Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Heterogeneous brand identities in Cross-IP mobile game collaborations: a case study of visual design characteristics
Full text linkThe rise of mobile games has opened a new potential market for brands, but there is a lack of research focusing on mobile game collaboration designs, as they tend to focus on other industries despite the mobile game industry having a high rate of collaborations. The objective of this study is to extend the subject of brand collaboration design analysis into the mobile gaming landscape. In addition, we will analyze how mobile game collaborations incorporate brand identity extensions of heterogeneous visual identities into distinctive designs, and the rationale of how these collaborations work well despite the contrasting brand identities. To analyze, we used a qualitative case study method. The case study includes three cases of mobile game collaborations chosen based on heterogeneity, originality, and utilization. The main findings of this paper are the design characteristics of the collaborative product, which consists of six attributes. This study can be used as a reference for the key design characteristics in heterogeneous mobile game collaboration designs in the future
Cross-Age Face Verification Using Generative Adversarial Networks (GAN) with Landmark Feature
Full text linkCross-age face verification is a complex problem in biometric recognition in terms of aging, a naturally changing face structure, and face landmark configuration changes over time. In this paper, a new cross-age face verification method is proposed with a Generative Adversarial Network (GAN) and a mix of landmark-based features. Realistic aging of a face with identity-specific landmarks, such as eyes, nose, and mouth, is generated for effective face recognition in a range of age groups. Performance testing with an in-house collected face dataset of 200 face images exhibited effectiveness in changing face configuration and face shape transformations, such as a fuller face thinning and thin face becoming fuller. Comparison with direct face verification showed increased values of similarity, such as 32.57% to 63.80%, reduced values of feature distance, such as 0.6743 to 0.3620, and improvement in accuracy for the ArcFace, VGG-Face, and Facenet architectures. ArcFace exhibited an improvement in accuracy with an increase in value from 82.64% to 86.02%, VGG-Face with an improvement in value from 76.23% to 80.57%, and Facenet with an improvement in value from 67.54% to 74.48%. These observations validate the effectiveness of the proposed method in overcoming age-related complications and improving cross-age face verification performance. In future work, we plan to investigate a larger dataset and model refinement to realize performance improvement and real-life biometric suitability
A Hybrid Adaptive Gradient-Based Sled Dog Optimizer for Enhanced Robotic Decision-Making in Industrial Applications
Full text linkAs autonomous robotic systems are increasingly used in industrial applications, there is a growing need to create efficient and automated decision-making capabilities that can work in complex environments with a range of possible actions. RL offers an effective way to train robotic agents. Still, conventional RL techniques tend to have issues with slow and unstable policy learning, poor convergence, and weak exploration-exploitation balance. To solve this problem, this paper develops a Hybrid optimization approach that incorporates reinforcement learning, deep learning, and metaheuristic optimization for more robust robotic control and adaptability. The new approach utilizes a Deep Q-Network with Experience Replay for learning policies. At the same time, an Adaptive Gradient-Based Sled Dog Optimizer is used to improve and optimize decision-making. Epsilon-greedy selection combined with Noisy Network is used for hybrid exploration-exploitation, which helps learning. The effectiveness of the proposed method was validated against five existing methods, which include Conservative Q-Learning, Behavior Regularized Actor-Critic, Implicit Q-Learning, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic, over the three benchmark robotic datasets of MuJoCo, D4RL, and OpenAI Gym Robotics Suite. The vast majority of results provide compelling support for the argument that the proposed approach consistently outperformed the baseline approaches in terms of accuracy, precision, recall, stability, speed of convergence, and degree of generalization. The improvement in performance was confirmed by validation methods such as analyzing confidence intervals and computing results of p-values
A Comparative Study of Fuzzy Logic Controller, ANFIS, and HHOPSO Algorithms in the LEACH Protocol for Optimising Energy Efficiency and Network Longevity in Wireless Sensor Networks
Full text linkThis research provides a thorough analysis of the algorithms used in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for Wireless Sensor Networks (WSNs) to apply Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Harris Hawks Optimisation-Particle Swarm Optimisation (HHOPSO). The primary aim of this paper is to compare and measure these methods by how they save energy, prolong the network’s lifetime and choose the best cluster heads. We look at major indicators such as First Node Death (FND) and the number of rounds when 80% and 50% of nodes are still working, by testing 100 simulated network nodes. The HHOPSO is shown to do a better job at keeping node batteries alive and, at length the network in operation than both Fuzzy Logic and ANFIS. Moreover, ANFIS is more effective than Fuzzy Logic, because it can learn better from data. It is found that HHOPSO helps LEACH become more efficient and effective, contributing new information about how to manage energy and network performance in Wireless Sensor Networks. The document shows the effectiveness of advanced algorithms in keeping sensor networks running longer and offers ideas on how to evaluate them in various network settings
Eco-design furniture and interior elements: aesthetics and innovation of splitting waste rattan weaving and production efficiency
Full text linkRattan is a natural material with high fiber content. Rattan is cut into various sizes and types to be utilized. The process of cutting and slicing generally leaves waste in the form of elongated rattan hearts of irregular shapes and sizes. The amount is large, almost 30%, and is not utilized. However, waste from the splitting process in the rattan industry is usually disposed of through burning, leading to substantial waste. Therefore, this study aims to reduce rattan splitting waste through weaving techniques and then use it as a basis for innovation in furniture products and interior elements. The study was conducted using an experimental research method to produce woven sheets. Karen weaving has been applied to materials with similar characteristics, such as lidi, vetiver, and bamboo. Data were obtained through observation, interviews, and documentation. Data analysis, with inferential analysis, tests the hypothesis by generalizing. Following the weaving experiment, natural coloring and interior product design were conducted. The design process included sketching, creating shop drawings, modeling, and bringing the design to life through prototypes. The results consist of woven rattan fiber strip sheets with a striped pattern, light brown color, textured, somewhat rigid, yet flexible enough to be folded or rolled lengthwise. Various techniques can be applied to crafting products, such as gluing, bending, rolling, folding, and sewing. The most effective application of woven rattan fiber sheets is fixed attachment on a flat surface with a flat direction. The contribution of research results can add alternative materials for the creation of craft products, furniture, and interiors made from rattan waste woven sheets. These rattan fiber strip sheets can be applied in 2D and 3D homeware product designs, serving decorative and protective functions
Emotion as interface and the cultural politics of synthetic empathy
Full text linkThis paper investigates how emotion functions as an interface in artificial intelligence (AI)-mediated communication systems, with a critical focus on the cultural politics embedded in synthetic empathy. Drawing from affect theory, critical communication studies, and posthumanist perspectives, the study employs a qualitative, discourse-analytical approach to examine how emotional responsiveness is simulated, packaged, and operationalized in human-machine interactions. Empirical cases include AI-powered therapeutic bots, emotionally adaptive voice assistants, and automated customer service agents. The analysis reveals that synthetic empathy, rather than reflecting genuine emotional understanding, primarily serves as a mechanism for behavioral optimization aligned with neoliberal market logics. Emotion, when coded into technological interfaces, becomes a regulatory tool—modulating user engagement while concealing asymmetries in care, power, and agency. Furthermore, the cultural scripting of empathy in AI systems tends to reproduce dominant affective norms, marginalize non-normative emotional expressions, and depoliticize the labor of care, thus reinforcing structural inequities under the guise of affective neutrality. The contribution of this paper lies in its critical interrogation of emotional design as a site of power negotiation in digital systems, highlighting how affective interfaces participate in broader sociotechnical processes of commodification and control. By situating synthetic empathy within cultural, ethical, and political frameworks, the study offers a novel theoretical lens for understanding the implications of emotional AI. It calls for a reimagining of emotional mediation in AI that prioritizes cultural specificity, relational ethics, and the recognition of human vulnerability—thereby contributing to the development of more just and accountable communicative technologies in the digital age
Study and Analysis of Adaptive PI Control for Pitch Angle on Wind Turbine System
Full text linkIn the current work, a study is proposed using the engineering program MATLAB through computer tests of a simulation model for modifying the tilt angle in wind turbines, with a study of the effect of changing the angle of the wind turbine on the mechanical energy resulting from changing wind speed. Variable wind speeds reduce turbine efficiency; pitch control mitigates this. A PI-based pitch controller adjusts blade angles to maintain optimal ?.20 kW model achieved 15% higher power output at variable speeds. ? (tip-speed ratio) and Cp, ? the ratio of blade tip speed to wind speed, determines turbine efficiency. Unlike prior fixed-speed models, our variable-speed design adapts to turbulent winds via real-time pitch adjustment. This approach aids in stabilizing grid integration for renewable energy systems. While pitch control improves turbine efficiency, existing studies lack real-time adaptive strategies for variable wind speeds. our work optimizes pitch angles dynamically using MATLAB simulations. We propose a data-driven pitch control model for 5 kW and 20 kW turbines, validated under turbulent wind conditions. This study aims to maximize power output by correlating pitch angle (?) and tip-speed ratio (?) via MATLAB simulations. As a research contribution, the turbine characteristic curve is examined, as changes occur with changes in lambda, and the Cp Max is obtained at the optimal lambda. Assuming that beta is chosen from the curves to determine how it changes and its effect on operation at a given Cp, a given lambda is determined from the curve. Torque can be recognized as the first variable, both mathematically and physically. A change in torque affects speed, and thus affects lambda. Since there is a relationship between turbine speed and wind speed with lambda, turbine speed also depends on mechanical speed. The aim of the study is to design and build a simulation model using a mathematical representation of a wind turbine to study the effect of tilt angle control on handling changes in wind speed. The research contributions include the design of two models: one with a capacity of 5 kW and the other with a capacity of 20 kW. The first model uses a constant speed, while the second uses a variable wind speed. To stabilize the output at rated power, the turbine is angled. Using the wind turbine simulation model and some proposed tests, we can determine the behavior of the system as speed changes
Performance Enhancement of BLDC Motor Drive Systems Using Fuzzy Logic Control and PID Controller for Improved Transient Response and Stability
Full text linkCurrently, systems generally need control units, which requires designing them to analyze the behavior of the system when there are suitable characteristics of the motor according to the required application. The electric motor is very important in many applications and is widely used because of the high-efficiency mechanical power, small sizes, and relatively high torques that these electrical machines have. Improving the performance of systems requires control units, which are of the types of traditional PID, expert Fuzzy, and intelligent control systems. Two systems were proposed, a system that relies on a traditional control unit and a system that relies on fuzzy logic to improve and raise the efficiency of performance and handle system fluctuations resulting from disturbances and different operating conditions. Simulation tests were conducted using MATLAB. The effectiveness of the proposed controllers is evaluated through measurement criteria including efficiency improvements, torque ripple reduction, or settling time. Simulation results for both the closed-loop system using the conventional controller and the expert controller showed that the improvement in system performance can be determined according to criteria that include response speed as well as the overshoot and undershoot rates. Specifically, the settling time using the conventional controller was 3.05 msec. The rise time using the conventional controller was 205.406 msec, while using the expert controller it was 205.406 msec. The overshoot rate (%) using the conventional controller was 18.452%, while using the expert controller it was 6.989%. The undershoot rate using the conventional controller was 6.633%, while using the expert controller it was 1.987%
Recent Advances in Energy-Efficient Fractional-Order PID Control for Industrial PLC-Based Automation: A Review
Full text linkThrough intelligent control and data-driven decision-making, Industry 4.0 transforms industrial automation by combining the digital, physical, and virtual worlds. The use of advanced control techniques, especially Fractional-Order PID (FOPID) controllers, has drawn a lot of attention due to the rising need for accurate and energy-efficient industrial automation. By examining recent developments in the application of energy-efficient FOPID controllers for Programmable Logic Controller (PLC) based automation systems, this review tries to bridge a gap in the body of literature. The study thoroughly examines more than ten years of research, classifying contributions according to optimization, fractional calculus approximations, and control design techniques. The reported results from various studies are compared using key performance indicators like energy consumption, ISE, ITAE, and IAE. The results show that FOPID controllers continuously perform better than classical PID in terms of energy efficiency, robustness, and control accuracy. However, there are still difficulties in striking a balance between real-time constraints and computational complexity, particularly in industrial settings. This review emphasizes how FOPID controllers can be used to achieve automation that is Industry 4.0 compatible, adaptive, and energy-efficient. It also emphasizes the necessity of future studies into hybrid optimization and lightweight implementation for nextgeneration PLC systems, as well as the need for standardized benchmarking frameworks
An Integrated Deep Learning Framework Combining LSTM-CRF, GRU-CRF, and CNN-CRF with Word Embedding Techniques for Arabic Named Entity Recognition
Full text linkNamed entity recognition (NER) is the main function of natural language processing (NLP) and has many applications. Arabic NER systems aim to identify and classify Arabic NEs in Arabic text, which provide unique problems due to the language's complex morphology and syntactic structures. This paper provides an integrated deep learning system that incorporates three deep learning architectures—LSTM-CRF, GRU-CRF, and CNN-CRF—as well as three word embedding techniques: GloVe, Word2Vec, and FastText, all trained on Arabic corpus. To develop NER state-of-the-art in Arabic language, the present paper proposed a 3-stage process of pre-processing, feature extraction, and a combination of various deep network schemes. In the preprocessing section, operations such as removing irrelevant words, correcting words, etc. will be used to improve the system's efficiency. In the feature extraction section, three-word embedding methods, Glove, word2vec, and fasttext, which are trained with Arabic texts, are used, and finally, three LSTM-CRF, GRU-CRF, and CNN-CRF models are trained with each word embedding, and the results they are combined. Experimental results on benchmark dataset, ANERcorp show that our methodology is effective, with an accuracy of 94.39%, which outperforms other cutting-edge methods. However, combining multiple deep learning models with word embeddings increases computational complexity and resource requirements, potentially complicating implementation in resource-constrained contexts. Future efforts will concentrate on optimizing the framework to lower computational costs while keeping good performance