Intelligent Operation Research Articles

Radio frequency fingerprint-based drone identification and classification using Mel spectrograms and pre-trained YAMNet neural

The convergence of drones with the Internet of Things (IoT) has paved the way for the Internet of Drones (IoD), an interconnected network of drones, ground control systems, and cloud infrastructure that enables enhanced connectivity, data exchange, and autonomous operations. The integration of drones with the IoD has opened up new possibilities for efficient and intelligent aerial operations, facilitating advancements in sectors such as logistics, agriculture, surveillance, and emergency response This paper introduces a novel approach for drone identification and classification by extracting radio frequency (RF) fingerprints and utilizing Mel spectrograms as distinctive patterns. The proposed approach converts RF signals to audio signals and leverages Mel spectrograms as essential features to train neural networks. The YAMNet neural network is employed, utilizing transfer learning techniques, to train the dataset and classify multiple drone models. The initial classification layer achieves an impressive accuracy of 99.6% in distinguishing between drones and non-drones. In the subsequent layer, the model achieves 96.9% accuracy in identifying drone types from three classes, including AR Drone, Bebop Drone, and Phantom Drone. At the third classification layer, the accuracy ranges between 96% and 97% for identifying the specific mode of each drone type. This research showcases the efficacy of Mel spectrogram-based RF fingerprints and demonstrates the potential for accurate drone identification and classification using pre-trained YAMNet neural networks.

Predictive Control of an Intelligent Energy-saving Operation System Based on Deep Learning

An intelligent energy-saving operation system is a high-tech product specifically designed to transform the air conditioning systems, motor systems, and lighting systems, to reduce energy consumption. The concentration of equipment distribution within these systems leads to a strong coupling relationship between them. By conducting an overall energy efficiency prediction, the intelligent energy-saving operation system can fully explore its energy-saving potential. The existing research methods for the online control process of intelligent energy-saving operation systems are not accurate enough to predict energy-saving operations when numerous devices are involved. Consequently, this article focuses on studying the predictive control of an intelligent energy-saving operation system using deep learning techniques. The Generalized Regression Neural Network (GRNN) network is selected to describe the energy consumption of the system. The Beetle Antennae search algorithm is then employed to iteratively optimize the smoothing factor of the model, eliminating the need to rely on experiential parameter determination and enhancing the predictive performance of the model. For the predictive control of the intelligent energy-saving operation system, the optimized GRNN network model serves as the prediction model. The primary control objective is to minimize energy consumption while maintaining a unified carrying capacity, thus achieving intelligent energy-saving effects. Experimental results validate the effectiveness of the model.

Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit

To achieve the economic and environmentally friendly operation of circulating fluidized bed (CFB) units, it is imperative to conduct optimization to obtain an economical mode of pollutant removal. This article focuses on the multi-objective optimization between SO2-NOx emissions and thermal efficiency for CFB units. According to the operation data and production mechanism of pollutants, models of SO2-NOx emission concentration, bed temperature, and oxygen content based on a convolutional neural network–bidirectional long short-term memory–attention mechanism (CNN-BiLSTM-Attention) were established. Then, an improved quantum genetic algorithm was used to find the optimal input variables of the SO2-NOx emission model. The proposed modeling method was evaluated, and it more accurately simulated the trends of actual operation data than other models under different operating conditions. Combining these models with economic calculations, the operating costs under typical conditions were reduced by 3.20% and 1.82% respectively, and the thermal efficiency increased by 0.72% and 1.07%, which contributes to economical and intelligent operation of the unit.

Reinforcement learning method for the multi-objective speed trajectory optimization of a freight train

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line.

Automated and Intelligent Satellite Mission Operations Experiences with the N2 Satellite Ground Segment and Future Directions

Automated and Intelligent Satellite Mission Operations Experiences with the N2 Satellite Ground Segment and Future Directions

Digital Collaborations Set To Begin Unlocking Production

_ In the brave new world of oil and gas production, digital alliances are emerging as a powerful tool for minimizing costs and maximizing efficiencies while ensuring safe and reliable operations. In the fast-paced world of business and innovation, an old proverb often rings true: “If you want to go fast, go alone. If you want to go far, go together.” Innovative industry collaborations are making a difference, with traditional oil and gas service firms like Baker Hughes and SLB forming strategic partnerships with cutting-edge digital technology providers, leveraging the expertise of both to advance abilities and operations. By leveraging technology such as artificial intelligence, automation, and remote operations, the oil and gas industry can monitor valuable resources with increased granularity and deploy its talented teams to concentrate on mission-critical endeavors, driving growth and maximizing their potential for success. By harnessing the power of digital, oil and gas companies can now access real-time data, enabling them to make more informed decisions and streamline processes. On the flip side, the digital technology companies involved in these partnerships gain valuable insights and domain knowledge from their energy counterparts, leading to tailored solutions to meet the specific needs of the energy sector and potentially opening new market opportunities. Need for Value The driving force behind these collaborations is the shift from a grow-at-all-costs mentality to one focused on value per barrel, according to Nikhil Patel, partner at McKinsey & Company. “These collaborations reflect a broader need for value from digital,” said Patel. “We’ve had multiple rounds of market price challenges and multiple rounds of oil and gas companies pulling the usual levers to bring costs down and production up. Those cannot be pulled much further. Digital remains one of the few means to improve performance meaningfully.” The challenge, Patel said, is finding a way to thrive and succeed at the goal of increasing the value per barrel of production. “Digital is central to that,” he said. “Digital transformation isn’t new to the industry, which has invested billions of dollars into digital. The results, however, have been ambiguous to date.” Patel said there’s a question about what is holding the oil and gas industry back from delivering on the full promise of digital. “While everyone believes in its potential, many companies, after a decade of trying to make it work, are stepping back and asking, ‘What will it take?’”, he said, adding that McKinsey has found in its research on the degree of success and failure that less than 30% of digital transformations succeed. The answer can be found at the heart of those collaborative partnerships that understand and respect the knowledge and skills of each partner. “Oil and gas companies are really good at producing barrels in a capital- and risk-heavy environment, but struggle to keep up with digital change,” he said. “The tech companies are setting the pace of digital change but aren’t built to drive change into the oil patch. They’ve recognized that they can do something special by combining their strengths and offsetting their gaps.”

An Innovative Future through Robotic Process Automation, a Case Study

Abstract Artificial intelligence and robotic process automation are among the next generation of digital technologies that are transforming the workplace and our daily lives. And digital transformation has become a critical strategy for many businesses and their leadership. In today's fast-paced and ever-changing business environment, the cornerstone of organizational success is innovation. As the business environment is in perpetual modification and development, it requires businesses to be adaptive to both external and internal factors. The instability of the organizational environment increases the necessity and benefits of employee innovation. The authors analyze the extensive literature defining artificial intelligence, focusing on automation and specifically the role robotic process automation has in increasing organizational efficiency, reducing cost, and ensuring quality. In expanding on the scientific literature surrounding robotic process automation, the article analyses a case study of a Romanian tech company whose complex internal technology ecosystem has resulted in a disconnected landscape which required constant multi-system manual intervention in order to ensure process and data cohesion. The authors present the company’s response to the increased consumption of human capital on repetitive tasks that were increasingly subject to errors, in the form of robotic process automation and levels of machine learning and artificial intelligence. In following the progress of the company towards adopting robotic process automation to increase efficiency, speed, accuracy, and security of processes and tasks, the authors identify the benefits of directing human labor towards more meaningful activities and allowing the repetitive tasks to be shifted to robotic labor. The authors followed the progress of a service technology solutions organization that has established an IT intelligent operation ecosystem, with new strategies across various key departments such as finance, human resources, and Internal Audit, followed by their leadership perspective and thinking for the company's next steps in this case study.

Cold War History Studies in China in the 21st Century: The State of the Field

Abstract This survey explains how the field of Cold War studies has been able to survive and even flourish in the People's Republic of China (PRC) from 2000 to the present, despite all the practical and political obstacles. It reviews several areas that Chinese scholars have been exploring: the economic Cold War; foreign intelligence operations and psychological warfare; nuclear strategies; the sciences during the Cold War and overseas education projects; and China's policies toward neighboring countries during the Cold War. The article outlines the major practical challenges facing Chinese scholars and the potential for overcoming these challenges.

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

Leak monitoring is essential for the intelligent operation and maintenance of marine systems, and can effectively prevent catastrophic accidents on ships. In response to this challenge, a machine vision-based leak model is proposed in this study and applied to leak detection in different types of marine system in complex engine room environments. Firstly, an image-based leak database is established, and image enhancement and expansion methods are applied to the images. Then, Standard Convolution and Fast Spatial Pyramid Pooling modules are added to the YOLOv5 backbone network to reduce the floating-point operations involved in the leak feature channel fusion process, thereby improving the detection speed. Additionally, Bottleneck Transformer and Shuffle Attention modules are introduced to the backbone and neck networks, respectively, to enhance the feature representation performance, select critical information for the leak detection task, and suppress non-critical information to improve detection accuracy. Finally, the proposed model’s effectiveness is verified using leak images collected by the ship’s video system. The test results demonstrate that the proposed model exhibits excellent recognition performance for various types of leak, especially for drop-type leaks (for which the accuracy reaches 0.97).

A Whole-of-Society Approach to Organise for Offensive Cyberspace Operations: The Case of the Smart State Sweden

Threat actors conduct offensive cyberspace operations for many purposes, such as espionage, to destroy information assets, and cybercrime. These operations are possible thanks to the innovation and development of information and communications technologies (ICT). Interconnected information systems have transformed societies positively. However, specific states exploit these systems' vulnerabilities to advance their strategic national interests. Therefore, it is important to know how a state can organise itself to defend against threat actors. The purpose of this research is to present how the smart state Sweden can through a whole-of-society approach organise for Offensive Cyberspace Operations. The intent is to conduct an active and independent foreign-, security- and defence policy, but also as a base for deterrence and defence. This article is based on a mixed methods approach. It uses the case study research strategy to discover new information. Fourteen men and women participated in individual semi-structured interviews. The respondents ranged in age from 40 to 65 with more than 20 years of experience in cyberspace operations, intelligence operations, military operations, special forces operations, and knowledge and understanding about information warfare and information operations. The analytic strategies include thematic analysis and quantitative methods to interpret the data. The results show many themes, but the article is especially focused on the themes of Operations, Capability, Policy & Governance, and Legal Frameworks. Finally, a conceptual map of a whole-of-society approach to organise for offensive cyberspace operations is presented inferred from the themes, codes, and content, and mapped to each responsible agency based on the interviews and codes. The answer to the research question is that Sweden should have a whole-of-society approach to organise for Offensive Cyberspace Operations to project power in and through cyberspace with the intent to conduct an active and independent foreign, security and defence policy and for deterrence, as described in Figure 2.

An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay’s trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles’ in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP’s convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable.

A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings

This article aims to systematically summarize the methods for intelligent operation of large public buildings, the integration and application of related technologies, as well as their development trends and challenges. (1) Background: In response to the rapid development and future needs of intelligent operation and maintenance, this study summarizes the development process of intelligent operation and maintenance in building operations, as well as relevant technical achievements and challenges; (2) Method: Quantitative and qualitative bibliometric statistical methods were used for overall analysis; (3) Result: Based on system theory, a B-IRO model was developed, and the current status of intelligent operation- and maintenance-related technologies and applications was sorted out. A framework for the entire industry was established, and future development trends were proposed as further research directions.

HUMINT Operations Abroad: Challenges to Japan’s Intelligence Services

The purpose of this article is to explore a rather hypothetical assumption about Japan’s intelligence capabilities and the perspective of the emergence of case officers capable to run human intelligence (HUMINT) operations abroad. To examine this notion, the article first draws on the concept of intelligence culture and defines and analyzes the key attributes of an outstanding case officer as seen by top-tier intelligence agencies. Then, the study provides a brief overview of the historical development of the Japanese experience with HUMINT from the beginning of the twentieth century until the present. The final section dwells in bigger detail on the current state of affairs of Japan’s Intelligence Community and concludes that much importance should be paid to spotting and training the nation’s case officers.

Prospects for the application of artificial intelligence for the development intelligent operating rooms

Prospects for the application of artificial intelligence for the development intelligent operating rooms

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Abstract Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.

Intelligent safe operation and maintenance of oil and gas production systems: Connotations and key technologies

Oil and gas production systems have the characteristics of high operation and maintenance risk and great accident influence. With the deep integration of informationization and industrialization, the development direction and necessary choice of the oil and gas industry is to develop the oil and gas production system into the interconnected, multi-domain interactive cyber-physical intelligent system. In order to avoid or reduce the complex, diverse and potentially unknown safety risks in the process of oil and gas production, improve the safety and reliability of oil and gas production system and increase the production efficiency, this paper analyzes the safety problems occurring in the intelligentization process of oil and gas production system and constructs a system from the perspective of operation and maintenance based on key elements of intelligent safe operation and maintenance technology, combined with the typical production scenarios in the oil and gas production industry. And the following research results are obtained. First, the connotation of intelligent safe operation and maintenance technology is clarified, the key elements and existing problems and challenges of intelligent safe operation and maintenance technology are analyzed, and the “1-2-3-4-5-6” intelligent safe operation and maintenance technology system of oil and gas production system is constructed, which empowers six key technologies with key elements of oil and gas production to realize the essential safety of oil and gas production system. Second, the intelligent safe operation and maintenance technology actively promotes the application and implementation of condition monitoring, health management, risk assessment, intelligent early warning technologies in typical production scenarios such as drilling and extraction, storage and transportation, refining and chemical industry in up, middle and down streams of oil and gas production. Third, in view of the characteristics of oil and gas production system under digital transformation, it is proposed to develop the intelligent safe operation and maintenance technology with the functions of intelligent decision-making, active prevention and comprehensive safety in the future to help the safe construction in the field of oil and gas production and promote the safe and healthy development of the oil and gas industry. In conclusion, the research on intelligent safe operation and maintenance technology system of oil and gas production system is conducive to the safe construction in the field of oil and gas production, which will not only provide technical support for the realization of trouble-free oil and gas production system, but also provide reference for the intelligent development of the world oil and gas industry.

The Future of Management: DAO to Smart Organizations and Intelligent Operations

In the future, management in smart societies will revolve around knowledge workers and the works they produce. This article is committed to explore new management framework, model, paradigm, and solution for organizing, managing, and measuring knowledge works. First, the parallel management framework is presented that would allow for the virtual-real interactions of humans in social space, robots in physical space, and digital humans in cyberspace to realize descriptive, predictive, and prescriptive intelligence for management. Then, the management foundation models are proposed by fusing scenarios engineering with artificial intelligence foundation models and cyber–physical-social systems. Moreover, the new management paradigm driven by decentralized autonomous organizations and operations is formulated for the advancement of smart organizations and intelligent operations. On these basis, the management operating systems that highlight features of simple intelligence, provable security, flexible scalability, and ecological harmony are finally put forward as new management solution.

Crowd Sensing Intelligence for ITS: Participants, Methods, and Stages

The construction of transportation 5.0 or the so-called society-centered intelligent transportation systems (ITS) has aroused higher requirements for the intelligent sensing capability to seamlessly integrate Cyber-Physical-Social Systems (CPSS). Crowd Sensing Intelligence (CSI), as a promising paradigm, leverages the collective intelligence of heterogeneous sensing resources to gather data and information from CPSS. Our first Distributed/Decentralized Hybrid Workshop on Crowd Sensing Intelligence (DHW-CSI) has been focused on principles and high-level processes of organizing and operating CSI. This letter reports the discussion results of the second DHW-CSI addressing the participants, methods, and stages of CSI for ITS. We categorized sensing participants into three kinds, i.e., biological, digital, and robotic. Then we summarized three methods to enable sensing intelligence, i.e., foundation models, scenarios engineering, and human-oriented operating systems. Finally, we anticipated that the progression of CSI will experience three stages, from algorithmic intelligence to linguistic intelligence, and eventually to imaginative intelligence.

Design of Reactive Power Online Monitoring System of Intelligent Distribution Transformer Based on Edge Computing

Due to the unbalanced development of various regions, in most regions, the distribution transformer of medium voltage line has not realized automatic monitoring, so the staff cannot monitor it in real-time. Therefore, an online monitoring system for the reactive power status of intelligent distribution transformers based on edge computing is designed. The system hardware structure configuration consisting of the master station layer, the edge layer and the acquisition layer is constructed, and the system software functional structure consisting of data processing, online monitoring, and system management is designed. On this basis, the continuous sampling data of the reactive power of the intelligent distribution transformer is collected, and based on edge computing, the reactive power operation characteristics of the intelligent distribution transformer are obtained and matched to judge the reactive state of the intelligent distribution transformer operation, so as to realize online monitoring of the reactive state of the intelligent distribution transformer. The experimental results show that the online monitoring accuracy and efficiency of the intelligent distribution transformer reactive state are high.

Network Intelligence for NFV Scaling in Closed-Loop Architectures

Characteristics like self-managing, self-adaptation, and self-organization are the main objectives of intelligent network operation. AI and Machine Learning (ML) algorithms will enable future networks to operate entirely autonomously. However, current network architectures are not fully prepared to include and properly handle the promised Network Intelligence (NI). This article looks at scaling, one key Management and Orchestration (MANO) operation that allows the network to adapt to unexpected changes. We show how different scaling methods can fit the Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), a well-established framework that allows architectural-based adaptation. Using a cloud-based scenario, we compare and highlight architectural differences between two prominent scaling methods, one based on Reinforcement Learning (RL) and the other based on classical control theory, showing that only the data-driven approach is adaptable enough to achieve automation. We conclude the article by pointing toward future research in autonomous, adaptive networks.