Hasil untuk "Acoustics in engineering. Acoustical engineering"

Andriy Miranskyy

Quantum computing is increasingly practiced through programming, yet most educational offerings emphasize algorithmic or framework-level use rather than software engineering concerns such as testing, abstraction, tooling, and lifecycle management. This paper reports on the design and first offering of a cross-listed undergraduate--graduate course that frames quantum computing through a software engineering lens, focusing on early-stage competence relevant to software engineering practice. The course integrates foundational quantum concepts with software engineering perspectives, emphasizing executable artifacts, empirical reasoning, and trade-offs arising from probabilistic behaviour, noise, and evolving toolchains. Evidence is drawn from instructor observations, student feedback, surveys, and analysis of student work. Despite minimal prior exposure to quantum computing, students were able to engage productively with quantum software engineering topics once a foundational understanding of quantum information and quantum algorithms, expressed through executable artifacts, was established. This experience report contributes a modular course design, a scalable assessment model for mixed academic levels, and transferable lessons for software engineering educators developing quantum computing curricula.

Sertac Kilickaya, Levent Eren

Purpose: The primary aim of this study is to enhance fault diagnosis in induction machines by leveraging the Padé Approximant Neuron (PAON) model. While accelerometers and microphones are standard in motor condition monitoring, deep learning models with nonlinear neuron architectures offer promising improvements in diagnostic performance. This research investigates whether Padé Approximant Neural Networks (PadéNets) can outperform conventional Convolutional Neural Networks (CNNs) and Self-Organized Operational Neural Networks (Self-ONNs) in the diagnosis of electrical and mechanical faults from vibration and acoustic data. Methods: We evaluate and compare the diagnostic capabilities of three deep learning architectures: one-dimensional CNNs, Self-ONNs, and PadéNets. These models are tested on the University of Ottawa's publicly available constant-speed induction motor datasets, which include both vibration and acoustic sensor data. The PadéNet model is designed to introduce enhanced nonlinearity and is compatible with unbounded activation functions such as LeakyReLU. Results and Conclusion: PadéNets consistently outperformed the baseline models, achieving diagnostic accuracies of 99.96%, 98.26%, 97.61%, and 98.33% for accelerometers 1, 2, 3, and the acoustic sensor, respectively. The enhanced nonlinearity of PadéNets, together with their compatibility with unbounded activation functions, significantly improves fault diagnosis performance in induction motor condition monitoring.

Abhik Roychoudhury, Corina Pasareanu, Michael Pradel et al.

Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.

Xin-She Yang

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.

Aurora Ramírez, José Raúl Romero, Carlos García-Martínez et al.

Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multi-objective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R utilities.

Ina K. Schieferdecker

The effectiveness of model-driven software engineering (MDSE) has been successfully demonstrated in the context of complex software; however, it has not been widely adopted due to the requisite efforts associated with model development and maintenance, as well as the specific modelling competencies required for MDSE. Concurrently, artificial intelligence (AI) methods, particularly deep learning methods, have demonstrated considerable abilities when applied to the huge code bases accessible on open-source coding platforms. The so-called big code provides the basis for significant advances in empirical software engineering, as well as in the automation of coding processes and improvements in software quality with the use of AI. The objective of this paper is to facilitate a synthesis between these two significant domains of software engineering (SE), namely models and AI in SE. The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, ai4se. In light of the aforementioned considerations, a vision of AI-assisted big models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development. Finally, the pair modelling paradigm is proposed for adoption by the MDSE industry.

Ruochun Zhang, Bonaventura Tagliafierro, Colin Vanden Heuvel et al.

This paper introduces DEM-Engine, a new submodule of Project Chrono, that is designed to carry out Discrete Element Method (DEM) simulations. Based on spherical primitive shapes, DEM-Engine can simulate polydisperse granular materials and handle complex shapes generated as assemblies of primitives, referred to as clumps. DEM-Engine has a multi-tier parallelized structure that is optimized to operate simultaneously on two GPUs. The code uses custom-defined data types to reduce memory footprint and increase bandwidth. A novel "delayed contact detection" algorithm allows the decoupling of the contact detection and force computation, thus splitting the workload into two asynchronous GPU streams. DEM-Engine uses just-in-time compilation to support user-defined contact force models. This paper discusses its C++ and Python interfaces and presents a variety of numerical tests, in which impact forces, complex-shaped particle flows, and a custom force model are validated considering well-known benchmark cases. Additionally, the full potential of the simulator is demonstrated for the investigation of extraterrestrial rover mobility on granular terrain. The chosen case study demonstrates that large-scale co-simulations (comprising 11 million elements) spanning 15 seconds, in conjunction with an external multi-body dynamics system, can be efficiently executed within a day. Lastly, a performance test suggests that DEM-Engine displays linear scaling up to 150 million elements on two NVIDIA A100 GPUs.

Samah Syed, Angel Deborah S

In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We address this task by incorporating generated code and comment pairs. The initial dataset comprised 9048 pairs of code and comments written in C, labeled as either Useful or Not Useful. To augment this dataset, we sourced an additional 739 lines of code-comment pairs and generated labels using a Large Language Model Architecture, specifically BERT. The primary objective was to build classification models that can effectively differentiate between useful and not useful code comments. Various machine learning algorithms were employed, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting, Random Forest, and a Neural Network. Each algorithm was evaluated using precision, recall, and F1-score metrics, both with the original seed dataset and the augmented dataset. This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.

Simon Raedler, Matthias Rupp, Eugen Rigger et al.

Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance for the engineering of complex systems. In previous work, a model-based engineering approach integrating the formalization of machine learning tasks using the general-purpose modeling language SysML is presented. However, formalized machine learning tasks still require the implementation in a specialized programming languages like Python. Therefore, this work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks by integrating model transformation to generate executable code. The method focuses on the modifiability and maintainability of the model transformation so that extensions and changes to the code generation can be integrated without requiring modifications to the code generator. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Based thereon, quality attributes of model transformations are assessed and discussed. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation. Further, the work builds a theoretical basis for standardizing data-driven engineering implementation in practice.

Shuai Chen, Hongliang Huang

In the far-reaching aquaculture, it is very common that the netting is damaged, the cage is deformed, and a large number of aquaculture fish escape. In this case, it is necessary to strengthen the research on related supporting technologies, improve the construction of the supporting service system of the industrial chain, explore new means to ensure aquaculture safety, and conquer key engineering technologies. Using fishery acoustics technology and satellite communication technology, the fish density around the cage can be monitored continuously for a long time. In case of abnormal situation, the alarm can be given quickly, and the loss of fish escape can be effectively prevented, and the safety of cage culture can be guaranteed.

Kristian Jambroši ć, M. Horvat, S. Fajt

* This paper presents the development of the scientific field of acoustics and electroacoustics at the University of Zagreb in the period that started with the establishment of the Department of Electroacoustics in 1954. Initially, the department was a part of the Faculty of Engineering, and from 1963 to the present it has been associated with the Faculty of Electrical Engineering. To this day, the department has been a unique and central institution on the territory of Croatia that covers research and teaching in many fields of acoustics. Over the years, scientific and professional activities undertaken by the members of the department influenced the development of certain branches of technology and industrial products, particularly in the fields of electroacoustic transducer technology and audiometry, but also in the design and evaluation procedures implemented in room acoustics, building acoustics and noise control. Many engineers, experts and scientists have received their education in this field through the great effort invested into the teaching activities by the department staff. In turn, this led to the launch of several companies that tackle everyday acoustic-related issues, but also to the development of both specialized and multidisciplinary research teams needed in various scientific projects that involve acoustics.

Zeinab Abou Khalil, Stefano Zacchiroli

We introduce the General Index of Software Engineering Papers, a dataset of fulltext-indexed papers from the most prominent scientific venues in the field of Software Engineering. The dataset includes both complete bibliographic information and indexed ngrams (sequence of contiguous words after removal of stopwords and non-words, for a total of 577 276 382 unique n-grams in this release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the 1971-2020 period.The dataset serves use cases in the field of meta-research, allowing to introspect the output of software engineering research even when access to papers or scholarly search engines is not possible (e.g., due to contractual reasons). The dataset also contributes to making such analyses reproducible and independently verifiable, as opposed to what happens when they are conducted using 3rd-party and non-open scholarly indexing services.The dataset is available as a portable Postgres database dump and released as open data.

Anam Fatima, Navira Qayyum, Mohd Zaid et al.

Abstract This research work focuses on evaluating the suitability of agricultural waste natural fibre namely rice, wheat and mustard as sustainable bio-composite that can also be used as an acoustic absorber for industrial noise control. Rice straw pulp was used in this method, pulp was extracted from rice straw then it was combined with binders to form bio composites. It was then tested for different properties. Porosity is highest when rice straw pulp was mixed with natural binders (starch). Hardness and thermal resistance was observed maximum in products made using cement and lime binders and moisture content was found maximum in composites made from natural binders. This means that composites made from rice straw pulp with natural binders has better sound absorption capacity as compared to those made using synthetic binders but composites with synthetic binders have better hardness. Products made from these methods can be used for biomedical, automobiles, packaging, acoustics and other engineering applications.

Mustahseen M. Indaleeb, Sourav Banerjee

Simultaneous occurrence of Dirac-like cones at the center of the Brillouin zone (Γ) at two different energy states is termed Dual-Dirac-like cones (DDC) in this article. The occurrence of DDC is a rare phenomenon. Thus, the generation of multiple Dirac-like cones at the center of the Brillouin zone is usually non-manipulative and poses a challenge to achieve through traditional accidental degeneracy. However, if predictively created, DDC will have multiple engineering applications with acoustics and vibration. Thus, the possibilities of creating DDC have been identified herein using a simple square periodic array of tunable square phononic crystals (PnCs) in air media. It was found that antisymmetric deaf bands may play critical roles in tracking the DDC. Hence, pivoting on the deaf bands at two different energy states, an optimized tuning parameter was found to achieve Dirac-like cones at two distinct frequency states, simultaneously. Orthogonal wave transport identified as key Dirac phenomena was achieved at two frequencies, herein. It was identified that beyond the Dirac-like cone, the Dirac phenomena remain dominant when a doubly degenerated state created by a top band with positive curvature and a near-flat deaf band are lifted from a bottom band with negative curvature. Utilizing a mechanism of rotating the PnCs near a fixed deaf band, frequencies are tracked to form the DDC, and orthogonal wave transport is demonstrated. Exploiting the dispersion behavior, unique acoustic phenomena, such as ballistic wave transmission, pseudo diffusion and acoustic cloaking are also demonstrated at the Dirac frequencies using numerical simulation. The proposed tunable acoustic PnCs will have important applications in acoustic and ultrasonic imaging, waveguiding and even acoustic computing.

Lillian N. Usadi, S. Firebaugh, H. Elbidweihy et al.

The advent of micro/nanorobotics promises to transform the physical, chemical, and biological domains by harnessing opportunities otherwise limited by size. Most notable is the biomedical field in which the ability to manipulate micro/nanoparticles has numerous applications in biophysics, drug delivery, tissue engineering, and microsurgery. Acoustics, the physics of vibrational waves through matter, offers a precise, accurate, and minimally invasive technique to manipulate microrobots or microparticles (stand-ins for microrobots). One example is through the use of flexural vibrations induced in resonant structures such as Chladni plates. In this research, we developed a platform for precise two-dimensional microparticle manipulation via acoustic forces arising from Chladni figures and resonating microscale membranes. The project included two distinct phases: (1) macroscale manipulation with a Chladni plate in air and (2) microscale manipulation using microscale membranes in liquid. In the first phase (macroscale in air), we reproduced previous studies in order to gain a better understanding of the underlying physics and to develop control algorithms based on statistical modeling techniques. In the second phase (microscale in liquid), we developed and tested a new setup using custom microfabricated structures. The macroscale statistical modeling techniques were integrated with microscale autonomous control systems. It is shown that control methods developed on the macroscale can be implemented and used on the microscale with good precision and accuracy.

Mamdouh Alenezi

Digital transformation is a hot topic in the current global environment as a large number of organizations have been working to adopt digital solutions. Software engineering has also emerged to be a more important role as a large number of systems, either traditional or smart, are dependent on the software that collects, store, and process data. The role of software engineers has also become crucial in digital transformation. In this regard, this paper aims to examine the trends of software engineering and the role of software engineers in digital transformation. In addition to this, this paper also examines the importance of secure software development in digital transformation. It can be concluded that software engineering is an integral part of digital transformation as all digital systems make use of software to perform their functions efficiently. Software act as a bridge between digital systems and humans to use the systems interactively and efficiently.

Jie Wang, C. Zheng, Leilei Chen et al.

A shape optimization approach based on isogeometric wideband fast multipole boundary element method (IGA WFMBEM) in 2D acoustics is developed in this study. The key treatment is shape sensitivity analysis by using the adjoint variable method under isogeometric analysis (IGA) conditions. A set of efficient parameters of the wideband fast multipole method has been identified for IGA boundary element method. Shape optimization is performed by applying the method of moving asymptotes. IGA WFMBEM is validated through an acoustic scattering example. The proposed optimization approach is tested on a sound barrier and two multiple structures to demonstrate its potential for engineering problems.

Orçun Saf, H. Erol, A. Kutlu

Abstract The reduction of noise in vehicle body components is one of the main themes of the acoustics profession for vehicles. At high vehicle cruising speeds, the predominant contributor to wind noise occurs via the sealing system. Accordingly, understanding the sound transmission loss (STL) through a vehicle’s sealing system is an essential engineering context for reducing interior wind noise levels. The STL through a sealing system is affected by the material, cross-section design of the profiles, overall design of the sealing system and the dynamics of the system. In this study, a brief method is described to explore the acoustic properties of vehicle door seals. Material characterization is performed both for hyperelastic and viscoelastic regions and realized using mechanical tests. The method described for studying the STL covers an analytical approach, numerical analysis, and, finally, experimental validations for several door seals, which form a major part of the sealing system located around a passenger door (closing all direct noise paths and attenuating noise from the body). Finally, the effects of fundamental parameters on the sound transmission properties are presented.

A. Remya, B. Vishwash, Christine Lee et al.

K. Y. Ng, E. Frisk, M. Krysander

This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from an initial observer-based residuals setup. The best candidates from all potential additional residuals are selected using the concept of sequential residual generation to ensure best fault isolation performance for the least number of additional residuals required. A simulation testbed is used to generate realistic engine data for the design of the additional residuals and the fault isolation performance is verified using structural analysis method.