This paper proposes auction based energy trading among electric vehicles (EVs) with consideration of practical battery status. At an arbitrary time, each EV can be a seller or a buyer according to their residual battery level and required energy for reaching the pre-defined destination. Under energy trading among EVs, there is an energy pool managed by an auctioneer in the market that gathers surplus energy from sellers and supplies it to buyers based on auction mechanism. Each user individually decides the initial bidding price and the amount of trading energy according to an expected cost savings compared with trading from macrogrids as well as considering their battery efficiency. Notably, we provide a battery status model of EVs for designing well-defined utilities of EV users. Also, we applied a naive auction process to distribute the energy based on individual user's preferred transaction price for maximizing the payoff of all participating users.

This paper proposes a novel user-preference-aware power scheduling scheme for application at an electric vehicle (EV) charging facility. Here, the preference of the EV user is characterized as a utility function by considering two different factors: 1) a satisfaction factor according to the charged energy, and 2) payment for the received charging service. As a key component of the power scheduling method, this paper proposes a two-stage power charging method and analyzes the advantage of the proposed method from a monetary perspective. In addition, this paper analyzes the economic benefits of a charging facility and EVs using the single-leader, multi-follower Stackelkberg game. By showing the existence of a unique best response for each participant, this paper presents the improvement of financial profit of EV users and charging facility through comparison with the case where the charging facility does not apply a proper power management scheme. Based on the actual world datasets, this paper shows that the proposed power scheduling scheme is feasible for actual environment. With satisfying the constraints, it is possible to reduce overall electricity cost up to 8.59% compared to the case without considering the peak power in EV charging facility.

Dental caries is a prevalent global condition, and its diagnosis often requires direct clinical examination by a dentist. However, access to traditional dental care can be limited due to high costs, availability, and patient discomfort. To address these limitations, this study introduced a remote caries detection model using a ResBlock-AutoEncoder that generates domain-specific pre-trained weights. The model demonstrated exceptional performance, achieving an accuracy of 0.9989, an F1-score of 0.9979, and a precision of 1.0, while maintaining a low average inference time of 5.7939 seconds. Furthermore, Grad-CAM was employed to enhance interpretability by visually localizing caries, ensuring model reliability. Notably, this high precision is attributed to the specific characteristics of frontal oral images, which allow for clearer visibility of caries compared to other imaging angles. However, this also introduces a potential limitation, as it does not account for variability in other perspectives of oral images. To improve generalization, future research will incorporate multi-angle dental images.

With the widespread of electric vehicles (EV), the necessity of installing EV charging facility has rapidly increased. Since operating EV charging facility are costly and complex, choosing a proper power operation scheme is critical in implementing a cost-effective charging service. Therefore, this article proposes an efficient power operation scheme for EV charging facility by jointly analyzing not only monetary issues with various power rate policies and battery wear-out costs of the energy storage system (ESS) but also the uncertainty issues caused by EV users' behavior under different charging requirements. By considering the issues, this article analyzes and formulates the problem with robust optimization techniques. By using the real-world EV operation datasets, the effectiveness of the proposed scheme is analyzed in terms of daily peak power and total cost. In addition, by applying the proposed scheme in an actual power system, it is shown that the proposed scheme is able to reduce the overall cost by 20.5% and reduce peak power by 17.3% compared to other benchmark models.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer’s gait and diagnose balance issues. This approach can be utilized to improve a user’s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.