.e. mapping, path planning, visual perception and trajectory tracking, with operator knowledge. Especially in USAR scenarios, this coupled initiative has the main advantage of enhancing the overall performance of a rescue mission. In-field experiments with rescue responders have been carried out to evaluate the effectiveness of this operational model.

Keywords: mixed-initiative planning and control, operator control interfaces for rescue robotics, situation awareness, urban search, rescue robotics

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

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Authors: Matteo Rossini, Alberto Portioli Staudacher

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The market competition is moving from the single firm to the whole supply chain one because of increasing competition and growing need for operational efficiencies and customer orientation. Supply chain management allows companies to look beyond their organizational boundaries to develop and leverage resources and capabilities of their supply chain partners. This leads to create competitive advantages in the marketplace and because of this SCM has acquired strategic importance. Lean Approach is a management strategy that focuses on reducing every type of waste present in an organization. This approach is becoming more and more popular among supply chain managers. The supply chain application of lean approach is low diffused. It is not well studied which are the impacts of lean approach principles in a supply chain context. In literature there are only few studies simulating the lean approach performance in single products supply chain. This research work studies the impacts of lean principles implementation along a supply chain. To achieve this, a simulation model of a three-echelon multiproduct product supply chain has been built. Kanban system (and several priority policies) and setup time reduction degrees are implemented in the lean-configured supply chain to apply pull and lot-sizing decrease principles respectively. To evaluate the benefits of lean approach, lean supply chain is compared with an EOQ-configured supply chain. The simulation results show that Kanban system and setup-time reduction improve inventory stock level. They also show that logistics efforts are affected to lean implementation degree. The paper concludes describing performances of lean supply chain in different contexts.

Keywords: inventory policy, Kanban, lean supply chain, simulation study, supply chain management, planning

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Authors: Keyao Li, Sai On Cheung

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Incorporation of alternative dispute resolution (ADR) mechanism has been the main feature of current trend of construction project dispute resolution (CPDR). ADR approaches have been identified as efficient mechanisms and are suitable alternatives to litigation and arbitration. Moreover, the use of ADR in this multi-tiered dispute resolution process often leads to repeated evaluations of a same dispute. Multi-tiered CPDR may become a breeding ground for cognitive biases. When completed knowledge is not available at the early tier of construction dispute resolution, disputing parties may form preconception of the dispute matter or the counterpart. This preconception would influence their information processing in the subsequent tier. Disputing parties tend to search and interpret further information in a self-defensive way to confirm their early positions. Their imbalanced information collection would boost their confidence in the held assessments. Their attitudes would be hardened and difficult to compromise. The occurrence of cognitive bias, therefore, impedes efficient dispute settlement. This study aims to explore ways to minimize bias in CPDR. Based on a comprehensive literature review, three types of bias minimizing approaches were collected: strategy-based, attitude-based and process-based. These approaches were further operationalized into bias minimizing measures. To verify the usefulness and practicability of these bias minimizing measures, semi-structured interviews were conducted with ten CPDR third party neutral professionals. All of the interviewees have at least twenty years of experience in facilitating settlement of construction dispute. The usefulness, as well as the implications of the bias minimizing measures, were validated and suggested by these experts. There are few studies on cognitive bias in construction management in general and in CPDR in particular. This study would be the first of its type to enhance the efficiency of construction dispute resolution by highlighting strategies to minimize the biases therein.

Keywords: bias, construction project dispute resolution, minimization, multi-tiered, semi-structured interview

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Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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This research addresses the need for a comprehensive framework that can guide the design and assessment of multi-level public spaces and public realms and their impact on the built environment. The study aims to understand and measure the neural mechanisms involved in this process. By doing so, it can lay the foundation for vertical and volumetric urbanism and ensure consistency and excellence in the field while also supporting scientific research methods for urban design with cognitive neuroscientists. To investigate these aspects, the paper focuses on the neighborhood scale in Hong Kong, specifically examining multi-level public spaces and quasi-public spaces within both commercial and residential complexes. The researchers use predictive Artificial Intelligence (AI) as a methodology to assess and comprehend the applicability of the urban design framework for vertical and volumetric urbanism. The findings aim to identify the factors that contribute to successful public spaces within a vertical living environment, thus introducing a new typology of public spaces.

Keywords: vertical urbanism, scientific research methods, spatial cognition, urban design guideline

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Authors: Hefzollah Mohammadian, Ensieh Hajeb, Mohamad Baqer Heidari

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In this paper, for the protection and safety of tanks gases (flammable materials) and also due to the considerable economic value of the reservoir, the new system for the protection, the conservation and fire fighting has been cloned. The system consists of several parts: the Sensors to detect heat and fire with Nanotechnology (nano sensor), Barrier for isolation and protection from a range of two electronic zones, analyzer for detection and locating point of fire accurately, Main electronic board to announce fire, Fault diagnosis in different locations, such as relevant alarms and activate different devices for fire distinguish and announcement. An important feature of this system, high speed and capability of fire detection system in a way that is able to detect the value of the ambient temperature that can be adjusted. Another advantage of this system is autonomous and does not require human operator in place. Using nanotechnology, in addition to speeding up the work, reduces the cost of construction of the sensor and also the notification system and fire extinguish.

Keywords: analyser, barrier, heat resistance, general fault, general alarm, nano sensor

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Authors: Mehrab Masayeed Habib, Tasneem Sanjana, Ahmed Amin Rumel

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Automated car anti-collision system is a trending technology of science. A car anti-collision system is an automobile safety system. The aim of this paper was to describe designing a car anti-collision system device to reduce the severity of an accident. The purpose of this device is to prevent collision among cars and objects to reduce the accidental death of human. This project gives an overview of secure & smooth journey of car as well as the certainty of human life. This system is controlled by microcontroller PIC. Sharp distance sensor is used to detect any object within the danger range. A crystal oscillator is used to produce the oscillation and generates the clock pulse of the microcontroller. An LCD is used to give information about the safe distance and a buzzer is used as alarm. An actuator is used as automatic break and inside the actuator; there is a motor driver that runs the actuator. For coding ‘microC PRO for PIC’ was used and ’Proteus Design Suite version 8 Software’ was used for simulation.

Keywords: sharp distance sensor, microcontroller, MicroC PRO for PIC, proteus, actuator, automobile anti-collision system

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Yun-Tsuen Chen, Shih-Ting Huang, Pi-Fen Cheng, Heng-Hua Wang, Hui-Zhu Chen

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This paper discusses the experience of caring for a patient diagnosed with breast cancer and later received total mastectomy during a 2nd trimester pregnancy. She was hospitalized from January 31 to February 4, 2018. Using 'Gordon’s 11 Functional Health Patterns' through physical exams and interviews, the researcher assessed the patient’s physical and mental health and determined the patient to have anxiety, acute pain, and body image disturbance. After establishing a strong relationship with the patient, the researcher helped the patient express her anxiety and personal feelings. A multi-specialist team was formed to evaluate both the patient and her unborn child, before, during, and after surgery. This individualized care allowed the patient and her child to optimize the post-operative results. Aside from medication, the patient also received non-medicinal treatment, including improvement of sleep quality with body positioning, diaphragmatic breathing exercises for pain and stress relief after surgery. Throughout hospitalization, the patient’s physical and emotional needs were addressed daily with listening sessions and empathy. The patient’s husband was also incorporated in the patient’s recovery by teaching both he and the patient how to change the sterile wound dressing, which may have the added benefit of improving marital relationships through shared activities of nurturing. The patient was also given advice about how to improve self-confidence through clothing. Lastly, the patient was encouraged to join a support group for breast cancer patients. Through the sharing of experience in groups and within the family, the patient was helped to adapt to the change of her appearance and re-establish her self-confidence. This level of care expedited the patient’s return to her family life and role of being a mother.

Keywords: anxiety, body image disturbance, breast cancer during pregnancy, multi-specialist team

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Authors: Farheen Tabassum, Shoab Ahmed Khan

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The brutality of attacks on networks and decisive infrastructures are on the climb over recent years and appears to continue to do so. Distributed Denial of service attack is the most prevalent and easy attack on the availability of a service due to the easy availability of large botnet computers at cheap price and the general lack of protection against these attacks. Application layer DDoS attack is DDoS attack that is targeted on wed server, application server or database server. These types of attacks are much more sophisticated and challenging as they get around most conventional network security devices because attack traffic often impersonate normal traffic and cannot be recognized by network layer anomalies. Conventional techniques of single-hosted security systems are becoming gradually less effective in the face of such complicated and synchronized multi-front attacks. In order to protect from such attacks and intrusion, corporation among all network devices is essential. To overcome this issue, a collaborative intrusion detection system (CIDS) is proposed in which multiple network devices share valuable information to identify attacks, as a single device might not be capable to sense any malevolent action on its own. So it helps us to take decision after analyzing the information collected from different sources. This novel attack detection technique helps to detect seemingly benign packets that target the availability of the critical infrastructure, and the proposed solution methodology shall enable the incident response teams to detect and react to DDoS attacks at the earliest stage to ensure that the uptime of the service remain unaffected. Experimental evaluation shows that the proposed collaborative detection approach is much more effective and efficient than the previous approaches.

Keywords: Distributed Denial-of-Service (DDoS), Collaborative Intrusion Detection System (CIDS), Slowloris, OSSIM (Open Source Security Information Management tool), OSSEC HIDS

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

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Authors: Grzegorz Sierpiński, Ireneusz Celiński, Marcin Staniek

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The article discusses multi modal mobility in contemporary societies as a main planning and organization issue in the functioning of administrative bodies, a problem which really exists in the space of contemporary cities in terms of shaping modern transport systems. The article presents classification of available resources and initiatives undertaken for developing multi modal mobility. Solutions can be divided into three groups of measures–physical measures in the form of changes of the transport network infrastructure, organizational ones (including transport policy) and information measures. The latter ones include in particular direct support for people travelling in the transport network by providing information about ways of using available means of transport. A special measure contributing to this end is a trip planner. The article compares several selected planners. It includes a short description of the Green Travelling Project, which aims at developing a planner supporting environmentally friendly solutions in terms of transport network operation. The article summarizes preliminary findings of the project.

Keywords: mobility, modal split, multimodal trip, multimodal platforms, sustainable transport

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Authors: Nora Arbaoui, Rachid Tadili

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The experiment adopted a natural technique of heating and cooling an agricultural greenhouse to reduce the fuel consumption and CO2 emissions based on the heating of a transfer fluid that circulates inside the greenhouse through a solar copper coil positioned at the roof of the greenhouse. This experimental study is devoted to the performance evaluation of a solar heating system to improve the microclimate of a greenhouse during the cold period, especially in the Mediterranean climate. This integrated solar system for heating has a positive impact on the quality and quantity of the products under the study greenhouse.

Keywords: solar system, agricultural greenhouse, heating, storage

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Authors: Cassandra Pratt Romero, Andres Gomez de Silva Garza

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This paper describes a computational system designed to imitate human inspiration during musical composition. The system is called MIS (Musical Inspiration Simulator). The MIS system is inspired by media to which human beings are exposed daily (visual, textual, or auditory) to create new musical compositions based on the emotions detected in said media. After building the system we carried out a series of evaluations with volunteer users who used MIS to compose music based on images, texts, and audio files. The volunteers were asked to judge the harmoniousness and innovation in the system's compositions. An analysis of the results points to the difficulty of computational analysis of the characteristics of the media to which we are exposed daily, as human emotions have a subjective character. This observation will direct future improvements in the system.

Keywords: human inspiration, musical composition, musical composition by computer, theory of sensation and human perception

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Authors: Linda Torresin

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This paper is devoted to the Russian as a Foreign Language (RFL) textbook, which is understood as a fundamental element of the RFL system. The aim of the study is to explore the role of the RFL textbook in modern RFL teaching theories and practices. It is suggested that the RFL textbook is not a secondary factor but contributes to the advancement and rewriting of both RFL theories and practices. This study applies to the RFL textbook theory's recent pedagogical developments in education. Therefore, the RFL system is conceived as a complex adaptive system whose elements (teacher, textbook, students, etc.) interact in a dynamic network of interconnections. In particular, the author shows that the textbook plays a central role in the RFL system since it may change and even renew RFL teaching from both theoretical and practical perspectives. On the one hand, in fact, the use of an RFL textbook may impact teaching theories: that is, the textbook may either consolidate preexisting theories or launch new approaches. On the other hand, the RFL textbook may also influence teaching practices by reinforcing the preexisting ones or encouraging teachers to try new strategies instead. All this allows the RFL textbook, within the RFL complex adaptive system, to exert an influence on the specific teaching contexts in which Russian is taught, interacting with the other elements of the system itself. Through its findings, this paper contributes to the advancement of research on RFL textbook theory.

Keywords: adaptive system, foreign language textbook, teaching Russian as a foreign language, textbook of Russian as a foreign language

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Authors: Michal Kopcek, Tomas Skulavik, Michal Kebisek, Gabriela Krizanova

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The operation of nuclear power plants involves continuous monitoring of the environment in their area. This monitoring is performed using a complex data acquisition system, which collects status information about the system itself and values of many important physical variables e.g. temperature, humidity, dose rate etc. This paper describes a proposal and optimization of communication that takes place in teledosimetric system between the central control server responsible for the data processing and storing and the decentralized measuring stations, which are measuring the physical variables. Analyzes of ongoing communication were performed and consequently the optimization of the system architecture and communication was done.

Keywords: communication protocol, transmission optimization, data acquisition, system architecture

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Hafez Abdo, Rob Ackrill

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Renewable energy (RE) promotion in the UK relies on multiple policy instruments, which are required to overcome the path dependency pressures favouring fossil fuels. These instruments include targeted funding schemes and economy-wide instruments embedded in the tax code. The resulting complexity of incentives raises important questions around the coherence and effectiveness of these instruments for RE generation. This complexity is exacerbated by UK RE policy being nested within EU policy in a multi-level governance (MLG) setting. To gain analytical traction on such complexity, this study will analyse policies promoting the on-farm generation of energy for heat and power, from farm and food waste, via anaerobic digestion. Utilising both primary and secondary data, it seeks to address a particular lacuna in the academic literature. Via a localised, in-depth investigation into the complexity of policy instruments promoting RE, this study will help our theoretical understanding of the challenges that MLG and path dependency pressures present to policymakers of multi-dimensional policies.

Keywords: anaerobic digestion, energy, green, policy, renewable, tax, UK

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

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Authors: Han Wang, Zhuoxian Dai, Zhe Zhu, Hui Zhang, Zhenyu Zeng

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In traditional microscopic traffic simulation, various approaches have been suggested to implement the single-agent behaviors about lane changing and intelligent driver model. However, when it comes to very large metropolitan areas, microscopic traffic simulation requires more resources and become time-consuming, then macroscopic traffic simulation aggregate trends of interests rather than individual vehicle traces. In this paper, we describe the architecture and implementation of the actor system of microscopic traffic simulation, which exploits the distributed architecture of modern-day cloud computing. The results demonstrate that our architecture achieves high-performance and outperforms all the other traditional microscopic software in all tasks. To the best of our knowledge, this the first system that enables single-agent behavior in macroscopic traffic simulation. We thus believe it contributes to a new type of system for traffic simulation, which could provide individual vehicle behaviors in microscopic traffic simulation.

Keywords: actor system, cloud computing, distributed system, traffic simulation

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Authors: Minho Kwak, Suhwan Yun, Choonsoo Park

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Nose shape optimizations of high-speed train are performed for the improvement of aerodynamic characteristics. Based on the commercial train, KTX-Sancheon, multi-objective optimizations are conducted for the improvement of the side wind stability and the micro-pressure wave following the optimization for the reduction of aerodynamic drag. 3D nose shapes are modelled by the Vehicle Modeling Function. Aerodynamic drag and side wind stability are calculated by three-dimensional compressible Navier-Stokes solver, and micro pressure wave is done by axi-symmetric compressible Navier-Stokes solver. The Maxi-min Latin Hypercube Sampling method is used to extract sampling points to construct the approximation model. The kriging model is constructed for the approximation model and the NSGA-II algorithm was used as the multi-objective optimization algorithm. Nose length, nose tip height, and lower surface curvature are design variables. Because nose length is a dominant variable for aerodynamic characteristics of train nose, two optimization processes are progressed respectively with and without the design variable, nose length. Each pareto set was obtained and each optimized nose shape is selected respectively considering Honam high-speed rail line infrastructure in South Korea. Through the optimization process with the nose length, when compared to KTX Sancheon, aerodynamic drag was reduced by 9.0%, side wind stability was improved by 4.5%, micro-pressure wave was reduced by 5.4% whereas aerodynamic drag by 7.3%, side wind stability by 3.9%, micro-pressure wave by 3.9%, without the nose length. As a result of comparison between two optimized shapes, similar shapes are extracted other than the effect of nose length.

Keywords: aerodynamic characteristics, design variable, multi-objective optimization, train nose shape

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

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In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: constraint programming, factors considered in scheduling, machine learning, scheduling system

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Authors: M. Areekijseree, W. Pongsawat, M. Pumipaiboon, C. Thepsithar, S. Sengsai, T. Chuen-Im

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Morphological interaction of porcine cumulus-oocyte complexes (pCOCs) was investigated on in vitro condition using electron microscope (SEM and TEM). The totals of 1,923 oocytes were round in shape, surrounded by zona pellucida with layer of cumulus cells ranging between 59.29-202.14 µm in size. They were classified into intact-, multi-, partial cumulus cell layer oocyte, and completely denuded oocyte, at the percentage composition of 22.80% 32.70%, 18.60%, and 25.90 % respectively. The pCOCs classified as intact- and multi cumulus cell layer oocytes were further culturing at 37°C with 5% CO2, 95% air atmosphere and high humidity for 44 h in M199 with Earle’s salts supplemented with 10% HTFCS, 2.2 mg/mL NaHCO3, 1 M Hepes, 0.25 mM pyruvate, 15 µg/mL porcine follicle-stimulating hormone, 1 µg/mL LH, 1µg/mL estradiol with ethanol, and 50 µg/mL gentamycin sulfate. On electron microscope study, cumulus cells were found to stick their processes to secrete substance from the sac-shape end into zona pellucida of the oocyte and also communicated with the neighboring cells through their microvilli on the beginning of incubation period. It is believed that the cumulus cells communicate with the oocyte by inserting the microvilli through this gap and embedded in the oocyte cytoplasm before secreting substance, through the sac-shape end of the microvilli, to inhibit primary oocyte development at the prophase I. Morphological changes of the complexes were observed after culturing for 24-44 h. One hundred percentages of the cumulus layers were expanded and cumulus cells were peeling off from the oocyte surface. In addition, the round-shape cumulus cells transformed themselves into either an elongate shape or a columnar shape, and no communication between cumulus neighboring cells. After 44 h of incubation time, diameter of oocytes surrounded by cumulus cells was larger than 0 h incubation. The effect of hormones in culture medium is exerted by their receptors present in porcine oocyte. It is likely that all morphological changes of the complexes after hormone treatment were to allow maturation of the oocyte. This study demonstrated that the association of hormones in M199 could promote porcine follicle activation in 44 h in vitro condition. This culture system should be useful for studying the regulation of early follicular growth and development, especially because these follicles represent a large source of oocytes that could be used in vitro for cell technology.

Keywords: cumulus cells, electron microscopy, in vitro, porcine oocyte

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Authors: Alokananda Ghosh, Shraban Sarkar

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The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

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Authors: Christina Peristeri, Tobias Berg, Domenico Caridi, Paul Hutcheson, Robert Winstanley

Abstract:

In recent years the demand for rapid innovations in the automotive industry has led to the need for accelerated simulation procedures while retaining a detailed representation of the simulated phenomena. The project’s aim is to create a fast transient workflow for external aerodynamic CFD simulations of road vehicles. The geometry used was the SAE Notchback Closed Cooling DrivAer model, and the simulation results were compared with data from wind tunnel tests. The meshes generated for this study were of two types. One was a mix of polyhedral cells near the surface and hexahedral cells away from the surface. The other was an octree hex mesh with a rapid method of fitting to the surface. Three different grid refinement levels were used for each mesh type, with the biggest total cell count for the octree mesh being close to 1 billion. A series of steady-state solutions were obtained on three different grid levels using a pseudo-transient coupled solver and a k-omega-based RANS turbulence model. A mesh-independent solution was found in all cases with a medium level of refinement with 200 million cells. Stress-Blended Eddy Simulation (SBES) was chosen for the transient simulations, which uses a shielding function to explicitly switch between RANS and LES mode. A converged pseudo-transient steady-state solution was used to initialize the transient SBES run that was set up with the SIMPLEC pressure-velocity coupling scheme to reach the fastest solution (on both CPU & GPU solvers). An important part of this project was the use of FLUENT’s Multi-GPU solver. Tesla A100 GPU has been shown to be 8x faster than an Intel 48-core Sky Lake CPU system, leading to significant simulation speed-up compared to the traditional CPU solver. The current study used 4 Tesla A100 GPUs and 192 CPU cores. The combination of rapid octree meshing and GPU computing shows significant promise in reducing time and hardware costs for industrial strength aerodynamic simulations.

Keywords: CFD, DrivAer, LES, Multi-GPU solver, octree mesh, RANS

Procedia PDF Downloads 112

Authors: Fang Liu, JiaJia Yao, GuanLin Wu, ZuMaoLi, XueYan Yang, HePeng Zhang, ZhiPeng Sun, JunShuai Xue

Abstract:

The explosive increasing needs of data processing and information storage strongly drive the advancement of the binary logic system to multiple-valued logic system. Inherent negative differential resistance characteristic, ultra-high-speed switching time, and robust anti-irradiation capability make III-nitride resonant tunneling diode one of the most promising candidates for multi-valued logic devices. Here we report the monolithic integration of GaN resonant tunneling diodes in series to realize multiple negative differential resistance regions, obtaining at least three stable operating states. A multiply-by-three circuit is achieved by this combination, increasing the frequency of the input triangular wave from f0 to 3f0. The resonant tunneling diodes are grown by plasma-assistedmolecular beam epitaxy on free-standing c-plane GaN substrates, comprising double barriers and a single quantum well both at the atomic level. Device with a peak current density of 183kA/cm² in conjunction with a peak-to-valley current ratio (PVCR) of 2.07 is observed, which is the best result reported in nitride-based resonant tunneling diodes. Microwave oscillation event at room temperature was discovered with a fundamental frequency of 0.31GHz and an output power of 5.37μW, verifying the high repeatability and robustness of our device. The switching behavior measurement was successfully carried out, featuring rise and fall times in the order of picoseconds, which can be used in high-speed digital circuits. Limited by the measuring equipment and the layer structure, the switching time can be further improved. In general, this article presents a novel nitride device with multiple negative differential regions driven by the resonant tunneling mechanism, which can be used in high-speed multiple value logic field with reduced circuit complexity, demonstrating a new solution of nitride devices to break through the limitations of binary logic.

Keywords: GaN resonant tunneling diode, negative differential resistance, multiple-valued logic system, switching time, peak-to-valley current ratio

Procedia PDF Downloads 97

Authors: Md. Shafiullah, M. Shafiul Alam, Bandar Suliman Alsharif

Abstract:

Electrical quantities such as frequency, voltage, current are being fluctuated due to abnormalities in power system. Most of the abnormalities cause fluctuation in system frequency and sometimes extreme abnormalities lead to system blackout. To protect the system from complete blackout planned and proper islanding plays a very important role even in case of extreme abnormalities. Islanding operation not only helps stabilizing a faulted system but also supports power supplies to critical and important loads, in extreme emergency. But the islanding systems are weaker than integrated system so the stability of islands is the prime concern when an integrated system is disintegrated. In this paper, different impacts on a load rich island have been studied and a frequency based auto-load shedding scheme has been developed for sudden load addition, generation outage and combined effect of both to the island. The developed scheme has been applied to Khulna-Barisal Island to validate the effectiveness of the developed technique. Various types of abnormalities to the test system have been simulated and for the simulation purpose CYME PSAF (Power System Analysis Framework) has been used.

Keywords: auto load shedding, FS&FD relay, impact study, island, PSAF, ROCOF

Procedia PDF Downloads 451