Digital Library
Vol. 22, No. 1, Feb. 2026
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Yeong-jin Hwang, Young-bin Jeong, Kwang-il Hwang
Vol. 22, No. 1, pp. 1-12, Feb. 2026
https://doi.org/10.3745/JIPS.04.0364
Keywords: Decision Fusion, Optical Character Recognition (OCR), Scene Text Detection (STD), Text SLAM, Text Tracking
Show / Hide AbstractSimultaneous localization and mapping (SLAM) is a core technology in robotics and autonomous navigation. Visual SLAM has gained attention through advances in object recognition enabling landmark-based mapping. However, a critical gap exists in handling inconsistencies arising from repeated text recognition processes, a challenge that existing studies have largely overlooked. To address this, we propose an innovative algorithm that enhances text recognition accuracy for mobile platforms. Our method excels in tracking objects and consistently recognizing and determining textual content in recurrent scenes. Through rigorous experimentation, we demonstrate that our algorithm significantly improves real-time text recognition accuracy by mitigating errors inherent in conventional approaches. This advancement not only refines the reliability of text-based Visual SLAM but also broadens its applicability in dynamic, text-rich environments. Our work paves the way for more robust and efficient autonomous navigation systems, particularly in urban landscapes where textual cues are abundant. -
Xiaona Lan
Vol. 22, No. 1, pp. 13-20, Feb. 2026
https://doi.org/10.3745/JIPS.02.0233
Keywords: Color Matching, Cultural and Creative Product, “Four Symbols” Motif, Packaging Design
Show / Hide AbstractThis paper presents a concise introduction to the cultural and creative packaging design and the “Four Symbols” motif, followed by an analysis of two designs. In the analysis process, a convolutional neural network (CNN) algorithm was applied to generate preliminary design scores, and then further evaluation was conducted according to the scores. It was found that the CNN algorithm could be used for preliminary scoring of the packaging design. The CNN-generated scores for both designs showed strong alignment with manual evaluation scores. The graphic and color matching of the “Four Symbols” motif in the two designs enhanced the cultural heritage of the products and effectively engaged consumer interest. -
Yanfeng Wang, Ning Ma
Vol. 22, No. 1, pp. 21-33, Feb. 2026
https://doi.org/10.3745/JIPS.02.0234
Keywords: Attention Mechanism, BERT, DuReader2, Machine Reading Comprehension
Show / Hide AbstractMachine reading comprehension (MRC) is a fundamental task in natural language processing (NLP), with existing models struggling to capture long-range dependencies and handle complex semantic nuances, particularly in Chinese. This paper proposes the Collaborative Semantic Reader (C-S Reader), a novel model that combines RoBERTa_wwm_ext pre-training and multi-level attention mechanisms to enhance semantic understanding. Experiments on the DuReader2 dataset show that C-S Reader significantly outperforms baseline models in both the Rouge-L and BLEU-4 scores, demonstrating its effectiveness in processing long documents and capturing complex semantic relationships. Our work provides a scalable solution for Chinese MRC tasks and highlights future challenges, including long-range dependency modeling and ambiguity in complex questions. -
Seog-Min Lee
Vol. 22, No. 1, pp. 34-48, Feb. 2026
https://doi.org/10.3745/JIPS.04.0365
Keywords: BERT Embeddings, Contemporary Topic Models, Deep Learning, LLM-based Evaluation, Semantic Evaluation Metrics
Show / Hide AbstractTopic modeling has evolved from statistical methods such as latent Dirichlet allocation (LDA) to neural hybrid models including BERTopic, which utilize bidirectional encoder representations from transformers (BERT) embeddings. However, traditional statistical evaluation metrics overlook the semantic richness of these neural representations, limiting model assessment capabilities. This paper introduces semantic-based evaluation metrics that leverage deep learning embeddings and validates them through both statistical comparison and large language model (LLM)-based assessment. This study evaluated three synthetic datasets with systematically varying topic overlap and one public dataset (20 Newsgroups). Analysis across 9,608 synthetic documents with 45 topics and a stratified sample of 1,000 documents from 20 Newsgroups shows that semantic metrics achieve improved discrimination compared to statistical baselines. Specifically, semantic coherence shows a 38.1% discriminative range versus 5.0% for statistical measures, representing a 7.62× improvement. Semantic distinctiveness achieves 1.57× higher discrimination than statistical methods. Semantic methods also maintain consistent discrimination quality for diversity metrics, with stable progression across similarity levels. LLM assessments, serving as proxies for human judgment, demonstrate inter-model agreement through a weighted three-model ensemble (mean pairwise Spearman ρ=0.937) and positive correlation with semantic metrics on public datasets (ρ=0.632–0.671). Domain-specific validation and multilingual extension constitute future work. -
Jun Ji, Fei-fei Xing, Dan Su, Yao-dong Cui
Vol. 22, No. 1, pp. 49-61, Feb. 2026
https://doi.org/10.3745/JIPS.01.0115
Keywords: Dynamic programming, Layout Value, Rectangular, Two-Dimensional Packing
Show / Hide AbstractThe unconstrained two-dimensional cutting stock problem is a typical NP-hard problem with high complexity. When generating the layout, both the utilization rate of the sheet and simplification of the cutting process must be considered. This study presents an algorithm for generating a same-size T-shape layout. The same-size T-shape layout is suitable for the cutting and punching processes in actual production. It includes two segments in different directions, each consisting of strips of only one size and direction. First, the algorithm determines the optimal same-size strip through dynamic programming; subsequently, it determines the layout of the same-size strip in the composite strip and the composite strip in the segment by solving the knapsack problem. Finally, two segments are selected to generate the layout with the highest piece value. Using 37 benchmark test problems from the literature, the proposed algorithm was compared with four advanced and effective layout algorithms. Our algorithm achieved optimal results for 16 test problems, and the ratio of the calculated results to the optimized results for the remaining test problems reached 99.9%. The average calculation time for each test problem was only 1.3 seconds. The experimental results indicate that the proposed algorithm offers advantages in terms of the computation time and sheet utilization rate. The algorithm not only achieves good optimization results within a reasonable time but also simplifies the cutting process while meeting the engineering requirements. -
Zhiqin Zhao, Liang Luo
Vol. 22, No. 1, pp. 62-74, Feb. 2026
https://doi.org/10.3745/JIPS.01.0116
Keywords: Image denoising, Low Rank Matrix Approximation, Markov-Chain Monte Carlo, Slice Sampling
Show / Hide AbstractA new method for non-local random denoising using slice sampling is introduced. This method can significantly enhance the efficiency of non-local image denoising algorithms. The proposed algorithm consists of two stages: first, similar image patches are searched using slice sampling, and then a denoising algorithm is designed to reconstruct the original image using these similar patches. Low-rank matrix approximation methods are used to obtain estimates of clean patches, and a clean denoised image is generated through superposition. The theoretical analysis and experimental tests demonstrate that this algorithm can overcome the dependence on proposal distributions in traditional random algorithms. The experimental results on benchmark images with additive Gaussian noise show that the proposed method can achieve good performance compared to state-of-the-art methods such as BM3D. Specifically, for the test image “Lena” with a noise standard deviation of 20, this method can achieve a peak signal-to-noise ratio (PSNR) of 32.82 dB and a structural similarity index measure (SSIM) of 0.87. For the “Barbara” image with the same noise level, the PSNR is 31.50 dB and the SSIM is 0.89. These results confirm the effectiveness of the algorithm in denoising performance and edge preservation. -
Kang-Won Lee, Tiger (Dong Geon) Shin
Vol. 22, No. 1, pp. 75-87, Feb. 2026
https://doi.org/10.3745/JIPS.04.0366
Keywords: AI and Blockchain, IoT-Enabled Systems, Smart Contracts, Smart Machines
Show / Hide AbstractThis paper introduces D-MIX, a novel blockchain-based platform to decentralize mixology by enabling sharing, execution, and monetizing original drink recipes. D-MIX enables creators (baristas and bartenders) to securely upload recipes, which then get autonomously reproduced using smart machines called D-MIXers. These drink dispensing machines create drinks according to recipes with precision and consistent quality aided by the D-MIX AI engine. When recipes are used, fair royalty distribution is guaranteed via smart contracts. By integrating blockchain technology with AI-assisted barista/bartender machines, D-MIX bridges the physical and digital realms, offering a solution that can democratize access to exclusive recipes that are only available to locals. Extensive evaluation in the testbed and pilot trials validate the system's performance and effectiveness that it can achieve high precision in recipe reproduction and recreate real-world recipes. -
Xueping Han
Vol. 22, No. 1, pp. 88-99, Feb. 2026
https://doi.org/10.3745/JIPS.01.0117
Keywords: Average Delay, Collision probability, power consumption, Resource Allocation, Success Rate
Show / Hide AbstractCurrently, most Internet of Things (IoT) resource-allocation solutions are based on centralized management, rendering it difficult to successfully establish diverse and dynamic IoT networks. Consequently, the fields of reinforcement learning and distributed computing require additional technological advancements. We propose an innovative approach for slot scheduling in IoT networks. This approach focuses on the utilization of distributed resource blocks. The purpose of this work is to demonstrate, via simulations, the influence of distributed slot assignment on the signal-to-interference ratio (SIR) and the probability of accidents occurring. The results of this research suggest that the proposed approach, in which each device in the IoT network is provided with an appropriate slot that possesses acceptable SIR levels, was successful. As the process of distributed slot allocation progresses, it is beneficial to build network convergence by utilizing the learning capabilities of each device. This was accomplished using a distributed slot-allocation process. Therefore, the existing bandwidth can be utilized more efficiently. -
Kai Wang
Vol. 22, No. 1, pp. 100-116, Feb. 2026
https://doi.org/10.3745/JIPS.04.0367
Keywords: Barrage Sentiment, Subtitle Topic, Similarity Calculation, Video Recommended
Show / Hide AbstractBullet-screen videos contain rich user-generated data. It is of great practical significance to utilize sentiment analysis technology and topic models to identify video topics by fusing multi-dimensional features. A novel video recommendation method (MSSA) based on multi-source sentiment analysis is proposed by fusing the sentiment features of bullet screens and the topic features of video subtitles. Firstly, the method performs sentiment analysis on the bullet screens and subtitles of videos, and constructs a user sentiment feature matrix and a video sentiment feature matrix. Secondly, the method clusters user groups with similar sentiment tendencies by extracting the temporal information of bullet screens posted by users. Next, the topic feature vectors of subtitle texts in video clips are calculated to obtain the topic similarity matrix among videos by fusing with the video label information. Afterwards, a sentiment-oriented video set is generated according to the differences in sentiment polarity between bullet screens and subtitles. Finally, online recommendation of bullet-screen videos is achieved by introducing a recommendation heat index. The MSSA method is validated on real-world datasets, and it conducts comparative experiments with other state-of-the-art methods to assess its recommendation coverage and accuracy. The experimental results show that the MSSA method can effectively enhance the performance of user sentiment clustering and the semantic alignment of video content topics, enabling it to effectively explore user interest characteristics, and optimize the quality of personalized video recommendation services.
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