https://reunir.unir.net/handle/123456789/12807 2024-11-04T17:29:08Z 2024-11-04T17:29:08Z García, Vicente Wu, Shaofei https://reunir.unir.net/handle/123456789/12841 2022-05-19T06:46:43Z

Editor’s Note García, Vicente; Wu, Shaofei The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:56:59Z No. of bitstreams: 1 ijimai_6_4_0.pdf: 490509 bytes, checksum: 85e87cc3d0eb1adf225a8da55469b83c (MD5); Made available in DSpace on 2022-04-08T09:56:59Z (GMT). No. of bitstreams: 1 ijimai_6_4_0.pdf: 490509 bytes, checksum: 85e87cc3d0eb1adf225a8da55469b83c (MD5)

Zang, Jingfeng Xu, Ningxue Liu, Riu Shi, Yuhuan https://reunir.unir.net/handle/123456789/12840 2022-04-08T09:52:20Z

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion Zang, Jingfeng; Xu, Ningxue; Liu, Riu; Shi, Yuhuan Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:52:20Z No. of bitstreams: 1 ijimai_6_4_14.pdf: 1449944 bytes, checksum: 78c7983b43bb2495d53e4e396d24648e (MD5); Made available in DSpace on 2022-04-08T09:52:20Z (GMT). No. of bitstreams: 1 ijimai_6_4_14.pdf: 1449944 bytes, checksum: 78c7983b43bb2495d53e4e396d24648e (MD5)

Xu, Guiming https://reunir.unir.net/handle/123456789/12839 2022-04-08T09:46:27Z

The Construction Site Management of Concrete Prefabricated Buildings by ISM-ANP Network Structure Model and BIM under Big Data Text Mining Analytic Network Process (ANP) Xu, Guiming In the construction industry, prefabricated buildings have developed rapidly in recent years due to their various excellent properties. To expand the application of big data text mining and Building Information Model (BIM) in prefabricated building construction, with concrete as a form of expression, the construction management of concrete prefabricated buildings is discussed. Based on the Interpretative Structural Model (ISM) and Analytic Network Process (ANP), the importance of the safety factors on the construction sites of concrete prefabricated buildings are assessed. Based on BIM, an optimized construction management platform for concrete prefabricated buildings is built, whose realization effects are characterized. The results show that prefabricated buildings have developed rapidly from 2017 to 2019. Compared with traditional buildings, they can significantly reduce the waste of resources and energy, and the savings of water resource utilization can reach 80%. Among the various safety impact elements, construction management has the greatest impact on construction safety, and the corresponding weight value is 0.3653. The corresponding weight of construction personnel is 0.2835, the corresponding weight of construction objects is 0.1629, the corresponding weight of construction technology is 0.1436, and the corresponding weight of construction environment is 0.0448. This building construction management platform is able to control the construction progress in real-time and avoid the occurrence of construction safety accidents. The final layout of the construction site shows a good effect, and the deviation between the actual construction schedule and the expected construction schedule is small, which is of great significance for the smooth development of concrete prefabricated buildings. This is a catalyst for the future development of concrete prefabricated buildings and the application of big data technology. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:46:27Z No. of bitstreams: 1 ijimai_6_4_15.pdf: 979715 bytes, checksum: 14f5a152ff648b1f2ce6d7e6728b1d72 (MD5); Made available in DSpace on 2022-04-08T09:46:27Z (GMT). No. of bitstreams: 1 ijimai_6_4_15.pdf: 979715 bytes, checksum: 14f5a152ff648b1f2ce6d7e6728b1d72 (MD5)

Li, You Wang, Juan Ji, Yiming https://reunir.unir.net/handle/123456789/12838 2022-04-08T09:23:42Z

Function Analysis of Industrial Robot under Cubic Polynomial Interpolation in Animation Simulation Environment Li, You; Wang, Juan; Ji, Yiming In order to study the effect of cubic polynomial interpolation in the trajectory planning of polishing robot manipulator, firstly, the articular robot operating arm is taken as the research object, and the overall system of polishing robot operating arm with 7 degrees of freedom is constructed. Then through the transformation of space motion and pose coordinate system, Denavit-Hartenberg (D-H) Matrix is introduced to describe the coordinate direction and parameters of the adjacent connecting rod of the polishing robot, and the kinematic model of the robot is built, and the coordinate direction and parameters of its adjacent link are described. A multi-body Dynamic simulation software, Automatic Dynamic Analysis of Mechanical Systems (ADAMS), is used to analyze the kinematic simulation of the robot operating arm system. Finally, the trajectory of the robot manipulator is planned based on the cubic polynomial difference method, and the simulation is verified by Matrix Laboratory (MATLAB). Through calculation, it is found that the kinematic model of polishing robot operating arm constructed in this study is in line with the reality; ADAMS software is used to generate curves of the rotation angles of different joint axes and the displacement of end parts of the polishing robot operating arm changing with time. After obtaining relevant parameters, they are put into the kinematic equation constructed in this study, and the calculated position coordinates are consistent with the detection results; moreover, the polishing robot constructed in this study can realize the functions of deburring, polishing, trimming, and turning table. MATLAB software is used to generate the simulation of the movement trajectory of the polishing robot operating arm, which can show the change curve of angle and angular velocity. The difference between the angle at which the polishing robot reaches the polishing position, the change curve of angular velocity, and the time spent before and after the path optimization is compared. It is found that after path optimization based on cubic polynomial, the change curve of the polishing robot's angle and angular velocity is smoother, and the time is shortened by 17.21s. It indicates that the cubic polynomial interpolation method can realize the trajectory planning of the polishing robot operating arm, moreover, the optimized polishing robot has a continuous and smooth trajectory, which can improve the working efficiency of the robot. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:23:42Z No. of bitstreams: 1 ijimai_6_4_11.pdf: 1076311 bytes, checksum: 37abc286f9822efb9dcabe5238f06721 (MD5); Made available in DSpace on 2022-04-08T09:23:42Z (GMT). No. of bitstreams: 1 ijimai_6_4_11.pdf: 1076311 bytes, checksum: 37abc286f9822efb9dcabe5238f06721 (MD5)

Heydarpour, F. Abbasi, E. Ebadi, M. J. Karbassi, S. M. https://reunir.unir.net/handle/123456789/12837 2022-04-08T09:17:48Z

Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks Heydarpour, F.; Abbasi, E.; Ebadi, M. J.; Karbassi, S. M. Cancer is an uncontrollable growth of abnormal cells in any tissue of the body. Many researchers have focused on machine learning and artificial intelligence (AI) based on approaches for cancer treatment. Dissimilar to traditional methods, these approaches are efficient and are able to find the optimal solutions of cancer chemotherapy problems. In this paper, a system of ordinary differential equations (ODEs) with the state variables of immune cells, tumor cells, healthy cells and drug concentration is proposed to anticipate the tumor growth and to show their interactions in the body. Then, an artificial neural network (ANN) is applied to solve the ODEs system through minimizing the error function and modifying the parameters consisting of weights and biases. The mean square errors (MSEs) between the analytical and ANN results corresponding to four state variables are 1.54e-06, 6.43e-07, 6.61e-06, and 3.99e-07, respectively. These results show the good performance and efficiency of the proposed method. Moreover, the optimal dose of chemotherapy drug and the amount of drug needed to continue the treatment process are achieved. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:17:48Z No. of bitstreams: 1 ijimai_6_4_2.pdf: 1191973 bytes, checksum: 343ea97ecbb1518af534e6b5dccfdb70 (MD5); Made available in DSpace on 2022-04-08T09:17:48Z (GMT). No. of bitstreams: 1 ijimai_6_4_2.pdf: 1191973 bytes, checksum: 343ea97ecbb1518af534e6b5dccfdb70 (MD5)

Magdin, Martin Držík, D. Reichel, J. Koprda, S . https://reunir.unir.net/handle/123456789/12836 2022-04-08T08:58:38Z

The Possibilities of Classification of Emotional States Based on User Behavioral Characteristics Magdin, Martin; Držík, D.; Reichel, J.; Koprda, S . The classification of user's emotions based on their behavioral characteristic, namely their keyboard typing and mouse usage pattern is an effective and non-invasive way of gathering user's data without imposing any limitations on their ability to perform tasks. To gather data for the classifier we used an application, the Emotnizer, which we had developed for this purpose. The output of the classification is categorized into 4 emotional categories from Russel's complex circular model - happiness, anger, sadness and the state of relaxation. The sample of the reference database consisted of 50 students. Multiple regression analyses gave us a model, that allowed us to predict the valence and arousal of the subject based on the input from the keyboard and mouse. Upon re-testing with another test group of 50 students and processing the data we found out our Emotnizer program can classify emotional states with an average success rate of 82.31%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:58:38Z No. of bitstreams: 1 ijimai_6_4_10.pdf: 1062017 bytes, checksum: b37a6b2c6c7d6758145e11aa052a7150 (MD5); Made available in DSpace on 2022-04-08T08:58:38Z (GMT). No. of bitstreams: 1 ijimai_6_4_10.pdf: 1062017 bytes, checksum: b37a6b2c6c7d6758145e11aa052a7150 (MD5)

Sánchez-Prieto, José Carlos Cruz-Benito, Juan Therón, Roberto García-Peñalvo, Francisco https://reunir.unir.net/handle/123456789/12835 2022-04-08T08:40:36Z

Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students Sánchez-Prieto, José Carlos; Cruz-Benito, Juan; Therón, Roberto; García-Peñalvo, Francisco In recent years, the use of more and more technology in education has been a trend. The shift of traditional learning procedures into more online and tech-ish approaches has contributed to a context that can favor integrating Artificial-Intelligence-based or algorithm-based assessment of learning. Even more, with the current acceleration because of the COVID-19 pandemic, more and more learning processes are becoming online and are incorporating technologies related to automatize assessment or help instructors in the process. While we are in an initial stage of that integration, it is the moment to reflect on the students' perceptions of being assessed by a non-conscious software entity like a machine learning model or any other artificial intelligence application. As a result of the paper, we present a TAM-based model and a ready-to-use instrument based on five aspects concerning understanding technology adoption like the AI-based assessment on education. These aspects are perceived usefulness, perceived ease of use, attitude towards use, behavioral intention, and actual use. The paper's outcomes can be relevant to the research community since there is a lack of this kind of proposal in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:40:35Z No. of bitstreams: 1 ijimai_6_4_8.pdf: 674624 bytes, checksum: 4af5d563c8d56c0f37fb2189ae2ccb1d (MD5); Made available in DSpace on 2022-04-08T08:40:36Z (GMT). No. of bitstreams: 1 ijimai_6_4_8.pdf: 674624 bytes, checksum: 4af5d563c8d56c0f37fb2189ae2ccb1d (MD5)

Muñoz Lezcano, Sergio López Hernández, Fernando Carlos Corbi, Alberto https://reunir.unir.net/handle/123456789/12834 2023-05-03T10:03:15Z

Data Science Techniques for COVID-19 in Intensive Care Units Muñoz Lezcano, Sergio; López Hernández, Fernando Carlos; Corbi, Alberto Data scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AI+IP+ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:26:44Z No. of bitstreams: 1 ijimai_6_4_1_0.pdf: 1529347 bytes, checksum: 4fa2a340718d14f8bdf39735c1674fc7 (MD5); Made available in DSpace on 2022-04-08T08:26:44Z (GMT). No. of bitstreams: 1 ijimai_6_4_1_0.pdf: 1529347 bytes, checksum: 4fa2a340718d14f8bdf39735c1674fc7 (MD5)

Jin, Xin Xiao, Yong Li, Shiqi Wang, Suying https://reunir.unir.net/handle/123456789/12833 2022-04-08T08:18:18Z

Deep Learning-based Side Channel Attack on HMAC SM3 Jin, Xin; Xiao, Yong; Li, Shiqi; Wang, Suying SM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:18:18Z No. of bitstreams: 1 ijimai_6_4_12.pdf: 1220803 bytes, checksum: be1a251509cad3220ce9b37f8440a5e1 (MD5); Made available in DSpace on 2022-04-08T08:18:18Z (GMT). No. of bitstreams: 1 ijimai_6_4_12.pdf: 1220803 bytes, checksum: be1a251509cad3220ce9b37f8440a5e1 (MD5)

Zhou, Shenghan Chen, Bang Zhang, Yue Liu, HouXiang Xiao, Yiyong Pan, Xing https://reunir.unir.net/handle/123456789/12816 2022-04-05T10:29:29Z

A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field Zhou, Shenghan; Chen, Bang; Zhang, Yue; Liu, HouXiang; Xiao, Yiyong; Pan, Xing As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T10:29:29Z No. of bitstreams: 1 ijimai_6_4_13.pdf: 1302128 bytes, checksum: d2a3fbe28ce1fbeef4eeca8c4caacbc6 (MD5); Made available in DSpace on 2022-04-05T10:29:29Z (GMT). No. of bitstreams: 1 ijimai_6_4_13.pdf: 1302128 bytes, checksum: d2a3fbe28ce1fbeef4eeca8c4caacbc6 (MD5)

García Nieto, Paulino José García-Gonzalo, Esperanza Sánchez Lasheras, Fernando Bernardo Sánchez, Antonio https://reunir.unir.net/handle/123456789/12815 2022-04-05T10:04:50Z

Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model García Nieto, Paulino José; García-Gonzalo, Esperanza; Sánchez Lasheras, Fernando; Bernardo Sánchez, Antonio The purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T10:04:50Z No. of bitstreams: 1 ijimai_6_4_4.pdf: 1114067 bytes, checksum: d4b8380c297df951f27b5f54089ec2f8 (MD5); Made available in DSpace on 2022-04-05T10:04:50Z (GMT). No. of bitstreams: 1 ijimai_6_4_4.pdf: 1114067 bytes, checksum: d4b8380c297df951f27b5f54089ec2f8 (MD5)

Rehman, I Ullah, S Khan, Dawar https://reunir.unir.net/handle/123456789/12814 2022-04-05T09:59:03Z

Multi Layered Multi Task Marker Based Interaction in Information Rich Virtual Environments Rehman, I; Ullah, S; Khan, Dawar Simple and cheap interaction has a key role in the operation and exploration of any Virtual Environment (VE). In this paper, we propose an interaction technique that provides two different ways of interaction (information and control) on complex objects in a simple and computationally cheap way. The interaction is based on the use of multiple embedded markers in a specialized manner. The proposed marker like an interaction peripheral works just like a touch paid which can perform any type of interaction in a 3D VE. The proposed marker is not only used for interaction with Augmented Reality (AR), but also with Mixed Reality. A biological virtual learning application is developed which is used for evaluation and experimentation. We conducted our experiments in two phases. First, we compared a simple VE with the proposed layered VE. Second, a comparative study is conducted between the proposed marker, a simple layered marker, and multiple single markers. We found the proposed marker with improved learning, easiness in interaction, and comparatively less task execution time. The results gave improved learning for layered VE as compared to simple VE. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:59:03Z No. of bitstreams: 1 ijimai_6_4_6.pdf: 1665600 bytes, checksum: 49124bc089a60d2f9492c14012ecbe4f (MD5); Made available in DSpace on 2022-04-05T09:59:03Z (GMT). No. of bitstreams: 1 ijimai_6_4_6.pdf: 1665600 bytes, checksum: 49124bc089a60d2f9492c14012ecbe4f (MD5)

Jain, Minni Jaswani, Aman Mehra, Ankita Mudgal, Laqshay https://reunir.unir.net/handle/123456789/12813 2022-04-05T09:54:02Z

Rumour Source Detection Using Game Theory Jain, Minni; Jaswani, Aman; Mehra, Ankita; Mudgal, Laqshay Social networks have become a critical part of our lives as they enable us to interact with a lot of people. These networks have become the main sources for creating, sharing and also extracting information regarding various subjects. But all this information may not be true and may contain a lot of unverified rumours that have the potential of spreading incorrect information to the masses, which may even lead to situations of widespread panic. Thus, it is of great importance to identify those nodes and edges that play a crucial role in a network in order to find the most influential sources of rumour spreading. Generally, the basic idea is to classify the nodes and edges in a network with the highest criticality. Most of the existing work regarding the same focuses on using simple centrality measures which focus on the individual contribution of a node in a network. Game-theoretic approaches such as Shapley Value (SV) algorithms suggest that individual marginal contribution should be measured for a given player as the weighted average marginal increase in the yield of any coalition that this player might join. For our experiment, we have played five SV-based games to find the top 10 most influential nodes on three network datasets (Enron, USAir97 and Les Misérables). We have compared our results to the ones obtained by using primitive centrality measures. Our results show that SVbased approach is better at understanding the marginal contribution, and therefore the actual influence, of each node to the entire network. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:54:02Z No. of bitstreams: 1 ijimai_6_4_5.pdf: 883619 bytes, checksum: 28c8eb329098e5d62a8071fbf03b8175 (MD5); Made available in DSpace on 2022-04-05T09:54:02Z (GMT). No. of bitstreams: 1 ijimai_6_4_5.pdf: 883619 bytes, checksum: 28c8eb329098e5d62a8071fbf03b8175 (MD5)

Dejene, Dawit Tiwari, Basant Tiwari, Vivek https://reunir.unir.net/handle/123456789/12812 2022-04-05T09:40:11Z

TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT Dejene, Dawit; Tiwari, Basant; Tiwari, Vivek The Internet of Things (IoT) is an emerging technology, which comprises wireless smart sensors and actuators. Nowadays, IoT is implemented in different areas such as Smart Homes, Smart Cities, Smart Industries, Military, eHealth, and several real-world applications by connecting domain-specific sensors. Designing a security model for these applications is challenging for researchers since attacks (for example, zero-day) are increasing tremendously. Several security methods have been developed to ensure the CIA (Confidentiality, Integrity, and Availability) for Industrial IoT (IIoT). Though these methods have shown promising results, there are still some security issues that are open. Thus, the security and authentication of IoT based applications become quite significant. In this paper, we propose TD2SecIoT (Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT), which incorporates Elliptic Curve Cryptography (ECC) and Nth-degree Truncated Polynomial Ring Units (NTRU) methods to ensure confidentiality and integrity. The proposed method has been evaluated against different attacks and performance measures (quantitative and qualitative) using the Cooja network simulator with Contiki-OS. The TD2SecIoT has shown a higher security level with reduced computational cost and time. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:40:11Z No. of bitstreams: 1 ijimai_6_4_16.pdf: 1476946 bytes, checksum: 16dcdb17bb3d3585f2a3c21f1d4786ac (MD5); Made available in DSpace on 2022-04-05T09:40:11Z (GMT). No. of bitstreams: 1 ijimai_6_4_16.pdf: 1476946 bytes, checksum: 16dcdb17bb3d3585f2a3c21f1d4786ac (MD5)

Cantabella, Magdalena Martínez-España, Raquel López, Belén Muñoz, Andrés https://reunir.unir.net/handle/123456789/12811 2022-04-05T09:13:52Z

A Fine-Grained Model to Assess Learner-Content and Methodology Satisfaction in Distance Education Cantabella, Magdalena; Martínez-España, Raquel; López, Belén; Muñoz, Andrés Learning Management System (LMS) platforms have led to a transformation in Universities in the last decade, helping them to adapt and expand their services to new technological challenges. These platforms have made possible the expansion of distance education. A current trend in this area is focused on the evaluation and improvement of the students’ satisfaction. In this work a new tool to assess student satisfaction using emoticons (smileys) is proposed to evaluate the quality of the learning content and the methodology at unit level for any course and at any time. The results indicate that the assessment of student satisfaction is sensitive to the period when the survey is performed and to the student’s study level. Moreover, the results of this new proposal are compared to the satisfaction results using traditional surveys, showing different results due to a more accuracy and flexibility when using the tool proposed in this work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:13:52Z No. of bitstreams: 1 ijimai_6_4_9.pdf: 844527 bytes, checksum: 5678413b7c54c73f979a94a5d43c68e4 (MD5); Made available in DSpace on 2022-04-05T09:13:52Z (GMT). No. of bitstreams: 1 ijimai_6_4_9.pdf: 844527 bytes, checksum: 5678413b7c54c73f979a94a5d43c68e4 (MD5)

Mahmoud, Karar Abdel-Nasser, Mohamed Kashef, Heba Puig, Domenec Lehtonen, Matti https://reunir.unir.net/handle/123456789/12810 2022-04-05T09:08:16Z

Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics Mahmoud, Karar; Abdel-Nasser, Mohamed; Kashef, Heba; Puig, Domenec; Lehtonen, Matti In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:08:16Z No. of bitstreams: 1 ijimai_6_4_17.pdf: 963471 bytes, checksum: 044ebe7277c34bcd250cf9b400fdfc0a (MD5); Made available in DSpace on 2022-04-05T09:08:16Z (GMT). No. of bitstreams: 1 ijimai_6_4_17.pdf: 963471 bytes, checksum: 044ebe7277c34bcd250cf9b400fdfc0a (MD5)

Das, Apangshu Pradhan, Sambhu Nath https://reunir.unir.net/handle/123456789/12809 2022-04-05T09:01:12Z

An Elitist Non-Dominated Multi-Objective Genetic Algorithm Based Temperature Aware Circuit Synthesis Das, Apangshu; Pradhan, Sambhu Nath At sub-nanometre technology, temperature is one of the important design parameters to be taken care of during the target implementation for the circuit for its long term and reliable operation. High device package density leads to high power density that generates high temperatures. The temperature of a chip is directly proportional to the power density of the chip. So, the power density of a chip can be minimized to reduce the possibility of the high temperature generation. Temperature minimization approaches are generally addressed at the physical design level but it incurs high cooling cost. To reduce the cooling cost, the temperature minimization approaches can be addressed at the logic level. In this work, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) based multi-objective heuristic approach is proposed to select the efficient input variable polarity of Mixed Polarity Reed-Muller (MPRM) expansion for simultaneous optimization of area, power, and temperature. A Pareto optimal solution set is obtained from the vast solution set of 3n (‘n’ is the number of input variables) different polarities of MPRM. Tabular technique is used for input polarity conversion from Sum-of-Product (SOP) form to MPRM form. Finally, using CADENCE and HotSpot tool absolute temperature, silicon area and power consumption of the synthesized circuits are calculated and are reported. The proposed algorithm saves around 76.20% silicon area, 29.09% power dissipation and reduces 17.06% peak temperature in comparison with the reported values in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:01:12Z No. of bitstreams: 1 ijimai_6_4_3.pdf: 1460901 bytes, checksum: 851af4d29c4bcbb38d17213b7b1d39a7 (MD5); Made available in DSpace on 2022-04-05T09:01:12Z (GMT). No. of bitstreams: 1 ijimai_6_4_3.pdf: 1460901 bytes, checksum: 851af4d29c4bcbb38d17213b7b1d39a7 (MD5)

Prasad Mudigonda, Krishna Siva Sharma, Poonam https://reunir.unir.net/handle/123456789/12808 2022-04-05T08:56:48Z

Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet Prasad Mudigonda, Krishna Siva; Sharma, Poonam Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T08:56:48Z No. of bitstreams: 1 ijimai_6_4_7.pdf: 691431 bytes, checksum: ce25fc0f7aa8a30f00141ace94a4ce6a (MD5); Made available in DSpace on 2022-04-05T08:56:48Z (GMT). No. of bitstreams: 1 ijimai_6_4_7.pdf: 691431 bytes, checksum: ce25fc0f7aa8a30f00141ace94a4ce6a (MD5)