Fuzzy A* for optimum Path Planning in a Large Maze

 Traditional A* path planning, while guaranteeing the shortest path with an admissible heuristic, often employs conservative heuristic functions that neglect potential obstacles and map inaccuracies. This can lead to inefficient searches and increased memory usage in complex environments. To address this, machine learning methods have been explored to predict cost functions, reducing memory load while maintaining optimal solutions. However, these require extensive data collection and struggle in novel, intricate environments. We propose the Fuzzy A* algorithm, an enhancement of the classic A* method, incorporating a new determinant variable to adjust heuristic cost calculations. This adjustment modulates the scope of scanned vertices during searches, optimizing memory usage and computational efficiency. In our approach, unlike traditional A* heuristics that overlook environmental complexities, the Fuzzy A* employs a dynamic heuristic function. This function, leveraging fuzzy logic principles, adapts to varying levels of environmental complexity, allowing a more nuanced estimation of the path cost that considers potential obstructions and route feasibility. This adaptability contrasts with standard machine learning-based solutions, which, while effective in known environments, often falter in unfamiliar or highly complex settings due to their reliance on pre-existing datasets. Our experimental framework involved 100 maze-solving trials in diverse maze configurations, ranging from simple to highly intricate layouts, to evaluate the effectiveness of Fuzzy A*. We employed specific metrics such as path length, computational time, and memory usage for a comprehensive assessment. The results showcased that Fuzzy A* consistently found the shortest paths (99.96% success rate) and significantly reduced memory usage by 67% and 59% compared to Breadth-First-Search (BFS) and traditional A*, respectively. These findings underline the effectiveness of our modified heuristic approach in diverse and challenging environments, highlighting its potential for real-world pathfinding applications

Enhancing UAV Navigation in Dynamic Environments: A Detailed Integration of Fick's Law Algorithm for Optimal Pathfinding in Complex Terrains

In the realm of Unmanned Aerial Vehicles (UAVs), efficient navigation in complex environments is crucial, necessitating advanced pathfinding algorithms. This study introduces the Fick's Law Algorithm (FLA) for UAV path optimization, drawing inspiration from the principles of molecular diffusion, and positions it in the context of existing algorithms such as A* and Dijkstra's. Through a comparative analysis, we highlight FLA's unique approach and advantages in terms of computational efficiency and adaptability to dynamic obstacles. Our experiment, conducted in a simulated three-dimensional space with static and dynamic obstacles, involves an extensive quantitative analysis. FLA's performance is quantified through metrics like path length reduction, computation time, and obstacle avoidance efficacy, demonstrating a marked improvement over traditional methods. The technical foundation of FLA is detailed, emphasizing its iterative adaptation based on a cost function that accounts for path length and obstacle avoidance. The algorithm's rapid convergence towards an optimal solution is evidenced by a significant decrease in the cost function, supported by data from our convergence graph. Visualizations in both 2D and 3D effectively illustrate the UAV’s trajectory, highlighting FLA's efficiency in real-time path correction and obstacle negotiation. Furthermore, we discuss FLA's practical implications, outlining its adaptability in various real-world UAV applications, while also acknowledging its limitations and potential challenges. This exploration extends FLA's relevance beyond theoretical contexts, suggesting its efficacy in real-world scenarios. Looking ahead, future work will not only focus on enhancing FLA's computational efficiency but also on developing specific methodologies for real-world testing. These include adaptive scaling for different UAV models and environments, as well as integration with UAV hardware systems. Our study establishes FLA as a potent tool for autonomous UAV navigation, offering significant contributions to the field of dynamic path optimization

Adaptive Cyber-Defense for Unmanned Aerial Vehicles: A Modular Simulation Model with Dynamic Performance Management

In light of escalating cyber threats, this study tackles the cybersecurity challenges in UAV systems, underscoring the limitations of static defense mechanisms. Traditional security approaches fall short against the sophisticated and evolving nature of cyber-attacks, particularly for UAVs that depend on real-time autonomy. Addressing this deficiency, we introduce an adaptive modular security system tailored for UAVs, enhancing resilience through real-time defensive adaptability. This system integrates scalable, modular components and employs machine learning techniques—specifically, neural networks and anomaly detection algorithm to improve threat prediction and response. Our approach marks a significant leap in UAV cybersecurity, departing from static defenses to a dynamic, context-aware strategy. By employing this system, UAV stakeholders gain the flexibility needed to counteract multifaceted cyber risks in diverse operational scenarios. The paper delves into the system's design and operational efficacy, juxtaposing it with conventional strategies. Experimental evaluations, using varied UAV scenarios, measure defense success rates, computational efficiency, and resource utilization. Findings reveal that our system surpasses traditional models in defense success and computational speed, albeit with a slight increase in resource usage a consideration for deployment in resource-constrained contexts. In closing, this research underscores the imperative for dynamic, adaptable cybersecurity solutions in UAV operations, presenting an innovative and proactive defense framework. It not only illustrates the immediate benefits of such adaptive systems but also paves the way for ongoing enhancements in UAV cyber defense mechanisms

Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges

Advancing UAV Path Planning System: A Software Pattern Language for Dynamic Environments

In the rapidly advancing domain of Unmanned Aerial Vehicle (UAV) technologies, the capability to navigate dynamic and unpredictable environments is paramount. To this end, we present a novel design pattern framework for real-time UAV path planning, derived from the established Pattern Language of Program Community (PLOP). This framework integrates a suite of software patterns, each selected for its role in enhancing UAV operational adaptability, environmental awareness, and resource management. Our proposed framework capitalizes on a blend of behavioral, structural, and creational patterns, which work in concert to refine the UAV's decision-making processes in response to changing environmental conditions. For instance, the Observer pattern is employed to maintain real-time environmental awareness, while the Strategy pattern allows for dynamic adaptability in the UAV's path planning algorithm. Theoretical analysis and conceptual evaluations form the backbone of this research, eschewing empirical experiments for a detailed exploration of the design's potential. By offering a systematic and standardized approach, this research contributes to the UAV field by providing a robust theoretical foundation for future empirical studies and practical implementations, aiming to elevate the efficiency and safety of UAV operations in dynamic environments

Mengukur Tingkat Keselarasan Information Technology dan Bisnis (Studi Kasus Perusahaan Start-up Digital Wilayah Jawa)

Abstract. The alignment between information technology (IT) and business becomes a main issue for a digital start-up which adopts technology as a support for the business activities. As one of the growing business in Indonesia, this organization gets special attention from the government that it will be able to become the backbone of the nation's economy. This has led to a necessity of measuring the alignment level between IT and business at digital start-ups in order to describe the conditions for developing strategies to increase competitive benefits. According to that necessity, the research on the assessment of the alignment between IT and business is conducted by using the Luftman SAM (Strategic Alignment Maturity) model to 30 samples of IT start-up companies from various business fields in Java where the center of digital start-up exists in Indonesia. Based on the results of this study, it was found that the average digital start-up has a pretty good alignment level of 3.45. This shows that the strategic alignment between IT and business is starting to integrate in all functional units. Keywords: alignment maturity, Luftman, SAM, start-up.Abstrak. Keselarasan teknologi informasi (IT) dan bisnis telah menjadi masalah utama start-up digital yang memanfaatkan teknologi sebagai penunjang dalam kegiatan bisnis. Sebagai salah satu organisasi yang marak berkembang di Indonesia, organisasi ini mendapatkan perhatian khusus dari pemerintah untuk mampu menjadi tulang punggung perekonomian bangsa. Hal ini menyebabkan perlunya pengukuran tingkat keselarasan IT dan bisnis pada start-up digital agar dapat menggambarkan kondisi keselarasan yang terjadi sehingga mampu menyusun strategi untuk semakin meningkatkan keuntungan kompetitif. Berdasarkan kebutuhan ini peneliti melakukan penilaian keselarasan antara IT dan bisnis dengan menggunakan model Luftman SAM (Strategic Alignment Maturity) terhadap 30 sampel perusahaan start-up IT dari berbagai bidang bisnis yang berada di wilayah Jawa dimana pusat pertumbuhan start-up digital di Indonesia berada. Berdasarkan hasil penelitian ini didapatkan bahwa rata-rata start-up digital memiliki tingkat keselarasan yang cukup baik yakni sebesar 3,45. Hal ini menunjukkan bahwa keselarasan strategis antara IT dan bisnis mulai terbentuk dan terintegrasi di seluruh unit fungsional. Kata Kunci: keselarasan IT dan bisnis, Luftman, SAM, start-up

Comparison of exponential smoothing and neural network method to forecast rice production in Indonesia

Rice is the most important food commodity in Indonesia. In order to achieve affordability, and the fulfillment of the national food consumption according to the Indonesia law no. 18 of 2012, Indonesia needs information to support the government's policy regarding the collection, processing, analyzing, storing, presenting and disseminating. One manifestation of the Information availability to support the government’s policy is forecasting. Exponential smoothing and neural network methods are commonly used to forecasting because it provides a satisfactory result. Our study are comparing the variants of exponential and backpropagation model as a neural network to forecast rice production. The evaluation is summarized by utilizing Mean Square Percentage Error (MAPE), Mean Square Error (MSE). The results show that neural network method is preferable than the statistics method since it has lower MSE and MAPE values than statistics method

Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric

This study conducts a comparative analysis of three prominent machine learning models: Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) in the field of automatic speech recognition (ASR). This research is distinct in its use of the LibriSpeech 'test-clean' dataset, selected for its diversity in speaker accents and varied recording conditions, establishing it as a robust benchmark for ASR performance evaluation. Our approach involved preprocessing the audio data to ensure consistency and extracting Mel-Frequency Cepstral Coefficients (MFCCs) as the primary features, crucial for capturing the nuances of human speech. The models were meticulously configured with specific architectural details and hyperparameters. The MLP and CNN models were designed to maximize their pattern recognition capabilities, while the RNN (LSTM) was optimized for processing temporal data. To assess their performance, we employed metrics such as precision, recall, and F1-score. The MLP and CNN models demonstrated exceptional accuracy, with scores of 0.98 across these metrics, indicating their effectiveness in feature extraction and pattern recognition. In contrast, the LSTM variant of RNN showed lower efficacy, with scores below 0.60, highlighting the challenges in handling sequential speech data. The results of this study shed light on the differing capabilities of these models in ASR. While the high accuracy of MLP and CNN suggests potential overfitting, the underperformance of LSTM underscores the necessity for further refinement in sequential data processing. This research contributes to the understanding of various machine learning approaches in ASR and paves the way for future investigations. We propose exploring hybrid model architectures and enhancing feature extraction methods to develop more sophisticated, real-world ASR systems. Additionally, our findings underscore the importance of considering model-specific strengths and limitations in ASR applications, guiding the direction of future research in this rapidly evolving field

SEGMENTASI CITRA MEDIS MENGGUNAKAN FUZZY LEVEL SET BERBASIS KOMPUTASI PARALLEL GPU CUDA

Image segmentation is a process that is intended to get the objects contained in the image by dividing the image into several regions that have similar attributes on the object. The main purpose of image segmentation is to facilitate the analysis process so the results more meaningful. Image segmentation is usually used to locate objects and boundaries in an image. In the medical world in particular, areas that have similar attributes often found in the image tissues or organs in humans was observed with medical devices such as organs and ultrasound scanning. In this study examined how image segmentation using fuzzy level set by utilizing the GPU via a platform called CUDA. The result is a developed image segmentation 141x able to accelerate faster than previously done by the CPU

Sistem Informasi Status Antrian Service Pada PT. Nissan Gunung Latimojong Makassar Berbasis Website

Penelitian ini bertujuan ( 1 ) Merancang sistem informasi status antrianrnservice berbasis web, sehingga dapat membantu pelanggan untuk memantau hasilrnservice mobilnya dari jarak jauh. ( 2 ) Mengimplementasi sistem tersebut Untukrnmenghasilkan informasi status antrian service berbasis web.rnrnJenis metode penelitian yang digunakan adalah metode penelitianrnpenggambaran system menggunakan diagram Flowmap, Diagram Kontkes,rnDiagram Aliran Data (DFD). Hal ini dilakukan untuk memberikan gambaranrnmelalui desain sistem mengenai manfaat sistem informasi sebagai suatu sistemrnyang memudahkan pengguna dan merancang sebuah sistem informasi statusrnantrian service mobil berbasis website dan MySQL.rnrnHasil penilitian berdasarkan program mampu membantu proses antrianrnservice mobil pada PT.Nissan Gunung Latimojong Makassar. Dan pelanggan bisarnmemantau service mobil dari jarak jauh berdasarkan program.rnKata Kunci : sistem informasi, status antrian service.rn57 hlm.; 29 cm