Intelligent Decision Technologies - Volume Pre-press, issue Pre-press - Journals (original) (raw)

Article Type: Research Article

Abstract: Deep learning (DL) is the basis of many applications of artificial intelligence (AI), and cloud service is the main way of modern computer capabilities. DL functions provided by cloud services have attracted great attention. At present, the application of AI in various fields of life is gradually playing an important role, and the demand and enthusiasm of governments at all levels for building AI computing capacity are also growing. The AI logic evaluation process is often based on complex algorithms that use or generate large amounts of data. Due to the higher requirements for the data processing and storage capacity …of the device itself, which are often not fully realized by humans because the current data processing technology and information storage technology are relatively backward, this has become an obstacle to the further development of AI cloud services. Therefore, this paper has studied the requirements and objectives of the cloud service system under AI by analyzing the operation characteristics, service mode and current situation of DL, constructed design principles according to its requirements, and finally designed and implemented a cloud service system, thereby improving the algorithm scheduling quality of the cloud service system. The data processing capacity, resource allocation capacity and security management capacity of the AI cloud service system were superior to the original cloud service system. Among them, the data processing capacity of AI cloud service system was 7.3% higher than the original cloud service system; the resource allocation capacity of AI cloud service system was 6.7% higher than the original cloud service system; the security management capacity of AI cloud service system was 8.9% higher than the original cloud service system. In conclusion, DL plays an important role in the construction of AI cloud service system. Show more

Keywords: Cloud service mode, deep learning, artificial intelligence, cloud service system construction

DOI: 10.3233/IDT-230150

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Wu, Zongfu | Hou, Fazhong

Article Type: Research Article

Abstract: Due to the large scale and spatiotemporal dispersion of 3D (three-dimensional) point cloud data, current object recognition and semantic annotation methods still face issues of high computational complexity and slow data processing speed, resulting in data processing requiring much longer time than collection. This article studied the FPFH (Fast Point Feature Histograms) description method for local spatial features of point cloud data, achieving efficient extraction of local spatial features of point cloud data; This article investigated the robustness of point cloud data under different sample densities and noise environments. This article utilized the time delay of laser emission and reception …signals to achieve distance measurement. Based on this, the measured object is continuously scanned to obtain the distance between the measured object and the measurement point. This article referred to the existing three-dimensional coordinate conversion method to obtain a two-dimensional lattice after three-dimensional position conversion. Based on the basic requirements of point cloud data processing, this article adopted a modular approach, with core functional modules such as input and output of point cloud data, visualization of point clouds, filtering of point clouds, extraction of key points of point clouds, feature extraction of point clouds, registration of point clouds, and data acquisition of point clouds. This can achieve efficient and convenient human-computer interaction for point clouds. This article used a laser image recognition system to screen potential objects, with a success rate of 85% and an accuracy rate of 82%. The laser image recognition system based on spatiotemporal data used in this article has high accuracy. Show more

Keywords: Laser recognition, image system, spatiotemporal data, point cloud data

DOI: 10.3233/IDT-230161

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Wei, Bo | Chen, Huanying | Huang, Zhaoji

Article Type: Research Article

Abstract: In order to solve the problem of low accuracy of evaluation results caused by the impact of throughput and transmission delay on traditional systems in 6G networks, this paper proposes a design method of network security processing system in 5G/6gNG-DSS of intelligent model computer. Supported by the principle of active defense, this paper designs a server-side structure, using ScanHome SH-800/400 embedded scanning module barcode QR code scanning device as the scanning engine. We put an evaluation device on the RISC chip PA-RISC microprocessor. Once the system fails, it will send an early warning signal. Through setting control, data, and cooperation …interfaces, it can support the information exchange between subsystems. The higher pulse width modulator TL494:4 pin is used to design the power source. We use the top-down data management method to design the system software flow, build a mathematical model, introduce network entropy to weigh the benefits, and realize the system security evaluation. The experimental results show that the highest evaluation accuracy of the system can reach 98%, which can ensure user information security. Conclusion: The problem of active defense network security is transformed into a dynamic analysis problem, which provides an effective decision-making scheme for managers. The system evaluation based on Packet Tracer software has high accuracy and provides important decisions for network security analysis. Show more

Keywords: Active defense, network security assessment, packet tracer, scanning engine, software flow design, mathematical model

DOI: 10.3233/IDT-230143

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Niu, Meng

Article Type: Research Article

Abstract: Electrical device automation in smart industries assimilates machines, electronic circuits, and control systems for efficient operations. The automated controls provide human intervention and fewer operations through proportional-integral-derivative (PID) controllers. Considering these devices’ operational and control loop contributions, this article introduces an Override-Controlled Definitive Performance Scheme (OCDPS). This scheme focuses on confining machine operations within the allocated time intervals preventing loop failures. The control value for multiple electrical machines is estimated based on the operational load and time for preventing failures. The override cases use predictive learning that incorporates the previous operational logs. Considering the override prediction, the control value is …adjusted independently for different devices for confining variation loops. The automation features are programmed as before and after loop failures to cease further operational overrides in this process. Predictive learning independently identifies the possibilities in override and machine failures for increasing efficacy. The proposed method is contrasted with previously established models including the ILC, ASLP, and TD3. This evaluation considers the parameters of uptime, errors, override time, productivity, and prediction accuracy. Loops in operations and typical running times are two examples of the variables. The learning process results are utilized to estimate efficiency by modifying the operating time and loop consistencies with the help of control values. To avoid unscheduled downtime, the discovered loop failures modify the control parameters of individual machine processes. Show more

Keywords: Control loop, electrical automation, override control, PID, predictive learning

DOI: 10.3233/IDT-230125

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Xiao, Jian

Article Type: Research Article

Abstract: Machine learning algorithms have been widely used in risk prediction management systems for financial data. Early warning and control of financial risks are important areas of corporate investment decision-making, which can effectively reduce investment risks and ensure companies’ stable development. With the development of the Internet of Things, enterprises’ financial information is obtained through various intelligent devices in the enterprise financial system. Big data provides high-quality services for the economy and society in the high-tech era of information. However, the amount of financial data is large, complex and variable, so the analysis of financial data has huge difficulties, and with …the in-depth application of machine learning algorithms, its shortcomings are gradually exposed. To this end, this paper collects the financial data of a listed group from 2005 to 2020, and conducts data preprocessing and Feature selection, including removing missing values, Outlier and unrelated items. Next, these data are divided into a training set and a testing set, where the training set data is used for model training and the testing set data is used to evaluate the performance of the model. Three methods are used to build and compare data control models, which are based on machine learning algorithm, based on deep learning network and the model based on artificial intelligence and Big data technology proposed in this paper. In terms of risk event prediction comparison, this paper selects two indicators to measure the performance of the model: accuracy and Mean squared error (MSE). Accuracy reflects the predictive ability of the model, which is the proportion of all correctly predicted samples to the total sample size. Mean squared error is used to evaluate the accuracy and error of the model, that is, the square of the Average absolute deviation between the predicted value and the true value. In this paper, the prediction results of the three methods are compared with the actual values, and their accuracy and Mean squared error are obtained and compared. The experimental results show that the model based on artificial intelligence and Big data technology proposed in this paper has higher accuracy and smaller Mean squared error than the other two models, and can achieve 90% accuracy in risk event prediction, which proves that it has higher ability in controlling financial data risk. Show more

Keywords: Machine learning algorithm, financial risk control, big data control, cyborg sensation, Internet of Things

DOI: 10.3233/IDT-230156

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Zhao, Lihui

Article Type: Research Article

Abstract: Due to the lack of data security protection, a large number of malicious information leaks, which makes building information security (InfoSec) issues more and more attention. The construction information involves a large number of participants, and the number of construction project files is huge, leading to a huge amount of information. However, traditional network security information protection software is mostly passive, which is difficult to enhance its autonomy. Therefore, this text introduced data sharing algorithm in building InfoSec management. This text proposed an Attribute Based Encryption (ABE) algorithm based on data sharing, which is simple in calculation and strong in …encrypting attributes. This algorithm was added to the building InfoSec management system (ISMS) designed in this text, which not only reduces the burden of relevant personnel, but also has flexible control and high security. The experimental results showed that when 10 users logged in to the system, the stability and security of the system designed in this text were 87% and 91% respectively. When 20 users logged in to the system, the system stability and security designed in this text were 89% and 92% respectively. When 80 users logged in to the system, the system stability and security designed in this text were 94% and 95% respectively. It can be found that the stability and security of the system have reached a high level, which can ensure the security of effective management of building information. Show more

Keywords: Data sharing, attribute based encryption, construction information, security management system

DOI: 10.3233/IDT-230144

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Chen, Huanying | Wei, Bo | Huang, Zhaoji

Article Type: Research Article

Abstract: In the age of big data, electronic data has developed rapidly and gradually replaced traditional paper documents. In daily life, all kinds of data are saved in the form of electronic documents. In this regard, people have strengthened the development of electronic depository system. Electronic storage refers to the storage of actual events in the form of electronic data through information technology to prove the time and content of events. Its application scenarios are very extensive such as electronic contracts, online transactions and intellectual property rights. However, due to the vulnerability of electronic data, the existing electronic data depository system …has certain security risks, and its content is very easy to be tampered with and destroyed, resulting in the loss of depository information. Due to the complexity of the operation of the existing electronic data depository system, some users are likely to reduce the authenticity of the depository information due to the non-standard operation. In order to solve the problems existing in the current electronic data storage system, this paper designed an electronic data storage system based on cloud computing and blockchain technology. The data storage of cloud computing and blockchain was decentralized, and its content cannot be tampered with. It can effectively ensure the integrity and security of electronic information, which is more suitable for the needs of electronic storage scenarios. This paper first introduced the development of electronic data depository system and cloud computing, and optimized the electronic data depository system through the task scheduling model of cloud computing. Finally, the feasibility of the system was verified through experiments. The data showed that the functional efficiency of the system in the electronic data sampling point storage function, the upload of documents to be stored, the download of stored documents, the view of stored information function and the file storage and certificate comparison verification function has reached 0.843, 0.821, 0.798, 0.862 and 0.812 respectively. The final function indexes of each function of the traditional electronic data depository system were 0.619, 0.594, 0.618, 0.597 and 0.622 respectively. This data shows that the electronic data storage system based on cloud computing and blockchain modeling can effectively manage electronic data and facilitate relevant personnel to verify electronic data. Show more

Keywords: Electronic data, deposit system, cloud computing, blockchain technology, task scheduling model

DOI: 10.3233/IDT-230152

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Zhang, Zhenyuan

Article Type: Research Article

Abstract: With the rapid development of the economy, the demand for electric power is increasing, and the operation quality of the power system directly affects the quality of people’s production and life. The electric energy provided by the electric power system is the foundation of social operation. Through continuous optimization of the functions of the electric power system, the efficiency of social operation can be improved, and economic benefits can be continuously created, thereby promoting social progress and people’s quality of life. In the power system, the responsibility of the power distribution network (PDN) is to transmit electricity to all parts …of the country, and its transmission efficiency would directly affect the operational efficiency of the power system. PDN scheduling plays an important role in improving power supply reliability, optimizing resource allocation, reducing energy waste, and reducing environmental pollution. It is of great significance for promoting social and economic development and environmental protection. However, in the PDN scheduling, due to the inflexibility of the power system scheduling, it leads to the loss and waste of electric energy. Therefore, it is necessary to upgrade the operation of the PDN automatically and use automation technology to improve the operational efficiency and energy utilization rate of the power system. This article optimized the energy-saving management of PDN dispatching through electrical automation technology. The algorithm proposed in this paper was a distribution scheduling algorithm based on electrical automation technology. Through this algorithm, real-time monitoring, analysis, and scheduling of PDNs can be achieved, thereby improving the efficiency and reliability of distribution systems and reducing energy consumption. The experimental results showed that before using the distribution scheduling algorithm based on electrical automation technology, the high loss distribution to transformation ratios of power distribution stations in the first to fourth quarters were 21.93%, 22.95%, 23.61%, and 22.47%, respectively. After using the distribution scheduling algorithm, the high loss distribution to transformation ratios for the four quarters were 15.75%, 13.81%, 14.77%, and 13.12%, respectively. This showed that the algorithm can reduce the high loss distribution to transformation ratio of power distribution stations and reduce their distribution losses, which saved electric energy. The research results of this article indicated that electrical automation technology can play an excellent role in the field of PDN scheduling, which optimized the energy-saving management technology of PDN scheduling, indicating an advanced development direction for intelligent management of PDN scheduling. Show more

Keywords: Electrical automation, power distribution network scheduling, energy saving management, power energy, distribution dispatching algorithm

DOI: 10.3233/IDT-230121

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Li, Guozhang | Xing, Kongduo | Alfred, Rayner | Wang, Yetong

Article Type: Research Article

Abstract: With the passage of time, the importance of spatio-temporal data (STD) is increasing day by day, but the spatiotemporal characteristics of STD bring huge challenges to data processing. Aiming at the problems of image information loss, limited compression ratio, slow compression speed and low compression efficiency, this method based on image compression. This article intended to focus on aircraft trajectory data, meteorological data, and remote sensing image data as the main research objects. The research results would provide more accurate and effective data support for research in related fields. The image compaction algorithm based on deep learning in this article …consisted of two parts: encoder and decoder, and this method was compared with the JPEG (Joint Photographic Experts Group) method. When compressing meteorological data, the algorithm proposed in this paper can achieve a maximum compaction rate of 0.400, while the maximum compaction rate of the JPEG compaction algorithm was only 0.322. If a set of aircraft trajectory data containing 100 data points is compressed to 2:1, the storage space required for the algorithm in this paper is 4.2 MB, while the storage space required for the lossless compression algorithm is 5.6 MB, which increases the compression space by 33.33%. This article adopted an image compaction algorithm based on deep learning and data preprocessing, which can significantly improve the speed and quality of image compaction while maintaining the same compaction rate, and effectively compress spatial and temporal dimensional data. Show more

Keywords: STD processing, picture data compaction, high-performance image compaction algorithms, compaction rate, JPEG compaction algorithm

DOI: 10.3233/IDT-230234

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Zhang, Xuxia | Chen, Weijie | Wang, Jian | Fang, Rang

Article Type: Research Article

Abstract: With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion …can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model. Show more

Keywords: Network security, machine learning algorithm, situation awareness, data analysis

DOI: 10.3233/IDT-230238

Citation: Intelligent Decision Technologies, vol. Pre-press, no. Pre-press, pp. 1-13, 2023