Traditional CAD systems have expanded human capabilities in numerical calculations and graphics processing, but they still have the following shortcomings without intelligent support: Failure to provide effective computer support throughout the design process; Inability to handle fuzzy knowledge or design problems described by insufficient conditions; lack of macroscopic knowledge structure analysis, design thinking principles and specific methods and skills to implement design, processing algorithms are mixed with the environment representing the processing object, which makes it difficult to change with the change of the environment or the replacement of processing technology and make necessary modifications and expansions. Based on the idea of intelligent engineering, this article uses simulated annealing method and genetic algorithm to solve the complex clock and watch movement CAD layout problem. Layout problems widely exist in production fields such as machinery manufacturing, clothing, leather, packaging and printing, glass, urban planning, factory layout, etc. The main manifestations are: Cutting one-dimensional bars into pieces of different lengths workpiece to maximize material utilization, such as the problem of cutting steel pipes according to customer orders.

Head injuries pose a significant risk in industries such as construction and manufacturing, making helmet use essential for safety. This paper presents a comparative analysis of six YOLO (You Only Look Once) models-YOLOv5mu, YOLOv6m, YOLOv7, YOLOv8m, YOLOv9 (m, GELAN-c, GELAN-e), and YOLOv10m-for helmet detection using a public dataset. The models are evaluated for mean Average Precision (mAP), frames per second (FPS), training time, and model size. YOLOv9 (GELAN-e) achieves the highest accuracy with 96.17% mAP at 0.1% confidence and 82.71% at 70% confidence. YOLOv8m is the fastest, achieving 45.37 FPS, while YOLOv10m has the highest mAP (85.10%) at 70% confidence and is the most compact (33.5 MB). YOLOv9 (GELAN-c) offers the shortest training time (1.32 hours), while YOLOv9 strikes the best balance between accuracy, speed, and size. These findings suggest YOLOv9 as an excellent choice for applications requiring both accuracy and efficiency, while YOLOv8m is optimal for real-time scenarios.

Real-time weather data has the potential to significantly improve agricultural practices by optimizing irrigation, pest control, and crop management. This paper explores how integrating real-time weather information into farming operations can enhance decision-making, reduce resource waste, and increase crop yields. Key findings include the benefits of timely irrigation adjustments and predictive pest management strategies.

A black hole is an area in space with immense gravity that nothing (not even light) can escape from it. They are formed usually when a star dies. These black holes can be found by the detectors in space. They detect the gravitational waves by the ripples' effect. The isolated black holes that are invisible can be found using a method called gravitational lensing. Various black holes have been found using these methods. These black holes do not emit any light. This makes it difficult for scientists to observe the black holes directly. The effects of black holes on the nearby matter and light were observed by the scientists to detect a black hole. In this paper, a comprehensive review is presented about the techniques that can be used to detect and analyze black holes using machine learning. Information regarding the technique along with the conditions has been summarized in the paper. Other issues regarding the technology limitations, research challenges, and future trends are also discussed.

Multi-objective optimization is critical in SCM to balance conflicting objectives such as cost minimization and service level maximization. This paper presents a comprehensive study on the application of Simulated Annealing (SA) as a metaheuristic approach to address these challenges. The study includes detailed mathematical formulations, Python code implementation, and empirical evaluations using real-world SCM datasets to demonstrate SA's effectiveness and practical application.

Accurately parameterizing human gait cycles is crucial for developing control systems for lower-limb wearable robots. Previous studies on characterizing human gait cycle investigated the use of human-inspired phase variables, but they lack robustness and adaptability to accommodate for transitions that occur between locomotor tasks. This paper proposes augmenting the traditional phase variables with a canonical dynamical system. This system considers the differences between the estimated and true thigh angular velocities, based on which the Fourier series coefficients and phase information will be dynamically updated via an adaptive frequency oscillator. Experiments with six able-bodied participants on level ground, inclines, and declines walking show that the augmented phase variable adapts to various locomotor tasks and transitions better than the traditional approach.

Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the number of keywords, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches not only for sets of keywords but also for any number of wildcards positioned anywhere within the keyword set. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical in computation and communication, and the experimental comparisons illustrate that PEWS achieves significant improvements in computational efficiency.

Load flow analysis is fundamental to power system operation and planning. While nonlinear methods offer high precision, their computational complexity limits real-time applications. Linear methods, although computationally efficient, often compromise accuracy. This paper proposes a hybrid linear approach combining two linear load flow methods to address this challenge. The proposed method significantly enhances accuracy compared to traditional linear methods while maintaining computational efficiency. Extensive testing demonstrates the effectiveness of the hybrid approach in improving load flow analysis for distribution networks.

Diabetic Retinopathy (DR) and Glaucoma (GL) are critical eye diseases that can lead to vision loss if not identified early. Manual diagnosis using fundus images is time-consuming and error-prone, highlighting the need for automated methods for efficient and accurate classification. Deep learning models improve diagnostic performance but still face challenges such as class imbalance and similarities between disease stages, which complicate early detection.To address these challenges, we propose an Enriched Feature-based Multi-Class (EFMC) deep learning classifier for detecting DR and GL from fundus images. The EFMC classifier operates in three stages: extracting domain-specific features through unsupervised learning, enriching these features with multi-scale filters, and classifying them using multiple CNN classifiers with a hybrid loss function. This approach effectively tackles class similarity and imbalance. Experimental results on benchmark datasets show that the EFMC system outperforms state-of-the-art models, achieving 98.75% accuracy and a 98% F1-score.

Grid forming converters can offer fast voltage support during grid disturbances through a prompt injection of reactive current. Nevertheless, the large overcurrents experienced during grid faults can trigger the converter current limiter, which is often responsible of transient instability problems. While over-dimensioning the power rating of the converter enhances its grid supporting capabilities, it significantly increases the costs. Online monitoring of the semiconductor junction temperature, instead, enables the converter to perform a safe over-current operation for a limited time interval, potentially improving the transient stability without converter overdimensioning. Nevertheless, the transient stability analysis and current limitation design considering thermal dynamics is very challenging due to the high order of the model, which makes conventional methodologies based on second-order models not applicable. This article proposes a temperaturedependent current limitation in grid-forming converters, enabling overcurrent operation and significantly improving transient stability. A methodology for transient stability analysis is proposed based on numerical methods applied to the nonlinear state-space model of the converter under thermally stressed conditions during over-current scenarios caused by grid faults. The proposed method is implemented in a simulation of a grid-forming medium voltage Modular Multilevel Converter (MMC) considering the dynamics of junction temperatures.

Differentially private data generation aims to protect individual privacy while maintaining data utility for analytical purposes. However, achieving a balance between high utility and fairness across various demographic groups remains a challenge. This research introduces a framework that quantitatively evaluates the impact of different privacy mechanisms on utility and fairness. Through experiments with synthetic datasets, we examine the consequences of varying privacy levels on machine learning task performance. The results demonstrate that stricter privacy measures often result in reduced utility and increased fairness disparities. On the other hand, loosening privacy constraints can improve both utility and fairness but may threaten individual privacy. Additionally, we explore techniques that enhance fairness without significantly compromising utility. These findings highlight the intricate relationships among utility, fairness, and privacy, providing valuable perspectives for advancing responsible data generation methodologies.

In recent years, Unmanned Aerial Vehicles (UAVs) have attracted a lot of attention due to their flexibility and high mobility. Nowadays, UAVs have been widely used in various fields, such as military fields and social fields. However, due to the increasingly complex environments faced by UAVs and the rising demands on UAV systems, traditional UAV control methods are no longer able to achieve efficient control of UAVs under multi-constraint situations. Reinforcement Learning (RL), as an emerging robot control technology, is well suited to the needs of UAV systems in terms of its ability to interact with and learn from the environment. Therefore RL-based UAV systems are gradually becoming a new trend in research. Nonetheless, as a new research field, it faces many challenges, such as high dimensional spaces and dynamic environments. It is necessary to provide a comprehensive overview and analysis of existing specific RL methods applied to UAV systems, and to understand the challenges as well as possible solutions for RL-based UAV systems. In this paper, we first provide a comprehensive overview and summary of the application of RL in different UAV application scenarios based on the classification of RL methods. After that, we systematically analyze the challenges and solutions faced by RL applied to UAV systems given the existing relevant literature. Finally, we discuss the potential research directions for RL-based UAV systems.

Agile software development projects focus on running software [BBvB + 01]. Requirements engineering will be performed as far as it is required by the development team. The agile procedure seeks to reduce bureaucracy. However, a risk exists that a focus on the completion of development tasks leads to technical debt. Enhanced requirements engineering could face this risk. In order to reduce the risk of technical debt in agile projects the use of a code generator could be solution a more pragmatically. In this paper, a concept for a code generator will be elaborated.

In the rapidly evolving field of multimedia services, video streaming has become increasingly prevalent, demanding innovative solutions to enhance user experience and system efficiency. This paper introduces a novel approach that integrates user digital twins-a dynamic digital representation of a user's preferences and behaviors-with traditional video streaming systems. We explore the potential of this integration to dynamically adjust video preferences and optimize transcoding processes according to real-time data. The methodology leverages advanced machine learning algorithms to continuously update the user's digital twin, which in turn informs the transcoding service to adapt video parameters for optimal quality and minimal buffering. Experimental results show that our approach not only improves the personalization of content delivery but also significantly enhances the overall efficiency of video streaming services by reducing bandwidth usage and improving video playback quality. The implications of such advancements suggest a shift towards more adaptive, user-centric multimedia services, potentially transforming how video content is consumed and delivered.

This paper presents a design approach for dual-band dual-polarized (DBDP) waveguide antenna array. A novel transmission network using orthogonal parallel plate waveguide (PPW) is proposed for the excitation of dual polarization. Dual-band bandstop structures are incorporated into the PPW feeding network. They suppress the leakage between the two polarizations and improve the transmission efficiency of the network. They also enable a wide range of frequency ratio from 1 to 2.64 between the two bands. A new orthogonal linear signal generator (LSG) enables ultra-wideband, dual-polarized quasi-TEM excitation, ensuring consistent and reliable performance across a broad bandwidth. Fed by four sequentially rotated single-ridge 1-to-4 power dividers, the LSG effectively generates dual-polarized signals. For demonstration, a K/Ka-band shared-aperture dual-polarized array antenna has been designed, analyzed and experimentally verified. The array features a wide impedance bandwidth (17.7-21.2 GHz and 27.5-31 GHz), high isolation (>50 dB), and excellent cross polarization discrimination (XPD) (>40 dB). The prototype demonstrates high aperture efficiency up to 80% and consistent radiation patterns at both bands, affirming its potential for SATCOM systems.

Communication is crucial for Dual Side Control (DSC) of Inductive Power Transfer (IPT), which uses multiple variables in two separate converters for efficiency optimization. To avoid add-on communication systems, this letter proposes to implement Talkative Power Conversion (TPC)-added utility communication by modulated power conversion without intrusive changes to hardware, control or power conversion. The secondary converter is used to transmit feedback information and the Phase Locked Loop (PLL) typical of synchronous DSC is relocated to the primary side as the receiver, avoiding hardware additions. Based on Continuous Phase Frequency Shift Keying (CPFSK), the communication process is then designed to act as quasi steady state to the power transfer and control processes. The strategy is demonstrated to actively close a DSC loop in experiment with an average frequency shift of only 0.15 Hz. No added perturbations are visible in time domain voltage or currents, which confirms the nonintrusive nature of the proposed method.

This study deals with flexibility quantification from space and water heating in about a million houses equipped with heat pumps, for providing emergency power support in a renewable dominated power-system. As electricity consumption determines the flexibility potential, this is estimated using integrated physics-based models involving a heat pump, space and water heating. The flexibility estimations are between 2.1 GW and 0.5 GW, at outdoor ambient temperatures ranging between -10◦C and 10◦C respectively, for a power-system with a dimensioning fault of 1.45 GW. The reduction in electric energy is between 5.4 GWh and 3.2 GWh, when the indoor and water temperatures are reduced from 20◦C and 55◦C, to 18◦C and 50◦C respectively over 17.25 hours, considering 24 hours recovery period. Additionally, electricity consumption as a function of time serves as valuable information for power balancing. Furthermore, a modified Nordic-32 bus system with a high share of renewable power installations is proposed. In this system, the loss of a major generation corresponding to 12% of the load, causes an instantaneous frequency deviation below 48 Hz. This can be limited by providing emergency power support using house heating flexibility corresponding to at least 5.9% of the load.

Predictive Control optimization typically relies on exhaustive enumeration of all possible voltage vectors within the finite control set. For simpler implementation the computational burden is not a concern, but the limited control set can demand a very high sampling frequency to reach good steady state performance. Indirect Predictive Control or Modulated Predictive Control addresses this by using a modulation stage, but classic implementation depends on duty cycle calculation that can limit constraints and non linearities in the cost function. This paper introduces the Informed Binary Search, an intelligent search algorithm capable of handling any constraints within the cost function, similarly to Finite Control Set, but with the benefit of fixed switching frequency. Experimental results on a two-level VSI driving an induction motor is shown but the method can be applied to every setup of machine and inverter. The results are compared with PTC, PTC with DSVM and MPTC and the metrics show improvements in torque and flux ripple and improvements of current THD up to 30% over classic M2PC while having similar computational burden.