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Motive, form and function of international R&D alliances: Evidence from the Chinese IT industry

Journal of High technology Management Research 2005年第2期16卷 173-191页

作者： Wu, Jian Callahan, John Carleton University Telecommunications Technology Management Program Department of Systems and Computer Engineering 1125 Colonel By Drive Ottawa Ont. K1S 5B6 Canada

This article adds to the research literature on international R&D alliances by focusing on alliances formed by multinational corporations with Chinese organizations. Using publicly available information, we developed a sample of 80 international R&D alliances between multinational corporations and Chinese organizations in the information technology industry in China formed during the period from 1997 to 2003. We use these data to test hypotheses on the connections between the declared motives, organizational form and R&D activity function of such alliances. Our analysis shows that the two most frequent motives of multinational corporations when establishing R&D alliances with Chinese organizations are establishing vertical linkages and obtaining human resources. Within the context of a strong preference for non-equity-based cooperative agreements, our analysis also provides some evidence for an association between the choice of form and motive, strong evidence for an association between function and motive, and strong evidence for an association between the form and function. © 2005 Elsevier Inc. All rights reserved.

关键词： Information technology

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Best-so-far ABC based nanorobot swarm

Best-so-far ABC based nanorobot swarm

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3rd International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2011

作者： Nantapat, Touchakorn Kaewkamnerdpong, Boonserm Achalakul, Tiranee Sirinaovakul, Booncharoen Department of Computer Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Biological Engineering Program King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9780769544441

Nanotechnology has continuously advanced with many tremendous promises to offer. The coming of nanorobots seems inevitable. Medical applications would be one of the first utilization of nanorobots. Nanorobot may become a solution for many currently incurable diseases as it can be viewed as a medical instrument that can be released inside human body for drug delivery and diagnosis [1-3]. In order to effectively utilize nanorobots, the concept of swarm intelligence is extensively studied in the literature [4-8]. In this study, we proposed a framework based on swarm intelligence concept for nanorobot control in medical applications. We adopted a variation of Artificial Bee Colony (ABC) called Best-so-far ABC to regulate nanorobot behavior. Best-so-far ABC biases solutions toward the best position of current iteration. The proposed framework is demonstrated in wounds healing application, nanorobots operate inside a network of blood vessels as artificial platelets to assist in hemostasis process to stop bleeding. Nanorobots must manage to attend to all multiple wounds. The demonstration is one example of possible medical applications of the swarm-intelligence-based nanorobot framework. © 2011 IEEE.

关键词： Nanorobotics

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT technology

Enabling Smart Home Energy Management through Gesture-Based ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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Biology-inspired sensor design

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IEEE Potentials 2009年第6期28卷 19-24页

作者： Stroble, Jacquelyn K. Watkins, Steve Stone, Robert American Institute of Physics Discoveries Breakthroughs Inside Science Program United States Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology United States Mechanical Industrial and Manufacturing Engineering Department Oregon State University United States

Biology has played a key role in influencing the development and optimization of a significant variety of sensors that provide engineers new ideas for improvements to current technology, new sensor technology, and potential sensor miniaturization. Biological systems, such as plants and insects offer significant methods of flight, imaging, sensing, adaptation to environment, and locomotion. The biological domain provides inspiration at many levels, such as cellular, organism, and species. A biological system is any biological organism, organism subsystem, or portion of an organism that is observed to exist. Abstraction of the biological system using analogical reasoning allows an engineering designer to look for a specific solution through mimicry in an approach to biology-inspired design. The analogous approach relies on the idea of figuring out the basis of overall biological system and of each part or subsystem that achieves the overall goal.

关键词： Biological systems

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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IoT-Integrated Machine Learning for Real-Time Soil Nutrient Monitoring and Horticultural Crop Recommendations 10

IoT-Integrated Machine Learning for Real-Time Soil Nutrient ...

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Wahid, Abdul Parenreng, Jumadi Mabe Aswandi Alwi, Ananta Dwi Prayoga Zain, Satria Gunawan Setialeksana, Wirawan Universitas Negeri Makassar Informatics and Computer Engineering Department Makassar Indonesia Politeknik Negeri Lhokseumawe Network Engineering Technology Study Program Lhokseumawe Indonesia

ISBN: (纸本)9798331521295

Farmers and agricultural newcomers often rely on intuition for plant nutrient management, leading to inaccuracies. This study presents a real-time soil nutrient monitoring and plant recommendation system using IoT and machine learning. The prototype integrates an ESP32 microcontroller, NPK sensor, soil pH sensor, and DHT11 with an application and a machine learning model. The model employs a hard voting method combining random forest, LightGBM, and support vector machine algorithms, deployed via Flask API and hosted on Vercel. Testing showed the system effectively monitors soil nutrients in real-time, with a pH sensor error margin of ±0.51 and NPK sensor readings displaying a non-linear increase in levels. The hard voting method achieved 98.5% accuracy, with recommendation processing taking less than one second (371.17ms) and CPU usage at 7%. System performance depends on internet speed, while recommendation accuracy is influenced by temperature and humidity. This system offers a practical solution for precision agriculture by optimizing nutrient application and plant recommendations. © 2024 IEEE.

关键词： pH sensors

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Multi-parameter prediction of oil palm fruit quality through near infrared spectroscopy combined with chemometric analysis

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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2025年

作者： Muhammad Achirul Nanda Kharistya Amaru S. Rosalinda Inna Novianty Tusan Park Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial Technology Universitas Padjadjaran Sumedang 45363 Indonesia Computer Engineering Technology Study Program College of Vocational Studies IPB University Bogor 16680 Indonesia Department of Smart Bio-Industrial Mechanical Engineering Kyungpook National University Daegu 41566 Republic of Korea Department of Applied Biosciences Kyungpook National University Daegu 41566 Republic of Korea Smart Agriculture Innovation Center Kyungpook National University Daegu 41566 Republic of Korea

Early information concerning water, oil, and free fatty acid (FFA) content in palm fruit on-site is crucial in determining the commercial value of fresh fruit bunches (FFB) and maintaining oil palm quality. Conventional methods are destructive, labor-intensive, single-target, costly, and time-consuming. Therefore, this study aimed to develop a method for the multi-parameter prediction of oil palm fruit quality using near-infrared (NIR) spectroscopy. Empirical Wavelet Transform (EWT) and Gaussian Process Regression (GPR) were implemented as a novel chemometric method. A total of 250 fruit samples of the Tenera variety ( Elaeis guineensis Jacq. var. tenera) with various maturity levels were collected from the Cikabayan Oil Palm Plantation. Each sample was scanned using an NIR instrument at 1000–1500 nm wavelengths to obtain absorbance data. The EWT was applied to decompose the NIR spectra into empirical modes, and GPR was used to build a regression model for fitting. Based on numerical analysis, the combination of EWT and GPR produced the root mean square error (RMSE) values of 2.877 ± 0.900 % (R 2  = 0.955 ± 0.018), 1.256 ± 0.543 % (R 2  = 0.942 ± 0.030), and 0.065 ± 0.04 % (R 2  = 0.964 ± 0.044) for water, oil, and FFA content, respectively. The results showed that the model could accurately predict the internal quality of oil palm fruit without the need for solvents or reagents, supporting environmental sustainability. These were expected to enhance oil palm production management by improving quality control, optimizing harvest timing, and promoting sustainability across the value chain.

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning 9

Features Reduction with PCA Technique for Malware Detection ...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Mantoro, Teddy Ginanjar, Agung Ayu, Media Anugerah Aditiawarman, Umar Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia

ISBN: (纸本)9798350370126

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688. © 2023 IEEE.

关键词： Principal component analysis

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Semantic Visual Simultaneous Localization and Mapping: A Survey

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7426-7449页

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Zhejiang University of Technology Institute of Computer Vision College of Computer Science and Technology Hangzhou 310023 China Istituto Italiano di Tecnologia Human-Robot Interfaces and Interaction Laboratory Genoa 16163 Italy National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Universitã di Genova Ph.D. Program of National Interest in Robotics and Intelligent Machines (DRIM) Genoa 16126 Italy Hangzhou Normal University School of Information Science and Technology Hangzhou 311121 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby 2800 Denmark Tianjin University of Technology Institute of Computer Vision School of Computer Science and Engineering Tianjin 300384 China

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emphasizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications. © 2000-2011 IEEE.

关键词： localization mapping semantic information Semantic visual SLAM SLAM datasets

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