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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 16-24页

作者： shouno, Kazuhiro sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOs process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Action Robust Reinforcement Learning with Highly Expressive Policy

Action Robust Reinforcement Learning with Highly Expressive ...

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2024 IEEE International Conference on systems, Man, and Cybernetics, sMC 2024

作者： Kim, seongIn shibuya, Takeshi University of Tsukuba Doctor's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Japan

ISBN: (纸本)9781665410205

In traditional reinforcement learning, there can be a degradation in the control performance of the policy when the environmental parameters differ between the training and application phase. The policy that minimizes this degradation is referred to as a robust policy. A framework called Noisy action Robust Markov Decision Process (NR-MDP) was proposed for training robust policies, and the Action Robust Deep Determin-istic Policy Gradient (AR-DDPG) algorithm was introduced as a method for solving NR-MDP. The optimal policy in NR-MDP includes policies following various probability distributions, whereas AR-DDPG is restricted to deterministic policies. We propose a new robust reinforcement learning method called Action Robust Q-Learning (AR-QL) that enables the training of optimal policies in NR-MDP by leveraging various sampling techniques to extend the representational capacity of policies, targeting an improvement in policy robustness. To validate this, we confirmed that AR-QL can acquire the optimal policy for a simple NR-MDP problem, for which AR-DDPG fails to obtain the optimal policy. Furthermore, we confirmed that the robust performance of policy trained by AR-QL in the OpenAI's InvertedPendulum environment surpasses that of policy trained by AR-DDPG. © 2024 IEEE.

关键词： Deep reinforcement learning

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Explainable Reinforcement Learning via Causal Model Considering Agent's Intention

Explainable Reinforcement Learning via Causal Model Consider...

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2024 IEEE International Conference on systems, Man, and Cybernetics, sMC 2024

ISBN: (纸本)9781665410205

Explaining agent's decision can offer valuable insights for designers and end-users. One proposed method for explaining an agent's decision-making involves representing a causal relation between state components and action as a causal model and providing explanations for the decisions made using causal model. However, traditional causal model often faces structural limitation, restricting the range of representable control problems. Additionally, providing accurate explanation becomes challenging in control problems with various types of rewards because agent's intention of an action is unknown. In this study, we introduce a causal model capable of representing a broader range of control problems and a method to provide accurate explanations in control problems with various types of reward structures. Through redefining the relationships between nodes in the causal model, we have enabled a broader representation of control problems. Also, by incorporating agent's intention into the explanation, we have achieved to provide a more precise explanation. To validate the effectiveness of our proposed method, we conducted experiments using OpenAI's LunarLander environment. Using a proposed causal model, we defined the causal model of LunarLander, which could not be represented by conventional causal models. Furthermore, by incorporating the intentions of an agent into the explanation, novel interpretations previously inaccessible have become feasible. © 2024 IEEE.

关键词： Reinforcement learning

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Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure

Reinforcement Learning to Efficiently Recover Control Perfor...

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2022 IEEE International Conference on systems, Man, and Cybernetics, sMC 2022

作者： Kobayashi, shoki shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Extreme environments, such as space and underwater, are difficult for humans to enter because they involve risks, hence it is necessary to employ autonomous robots instead of humans. When robots fail in extreme environments, it is essential for the robot to automatically recover control following the rules of failure because humans cannot repair the robot directly. Reinforcement learning is expected to automatically acquire the control rule;however, the retrieval of the control rule requires significant trial-and-error. Imitation learning cannot acquire the control rule if no suitable expert data exist. Methods combining imitation learning and reinforcement learning reduce the number of trial-and-errors;however, they are still not effective against robot failure because these methods cannot utilize expert data effectively. This paper proposes a reinforcement learning method that efficiently recovers control performance from failures by utilizing both the control rules prepared by the designer and multiple discriminators to calculate measures similar to expert data. Experimental results show that the proposed method recovers the control performance with fewer episodes than the conventional method. The main contribution of the proposed method is its efficiency against robot failure through utilizing expart data prepared for failure by the designers for imitation learning. © 2022 IEEE.

关键词： Reinforcement learning

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Centralized Training with Decentralized Execution Reinforcement Learning for Cooperative Multi-agent systems with Communication Delay 61

Centralized Training with Decentralized Execution Reinforcem...

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61st Annual Conference of the society of Instrument and Control Engineers of Japan, sICE 2022

作者： Ikeda, Takuma shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Japan

ISBN: (数字)9784907764784

ISBN: (纸本)9784907764784

In cooperative multi-agent systems, efficient coordination among agents is important when accomplishing tasks. VFFAC is a method that learns the communication system between agents and their interactions with the environment to obtain policies with high performance. However, this method results in decreased performance of policy in environments with a delay in communication. Furthermore, there is no formulation of the control problem of a cooperative multi-agent system with communication delays in unknown environments. In this study, we formulated a decision-making problem in a cooperative multi-agent system with an unknown environment model and a certain length delay in communication. We also propose a method to handle communication delays by using the history of information obtained through communication. We demonstrated that the proposed method successfully learns policy with high rewards through simulated experiments in an environment with a communication delay. © 2022 The society of Instrument and Control Engineers - sICE.

关键词： Reinforcement learning

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The Banking Industry is Undergoing a Digital Transformation as Part of Industrial 4.0 10

The Banking Industry is Undergoing a Digital Transformation ...

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10th International Conference on smart Computing and Communication, ICsCC 2024

作者： sardjono, Wahyu Pranata, Dewi sagita Ramdhana, Aditya Agung Dwi BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Mathematical & Engineering Sciences King's College London Cyber Security Studi Program Faculty of Natural Department of Informatics West Minster Strands WC2R 2LS United Kingdom University of Airlangga Master of Management Faculty of Economics and Business Surabaya60286 Indonesia

ISBN: (纸本)9798350363104

In the current day, digital transformation is imperative;both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. successful transformation, however, does not happen by accident;in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment. © 2024 IEEE.

关键词： Commerce

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Implementation and Performance Evaluation of Collective Communications Using CIRCUs on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUs which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUs only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUs for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUs system briefly followed by the design, implementation and preliminary performance evaluation on Intel stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Comparing Classification Algorithms to Analyze Twitter sentiment on Public Opinion on Fuel Oil 24

Comparing Classification Algorithms to Analyze Twitter Senti...

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24th International seminar on Intelligent Technology and Its Applications, IsITIA 2023

作者： Widarma, Gita Novita, Rice Mustakim Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Information Systems Department Pekanbaru Indonesia Postgraduate Programs Universitas Negeri Padang Information Systems Pekanbaru Indonesia Postgraduate Programs Institut Pertanian Bogor Master of Informatics Engineering Pekanbaru Indonesia Postgraduate Programs Universiti Teknologi Malaysia Master of Information Systems Management Pekanbaru Indonesia

ISBN: (纸本)9798350313956

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle swarm Optimization (PsO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle swarm Optimization (PsO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%. © 2023 IEEE.

关键词： Decision trees

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study on In-clothes Body Weight support system to support Treating and Recovering Knee Arthopathy

Study on In-clothes Body Weight Support System to Support Tr...

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2022 IEEE International Conference on systems, Man, and Cybernetics, sMC 2022

作者： Netsu, Kohki Kawamoto, Hiroaki sankai, Yoshiyuki Univetsity of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Center for Cybernics Research Ibaraki Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Patients with knee arthropathy suffer from pain owing to damage at the knee joint cartilage and experience difficulty in walking as the condition worsens. As one of treatments of knee arthropathy, an implantation of cellular cartilage has been performed. After implantation of cellular cartilage, the knee joint should avoid overweight to prevent cell damage form body weight and apply appropriate mechanical stress to articular cartilage to promote cartilage regeneration. Therefore, a weight support system which can adjust the amount of weight in everyday use is required. This study aims to develop an in-clothes body weight support system to protect the knee joint during daily walking to support knee arthropathy treatment and recovery in regenerative medicine, and confirm basic performance of the system through experiments with able-bodied person. The system had frame structure with two-node link from the groin to the foot with the user's knee joint as the center of rotation. It consisted of load-bearing seat, height adjustment mechanism, thigh cuff, mechanical knee joint, and, ground reaction force sensor shoes, and was designed to be sufficiently thin to fit inside clothes. In the 10-Meter walking test, the system could adjust the amount of supporting weight and support two-thirds, one-half, and one-third of total body weight of an able-bodied participant during walking. In conclusion, we confirmed that the developed system had basic performance of weight support. © 2022 IEEE.

关键词： Cartilage

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Minimizing the Number of stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer science and information Technology, ICOsNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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