## 2 Related Works  
In Reference [2], a new non-destructive evaluation method for cable tension monitoring was proposed on the basis of the elasto-magnetic effect and the self-induction phenomenon. The method was called the elasto-magnetic induction (EMI) method. This paper simplified the primary coil and induction unit of the traditional elasto-magnetic (EM) sensor into a self-induction coil. In addition, experimental verification of steel cable specimens was performed to verify the validity of EMI methods, and data processing and tension calculation methods were proposed to handle tension monitoring. Reference [3] deals with the analysis of subsea cable tension, which is essential for laying work to place subsea cables. In [2], the fatigue failure of subsea cables was mainly studied. Mathematical modeling was used in consideration of a number of parameters to solve and analyze the problems. Two types of subsea cable tension analyses have been studied: tensional analysis of catenary configurations and tensional analysis of lazy wave configurations. The two analyses were solved using minimization through a gradient-based approach to tension analysis of different subsea cables. Reference [4] proposed for the accurate measurement of cable tension, which is essential in the construction and maintenance stages for cable-supported bridge systems. In addition, to replace the numerical iterative process based on the transverse vibration equation of a cable, a practical formula for estimating the cable tension in a simple explicit form including bending stiffness of the cable has been proposed. Reference [5] proposed a practical formula for the vibration method considering the effect of flexural rigidity and sag of a cable into account. The corresponding formula is based on a high-accuracy approximation to the equation of the inclined cable with flexural rigidity, and the cable tension is easily estimated by these formulas using the measured natural frequency in the low-order modes. In [6], the authors proposed a novel technique for estimating cable tension from measured natural frequencies. The proposed method can simultaneously grasp the tension, flexural rigidity, and axial rigidity of the cable system. First, a finite element model that can consider both sag-expandability and flexural rigidity for the target cable system was built. Next, a frequency-based sensitivity update algorithm to identify the model was applied. Previous theories of calculating cable tension can cause unacceptable errors in many applications because cable sag and bending stiffness are ignored. Therefore, Reference [7] proposed the empirical formulas to estimate cable tension on the basis of the cable fundamental frequency only, which could be derived by using the solutions by means of energy method and fitting the exact solutions of cable vibration equations where the cable sag and bending stiffness are, respectively, considered. Conventional vibration methods for estimating cable tension are mainly based on measured acceleration responses. These methods are relatively expensive and time-consuming because they require the installation of contact sensors and data collection systems. In [8], a contactless vision-based sensor was proposed as an alternative to determining cable tension. The proposed method was applied to measure the cable forces for the cable-supported roof structure of the Hard Rock Stadium in Florida, and a series of field tests were conducted at various roof erection stages to ensure that the cable force reached its design values. In [9], the authors proposed an automated cable tension monitoring system using deep learning and wireless smart sensors that can estimate cable tension forces. Using a region-based convolution neural network, a fully automated peak-picking algorithm tailored to cable vibration was developed, and a vibration-based tension estimation method to automated cable tension monitoring was applied.