Article
Engineering, Electrical & Electronic
Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jong-Keuk Park, Inho Kim
Summary: In this study, a novel neuron implementation model is proposed, which enhances neural and synaptic dynamics using time-embedded floating-point arithmetic for better biological plausibility and low-power consumption. The proposed algorithm enables sharing temporal information with a membrane potential to minimize memory usage and reduce dynamic power consumption.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Computer Science, Hardware & Architecture
Chen Wu, Mingyu Wang, Xinyuan Chu, Kun Wang, Lei He
Summary: This article proposes a low-precision floating-point (LPFP) quantization method for FPGA-based acceleration, which overcomes the limitations of existing methods by achieving optimal 8-bit data representation without the need for re-training. Experimental results demonstrate that our method significantly improves the throughput, especially for VGG16 and YOLO, compared to existing FPGA accelerators.
ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Volodymyr Shymkovych, Sergii Telenyk, Petro Kravets
Summary: This article introduces a method for implementing the Gaussian activation function of radial-basis (RBF) neural networks on field-programmable gate arrays (FPGAs), with different FPGA chips used for modeling purposes. RBF neural networks of various topologies have been synthesized and investigated, with hardware implementation suitable for real-time control systems for high-speed objects.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Chao Cui, Yun Zhao, Yong Xiao, Weibin Lin, Di Xu
Summary: The hardware-efficient elliptic curve cryptography architecture proposed in this paper utilizes adders for scalar multiplication, achieving good performance through algorithm improvements and optimal target selection. The synthesis design demonstrates excellent performance in terms of execution time and gate area for different field orders, with significantly less hardware resources utilized compared to other processors.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Amer Aljaedi, Sajjad Shaukat Jamal, Muhammad Rashid, Adel R. Alharbi, Mohammed Alotaibi, Dalal J. Alanazi
Summary: This paper presents a novel hardware design for a compact crypto processor dedicated to elliptic-curve point multiplication over GF(2(233)). The design focuses on minimizing hardware usage, achieved through an iterative bit-serial finite field modular multiplier. The same multiplier is also used for modular squares and inversion computations, further optimizing the hardware footprint. The implementation on Virtex-6 and Virtex-7 FPGA devices demonstrates the practicality of the proposed crypto processor for efficient and compact cryptographic computations.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Jing Chen, Yingjiao Rong, Quanmin Zhu, Budi Chandra, Hongxiu Zhong
Summary: A back propagation algorithm for polynomial nonlinear models using generalized minimal residual method is proposed in this paper, which offers several advantages over the traditional gradient descent iterative algorithm. The algorithm is based on Arnoldi's method and can adaptively compute the step-length, making it suitable for large-scale system identification.
SYSTEMS & CONTROL LETTERS
(2021)
Article
Computer Science, Information Systems
Muhammad Rashid, Omar S. Sonbul, Muhammad Yousuf Irfan Zia, Muhammad Arif, Asher Sajid, Saud S. Alotaibi
Summary: This paper presents a hardware accelerator architecture for elliptic curve point multiplication (ECPM) over GF(2233) that is optimized for throughput and area efficiency. The design reduces clock cycles using a bit-parallel Karatsuba modular multiplier and minimizes hardware resources through a consolidated arithmetic unit and leveraging existing hardware resources for inverses. The results demonstrate that the proposed accelerator is suitable for applications that require optimized ECPM implementations in terms of throughput and area.
Article
Computer Science, Information Systems
Kunming Zheng, Qiuju Zhang, Li Peng, Shuisheng Zeng
Summary: This study proposes an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method for efficient and precise control of robots with complex dynamic characteristics, while reducing control costs.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Satyajit Bora, Roy Paily
Summary: This article introduces a novel iterative binary division method with the aim of reducing delays in hardware implementation, and conducts a study on area, power consumption, and delays. The study shows that the divider designed at the UMC 40nm technology node has small area and power consumption, as well as low latency.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Julio Villalba, Javier Hormigo
Summary: This article proposes a high-radix floating-point representation for efficient floating-point addition in FPGA devices. The study shows that in practical and common cases, the high-radix format can significantly reduce execution time and area, improving performance.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Botao Xiong, Sheng Fan, Xintong He, Tu Xu, Yuchun Chang
Summary: This paper introduces a small floating-point multiplier scheme for implementing convolutional neural networks on FPGAs and proposes a small logarithmic floating-point multiplier implemented in the logarithmic domain, which can support multiple accuracy levels.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Computer Science, Hardware & Architecture
Tulio Araujo, Matheus B. R. Cardoso, Erivelton G. Nepomuceno, Carlos H. Llanos, Janier Arias-Garcia
Summary: This study addresses the issue of numerical data representation in computer science, focusing on the design of adder circuits in various formats. The hardware implementation of the Round-to-Nearest (RN) representation is discussed, with a proposed architecture for binary and floating-point adders. Results show promising applications for future research and development.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mahdi Shafiei, Hassan Daryanavard, Ahmad Hatam
Summary: In this paper, a floating-point convolution core is proposed for edge processors to implement CNN. By analyzing conventional CNN networks, a 32-bit 3x3 convolution core is designed using a 10-input adder and modified multipliers. The minimum bit width of 13 is chosen for floating-point numbers. The new scalable core supports filter sizes of 1x1, 3x3, and 5x5.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Wenzhe Zhao, Qiwei Dang, Tian Xia, Jingming Zhang, Nanning Zheng, Pengju Ren
Summary: In recent years, low-precision fixed-point computation has been widely used for neural network inference on FPGAs. However, this approach has limitations when dealing with certain neural networks, such as those for super-resolution scaling and image denoising, which require fine-tuning. To overcome this, this paper proposes an FPGA-friendly floating-point data format that achieves the same storage density as int8 without sacrificing inference accuracy. Additionally, an FPGA-based neural network accelerator compatible with the proposed format is presented, achieving comparable resource consumption and execution efficiency to 8-bit fixed-point accelerators and outperforming previous works in terms of performance.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Computer Science, Information Systems
Mioara Joldes, Bogdan Pasca
Summary: This article revisits and improves a floating-point implementation of probit, achieving better performance and setting benchmarks for new custom and double-precision FP implementations. It proposes single-precision and generic custom-precision architectures that offer a user-selectable trade-off between accuracy and resource utilization. The proposed cores outperform existing FPGA implementations in terms of area, latency, and accuracy.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2021)
Article
Automation & Control Systems
Hojin Choi, Seul Jung
ASIAN JOURNAL OF CONTROL
(2020)
Article
Automation & Control Systems
Sang-Deok Lee, Seul Jung
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2019)
Article
Automation & Control Systems
Sang-Deok Lee, Seul Jung
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2019)
Article
Automation & Control Systems
Sang Deok Lee, Seul Jung
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2020)
Article
Automation & Control Systems
Seul Jung
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2020)
Proceedings Paper
Automation & Control Systems
Dhruv Talwar, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Manuj Trehan, Aniket Gupta, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Kumar Saurav, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Hyun W. Kim, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Sang D. Lee, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Sang D. Lee, Seul Jung
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Seul Jung
2019 7TH INTERNATIONAL CONFERENCE ON ROBOT INTELLIGENCE TECHNOLOGY AND APPLICATIONS (RITA)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
S. D. Lee, S. Jung
2019 7TH INTERNATIONAL CONFERENCE ON ROBOT INTELLIGENCE TECHNOLOGY AND APPLICATIONS (RITA)
(2019)
Proceedings Paper
Automation & Control Systems
S. D. Lee, S. Jung
2019 12TH ASIAN CONTROL CONFERENCE (ASCC)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Sang D. Lee, Seul Jung
2019 FIRST INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION, CONTROL, ARTIFICIAL INTELLIGENCE, AND ROBOTICS (ICA-SYMP 2019)
(2019)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)