System Requirements Research Articles

Real time implementation of downlink orthogonal frequency division multiplexing based non-orthogonal multiple access transceiver using NI USRP platform

The growing fame and utilization of wireless multimedia approaches have led to the advancement of the wireless system. The fifth generation (5G) of wireless communication was developed to serve users with enhanced proficiency, low latency, reliable communication, and lesser battery exhausts. The non-orthogonal multiple access (NOMA) scheme is a proficient multiple access scheme to fulfil the requirement of a 5G mobile system. NOMA enables a remarkable enhancement in the systems throughput and ability to connect devices. NOMA distinguishes each user by allocating a distinct power level, superimposing all the user’s signals utilizing superposition coding while transmitting and at the reception, each user’s signals are decoded by employing successive interference cancellation (SIC). This study builds a real time downlink orthogonal frequency division multiplexing based NOMA (OFDM-NOMA) transceiver system using the NI USRP 2944R and 2901 platforms. The performance evaluation of the proposed OFDM-NOMA system is carried out in terms of signal to noise ratio by adjusting transmitting gain and the distance of users from the base station system. Experimental results show that the signal-to-noise ratio (SNR) of each user relies on the power allocation factor and proximity of users from the base station, and SIC output is compared with constellation variations.

Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

AbstractRenewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Sustainable residency training in urology in context of current changes in the German healthcare system

Promoting sustainable structures and measures in residency is crucial to meet current and future requirements of the German healthcare system. This process does not only involve the integration of social, ecological, and economic aspects into everyday professional life but also into residency training. Specifically, sustainable structures and measures in residency include initiatives such as structured training curricula, simulation-based training, digital training opportunities, flexible working time models, and gender equality. In addition, consideration of environmental aspects as well as expansion of quality management programs are essential. Aholistic approach that considers both the efficiency and the needs of physicians and their patients is the key to sustainable urologic residency programs. This way, urology will remain an attractive specialty for future generations.

Anomaly detection of power grid data based on storm cloud platform sliding window

Abstract To realize the monitoring and diagnosis of multi-source data in the power industry and meet the real-time processing requirements of the power system, the storm-distributed real-time computing platform is introduced to process the data. On this platform, the streaming data processing model based on a sliding window is deployed to realize the anomaly diagnosis of data flow. By calculating the predicted value of a point and the confidence interval of the predicted value of the point, we can judge whether the actual point is an abnormal value. By testing the data processing capacity of a cluster and a single machine, the experiment shows that in the cluster environment, reasonably setting the component parallelism can improve the throughput of flow computing.

MODERN REQUIREMENTS FOR THE TYPOLOGY AND SYSTEM OF EXERCISES IN TEACHING FOREIGN LANGUAGES

This article provides for the study of the typology and system of exercises used in teaching foreign languages, the identification of modern requirements for them, and the analysis of the methodological foundations for the formation of an effective system of exercises. The article uses methods of historical-comparative analysis, systematic approach, and contextual analysis. Theoretical sources on typologies and systems of exercises developed in different periods have been studied, a comparative analysis of which has been carried out. As a result of the study, various typologies of exercises (language and speech exercises, receptive and reproductive exercises, complex language exercises, etc.) were identified and analyzed. The basic requirements for the exercise system are established: the development of cognitive processes, the inclusion of all linguistic aspects and types of speech activity, the growing level of difficulty, interdependence, repeatability, and communicative orientation. Also, the importance of exercises and the requirements for them have been determined in the methodology of Modern Language Teaching. Exercises are considered the basis of the process of teaching a foreign language, the formation and application of which depends on a number of factors. An effective exercise system should be aimed at the balanced development of cognitive, linguistic, and communicative aspects. A system of exercises developed on the basis of modern approaches makes it possible to effectively formulate the communicative competence of language learners.

A home-based tele-rehabilitation exercise system for patients after knee replacement surgery

BackgroundKnee arthritis is a destructive disorder that affects the knee joints and causes pain and reduced mobility. Drug treatments, weight loss, and exercise control the symptoms of the disease, but these methods only delay the disease process and eventually, knee joint replacement surgery will be needed. After surgery, with the help of a proper physiotherapy program, full recovery takes an average of 6–12 months. However, currently, there is no similar tool to facilitate this process in Iran.ObjectiveThe purpose of this research is to design and develop the prototype of a rehabilitation system for patients after knee replacement surgery, which provides patients with information and appropriate physiotherapy programs.MethodsThis study was development-applied and was done in three stages. In the first stage, the needs and content of education and therapeutic exercises were prepared in the form of a checklist, and then the necessity of each item was checked in the evaluation sessions. In the second stage, the prototype of the system was developed using Adobe XD software and based on the requirements approved from the previous stage. In the third stage, the usability of the program was analyzed from the point of view of experts using the exploratory evaluation method with Nielsen’s 13 principles of usability.ResultsAt first, the system requirements were extracted and prepared in two checklists of content (51 exercises) and capabilities (60 items). Then after a survey of experts based on the Delphi technique, content information (43 exercises) and functional and non-functional requirements (53 items) were obtained. A survey of experts helped to finalize the information elements, categorize them, and prepare the final version of the checklists. Based on this, the system requirements were classified into 11 categories, and the training content was classified into 3 training periods. Finally, the design and development of the system was done. This system has admin, physician, and patient user interfaces. The result of usability showed that this system is efficient and there are only a few problems in the feature of helping users to detect and recover from errors.ConclusionIt seems necessary to develop a system based on the principles of usability by therapists and rehabilitation specialists to train and monitor the remote rehabilitation process of patients after knee joint replacement at home. And the importance of involving stakeholders in the design and development of remote rehabilitation systems is not hidden from anyone. Kara system has all of the above.

ENRICHMENT OF SYSTEMS THINKING FOR PROBLEM SOLVING IN ENGINEERING SYSTEMS USING THE HERMENEUTIC LOOP: CASE STUDIES FOR DESIGNERS IN THE FIELD OF REMOTE COMMUNICATION

The systems thinking approach to problem solving is a dynamic and productive approach. However, emphasizing only the objective aspects of a phenomena and neglecting their subjective aspects can decrease its problem solving power. In order to enhance the problem solving power of systems thinking, it is proposed in this article to enrich systems thinking by using the hermeneutic loop. This applied research adopts practical aspects of systems thinking and hermeneutics in understanding and solving problems more effectively. The proposed approach is comprised of an additive loop, named the hermeneutic loop, concerning simultaneously “the whole system and its parts” and “subjectively and objectively”. Requirements of systems thinking are highlighted involving the holism and life cycle of the system. A summary of the considerations for systems thinking regarding holism and lifetime are as follows: (1) feedback in lifetime, (2) feed forward, (3) combining human and tools, (4) resources, (5) stock and flow and (6) carrying capacity (saturation). The utilization of this enriched systems thinking is investigated in three real engineering design processes, including case studies for designers in the field of remote communication. As the result, the proposed enriched systems thinking provides an improved understanding of the whole, which improves the solving power of the designers of components, and adds subjective product design to the objective design.

A robust method for multi object tracking in autonomous ship navigation systems

A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the Multi-Object Tracking employs You Only Look Once version 7 detector for object detection. In the data association part, a module for onboard camera motion compensation is developed, a maritime dynamic spatial information-based intersection-over-union is presented as a similarity metric, and a progressive refinement cascade matching strategy is designed to enhance the tracker's sea-surface multi-object tracking capabilities. The Jari Maritime Tracking Dataset is utilised to validate the effectiveness performance of the proposed method. Experimental results demonstrate that compared to the earlier process, the proposed method exhibits a significant enhancement in multiple object tracking accuracy, with an increase of 27.8% and an achieved score of 81.3. In particular, it reduces the number of identifications switching and missed targets, achieving holistically preferable performance. Meanwhile, the speed of the proposed Multi-Object Tracking fulfils the engineering application requirements for an autonomous ship navigation system.

Blockchain Technology and Smart Contract Application in Security Management of Intelligent Chemical Plants

As blockchain technology and smart contracts develop, computer technology is constantly integrating with smart chemical plants. Due to the continuous development of intelligent chemical plants, their systems have gradually become large and dispersed, posing a threat to safety management. In order to improve the performance of intelligent security management systems, the study first explores the principles of blockchain and smart contract technology, and then combined with the requirements of intelligent chemical plant security management systems, designs an intelligent security management system based on blockchain and smart contract technology. The experimental results showed that compared to systems without smart contract support, the communication success rate between nodes was lower. The error rates of blockchain-based encryption systems, deep learning-based encryption systems and improved data encryption systems proposed in the study were 0.22, 0.07 and 0.09, respectively. The packet loss rates were 0.13, 0.04 and 0.05, respectively. The lower the bit error rate and packet loss rate of the encryption system, the clearer the illegal eavesdropping information. The experimental results indicate that the intelligent security management system designed in this study has good encryption performance and a higher communication success rate. The results have certain reference value in security management application in intelligent chemical plants.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

Stretching Aligned Hydrogen Bonding Network to Evoke Mechanically Robust and High-Energy-Density P(VDF-HFP) Dielectric Film Capacitors.

Polymer-based dielectric film capacitors are essential energy storage components in electronic and power systems due to their ultrahigh power density and ultra-fast charge storage/release capability. Nonetheless, their relatively low energy density does not fully meet the requirements of power electronics and pulsed power systems. Herein, a scalable composite dielectric film based on a ferroelectric polymer with edge hydroxylated boron nitride nanosheets (BNNS-OH) is fabricated via the construction of a hydrogen bonding network and stretching orientation strategy. The presence of hydroxyl groups on boron nitride aids in forming a robust hydrogen bonding network within the ferroelectric polymer, leading to a significant increase in Young's modulus and superior dielectric performance. Furthermore, the stretching process aligns the BNNS-OH and the hydrogen bonding network along the drawing direction via covalent and hydrogen bonding interaction, resulting in a remarkable tensile strength (109MPa), breakdown strength (688 MVm-1), and energy density (28.2 Jcm-3), outperformingmostrepresentative polymer-based dielectric films. In combining the advantages of a simple preparation process, extraordinary energy storage performance, and low-cost raw materials, this strategy is viable for large-scale production of polymer-based dielectric films with high mechanical and dielectric performance and opens a new path for the development of next-generation energy storage applications.

Protective layer constructed by liquid phase quenching for long lifespan potassium ion batteries

Manganese-based transition metal layered oxides have garnered considerable attention for potassium-ion batteries (KIBs) due to their high energy density, high conversion efficiency, long lifespan and simple manufacturing processes. However, the slow K+ diffusion rate and poor stability fail to meet the increasing requirements of the energy storage system. The quenching technology was employed herein to in-situ form a thin protective layer of KMn4(PO4)3 with a high K+ diffusion rate on the surface of K0.5Mn0.85Co0.1Fe0.05O2. The introduction of the protective layer enhances electrochemical kinetics to promote high-rate electrochemical performance and reinforcing the ion diffusion dynamics, thus alleviating stress caused by uneven K+ distribution. Meanwhile, the reduction in Mn3+ content plays a crucial role in suppressing the Jahn-Teller distortion effect, contributing to enhanced cyclic stability. It exhibits an excellent electrochemical performance with a reversible specific capacity of 100.5 mAh g−1 at 0.2C, maintaining 72.97 % capacity after 500 cycles at 2C (1C=100 mA g−1). Coupled with graphite as the anode, the full cell shows a capacity retention of 73.53 % after 650 cycles at 2C, highlighting the potential for future practical applications.

Intelligent warehousing performance management based on Internet of Things and automation technology in the context of green manufacturing

At present, there is a significant imbalance in the development level of warehousing in China, and most enterprises still use traditional material identification technology, resulting in many unnecessary manual operations in the management process, thereby reducing efficiency and causing waste. Therefore, in the context of green manufacturing, we propose an intelligent warehousing performance management solution based on Internet of Things technology and automation technology. By conducting research on the development and research status of the Internet of Things and intelligent warehousing at home and abroad, this paper delves into the various requirements of intelligent warehousing systems and formulates a targeted architecture for this system. Through experiments, it has been proven that the intelligent warehouse performance evaluation model improved by dual technology can effectively improve warehouse management level, reduce enterprise costs, and enhance enterprise efficiency. After in-depth analysis, it is concluded that this method has considerable adequacy and practicality in evaluating the performance management of intelligent warehousing. Therefore, in the context of green manufacturing, the intelligent warehouse performance management system built by adding IoT technology and automation technology has achieved automation and intelligence in performance management evaluation and warehouse entry and exit management, and has extremely high practical value.

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Integrated Bionic Human Retina Process and In‐Sensor RC System Based on 2D Retinomorphic Memristor Array

AbstractThe rapid development of machine vision has put forward higher requirements for image perception and visual learning systems. However, limited by the von Neumann bottleneck, traditional artificial vision systems rely on complex optical and circuit designs, and the separate distribution of sensors, memory, and computing modules results in high energy consumption and high latency. Memristor‐based reservoir computing (RC) system with integrated sensing and memory functions provides a solution to effectively improve the computational efficiency of artificial vision networks. Here, a complete artificial visual recognition system based on in‐sensor RC is developed by combining the excellent optoelectronic synaptic properties of 2D WS2 memristor array. Through effectively regulating the conductance of memristors by light or electrical stimulation, image information is converted into high‐dimensional information in the reservoir layer, and the classification can be completed by simple training, significantly reducing the training complexity and cost consumption. With this system, an effective recognition of hand‐written numbers with an accuracy of 88.3% is demonstrated, and a 100% recognition of traffic signals of different colors is achieved. The proposed in‐sensor RC system advances the development and application of artificial vision recognition, paving the way for efficient machine learning and neuromorphic vision systems.

Hybrid Radix-4 SESA/SEDA Adders for Medical Image Processing

Adders play a critical role as primary modules in circuit design, supporting error-tolerant applications such as machine learning, digital image processing, and signal processing. Therefore, the development of adders with low power consumption and energy efficiency is of utmost importance. Approximate adders outperform exact adders in VLSI chips. Among approximate adders, the hybridization of Single Exact and Single Approximation (SESA) and Single Exact and Dual Approximation (SEDA) techniques has emerged as a promising solution. These hybrid adders offer superior power and energy savings relative to other accurate and approximate adder designs. In this scheme, we propose the integration of hybrid radix-4 adders with approximation adders to achieve enhanced performance and pave the way for future hybrid approximate adder circuits. Extensive simulations validated the efficacy of the proposed approach. Furthermore, practical applications demonstrated the versatility and potential of hybrid adders in real-world scenarios. The proposed methodology ensures cost-effectiveness and facilitates seamless future updates and extensions. Overall, this research contributes to the advancement of efficient and low-power adder designs, addressing the evolving requirements of modern digital systems.

Cryogenic system of the high performance 1.3 GHz 9-cell superconducting radio frequency prototype cryomodule

High quality factor 1.3 GHz 9-cell superconducting radio frequency (SRF) cavity cryomodule is the core technology of the international advanced accelerator. China’s first high-quality factor 1.3 GHz 9-cell SRF cavity cryomodule was developed, assembled and tested at the Institute of High Energy Physics (IHEP), Chinese Academy of Sciences for the Dalian Advanced Light Source (DALS) and Circular Electron Positron Collider (CEPC) R&D. The 9-cell cavities in the cryomodule achieved a high average intrinsic quality factor (Q0) of 3.8 × 1010 at 16 MV/m and 3.6 × 1010 at 21 MV/m in the horizontal test. With such high specifications, the SRF cavity system has corresponding requirements for the cryogenic system. Key criteria for stable and reliable operation include a dependable cryogenic refrigeration system and process design, good thermal insulation, pressure control, temperature uniformity, and quench protection, etc. IHEP has successfully completed cryogenic testing development of the first 1.3 GHz 9-cell SRF prototype cryomodule. The whole cooldown period was 74 h, and the automatic cooldown was completed. The successful development and implementation of the 1.3 GHz superconducting cryomodule cryogenic system will provide a stable and reliable test environment for advanced superconducting cavity cryogenic testing, which will be critical in promoting the further development of the Free Electron Laser (FEL) and CEPC projects.

The Effect of Napier Grass+Cowpea Intercropping and Different Fertilizer Levels on Soil Characteristics and Economics

Background: The field experiment was conducted to check the optimization of fertilizer requirement for napier grass and cowpea intercropping system at the farm of Lovely Professional University, Phagwara, Punjab during kharif season (2022). Methods: Three replications of factorial RBD design with 4 cropping patterns (I1, I2, I3 and I4) and 3 fertilizer levels (F1, F2 and F3) were set up. Study was conducted on total twelve treatment combinations, I1 (Sole napier with 100, 75 and 125% RDF), I2 (Sole Cowpea with 100, 75 and 125% RDF), I3 (Napier grass+cowpea intercropping in 1:2 ratio with 100, 75 and 125% RDF) and I4 (Napier grass+cowpea intercropping in 1:3 ratio with 100, 75 and 125% RDF). Result: The results showed that in the interaction effect of intercropping and fertilizer levels the the highest value of pH and EC was found to be in I1F2- Sole Napier+75% RDF (8.04 and 0.42 mhos cm-1, respectively). Higher OC was found in I4F3- Napier+cowpea (1:3)+125% RDF (18.23%) as compared to sole napier. However, sole cowpea plots recorded highest OC (18.60 %) in I2F3- Sole Cowpea+125% RDF but lowest pH and EC. The lowest N, Pand K in soil were seen in I1F2- Napier grass+75% RDF (90.00, 10.93 and 124.4 kg ha-1, respectively) in interaction of intercropping and fertilizer levels among treatment combinations of napier grass. I2F3- Sole cowpea+125% RDF recorded (225, 20.68 and 214.93 kg ha-1) N, P and K, respectively, which was highest among all the treatment combinations. Highest net returns and B: C ratio of 3.37 was recorded from intercropping ratio 1:3 and 125% RDF which was followed by 100% RDF (3.13) and 75% RDF (2.77).

Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets.

Spesifikasi Kebutuhan Perangkat Lunak Sistem Informasi E-Survey Badan Litbang dan Diklat Kementerian Agama dengan standar ISO/IEC/IEEE 29148:2018

The digitalization of survey processes is crucial in today's era, and this research aims to design the software requirements specification for the E-Survey Information System for the Research and Training Agency of the Ministry of Religious Affairs of the Republic of Indonesia. The methodology employed follows the ISO/IEC/IEEE 29148:2018 standard in developing the System Requirements Specification (SRS) document. This comprehensive SRS outlines the purpose, scope, product overview, user characteristics, limitations, functional requirements, non-functional requirements, database design, interface, and use case diagrams. The functional requirements encompass survey management, survey question management, user management, role management, and accessing surveys. Non-functional requirements address performance and user-friendliness aspects. The result is a detailed SRS document that serves as a guideline for the development team in building an integrated and modern E-Survey application. This application will streamline and digitize the survey activities conducted by the Research and Training Agency, enabling efficient data collection, processing, and analysis. By following industry standards, the developed system aims to enhance productivity, centralize survey operations, and support informed decision-making processes related to strengthening religious moderation and harmony among religious communities in Indonesia