Computer Hardware Research Articles

A Study on Cyclical Learning Rates in Reinforcement Learning and Its Application to Temperature and Power Consumption Control of Refrigeration System

In recent years, with the advancement of computer hardware technology, an increasing number of complex control systems have begun employing reinforcement learning over traditional PID controls to address the challenge of managing multiple outputs simultaneously. In this study, we have for the first time adopted the cyclical learning rate method, which is widely used in deep learning, and applied it to deep reinforcement learning. Utilizing MATLAB Simulink, a detailed simulation model was developed with the RSD-4TFK5J model refrigeration storage as the reference object. We precisely evaluated the effects of various cyclical learning rate strategies on the training process of the model. The simulation results demonstrate the effectiveness of the cyclical learning rate method during the training phase, showcasing its potential to enhance learning efficiency and system performance in complex control environments.

FOUR COORDINATE FLOORING LAYOUT OF PORTAL COMPOSITION

The article highlights the relevance and formulates the problem of creating small-sized workbenches with computer hardware, inspecting the data generators and the design of such workbenches, and proposing options for a table-top workbench with a portal layout. The example of the preparation of a folding part shows the procedure for breaking up the ceramic program.

Tiny? Make it mighty! Maximizing a limited-budget upgrade of a pint-sized hospital library using UX methods

Introduction The University of Ottawa Heart Institute’s Berkman Library space is outdated. Budget constraints and tiny square footage leave little room for error. A needs assessment using user experience (UX) research methods was conducted from 2022 to 2023 to inform strategic decisions on updating and reorganizing furnishings to better support library patrons and their needs. Methods Data was collected via an electronic survey, “guerilla” interviews, observations of library patrons, and a physical survey of communal spaces in the building. Resulting qualitative data were compiled and examined for common themes. Low fidelity mockups of furnishings and space arrangements were prototyped and presented to patrons for feedback. Results Quiet was one of the most valued attributes of the library space and showed itself to be a unique quality of the library when compared to communal spaces within the hospital. Survey and interview responses consistently cited soft, comfortable furnishings as desirable additions. Observed behaviours support the continued need for desks with a deep surface area to accommodate multiple devices used in tandem. Flexible use of computer hardware, better access to power outlets, and adjustable lighting were identified as additional gaps. Discussion Methods showcase light-weight space assessment strategies that are of particular interest to solo librarians or small library teams working in a hospital environment. Results identify library qualities that address institutional gaps and provide insight into the motivators, needs, and behaviours of hospital staff. Centering patron behaviours and preferences in the project’s methodology provides data to support decision-making for near term upgrades and long-term library policy.

Performance Characterization of Hardware/Software Communication Interfaces in End-to-End Power Management Solutions of High-Performance Computing Processors

Power management (PM) is cumbersome for today’s computing systems. Attainable performance is bounded by the architecture’s computing efficiency and capped in temperature, current, and power. PM is composed of multiple interacting layers. High-level controllers (HLCs) involve application-level policies, operating system agents (OSPMs), and PM governors and interfaces. The application of high-level control decisions is currently delegated to an on-chip power management unit executing tailored PM firmware routines. The complexity of this structure arises from the scale of the interaction, which pervades the whole system architecture. This paper aims to characterize the cost of the communication backbone between high-level OSPM agents and the on-chip power management unit (PMU) in high performance computing (HPC) processors. For this purpose, we target the System Control and Management Interface (SCMI), which is an open standard proposed by Arm. We enhance a fully open-source, end-to-end FPGA-based HW/SW framework to simulate the interaction between a HLC, a HPC system, and a PMU. This includes the application-level PM policies, the drivers of the operating system-directed configuration and power management (OSPM) governor, and the hardware and firmware of the PMU, allowing us to evaluate the impact of the communication backbone on the overall control scheme. With this framework, we first conduct an in-depth latency study of the communication interface across the whole PM hardware (HW) and software (SW) stack. Finally, we studied the impact of latency in terms of the quality of the end-to-end control, showing that the SCMI protocol can sustain reactive power management policies.

Rancang Bangun Lengan Robot Berbasis Arduino Nano

Technological advances in the Industrial Revolution 4.0 era have had a big impact, especially in the industrial sector with the emergence of automatic robots which help increase efficiency and productivity. This research discusses the design of an Arduino Nano-based robot arm that is controlled using a joystick system. The objective of this research is to establish a robotic arm mechanical system that is responsive to the movement of the joystick and sevo motor as a driver of the closed feedback system and to identify the parameters for the success of the hard ware and software system in moving objects. The research stages used involve design analysis, device assembly, and system programming. The research results show that the developed robot arm is able to effectively move objects with success parameters including the type of shape and physical structure of the object being lifted. This research found that objects with simple shapes such as blocks have a higher level of ease of lifting, while soft physical structures also influence ease of lifting.

Effect of Technological Pedagogical and Content Knowledge Framework based Capacity Building Programme Module (CBPM) on Teaching Aptitude of Pre-Service Teachers

Technology integration in education transcends the mere implementation of technological artefacts, encompassing theoretical frameworks and evidence-based research findings to enhance pedagogical practices rather than being limited to the rudimentary application of computer hardware and software in instructional delivery. This study employed a quasi-experimental methodology, utilising a pre-test/post-test equivalent group design incorporating the Solomon four-group paradigm to generate quantitative data from pre-service teachers enrolled in undergraduate teacher education programmes. Statistical analyses, including descriptive measures of means, standard deviations and standard errors of means, demonstrated a significant favourable influence of a Technological Pedagogical and Content Knowledge (TPACK) framework-based capacity-building programme Module on the teaching aptitude of pre-service teachers.

National STEM Education Framework: Teachers’ Perspectives on the 2015-2022 Curriculum Cycle

Throughout the world, nations are busy investing in Science, Technology, Engineering and Mathematics (STEM) to prepare innovative minds that can spearhead the development and sustainable growth of their economies. Zimbabwe’s participation in the global economy is premised on the preference for STEM education system. The introduction of the Competence-Based Curriculum (CBC) in Zimbabwe meant a shift in the general aims and objectives of the education system. The transformation meant a move from an academic oriented and content-focused curriculum to one that is skills-based, giving prominence to continuous assessment over final examinations. To explore classroom practitioners’ perceptions of the first cycle of the CBC, 20 purposively selected STEM teachers participated in the study. A qualitative research approach and case study design were adopted as study methodology. Thematically analysed data were collected through focus group interviews with participants. The study focused on support systems available for STEM education in secondary schools and how the absence of the national framework affects its implementation. The study found that there were limited support systems for STEM education as some schools did not have adequate facilities and equipment such as science laboratories, apparatus, science consumables, computer laboratories, computer hardware and software among others. It came out from the study that the absence of national framework had negative effect on the implementation of STEM education in secondary schools. The study concluded that though the secondary schools experienced constraints in implementing STEM education there were noticeable pockets of good practices in some schools, for example, learners graduating with competences for life skills. The study recommended that the schools should engage stakeholders for support in provision of required resources. The Ministry of Primary and Secondary Education should expedite the development of a national framework for the effective implementation of STEM education.

Edukasi Teknologi untuk Meningkatkan Literasi Digital dan Keselamatan Penggunaan Gawai pada Remaja Desa Riring Rumahsoal

Information technology has become an integral part of modern life, including in Indonesia. However, access to technology in remote areas such as Riring Rumahsoal Village, West Seram Regency, is still limited. The technology education activity carried out on 19 October 2024 aimed to increase the understanding of teenagers in the village about information technology, including its positive and negative impacts. Through observation, lecture, question and answer, and evaluation methods, this activity succeeded in increasing the knowledge of teenagers about various computer hardware, their functions, and how they work. More importantly, this activity succeeded in making teenagers aware of the potential radiation hazards that can be caused by excessive and inappropriate use of electronic devices. The evaluation results showed a significant increase in teenagers' awareness of the importance of using technology wisely and safely. They now better understand the health risks associated with exposure to electromagnetic radiation and the importance of maintaining a safe distance when using electronic devices. It is hoped that this activity can be the first step in encouraging wiser and safer use of technology among teenagers in Riring Rumahsoal Village. In the future, continuous efforts need to be made to improve community access to information on technology and health, as well as provide adequate facilities to support optimal utilisation of technology.

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

FLEXPART version 11: improved accuracy, efficiency, and flexibility

Abstract. Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles. This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/, last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development.

Knowledge, attitude, and perception/practice towards artificial intelligence among dental students and dental professionals - a systematic review

Artificial intelligence (AI) is a broad term encompassing advanced technologies that aim to mimic human intelligence in computer hardware and software, achieving human-level performance. By studying the attitudes and practices of dental professionals and students, we can better integrate AI into the dental field. Hence, the aim is to examine the knowledge, attitudes, and perceptions/practices towards AI among dental students and dental professionals. This study protocol was based on preferred reporting items for overviews of reviews (PRIOR) guidelines. A broad electronic search was carried out in the following databases, such as PubMed, Scopus, Google Scholar, Cochrane database of systematic reviews, and Trip database to find related studies and a total of 28 articles were selected for the systematic review. In this review, the average knowledge percentage among dental students was around 56.9, while dental professionals demonstrated a higher average percentage of 66.42. 82% of postgraduate students showed greater openness to integrating AI at an advanced level of dental education. The review suggests a way for dental students and dental professionals to improve their artificial intelligence understanding and skills.

From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL‐Inspired Interface

ABSTRACTWhile most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single‐socket and NUMA configurations show good strong and weak scaling.

Fault3DNnet: A lightweight 3D seismic fault detection network with bidirectional decoding

Fault detection from seismic data is a critical component of hydrocarbon exploration and production. In recent years, deep-learning techniques have made tremendous achievements and breakthroughs in seismic fault detection. Especially U-nets, represented by FaultSeg3D and its variants, are a dominant method for 3D deep-learning-based seismic fault interpretation. The incorporation of state-of-the-art network modules or training strategies with U-nets continuously improves the performance of fault detection for field seismic data, usually at the cost of increased computational intensity and hardware consumption. We design a lightweight bidirectional decoding network, Fault3DNnet, for fault detection from 3D seismic data volume. To make it simple and concise, our Fault3DNnet is equipped with basic conventional convolution and regular operation modules. The two decoding branches in Fault3DNnet could provide complementary information and improve fault detection performance. The superiority of Fault3DNnet is verified using publicly available synthetic and four field seismic data sets. The experimental results indicate that the method predicts fault structures with improved completeness and continuity compared with FaultSeg3D. Meanwhile, the parameter and computational quantity of the network are only 1/7 and 1/5 of FaultSeg3D, respectively. With the same graphics processing unit memory consumption, Fault3DNnet can handle the size of approximately six times the volume of 3D seismic data during inferencing as that of FaultSeg3D. It helps to improve prediction stability and decrease discontinuous artifacts at the stitching boundaries when predicting the large field data in chunks. In general, our Fault3DNnet delivers superior fault detection results while greatly reducing computational intensity and memory usage.

Fault Diagnosis Technology for Complex Mechanical Systems Based on Deep Learning

Traditional fault diagnosis methods are usually based on expert knowledge and experience, but this method often relies on manual analysis and judgment, which is time-consuming and inaccurate. Deep learning has become a hot spot because of the development of optimization algorithms and computer hardware, and has achieved great success in many applications. In the field, neural network, as a popular tool, can establish a feature recognition system between perceptron and classifier through training. This system includes the representation process of input layer, the promotion process of hidden layer and output layer. While ensuring the safe, stable and reliable operation of equipment and personal safety, improving the production efficiency of enterprises and enhancing the international competitiveness and influence of the industry are the new development trends of the design, production and maintenance guarantee system of manufacturing industry worldwide. At the same time, comprehensive testing and evaluation records of the operating status of the transmission equipment of the rolling mill can provide reliable reference for the diagnosis of faults and major and medium repairs of this type of rolling mill in the future. In practical engineering, due to different working conditions and complex environments, the types of faults that occur are unpredictable, which may result in a lack of samples available for training fault diagnosis models. This article mainly discusses and analyzes in depth the fault diagnosis technology of complex mechanical systems based on deep learning algorithms.

How can artificial intelligence be used to detect and mitigate zero-day vulnerabilities?

A zero-day exploit is a cyberattack that uses unknown or unaddressed security flaws in computer software, hardware, or firmware. Zero-day vulnerabilities pose very significant threats to cyber security. While traditional methods have been effective, they are lacking in many aspects due to rapidly evolving cyber threats. Hence, this paper examines artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities. The research delves into supervised and unsupervised models and algorithms like Naive Bayes. The findings suggest that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Hardware Design Strategies Oriented to Edge Computing and AgriFood Electronics for Image Processing using Cellular Automata

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

One molecule to couple them all: Toward realistic numbers of molecules in multiscale molecular dynamics simulations of exciton-polaritons.

Collective strong coupling of many molecules to the confined light modes of an optical resonator can influence the photochemistry of these molecules, but the origin of this effect is not yet fully understood. To provide atomistic insights, several approaches have been developed based on quantum chemistry or molecular dynamics methods. However, most of these methods rely on coupling a few molecules (or sometimes only one) to a single cavity mode. To reach the strong coupling regime with such a small number of molecules, much larger vacuum field strengths are employed than in experiments. To keep the vacuum field realistic and avoid potential artefacts, the number of coupled molecules should be significantly increased instead, but that is not always possible due to restrictions on computational hardware and software. To overcome this barrier and model the dynamics of an arbitrarily large ensemble of molecules coupled to realistic cavity fields in atomistic molecular dynamics simulations, we propose to coarse-grain subsets of molecules into one or more effective supermolecules with an enhanced dipole moment and concerted dynamics. To verify the validity of the proposed multiscale model, we performed simulations in which we investigated how the number of molecules that are coupled to the cavity affects excited-state intra-molecular proton transfer, polariton relaxation, and exciton transport.

VR Simulation: Advancing Practical Skills in Computer Science Education

This study presents the successful integration of Virtual Reality (VR) tools as an element in the computer science curriculum at the German University of Technology, Oman. The research study investigates the use of MyScore, a VR application developed by RWTH Aachen University - Teaching & Research Field Engineering Hydrology as an environment for computer hardware assembly and troubleshooting exercises. The VR simulation utilized in this study acts as a gamified environment, incorporating elements like scorekeeping and interactive challenges to enhance practical learning in computer science, akin to game-based strategies that engage and motivate students. Over some weeks, the project involved seven students and one tutor in immersive learning sessions to develop applied skills in computer hardware assembly with VR technology over conventional teaching methods. Structured surveys and direct observations were used for data collection through students' interactions with the VR sessions. We also evaluated the student's learning experiences as well as the usability of technology. The results showed that the user-friendliness of the VR interface and its ability to accurately simulate real-world tasks associated with computer assembly received both positive and negative reactions. While some students praised the technology for being pretty cutting-edge, some criticized it as unrealistic or non-interactive. We also received complaints that ranged from actually moving around in the virtual space to navigating only through teleportation spark points. Specifically, two students got motion sickness, which is a well-known obstacle associated with VR environments, and consequently left the sessions prematurely. The feedback of the instructor pointed out that setting up the VR equipment is straightforward and described some potential methods. Still, a call for more reliability in simulations and better interfaces began to surface as nevertheless less controversial agenda items. However, the instructor saw how VR has the potential to revolutionize educational practices. Providing interactive views between the VR device and students that can act as new learning methods. The present study extends the discussion on educational technology by providing empirical evidence of the benefits and challenges experienced in deploying VR for technical education implementation. These findings thus support the continued development and validation of VR technologies in pursuit of improved commitment, cost-effectiveness, immersion, and environment quality that may maximize learning outcomes in future educational applications.

The Method of Diversity-Based Ensuring the Reliability of the Router in the IIoT System

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.