Predicting the Remaining Useful Life (RUL) of turbofan engines is a vital component in the field of prognostics and health management (PHM). Accurate estimation of RUL enables timely maintenance and prevents sudden failures, which is essential for ensuring safety and reducing operational costs. This study introduces a novel hybrid deep learning model referred to as RCBLA (Residual Convolutional Bidirectional Long Short-Term Memory with Attention), designed specifically to handle the temporal and sequential characteristics of engine sensor data. The model architecture combines residual convolutional layers to enhance feature extraction, Temporal Convolutional Networks (TCN) for capturing long-range dependencies, Bidirectional LSTM layers for understanding both past and future contexts, and an attention mechanism to focus on the most critical time steps. Layer normalization is applied to improve convergence and training stability. The proposed model is evaluated using the FD001 and FD003 subsets of the C-MAPSS dataset. A piecewise linear degradation model is adopted, and only sensor measurements are used as input features. A custom learning rate scheduler is applied to optimize model convergence, and a dropout rate of 0.7 and 0.5 is used to prevent overfitting. The results demonstrate the model’s strong predictive capabilities, achieving RMSE values of 14.389 and 12.46 for FD001 and FD003, respectively. The scoring function yields 279.23 and 282.56 for FD001 and FD003, respectively. These outcomes highlight the model’s robustness and suitability for real-world RUL prediction, offering a reliable framework for improving PHM systems in aerospace applications.
The Stochastic Canadian Traveller Problem (SCTP) models adaptive routing on a known graph in which the availability of an edge is revealed only after the traveller reaches an incident vertex, while edge-specific blocking probabilities are known in advance. This paper investigates simulation-based strategies for minimizing the expected travel cost. After summarizing the Optimistic Policy, Hindsight Optimization, Optimistic Rollout, and established UCT-based approaches, the paper formalizes and evaluates two original heuristic variants of Optimistic UCT, UCTO2 and UCTP, which had previously been proposed and implemented in a master's thesis. UCTO2 replaces complete depth-oriented rollout trajectories with progressive tree expansion and batch rollout evaluation of selected nodes. UCTP augments UCTO2 with a pruning mechanism that retains the more promising of alternative tree nodes associated with the same graph vertex. The methods are evaluated on two families of randomly generated graphs with 50 vertices, using ten graphs per family and 500 stochastic realizations per graph. In the reported experiments, UCTP achieved lower mean travel costs than the Optimistic Policy and UCTO while requiring substantially less computation time than the other evaluated UCT variants. HOP and ORO nevertheless attained the lowest overall mean costs. The evidence therefore supports the complete UCTP configuration as a favourable finite-budget alternative within the UCT family, not as a generally superior SCTP policy.
This study analyses and simulates a snake robot's locomotion in complex environments, exploring the impact of serpentine gait parameters like magnitude, shape, and curvature on its motion in linear and circular gaits. The research reveals how small errors in the approximation process can accumulate, significantly affecting the robot's path. Ninety-six scenarios are tested to validate the model and demonstrate that CoppeliaSim's simulations closely match Maple's numerical simulations, especially for longer snakes. The study highlights the minimal role of wheel positions in altering the trajectory and underscores the significance of design parameters and friction in the robot's motion. This comprehensive analysis enhances our understanding of snake robot locomotion and informs their development for diverse applications.
Forecasting intermittent demands is challenging due to their irregular and unpredictable demand pattern. This makes the businesses unprepared for upcoming demands, where the conventional methods often fail to predict the demand occurrence pattern sufficiently. In this paper, we proposed a two-step approach, "UR2CUTE," (Using Repetitively 2 CNN for Unsteady Timeseries Estimation), employing Convolutional Neural Networks (CNNs) specifically designed to handle the unique challenges of intermittent time series. CNNs, known for their effectiveness in capturing spatial and temporal patterns in data, offer a promising area to improve forecast accuracy in predicting time series demand patterns. Our approach presents a combined process for intermittent demand forecasting. A CNN model is initially designed as a binary classifier to determine demand occurrence. Afterward, a distinct CNN model is employed to estimate the magnitude of the demand. This dual-phase approach improves forecasting accuracy in intermittent demands, specifically in predicting the non-demand (Zero-Demand). The suggested approach notably surpasses traditional forecasting techniques, including Croston's method, which is tailored for intermittent demand forecasting. It also outperforms other methods like XGboost, Random Forest, ETR, Prophet, and AutoArima, especially in predicting the lead time demand distribution for sporadic demands. The deployment of dual CNN models facilitates a deeper understanding of intermittent demand dynamics. This, in turn, enhances supply chain management effectiveness and efficiency, offering a robust solution to the complex challenges of intermittent demand forecasting.
Since their introduction, function calls have become a widely used feature within the OpenAI API ecosystem. They reliably connect GPT’s capabilities with external tools and APIs, and they quickly found their way into the other LLMs. The challenge one can encounter is the number of tokens consumed per request since each definition of each function is always sent as an input. We propose a simple solution to effectively decrease the number of tokens by sending only the function corresponding to the user question. The solution is based on saving the functions to the vector database, where we use the similarity score to pick only the functions that need to be sent. We have benchmarked that our solution can decrease the average prompt token consumption by 210% and the average prompt (input) price by 244% vs the default function call. Our solution is not limited to specific LLMs. It can be integrated with any LLM that supports function calls, making it a versatile tool for reducing token consumption. This means that even cheaper models with a high volume of functions can benefit from our solution.
In this paper, we develop a mathematical model for modelling and simulation of the dam-break flow through various obstacles. The model used here is an extension of one-layer non-hydrostatic (NH-1L) model by considering varying channel width (Saint Venant). The capability of our proposed scheme to simulate free surface wave generated by dam-break flow through various obstacles is demonstrated, by performing two types of simulation with various obstacles, such as; bottom obstacle and channel wall contraction. It is shown that our numerical scheme can produce the correct surface wave profile, comparable with existing experimental data. We found that our scheme demonstrates the evolution of a negative wave displacement followed by an oscillating dispersive wave train. These well-captured dispersive phenomena, indicated both the appropriate numerical treatment of the dispersive term in our model and the performance of our model.
This paper considers the problem of distributed formation scaling of Multi-Agent Systems (MASs) under a switching-directed graph where the scaling of formation is determined by one leader agent. A directed-sensing graph where neighboring agents exchange their relative displacement and a directed-communication graph where neighboring agents exchange the information about formation scaling factor and velocity factors are used in this paper. One leader agent which decides the formation scaling factor as well as the velocity of the group is chosen among agents. It is shown that under a switching-directed graph, the group of agents achieves the desired formation pattern with the desired scaling factor as well as the desired group's velocity if the union of the sensing and communication graphs contains a directed spanning tree.
This paper proposes a deep learning model for speckle noise suppression in digital images. The model consists of two interconnected networks: the first network focuses on the initial suppression of speckle noise. The second network refines these features, capturing more complex patterns, and preserving the texture details of the input images. The performance of the proposed model is evaluated with different backbones for the two networks: ResNet-18, ResNet-50, and SENet-154. Experimental results on two datasets, the Boss steganography, and COVIDx CXR-3, demonstrate that the proposed method yields competitive despeckling results. The proposed model with the SENet-154 encoder achieves PSNR and SNR values higher than 37 dB with the two datasets and outperforms other state-of-the-art methods (Pixel2Pixel, DiscoGAN, and BicycleGAN).
The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98\% with equal or better generalization ability.
The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully.
Municipalities play a key role in public waste management ensuring effective and efficient service performance. In Colombia, the public utilities sector has undergone significant changes since decentralization and the entry of private companies into the sector. In this study, our purpose is to analyze user perceptions and their willingness to pay for additional services regarding waste management. By using data analysis methods and a Mamdani fuzzy inference system, we were able to identify users’ service requirements and expected quality. According to the results of our analysis, a combination of minimum coverage and low frequency resulted in a tariff increase of 7.05%. Furthermore, we recommend expanding the model to include other waste management services, such as solid waste collection, as well as to consider environmental aspects and sustainable practices.
Online shopping using virtual try-on technology is becoming popular and widely used for digital transformation because of sustainably sourced materials and enhancing customers’ experience. For practical applicability, the process is required for two main factors: (1) accuracy and reliability, and (2) the processing time. To meet the above requirements, we propose a state-of-the-art technique for generating a user’s visualization of model costumes using only a single user portrait and basic anthropometrics. To start, this research would summarize different methods of most virtual try-on clothes approaches, including (1) Interactive simulation between the 3D models, and (2) 2D Photorealistic Generation. In spite of successfully creating the visualization and feasibility, these approaches have to face issues of their efficiency and performance. Furthermore, the complexity of input requirements and the users’ experiments are leading to difficulties in practical application and future scalability. In this regard, our study combines (1) a head-swapping technique using a face alignment model for determining, segmenting, and swapping heads with only a pair of a source and a target image as inputs (2) a photorealistic body reshape pipeline for direct resizing user visualization, and (3) an adaptive skin color models for changing user’s skin, which ensures remaining the face structure and natural. The proposed technique was evaluated quantitatively and qualitatively using three types of datasets which include: (1) VoxCeleb2, (2) Datasets from Viettel collection, and (3) Users Testing to demonstrate its feasibility and efficiency when used in real-world applications
In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of the Differential Evolution algorithm. Twelve variants of the standard Differential Evolution with various settings of the control parameters are compared with 19 state-of-the-art adaptive variants of this algorithm. The results are analysed statistically to achieve significant differences. Three variants of adaptive Differential Evolution provided better results compared to other algorithms. Some adaptive variants of Differential Evolution perform significantly worse than the original Differential Evolution with the fixed setting of the control parameters.
Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web dependson the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. SupportVector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource; and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research.
In this paper, we consider a global optimization problem where the objective function is assumed to be Lipschitz-continuous with an unknown Lipschitz constant. Building upon the recently introduced BIRECT (BIsection of RECTangles) algorithm, we propose a new diagonal partitioning and sampling scheme. Our framework, named BIRECT-V (V for vertices), combines bisection with the sampling of two points. In the initial hyper-rectangle, these points are located at 1/3 and 1 along the main diagonal. Unlike most DIRECT-type algorithms, where evaluating the objective function at vertices is not suitable for bisection, our strategy, when combined with bisection, provides more comprehensive information about the objective function. However, the creation of new sampling points may coincide with existing ones at shared vertices, resulting in additional evaluations of the objective function and increasing the number of function evaluations per iteration. To overcome this issue, we propose modifying the original optimization domain to obtain a good approximation of the global solution. Experimental investigations demonstrate that this modification positively impacts the performance of the BIRECT-V algorithm. Our proposal shows promise as a global optimization algorithm compared to the original BIRECT and two popular DIRECT-type algorithms on a set of test problems. It particularly excels at high-dimensional problems
Evaluation of alternatives to making decisions still remains as the most difficult field for every manager. Considering that uncertainty, stress, emotions and many other factors still remain insurmountable during decision-making in the work of managers. The research will bring a contemporary approach to the evaluation of alternatives through the multi-stage method by conducting a series of exhibitions for an effective decision. Model will create a logical structure derivation of exhibitions by revealing options and paths toward strategic leadership. The research used mixed methods of data collection to create a more meaningful and integrative research design. The sample was elongated over a survey of 250 organizations. The research brings to the surface a clear analysis of the following path as a guide and practically used to gain differentiating advantages towards the long-term through Strategic Attractiveness Position in Industry (ST-API). From this analysis structure, a clearness leadership orientation is created for managers, a recommendation for strategic leadership, revealing a group of strategies to undertake depending on the ST-API dimension IFE (Internal Factor Evaluation) or ST-API dimension EFE (External Factor Evaluation) through crafting "Option's" since the organizations are concentrated in the vicinity of the corner (nook) in quad IV, conclusively in "growth and build". Occurrated in this axle, organizations are advised to orient their actions towards the "develop products" in order to go towards longevity and leaderism in the industry.
Badminton is one of the most popular sports in the world, especially in Asia. It has a parent organization called Badminton World Federation (BWF). Discussion about player strategies in winning various championships held by BWF is an interesting topic to discuss. This paper aims to analyze the hitting patterns of badminton players by paying attention to the sequence of types of strokes made by the players, including lobs, netting, smashes, drives, and dropshots. Sequential pattern discovery using the equivalent class algorithm (SPADE) is the appropriate method to identify these problems because it can determine the rules and probabilities of player's hitting patterns based on the order of the types of strokes. In this paper, we analyze the stroke pattern of the two top-ranked badminton players in the men's singles sector at the Malaysia Open 2022 championship, where Viktor Axelsen and Kento Momota met in the final. Based on the results of these research, we analyze the strategies and recommended hitting patterns from the information on the two players' patterns. The results of this study, in general, can be used as information for players to understand and analyze the opponent's performance or strategy before competing.
Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets.
Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.
In light of the recent severe Supply Chain (SC) disruptions that have occurred across multiple industries around the globe, three essential and linked themes have emerged in SC management: the well-being of employees, SC sustainability, and competition between SCs for limited resources. In this paper, we create a game-theoretic SC network model that incorporates together non-cooperative SC competition, employee productivity and engagement, and green investing. Each competing firm within the network seeks to maximise its profit by determining an optimal flow of products and allocation of green investments across the SC according to a predetermined budget. A carbon tax on emissions and consumer sustainability preferences are also included in the model. The model is solved using a Variational Inequality reformulation. The illustrative numerical examples presented in this paper have been inspired by the Maltese dairy industry and demonstrate the applicability of the model to real-world problems. The results highlight the significance of the employee engagement factor in enabling firms to adopt and realise more sustainable SC practices.
