Intelligent System Technologies Research Articles

Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Methodological Problems and Strategic Goals of the Work on Creation of the Theory and Technology of New Generation Intelligent Computer Systems

The main methodological problems and strategic goals of work on creating the theory and technology of intelligent computer systems of a new generation are considered. The problems of the current state of work in the field of artificial intelligence and their causes are studied, and the need for a transition to a new generation of intelligent computer systems is substantiated. The basic principles underlying such systems, as well as the corresponding complex technology, are presented. The transition from local automation when solving particular problems to complex automation of various areas of human activity is justified. The concept of an ecosystem of interoperable intelligent computer systems is proposed, which is the basis for complex automation. The architecture of the ecosystem, the directions of its evolution, and the classes of systems included in the ecosystem are considered.

Implementasi Profil Pelajar Pancasila melalui Kegiatan Ekstrakurikuler Robotik dan Smart Farmer di SMAS Santo Tarcisius Dumai

Santo Tarcisius High School is the first driving school in Dumai City, so Santo Tarcisius High School has the responsibility to implement the Pancasila Student Profile for its students. One of the characteristics of the Pancasila Student Profile is creative, namely being able to modify and produce something original, meaningful, useful and impactful. To realize the Pancasila Student Profile, an effort is needed to provide knowledge and skills to students in an interesting way, one of which is through robotics and smart farmer extracurricular learning activities. Based on the results of discussions with the school principal, the problems faced in implementing robotics and smart farming extracurricular activities include 1) Students have never had an understanding of computer and robot-based intelligent farming system technology, 2) They do not yet have teachers who are competent in the field of robotics, 3) Limited costs purchasing robotic electronic equipment and installing smart farmers at schools. Based on the problems above, the aim of this activity is to realize the Pancasila Profile, especially in the IT sector, through education on making Line Follower robots and smart farming systems for hydroponic plants. The implementation method is by dividing students into several groups to make Line Follower robots. The number of students in this activity is 150 students/class XI SMA Santo Tarcisius. Success in Beginner Community Service (PMP) is to improve students' skills in making robots and intelligent systems which is measured through the final product results, pre test and post test questionnaires, the product results from this PMP are line follower robots and smart farmer systems, the conclusion of the PMP This is to increase efficiency, productivity and sustainability in the agricultural sector through the application of the latest technology.

Recent Topology-based Routing Approaches in VANETs: A Review

Due to increasing both safety and efficiency of the traffic, Vehicular Ad Hoc Network (VANET) is a promising technology of Intelligent Transport Systems (ITS). Unique characteristics of VANETs including high mobility and strict delay constraints, require new routing solutions specific to these networks to be proposed. As one of those solutions, topology-based routing approaches aim to find the shortest path by managing routing tables. In this paper, recent topology-based routing approaches for VANETs are investigated in detail. Proactive, reactive and hybrid solutions are compared with respect to their advantages, disadvantages, updating procedures and network sizes. This paper will shed light on future studies since it provides detailed information about the current status of the literature in topology-based routing approaches in VANETs.

Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.

Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.

Research on a Ship Mooring Motion Suppression Method Based on an Intelligent Active Anti-Roll Platform

Conventional ship mooring in ports has many shortcomings such as a high safety risk, low efficiency and high labor intensity. In order to explore and develop the theory and key technologies of intelligent automatic mooring systems, this paper takes an intelligent mooring system based on a parallel anti-rolling mechanism as the research and development object. A new mooring method integrating ship hydrodynamics, mechanism kinematics and intelligent algorithms is proposed. Through numerical simulation and comparative analysis of the model, the motion inhibition effect of mooring ships under different working conditions is obtained. The results show that the control strategy and intelligent algorithm of the system can realize the active control of the wharf mooring ships and achieve the goal of improving wharf stability conditions through an intelligent mooring system.

Frequency-angular resolving LiDAR using chip-scale acousto-optic beam steering.

Thanks to its superior imaging resolution and range, light detection and ranging (LiDAR) is fast becoming an indispensable optical perception technology for intelligent automation systems including autonomous vehicles and robotics1-3. The development of next-generation LiDAR systems critically needs a non-mechanical beam-steering system that scans the laser beam in space. Various beam-steering technologies4 have been developed, including optical phased array5-8, spatial light modulation9-11, focal plane switch array12,13, dispersive frequency comb14,15 and spectro-temporal modulation16. However, many of these systems continue to be bulky, fragile and expensive. Here we report an on-chip, acousto-optic beam-steering technique that uses only a single gigahertz acoustic transducer to steer light beams into free space. Exploiting the physics of Brillouin scattering17,18, in which beams steered at different angles are labelled with unique frequency shifts, this technique uses a single coherent receiver to resolve the angular position of an object in the frequency domain, and enables frequency-angular resolving LiDAR. We demonstrate a simple device construction, control system for beam steering and frequency domain detection scheme. The system achieves frequency-modulated continuous-wave ranging with an 18° field of view, 0.12° angular resolution and a ranging distance up to115 m. The demonstration can be scaled up to an array realizing miniature, low-cost frequency-angular resolving LiDAR imaging systems with a wide two-dimensional field of view. This development represents a step towards the widespread use of LiDAR in automation, navigation and robotics.

An Improved YOLOv7 Model Based on Visual Attention Fusion: Application to the Recognition of Bouncing Locks in Substation Power Cabinets

With the continuous progress of intelligent power system technology, in order to meet the needs of substation operation and maintenance, a target detection algorithm is applied to identify the status of equipment switches. YOLOv7, as the latest achievement of YOLO (You Only Look Once) series algorithms, has good speed and accuracy in target detection tasks. However, when the generalized network is applied in a specific scenario, its advantages are not obvious due to its high weight and poor portability. In this paper, an improved GF-YOLOv7 network model is proposed to apply in the recognition of the status of bounce locks in a substation. The MobileViT module is used to improve the feature extraction ability of the backbone network. Referring to the CBAM feature attention mechanism, the channel attention module and the spatial attention module are used to design a more lightweight feature fusion network. The experimental results in the test set show that the proposed network can significantly reduce the network weight and improve the detection accuracy on the basis of a small reduction in the detection speed, and the accuracy reaches 97.8%, which can meet the needs of the detection task of substation bounce locks.

Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels

With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme.

Application technology of intelligent weak current engineering system in intelligent building

The popularization of electronic equipment makes the weak current system more and more important. This paper mainly describes the development of intelligent weak current engineering system in China and the application technology of intelligent weak current engineering system in the field of intelligent building in today's advanced science and technology and people's increasingly high demand for housing. On this basis, the development direction of the intelligent weak current engineering system in China is expounded, and a concrete example is given to illustrate how the intelligent weak current engineering system can realize the comfort and safety of people's houses in real life. It is believed that with the economic development of China and the improvement of intelligent weak current engineering system technology, people's working environment in intelligent buildings will become better and more convenient.

Optimalisasi Penentuan Kriteria Siswa Kelas Unggul dengan Metode Simple Additive Weighting

Education has an important role that is very decisive, not only for the development and realization of individuals but also for the development of the nation and state. This relates to the same quality of education and must be provided to Indonesian citizens, both normally and differently. The process of class division every new academic year is usually still carried out conventionally, namely by means of the sorting method and does not involve intelligent system technology. Therefore, not all students who enter the superior class have the desired criteria. The use of a Decision Support System (DSS) is expected to help the decisions made in selecting and determining who is the best student, considering that so far no particular method has been used in selecting students so that sometimes decisions are considered less objective and not on target. This decision support system using the simple additive weighting (SAW) method is processed with data sourced from student data at SMP Negeri 25 Padang. This research was conducted by finding the weight value for each attribute, then a ranking process was carried out that would determine the optimal alternative, namely the best students. Based on the analysis of the decision support system with this method is done by taking 3 criteria, namely the criteria for attitudes, criteria for value and criteria for ranking. Where the results of the researcher are the rankings that can support the best decisions for the criteria of superior class students. From the researcher, it can be concluded that the best students have high attitude criteria, high scoring criteria, and high ranking criteria. Based on the data that has been researched, there are students who score Exelia Monica with code A5 having a value of 3.

The Integral Role of Intelligent IoT System, Cloud Computing, Artificial Intelligence, and 5G in the User-Level Self-Monitoring of COVID-19

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19’s economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19.

Construction technology of intelligent manufacturing service systems driven by industrial big data

Construction technology of intelligent manufacturing service systems driven by industrial big data

An Answer Verification Approach to Objective Questions in Intelligent Tutoring Systems

An approach to develop a problem solver for automatic answer verification to objective questions in intelligent tutoring systems constructed using OSTIS (Open Semantic Technology for Intelligent Systems) technology is сonsidered. The problem solver is developed based on multi-agent technology within OSTIS. The proposed problem solver automatically verifies the correctness and completeness of the user’s answer at the semantic level by starting the corresponding sc-agent sets according to the type of the questions. The objective question answer verification is one of the main purposes of the developed problem solver, it can also perform the calculation of the similarity between any two semantic graphs.

Organization of online learning using the intelligent metasystem of open semantic technology for intelligent systems

Distance learning today allows you to create a system of mass continuous self-learning, universal exchange of information, regardless of the presence of time and space zones. For more effective online learning, it is necessary to introduce artificial intelligence technologies and methods for implementing learning systems. In this paper, the objects of research were: 1) the capabilities of the OSTIS open semantic technology, the capabilities of the IMS.OSTIS metasystem for organizing online learning; 2) machine learning method – classification. Results of the study: 1) an online course was organized using the IMS.OSTIS metasystem of the OSTIS open semantic technology; 2) for the analysis and visualization of training data, a machine learning method is implemented – classification. The results of the implementation of the online course were obtained using the semantic technology for designing an intelligent learning system: the IMS.OSTIS metasystem using the graphical semantic code SCg. The OSTIS kernel requires a machine with the Ubuntu operating system installed, which is a GNU/Linux distribution based on Debian GNU/Linux, an operating system based on the Linux kernel. The paper also shows an example of using the machine learning method – classification. This method allows you to classify data. Intelligent processing and visualization of data were carried out based on the results of testing students in order to classify them into letter categories A, B, C, D according to a set of features: scores and points of the average score. The high-level Python library Pandas was used, this is a library for data analysis. To visualize the results of data processing, the Matplotlib library in Python was used

Digital twin-driven intelligent maintenance decision-making system and key-enabling technologies for nuclear power equipment

In the life cycle of nuclear power equipment (NPE), the long-term high-safety maintenance services play a vital role in ensuring their optimal operation. However, as the complex system equipment with high-safety requirements and high costs, there are lots of limitations of traditional time-based maintenance strategies for NPE. For example, the maintenance service process is invisible, the condition monitoring is mainly based on manual inspection, and the maintenance decision-making mainly depends on personal experience passively. Digital twins (DT) are an effective way to break the “information isolated island” in the whole life cycle, which can give full play to the value of data to realize the visualization of operation process of NPE. Nevertheless, nowadays, the application of DT in the field of nuclear industry is at the exploration stage, and there is lacking systematic and practical research. Thus, a novel DT-driven intelligent maintenance decision-making system involving three key-enabling technologies is proposed in this paper. Firstly, the DT-driven maintenance service mode is introduced, and its corresponding system framework is built. Then, the key enabling technologies such as DT modeling, condition monitoring and dynamic pre-alarm, and systematic intelligent maintenance decision-making and verification are expounded in detail. Finally, the cooling water pump is regarded as the case to verify the proposed method. The DT prototype system is developed and verified in the novel system, which demonstrates the novel system and the three key-enabling technologies are feasible and practical.

Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors.

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.

Cyber-Physical System based E-Health: Knee Joint Physical Therapy Monitoring

A cyber-physical system (CPS) uses sensors/actuators to associate the physical world with a system capable of computing. CPSs include software systems, communication technologies, and sensors/actuators, including intelligent embedded system technologies to interact with the real world. It is possible to monitor and control all physical processes without human factors by connecting the mechanical systems equipped with information technologies and establishing communication among themselves. In this study, a CPS system that offers a cost-effective and easy-to-apply physical therapy opportunity is proposed for post-hospital home follow-up of knee osteoarthritis, which can be seen in many people over a certain age. The proposed system consists of a hardware module consisting of two potentiometers, a microcontroller and a WiFi module, and a software module to monitor the movement of the knee joint point daily and to save the data in the database. Maximum knee flexion angle, exercise duration and success rate measurements were used to measure the progress of physical therapy. The CPS prototype developed for the healthcare field will make significant contributions to reducing the duration of physical therapy and increasing its reliability by eliminating the possibility of wrong/incomplete exercises.

INTELLIGENT TRANSPORT SYSTEMS AS TRAFFIC FLOW MANAGEMENT TOOL (THE CASE OF KYIV)

The low quality of public transport services, the inconvenience of routes, the long duration of trips and several other problems encourage Kyiv residents to increasingly choose a car for everyday correspondence, which, in turn, increases delays, travel time and leads to environmental pollution of territories. These problems are especially acute in large cities, where numerous industrial, labour, and cultural and household connections are within the city. There is a need to create a single efficient transport system that serves as a city and implement effective traffic flow management approaches. 
 First of all, the article defines the concepts of "traffic flow management", "urban mobility" and "urban mobility regime". Four groups of problems of urban transport systems were identified and two ways of solving them were formed: extensive and intensive. Using the instrumental case of Kyiv, the level of implementation of systems for monitoring and managing traffic flows in Ukrainian cities was evaluated. The levels of the legal framework regulating the urban mobility of the population in Ukraine are described. The characteristics of intelligent transport systems and their key subject areas have been studied. Commonly accepted services for users of intelligent transport systems (ITS) are distinguished and segmented according to the needs of these system users. Features, methods of application and technologies of intelligent transport systems are presented. Services that are a priority for potential users of intelligent transport systems in Ukrainian cities are highlighted. The world experience of the formation and development of intelligent transport systems, as well as European programs for their implementation, is characterized. A SWOT analysis of the implementation of intelligent transport systems in Kyiv was carried out and recommendations were made for the creation of an ITS implementation program in Kyiv. 
 The information base of the article consists of publications by prominent Ukrainian and foreign researchers, data from the Tom Tom transport analytics service, the current legal framework of Ukraine, which regulates urban transport systems, and urban development strategies. The following methods of scientific research are used in the research process: dialectical method; method of scientific abstraction, analysis and synthesis; method of induction and deduction; comparative method; method of systematization and graphic method.

Integrating Transparency, Trust, and Acceptance: The Intelligent Systems Technology Acceptance Model (ISTAM)

Intelligent systems such as technologies related to artificial intelligence, robotics, machine learning, etc. open new insights into data and expand the concept of work in myriad domains. These technologies, while potentially useful, face high barriers to widespread adoption and acceptance by industries and citizens alike. The complexity and multi-dimensionality inherent in intelligent systems often renders traditional validation efforts (e.g., traceability analysis) impossible. In addition, contexts where predictions or computer-generated recommendations have real-world consequences, such as in medical prognosis, financial investing, or military applications introduce new risks and a host of moral and ethical concerns that can further hinder the widespread adoption of intelligent systems. Naturally, such reluctance by would-be users limits the potential of intelligent systems to solve real-world problems. This article reviews the challenges to technology acceptance through the lens of system transparency and user trust, and extends the Technology Acceptance Model (TAM) structure with issues germane to intelligent systems. We examine several prospective transparency frameworks that could be adopted and used by Human-Computer Interaction (HCI) practitioners involved in systems development. Our intention is to assist practitioners in the design of more transparent systems with a specific eye towards enhancing trust and acceptance in intelligent systems. Further, as a result of our review, we suggest that the well-known TAM should be expanded in the context of intelligent systems to include trust and transparency as key elements of the model. Finally, we conclude with a research agenda that might offer empirical evidence showing how transparency might enhance acceptance and use of intelligent systems.