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.

This paper presents technical requirements of on-channel repeaters (OCRs) to broadcast the terrestrial digital multimedia broadcasting (T-DMB) signals in single frequency network (SFN) environment. In order to meet such requirements, the configuration and implementation method of the equalization OCR are proposed. The proposed equalization OCR does not have only short processing delay, but also provides high quality transmitting signals with high power because of the rejection capability of a feedback signal introduced by incomplete antenna isolation and multipath signals existing between a main transmitter and the OCR. Computer simulations and laboratory tests results are provided to figure out performance of the proposed equalization OCR.

Digital on-channel repeater (DOCR) is regarded as a desirable solution to effectively build a wide-area network of mobile terrestrial-digital multimedia broadcasting (T-DMB) services. On the other hand, the DOCR mainly suffers from interference effects caused by a strong feedback signal and a time-varying channel dispersion. In this paper, we propose a novel DOCR for use in the T-DMB system in order to mitigate effectively both interference effects. First, we present an energy equalizer using the scarce pilot structure of T-DMB system to estimate a channel state information on every OFDM symbol. An optimum frequency domain channel estimation (FDCE) algorithm is then derived for the energy equalizer. In addition, a moving average filter (MAF) is employed to mitigate the phase distortion of truncated equalizer coefficients in deep null channel. Finally, in the performance evaluations, it is shown that the optimum FDCE outperforms a perfect FDCE. It is also shown that the DOCR equipped with the proposed interference cancellation can solve the problems caused by a limited channel information update, a phase distortion of the equalizer, and a residual feedback signal.

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.

In this paper, we propose a novel equalization on-channel repeater (OCR) with a feedback interference canceller (FIC) to relay terrestrial digital multimedia broadcasting signals in single frequency networks. The proposed OCR not only has high output power by cancelling the feedback signals caused by insufficient antenna isolation through the FIC, but also shows better output signal quality than the conventional OCR by removing multipath signals existing between the main transmitter and the OCR through an equalizer. In addition, computer simulations and laboratory test results demonstrate that the proposed OCR successfully cancels feedback signals and compensates channel distortions and provides a higher quality transmitting signal with higher output power than conventional OCRs.