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Background Although post-inflammatory hyperpigmentation (PIH) is a common adverse event following laser procedures, studies evaluating its risk remain limited.Objective To analyze PIH risk after 532 nm Q-switched Nd:YAG laser (QSNYL) treatment for solar lentigines and examine the efficacy of triple combination cream (TCC) for its prevention.Methods In this single center, investigator-blinded, randomized controlled study, participants with solar lentigo either received TCC or emollient from 2 weeks post-QSNYL treatment. The occurrence of PIH was determined by three independent and blinded dermatologists. In vivo skin measurements and sun exposure questionnaires were examined to evaluate the risk of PIH.Results A total of 28 patients with 67 solar lentigines were included in the analysis. In the control group, PIH occurred in 55.3% of the lesions. Risk factors for the occurrence of PIH were the increased erythema at weeks 2 (OR, 1.32; p = 0.035) and outdoor activity during 1–5 pm (OR, 8.10; p = 0.038). Treatment with TCC from 2 weeks post-QSNYL treatment significantly decreased the incidence of PIH (31.0% vs. 55.3%, p = 0.048).Conclusion Post-laser erythema and outdoor activity at the daytime are prognostic factors for the occurrence of PIH. Administering TCC could be considered for the prevention of PIH in high-risk patients.

Abstract Planar object tracking (POT) is crucial in vision‐based robotic applications. Despite significant advancements, tracking planar objects under real‐world conditions remains a challenge owing to various factors. The method proposed by the authors leverages SIFT features and generalized Hough transform, which is enhanced by spatial locality, to mitigate background clutter and false matching. The experimental results demonstrate that this method significantly outperforms existing approaches, achieving higher precision across various alignment error thresholds.

Conversational recommender systems (CRSs) have garnered increasing attention for their ability to provide personalized recommendations through natural language interactions. Although large language models (LLMs) have shown potential in recommendation systems owing to their superior language understanding and reasoning capabilities, extracting and utilizing implicit user preferences from conversations remains a formidable challenge. This paper proposes a method that leverages LLMs to extract implicit preferences and explicitly incorporate them into the recommendation process. Initially, LLMs identify implicit user preferences from conversations, which are then refined into fine-grained numerical values using a BERT-based multi-label classifier to enhance recommendation precision. The proposed approach is validated through experiments on three comprehensive datasets: the Reddit Movie Dataset (8413 dialogues), Inspired (825 dialogues), and ReDial (2311 dialogues). Results show that our approach considerably outperforms traditional CRS methods, achieving a 23.3% improvement in Recall@20 on the ReDial dataset and a 7.2% average improvement in recommendation accuracy across all datasets with GPT-3.5-turbo and GPT-4. These findings highlight the potential of using LLMs to extract and utilize implicit conversational information, effectively enhancing the quality of recommendations in CRSs.

In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user’s choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot’s real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.

Sequential recommendation systems aim to predict users’ subsequent interactions by analyzing their historical behavior. Significant advancements have been achieved in this field through the utilization of models such as RNN, GRU, and models incorporating the self-attention mechanism. Most state-of-the-art methods enhance recommendation effectiveness by incorporating side information, such as category or brand, within the attention layer to enrich item representations. However, in our pilot experiments, we found that increasing the amount of side information could negatively impact the recommendation performance when integrated directly into the attention layer due to the learning of interdependency among them. To alleviate this problem, we propose Independent Representation of side Information on each item for Sequential recommendation (IRIS). IRIS learns pairs of both item and side information independently, without any fusion among side information in attention layer. In contrast to existing approaches, IRIS avoids direct fusion among side information in the attention layer, opting instead for a novel strategy of learning independent pairs of both item and side information. Our proposed method constructs an attention layer specifically designed to learn these pairs, thus avoiding potential distortions to item representations caused by intermixing side information. Experiments on four public datasets show that IRIS outperforms state-of-the-art sequential recommendation models. Ablation study also indicates robust scalability of side information within IRIS.

Deep learning models have significantly addressed the challenges of multivariate time series forecasting. Recently, Transformer-based models which have primarily focused on either temporal or inter-variate (spatial) dependencies have demonstrated exceptional performance. These models decide whether to embed multivariate time series data temporally or spatially. Hence, we propose the dual embedded transformer (DEformer) which simultaneously considers both temporal and spatial dependencies. Our model enables capturing these dependencies independently through two distinct encoders. The temporal encoder, which processes the entire time series of each variate, is designed to learn temporal dependency. Conversely, the spatial encoder, which processes the multivariate data at each time step, is optimized to capture spatial dependency. Both encoders share an identical architecture, comprising an attention mechanism to model correlations and a feed-forward network to enhance feature representation. Through empirical studies on challenging real-world datasets, we not only demonstrate that our method can outperforms state-of-the-art approaches, but also prove the performance of independently embedding both dependencies through ablation study.

Virtual reality (VR) is used in many fields, including entertainment, education, training, and healthcare, because it allows users to experience challenging and dangerous situations that may be impossible in real life. Advances in head-mounted display technology have enhanced visual immersion, offering content that closely resembles reality. However, several factors can reduce VR immersion, particularly issues with the interactions in the virtual world, such as locomotion. Additionally, the development of locomotion technology is occurring at a moderate pace. Continuous research is being conducted using hardware such as treadmills, and motion tracking using depth cameras, but they are costly and space-intensive. This paper presents a walk-in-place (WIP) algorithm that uses Mocopi, a low-cost motion-capture device, to track user movements in real time. Additionally, its feasibility for VR applications was evaluated by comparing its performance with that of a treadmill using the absolute trajectory error metric and survey data collected from human participants. The proposed WIP algorithm with low-cost Mocopi exhibited performance similar to that of the high-cost treadmill, with significantly positive results for spatial awareness. This study is expected to contribute to solving the issue of spatial constraints when experiencing infinite virtual spaces.

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems.

With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve the dataset sparsity and increase the accuracy of the prediction results by using item features with user responses. A method based on the content-based filtering concept is proposed to extract category rates from the user–item matrix according to the user preferences and to organize these into vectors. Thereafter, we present a method to filter the user–item matrix using the extracted vectors and to regenerate the input matrix for collaborative filtering (CF). We compare the prediction results of our approach and conventional CF using the mean absolute error and root mean square error. Moreover, we calculate the sparsity of the regenerated matrix and the existing input matrix, and demonstrate that the regenerated matrix is more dense than the existing one. By computing the Jaccard similarity between the item sets in the regenerated and existing matrices, we verify the matrix distinctions. The results of the proposed methods confirm that if the regenerated matrix is used as the CF input, a denser matrix with higher predictive accuracy can be constructed than when using conventional methods. The validity of the proposed method was verified by analyzing the effect of the input matrix composed of high average ratings on the CF prediction performance. The low sparsity and high prediction accuracy of the proposed method are verified by comparisons with the results by conventional methods. Improvements of approximately 16% based on K-nearest neighbor and 15% based on singular value decomposition, and a three times improvement in the sparsity based on regenerated and original matrices are obtained. We propose a matrix reconstruction method that can improve the performance of recommendations.

Recently, recommender systems have been used in various fields. However, they are still plagued by many issues, including cold-start and sparsity problems. The cold-start problem occurs when users are unable to make recommendations to other users owing to a complete lack of information about certain items. This problem can exist both at the user side and the item side. User-side cold-start problems occur when new users access the systems; item-side cold-start problems occur when new items are added to databases. In this study, we addressed the item-side cold-start problem using the concept of weak supervision. First, a new process for identifying feature based representative reviewers in a rater group was designed. Then, we developed a method to predict the expected preferences for new items by combining content-based filtering and the preferences of representative users. Through extensive experiments, we first confirmed that in comparison to existing methods, the proposed approach provided enhanced accuracy, which was evaluated by determining a mean absolute error for the average ratings. Then, we compared the proposed scheme with the collaborative filtering (CF) and neural CF approaches (NCF). The estimation by the proposed approach was 21% and 38% more accurate than CF and NCF in terms of mean absolute error (MAE), respectively. In future, the proposed framework can be applied in various recommender systems as a core function.

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.

In this study, seven of the most commonly applied covering materials in smart farms are selected as the representative samples for analysis, namely, glass, soft film (polyethylene, PE), soft film (polyolefin, PO), rigid plastic film (ethylene tetra fluoro ethylene, ETFE), rigid plastic sheet (poly methyl methacrylate, PMMA), rigid plastic sheet (polycarbonate, PC double layer), and woven film. For each covering material, visible light transmittance and reflectivity, solar radiation transmittance and reflectivity, thickness, solar heat gain coefficient, and the coefficient of heat transmission are measured according to the test methods in the Korean Industrial Standards (KS) to derive input data for the respective materials. In addition, using the optical and thermal input data as derived above, simulations are performed on the cooling load and daylight characteristics of smart farms to derive basic reference data for the selection of adequate covering materials for the design. Based on the analysis result of the daylight characteristics for each covering material, for a shading rate of 60%, the average values of indoor illuminance were 19,879 lux, 20,012 lux, 19,393 lux, 19,555 lux, 16,560 lux, 16,228 lux, and 11,173 lux for glass, PE film, PO film, ETFE, woven film, PMMA, and PC double layer, respectively, between 6 a.m. and 8 p.m., which correspond to the hours when daylight enters indoor spaces. Considering the target light intensity for strawberry growth at 10,000–40,000 lux, the above results confirm that all the sample covering materials had an indoor illuminance level above the lower limit range of the target light intensity. For the cooling load evaluation, the PC double layer had the lowest value of 482.8 W/m2, and PO film had the highest value of 633.8 W/m2. The difference between the cooling loads of the PC double layer and the PO film is 151 W/m2, which amounts to 23.8%, thus indicating that the selected covering material significantly impacts the cooling load. The cooling load exhibited a pattern similar to that of the coefficient of heat transmission and solar heat gain coefficient, which are key influencing factors for most of the sample materials. However, for PMMA, the cooling load was low because it had a higher coefficient of heat transmission than other materials, but its solar heat gain coefficient was relatively low. Another possible reason for the difference is that the solar heat gain coefficient impacts the cooling load. When the cooling set temperature was controlled from Case 1-1 to Case 1-2, the cooling load decreased by 10.7% on average. In addition, when the cooling set temperature changed from Case 1-1 to Case 1-3, the cooling load decreased by 26.1% on average. For cooling set temperature control, maintaining the temperature around the lower temperature range of the optimal growth temperature of the plants increases the yield, but it also incurs increased cooling load and cost. In terms of cost only, while maintaining the cooling temperature for 24 h at 30 °C, which is the upper limit of the optimal growth temperature, would be advantageous, it will lead to a deterioration of the quality and reductions in yield. Therefore, as follow-up studies for further investigation of the findings of this research, there is the need for an evaluation of the yield and quality with respect to the range of cooling set temperatures. When the internal shading rate was increased to 40% (Case 2-2) with reference to Case 2-1, which was a greenhouse without the application of internal shading, the cooling load decreased by 27.4% on average. Furthermore, when the internal shading rate increased to 50% (Case 2-3) with reference to Case 2-1, the cooling load decreased by 29.3% on average. When the internal shading rate increased to 60% (Case 2-4), the cooling load decreased by 31.5% on average.

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

Renewable energy generation capacity in Korea is expected to reach about 63.8 GW by 2030 based on calculations using values from a power plan survey (Korea’s renewable energy power generation project plan implemented in September 2017) and the “3020” implementation plan prescribed in the 8th Basic Plan for Long-Term Electricity Supply and Demand that was announced in 2017. In order for the electrical grid to accommodate this capacity, an appropriate power system reinforcement plan is critical. In this paper, a variety of scenarios are constructed involving renewable energy capacity, interconnection measures and reinforcement measures. Based on these scenarios, the impacts of large-scale renewable energy connections on the future power systems are analyzed and a reinforcement plan is proposed based on the system assessment results. First, the scenarios are categorized according to their renewable energy interconnection capacity and electricity supply and demand, from which a database is established. A dynamic model based on inverter-based resources is applied to the scenarios here. The transmission lines, high-voltage direct current and flexible alternating current transmission systems are reinforced to increase the stability and capabilities of the power systems considered here. Reinforcement measures are derived for each stage of renewable penetration based on static and dynamic analysis processes. As a result, when large-scale renewable energy has penetrated some areas in the future in Korean power systems, the most stable systems could be optimally configured by applying interconnection measure two and reinforcement measure two as described here. To verify the performance of the proposed methodology, in this paper, comprehensive tests are performed based on predicted large-scale power systems in 2026 and 2031. Database creation and simulation are performed semi-automatically here using Power System Simulator for Engineering (PSS/E) and Python.

A distributed system guarantees the acceptance of Byzantine fault tolerance (BFT) for information transmission. Practical BFT (pBFT) and asynchronous BFT (aBFT) are among the various available forms of BFT. Distributed systems generally share information with all participating nodes. After information is shared, the systems reshare it. Thus, ensuring BFT consumes a considerable amount of time. Herein, we propose Decision search protocols that apply the gossip protocol, denoted by DecisionBFT, for distributed networks with guaranteed BFT. Each protocol in DecisionBFT is completely asynchronous and leaderless; it has an eventual consensus but no round-robin or proof-of-work. The core concept of the proposed technology is the consensus structure, which is based on the directed acyclic graph (DAG) and gossip protocol. In the most general form, each node in the algorithm has a set of k neighbors of most preference. When receiving transactions, a node creates and connects an event block with all its neighbors. Each event block is signed by the hashes of the creating node and its k peers. The consensus structure of the event blocks utilizes a DAG, which guarantees aBFT. The proposed framework uses Lamport timestamps and concurrent common knowledge. Further, an example of a Decision search algorithm, based on the gossip protocol DecisionBFT, is presented. The proposed DecisionBFT protocol can reach a consensus when 2/3 of all participants agree to an event block without any additional communication overhead. The DecisionBFT protocol relies on a cost function to identify the k peers and generate the DAG-based consensus structure. By creating a dynamic flag table that stores connection information between blocks, the gossip protocol achieves a consensus in fewer steps than that in the case of the existing aBFT protocol.

This paper has been focused on developing a new hybrid mount for shipboard equipment used in naval surface ships and submarines. While the hybrid mount studied in our previous research was 100 kg-class series-type mount, the new hybrid mount has been designed as an inertia-type mount capable of supporting a static of 500 kg. The proposed mount consists of a commercial rubber resilient mount, a piezostack actuator and an inertial mass. The piezostack actuator connected with the inertial mass generates actively the control force. The performances of the proposed mount with a newly designed specific controller have been evaluated in accordance with US military specifications and compared with the passive mount. An isolation system consisting of four proposed mounts and auxiliary devices has been also tested. Through a series of experimental tests, it has been confirmed that the proposed mount provides better performance than the US Navy's standard passive mounts.

Recently, the Internet of things (IoT) became useful in various applications based on the web communication technology. The IoT has great potential in several service domains including cultural, educational, or medical areas. We consider a recommendation technique suitable for the IoT-based service. A personalized recommender system often relies on user preferences for better suggestions. We notice that we need a different recommendation approach in the IoT platform. While the conventional recommendation approaches rely on user preferences provided by users, these approaches may not be suitable for the IoT environment. The conventional systems utilize user ratings for items to compose recommendation list. This implies that the systems require additional user activities such as adding their preferences. We notice that the IoT environment can naturally provide user information such as users’ item selection history without users’ additional actions. We propose a recommendation model that does not require users’ additional actions and is more suitable for the IoT environment. We examine the usability of the bandwagon effect to build a new recommender system based on users’ selection history. We first consider the bandwagon effects in movie recommendation domain and show its usefulness for the IoT. We then suggest how to use the bandwagon effect in recommender systems with IoT.