# Selected Papers from IEEE ICASI 2018
## 2 The Topics of Applied System Innovation
The numerical results demonstrated the validity of the proposed method and that the tracking system was asymptotically stable. [PERSON] et al. reported on \"Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms\" [5]. In this study, resource blocks (RB) were allocated to user equipment (UE) according to the evolutional algorithms for long-term evolution (LTE) systems. The particle swarm optimization (PSO) algorithm is one of these evolutionary algorithms, which imitates the foraging behavior of a flock of birds through learning and grouping the best experience. The authors proposed a refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results showed that the proposed GA with 100 populations could converge to suboptimal solutions in 200 generations. The proposed GA and RPSO improved system capacity performance as compared with SPSO by 2.0 and 0.6 UEs, respectively. [PERSON] reported on the \"Application of Aesthetic Principles to the Study of Consumer Preference Models for Vase Forms\" [6]. The objective of this study was to investigate consumers' preference models from their preferred product forms. The esthetic principles of symmetry, minimalism, and cohesion were applied to the case studies of the design of various vase forms. A quantitative approach of evaluating vase forms by the aesthetic principles was proposed. A conjoint analysis of the vase features and attributes was carried out in order to determine their correlations with consumer preferences. Consumers were classified into six groups of different aesthetic conceptions by cluster analysis. The consumers' preference model could be determined by indices including Pearson's R and Kendall's tau. This approach provides designers with an effective way of determining the right direction for new form designs and renovation. [PERSON] et al. reported on \"An Emotion-Aware Personalized Music Recommendation System Using a Convolutional Neural Networks Approach\" [7]. In this paper, the authors proposed an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, the authors combined the outputs of two approaches, the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, the authors generate the implicit user rating for music to extract the correlation between the user data and the music data. For performance comparison, the authors take the content similarity music recommendation system (CSMRS), as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE), as the baseline systems. Experimental results showed that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, the authors built the Android and iOS APPs to obtain realistic data of user experience on the EPMRS. The collected feedback from anonymous users also showed that the EPMRS sufficiently reflect their preference on music. [PERSON] et al. reported \"Study on Cleaning the Surface of Stainless Steel 316 Using Plasma Electrolysis Technology\" [8]. This research utilized a plasma electrolysis technique to clean the surface of stainless steel 316. The resulting microstructure enhances the self-cleaning properties of the stainless steel surface. The position of the cathode electrode is varied to enlarge the total surface being processed and achieves a uniform processing surface. The authors proposed a self-made plasma electrolysis reaction system supplemented with a three-axis platform to control the speed at which the cathode electrode moves. The electrolyte is an aqueous solution of sodium bicarbonate (NaHCO3) and water.