Details of a Researcher - Nishi, Hiroaki

Papers - Nishi, Hiroaki

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Proposal of Feature Value Selection Method for Time-Critical Learning

Yuyama K., Nishi H.

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA) 2018-September 1365 - 1371 2018.10

ISSN 9781538671085

The development of IoT has led to the creation of a data-enriched environment that enables data gathering by using distributed sensors and terminals. However, in this environment, the cost of data analysis has increased. Machine learning has gained attention for reducing the cost because enabling automatic data analysis, as well as multidimensional data, is expected. However, for enormous data, such as Big Data, we still have to pay costs. Therefore, selecting feature values when using machine learning technology is essential, especially as inputs of a classifier. Selecting the feature values increases its estimation accuracy. Moreover, the time cost, as well as calculation cost, needs consideration for the actual time-critical use of machine learning, especially in its learning process. Therefore, in this study, we proposed an algorithm that selected suitable feature values in required time. The proposed method consists of two stages: stepwise input selection stage using ANOVA and feature deletion stage according to the contribution rate of the features to estimate accuracy. These selection and deletion processes continue until the required processing time. We confirmed the efficiency of the proposed method by using an environment of a crystallization process in a factory and a household's occupancy estimation. A comparison with the original stepwise input method proved that the proposed method improved the estimation accuracy by 2% and 5% in the estimation of the substance amount of the crystallization process and household's occupancy, respectively.

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Request Distribution for Heterogeneous Database Server Clusters with Processing Time Estimation

Omori M., Nishi H.

Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018) 278 - 283 2018.09

ISSN 9781538648292

Recently, data traffic on the Internet has increased due to the rapid growth of various Internet-based services. The convergence of user requests means that servers are overloaded. To solve this problem, service providers generally install multiple servers and distribute requests using a load balancer. The existing load balancing algorithms do not estimate the size of the load of unknown requests. However, the requested contents are heterogeneous and complex, so the size of the load is dependent on the servers and the contents of the requests. In this study, we propose a load balancing algorithm that distributes the requests based on estimates of the processing time, which avoids mismatches between the characteristics of servers and the request contents. The processing time for requests is estimated based on the requested contents by online machine learning, and a strategy to cover the latency of machine learning is proposed and partially conducted. To test the algorithm, we built a model of multiple database servers and performed an experiment using real log data for database requests. The simulation results showed that the proposed algorithm reduced the average processing time for requests by 94.5% compared with round robin and by 28.3% compared with least connections.

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Task Allocating Service-oriented Network for Smart Community Applications

Kinoshita S., Hiroaki N.

Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018) 140 - 145 2018.09

ISSN 9781538648292

Smart community is the extended concept of the smart grid. Many services, including, but not limited to infrastructure services are deployed in the smart community. Some of the services in the smart community are time critical and require low latency between requests and responses. However, commonlyused cloud computing architecture is not designed with an emphasis on the latency. Therefore, the network architecture for low latency required services are necessary. This paper proposes a serviceproviding network for smart communities. In the proposed network, service-oriented router is utilized to provide the service in intermediate nodes between a cloud server and an end node. By responding to therequest at the intermediate node instead of the cloud where is close to the end devices, the latency is reduced. To demonstrate the feasibility of the proposed network, a simulation of the proposed network with two time-critical services were carried out in the network simulator ns-3. The ancillary service and intersection accident avoidance service are chosen to be considered in the simulation since they are attention-grabbing time-critical services for the smart community. The reduction of both latency and variation of the latency in both services was observed in the simulation. For the ancillary service, the proposed method has reduced the average latency to 10.85 ms that is low enough to provide stable service. The result shows the feasibility of application task allocation between intermediate SoR nodes based on a latency requirement in the smart community.

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Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach

Yoshida M., Kleisarchaki S., Gtirgen L., Nishi H.

19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018 (19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018) 2018.08

ISSN 9781538647257

Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., CO2) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.

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Building Thermal Performance Assessments Using Simple Sensors for the Green New Deal in Japan

Sakuma Y., Nakajo Y., Nishi H.

IEEE International Symposium on Industrial Electronics (IEEE International Symposium on Industrial Electronics) 2018-June 691 - 696 2018.08

ISSN 9781538637050

The development of smart communities has provided additional sources of data for innovative new services. In residential areas, Home Energy Management Systems (HEMS) provide a data stream from the home, including room temperature and the status of heating, ventilation, and air conditioning (HVAC) appliances. The data is of interests to both residents and utilities for energy saving purposes. The Green New Deal is a scheme that subsides building improvement to increase energy efficiency by insulating buildings and introducing energy-saving electric appliances. However, the scheme is often criticized due to high installation costs. Therefore, a simple and low cost method to assess building thermal performance is important to select those homes, that can benefit the most from the scheme. In this study, a novel method for assessing building thermal performance based on HEMS data is proposed. The thermal equilibrium of a house is modeled to state the system identification problem to estimate the unknown parameters, such as heat transfer coefficient and heat capacity of a house. The unknown parameters are estimated by using Kalman Filter. In this way, the thermal performance of building can be assessed remotely while residents live their daily lives. Estimated parameters are then validated by considering those elements affected by the behavior of residents. The proposed method was found to estimate the heat transfer coefficient well and thus aid in effectively targeting Green New Deal projects.

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スマートシティインフラの展望

西宏章

技術雑誌「スマートグリッド」特集エネルギーを中心とした情報収集・管理技術大河出版 2018.07

Data prediction for response flows in packet processing cache

Yamaki H., Nishi H., Miwa S., Honda H.

Proceedings - Design Automation Conference (Proceedings - Design Automation Conference) Part F137710 2018.06

ISSN 9781450357005

We propose a technique to reduce compulsory misses of packet processing cache (PPC), which largely affects both throughput and energy of core routers. Rather than prefetching data, our technique called response prediction cache (RPC) speculatively stores predicted data into PPC without additional access to the low-throughput and power-consuming memory (i.e., TCAM). RPC predicts the data related to a response flow at the arrival of the corresponding request flow, based on the request-response model of internet communications. RPC can improve the cache miss rate, throughput, and energy-efficiency of PPC systems by 15.3%, 17.9%, and 17.8%, respectively.

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Time slotted channel hopping scheduling based on the energy consumption of wireless sensor networks

Matsui T., Nishi H.

Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018 (Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018) 605 - 610 2018.06

ISSN 9781538619469

Wireless Sensor Nnetworks (WSNs) are becoming increasingly widespread owing to the introduction of the Internet of Things (IoT) and the networks' comparatively low cost and usability. WSNs are usually battery-powered, owing to their low power consumption, which however limits their computational performance. The lifetime of a WSN strongly depends on its communication-related power consumption. Time Slotted Channel Hopping (TSCH) solves the problem of power consumption by featuring both time-division and frequency- division multiplexing in the system's Media Access Control (MAC) layer. In this paper, Pruning-based Coloring Scheduling (PRCOS) is proposed, which is an algorithm for TSCH scheduling that accounts for the power consumption to extend the system's lifetime. Using mathematical analysis, we show that the proposed algorithm decreases the transfer time and energy consumption by factors of 0.43 and 0.57, respectively, compared with existing algorithms.

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Slightly-slacked dropout for improving neural network learning on FPGA

Sota Sawaguchi, Hiroaki Nishi

ICT Express (ELSEVIER) 4 ( 2 ) 75 - 80 2018.06

ISSN 2405-9595

© 2018 The Korean Institute of Communications and Information Sciences (KICS) Neural Network Learning (NNL) is compute-intensive. It often involves a dropout technique which effectively regularizes the network to avoid overfitting. As such, a hardware accelerator for dropout NNL has been proposed; however, the existing method encounters a huge transfer cost between hardware and software. This paper proposes Slightly-Slacked Dropout (SS-Dropout), a novel deterministic dropout technique to address the transfer cost while accelerating the process. Experimental results show that our SS-Dropout technique improves both the usual and dropout NNL accelerator, i.e., 1.55 times speed-up and three order-of-magnitude less transfer cost, respectively.

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Information and communication platform for providing smart community services: System implementation and use case in Saitama city

Nishi H.

Proceedings of the IEEE International Conference on Industrial Technology (Proceedings of the IEEE International Conference on Industrial Technology) 2018-February 1375 - 1380 2018.04

ISSN 9781509059492

Smart communities, smart cities, smart towns, and similar smart terminologies are trending recently, along with the Internet of Things (IoT) and the Cloud, as popular new information and communication technologies (ICTs). This paper proposes a synergistic solution to urbanization problems from a technological perspective that is beneficial both to residents and to local government. Case studies of a smart community implementation in Saitama city are also provided, from the viewpoint of integrated smart infrastructure services.

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An Information Platform for Smart Communities Realizing Data Usage Authentication and Secure Data Sharing

Niwa A., Nishi H.

Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017 (Proceedings - 2017 5th International Symposium on Computing and Networking, CANDAR 2017) 2018-January 119 - 125 2018.04

ISSN 9781538620878

The deployment of Information and Communication Technology in existing infrastructure has increased interest in smart communities. Smart communities provide various services such as ancillary services, road traffic information management, and health care management. Each service has distinctive characteristics such as processing delay, the need for anonymity, computational cost, and the amount of data. Therefore, to optimize the system, locations to provide these services should be organized. In addition, secondary use of personal data is assumed in smart communities. Thus, measures to provide privacy protection should be implemented. In this paper, a new information platform is proposed, which allows management of a user's privacy policy, management of data usage, smooth data sharing, and adequate service location organization by introducing the concept of 'Relationship.' Response time associated with organization was evaluated on the implemented platform to ensure the feasibility of the proposed method, and it was about 15 minutes when the number of Relationships is 12,800.

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Blind Watermarking Method for Anonymized Data

Ohno Y., Iwai T., Nishi H.

ISSN 9781538620878

The secondary use of data in consideration of individual privacy has been gaining attention. Data anonymization needs to be applied to enable both secondary use and privacy protection. In distributing anonymized data, there is a concern that data users might re-distribute the data to a third party without permission. To tackle this issue, a watermarking technique that correlates data users (IDs) with anonymized data has been studied. In existing studies, a non-blind detection method has been employed to obtain the IDs; however, the problem is the use of original data in the detection, which deteriorates the applicability. In this paper, a watermarking technique in combination with a blind detection method is proposed. We evaluate and compare the proposed watermarking method with an existing non-blind method in terms of information loss, tolerance against attacks, and the processing time of watermarking and detection.

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A privacy-preserving sharing method of electricity usage using self-organizing map

Yuichi Nakamura, Keiya Harada, and NISHI HIROAKI

Journal of ICT Express, The Korean Institute of Communications and Information Sciences (ScienceDirect) 4 ( 1 ) 24 - 29 2018.01

Research paper (scientific journal), Accepted, ISSN 2405-9595

© 2018 The Korean Institute of Communications Information Sciences Smart meters for measuring electricity usage are expected in electricity usage management. Although the relevant power supplier stores the measured data, the data are worth sharing among power suppliers because the entire data of a city will be required to control the regional grid stability or demand–supply balance. Even though many techniques and methods of privacy-preserving data mining have been studied to share data while preserving data privacy, a study on sharing electricity usage data is still lacking. In this paper, we propose a sharing method of electricity usage while preserving data privacy using a self-organizing map.

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Improving energy efficiency in data centers by controlling task distribution and cooling

Nakajo Y., Athavale J., Yoda M., Joshi Y., Nishi H.

ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, InterPACK 2018 (ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, InterPACK 2018) 2018

ISSN 9780791851920

The rapid growth in cloud computing, the Internet of Things (IoT), and data processing via Machine Learning (ML), have greatly increased our need for computing resources. Given this rapid growth, it is expected that data centers will consume more and more of our global energy supply. Improving their energy efficiency is therefore crucial. One of the biggest sources of energy consumption is the energy required to cool the data centers, and ensure that the servers stay within their intended operating temperature range. Indeed, about 40% of a data center’s total power consumption is for air conditioning[1]. Here, we study how the server air inlet and outlet, as well as the CPU, temperatures depend upon server loads typical of real Internet Protocol (IP) traces. The trace data used here are from Google clusters and include the times, job and task ID, as well as the number and usage of CPU cores. The resulting IT loads are distributed using standard load-balancing methods such as Round Robin (RR) and the CPU utilization method. Experiments are conducted in the Data Center Laboratory (DCL) at the Georgia Institute of Technology to monitor the server outlet air temperature, as well as real-time CPU temperatures for servers at different heights within the rack. Server temperatures were measured by on-line temperature monitoring with Xbee, Raspberry PI, Arduino, and hot-wire anemometers. Given that the temperature response varies with server position, in part due to spatial variations in the cooling airflow over the rack inlet and the server fan speeds, a new load-balancing approach that accounts for spatially varying temperature response within a rack is tested and validated in this paper.

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On the effectiveness of using network device state information for network path selection

Janaka Wijekoon, Divya Amunugama, Hiroaki Nishi

Procedia Computer Science (Elsevier) 130 134 - 141 2018

© 2018 The Authors. Published by Elsevier B.V. Network path selection defines the methodology of selecting the best routes and forwarding traffic in a network service provider (NSP). NSPs use routing protocols that are optimized for a single arbitrary metric (i.e., administrative weight), which is commonly calculated according to the link state information, to select network paths. Despite the advantages, link-state protocols lack the ability to select network paths by considering the states of network devices such as the effect of routers for network path selection. Apparently, studying possible techniques for selecting network paths based on the state information of network devices, e.g., routers, has become obligatory. This paper studies the effectiveness of using network device state information for network path selection by presenting the Extended Server Link Router state Routing Protocol (ESLR). ESLR uses the state information of network devices as parameters to calculate a composite route metric; ESLR selects the network paths based on the cumulative propagation delay. By simulating ESLR using an ISP topology, the proposed protocol is examined for the effectiveness of using network device state information for network path selection.

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On the Effectiveness of IP-Routable Entire-Packet Encryption Service over Public Networks (November 2018)

Rajitha Tennekoon, Janaka Wijekoon, Hiroaki Nishi

IEEE Access (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC) 6 73170 - 73179 2018

ISSN 2169-3536

© 2013 IEEE. The Internet is an unsecured public network accessed by approximately half of the world population. There are several techniques, such as cryptography, end-to-end encryption, and tunneling, used to preserve data security and integrity and to reduce information theft. This is because the security of data transmission over public networks is an ever-questionable issue. However, none of the above techniques are capable of providing the flexibility of changing either the algorithm or its key at the intermediary routers according to the requirements of stakeholders, e.g., ISPs or Internet users. Although the transmitted data are encrypted and unreadable, the metadata contained in the packet headers are readable during traversal. Nonetheless, service-based Internet architectures, e.g., IoT architectures, demand the analysis the data streams at the intermediary routers to provide smart services such as strengthening the security of the data streams. To this end, this paper proposes a method to use service-oriented routers for providing secure data transmission by encrypting data packets including the header and trailer information. A prototype of the proposed method is implemented on the ns-3 simulator, and this paper discusses the implementation notes and evaluation of the test results. The test results demonstrate that there is only an average processing cost of 180.14/191.35, 213.96/257.41, 157.56/170.68, and 235.48/ 249.49~\mu \text{s} for encrypting the total encrypted combined packets/total encrypted separate packets using IDEA, DES, AES-GCM, and AES-CTR encryption algorithms, respectively, with a 256-bit key space. This is significantly lower than the tolerable transmission delay (150 ms) defined by the ITU-T.

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Transcriptomic features of KRT7 positive cells derived from BMP4-treated human induced pluripotent stem cells (hiPSCs)

Hiroaki Nishi

Human Reproduction 2018

Anonymization method based on sparse coding for power usage data

Keiya Harada, Yuta Ohno, Yuichi Nakamura, Hiroaki Nishi

2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) (IEEE) 571 - 576 2018

Accepted, ISSN 1935-4576

In recent years, there have been rapid increases in the number of network-connected devices such as computers, smartphones, and Internet of Things devices. Thus, large amounts of data have been accumulated such as locational data, website search histories, and power usage data. These data are used in various types of services. However, these data cannot he used easily for secondary purposes in some countries because of privacy problems. Therefore, privacy protection is necessary to apply these data in secondary uses where data anonymization is the usual solution. Many conventional methods are used for anonymizing power usage data, but the conventional method has three problems. First, it cannot anonymize time-series data. Second, the information loss is so large in the conventional method that the anonymized data are no longer suitable for secondary uses. Third, the conventional method cannot preserve the type of electrical appliance used. In this study, we propose a method for anonymizing power demand data, where sparse coding is used to solve the three problems that affect the conventional method. The proposed method can anonymize time series-data and it allows data to be analyzed at a chosen time. The proposed method was used to anonymize power usage data from the Urban Design Center Misono (UDCMi) and the experimental error rate decreased compared with the conventional method. The dictionary produced using the proposed method represents the electrical appliance data.

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Presence of Visceral Adiposity Fans the Flame of Mortality Amongst Sarcopenic Patients with Liver Cirrhosis

Hiroaki Nishi

Hepatology 2018

Analysis of batteries in the built environment: An overview on types and applications

Jan Haase, Fares AlJuheshi, Hiroaki Nishi, Joern Ploennigs, Kim Fung Tsang, Nasser Aljuhaishi, Mahmoud Alahmad

Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (IEEE) 2017-January 8113 - 8118 2017.12

ISSN 1553-572X

© 2017 IEEE. Recent trends in the applications of batteries in the built environment are improving the efficiency of batteries and lowering costs. This paper introduces various types of battery technologies such as sodium sulfur, lithium ion, flow and lead acid batteries and discusses their models. Various applications of batteries such as adaptive battery systems, Battery Electrical Vehicles (BEVs), Battery Energy Storage Systems (BESS), the Internet of Things (IoT), and Smart Grid and Smart Environment applications are also discussed. In their selection and use of batteries, scholars are ultimately looking to maximize occupant comfort whilst keeping costs low and optimizing the energy efficiency of buildings. This paper will provide a review of current trends in this field.

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