A digital twin has recently received considerable attention in various industry domains. The digital twin replicates physical objects (e.g., people, objects, spaces, systems, and processes) in the real world into digital objects in the digital world. It also provides various simulations to solve problems in the real world or to improve situational operations. Therefore, the digital twin is a convergence of various technologies, such as advanced machine-learning algorithms, data analytics, super-resolution visualization and modeling, and simulation. Because the digital twin is a complicated technology, a step-by-step implementation that includes many technology elements should be considered to create a digital twin model. In this study, implementation layers are introduced to guide practical implementations of the digital twin. In addition, technology elements were suggested for the presented implementation layers. Because the suggested technology elements include clear technology definitions, various application domains (e.g., energy, transportation, logistics, environment, manufacturing, and smart cities) can easily utilize the introduced implementation layers and technology elements according to the intended purpose. Furthermore, this paper describes the evolution of digital twins. Digital twin technology has evolved continuously since 2002, when the digital twin concept was first introduced. In the described evolution levels, we show the future aspects of digital twin technology, according to the technological evolution direction. Therefore, the digital twin model can be efficiently created by considering the evolution direction and future aspects by using the suggested digital twin evolution levels.

In order to quickly and effectively respond to a newly received criminal case, information regarding the type and severity of the case is crucial for authorities. This paper designs and develops a crime type and risk level prediction technique based on machine learning technology and verifies its performance. The designed technology can predict crime type and crime risk level using a text-based criminal case summary, which is criminal case receipt data. For the text-based criminal case summary data, the KICS data format is considered, which is actual policing data that contains information about criminal cases. For the crime type, 21 representative types of crimes are considered; therefore, the system can predict one of 21 types of crime for each criminal case. Furthermore, to predict the crime risk level, we developed a crime risk calculation formula. The developed formula calculates the crime risk level and outputs the risk score in numerical terms considering the severity and damage level of the criminal case. To predict the crime type and crime risk score, both DNN and CNN-based prediction models were designed and developed. The performance evaluation section shows that, in the case of crime type prediction, the proposed prediction models can achieve better performance than traditional classification algorithms such as naïve Bayes and SVM. The performance of the CNN-based crime type prediction model is about 7% and 8% better than those of the SVM algorithm and the naïve Bayes algorithm, respectively. The performance of the designed technology was comprehensively analyzed and verified through various performance measurement parameters. It is also developed in the form of a software platform with a GUI, allowing field personnel (e.g. police officers) to intuitively identify the type of criminal case and the level of risk from a text-based criminal case summary upon receipt of a new criminal case.

Faster-than-Nyquist (FTN) signaling is a useful communication technique for high spectral efficiency. However, since orthogonality between symbols is destroyed when a symbol rate faster than the Nyquist rate is used, intersymbol interference (ISI) is inevitably generated. Interference cancellation and signal detection processes are required to reduce the effect of ISI on the FTN receiver. Furthermore, FTN signaling in the multipath fading channel is a complicated problem because nonlinear interference from FTN signaling is combined with the additional nonlinear interference from the multipath fading channel. This paper proposes a deep learning-based signal detection technique for the FTN signal received through a multipath fading channel. The proposed technology considers the normal FTN signaling-based transmitter without additional signal processing schemes such as precoding and power allocation. We designed a recurrent neural network (RNN)-based deep learning structure and applied it to the FTN signaling-based system. For the RNN structure, both unidirectional long short-term memory (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) architectures were considered. Simulation results showed that both Uni-LSTM and Bi-LSTM performed better than the conventional BCJR algorithm when the FTN factor $\tau$ $=$ 0.8 and 0.7. Furthermore, the Bi-LSTM architecture achieved the best performances in all simulations. The proposed LSTM-based FTN detector can detect transmitted symbols without estimating channel coefficients and SNR.

Many efforts to achieve cost savings through simulations have been ongoing in the cyber-physical system (CPS) industry and manufacturing field. Recently, the concept of digital twins has emerged as a promising solution for cost reduction in various fields, such as smart cities, factory optimization, architecture, and manufacturing. Digital twins offer enormous potential by continuously monitoring and updating data to study a wide range of issues and improve products and processes. However, the practical implementation of digital twins presents significant challenges. Additionally, while various studies have introduced the concepts and roles of digital twin systems and digital components, further research is needed to explore efficient operation and management strategies. This paper aims to present digital entity management methodology for the efficient implementation of digital twin systems. Our proposed class-level digital entity management methodology constructs complex and repetitively used digital entities into digital entity classes. This approach facilitates the abstraction, inheritance, and upcasting of digital entity classes. By leveraging class-level management and easily reusable and modifiable digital entities, the implementation of low-complexity digital twin systems becomes feasible. The proposed methodology aims to streamline the digital twin implementation process, addressing complex technical integration and practical implementation challenges.

This paper investigates the integration of an intelligent reflecting surface (IRS) and a jammer controlled interference to enhance the physical layer security (PLS) of wireless powered communication networks (WPCNs). In 6G networks, where low-power Internet of Things (IoT) devices are prevalent, security and energy efficiency are critical. While existing studies primarily focus on IRS-assisted methods to improve PLS by manipulating signal reflections, such approaches may not fully degrade the reception of eavesdroppers. We address this limitation by proposing an approach that combines IRS-assisted signal reflection with controlled jamming to further degrade the reception of eavesdroppers. Specifically, we jointly optimize the downlink/uplink time allocation, IRS phase shift matrices, and jamming interference to maximize the sum secrecy throughput. To address the non-convex nature of the problem, we develop an alternating optimization algorithm that decouples the problem into subproblems, ensuring efficient convergence to a locally optimal solution. Through detailed performance analysis, we demonstrate the superiority of our proposed scheme in terms of secrecy throughput and energy efficiency. Numerical results reveal that, compared to traditional IRS-assisted or non-jamming schemes, our approach yields substantial performance gains. This highlights the potential of integrating IRS and jamming to provide more secure and energy-efficient communication solutions for WPCNs, particularly in the context of 6G IoT applications.

For Eureka-147-based digital audio broadcasting (DAB) system, frequency band III has been generally used in many countries. However, in some countries, other legacy broadcasting services have been adopted in the same frequency band. Especially, in South Korea, T-digital multimedia broadcasting (DMB) service has been already broadcasted through the band III since early 2005. In this paper, to adopt the DAB system in band III, channel allocation method and interference analysis are investigated for Korea broadcasting environment with legacy T-DMB. To allocate DAB channel, actual FM radio broadcasting stations and geographical features are considered. Furthermore, interference analysis and evaluation are performed between co-channel broadcasting areas and adjacent channel broadcasting areas. These evaluation results provide the DAB service channel allocation method in broadcasting environment with legacy broadcasting system, and can be used as a reference to determine the DAB service channel allocation through the frequency band with existing broadcasting system.