Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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NMPC Based-Trajectory Tracking and Obstacle Avoidance for Mobile Robots
Get PDFThis paper presents the design of a Nonlinear Model Predictive Controller (NMPC) for a wheeled Omnidirectional Mobile Robot (OMR) in order to track a desired trajectory in the presence of previously unknown static and dynamic obstacles in the environment around the robot. A laser rangefinder sensor is used to detect the obstacles where each obstacle occupies numerous points of every sensor reading. The points that belong to each obstacle are then clustered together using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. This research introduces a novel approach to represent obstacles as multiple rotated ellipses, enabling a more accurate representation of complex obstacle shapes without overestimating their boundaries, thereby allowing the robot to navigate through narrow passages. CoppeliaSim robotic simulator is utilized to create the virtual simulation environment as well as simulate the OMR dynamics. MATLAB with the help of the CasADi toolbox is used for the process of the laser rangefinder readings and the implementation of NMPC, respectively. Â To validate the effectiveness and robustness of the proposed approach, three simulation scenarios are conducted, each involving distinct trajectories and varying densities of static and/or dynamic obstacles. The proposed control architecture exhibits remarkable performance, enabling the OMR to effectively navigate through narrow passages and avoid multiple static and dynamic obstacles while closely adhering to the desired trajectory
Shaping the academic productivity: Theory on the early scientific article writing among the lecturers
Get PDFThe study aims at uncovering the factors that influence the early productivity of lecturers in writing scientific articles. The main problem under review is how the early writing experience shapes the continuity of academic productivity. In conducting the study, the qualitative approach of Grounded Theory was used with the data gathered from in-depth interviews with 18 lecturers throughout Indonesia. The research participants were purposively selected and were interviewed in stages by means of theoretical sampling. Then, the data were analyzed by using ATLAS.ti, and the data validity was tested by using method and theory triangulation. The results of the study show that publication productivity does not take place naturally but is shaped through social intervention, personal strategy, technology mastery, academic literacy, reading habit, and target stipulation. These results highlight the need to foster a supportive academic ecosystem to sustain lecturers’ writing productivity over time
Retaining humorous content from marked stand-up comedy text
Get PDFIdentifying humor in stand-up comedy texts has distinct issues due to humor's subjective and context-dependent characteristics. This study introduces an innovative method for humor retention in stand-up comedy content by employing a pre-trained BERT model that has been fine-tuned for humor classification. The process commences with the collection and annotation of a varied assortment of stand-up comedy writings, categorized as hilarious or non-humorous, with essential comic elements like punchlines and setups highlighted to augment the model's comprehension of humor. The texts undergo preprocessing and tokenization to be ready for input into the BERT model. Upon refining the model using the annotated dataset, predictions regarding humor retention are generated for each text, yielding classifications and confidence scores that reflect the model's certainty in its predictions. The criterion for prediction confidence is set to categorize texts as "retaining humor." The results indicate that prediction confidence is a dependable metric for humor retention, with elevated confidence scores associated with enhanced accuracy in comedy classification. Nonetheless, the analysis reveals that text length does not affect the model's confidence much, contradicting the presumption that lengthier texts are more prone to comedy. The findings underscore the significance of environmental and linguistic elements in comedy detection, indicating opportunities for model enhancement. Future efforts will concentrate on augmenting the dataset to encompass a broader range of comic styles and integrating more contextual variables to improve prediction accuracy, especially in intricate or ambiguous comedic situation
Lessons from comedy group management in Yogyakarta: a case study of organizational management practices of “Double S†group as a microenterprise
Get PDFPerforming arts organizations at the micro level often face challenges threatening their sustainability. The big challenge is how good organizational management practices can be done with limited resources, local competition, and changing market dynamics. This research aims to examine and improve the effectiveness of micro-organizational management in the context of performing arts, focusing on a case study of the comedy group “Double S†in Yogyakarta. This research is exploratory, using a qualitative approach and case studies as an analytical framework. Data were collected through in-depth interviews with group members, direct observation of performances, and document analysis related to organizational management. Organizational management concepts, such as strategic planning, organizing, and leadership, were applied in the context of micro-performing arts. The results showed that comedy groups as micro organizations have unique characteristics of organizational management practices, namely, organizational management in the context of a very flexible comedy group and the emergence of the central role of the artist who doubles as the group manager. Although it is done with complete improvisation and tends to be unstructured, the flexibility of organizational management has become a strength for the comedy group. The flexibility of management allows the comedy group to adapt to various uncertainties. Microorganizations need managers who have improvisational skills in organizational management. This research contributes to understanding organizational management at the micro level in the performing arts industry. The practical implication is to guide similar performing arts groups to improve competitiveness and sustainability. This study can also serve as a reference for further research on micro-level performing arts organization management and its influence on the development of local performing arts
Impact of radicalism and terrorism through social media among youth in the Film The Lone Wolf Next Door
Get PDFThis research delves into the detrimental impacts of social media utilization among young individuals as a conduit for radicalism and terrorism propagation. Focusing on "The Lone Wolf Next Door," the study illustrates the journey of Alif Himawan, a high school student entrapped in jihad ideology disseminated through social platforms. Alif's trajectory escalates as he acquires bomb-making skills, culminating in church suicide bombings. The research aims to portray societal realities through cinematic portrayal, drawing from the 2016 Medan tragedy involving Ivan Armadi Hasugian as inspiration. By analyzing the filmmaking process of "The Lone Wolf Next Door," the study endeavors to shed light on pertinent issues. Employing a realism approach, the film is not only scrutinized as a cinematic entity but also serves as a discourse platform regarding realism's intersection with radicalism and terrorism. Methodologically, descriptive qualitative analysis facilitates reality reconstruction, utilizing primary and secondary data from literature reviews, observations, and interviews. The findings highlight the pivotal role of film in broadening deradicalization efforts targeting the youth demographic. In conclusion, "The Lone Wolf Next Door" not only serves as a cinematic narrative but also contributes to the discourse on countering radicalism, emphasizing the significance of media in societal intervention strategies
Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features
Get PDFCucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases
Photovoltaic Energy Anomaly Detection using Transformer Based Machine Learning
Get PDFThis study uses the Anomaly Transformer model to find anomalies in photovoltaic energy generation in Malang, Indonesia. The main background of this study is the lack of satellite monitoring in this region and the importance of annual data for electricity generation forecasting. Temperature scattered direct solar radiation, and hourly electricity production are all part of the dataset used which is only available since 2019. Anomalies were detected at 05.00 and 16.00 WIB, indicating instability in the energy supply due to high temperatures in the morning and heavy rain in the afternoon. Detection of these anomalies is important to improve the efficiency and reliability of photovoltaic systems, reduce operational costs, and reduce the risk of system failure. Indonesia has many challenges for photovoltaic energy generation due to its unique location, with many islands located close to the equator. The use of the Anomaly Transformer algorithm improves the accuracy of anomaly detection over conventional methods. This algorithm helps to find complex patterns in very large time series. The results show that the anomaly transformer model can effectively detect anomalous patterns. It offers ideas to improve the stability and efficiency of photovoltaic systems in Malang and other areas with comparable environmental conditions. Improved energy efficiency and environmental sustainability are the results of anomaly pattern detection
Altitude Controller Based on Artificial Neural Network Genetic Algorithm for a Quadcopter MAV
Get PDFMechanical systems with high dynamic complexity often face challenges due to unmodeled uncertainties and external perturbations, making effective control difficult. Therefore, new advanced, robust, intelligent control theories have been developed through the sudden advance of computational power in recent years. In this research work, these new theories of automatic control are used, mainly based on what is currently called Artificial Intelligence (AI) algorithms, to develop a novel altitude controller based on the theory of Genetic Algorithms (GA) and Artificial Neural Networks (ANN).Theperformance of the designed controller is evaluated by employing the numerical simulation model in MATLAB SIMULINK, which was created for the commercial MAV Mambo Parrot. The developed intelligent ANN-GA controller uses the Levenberg-Marquardt optimization method and a Genetic Algorithm (GA) to improve Artificial Neural Network performance. The initial PID gains are obtained according to the GA, generating optimal values that initialize the neural network and contribute to optimal performance of the ANN training through evaluation of (Mean Square Error) MSE and (Integral Time Absolute Error) ITAE; the ANN takes then, the adequate output and signals as data from input to calculate the required combination of gains as output for MAV altitude controller. Simulation results demonstrate that the self-tunable controller improves the settling time, decreasing by 31.6% compared to the original PID controller. The certainty of the implemented controller opens new routes for automatic control strategies based on artificial intelligence algorithms for the complex nonlinear dynamics of unmanned aircraft
Improving sustainability of precast concrete sandwich wall panels through stone waste aggregates and supplementary cementitious material
Get PDFThis study aims to enhance the sustainability of precast concrete sandwich wall panels by replacing 100% of natural aggregates with stone waste and 30% of cement with supplementary cementitious materials. The panels, consisting of two 60 mm thick concrete wythes reinforced with 1% steel fibers, were connected using basalt fiber-reinforced polymer (BFRP) connectors and separated by high-density expanded polystyrene (EPS) insulation (30 kg/m³). Full-scale panels were tested for flexural strength, showing that the inclusion of sustainable materials increased the failure load by 96% compared to conventional panels, with steel fiber-reinforced panels achieving a failure load of 110.5 kN. Panels incorporating stone waste aggregates demonstrated a 71% increase in strength compared to control samples. These results highlight that using stone waste and supplementary materials not only improves environmental sustainability but also enhances structural performance, making these panels a viable option for eco-friendly construction
Accelerating Convergence in Data Offloading Solutions: A Greedy-Assisted Genetic Algorithm Approach
Get PDFData offloading, a technique that distributes data across the network, is crucial for alleviating congestion and enhancing system performance. One challenge in this process is optimizing web caching, which can be modeled as a dynamic knapsack problem in edge networks. This study introduces a Greedy-Assisted Genetic Algorithm (GA-Greedy) to tackle this challenge, accelerating convergence and improving solution quality. The greedy heuristic is integrated into the GA at two stages: during initialization to create a superior starting population, and at the end of each iteration to refine solutions generated through genetic operations. The GA-Greedy’s effectiveness was evaluated using the IRcache dataset, focusing on hit ratio—an indicator of successful cache accesses that reduces network load and speeds up data retrieval. Results show that GA-Greedy outperforms traditional GA and standalone greedy algorithms, especially with smaller cache sizes. For instance, with a 3K cache size, the half-greedy GA achieved a hit ratio of 0.55, compared to 0.2 for the pure GA and 0.1 for the greedy algorithm. Similarly, the full-greedy GA reached a hit ratio of 0.45. By enhancing convergence and guiding the search, GA-Greedy enables more efficient data distribution in edge networks, reducing latency and improving user experience